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Harm Reduct JHarm Reduction Journal1477-7517BioMed Central London 1477-7517-2-31574828310.1186/1477-7517-2-3EditorialWitch-hunt Drucker Ernest [email protected] Montefiore Medical Center, New York, USA2 Albert Einstein College of Medicine, New York, USA2005 4 3 2005 2 3 3 3 3 2005 4 3 2005 Copyright © 2005 Drucker; licensee BioMed Central Ltd.2005Drucker; 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.
Beginning two years ago, the US Dept of Health and Human Services began "special reviews" of all current research grants that involved harm reduction, sex and drugs, and continues its ban on funding of needle exchange. With Bush's second term, the campaign was extended to all US funded international programs that dealt with these issues and populations. And, most recently, the US has again undertaken to dominate the discourse within international organizations charged with drug control and AIDS policies – especially those of the UN. But the international harm reduction and human rights community is fighting back in several important ways, including "An Open Letter to the delegates of the Forty-eighth session of the Commission on Narcotic Drugs (CND) of the UN" prepared by a group of 334 well respected public health experts and human rights advocates, protesting U.S. pressure on the U.N. to withdraw its support from harm reduction. This editorial includes the letter and signatures as well as French, Spanish, and Russian versions of the letter as additional files.
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"This is a sharp time, now, a precise time - we live no longer in the dusky afternoon when evil mixed itself with good and befuddled the world. Now, by God's grace, the shining sun is up, and them that fear not light will surely praise it."
Arthur Miller, The Crucible, Act III
"I do not believe that the meaning of our Eighth Amendment, any more than the meaning of other provisions of our Constitution, should be determined by the subjective views of five members of this court and like-minded foreigners"
Justice Antonin Scalia, in his dissent from the US Supreme Court majority decision barring capital punishment for crimes committed by minors.
It is indeed "a sharp time" in the US for those of us who agree with "like-minded foreigners". This is especially so regarding matters of drug and AIDS policies based on harm reduction (HR) and public health – decriminalization of drug users, the need for safer injections, low thresholds for access to care, sex education and social supports that work to reduce risk. Today more people are imprisoned for drug use in the US than are incarcerated in the European Union for all crimes.
US conservatives have, in the same lethal moralistic tradition of our Salem witch-hunts of the 1600's and the McCarthy era, effectively obstructed and undermined our HR efforts at home for two decades. But now the Bush administration, emboldened by its re-election and in full warrior mode, has undertaken a newly invigorated global jihad against harm reduction. Americans who support HR are now to be made to feel "foreign", their moral compass, patriotism, and loyalty to "American core – values" placed in doubt.
Parallels to the death penalty are not casual: the failed punitive drug policies of the war on drugs are also official "death sentences", with many more lives lost to them each year than to all the judicially sanctioned executions of all the countries on earth. And, because preventable death is more then an analogy when it comes to public health, we can learn a lot from this Supreme Court case. Justice Anthony M. Kennedy, writing for the courts majority, recognized the "evolving standards of decency" that should shape our judgment of what constitutes a violation of our Constitution's Eighth Amendment and its prohibition against cruel and unusual punishments: "it is fair to say that the United States now stands alone in a world that has turned its face against the juvenile death penalty."
But Justice Scalia, significant as the most likely nominee for Chief Justice with the ailing incumbents imminent departure, saw it differently, the NY Times reporting that he reserved "his strongest dissent for (the majority's) reference to international developments that have left the United States alone in supporting juvenile executions". For while the majority opinion said the court was not bound by foreign developments, "it is proper that we acknowledge the overwhelming weight of international opinion" for its "respected and significant confirmation for our own conclusions", Scalia objected that this position implied that "the views of our own citizens are essentially irrelevant," and had wrongly given "center stage" to the "so-called international community." Assumedly this would be that same " international community" that our Declaration of Independence refers to in its famous opening paragraph where, attempting to justify our nations throwing off British rule, we are told that " a decent respect to the opinions of mankind requires that " we explain our course of action. But as the worlds "sole super- power" (tell China that) it appears that we no longer have to show a decent respect for any other nations opinions about human rights, nor it would seem for the relentlessly insistent biology of HIV.
The US harm maximization drug policies, which violate both human rights and the realities of infectious diseases, are immoral and dangerously misguided – sustained by demagogic politicians and mad moralists now in near absolute power in our country. As surely as capital punishment, these policies mete out death sentences on a massive scale to our most vulnerable citizens. These failed policies account for the continued annual incidence of 40,000 new HIV infections in the US.
Beginning two years ago, the US Dept of Health and Human Services (the parent agency of our National Institutes of Health, which funds most AIDS and drug research in the world) began "special reviews" of all current research grants that involved sex and drugs. Washington re-asserted the drive for mandated "abstinence only" and "faith based" programs, and continues the Federal ban on funding of needle exchange. Don't even bother applying for work on gay sex. With Bush's second term, the campaign was extended to all US funded international programs that dealt with these issues and populations. And, most recently, the US has again undertaken to dominate the discourse within international organizations charged with drug control and AIDS policies – especially those of the UN.
Now the US seeks to impose them as extra judicial capital punishment on the rest of the world. But the international harm reduction and human rights community is not going quietly – it is fighting back in several important ways and we can cite many successes that have already saved hundreds of thousands of lives. Henceforth we will chronicle this struggle in this journal.
As a start we are publishing An Open Letter to the delegates of the Forty-eighth session of the Commission on Narcotic Drugs (CND) of the UN prepared by a group of well respected public health experts and human rights advocates, protesting U.S. pressure on the U.N. to withdraw its support from harm reduction (see Additional file 1). The letter garnered 334 individual and organizational endorsements from fifty-six countries. The organizers of the letter are in the process of sending it to all country missions in Vienna as well as to UNODC Executive Director Antonio Maria Costa and representatives from UNICEF, WHO, UNAIDS, and the UN Office of the High Commissioner for Human Rights.
For more information about this letter contact Jonathan Cohen at Human Rights Watch – [email protected]
We at HRJ welcome your views, which can be submitted online (as Comments) at harmreductionjournal.com
• E Drucker PhD is Director, Division of Public Health and Policy Research, Professor of Epidemiology and Social Medicine and Professor of Psychiatry, and Family Medicine at Montefiore Medical Center/Albert Einstein College of Medicine in New York City. Dr. Drucker was a founder of the International Harm Reduction Association and Founding Chairman of Doctors of the World / USA. He is a currently a senior Soros Justice Fellow.
Appendix
An Open Letter to the delegates of the Forty-eighth session of the Commission on Narcotic Drugs (CND).
In a year when the United Nations Office on Drugs and Crime (UNODC) is chair of the governing body of the UN's Joint Programme on HIV/AIDS (UNAIDS), we write to express concern about U.S. efforts to force a UNODC retreat from support of syringe exchange and other measures proven to contain the spread of HIV among drug users. Injection drug use accounts for the majority of HIV infections in dozens of countries in Asia and the former Soviet Union, including Russia, China, all of Central Asia, and much of Southeast Asia. In most countries outside Africa, the largest number of new infections now occurs among injection drug users. As UNODC director Antonio Maria Costa noted at the July 2004 International AIDS Conference, effective responses to injection driven AIDS epidemics require expanded HIV prevention, including syringe exchange, rather than policies that accelerate HIV infections through widespread and indiscriminate imprisonment.
Unfortunately, recent events suggest that UNODC – under pressure from the United States – is being asked to withdraw support from proven HIV prevention strategies at precisely the moment when increased commitment to measures such as syringe exchange and opiate substitution treatment is needed. It is particularly alarming that the silencing of UNODC is occurring in a year when the agency is chair of UNAIDS' Committee of Co-sponsoring Organizations and in a year when HIV prevention is a focus of thematic debate at the 48th meeting of the CND. Among the events that have particularly heightened our concern are:
* Mr. Costa, who last year expressed support for positive changes in the Russian criminal code, expansion of syringe exchange in countries facing injection driven epidemics and other measures to reduce drug-related harm, has apparently been rebuked by the U.S. State Department. Following a meeting with Robert Charles, U.S. Assistant Secretary for International Narcotics and Law Enforcement Affairs, Mr. Costa pledged to review all UNODC electronic and printed documents for references to "harm reduction" and to be "even more vigilant in the future."
* In Southeast Asia, UNODC has suspended a program that sought reduce drug users' vulnerability to HIV prevention through approaches that emphasized public health and drug users' human rights, rather than punishment.
* Even syringe exchange, affirmed as an effective and essential part of HIV prevention by UNAIDS, WHO, and UN member nations, has become politically unpalatable. A November e-mail from a senior UNODC staff member asked junior staff to "to ensure that references to harm reduction and needle/syringe exchange are avoided in UNODC documents, publications and statements."
We recognize that UNODC is dependent on contributions from donor nations, and that the U.S. is the single largest donor to UN drug control. At the same time, the lives of hundreds of thousands depend on sound, scientific approaches to HIV prevention. Numerous studies, including U.S. government studies, have found that strategies such as syringe exchange and methadone maintenance demonstrably diminish HIV transmission and other health risks. The fact that U.S. delegates declare the evidence in support of syringe exchange "unconvincing," as they did in last year's CND session, should not be allowed to determine the course of the UN drug control and HIV prevention efforts, which are inextricably and essentially linked. Nor should UNODC – a co-sponsor of UNAIDS, and an agency with an essential role to play in the course of the HIV epidemic – be asked to refrain from public statements about needle exchange simply because they do not fall within the realm of what the U.S. deems acceptable.
Strategies that attempt solely to achieve abstinence from drug use do not constitute an acceptable alternative to programs, such as syringe exchange, that help active drug users protect themselves from HIV/AIDS. Experience has shown that "zero tolerance" drug control efforts can have the effect of driving injection drug users underground and away from drug treatment and other health services. This is particularly true where, as in many countries, counter-narcotics efforts lead to false arrest, beatings and extortion by police, prolonged detention without trial, forced drug treatment, disproportionate incarceration in cruel conditions and, in some cases, extrajudicial execution. Programs such as syringe exchange and opiate substitution, by contrast, both prevent HIV infection and can provide a bridge to other health services. Restricting these programs is a blatant infringement of drug users' human right to health.
As you gather this year to debate HIV/AIDS prevention and drug abuse, we respectfully urge you to support syringe exchange, opiate substitution treatment and other harm reduction approaches demonstrated to reduce HIV risk; to affirm the human rights of drug users to health and health services; and to reject efforts to overrule science and tie the hands of those working on the front lines. No less than the future of the HIV epidemic is at stake.
cc: Joint United Nations Programme on HIV/AIDS
World Health Organization
Office of the High Commissioner for Human Rights
International Narcotics Control Board
Organizations and individuals who have signed this letter as of March 1, 2005 are listed in Additional file 1.
For the French version of this Open Letter please see Additional file 2. For the Spanish version of this Open Letter please see Additional file 3. For the Russian version of this Open Letter please see Additional file 4.
Supplementary Material
Additional File 1
Open Letter to the delegates of the Forty-eighth session of the Commission and other additional information.
Click here for file
Additional File 2
French version of the Open Letter
Click here for file
Additional File 3
Spanish version of the Open Letter
Click here for file
Additional File 4
Russian version of the Open Letter
Click here for file
| 15748283 | PMC555579 | CC BY | 2021-01-04 16:36:50 | no | Harm Reduct J. 2005 Mar 4; 2:3 | utf-8 | Harm Reduct J | 2,005 | 10.1186/1477-7517-2-3 | oa_comm |
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J Circadian RhythmsJournal of Circadian Rhythms1740-3391BioMed Central London 1740-3391-3-31576047210.1186/1740-3391-3-3ResearchDaily rhythm of cerebral blood flow velocity Conroy Deirdre A [email protected] Arthur J [email protected] Rebecca Q [email protected] Department of Psychology, The Graduate School and University Center of the City University of New York, New York, USA2 Department of Neurology and Neuroscience, New York Presbyterian Hospital, New York, USA3 Department of Health Psychology, Albert Einstein Medical College at Yeshiva University, Bronx, USA2005 10 3 2005 3 3 3 21 12 2004 10 3 2005 Copyright © 2005 Conroy et al; licensee BioMed Central Ltd.2005Conroy 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
CBFV (cerebral blood flow velocity) is lower in the morning than in the afternoon and evening. Two hypotheses have been proposed to explain the time of day changes in CBFV: 1) CBFV changes are due to sleep-associated processes or 2) time of day changes in CBFV are due to an endogenous circadian rhythm independent of sleep. The aim of this study was to examine CBFV over 30 hours of sustained wakefulness to determine whether CBFV exhibits fluctuations associated with time of day.
Methods
Eleven subjects underwent a modified constant routine protocol. CBFV from the middle cerebral artery was monitored by chronic recording of Transcranial Doppler (TCD) ultrasonography. Other variables included core body temperature (CBT), end-tidal carbon dioxide (EtCO2), blood pressure, and heart rate. Salivary dim light melatonin onset (DLMO) served as a measure of endogenous circadian phase position.
Results
A non-linear multiple regression, cosine fit analysis revealed that both the CBT and CBFV rhythm fit a 24 hour rhythm (R2 = 0.62 and R2 = 0.68, respectively). Circadian phase position of CBT occurred at 6:05 am while CBFV occurred at 12:02 pm, revealing a six hour, or 90 degree difference between these two rhythms (t = 4.9, df = 10, p < 0.01). Once aligned, the rhythm of CBFV closely tracked the rhythm of CBT as demonstrated by the substantial correlation between these two measures (r = 0.77, p < 0.01).
Conclusion
In conclusion, time of day variations in CBFV have an approximately 24 hour rhythm under constant conditions, suggesting regulation by a circadian oscillator. The 90 degree-phase angle difference between the CBT and CBFV rhythms may help explain previous findings of lower CBFV values in the morning. The phase difference occurs at a time period during which cognitive performance decrements have been observed and when both cardiovascular and cerebrovascular events occur more frequently. The mechanisms underlying this phase angle difference require further exploration.
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Background
It has been well documented that cerebral blood flow velocity (CBFV) is lower in sleep [1-7] and in the morning shortly after awakening [8-10] than in the afternoon or evening. Generally accepted theories about the time of day changes in CBFV attribute the fall in CBFV to the physiological processes of the sleep period and the increase during the day to waking processes. The low CBFV in the morning is thought to be a consequence of the fall in the overall reduced metabolic level [8,10,11] and reduced cognitive processing [12]. Additionally, the reduced physical activity [13], reduced body temperature, and the recumbent sleeping position have also been proposed as contributors [14] to the decline in CBFV and analogous brain processes.
An alternative to these explanations that attribute changes in CBFV to sleep and wake dependent processes is that this pattern of fluctuation reflects an endogenous process with circadian rhythmicity. The decline of CBFV across the sleep period and rise after subjects are awakened in the morning resemble the endogenous circadian changes in core body temperature (CBT), a reliable index of endogenous circadian rhythmicity. Both patterns are low during sleep, start to rise in the morning, reach their peak in the late afternoon, and then drop during the sleep period.
The aim of this study was to examine CBFV over ~30 hours of sustained wakefulness to unmask and quantify contributions of the endogenous circadian system. By not permitting sleep, the evoked changes dependent on this change of state will not contribute to the observed CBFV changes. We hypothesized that time of day changes in CBFV are due to endogenous circadian regulation. Previous studies have been limited by several factors. First, the environmental conditions (light level) and the behavior of the subject (sleep, meals, and caffeine intake) were not controlled [15,13,1,16]. Second, CBFV measurements were obtained at only a few circadian points. For example, Ameriso et al. [15] and Qureshi et al. [16] assessed CBFV between 6–8 am, 1–3 pm, and 7–9 pm. Diamant et al [13] assessed CBFV during the first 15 minutes of every hour across a 24 hour period. Given these brief time periods, the findings are only a schematic of the 24 hour profile. Third, primary output markers of the endogenous circadian pacemaker (such as core body temperature and melatonin production) were not assessed.
We employed the "constant routine" protocol, which was designed specifically to unmask underlying circadian rhythms in constant conditions [17]. CBFV was collected by Transcranial Doppler (TCD) ultrasonography for the entire study period. Core body temperature and salivary dim-light melatonin onset (DLMO) were measured for determination of circadian phase. Continuous electroencephalography (EEG) was performed to ensure wakefulness across the study. Additionally, measurements of blood pressure, heart rate, and end tidal carbon dioxide (EtCO2), three of the main regulators of CBFV, were collected every half hour.
Methods
Subject selection
Twelve subjects (10 men and 2 women; ages 19–38, mean 28 years) agreed to participate. One subject discontinued her participation because of a headache 15 hours into the study. Subjects were in good health, as assessed by medical history, semi-structured clinical interview, and physical exam. Information regarding menstrual cycle was not obtained from female subjects. Subjects also underwent an independent standard cerebrovascular assessment and were determined to be normal. They reported no symptoms of sleep problems (such as insomnia, obstructive sleep apnea, narcolepsy, or restless legs syndrome).
Subjects that were selected to participate kept to a designated sleep-wake schedule (that was negotiated from the subject's typical pattern) and filled out a sleep diary for the two weeks prior to the time in the laboratory. According to sleep diary reports, bedtimes ranged from 10:30 pm to 1:00 am and waketimes ranged from 6:00 am to 10:00 am. Alcohol and caffeine intake was discontinued for the entire week before the study. During the data collection, subjects were not permitted either alcohol or caffeine. All subjects were non-smokers.
Laboratory constant routine protocol
The study protocol was approved by the Institutional Review Boards of New York Presbyterian Hospital – Weill Medical College of Cornell University and The City College of New York. Subjects gave written and informed consent before participating. Subjects arrived at the sleep laboratory between 9:30 am and 10:00 am. They were oriented to the study procedures and to their bedroom. Electrodes were placed on the subject's head and face as they sat in a chair next to the bed. Data collection began at 11 am. Subjects remained in bed and awake in a semi recumbent position for 30 hours in an established "constant routine" (CR) protocol. Subjects remained in low (<25 lux) light levels which have been shown to have little or no entraining effect on the circadian pacemaker [18]. They were not allowed to get out of bed to urinate. Instead they urinated in private in a urinal or bedpan. Subjects remained awake from 11:00 a.m. on Day 1 until 5 p.m. on Day 2. Throughout the study, subjects were provided small meals (Ensure ® liquid formula plus one-quarter nutritional food bar) every 2 hours. Subject's typical total food and liquid intake for a day and a quarter were divided into 15 relatively equal portions. Only one subject participated in the CR per 30-hour period.
This protocol represents a modified CR in two ways. First, subjects were allowed to watch television and were therefore were not in "time isolation." Television content was monitored so that subjects were not exposed to programs with highly emotional themes. Second, subjects needing to defecate were allowed to go to the bathroom, which was located a few steps away from the bedside. We chose this method as an alternative to using the bedpan to ensure subject's comfort and study compliance. Three subjects (subjects 05, 06, and 10) got out of bed once at 3:30, 21:30, and 15:30, respectively, to defecate. One subject, subject 12, got out of bed twice, at 22:30 and 6:35. Subject 10 used the bathroom only during the adaptation period. A paired-samples t-test was conducted to evaluate the impact of getting out of bed to defecate on subject's CBT and CBFV values. The CBT and CBFV values in the two hours before getting up were compared to the two hours after the subject got up. Subjects 5 showed a slight decrease in CBT from before (M = 98.12, SD = 0.14) to after the subject returned to the bed (M = 97.91, SD = 0.08), t(3) = -5.17, p = .014). Subject 6 showed a decline in CBFV from before (M = 56.14, SD = 2.3) to after the subject returned to the bed (M = 45.67, SD = 3.7), t(3) = 5.49, p = 0.012). There were no other significant differences detected between these two time periods for subject 5's CBFV, subject 6's CBT, or for both times subject 12 got out of the bed. By visual inspection, the overall shape of the curves in these subjects was not affected and therefore these subject's data were included in subsequent analyses.
Transcranial Doppler ultrasound recordings
The current study utilized TCD ultrasonography to measure cerebral blood flow velocity. TCD is a non-invasive instrument (consisting of one or two 2-Mhz transducers fitted to a headband, MARC500, Spencer Technologies, Nicolet Biomedical Inc) that is used predominantly as a diagnostic tool to assess cerebral hemodynamics in normal and pathological conditions. TCD ultrasonography is predicated on a theory that involves the measurement of moving objects when combined with radar. When the instrument emits the sound wave, it is reflected by the blood cells that are moving in the vector of the sound wave [19].
CBFV was measured using either the right or left middle cerebral artery (MCA) using TCD sonography (TCD: DWL Multidop X-2, DWL Elektronische Systeme GmbH, D-78354 Sipplingen/Germany) through the temporal window. An observer who was present continuously during the recordings evaluated the quality of the signal. This enabled long-term recording of CBFV throughout the study. Fast Fourier Transformation (FFT) of the signal was used to analyze the velocity spectra. The mean velocity of the MCA was obtained from the integral of the maximal TCD frequency shifts over one beat divided by the corresponding beat interval and expressed in cm/sec. Analysis was conducted off line.
Measurement of standard markers of the circadian pacemaker
Body temperature recordings
Core body temperature was recorded at 1-minute intervals with an indwelling rectal probe (MiniMitter, Co. Bend, OR). A wire lead connected the sensor out of the rectum to a data collection system worn on the belt. Temperature readings were collected and saved into the device and monitored at hourly intervals by the investigator. After the study, the recordings were visually inspected and artifacts resulting from removal or malfunction of the probe were excluded from further analysis.
Salivary melatonin
Salivary samples of 3 ml were collected every hour from 11:00 a.m. on Day 1 to 4:00 p.m. on Day 2. Ten of these samples were used only for the determination of the timing of the salivary dim light melatonin onset (DLMO). For nine subjects, salivary DLMO was assessed across a ten-hour time window that included the ten hours before the CBT minimum. Immediately after collection, each saliva sample was frozen and stored at -20°C. Saliva samples were assayed using Bühlmann Melatonin Radio Immunoassay (RIA) test kit for direct melatonin in human saliva (American Laboratory Products Co., Windham, NH). Analysis was conducted at New York State Institute for Basic Research. Salivary DLMO time was selected based on two criteria. The saliva sample needed to have melatonin concentration 3 pg/ml or above and later samples needed to show higher levels (Bühlmann laboratories). Second, the 3 pg/ml threshold needed to occur within 6–10 hours before core body temperature minimum [20].
Polygraphic recordings
Electroencephalography (EEG) was continually assessed across the 30 hours to ensure that subjects maintained wakefulness. The following montage was used according to the international 10–20 system: C3-A2, C4-A1, O1-A2, O2-A1, ROC-A1, LOC-A2, and submentalis electromyogram (EMG). One channel of electrocardiogram was continuously recorded by monitoring from two electrodes (one on each side of the body at the shoulder chest junction). The EEG software (Rembrant Sleep Collection Software Version 7.0) was used for data acquisition and display of the signals on a personal computer. Throughout the CR, the investigator (DAC) monitored the quality of the recordings. The recordings were scored by RQS and DAC.
Blood pressure, heart rate, and end-tidal CO2
An automated blood pressure cuff was placed on the bicep of the subject and inflated two times each hour in order to determine changes in blood pressure and heart rate over time. Blood pressure and heart rate in one subject (02) was recorded via a finger blood pressure monitor (Omron Marshall Products, Model F-88). Blood pressure and heart rate in subjects 03, 04, 05, 06, and 07 were recorded with Omron Healthcare, Inc, Vernon Hills, Illinois 60061 Model # HEM-705CP Rating: DC 6V 4W Serial No: 2301182L. Blood pressure and heart rate for subjects 08, 09 and 10 was recorded with a similar blood pressure monitor (CVS Pharmacy Inc, Woonsocket, RI 02895 Model # 1086CVS). Blood pressure and heart rate recordings were not measured in subjects 11 and 12. EtCO2 was continuously obtained. A nasal cannula for monitoring expired gases was placed under the nose. Relative changes in carbon dioxide content were measured by an Ohmeda 4700 Oxicap (BOC healthcare). Mean EtCO2 levels were analyzed off-line. EtCO2 recordings were not measured in subjects 11 and 12.
Data Analyses
Data reduction and statistical procedures
CBT and CBFV values were first subjected to data rejection. All CBT values less than 96 degrees were determined to be artifact and were rejected. All CBFV values less than 20 cm/sec were determined to be artifact according to the clinical criteria set by the staff neurologist. Data reduction was accomplished by averaging into one minute, 30 minute or hourly bins. Correlations presented here were performed on mean values in 30 minute bins. To ensure that circadian measurements were made under basal conditions, the first five hours of the constant routine were excluded from all analyses to eliminate effects of study adaptation. The last hour was excluded to eliminate confounding effects such as expectation effects.
The data are presented in this article in three ways. First, CBT and CBFV values were plotted according to time of day (Figures 1 and 2). Second, CBFV values were aligned according to the CBT nadir (Figure 3) and third, the CBFV nadir was aligned to the CBT nadir (Figure 4). To align CBFV to the CBT circadian nadir as shown in Figure 3, the CBT nadir of each individual subject was set to circadian time 0, or 0°. The CBFV value that corresponded to the CBT nadir was then also set to 0. Each half hour data point after the temperature nadir and corresponding CBFV values were then set to a circadian degree. There were a total of 48 data points across the 24 hour period. Therefore, each data point was equal to 7.5 degrees so that each data point would accumulate to 360°. Lastly, mean values were obtained for CBT and CBFV at each circadian degree.
Figure 1 24-hour Cosine Curve fit to Mean Core Body Temperature (°F). Time course of CBT according to time of day. Shown is a double plot of the group (n = 11) mean levels (+/- SEM) of CBT (blue diamonds) fit with a 24-hour cosine curve (purple squares). Time of day is shown on the abscissa. The ordinate shows CBT values (degrees F). The vertical line indicates where the data was double plotted. Also displayed in the upper right corner is the non-linear cosine curve fit for mean CBT, R2 = 0.62. The overall mean circadian phase position of the minimum was 6:05 am.
Figure 2 24-hour Cosine Curve fit to Mean Cerebral Blood Flow Velocity (cm/sec). Time course of CBFV according to time of day. Shown is a double plot of the group (n = 11) mean levels (+/- SEM) of CBFV (blue diamonds) fit with a 24-hour cosine curve (purple squares). Time of day is shown on the abscissa. The ordinate shows CBFV values (cm/sec). The vertical line indicates where the data was double plotted. Also displayed in the upper right corner is the non-linear cosine curve fit for mean CBFV, R2 = 0.67. The overall mean circadian phase position of the minimum was 12:02 pm.
Figure 3 Mean CBT and CBFV Aligned to CBT Nadir. Time course of mean CBFV and mean CBT aligned to the nadir of CBT and then averaged. Shown is a double plot of the group (n = 11) mean levels (+/-SEM) of CBT (purple squares) and CBFV (blue circles) aligned to the phase of the circadian temperature cycle. Circadian time in degrees is shown on the abscissa. The ordinate on the left shows CBT values (degrees F) and CBFV (cm/sec) on the right. The vertical line indicates the CBT nadir.
Figure 4 Mean CBT and CBFV Aligned to Their Respective Nadir. Time course of mean CBFV and mean CBT aligned to each of their respective nadirs and then averaged. Shown is a double plot of the group (n = 11) mean levels (+/-SEM) of CBT (purple squares) and CBFV (blue circles) aligned to the phase of the circadian temperature cycle. Circadian time in degrees is shown on the abscissa. The ordinate on the left shows CBT values (degrees F) and CBFV (cm/sec) on the right. The vertical line indicates both the CBT nadir and the CBFV nadir. The correlation coefficient between the aligned rhythms is 0.77 (p < 0.01).
To align the CBFV nadir to the CBT nadir, first, the lowest value of CBT and the lowest value of CBFV were identified and set to circadian time 0, or 0°. Each half hour data point after the CBT nadir and CBFV nadir were then set to a circadian degree. There were a total of 48 data points across the 24 hour period. Therefore, each data point was equal to 7.5 degrees so that each data point would accumulate to 360°. Lastly, mean values were obtained for CBT and CBFV at each circadian degree.
Estimation of circadian phase
A 24-hour non-linear multiple regression -cosine curve fit analysis was performed on the CBT and CBFV data (SAS Institute, Cary, NC). This technique constrains the circadian period of CBT and CBFV to be within 24 hours. This technique used the following equations: model cbt = &avg_cbt + r * cos((2 * 3.1415) * (hours-&max_cbt)/24; model cbfv = &avg_cbt + r * cos((2 * 3.1415) * (hours-&max_cbfv)/24, where & = constants that center the curve at the actual average for each series (vertical centering) and the predicted maximum at the actual maximum (horizontal centering); r = the amplitude of the cosine wave. An additional analysis was performed which also yielded the estimated clock time for the CBT nadir and CBFV nadir (Synergy software, Kaleidagraph Version 3.6). Third, the minimum of the circadian rhythm of CBT and salivary DLMO were also used as markers of the endogenous circadian phase. A paired t-test was used to determine the overall phase difference between CBT and CBFV.
Results
Eleven subjects completed the protocol. The TCD probe was placed on either the right or left temple, whichever gave the better signal. Mean isonation depth of the TCD signal was 56.5 mm for the right MCA and 55.6 mm for the left MCA (range 53–60 mm). The constant routine ranged from 28 to 30 hours in duration. Polygraphic recordings confirmed sustained wakefulness across essentially the entire protocol in all but one subject. Subjects that had difficulty remaining awake were monitored closely and aroused when needed by engagement in conversation. Results from the polygraphic recordings are not presented here. We do not present the results of the polygraphic recordings because, for the purposes of this study, these recordings were used solely to monitor whether subjects were awake or asleep. The first five hours and the final hour of data from the constant routine were excluded from analysis.
Core body temperature, cerebral blood flow velocity and the 24-hour day
A 24 hour non-linear multiple regression, cosine fit analysis revealed that the overall mean CBT rhythm (n = 11) fit a 24 hour cosine rhythm (R2 = 0.62, p < 0.01), Figure 1. The mean CBT across all subjects was 98.6 °F (+/- 0.03 °F). Figure 2 shows that a 24-hour non-linear multiple regression, cosine analysis fit a 24 hour cosine rhythm (R2 = 0.67, p < 0.01), Figure 2. The mean CBFV across subjects was 40.6 cm/sec (+/- 0.54 cm/sec). Salivary DLMO occurred 7.7 hours prior to the CBT nadir in nine subjects, which served only as a secondary measure of endogenous circadian phase position in those subjects. The mean salivary melatonin concentration across the ten hour window was 15.3 pg/ml (+/-3.05 pg/ml).
CBFV rhythm is 90 degrees out of phase with the CBT rhythm
The overall mean circadian position of CBT occurred at 6:05 am and the mean position of CBFV occurred at 12:02 pm (Figure 3), yielding a 6 hour or 90 degree statistically significant difference (t = 4.9, DF = 10, p < 0.01). In individual subject data, the differences ranged from 0 to 8.5 hours. In eight subjects, the CBFV phase occurred later than the respective CBT phase, with mean difference of 5.2 hours. In two subjects, the CBFV nadir occurred earlier than the respective CBT nadir, with a mean difference of 6 hours. In one subject, there was no difference between the phase of CBT and CBFV. However, this subject's CBT rhythm was highly unusual, with the nadir occurring at 11:35 am on Day 2. Nevertheless, we felt the most appropriate way to present the data was to include this subject in the overall analysis. When the phase of CBFV was shifted so that the lowest value was aligned to the lowest CBT value, the two parameters were highly correlated (see Figure 4; r = 0.77, n = 98, p < 0.01). While the difference in the two rhythms variability was large, Fisher's z-transformed values revealed that the amplitudes of the two parameters were similar. The amplitude of CBFV yielded a z score of 4.25 and CBT yielded a z score of 3.06.
Blood pressure recordings and systemic hemodynamic variables
A Pearson correlation revealed a positive relationship between CBT and heart rate (r = 0.40, p < 0.01) across the 24 hour period. Diastolic blood pressure (DBP) and CBT showed a negative correlation (r = -0.30, p < 0.05). EtCO2 showed a trend towards a direct relationship with CBFV (r = 0.24, p = 0.10). Blood pressure, heart rate, and EtCO2 served only as regulators of CBFV and were not analyzed according to circadian phase.
Discussion
This study is the first to use the constant routine (CR) protocol to determine whether the endogenous circadian pacemaker contributes to the previously reported diurnal changes in CBFV. The current work demonstrates that, with limited periodic external stimuli and a constant posture, there is 24-hour rhythmicity in CBFV. Subjects showed a cycle of approximately 24 hours in CBT, which has been previously demonstrated with the CR [21].
Figure 3 illustrates the intricate relationship between the rhythms across the study period. At approximately the CBT acrophase, the relationship between the two rhythms undergoes a transition. Between 180 and 240 degrees, CBFV is still rising and CBT is changing directions (first rising, reaching its peak and then falling). This period between 180 and 240 has been described as a "wake maintenance zone", a time in the circadian cycle during which humans are less likely to fall asleep [22]. In our subjects, the CBT is near its zenith or just starting to fall at this time and CBFV is still steadily rising. Higher values in CBT and CBFV are associated with activation and therefore these two endogenous rhythms may be promoting wakefulness during this "wake maintenance zone". However, at the end of this transition period, CBT is falling and CBFV is still rising, perhaps reflecting continued activation of the cerebral cortex. Whereas the two-process model predicts increased tendency to sleep as CBT falls [23], our finding may provide the mechanism by which wakefulness is effortlessly maintained before bedtime.
Figure 3 further illustrates that as wakefulness is extended past the subject's habitual bedtime (approximately 270 degrees), the two rhythms decline together. Between 0 and 60 degrees, CBFV steadily declines and CBT is steadily rising. The lower CBFV values in the morning may play a role in cognitive performance impairments [24], particularly the 3–4.5 hour phase difference in neurobehavioral functioning relative to the CBT rhythm that has been previously demonstrated in constant routine protocols [25].
Earlier studies using simultaneous EEG and TCD to continuously measure CBFV across the sleep period have concluded that, except for periods of REM sleep, [26,27], there is a linear decline in CBFV across the night during periods of non-REM sleep [1,28]. Other groups utilizing these techniques simultaneously speculated that the decline in CBFV through the night was a "decoupling" of cerebral electrical activity and cerebral perfusion during non-REM sleep [8-10]. In all studies [1,8-10,28], CBFV values were lower in the morning during wakefulness than during wakefulness prior to sleep at night. The current findings show that the decline in CBFV is present during wakefulness in the night time hours and therefore may not be attributed solely to sleep and associated changes that normally influence CBFV (including factors such as the shift to recumbency, and reduced activity, metabolic rate and respiratory rate).
Moreover, our interaction with the subjects and the monitoring of EEG for signs of sleep resulted in no sleep in all but one subject. The one exception was in a subject who lapsed into brief periods of sleep. Therefore, the fall in CBFV in 10 out of 11 subjects cannot be explained by the occurrence of non-REM sleep. It is possible, however, that the decline of CBFV across the night and early morning may be secondary to the sleep deprivation that is part of the constant routine. Brain imaging studies across sustained periods of wakefulness have shown significant decreases in absolute regional cerebral glucose metabolic rate in several areas of the brain [29-34].
The drop in CBT which preceded the parallel fall in CBFV needs to be considered as a possible explanation for the CBFV changes. The fall in CBT during sleeping hours is attributed in part to sleep-associated changes and in part to strong regular circadian forces independent of the sleep period. CBT is, in fact, one of the key and most extensively studied indices of the circadian phase. It is also known that CBT is highly correlated with brain temperature and brain metabolic rate [35]. Imaging studies have documented the intimate relation between brain activity and increased metabolic rate and oxygen delivery through perfusion. Therefore, it is plausible that CBT is a direct influence on CBFV or an index of decreased metabolic need for blood flow. The prevailing hypothesis that there is tight coupling of normal neuronal activity and blood flow was formulated over 100 years ago [36]. The drop in CBFV may be a consequence of the lowered cerebral activity secondary to lowered brain temperature. In contrast, two studies of exercise-induced hyperthermia showing decreased global and middle cerebral artery CBFV [37,38] do not support this hypothesized direct relationship between the two variables. However, one of the main purported mechanisms for the fall in CBFV in these exercise studies, the hyperventilation induced lowering of PaCO2, is unlikely present during waking while lying in bed at night. Therefore, CBT declines remain a plausible explanation for the portion of the 24 hours when CBFV declined.
Mechanisms of CBFV regulation
This protocol allowed the unique opportunity to evaluate blood pressure, heart rate, and EtCO2 in the absence of sleep, in subjects with constant posture, and highly restricted movements. While blood pressure clearly falls during sleep in normal individuals, the absence of sleep in the current study obviates the explanation that CBFV declines are secondary to lowered blood pressure. Furthermore, we sampled blood pressure throughout the day and night and found a weak inverse relationship between DBP and CBT. This finding is in contrast to a careful study of circadian influence on blood pressure in the absence of sleep which showed no change in blood pressure during the descending portion of the body temperature curve [39]. Nevertheless, our finding was weak and likely does not provide the explanation for the CBFV changes. The small-inverse relationship between Et CO2 and CBT is similar to that found by Spengler et al. [40], who showed a consistent but small amplitude circadian rhythm in mean end-tidal EtCO2 on a CR protocol. EtCO2 showed a trend towards a direct relationship with CBFV, which is consistent with previous studies showing that changes in EtCO2 are associated with changes in CBFV [41,42]. Heart rate was correlated with CBT, consistent with the findings of Van Dongen et al [39].
Clinical correlation
The approximate 6 hour (90 degree) phase angle difference between the CBFV and CBT suggests that CBFV continues to decline into the early to mid-morning hours. This finding is consistent with a time window in the morning during which several physiological changes have been observed. For example, cerebral vasomotor reactivity to hypocapnia, hypercapnia, and normoventilation has been found to be most reduced in the morning [15,16]. It is tempting to suggest that the the low CBFV values in the morning may also help explain the well established diurnal variation of the onset of cerebrovascular accidents (CVAs) [43]. A meta-analyses of 11,816 publications between 1966 to 1997 found that there was a 49% increased risk of strokes between 6 am and 12 pm [44]. This time period is in agreement with studies on myocardial infarction (MI) and sudden death [45]. The increased incidence of these events has been attributed, in part, to the surge of blood pressure [13,46,47] and platelet aggregability [48,49] in the morning when patients are getting out of bed. Our results demonstrate that even in the absence of surges in blood pressure, the phase of CBFV reaches its lowest values during the hours before 12 pm. This further suggests that the endogenous rhythm of CBFV may be associated with the risk of CVAs in the late morning hours even without changes in posture or activity.
Conclusion
Overall, the results demonstrate that CBFV, in the absence of sleep, exhibits properties of a circadian rhythm, as it rises and falls across a 24 hour period. The 6 hour (90 degree) phase angle difference in the CBFV rhythm with respect to the CBT rhythm may help explain previous findings of lower CBFV values in the morning. The phase difference occurs at a time period during which cognitive performance decrements have been observed and when both cardiovascular and cerebrovascular events occur more frequently. The mechanisms underlying this phase angle difference require further exploration.
List of abbreviations
CBFV Cerebral Blood Flow Velocity
CBT Core Body Temperature
TCD Transcranial Doppler
EtCO2 End tidal Carbon Dioxide
DLMO Dim Light Melatonin Onset
EEG Electroencephalogram
MCA Middle Cerebral Artery
FFT Fast Fourier Transformation
CR Constant routine
EMG Electromyogram
SBP Systolic Blood Pressure
DBP Diastolic Blood Pressure
CVA Cerebrovascular accident
MI Myocardial infarction
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
DAC coordinated, carried out, analyzed, and interpreted the study. AJS participated in the analysis and interpretation of the findings. DAC drafted the manuscript and AJS provided final approval of this version. RQS participated in data collection and data analysis. DAC and AJS co-designed the study. All authors read and approved the final manuscript.
Acknowledgements
The authors are grateful to the volunteer participants who completed this extremely difficult protocol, to the research assistants: Jason Birnbaum, Will Carias, RN, Laura Diaz, Boris Dubrovsky, Mathew Ebben, Ph.D., Carrie Hildebrand, Lars Ross, Greg Sahlem, Mathew Tucker, Ayesha Udin, to those who helped with the data analysis: Scott Campbell, Ph.D. of New York Presbyterian Hospital, White Plains, Abdeslem ElIdrissi, Ph.D. of The Institute for Basic Research, Staten Island, NY, Larry Krasnoff, Ph.D. of Digitas, New York, and Andrew Scott, MBA, to those who provided their expert advice: William Fishbein, Ph.D. of The City College of New York, Paul Glovinsky, Ph.D. of The Sleep Disorders Center, Albany, NY, Margaret Moline, Ph.D. of Eisai, Inc, Charles Pollak, MD of The Center for Sleep Medicine, New York Presbyterian Hospital-Cornell, and Alan Segal, MD of The Department of Neurology, New York Presbyterian Hospital, and to others who helped make this study possible: Stacy Goldstein, Neil B. Kavey, MD, Igor Ougorets, MD, and Jerry Titus.
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| 15760472 | PMC555580 | CC BY | 2021-01-04 16:39:13 | no | J Circadian Rhythms. 2005 Mar 10; 3:3 | utf-8 | J Circadian Rhythms | 2,005 | 10.1186/1740-3391-3-3 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-2-161574352810.1186/1742-4690-2-16ReviewDiscovery of adult T-cell leukemia Takatsuki Kiyoshi [email protected] Department of Internal Medicine II, Kumamoto University School of Medicine, 1-1-1 Honjo, Kumamoto 860-8556, Japan2005 2 3 2005 2 16 16 31 1 2005 2 3 2005 Copyright © 2005 Takatsuki; licensee BioMed Central Ltd.2005Takatsuki; 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.
Adult T-cell leukemia (ATL) was first reported as a distinct clinical entity in 1977 in Japan. The predominant physical findings are skin lesions, lymphadenopathy and hepatosplenomegaly. The ATL cells are of mature T-helper phenotype and have a characteristic appearance with indented nuclei. There is striking frequent hypercalcemia with increased numbers of osteoclasts. Central to the identification of the disease is a striking geographic clustering in southwestern Japan and the isolation of human T-cell lymphotropic virus type-1 (HTLV-1) from the cell lines of patients. Worldwide epidemiological studies have been made through international collaborations. Several diseases were found to be related to HTLV-1 infection. Moreover, it was noted that an immunodeficiency state may be induced by HTLV-1 infection. In Japan, HTLV-1 carriers have been estimated to be 1.2 million, and more than 700 cases of ATL have been diagnosed each year.
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Adult T-cell leukemia (ATL) was described as a distinct clinical entity in 1977 in Kyoto, Japan [1,2]. The illness is manifested by presentation in adult life: frequent skin lesions, lymphadenopathy, hepatosplenomegaly, and elevated white blood cell count with abnormal lymphoid cells. The abnormal ATL cells are of mature T-helper phenotype and have a characteristic appearance with especially indented or lobulated nuclei. There is strikingly frequent hypercalcemia with increased numbers of osteoclasts. Central to the identification of the syndrome is the striking geographic clustering in southwestern Japan and the isolation of the human T-cell lymphotropic virus type-1 (HTLV-1) from the cell lines of patients with ATL.
Background of our ATL study
It was around the 1973 that we came to recognize the existence of ATL, previously an unknown disease. I would like to give a retrospective view concerning the background of our studies on ATL.
Many clinicians in Japan probably feel that the descriptions of diseases in the literature from abroad differ from the features of the diseases observed in their practices in Japan. We can mention many examples in the field of hematology. One of these is that the incidence of chronic lymphocytic leukemia is quite low, being only 2% of all hematological malignancies. What differs is not only the incidence but also the detailed symptoms and signs of diseases. This vague feeling that an autochthonous pathology exists in Japan may be considered as key factor in the background of our study of ATL.
The second major factor is the recent progress made in basic immunology. Having an interest in immunoglobulin abnormalities, I studied multiple myeloma and related diseases in the days when even the word 'immunoglobulin' was not yet in use. The central theme of immunology at that time was to identify the structure of antibodies. Immunology has advanced rapidly, and myeloma was defined as a B-cell malignancy. Therefore, it was quite natural to enlarge the objectives of our clinical studies to all lymphoproliferative diseases.
At this stage of our research in Kyoto, joint studies with young researchers were initiated. This was the third and most important element of the background of our ATL study. I became acquainted with Drs. Takashi Uchiyama and Junji Yodoi, who are currently both working in Kyoto, during their postgraduate training. Dr. Yodoi prepared an antiserum against human thymocytes and invited us to direct our attention to T-cells.
In the course of examining patients with various lymphoproliferative disorders, we arrived at the conclusion that ATL was a disease which had not been described anywhere before. It was recognized that T-cell malignancy had a relatively high incidence among Japanese adults and that most of the patients with this disease were from Kyushu. This discovery was made from our bedside observations rather than from laboratory work. Thereafter, Dr. Uchiyama went to the National Cancer Institute, Bethesda to work with Dr. Thomas A. Waldmann and raised a monoclonal antibody, called 'Tac' antibody, which later played an important role in our ATL studies. Dr. Yodoi has developed a new field of research by identifying a novel cytokine, ATL-derived factor (ADF), which has proved to be important in redox regulation. At the end of 1981, I moved from Kyoto to Kumamoto, which is located in the middle of Kyushu. Our studies advanced remarkably there, due mainly to the efforts of excellent young co-workers including Drs. Kazunari Yamaguchi, Toshio Hattori, and Masao Matsuoka, who are now independently working in Tokyo, Sendai and Kyoto, respectively.
Development of virology
This discovery of ATL ushered in some dramatic developments in oncology, virology and, unexpectedly, neurology and other fields of medicine. When we reported a series of 13 patients with ATL in 1976 on the occasion of the 16th International Congress of Hematology in Kyoto, it was stated that 'attempt to elucidate leukemogenesis in this disease should be directed towards exploring the genetic background and a possible viral involvement'. HTLV (human T-cell leukemia virus), the pathogen of ATL, was first reported by Dr. Robert C. Gallo and his co-workers in Bethesda in 1980 and 1981. They isolated HTLV from cultured cells taken from a patient with an aggressive variant of mycosis fungoides and another with Sezary syndrome. Although both patients were said to have cutaneous T-cell lymphoma, they had some unusual features which, in retrospect, linked them to the clinical entity now called ATL.
In Japan, co-culturing of ATL cells with umbilical cord lymphocytes was first done successfully by Dr. Isao Miyoshi's group in Okayama, who obtained the cell line MT-1. Dr. Yorio Hinuma and his co-workers in Kyoto demonstrated that ATL patients have antibodies against presumed viral antigens on MT-1 cells by indirect immunofluorescence method. Subsequently, a retrovirus was isolated and called ATLV (adult T-cell leukemia virus). Since Dr. Mitsuaki Yoshida and his group in Tokyo showed that HTLV and ATLV are, in fact, identical, the term HTLV-1 (human T-cell lymphotropic virus type 1) has been commonly used.
Furthermore, the following observations have been successively reported to support the etiologic association of HTLV-1.
1. All patients with ATL have antibodies against HTLV-1.
2. The areas of high incidence of ATL patients correspond closely with those of high incidence of HTLV-1 carriers.
3. HTLV-1 immortalizes human T cells in vitro.
4. Monoclonal integration of HTLV-1 proviral DNA was demonstrated in ATL cells.
Thus, HTLV-1 is the first retrovirus directly associated with human malignancy.
Diagnosis and classification of ATL
The diagnostic criteria for HTLV-1 associated ATL have been defined as follows:
1. Presence of morphologically proven lymphoid malignancy with T-cell surface antigens (These abnormal T lymphocytes include typical ATL cells and the small and mature T lymphocytes with incised or lobulated nuclei that are characteristic of chronic or smoldering type).
2. Presence of antibodies to HTLV-1 in the sera.
3. Demonstration of monoclonal integration of HTLV-1 provirus by Southern blot method.
Virological study led us to classify the patients with ATL into four clinical subtypes according to the clinical features: acute, chronic, smoldering, and lymphoma. The acute type is the prototypic ATL in which patients exhibit increased number of ATL cells, frequent skin lesions, systemic lymphadenopathy, and hepatosplenomegaly. Most of these patients are resistant to chemotherapy and have a poor prognosis. In chronic ATL, white blood cell count is mildly increased, and skin lesions, lymphadenopathy, and hepatosplenomegaly are sometimes exhibited. In the past, chronic ATL was thought to be 'chronic T-lymphocytic leukemia'. Smoldering ATL is characterized by the presence of a few ATL cells in the peripheral blood over a period of years. Frequent symptoms are skin or pulmonary lesions. Chronic and smoldering ATL often progress to acute ATL after a long period (crisis). Lymphoma-type ATL is characterized by prominent systemic lymphadenopathy, with few abnormal cells in the peripheral blood. This type has been diagnosed as nonleukemic malignant lymphoma. Later, the Lymphoma Study Group in Japan proposed a practical diagnostic criteria for classifying ATL into these four subtypes.
Epidemiology of ATL and HTLV-1
HTLV-1 is endemic in southwestern Japan, especially Kyushu and Okinawa, in the Caribbean Islands, and in Central Africa. It has been revealed that there are HTLV-1 carriers in South America, Papua New Guinea, the Solomon Islands, South China, and other isolated populations in the world, including Ainus in Hokkaido and Aborigines in Australia. These epidemiological studies have been promoted by international collaborations, to which Drs. William A. Blattner in Bethesda, Guy de The in Paris, Kazuo Tajima in Nagoya, Shunro Sonoda in Kagoshima, and many other epidemiologists have made important contributions. Dr. Daniel Catovsky and his colleagues in London delineated the clinical features of ATL patients originating in the Caribbean islands.
HTLV-1 related diseases
On the other hand, several diseases have been found to be related to HTLV-1 infection. Drs. Antoinne Gessain and Guy de The first reported the association of tropical spastic paraparesis (TSP) and HTLV-1, and Dr. Mitsuhiro Osame and his co-workers in Kagoshima studied features of HTLV-1-associated myelopathy (HAM/TSP) in detail. HTLV-1 uveitis was subsequently described by a study group of ophthalmologists in Kyushu. HTLV-1 may also be associated with bronchopneumonitis, arthritis, polymyositis and other inflammatory conditions. Moreover, it was noted that an immunodeficiency state may be induced by HTLV-1 infection.
Prevention and treatment
In Japan, HTLV-1 carriers have been estimated to be 1.2 million, and more than 700 cases of ATL have been diagnosed each year. Majority of HTLV-1 transmission occurs via one of three routes, all of which require the passage of virus-infected cells. HTLV-1 carrier mothers transmit the virus to newborns mainly through breast milk. Dr. Shigeo Hino and his group in Nagasaki conducted intensive fieldwork regarding the mother-to-infant infection. Carrier mothers in Japan have been instructed to refrain from breastfeeding or modify the feeding procedures to prevent HTLV-1 infection. There is convincing evidence that HTLV-1 can be transmitted from individual to individual by sexual contact, especially males to females, and also through blood transfusions. All blood donated at the Red Cross Blood Centers in Japan has been subjected to HTLV-1 antibody testing beginning in November 1986.
Treatment of ATL is the most difficult task. Dr. Masanori Shimoyama in Tokyo has organized a multicenter study group for the chemotherapy of ATL. Many other trials have been reported: deoxycoformycin, α-interferon combined with azidothymidine, and so on. The use of humanized monoclonal antibodies against the interleukin-2 receptor has been championed by Dr. Waldmann's group. More recently, successful allogeneic bone marrow transplantation for patients with ATL has been reported from many institutions.
Addendum
Before my retirement from Kumamoto University in 1996, I edited a book titled "Adult T-cell Leukaemia" [3]. Many of the above-mentioned investigators contributed chapters to this book. I also would like to add that Dr. Matsuoka and I wrote a chapter on ATL in a textbook of leukemia [4]. It may be pertinent here to commemorate the establishment of the International Retrovirology Association, which was announced on the occasion of the 5th International Conference of Human Retrovirology held in Kumamoto on May 11–13, 1992.
A companion article by Robert C. Gallo recollects the events surrounding the discovery of the first human retrovirus, HTLV-I [5].
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Takatsuki K Uchiyama T Sagawa K Yodoi J Seno S, Takaku F, Irino S Adult T cell leukemia in Japan Topics in Hematology 1977 Amsterdam, Excerpta Medica 73 77
Uchiyama T Yodoi J Sagawa K Takatsuki K Uchino H Adult T-cell leukemia: Clinical and hematologic features of 16 cases Blood 1977 50 481 492 301762
Takatsuki K ed Adult T-cell Leukaemia 1994 Oxford, Oxford University Press
Matsuoka M Takatsuki K Henderson ES, Lister TA, Greaves MF Adult T-Cell Leukemia 2002 7 Leukemia, Philadelphia, Saunders 705 712
Gallo RC The discovery of the first human retrovirus: HTLV-1 and HTLV-2 Retrovirology 2005 2 17 15743526 10.1186/1742-4690-2-17
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J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-2-11573056010.1186/1743-0003-2-1EditorialObituary: Yukio Mano (1943–2004) Ikoma Katsunori [email protected] Department of Rehabilitation and Physical Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan2005 24 2 2005 2 1 1 23 2 2005 24 2 2005 Copyright © 2005 Ikoma; licensee BioMed Central Ltd.2005Ikoma; 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.
Yukio Mano, MD, PhD (1943–2004)
Associate Editor, Journal of NeuroEngineering and Rehabilitation
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I was terribly shocked to hear of the tragic and sudden passing of Yukio Mano on November 7, 2004, at the age of 61. He had not been well this past year but had been working continuously until just ten days before his death.
Yukio Mano (Figure 1) was born on August 26, 1943 in Aichi Prefecture, Japan. He studied medicine at Nagoya University School of Medicine, and graduated in 1968. After he completed his basic medical training in Japan, he began his medical career in the USA in 1972. He first worked as a resident at the Institute of Rehabilitation Medicine at New York University for two years, then in 1974 he moved to the Department of Neurology at Baylor College of Medicine working as an assistant instructor and resident for one year. In 1975, he became a research fellow at the University of Maryland School of Medicine, in the Neuromuscular Research Unit. Yukio Mano studied the most advanced techniques in the fields of rehabilitation medicine and neurology during his four-year stay in the USA. Upon returning to Japan in 1976, he resumed his research in rehabilitation medicine at Nagoya University and the National Center of Neurology and Psychiatry, Japan. In 1981, he became an associate professor in the Department of Neurology at Nara Medical University. He was responsible for running the rehabilitation department there as a specialist in rehabilitation medicine. Finally, he was granted a full professorship in Rehabilitation and Physical Medicine at Hokkaido University (Graduate) School of Medicine in 1995. Yukio Mano was committed to helping researchers studying rehabilitation medicine, as well as young medical doctors and therapists in the rehabilitation field. He extensively expanded the Rehabilitation Department of Hokkaido University, and my colleagues and I had expected his leadership to continue into the future.
His research interest was rehabilitation medicine, especially brain plasticity. He was the first Japanese developer of an apparatus that could deliver transcranial magnetic stimulation. Using this apparatus, he analyzed changes in the central nervous system resulting from various diseases, including brain plasticity after anastomosis of the musculocutaneous and intercostal nerves following cervical root avulsion, and cortical reorganization in training. The knowledge resulting from his research proved beneficial in the rehabilitation of disabled patients. He also emphasized a multidisciplinary approach to rehabilitation medicine and adopted new techniques from engineering. He received the Best Paper Award from ANNIE (Artificial Neural Networks in Engineering) in 2000 for his work entitled, "Adaptive FES Switching System for Hemiplegics".
Yukio Mano served as a council member of the International Society of Electrophysiology and Kinesiology (ISEK) and, in 2000, he organized the XIII Congress of the ISEK in Sapporo, Japan. He was also a member of the editorial board of the Journal of Electromyography and Kinesiology. He served as a council member of many Japanese societies and organized nationwide congresses in Japan even in the year he died. He welcomed the launch of the new Journal of NeuroEngineering and Rehabilitation (JNER) and was honored to be asked to join the Editorial Board as an Associate Editor. He was indeed fully active to his last day.
We cannot help praising him for all that he has accomplished in the fields of rehabilitation medicine, neurophysiology and kinesiology. We must also not forget that this remarkable scientist was also a caring family man. He always showed his love for his family as well as for his colleagues and friends. The loss of such an outstanding personality has been met with great sorrow by his family and the international scientific community. We will always remember him with great affection.
Figure 1 Yukio Mano, MD, PhD
1943–2004
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J Neuroengineering RehabilJournal of NeuroEngineering and Rehabilitation1743-0003BioMed Central London 1743-0003-2-51574062210.1186/1743-0003-2-5ResearchDesign considerations for a wearable monitor to measure finger posture Simone Lisa K [email protected] Derek G [email protected] Kessler Medical Rehabilitation Research and Education Corporation, West Orange, NJ, USA2 Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA2005 1 3 2005 2 5 5 31 1 2005 1 3 2005 Copyright © 2005 Simone and Kamper; licensee BioMed Central Ltd.2005Simone and Kamper; 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
Objective measures of hand function as individuals participate in home and community activities are needed in order to better plan and evaluate rehabilitation treatments. Traditional measures collected in the clinical setting are often not reflective of actual functional performance. Recent advances in technology, however, enable the development of a lightweight, comfortable data collection monitor to measure hand kinematics.
Methods
This paper presents the design analysis of a wearable sensor glove with a specific focus on the sensors selected to measure bend. The most important requirement for the glove is easy donning and removal for individuals with significantly reduced range of motion in the hands and fingers. Additional requirements include comfort and durability, cost effectiveness, and measurement repeatability. These requirements eliminate existing measurement gloves from consideration. Glove construction is introduced, and the sensor selection and glove evaluation process are presented.
Results
Evaluation of commercial bend sensors shows that although most are not appropriate for repeatable measurements of finger flexion, one has been successfully identified. A case study for sensor glove repeatability using the final glove configuration and sensors does show a high degree of repeatability in both the gripped and flat hand positions (average coefficient of variability = 2.96% and 0.10%, respectively).
Conclusion
Measuring functional outcomes in a portable manner can provide a wealth of information important to clinicians for the evaluation and treatment of movement disorders in the hand and fingers. This device is an important step in that direction as both a research and an evaluation method.
Finger flexionRange of Motionsensorshome monitoring
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Background
Rehabilitation research has recently begun to emphasize the use of objective outcome measures to assess the efficacy of rehabilitation treatment or intervention [1]. These goals could be greatly furthered through the development of wearable measurement systems that provide an opportunity to evaluate how the individual participates in home and community activities. Information collected in this manner can provide a more realistic snapshot of activity and function than traditional methods which restrict measurements to the clinical or research site. Data describing actual usage in the home is especially important for the hand as hand movement is so closely tied to performance of functional tasks. In order to understand how individuals truly interact with their environments, we wish to obtain quantitative measures of finger flexion and extension over longer periods of time than traditionally investigated (such as monitoring over a full circadian cycle).
Unfortunately, rehabilitation researchers have very few methods available to objectively evaluate hand use and function outside the clinic, especially for individuals with moderate to severe reduction in range of motion in the hand and fingers. Joint range of motion (ROM) is a primary measure in hand rehabilitation, and is traditionally assessed using manual goniometry. Manual methods, however, are limited to static measurements. In addition, they can be adversely affected by common issues such as inter- and intra-operator error and operator experience level [2].
In order to objectively measure hand use outside the clinic, the selected method must be both portable and capable of recording continuous streams of data over time. Automated measurement methods can meet these requirements as well as eliminate most operator-related issues. For example, 24-hour monitoring has proven useful for tracking parameters such as heart rate and blood pressure, and the same concept can be extended to other useful parameters, although currently no wearable devices are available to measure finger posture in a similar manner. Practical medical applications can include providing input for virtual reality therapy, measuring hand function in the planning of rehabilitation therapies, or evaluating the outcome of interventions under more realistic conditions.
Sensor gloves have been proposed to provide automatic measurements of finger and joint position during different activities, with the virtual reality industry continuing to drive the market for sensor gloves [3]. For example, Fifth Dimension Technologies (5DT, Irvine, CA) produces a 5-sensor and a 16-sensor version wireless sensor glove which transmits data to a nearby computer. Two joints per finger are captured with these expensive devices (proximal interphalangeal joint (PIP) and metacarpophalangeal joint (MCP)) using fiber optic sensors. The White Hand Group (Mississippi State University) is developing a lower-cost (<$500) flexion data glove using fiber optics that has two sensors per finger. The glove is tethered to a computer and is aimed at VR and gaming applications rather than accurate measurement applications.
CyberGlove (Immersion Corporation, San Jose, CA) contains 5 to 22 "patented bend-sensing technology" strain gauges to measure individual joint movements. This glove, however, is very expensive and difficult for stroke survivors to don. The Essential Reality (Mineola, NY) P5 glove also uses bend sensors, Abrams/Gentile flex sensors (wired flexion measurements), and infrared (IR) emitters (line-of-site wireless position and rotation measurements). The flex sensors are attached to each finger by rings between the proximal and distal interphalangeal joints (DIP). While price of this glove is appealing (<$200), the glove is not portable and requires the wearer to keep the top of the glove always facing a fixed antenna IR receiver.
The Humanglove™ (Humanware S.R.L., Pisa, Italy) is a flexible glove with 20 Hall effect sensors to measure bend. The Humanglove was evaluated for feasibility and repeatability in finger range of motion in all joints; work continues to establish the measurement accuracy [2].
Several research gloves have been reported with no clinical results. Karlsson et al. [4] determined finger flexion by measuring the pressure changes in airtight polyvinyl tubes on three fingers. Zurbrügg [5] measured flexion using potentiometers mounted on the back of the hand, although the construction is not durable for long term measurements. Hofmann and Henz [6] used inductive length encoders attached to a cotton glove to measure flexion and gestures in real time. The glove is not easy to put on, and the sensors can move around relative to the joint position. Jurgens et al. [7] proposed an innovative method using electrically conducting ink printed on a flexible polyester plastic for a low cost solution, although disadvantages include extreme sensitivity to small changes in temperature, and a moderately slow response time.
The existing glove systems do not meet the needs set forth in our device requirements. Although some gloves are lightweight, others such as force feedback and exoskeleton based gloves are too heavy and bulky for home use by individuals with hand impairments. Most are too expensive or require custom sizing to reduce errors; measurement errors are decreased when gloves fit more snugly [2]. However, donning a tight glove can be difficult to impossible for some individuals with movement disorders in the hand. Full gloves have other limitations as well. Actions such as wrist flexion and rotation in full fabric gloves can cause the glove material to move over the skin. In this case, friction can prevent the material from returning completely to the original position, leaving the sensors located in different positions over the joints [2]. This source of error is manifested as a drift in measured bend that is difficult to detect, characterize, and eliminate from recorded data streams.
The environment proposed for the glove use is also very challenging. A majority of the existing methods are not appropriate for wearable measurements in the home and community, and none are designed for use with clinical and rehabilitation populations who may exhibit significant range of motion restriction in the fingers.
The long term goal of this project, therefore, is to design, build, and clinically evaluate a novel low-cost wireless device to measure hand and finger activity while individuals participate in normal home and community activities. It will be used to study the loss of hand function that can occur following neurological disorders such as stroke and traumatic brain injury, and to evaluate how treatments can improve an individual's ability to function in the home and community environments.
This article describes the design process for the development of the glove, discussing wearability issues such as comfort, durability and weight and focusing on sensor characterization and selection. The results of an initial evaluation of measurement repeatability while wearing the glove are presented. Appropriate characterization of the glove must occur in two phases: evaluation of the sensors separately, and then evaluation of the entire glove after appropriate sensors have been identified and characterized. Full repeatability results and measurement accuracy will be reported in the future.
Methods
The creation of effective custom measurement systems requires detailed attention to the requirements and design analysis phases of the development process. While it may be tempting to solve multiple problems with one system, this often leads to overly complicated devices that take too long to complete, and may not actually meet the core requirements. To avoid this scenario, the sensor glove project focused specifically on a set of core requirements presented below, and pursued a multi-step analysis of the design to ensure the requirements were being appropriately addressed. The steps include an analysis of glove construction methods, characterisation of sensors, and exploration of sensor repeatability and accuracy on the bench and during subject trials.
The primary requirements fall into four categories.
1) Donning and Removing: The glove must be easy to don and remove for individuals having reduced range of motion in the hand and fingers.
2) Comfort and Durability: The glove must be lightweight and unobtrusive, and permit comfortable wearing for at least 24 hours. It must not restrict range of motion or represent a snag hazard during use.
3) Function: The glove must detect a wide range of activities, including fine motor activities such as writing. Measurements must be accurate and repeatable. The sensors must not move around, but remain in the same position with respect to the joints and phalanges over time. The system must allow the performance of normal daily activities, although use in water is not required. Measurements must be repeatable with an error no more than 5% of full scale.
4) Cost: Manufacturing cost is an important consideration for several reasons. An inexpensive device allows several to be deployed simultaneously to perform research studies more quickly. A low-cost glove can be considered disposable for sanitary reasons. Finally, wearers may unknowingly limit or modify their hand motions and activities in an attempt to protect an expensive device. A low-cost alternative ensures that more realistic and representative data are captured and recorded.
As noted, a variety of sensors have been employed to measure joint angle, including strain gauges or bend sensors, fiber optics, pneumatics, or Hall effect sensors. While each has advantages and disadvantages, the requirements for our glove preclude the use of all but the bend sensors. Using fiber optics to measure bend requires a light source such as a light emitting diode and a photo detector. The amount of bend is proportional to the attenuation of detected light in specially treated sections of fiber that pass over the tops of the finger joints. Disadvantages of this method include the complexity of glove construction and price. Hall effect sensors, which detect magnetic fields, and can be configured as proximity sensors to provide a linear output proportional to distance from a magnetic source. By placing a series of sensors on the back of a glove in a predefined pattern, the joint angle can be computed from the changing field strengths measured by the sensors when the fingers bend. While these devices are small, the resulting glove can be somewhat bulky and will require a magnetic source, adding to overall weight. Interference from other electromagnetic sources is also a concern. Strain gauges detect stretch produced by joint rotation. They may have very high accuracy, but are expensive and often delicate.
Bend sensors offer a lightweight and inexpensive alternative. These sensors are thin flexible membranes that change resistance when bent; increasing bend angle is generally associated with increased measured resistance. One or more layers of carbon and a conductive material are applied over a thin substrate. Depending on the sensor type, bending motion forces conductive particles further apart, increasing the resistance to current flow. These sensors are popular for detecting obstacles and measuring large changes in bend angle, and are proposed for accurate measurement of finger posture. However, most exhibit a time-varying creep behavior when held in a fixed bent position that reduces the accuracy of measurement. We sought to find bend sensors that would be feasible.
Glove Construction Methods
In order to explore the first two requirements, ease of donning and comfort/durability, several prototype glove systems were created in order to identify the best materials and construction methods to satisfy these requirements. Several materials were evaluated, including blends of Lycra®, Nylon and cotton. The material must exhibit stretch so that finger motion and bending are not restricted. While some blends resisted finger motions, most were flexible enough that the wearer could forget the glove was on. A discussion of the process and final selection of glove materials and application method can be found elsewhere [8].
The final glove material is a 93% Lycra® / 7% Nylon blend; it is used to create thin sleeves into which the sensors are inserted. One sleeve is attached to the back of each finger in order to locate the sensor directly over each joint; the optimal adhesive is very thin, double-sided toupee tape. Applying the sensors to the back of the fingers, rather than using a traditional glove that must be donned, allows easy application and removal for individuals who cannot fully open all fingers to put on a traditional glove. Total glove cost without sensors is less than $2.50.
Comfort and durability were evaluated over a 24 hour period. The glove survived intact and did not impede any activities other than showering and tucking in a shirt. Because the sensors are attached to the back of the hand, the palmar surface of the hand is uncovered and free of obstruction, leaving the sense of touch intact. While only one individual was used to narrow down the different prototype ideas, 24 individuals (12 with brain injury and 12 healthy controls) are currently participating in a study to more fully evaluate the glove configuration and performance.
Sensor Repeatability
An early decision was made to use an inexpensive bend sensor as the sensing element for its low profile, light weight, and low cost. While the first prototype glove has only 5 sensors, the design provides the flexibility to add additional sensors for all joints and for finger adduction/abduction. Sensors from several manufacturers were characterized in order to determine if measurements were repeatable and if large changes in finger posture and fine motor control could be captured. In addition, the calibration relationship between bend angle and measured resistance was evaluated.
The importance of repeatability testing cannot be overemphasized. The sensors were evaluated separately before being incorporated into the sensor glove. Two types of tests were performed using a set of tubes of known diameter: 1) determination of full scale and the resistance-bend relationship (using tube diameters: 4", 3", 2", and 1.5", 1" and 0.75"), and 2) evaluation of measurement repeatability (using the 3" diameter tube). A physical guide was placed on each tube to ensure that the sensor was placed in the same location on the tube each time it was applied. From four to ten sensors from each manufacturer were evaluated. To test the sensors, each was initially placed flat on the table for several seconds, then bent over a single calibration tube for 30 to 60 seconds, and then placed flat again. Resistance readings were taken during each phase. The expected profile for this activity should look like a rectangular wave between two resistance values: a lower resistance value in the flat position, then the higher value when bent on the tube, and ending with the original lower flat value. Multiple measurements were taken to evaluate the variation in sensor outputs, and to construct a bend resistance versus tube diameter relationship that could be used for calibration purposes.
Full scale is determined from the endpoints of the calibration relationship. The minimum measured resistance value corresponds to the flat position, and the maximum resistance value is represented by the resistance measured on the 0.75" calibration tube. The maximum value is an estimate of what would be observed in human subjects because the bending radius over a finger joint is not exactly replicated by bending over different diameter tubes; however, making this assumption allows the different sensors to be compared to one another for selection purposes. Full scale error is computed as the percent change in resistance measured while a sensor is fixed unmoving on a calibration tube with respect to the full scale range.
A second error is also reported, and is calculated as the percent change in the peak sensor resistance with respect to the magnitude of the step function rise in resistance when the sensor is positioned on a calibration tube. This error calculation was also selected for comparison because a number of everyday activities such as grasping and moving objects find the hand in roughly this position.
As discussed in the results section, additional analysis was performed to explore an unexpected time-varying behavior of the sensors. Collection of these data and of the data described in the next section was performed using a host computer with an 8 channel 16-bit A/D card. The sensors were connected to an interface box and data on all sensors was collected at 100 Hz using LabView (National Instruments, Austin, TX). Analysis was performed using Microsoft Excel.
Sensor Glove Repeatability
A major concern in developing a measurement method is that measurements are repeatable. If the bend sensors and the configuration of these sensors on the fingers will be used for several measurements during the same session, or for measurements over several sessions, then repeatability must be established before the data can be given credence. Rigorous validation of repeatability, however, is often lacking from descriptions of various "data gloves."
One method to evaluate repeatability of sensor glove-type devices has been proposed by Wise [9] and expanded by Dipietro [2]. The method was specifically developed for devices that perform semi- or fully-automated goniometric measurements. Repeatability was established using a custom mold created by each subject. The mold was made while the subject simulated a grip position around a wet mixture of Plaster of Paris. After the mold dried, it was used to ensure that the subject's finger positions were identical for several consecutive gripping actions on the mold. Testing with 5 healthy control subjects revealed errors that led to recommendations for improvements in measurement methodology. Dipietro [2] repeated these procedures with some clarifications in hand position in the evaluation of the Humanglove, and echoed the need for standardized testing protocols for sensor glove devices.
This standardized repeatability testing protocol procedure includes four tests. We will use two, Tests A and C, which focus on repeatability of multiple measurements over a single data collection session. Tests A and C are performed here because the first focus of the glove is for single data collection sessions (which can last up to 24 hours). The protocol used here follows the modifications proposed in [2]; an overview of the tests appears below.
Test A: A roughly cylindrical plaster mold is custom created for each subject to ensure that the fingers are flexed to the same position for each test. The participant clenches the mold for 6 seconds and then releases the mold for 6 seconds. This clench/release cycle is repeated 10 times. Repeatability measurements are taken from each sensor during the clench phases.
Test C: The participant places the hand on a table top and alternately raises the hand and lightly flexes the fingers, and returns the hand to the table top for 6 second each. Repeatability of the flat hand position is explored in this test. In order to achieve repeatability in hand and finger position, and outline of the hand profile is drawn on paper and placed on the table. This cycle is also repeated 10 times.
For each test above, the participant rested for at least 1 minute, and then repeated the entire test. This was done 10 times for both Test A and Test C, for a total of 100 grip/release cycles for each test. Descriptive statistics are computed (mean, standard deviation, and coefficient of variation). The percent coefficient of variation (standard deviation divided by the mean*100%) is used to compare the measurement variability among the five digits and between the two repeatability tests.
Results and Discussion
Glove Construction
Figure 1 shows a prototype of the sensor glove monitor. For the test shown here, one sensor was used to measure flexion of each metacarpophalangeal (MCP) joint. The sensors are located inside the beige sensors sleeves, which are attached to the back of the metacarpals and proximal phalanges. The sensors do not move relative to the joint under measurement. The forearm-mounted box contains signal conditioning. In the next prototype, the box will also contain a wireless transmitter and the cable from the left of the box will be removed, allowing the participant to move around freely. Instead of Velcro bands, a comfortable band of flexible material will hold the box to the forearm. Data will initially be transmitted wirelessly to a nearby laptop computer, and eventually transmitted wirelessly to a waist mounted data recorder. The sensors and glove sleeves weigh less than 7.1 grams; adding the signal conditioning box increases the weight to 85 grams. The final device will have the added weight of a battery and small wireless transmitter.
Figure 1 Prototype of the sensor glove monitor. The monitor is shown with five sensors placed on the MCP joints. Signal conditioning is contained in the box. The next prototype will include a wireless link for data download to an external computer, enabling the removal of the cable extending out of the back of the box.
Sensor Repeatability
Performing sensor evaluation and characterization early in the design process allowed us to identify several shortcomings of commercial bend sensors and eventually select an appropriate sensor.
The first sensor evaluated was the Abrams-Gentile Entertainment, Inc. (New York, NY) sensor patent #5,086,785. Attempts to measure repeatable bend resistance versus calibration tube diameter failed because the measured resistance decayed over time. The Abrams-Gentile sensor exhibits the most common behavior that we will refer to as Type A behavior, and it appears in Figure 2 as line "AG". The sensor reached a peak resistance value just as it was wrapped around the calibration tube, with an immediate decay in resistance over time. We expected that the sensor values would be constant; however, the drift in measured resistance prevented an accurate and repeatable measurement of bend. To eliminate other potential sources of error, the analysis was repeated on ten other sensors, and the problem finally isolated to the sensors by testing each directly using an ohmmeter.
Figure 2 Sensor time-varying properties. Each of several sensors is placed on a calibration tube for at least 30 seconds, and then stretched flat on a table in order to verify that a constant relationship exists between bend angle and measured resistance. Ideally, these curves should be flat but a significant time-varying decay renders most unusable for this application. This figure shows several representative curves for the three manufacturers evaluated. AG: Abrams-Gentile sensors, SS: SpectraSymbol sensors, FP: Flexpoint sensors (several types).
The average decay in resistance while on the tube was computed. After 30 seconds, the average error for the Abrams-Gentile sensors was 9.5% of full scale, and 24.4% of step function rise resistance (Table 1). The Abrams-Gentile sensor never settled on a final resistance value, but over an extended two-day data collection session continued to slowly decay. While these sensors are appropriate for many applications such as position detectors and indicators of gross movement, we determined that they are not appropriate for accurate and repeatable measurements of finger flexion.
Table 1 Sensor decay over time for three sensor types fixed over a 3-inch tube
% Full Scale % Step Function Rise
Time (sec) Abrams-Gentile Flexpoint Time (sec) Abrams-Gentile Spectra Symbol Flexpoint
0 0.0% 0.0% 0 0.0% 0.0% 0.0%
2 3.9% 0.3% 2 9.9% 15.2% 2.6%
4 5.2% 0.3% 4 13.4% 17.8% 3.2%
15 7.9% 0.6% 15 20.4% 25.7% 6.1%
30 9.5% 0.8% 30 24.4% 31.8% 8.9%
The same testing was repeated using sensors from Spectra Symbol (Salt Lake City, UT). Similarly, the step function rise in resistance measured on application to the 3" calibration ring dropped 31.8 % in the first 30 seconds (Figure 2, Type A: SS). Again, the sensor is better suited to sensing a change in angle, rather than the magnitude of the change. A calibration relationship was not explored because the magnitude of the error was so large.
Six different sensor configurations were evaluated from Flexpoint (South Draper, UT). These included flex sensors with an overlaminate adhered by a pressure sensitive adhesive (sensor #1), with a robust polyimide overlaminate (sensor #2), with no overlamination but with a stiff backer (sensor #3), and an overmolded sensor (sensor #4) for harsh environmental conditions. Representative contours for the 3" calibration test are shown in Figure 2, labelled "FP: #1, 2, 4, and 5" Sensor 3 exhibited the same large decays observed with the Type A Abrams-Gentile and SpectraSymbol sensors shown in the figure. In contrast, sensors 1 and 2 (Type B) responded to the initial fast bend over the tube with a slow rise in resistance that never reached a peak. When the sensors were removed from the tube and placed flat again, the resistance decayed but did not reach baseline values for many minutes. Sensor 4 (Type C) exhibited a fast response to a peak value, dropped 15% of the rise amount and then slowly recovered the 15% over several minutes, but never returned to the baseline value when placed flat at the end of the test. None of these sensors (1–4) is appropriate for the sensor glove. However, in consultation with the company describing our exact needs, a solution was identified. The bend sensors are generally not supplied without some type of protection layer. However, these layers tended to cause the observed decay problems, making these sensors inappropriate for this application. Evaluation of bare sensors (Figure 2, Type D) revealed the initial peak resistance followed by decay; however, the magnitude of the decay after 30 seconds was only 0.8% full scale or 8.9% of step function resistance rise, which is acceptable for this application. The bare version of the sensor is approximately $7.10 in low quantities. The average relationship between bend angle and resistance for 5 sensor trials is shown in Figure 3. The error results from all sensors are shown in Table 1.
Figure 3 Resistance – bend relationship of the Flexpoint sensor. The Flexpoint sensor has a nonlinear relationship between measured resistance and bend diameter, as found by measuring resistance for sensors wrapped around calibration tubes of different diameters. For illustration purposes, the relationship presented here is an average of several sensors; a separate relationship will be measured for each sensor used in the sensor glove.
Bend sensors are used in a number of university and home projects, despite our findings that most are not repeatable for moderate to fine resolution measurements. Instead, most are appropriate for binary ON/OFF applications, or applications that do not require high resolution or highly repeatable results. Examples include using the sensor as a "whisker" to sense the proximity of an object for collision detection, for detecting large changes in bend angle, or for more unique applications such as adding effects to music [10]. Others report early results for such implementations as measurement of foot flexion for biofeedback [11] although follow-up work on calibration and analysis methods is still pending. We located no references validating and using these sensors, and only one reference that indicated that the Abrams-Gentile sensor was "difficult to work with" [12]. For the measurement device described here, repeatability of measurement is an important criterion. Only the bare Flexpoint sensor was found to be appropriate for measuring fine changes in bend angle in a repeatable manner. If another sensor is used in this application, there is no way to determine the actual bend radius because a wide range of measured resistance values (caused by the decay) correspond to a single bend radius.
Sensor Glove Repeatability
Repeatability testing began with the evaluation of the sensors and final sensor selection, and continues by considering the performance of the entire glove. The sensor glove repeatability testing has been performed with one participant, who is the first in a study that will include repeatability testing for 6 healthy controls. All participants complete an Institutional Review Board consent form and the required HIPAA authorization forms. The plaster mold was created as shown in Figure 4.
Figure 4 Grip mold. The grip mold is custom made for each subject. It provides a repeatable position for the fingers to assume for multiple grip-release activities in order to evaluate repeatability of measurement.
The results of Test A reveal the repeatability of measurement in the grip hand position. Coefficient of variability for all five digits is less than 6% (thumb: 0.55%, index: 5.37%, middle: 1.91%, ring: 4.61%, pinkie: 2.36%). Figure 5 shows the mean and standard deviation of measured MCP joint position while gripping the mold 100 times. The mean values indicate the average resistance of the sensors when the fingers are gripping the mold. In this test, the actual mean value of bend is not critical, it just represents the joint position when the mold was made. In addition, these values are not calibrated. For this individual, the thumb MCP is the least bent (having the lowest resistance values) while the ring and pinkie fingers are significantly bent. The repeatability information is located within the very low coefficient of variation, or variation of the measured values about the mean.
Figure 5 Repeatability testing of grip position. Repeatability testing of one participant for the grip test (Test A). Means and standard deviations are shown. Mean values differ because each finger is in a different position when gripping the mold. Repeatability information is contained in the variation around the mean.
The results for Test C show the repeatability in the flat hand position with the sensors fully extended. Coefficient of variability for all five digits is less than 1% (thumb: 0.18%, index: 0.08%, middle: 0.05%, ring: 0.07%, pinkie: 0.15%). Figure 6 shows the mean and standard deviation of measured MCP joint position while placing the hand flat 100 times. Descriptive statistics and coefficient of variability for Tests A and C are shown in Table 2. The variation in measurements for the flat hand position is extremely small over the 100 cycles, which is very encouraging considering that the flat hand position is only guided by an outline of the hand on the tabletop. Five additional participants will complete this repeatability testing and results presented in the future.
These results are similar to [2] in that the measurement repeatability was better in the flat hand (Test C) case than in the grip mold case (Test A). Dipietro [2] speculated that the "hand is positioned more accurately by placing it flat...than by clenching the mold." Wise [9] also noted that "increasing forces produced errors in the glove measurements, especially in the MCP joints." We observed through separate experimentation that in the flat hand position, the musculature in the hand tends to relax. In the grip position, in contrast, the muscles must maintain at least a minimal contraction in order to prevent dropping the mold. Varying levels of grip force can be applied, and the finger positions can be shifted slightly while still holding the mold closely. The measured variations in resistance easily accounted for the observed variations in the grip repeatability test results. The solution was to ask the individual to only grip the mold with enough strength to hold it, and that instruction will be given to future participants. While executing these tests can be challenging, we must concur that standardized testing is vital to ensuring that the collected data are useful and repeatable.
Conclusion
The glove developed in this research is unconventional, and its uniqueness owes to the appropriate attention to the core requirements during the design analysis phase. The glove provides a novel method to evaluate actual functional capacity, starting with the dynamic evaluation of ROM as individuals participate in their normal daily activities.
The glove is not a generic solution, but a specific device to measure finger posture in an underserved population. Bend sensors were selected for their lightweight low profile, and for cost effectiveness. Although significant error can be introduced by using bend sensors, sensors with the appropriate repeatability characteristics have been identified. The bare Flexpoint sensors provided repeatable measurements with a 30 second error of 0.8% full scale, as compared to 9.5% for the next best solution, the Abrams-Gentile sensor. The overall glove configuration shows strong promise for providing repeatable measurements over long periods of time without undesired movement over the joints. Coefficient of variability averaged 2.96% and 0.10% across the five digits for the grip test and flat hand test, respectively. The glove is low cost; the total cost for the disposable portion of the device (glove material, adhesive and sensors) is less than $40, which is significantly less than any other reported solution. The glove can be used not only to measure flexion in individuals with reduced range of motion who cannot wear traditional measurement gloves, but also to measure passive ROM and cleaning activities in individuals who cannot initiate volitional activity in their affected hand.
Future directions include completion of the current study to establish repeatability and to identify calibration methods. The wireless link will be added midway through the study to provide full wearability and unencumbered movement, paving the way for extended studies in the home and community environments.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
LS made substantial contributions to the conception and design of the device and drafted the manuscript. DK identified a need for the device, contributed to the requirements and design of the device, and participated in revisions of the manuscript.
Figure 6 Repeatability testing of flat hand position. Repeatability testing of one participant for the flat hand test (Test C). Means and standard deviations are shown. Mean values are similar because all fingers are straight when data collection occurs.
Table 2 Variability in glove measurements for repeatability Tests A and C
Test A: Grip Mold Test C: Flat Hand
Mean (Ohms) SD (Ohms) CoV Mean (Ohms) SD (Ohms) CoV
Thumb 9138 50 0.55% 8257 15 0.18%
Index 14400 773 5.37% 8562 7 0.08%
Middle 27873 533 1.91% 7992 4 0.05%
Ring 51364 2368 4.61% 8231 6 0.07%
Pinkie 50629 1194 2.36% 7586 11 0.15%
Acknowledgements
This work was supported by a grant from the Foundation of University of Medicine and Dentistry of New Jersey, and by the Henry H. Kessler Foundation.
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| 15740622 | PMC555583 | CC BY | 2021-01-04 16:37:39 | no | J Neuroengineering Rehabil. 2005 Mar 1; 2:5 | utf-8 | J Neuroeng Rehabil | 2,005 | 10.1186/1743-0003-2-5 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-2-151573055610.1186/1743-422X-2-15ResearchMaternal plasma viral load and neutralizing/enhancing antibodies in vertical transmission of HIV: A non-randomized prospective study Kamara Paul [email protected] Loyda [email protected] Miguel [email protected] Heidi [email protected] Pauline [email protected] Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, 1665 University Blvd., Ryals Building, Room 217, Birmingham AL 35294-0022, USA2 Department of Microbiology and Medical Zoology, University of Puerto Rico, Medical Sciences Campus, School of Medicine, San Juan, Puerto Rico3 US Military HIV Research Program, Walter Reed Army Institute of Research, Division of Retrovirology, Silver Spring, MD 20910, USA4 Breast Center, Baylor College of Medicine, One Baylor Plaza, MS: BCM 600, 335A, Houston, TX 77030, USA2005 24 2 2005 2 15 15 5 10 2004 24 2 2005 Copyright © 2005 Kamara et al; licensee BioMed Central Ltd.2005Kamara 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
We examined the association and interaction between maternal viral load and antibodies in vertical transmission of HIV in a non-randomized prospective study of 43 HIV-1 infected pregnant women who attended the San Juan City Hospital, Puerto Rico, and their 45 newborn infants. The women and infants received antiretroviral therapy.
Methods
A nested PCR assay of the HIV-1 envelope V3 region and infant PBMC culture were performed to determine HIV status of the infants. Maternal and infant plasma were tested for HIV neutralization or enhancement in monocyte-derived macrophages.
Results
Twelve (26.7%) infants were positive by the HIV V3 PCR assay and 3 of the 12 were also positive by culture. There was a trend of agreement between high maternal viral load and HIV transmission by multivariate analysis (OR = 2.5, CI = 0.92, p = 0.0681). Both maternal and infant plasma significantly (p = 0.001 for both) reduced HIV replication at 10-1 dilution compared with HIV negative plasma. Infant plasma neutralized HIV (p = 0.001) at 10-2 dilution but maternal plasma lost neutralizing effect at this dilution. At 10-3 dilution both maternal and infant plasma increased virus replication above that obtained with HIV negative plasma but only the increase by maternal plasma was statistically significant (p = 0.005). There were good agreements in enhancing activity in plasma between mother-infant pairs, but there was no significant association between HIV enhancement by maternal plasma and vertical transmission.
Conclusion
Although not statistically significant, the trend of association between maternal viral load and maternal-infant transmission of HIV supports the finding that viral load is a predictor of maternal-infant transmission. Both maternal and infant plasma neutralized HIV at low dilution and enhanced virus replication at high dilution. The antiretroviral treatments that the women received and the small sample size may have contributed to the lack of association between HIV enhancement by maternal plasma and vertical transmission.
HIV vertical transmissionHIV neutralizationmaternal viral loadHIV enhancement
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Background
The rate of HIV-1 infection has been increasing rapidly among women of childbearing age. At the end of 2003 women accounted for 50% of adults living with HIV/AIDS worldwide [1]. Consequently, the number of pediatric AIDS cases due primarily to perinatal (peripartum or intrapartum) transmission is rapidly increasing. Mother-to-child transmission accounts for more than 90% of all HIV infections in infants and children worldwide. In 2003 an estimated 2.1 million children under 15 years were living with HIV/AIDS [1]. Zidovudine (ZDV) given as either an intensive or short course regimen significantly reduces perinatal transmission [2,3]. However, because of its cost, ZDV is not always available in poorer countries of the world. Successful use of nevirapine therapy in preventing perinatal transmission offers hope for more affordable treatment for poor women worldwide [4,5]. However, in 2003, only one in ten pregnant women was offered services for preventing mother-to-child HIV transmission [1]. Further, whether treated with ZDV or nevirapine, a portion of HIV-positive women still transmit virus to their offspring vertically and the problem of maternal-infant transmission through breast milk remains unsolved. Therefore, there is need for continued studies of viral and immunological factors associated with maternal-infant transmission of HIV so that other effective and affordable strategies to prevent transmission may be developed.
Although some studies show no association between the presence of HIV neutralizing antibodies in maternal sera and the risk of perinatal transmission [6,7], other studies report a reduction in the risk of vertical transmission in pregnant women whose sera contain neutralizing antibodies to HIV [8]. A number of studies have indicated lower transmission rates from infected pregnant women with high antibody titer or with high affinity/avidity antibody to conserved portion of HIV-1 glycoprotein 41 [9], to the CD4 binding site [10] or the V3 loop of glycoprotein 120 [11,12], and to the p24 Gag protein [13]. Other studies have reported that non-transmitting mothers more frequently have such antibodies to their own virus than do transmitting mothers and that transmitting mothers rarely have neutralizing antibody against their own children's isolates [14,15]. In contrast, a study by St. Louis, et al. [16] found no evidence that anti-V3 loop antibody protected against perinatal transmission. Further, a study by Lallemant, et al. [17] showed that mothers with higher antibody titers to peptides corresponding to the V3 region of gp120 and the immunodominant domain of gp41 had a higher risk of perinatal transmission. The authors hypothesized that women who display the broadest antibody response to V3 may be experiencing the greatest viral turnover [18] which could make them more at risk for transmitting virus to their offspring.
In contrast to neutralizing antibodies, non-neutralizing antibodies may enhance HIV infection by binding to the virus and facilitating its uptake by cell types that carry immunoglobulin (Fc) or complement receptors. Antibodies that enhance HIV replication in vitro by either Fc gamma receptor- or complement receptor-mediated endocytosis (FcγR-ADE or C'-ADE) have been identified in sera from HIV-1-infected individuals [19-25] and from many gp120-vaccinated volunteers [19,20]. An in vitro study of antibody dependent enhancement (ADE) of HIV-1 infection in human term syncytiotrophoblast cell cultures suggested that both FcγR-ADE and C'-ADE may contribute to maternal-infant transmission of HIV-1 [26]. Pancino, et al. [27], reported that mother-infant transmission of HIV was associated with maternal antibodies to the envelope gp160 and to a highly conserved domain of the trans-membrane glycoprotein. Mann, et al. [28], observed that certain combinations of antibody subclasses occurred more frequently in mothers who transmitted HIV-1 to their offspring than in non-transmitters and suggested that ADE may occur in mother-infant transmission of HIV-1. However, C'-ADE was not found to be associated with maternal-infant transmission of HIV [29] and the role of FcγR-ADE in maternal-infant transmission has not been determined.
Maternal plasma virus load has been shown to be strongly associated with perinatal transmission of HIV [27,30,31] and it was reported that there is no absolute threshold of maternal viral load below which HIV transmission does not occur [32]. Although transmission did not occur at a threshold below 2,000 copies/ml [18,33] or below 1000 copies/ml [34], more recent meta-analysis has demonstrated that occasional transmission does occur below a viral load threshold of 1,000 copies/ml [35]. Another study indicated that viral load correlated with vertical transmission in women at the clinical stage A1 (asymptomatic) of infection [36].
However, the association of both viral load and enhancing activity (presumably by FcγR-ADE) in maternal plasma and vertical transmission of HIV has not formerly been examined. Thus, we examined the association of these factors (independently and combined) in vertical transmission using samples from mother-infant pairs from San Juan, Puerto Rico, previously described by Melendez-Guerrero et al [37]. Neutralization/enhancement of a R5 tropic strain of HIV-1 subtype B by maternal plasma was examined in monocyte-derived macrophage cultures.
Results and Discussion
Study sample
A cohort of 43 HIV-1 subtype B infected pregnant women attending antenatal clinic at the San Juan City Hospital, Puerto Rico, was enrolled into a prospective study from their first antenatal visit until delivery [37]. Eleven women were enrolled during their first trimester, 24 during their second trimester, and 8 during their third trimester of pregnancy. Approximately forty-five HIV-1 infected women gave birth at the San Juan City hospital during 1998. All of the women recruited into the study received some form of antiretroviral therapy as detailed and referenced [3,38-44] in Table 1. All of the infants were enrolled into the study shortly after delivery and most (86%) were also enrolled in the antiviral protocols during their first six weeks of life (Table 1). One mother had triplets, therefore, a total of 43 mothers and 45 infants (regarded as 45 mother-infant pairs) were enrolled.
Table 1 Number and percent of HIV positive pregnant women and infants assigned to the different AIDS Clinical Trails Group (ACTG) protocols or to zidovudine (ZDV)
ACTG Protocol # Number of women (%) Dose and frequency
185 [38] 16 (37.2) Mother: ZDV according to 076 protocol [3] plus anti-HIV immune serum globulin (HIVIg) or immune globulin (Ig) (200 mg/kg) every 28 days followed by 1.0 mg/kg/hr continuous infusion during labor and delivery.
Infant: HIVIg (200 mg/kg) or normal Ig within 12 hrs of birth plus oral ZDV syrup (2.0 mg/kg) every 6 hours after birth (beginning within 8–12 h) and continuing for 6 weeks.
249 [39] 4 (9.3) Mother: Didanosine (ddi) IV (1.6 mg/kg) on day one, during pregnancy, followed by an oral dose (200 mg) one week after the initial dose. Oral ddi is then administered every 12 hrs until labor starts and every 12 hrs after delivery until 6 weeks post-partum. During labor and delivery patients receive a loading dose followed by continuous infusion.
Infant: Oral ZDV syrup (2.0 mg/kg) every 6 hours after birth and continuing to week 6.
250 [40] 5 (11.6) Mother: ZDV plus Nevirapine (200 mg/kg) single dose during labor.
Infant: Oral ZDV syrup (2.0 mg/kg) every 6 hours after birth and continuing to week 6 plus single dose of Nevirapine (2 mg/kg) after birth.
296 [41] 3 (7.0) Mother: ZDV as in protocol 185.
Infant: Oral ZDV syrup (2.0 mg/kg) every 6 hours after birth and continuing to week 6.
316 [42] 9 (20.9) Mother: Nevirapine (200 mg oral dose) or the corresponding placebo during delivery plus ZDV (as in 076).
Infant: ZDV perinatal prophylaxis (2.0 mg/kg) plus single 2.0 mg/kg oral dose of Nevirapine or Nevirapine placebo administered between 48 and 72 hrs of life.
324 [43] 3 (7.0) Mother: ZDV before and after delivery as is usual but oral administration of ZDV (300 mg) every 3 hrs, 3 doses total during delivery.
Infant: Oral ZDV syrup (2.0 mg/kg) every 6 hours after birth and continuing to week 6.
332 [44] 1 (2.3) Mother: Stravudine (d4T, 30–40 mg) during pregnancy and 0.05 mg/kg/hr during delivery in combination with 3TC 150 mg followed by 150 mg during delivery.
Infant: Stravudine (d4T, 1 mg/kg) single dose on day 35–42, in combination with 3TC (2.0 mg/kg/dose).
ZDV [3] 2 (4.7) Mother: ZDV (2.0 mg/kg) every 28 days followed by 1.0 mg/kg/hr continuous infusion during labor.
Infant: Oral ZDV syrup (2.0 mg/kg) every 6 hours after birth and continuing to week 6.
Demographic and clinical characteristics of the mothers and vertical transmission
The mean age of the mothers was 24 years (range 14–38 years; Table 2). Majority of the women (88.4%) were infected through heterosexual contact. Based on the 1993 CDC revised classification system for HIV infection and disease progression, only 6 of the mothers (those in the A3 and B3 categories) were classified as AIDS cases [45]. Approximately two-thirds (67.4%) of the mothers had viral load levels below 10,000 RNA copies/ml and were classified as having low viral loads (LVL) based on the categorization by Contopoulos-Ioannidis & Ioannidis [46]. The remaining 32.6% of mothers had viral loads above 10,000 RNA copies/ml and were classified as having high viral loads (HVL). CD4+ T cell counts in the women ranged from 23 to 1165 cells/mm3 of blood with a mean count of 425 cells/mm3. Twenty-six of the 43 women delivered their babies by normal vaginal delivery; the remaining women had cesarean sections (Table 2).
Table 2 Age, source of infection, clinical status and transmission outcome for HIV-positive mothers
Variable Number (%)
Age of mothers (years)a
< 25 14 (32.6)
25–30 12 (27.9)
> 30 16 (37.2)
Missing 1 (2.3)
Source of infection
Heterosexual contact 38 (88.4)
IV drug use 3 (7.0)
Unknown 2 (4.6)
Clinical status (CDC classification 45)
A1 11 (25.6)
A2 14 (32.6)
A3 1 (2.3)
B1 2 (4.6)
B2 10 (23.3)
B3 5 (11.6)
Viral load (copies/ml)b
<10,000 (range 83–9,078) 29 (67.4)
≥ 10,000 (range 10,220–484,703) 14 (32.6)
Mode of delivery
Vaginal 26 (60.5)
Cesarean 17 (39.5)
Transmission outcome
V3 PCR+ Infants 12 (26.7)
V3 PCR- Infants 33 (73.3)
Culture+ Infants 3c (6.7)
Culture- Infants 42 (93.3)
aMean age 24 years (range 14–28 years)
bMean viral load 28,112 copies/ml (range = 83 – 484,703 copies/ml)
cHIV infection confirmed by PCR and culture
Twelve (26.7%) of 45 infants were HIV V3 positive indicating that HIV transmission had occurred (Table 2). However, only three of these twelve infants were also HIV culture positive. This infection rate of approximately 7.0% observed is similar to the rate of 8.3% observed for ZDV treated mothers and infants in the 076 study [4]. Based on the V3 PCR results an equal number of mothers with low and high viral load (6 in each group) transmitted HIV to their infants. The mean log viral load of transmitting mothers 3.77 + 0.31 (median = 3.77) was higher than of the non-transmitting mothers 3.54 + 0.15 (median = 3.44) but the difference was not statistically significant (p = 0.474). This is probably due to the small sample size. The mothers of the three infants who were HIV culture positive all had high viral load levels (484,703, 11,642 and 10,220 RNA copies/ml) but the levels in two of the three were close to the 10,000 RNA copies/ml cut-off value used to distinguish LVL from HVL. Since perinatal transmission occurs mostly at or during delivery, the viral load in the genital tract (which may be similar to maternal plasma level) [47] may be an important determinant in maternal-infant transmission.
The mothers of HIV V3 positive infants had a non-significantly higher mean CD4+ T count (526 cells/mm3, median = 508 cells/mm3) compared to non-transmitting mothers (mean CD4+ T count of 413 cells/mm3; median = 336 cells/mm3). Maternal CD4 cell counts have been shown to be a less effective predictor of transmission of HIV than viral load [18]. Further, most of the women in the study (86%) had CD4+T cell counts above the 200 cells/mm3 level which is used to define AIDS. Only one of the three women whose infants were HIV culture positive had AIDS (CD4 = 149 cells/mm3). The other two women had CD4 counts of 510 and 538 cells/mm3.
Neutralization/enhancement of HIV treated with maternal and infant plasma
The effect of plasma (at 10-1 to 10-3 dilutions) from the mothers (transmitters and non-transmitters) and infants (HIV V3+ and HIV V3-) on HIV replication was examined in the neutralization/enhancement assay (Table 3). HIV p24 antigen in culture fluids was determined using the coulter p24 antigen assay kit (Coulter, Miami FL) according to the manufacturer's instructions. The mean value of HIV p24 antigen by dilution was calculated from seronegative samples. The percent change (increase or decrease) in p24 antigen of the maternal or infant plasma from the mean of the seronegative samples was calculated. Neutralization was defined as 70% or greater reduction in HIV p24 antigen in cultures treated with HIV positive plasma compared with cultures treated with HIV negative plasma. Enhancement was defined as 100% or greater increase in p24 antigen in cultures treated with HIV positive plasma compared to the p24 level in cultures treated with HIV negative plasma. Traditionally enhancement of HIV in PBMC cultures has been defined as a 1.5 to 2.5-fold or greater increase in virus replication as a result of treatment with immune sera [21,25,48]. Table 3 shows that at 10-1 dilution, over 90% of plasma samples from both groups of mothers (transmitters/non-transmitters) and infants (HIV V3+/ HIV V3-) neutralized HIV. At 10-2 dilution the percentages of plasma that neutralized HIV dropped to approximately 75% for both groups of mothers and to 58% and 70% for HIV V3+ and HIV V3- infants respectively. The percentages again dropped at 10-3 dilution to ≤ 67% for mothers and ≤ 33% for infants (Table 3). Chi-square or Fisher's exact test were used to compare the proportion of plasma from transmitter versus non-transmitter mothers and between HIV V3+ and HIV V3- infants that neutralized, enhanced or resulted in no change in HIV replication. No significant differences were found.
Table 3 Neutralization/enhancement of HIV infection by plasma from 12 transmitter and 33 non-transmitter mothers and 12 HIV V3 positive and 33 HIV V3 negative infants
Plasma Dilution and effect Transmitter mothers Number (%) Non-transmitter mothers Number (%) HIV V3+ infants Number (%) HIV V3- infants Number (%)
10-1
Neutralized 11 (92) 31 (94) 11 (92) 30 (91)
Enhanced 0 (0) 0 (0) 0 (0) 0 (0)
No change 1 (8) 2 (6) 1 (8) 3 (9)
10-2
Neutralized 9 (75) 25 (76) 7 (58) 23 (70)
Enhanced 1 (8) 2 (6) 2 (17) 1 (3)
No change 2 (17) 6 (18) 3 (25) 9 (27)
10-3
Neutralized 8 (67) 19 (58) 4 (33) 9 (27)
Enhanced 0 (0) 5 (15) 4 (33) 7 (21)
No change 4 (33) 9 (27) 4 (33) 17 (52)
The mean percent change in p24 antigen and standard errors for mothers and infants were plotted for each group by plasma dilution (Figure 1). The Wilcoxon signed-ranked test was used to determine whether the percent change was significantly different from zero. Both the maternal and infant plasma significantly (p = 0.001 for both groups) reduced HIV replication at low (10-1) dilution when compared with HIV negative sera (Figure 1). At 10-2 dilution the infant plasma still significantly (p = 0.001) reduced virus replication, but the maternal plasma lost neutralizing activity (Figure 1). At 10-3 dilution, the maternal plasma significantly increased virus replication (p = 0.005) above seronegative plasma and the infant plasma showed a non-significant increase (88%) in HIV replication (Figure 1). These findings of neutralization by plasma of HIV positive individuals at low dilutions and enhancement at higher dilutions are similar to data published by Jolly and Weiss [49], which showed that neutralizing and enhancing antibodies can occur simultaneously in sera of HIV-infected individuals. If neutralizing antibody is present, enhancement is seen only at high dilutions, whereas, if only enhancing antibody is present, enhancement is observed without, or at low, plasma/serum dilutions.
Figure 1 Neutralization/enhancement of HIV-1BaL by maternal and infant plasma diluted 10-1 – 10-3. Virus replication (determined by p24 antigen (pg/ml) in culture fluids collected 2–4 days post-infection) is compared to replication of virus treated with plasma from HIV-1 seronegative (sn) women. The data represent the average of all maternal and infant samples. Two independent infections were conducted with each sample in duplicate (4 replicates) for each of 43 maternal and 45 infant plasma. Maternal and infant plasma significantly reduced HIV replication (p = 0.001 for both) at 10-1 dilution compared to HIV negative sera. Infant plasma also significantly reduced HIV replication (p = 0.001) at 10-2 dilution. At 10-3 dilution maternal plasma significantly increased HIV replication (p = 0.005) above HIV negative sera and infant plasma showed a non-significant increase (88%) in HIV replication.
Enhancing activity in plasma from mothers and infants
Sixty-nine percent (31/45) of plasma from mother-infant pairs were also tested in the neutralization/enhancement assay at higher dilutions (10-4 to 10-6). Examination of enhancing activity in plasma of mothers and infants showed that there were good agreements (60% or greater) in enhancement status between mother-infant pairs. For example, at 10-4 dilution, the plasma of 7 infants of 9 mothers (78%) whose plasma enhanced HIV replication also exhibited enhancement, and at 10-6 dilution, the plasma of 8 infants of 10 mothers (80%) whose plasma enhanced HIV replication also exhibited enhancement (data not shown). Comparison of p24 antigen between transmitter and non-transmitter mothers or their infants at 10-4 to 10-6 dilutions showed no significant difference between the two groups of mothers or infants. The lack of association of enhancing activity and HIV transmission in this study is similar to the findings for C'-ADE by Gras, et al. [29]. We examined complement-independent (presumably FcγR-mediated antibody dependent) enhancement in primary human macrophages because we thought that this type of enhancement would be more relevant to HIV clinical disease and transmission. FcγR-mediated enhancement is characteristic of diseases such as dengue and feline infectious peritonitis for which ADE has been best demonstrated to occur [50,51]. In addition, macrophages are important target cells for infection and replication in vivo by most HIV-1 variants [52-54]. However, our results showed no association between enhancing activity in maternal or infant plasma and maternal-infant transmission of HIV. The negative results could in part be due to the various anti-retroviral protocols to which the women were assigned.
Correlation between maternal and infant p24 antigen levels and maternal viral load with maternal and infant p24 antigen
Using Spearman's correlation, we examined the association between maternal viral load and enhancing activity in vertical transmission of HIV by using mothers' characteristics (viral load and p24 antigen value) and the infants' p24 antigen values. A correlation matrix showed a positive association between mothers' p24 antigen values and those of their infants (Table 4). However, low positive or no associations were found between maternal viral load and maternal p24 antigen values at low dilutions (10-1 – 10-3) and low negative associations at higher dilutions (10-4 – 10-6). There were low positive associations between maternal viral load and the p24 antigen values of their infants at low dilutions (10-1 – 10-3) and low negative correlations at higher dilutions except at 10-5 dilution (Table 4). However, none of the values was statistically significant.
Table 4 Correlation (Spearman) of maternal and infant p24 antigen levels and maternal viral load with maternal and infant p24 antigen
Plasma dilution p24 antigen (M vs I) Correlationa log (p-value)b MVL and Mp24 Correlationc log (p-value)b MVL and Ip24 Correlationd log (p-value)b
10-1 0.412 (0.005) 0.12 (0.448) 0.09 (0.562)
10-2 0.59 (0.0001) 0.18 (0.249) 0.11 (0.473)
10-3 0.72 (0.0001) 0.14 (0.355) 0.04 (0.832)
10-4 0.40 (0.028) -0.15 (0.423) -0.16 (0.394)
10-5 0.75 (0.0001) -0.10 (0.581) 0.31 (0.091)
10-6 0.69 (0.0001) -0.34 (0.064) -0.31 (0.089)
M = mother; I = infant; MVL = maternal viral load; Mp24 = maternal p24 antigen; Ip24 = infant p24 antigen
aSpearman correlation coefficient between log values of maternal and infant p24 antigen.
bp-value to test for statistically significant correlation.
cSpearman correlation coefficient between log values of maternal viral load and maternal p24 antigen.
dSpearman correlation coefficient between log values of maternal viral load and infant p24 antigen.
Maternal viral load and enhancing antibodies as predictors of vertical transmission of HIV
Univariate analysis of type of treatment, stage of HIV disease, method of delivery and CD4+ T cell count of the mothers indicated no significant association with vertical transmission of HIV (p > 0.05). The combined effect of maternal viral load and enhancing antibodies as potential risk factors for the vertical transmission of HIV-1 was examined in a multivariable model. Log viral load was treated as a continuous variable in the model and enhancement was categorized as enhancement versus neutralization. There was a trend of association between maternal viral load and transmission of HIV-1 so that the higher the viral load, the more likely mothers were to transmit HIV-1 to their infants (OR= 2.5, CI= 0.93 – 6.67, p = 0.0681). This is in agreement with other studies discussed earlier that show viral load as a predictor of maternal-infant transmission [27,30,31]. The non-significant result in this study is probably due to the small sample size. Also, twice as many mothers (29) in this study had low plasma HIV RNA levels (<10,000 RNA copies/ml) compared to those with high viral load levels (14 mothers). The preponderance of women with low viral load levels may be the result of the effect of the different antiretroviral protocols that the women were given during pregnancy. Whereas Dickover, et al. [30] showed that women given zidovudine during gestation showed an eight-fold median decrease in plasma HIV RNA levels (p < 0.001), Sperling, et al., [32] have shown that ZDV treatment had only a minimal effect in decreasing maternal HIV RNA levels. In this study all of the women except one (given stravudine) received ZDV treatment and in most cases ZDV was given along with one other anti-retroviral drug or with HIVIg (Table 1). There was no significant association between enhancing activity (based on p24 antigen values) in the plasma of mothers who transmitted HIV-1 to their infants based on the HIV V3+ status of the infants (OR = 0.92, CI = 0.41 – 2.07, p = 0.8492).
Conclusion
In agreement with published data, multivariate analysis showed a trend of association between maternal viral load and maternal-infant transmission of HIV. The non-significant difference in the mean log viral load of transmitting and non-transmitting mothers is probably due to the small sample size. No significant associations were found between HIV antiretroviral treatment protocols, classification of HIV disease, method of infant delivery and CD4+ T cell count of the mothers and vertical transmission of HIV. Both maternal and infant plasma significantly neutralized HIV infection at low (10-1) dilution and enhanced virus replication at higher dilution (10-3). Neutralizing and enhancing antibodies can occur together in the blood of HIV positive individuals and the neutralizing effect can be lost at high plasma dilution.
There were good agreements in the neutralizing or enhancing activity of the plasma from mothers-infant pairs. That is, when plasma of the mothers neutralized or enhanced HIV infection, their infants' plasma showed similar activity. However, there was no significant association between virus enhancement by maternal plasma and vertical transmission of HIV. Thus, enhancing activity in plasma of these HIV-infected mothers was not a dominant factor in vertical transmission of HIV.
Methods
Collection, processing and testing of maternal and infant blood samples for HIV
Blood samples were taken from the mothers during each trimester of pregnancy and during delivery. Blood samples from the infants were collected from the umbilical cord at birth and at 1–2, 3–4 and 5–12 months. Blood was collected in vacutainer tubes containing ACD anticoagulant and centrifuged at 2500 rpm for 15 minutes. The plasma was stored frozen (-20°C) for use in maternal viral load and HIV neutralization/enhancement assays. The remaining blood was diluted in phosphate-buffered saline (PBS) pH 7.4 and processed by Ficoll-hypaque density gradient for cell isolation. For virus isolation, one million cells from each mother or infant were co-cultured with HIV seronegative donor cells previously stimulated by PHA as described in the ACTG virology manual [55]. The remaining cells (105) were stored frozen at -85°C. The HIV status of the infants was determined using a nested PCR assay of the HIV-1 envelope variable (V3) region as previously described [37,56]. This V3 PCR assay was conducted in duplicates and repeated on the infant samples at 1–2 months, 3–4 months and 5–11 months. Culture for HIV was repeated on infant samples collected at 3, 6 and 12 months and all except 3 infants were culture negative.
Determination of maternal viral load
HIV RNA copies in maternal plasma was determined by the amplicor HIV monitor test (Roche Diagnostics, Branchwater, NJ, USA) at the Puerto Rico ACTG-certified laboratory [37]. A 142 base-pair sequence in the HIV gag gene was amplified by RT-PCR for viral load determination. The mean log viral load was calculated and compared between transmitting and non-transmitting mothers to determine the effect of maternal viral load on HIV vertical transmission.
Preparation and titration of HIV-1BaL for the neutralization/enhancement assay
HIV-1BaL stock was prepared in primary macrophages as reported previously by Jolly [57]. Briefly, HIV-1BaL supernatant fluid (1 × 104.6 TCID50/ml) obtained from the NIH AIDS Research and Reference Reagent Program was used to inoculate fresh cultures of macrophages grown on 75 ml tissue culture flasks. The cultures were washed 24 hours later and incubated with fresh media. Supernatant fluids were harvested at 7 and 14 days post-infection, clarified by centrifugation at 1800 rpm for 10 minutes and used as stock virus for these studies. The stock contained 5 × 105 TCID50/ml.
Neutralization/enhancement assay
A neutralization/enhancement assay was conducted using maternal or infant samples and HIV-1BaL. Maternal plasma samples collected during the third trimester of pregnancy were used in these assays since most prenatal HIV infections occur in the third trimester [58]. Briefly, 10-fold dilutions of heat inactivated (56°C for 30 min) plasma samples were mixed 1:1 with 103 TCID50/ml of virus and pre-incubated at 37°C for 30 min. The mixtures were then inoculated into replicate cultures of monocyte-derived macrophages as prepared previously [57] in 8 well chamber slides and incubated at 37°C for 6 hours in a 5% CO2 incubator. The inocula were removed and the cells washed, and incubated with fresh media for up to 8 days. Culture supernatant fluids were then collected on days 2, 4, 6, and 8 and tested for p24 antigen using the Coulter assay (Coulter, Miami, FL, USA). Cultures treated with HIV-negative sera were used as controls. All maternal and infant plasma samples were tested at three 10-fold dilutions; 69% of samples were tested up through six 10-fold dilutions. Two independent assays were conducted for each maternal or infant plasma sample and each sample was run in duplicate on each assay (4 replicates).
Statistical analysis
Descriptive statistics such as mean, median, range were calculated to summarize maternal characteristics such as viral load, CD4+ T cell count and p24 antigen levels. The number of HIV vertical transmission among infants was summarized and the univariate association of maternal characteristics, such as, treatment (type of antiretroviral therapy), stage of disease based on the 1993 CDC classification [45], method of delivery (C-section vs. vaginal), and CD4+ T cell count, with HIV transmission in infants was evaluated using the Fisher's exact test. The simultaneous effect of maternal viral load and p24 levels on infant transmission was evaluated using the logistic regression model. Odds ratio and 95% confidence intervals were calculated for the effect of both factors in the model.
Correlation between maternal versus infant p24 antigen levels and between maternal viral load and maternal or infant p24 antigen levels was evaluated using the Spearman's correlation coefficient. Enhancement, decrease (neutralization), or no change in p24 levels compared to seronegative control was assessed for mother-infant pairs. The distribution of virus p24 antigen was compared between transmitting vs. non-transmitting mothers and between HIV V3+ infants and HIV V3- infants using Fisher's exact test.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
LMG and PEJ were involved in conception and design of the study, collection of the samples, interpretation of the data and drafting and critical review of the manuscript. PK and MA were involved in performing the laboratory experiments that resulted in the acquisition of the data, in data entry, and in drafting and revising the manuscript with LMG and PEJ. HLW was responsible for data analysis and interpretation along with PEJ, LMG and PK. All authors have read and approved the final manuscript.
Acknowledgements
This study was supported by the CFAR-RCMI supplement grant P30-AI-2767 from the NIAID/NIH, the "Research Centers in Minority Institutions" award G12RR-03051 from the National Center for Research Resources, NIH, award 3 S06 GM08224 from the National Institutes of General Medical Sciences, NIH, and awards RO1 AI 39194 and 5P30 AI27767 from the NIAID/NIH. We appreciate the contribution of the nurses who followed the mothers and infants in the study and the pediatrician in charge of the ACTG clinic. We thank Gill Nieves for help in developing viral macrocultures and M. Vega and G. Hillyer for help with phenotypic and viral load analyses, respectively. HIV-1BaL from Dr. Suzanne Gartner and Dr. Mikulas Popovic was obtained through the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH.
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| 15730556 | PMC555584 | CC BY | 2021-01-04 16:39:00 | no | Virol J. 2005 Feb 24; 2:15 | utf-8 | Virol J | 2,005 | 10.1186/1743-422X-2-15 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central London 1465-9921-6-231574828610.1186/1465-9921-6-23ResearchArginase attenuates inhibitory nonadrenergic noncholinergic nerve-induced nitric oxide generation and airway smooth muscle relaxation Maarsingh Harm [email protected] Marieke A [email protected] Johan [email protected] Herman [email protected] Department of Molecular Pharmacology, University Centre for Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands2005 4 3 2005 6 1 23 23 14 1 2005 4 3 2005 Copyright © 2005 Maarsingh et al; licensee BioMed Central Ltd.2005Maarsingh 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 evidence suggests that endogenous arginase activity potentiates airway responsiveness to methacholine by attenuation of agonist-induced nitric oxide (NO) production, presumably by competition with epithelial constitutive NO synthase for the common substrate, L-arginine. Using guinea pig tracheal open-ring preparations, we now investigated the involvement of arginase in the modulation of neuronal nitric oxide synthase (nNOS)-mediated relaxation induced by inhibitory nonadrenergic noncholinergic (iNANC) nerve stimulation.
Methods
Electrical field stimulation (EFS; 150 mA, 4 ms, 4 s, 0.5 – 16 Hz)-induced relaxation was measured in tracheal preparations precontracted to 30% with histamine, in the presence of 1 μM atropine and 3 μM indomethacin. The contribution of NO to the EFS-induced relaxation was assessed by the nonselective NOS inhibitor L-NNA (0.1 mM), while the involvement of arginase activity in the regulation of EFS-induced NO production and relaxation was investigated by the effect of the specific arginase inhibitor nor-NOHA (10 μM). Furthermore, the role of substrate availability to nNOS in EFS-induced relaxation was measured in the presence of various concentrations of exogenous L-arginine.
Results
EFS induced a frequency-dependent relaxation, ranging from 6.6 ± 0.8% at 0.5 Hz to 74.6 ± 1.2% at 16 Hz, which was inhibited with the NOS inhibitor L-NNA by 78.0 ± 10.5% at 0.5 Hz to 26.7 ± 7.7% at 8 Hz (P < 0.01 all). In contrast, the arginase inhibitor nor-NOHA increased EFS-induced relaxation by 3.3 ± 1.2-fold at 0.5 Hz to 1.2 ± 0.1-fold at 4 Hz (P < 0.05 all), which was reversed by L-NNA to the level of control airways in the presence of L-NNA (P < 0.01 all). Similar to nor-NOHA, exogenous L-arginine increased EFS-induced airway relaxation (P < 0.05 all).
Conclusion
The results indicate that endogenous arginase activity attenuates iNANC nerve-mediated airway relaxation by inhibition of NO generation, presumably by limiting L-arginine availability to nNOS.
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Background
The inhibitory nonadrenergic noncholinergic (iNANC) nervous system is the most effective bronchodilating neural pathway of the airways. Inhibition of nitric oxide synthase (NOS) markedly reduces the iNANC relaxation of both guinea pigs [1-3] and human airways [4,5], indicating that nitric oxide (NO) is a major neurotransmitter of the iNANC system. In addition, vasoactive intestinal polypeptide (VIP) has been implicated in iNANC relaxation [6,7], and colocalization of NOS and VIP has been demonstrated both in guinea pig [8] and in human airway nerves [9].
NO is generated by a family of NOS isoforms that utilize the semi-essential amino acid L-arginine, oxygen and NADPH as substrates to produce NO and L-citrulline [10]. Three isoforms of NOS have been identified: neuronal NOS (nNOS), endothelial NOS (eNOS) and inducible NOS (iNOS). In the airways, the constitutive NOS (cNOS) isoforms are mainly expressed in the iNANC neurons (nNOS), the endothelium (eNOS) and the epithelium (nNOS and eNOS), whereas iNOS, which is induced by proinflammatory cytokines during airway inflammation, is mainly expressed in macrophages and epithelial cells [11].
Another L-arginine metabolizing enzyme is arginase, which hydrolyzes L-arginine to L-ornithine and urea. Arginase is classically considered to be an enzyme of the urea cycle in the liver, but also occurs in extrahepatic tissues, including the lung [12,13]. Two distinct isoforms of arginase have been identified in mammals: arginase I, a cytosolic enzyme, mainly expressed in the liver, and arginase II, a mitochondrial enzyme, which is mainly expressed in extrahepatic tissues [13]. Extrahepatic arginase has been implicated in the regulation of NO synthesis by limiting the availability of intracellular L-arginine for NOS [12-15]. In addition, arginase might be involved in cell growth and tissue repair via the production of L-ornithine, a precursor of polyamines and proline [13]. Both arginase isoforms are constitutively expressed in the airways, particularly in the bronchial epithelium and in fibroblasts from peribronchial connective tissue [12]. Using a perfused guinea pig tracheal tube preparation, we have previously demonstrated that endogenous arginase activity is functionally involved in the regulation of airway smooth muscle tone [16]. Endogenous arginase potentiates methacholine-induced airway constriction by diminishing agonist-induced NO production, by competition with epithelial cNOS for the common substrate, L-arginine [16]. Previous studies had demonstrated that L-arginine availability is indeed a limiting factor for agonist-induced NO-production and airway relaxation [17].
A role for arginase in the iNANC system has been found in internal anal sphincter [18] and penile corpus cavernosum [19,20]. Thus, arginase inhibition increased electrical field stimulation (EFS)-induced relaxation of these preparations, indicating that endogenous arginase activity attenuates nNOS-mediated NANC relaxation.
The role of endogenous arginase in the regulation of iNANC-derived NO generation in the airways has not yet been investigated. In the present study, we demonstrated that endogenous arginase activity and L-arginine availability are importantly involved in the modulation of iNANC nerve-mediated NO-production and relaxation of guinea pig tracheal smooth muscle.
Methods
Animals
Male specific pathogen free HsdPoc:Dunkin Hartley guinea pigs (Harlan Heathfield, UK), weighing 500 – 800 g, were used in this study. The animals were group-housed in individual cages in climate-controlled animal quarters and given water and food ad libitum, while a 12-h on/12-h off light cycle was maintained.
All protocols described in this study were approved by the University of Groningen Committee for Animal Experimentation.
Tissue preparation
The guinea pigs were sacrificed by a sharp blow on the head. After exsanguination, the trachea was removed from the larynx to the bronchi and rapidly placed in a Krebs-Henseleit (KH) buffer solution of 37°C, gassed with 95% O2 and 5% CO2. The composition of the KH-solution in mM was: NaCl 117.50; KCl 5.60; MgSO4 1.18; CaCl2 2.50; NaH2PO4 1.28; NaHCO3 25.0 and D-glucose 5.50; pH 7.4. The trachea was prepared free of serosal connective tissue. Twelve single proximal tracheal open-ring preparations were mounted for isotonic recording (0.3 g preload) between two parallel platinum point-electrodes in water-jacketed (37°C) organ baths containing 20.0 ml of gassed KH-solution and indomethacin (3 μM), which remained present during the whole experiment to eliminate any influence of prostanoids.
Electrical field stimulation-induced relaxation experiments
After a 30 min equilibration period, tracheal preparations were relaxed with isoprenaline (0.1 μM) to establish basal tone. After a washout period of 30 min with three washes with fresh KH solution, maximal contraction of the tracheal preparations to histamine was determined with cumulative additions of the agonist (0.1, 1, 10 and 100 μM). After washout (30 min), the tracheal preparations were precontracted with histamine to 30% of the maximal histamine-induced tone in the presence of atropine (1 μM) to prevent EFS-induced cholinergic airway contraction. On the plateau, biphasic EFS (150 mA, 4 ms, 4 s, 0.5 – 16 Hz) was applied and frequency response curves (0.5 – 16 Hz in doubling steps) were recorded. Per preparation, one frequency response curve was performed. When used, the nonselective NOS inhibitor Nω-nitro-L-arginine (L-NNA; 100 μM), the specific arginase inhibitor Nω-hydroxy-nor-L-arginine (nor-NOHA; 10 μM), a combination of both inhibitors, or L-arginine (0.3, 1.0 or 5.0 mM) were applied 30 min prior to histamine-addition. In line with previous observations [21], neither the NOS inhibitor, nor the arginase inhibitor and L-arginine affected agonist-induced tone in the open-ring preparations. All measurements were performed in triplicate. After the final EFS-induced relaxation, followed by washout, isoprenaline (10 μM) was added to establish basal tone.
Data analysis
All individual relaxations elicited by EFS were estimated as peak height of the EFS-induced response, and were expressed as a percentage of maximal relaxation as established in the presence of isoprenaline. The contribution of NO to the EFS-induced relaxation was determined by the effect of the NOS inhibitor L-NNA. Similarly, the role of arginase activity in the modulation of EFS-induced airway relaxation was determined by the effect of the arginase inhibitor nor-NOHA. The role of substrate availability in EFS-induced airway relaxation was assessed by measuring the responses in the presence of various concentrations of exogenous L-arginine.
All data are expressed as means ± s.e.m. Statistical significance of differences was evaluated using a paired or unpaired two-tailed Student's t-test as appropriate, and significance was accepted when P < 0.05.
Chemicals
Histamine dihydrochloride, indomethacin, atropine sulphate, Nω-nitro-L-arginine, (-)-isoprenaline hydrochloride and L-arginine hydrochloride were obtained from Sigma Chemical Co. (St. Louis, MO, USA). Nω-hydroxy-nor-L-arginine was kindly provided by Dr J.-L. Boucher (Université Paris V).
Results
In guinea pig tracheal open-ring preparations, EFS induced a frequency-dependent relaxation of histamine-induced tone ranging from 6.6 ± 0.8% at 0.5 Hz to 74.6 ± 1.2% at 16 Hz. Incubation with the NOS inhibitor L-NNA caused a significant inhibition of the EFS-induced relaxation at 0.5 to 8 Hz, particularly at the lower frequencies. The effect of L-NNA ranged from 78.0 ± 10.5% inhibition at 0.5 Hz to 26.7 ± 7.7% inhibition at 8 Hz (P < 0.01 all; Fig. 1).
Figure 1 Role of NO and arginase in iNANC nerve-induced relaxation of guinea pig tracheal smooth muscle. Electrical field stimulation-induced relaxation of precontracted guinea pig tracheal open-ring preparations in the absence and presence of the NOS inhibitor L-NNA (100 μM), the arginase inhibitor nor-NOHA (10 μM) or a combination of both inhibitors. Results are means ± s.e.m. of 8 experiments. *P < 0.05 and **P < 0.01 compared to control, †P < 0.05 and ‡P < 0.01 compared to nor-NOHA-treated.
In contrast, incubation with the arginase inhibitor nor-NOHA significantly enhanced EFS-induced relaxation by 3.3 ± 1.2-fold at 0.5 Hz to 1.2 ± 0.1-fold at 4 Hz (P < 0.05 all; Fig. 1), that is, at the frequencies most sensitive to L-NNA. The increased relaxation in the presence of nor-NOHA was fully reverted by L-NNA (P < 0.05 all), to the level of control preparations in the presence of L-NNA alone (Fig. 1).
Incubation with L-arginine caused a dose-dependent increase of total EFS-induced relaxation, which was maximal at 5.0 mM L-arginine (data not shown). In the presence of 5.0 mM L-arginine, a significant increase in EFS-induced relaxation was observed at all frequencies compared to untreated preparations (P < 0.05 all, Fig. 2). At the lower frequencies, this increase was similar to the increase in EFS-induced relaxation observed after incubation with nor-NOHA (Fig. 2).
Figure 2 Role of L-arginine availability and arginase in iNANC nerve-induced relaxation of guinea pig tracheal smooth muscle. Electrical field stimulation-induced relaxation of precontracted guinea pig tracheal open-ring preparations in the absence and presence of exogenous L-arginine (5.0 mM) or the arginase inhibitor nor-NOHA (10 μM). Results are means ± s.e.m. of 5–13 experiments. *P < 0.05 and **P < 0.01 compared to control.
Discussion
Using perfused tracheal preparations, we have previously demonstrated that endogenous arginase activity is involved in the regulation of agonist-induced airway constriction by inhibition of NO production, presumably by competition with cNOS for L-arginine [16]. In the present study, we demonstrated that endogenous arginase activity is also involved in the regulation of iNANC nerve-mediated airway smooth muscle relaxation.
In line with previous observations [1], it was demonstrated that the NOS inhibitor L-NNA inhibited EFS-induced iNANC relaxation of guinea pig tracheal preparations. This inhibition was most pronounced at the lower frequencies, indicating a prominent role of nNOS-derived NO at these frequencies. By contrast, inhibition of arginase activity by nor-NOHA caused a considerable (up to 3.3-fold) increase in EFS-induced relaxation at low frequencies, indicating that endogenous arginase activity restricts iNANC nerve-mediated airway smooth muscle relaxation. The increased relaxation after arginase inhibition was completely reverted by L-NNA, clearly indicating that arginase activity attenuates iNANC nerve-mediated airway smooth muscle relaxation by limiting NO production, presumably by competition with nNOS for their common substrate, L-arginine.
The observation that exogenous L-arginine significantly enhanced the EFS-induced airway smooth muscle relaxation confirms that L-arginine is indeed a limiting factor in EFS-induced, NO-mediated airway smooth muscle relaxation under basal conditions. Remarkably, the effect of nor-NOHA was similar to that observed in the presence of the maximally effective L-arginine concentration, indicating that endogenous arginase activity is a major factor in regulating the neural NO-mediated airway smooth muscle relaxation.
Recently, we discovered that increased arginase activity is importantly involved in the pathophysiology of asthma by contributing to the allergen-induced NO-deficiency and subsequent airway hyperresponsiveness to methacholine after the early asthmatic reaction, by limiting the availability of L-arginine for cNOS to produce bronchodilating NO [22]. Arginase activity as well as expression was also considerably increased in two mouse models of allergic asthma, irrespective whether the animals were challenged with ovalbumin or with Aspergillus fumigatus [23]. Moreover, enhanced mRNA or protein expression of arginase I was observed in human asthmatic lung tissue, particularly in inflammatory cells and in the airway epithelium [23], while increased arginase activity was measured in asthmatic serum [24]. In guinea pig tracheal strips, it has previously been demonstrated that EFS-induced iNANC relaxation is reduced after ovalbumin-challenge, due to a deficiency of iNANC nerve-derived NO [25]. Thus, it is tempting to speculate that increased arginase activity could similarly be involved in allergen-induced reduced iNANC activity.
A role for arginase by restricting the L-arginine availability for nNOS in iNANC nerves has also been proposed in the pathophysiology of erectile dysfunction [19]. In support, increased expression and activity of arginase II contributing to reduced NO production has been demonstrated in diabetic cavernosal tissue [26]. Neuronal arginase may also be involved in gastrointestinal motility disorders, by reducing nNOS-mediated iNANC relaxation in the internal anal sphincter [18].
Conclusion
This is the first demonstration that endogenous arginase activity is functionally involved in iNANC nerve activity in the airways, by attenuating the generation of nNOS-derived NO. Disturbance of this novel regulation mechanism of airway responsiveness might be involved in the pathophysiology of allergic asthma.
Abbreviations
cNOS, constitutive nitric oxide synthase; EFS, electrical field stimulation; eNOS, endothelial nitric oxide synthase; iNANC, inhibitory nonadrenergic noncholinergic; iNOS, inducible nitric oxide synthase; KH, Krebs-Henseleit; L-NNA, Nω-nitro-L-arginine; NADPH, nicotinamide adenine dinucleotide phosphate; nNOS, neuronal nitric oxide synthase; nor-NOHA, Nω-hydroxy-nor-L-arginine; VIP, vasoactive intestinal polypeptide
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HMa designed and coordinated the study, performed a major part of the experiments, performed the statistical analysis and drafted the manuscript. MAT assisted substantially in performing the experiments. JZ participated in the design of the study, interpretation of results and final revision of the manuscript. HMe conceived of the study, participated in its design and direction, as well as in preparing the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors wish to thank Sijtze Blaauw for technical assistance. We thank the Netherlands Asthma Foundation for financial support (grant 00.24).
==== Refs
Tucker JF Brave SR Charalambous L Hobbs AJ Gibson A L-NG-nitro arginine inhibits non-adrenergic, non-cholinergic relaxations of guinea-pig isolated tracheal smooth muscle Br J Pharmacol 1990 100 663 664 2207492
Belvisi MG Stretton D Barnes PJ Nitric oxide as an endogenous modulator of cholinergic neurotransmission in guinea-pig airways Eur J Pharmacol 1991 198 219 221 1650703 10.1016/0014-2999(91)90626-2
Li CG Rand MJ Evidence that part of the NANC relaxant response of guinea-pig trachea to electrical field stimulation is mediated by nitric oxide Br J Pharmacol 1991 102 91 94 2043935
Belvisi MG Stretton CD Yacoub M Barnes PJ Nitric oxide is the endogenous neurotransmitter of bronchodilator nerves in humans Eur J Pharmacol 1992 210 221 222 1350993 10.1016/0014-2999(92)90676-U
Ellis JL Undem BJ Inhibition by L-NG-nitro-L-arginine of nonadrenergic-noncholinergic-mediated relaxations of human isolated central and peripheral airway Am Rev Respir Dis 1992 146 1543 1547 1456572
Ellis JL Farmer SG Effects of peptidases on non-adrenergic, non-cholinergic inhibitory responses of tracheal smooth muscle: a comparison with effects on VIP- and PHI-induced relaxation Br J Pharmacol 1989 96 521 526 2655804
Ellis JL Farmer SG The effects of vasoactive intestinal peptide (VIP) antagonists, and VIP and peptide histidine isoleucine antisera on non-adrenergic, non-cholinergic relaxations of tracheal smooth muscle Br J Pharmacol 1989 96 513 520 2720290
Shimosegawa T Toyota T NADPH-diaphorase activity as a marker for nitric oxide synthase in neurons of the guinea pig respiratory tract Am J Respir Crit Care Med 1994 150 1402 1410 7524981
Fischer A Hoffmann B Nitric oxide synthase in neurons and nerve fibers of lower airways and in vagal sensory ganglia of man. Correlation with neuropeptides Am J Respir Crit Care Med 1996 154 209 216 8680682
Moncada S Palmer RM Higgs EA Biosynthesis of nitric oxide from L-arginine. A pathway for the regulation of cell function and communication Biochem Pharmacol 1989 38 1709 1715 2567594 10.1016/0006-2952(89)90403-6
Ricciardolo FL Sterk PJ Gaston B Folkerts G Nitric oxide in health and disease of the respiratory system Physiol Rev 2004 84 731 765 15269335 10.1152/physrev.00034.2003
Que LG Kantrow SP Jenkinson CP Piantadosi CA Huang YC Induction of arginase isoforms in the lung during hyperoxia Am J Physiol 1998 275 L96 102 9688940
Wu G Morris SM Arginine metabolism: nitric oxide and beyond Biochem J 1998 336 1 17 9806879
Hey C Boucher JL Vadon-Le GS Ketterer G Wessler I Racke K Inhibition of arginase in rat and rabbit alveolar macrophages by N omega-hydroxy-D,L-indospicine, effects on L-arginine utilization by nitric oxide synthase Br J Pharmacol 1997 121 395 400 9179379
Boucher JL Moali C Tenu JP Nitric oxide biosynthesis, nitric oxide synthase inhibitors and arginase competition for L-arginine utilization Cell Mol Life Sci 1999 55 1015 1028 10484661 10.1007/s000180050352
Meurs H Hamer MA Pethe S Vadon-Le GS Boucher JL Zaagsma J Modulation of cholinergic airway reactivity and nitric oxide production by endogenous arginase activity Br J Pharmacol 2000 130 1793 1798 10952667 10.1038/sj.bjp.0703488
De Boer J Duyvendak M Schuurman FE Pouw FM Zaagsma J Meurs H Role of L-arginine in the deficiency of nitric oxide and airway hyperreactivity after the allergen-induced early asthmatic reaction in guinea-pigs Br J Pharmacol 1999 128 1114 1120 10556950 10.1038/sj.bjp.0702882
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Cox JD Kim NN Traish AM Christianson DW Arginase-boronic acid complex highlights a physiological role in erectile function Nat Struct Biol 1999 6 1043 1047 10542097 10.1038/14929
Kim NN Cox JD Baggio RF Emig FA Mistry SK Harper SL Speicher DW Morris SMJ Ash DE Traish A Christianson DW Probing erectile function: S-(2-boronoethyl)-L-cysteine binds to arginase as a transition state analogue and enhances smooth muscle relaxation in human penile corpus cavernosum Biochemistry 2001 40 2678 2688 11258879 10.1021/bi002317h
De Boer J Meurs H Coers W Koopal M Bottone AE Visser AC Timens W Zaagsma J Deficiency of nitric oxide in allergen-induced airway hyperreactivity to contractile agonists after the early asthmatic reaction: an ex vivo study Br J Pharmacol 1996 119 1109 1116 8937712
Meurs H McKay S Maarsingh H Hamer MA Macic L Molendijk N Zaagsma J Increased arginase activity underlies allergen-induced deficiency of cNOS-derived nitric oxide and airway hyperresponsiveness Br J Pharmacol 2002 136 391 398 12023942 10.1038/sj.bjp.0704725
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Bivalacqua TJ Hellstrom WJ Kadowitz PJ Champion HC Increased expression of arginase II in human diabetic corpus cavernosum: in diabetic-associated erectile dysfunction Biochem Biophys Res Commun 2001 283 923 927 11350073 10.1006/bbrc.2001.4874
| 15748286 | PMC555585 | CC BY | 2021-01-04 16:36:27 | no | Respir Res. 2005 Mar 4; 6(1):23 | utf-8 | Respir Res | 2,005 | 10.1186/1465-9921-6-23 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central London 1465-9921-6-231574828610.1186/1465-9921-6-23ResearchArginase attenuates inhibitory nonadrenergic noncholinergic nerve-induced nitric oxide generation and airway smooth muscle relaxation Maarsingh Harm [email protected] Marieke A [email protected] Johan [email protected] Herman [email protected] Department of Molecular Pharmacology, University Centre for Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands2005 4 3 2005 6 1 23 23 14 1 2005 4 3 2005 Copyright © 2005 Maarsingh et al; licensee BioMed Central Ltd.2005Maarsingh 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 evidence suggests that endogenous arginase activity potentiates airway responsiveness to methacholine by attenuation of agonist-induced nitric oxide (NO) production, presumably by competition with epithelial constitutive NO synthase for the common substrate, L-arginine. Using guinea pig tracheal open-ring preparations, we now investigated the involvement of arginase in the modulation of neuronal nitric oxide synthase (nNOS)-mediated relaxation induced by inhibitory nonadrenergic noncholinergic (iNANC) nerve stimulation.
Methods
Electrical field stimulation (EFS; 150 mA, 4 ms, 4 s, 0.5 – 16 Hz)-induced relaxation was measured in tracheal preparations precontracted to 30% with histamine, in the presence of 1 μM atropine and 3 μM indomethacin. The contribution of NO to the EFS-induced relaxation was assessed by the nonselective NOS inhibitor L-NNA (0.1 mM), while the involvement of arginase activity in the regulation of EFS-induced NO production and relaxation was investigated by the effect of the specific arginase inhibitor nor-NOHA (10 μM). Furthermore, the role of substrate availability to nNOS in EFS-induced relaxation was measured in the presence of various concentrations of exogenous L-arginine.
Results
EFS induced a frequency-dependent relaxation, ranging from 6.6 ± 0.8% at 0.5 Hz to 74.6 ± 1.2% at 16 Hz, which was inhibited with the NOS inhibitor L-NNA by 78.0 ± 10.5% at 0.5 Hz to 26.7 ± 7.7% at 8 Hz (P < 0.01 all). In contrast, the arginase inhibitor nor-NOHA increased EFS-induced relaxation by 3.3 ± 1.2-fold at 0.5 Hz to 1.2 ± 0.1-fold at 4 Hz (P < 0.05 all), which was reversed by L-NNA to the level of control airways in the presence of L-NNA (P < 0.01 all). Similar to nor-NOHA, exogenous L-arginine increased EFS-induced airway relaxation (P < 0.05 all).
Conclusion
The results indicate that endogenous arginase activity attenuates iNANC nerve-mediated airway relaxation by inhibition of NO generation, presumably by limiting L-arginine availability to nNOS.
==== Body
Background
The inhibitory nonadrenergic noncholinergic (iNANC) nervous system is the most effective bronchodilating neural pathway of the airways. Inhibition of nitric oxide synthase (NOS) markedly reduces the iNANC relaxation of both guinea pigs [1-3] and human airways [4,5], indicating that nitric oxide (NO) is a major neurotransmitter of the iNANC system. In addition, vasoactive intestinal polypeptide (VIP) has been implicated in iNANC relaxation [6,7], and colocalization of NOS and VIP has been demonstrated both in guinea pig [8] and in human airway nerves [9].
NO is generated by a family of NOS isoforms that utilize the semi-essential amino acid L-arginine, oxygen and NADPH as substrates to produce NO and L-citrulline [10]. Three isoforms of NOS have been identified: neuronal NOS (nNOS), endothelial NOS (eNOS) and inducible NOS (iNOS). In the airways, the constitutive NOS (cNOS) isoforms are mainly expressed in the iNANC neurons (nNOS), the endothelium (eNOS) and the epithelium (nNOS and eNOS), whereas iNOS, which is induced by proinflammatory cytokines during airway inflammation, is mainly expressed in macrophages and epithelial cells [11].
Another L-arginine metabolizing enzyme is arginase, which hydrolyzes L-arginine to L-ornithine and urea. Arginase is classically considered to be an enzyme of the urea cycle in the liver, but also occurs in extrahepatic tissues, including the lung [12,13]. Two distinct isoforms of arginase have been identified in mammals: arginase I, a cytosolic enzyme, mainly expressed in the liver, and arginase II, a mitochondrial enzyme, which is mainly expressed in extrahepatic tissues [13]. Extrahepatic arginase has been implicated in the regulation of NO synthesis by limiting the availability of intracellular L-arginine for NOS [12-15]. In addition, arginase might be involved in cell growth and tissue repair via the production of L-ornithine, a precursor of polyamines and proline [13]. Both arginase isoforms are constitutively expressed in the airways, particularly in the bronchial epithelium and in fibroblasts from peribronchial connective tissue [12]. Using a perfused guinea pig tracheal tube preparation, we have previously demonstrated that endogenous arginase activity is functionally involved in the regulation of airway smooth muscle tone [16]. Endogenous arginase potentiates methacholine-induced airway constriction by diminishing agonist-induced NO production, by competition with epithelial cNOS for the common substrate, L-arginine [16]. Previous studies had demonstrated that L-arginine availability is indeed a limiting factor for agonist-induced NO-production and airway relaxation [17].
A role for arginase in the iNANC system has been found in internal anal sphincter [18] and penile corpus cavernosum [19,20]. Thus, arginase inhibition increased electrical field stimulation (EFS)-induced relaxation of these preparations, indicating that endogenous arginase activity attenuates nNOS-mediated NANC relaxation.
The role of endogenous arginase in the regulation of iNANC-derived NO generation in the airways has not yet been investigated. In the present study, we demonstrated that endogenous arginase activity and L-arginine availability are importantly involved in the modulation of iNANC nerve-mediated NO-production and relaxation of guinea pig tracheal smooth muscle.
Methods
Animals
Male specific pathogen free HsdPoc:Dunkin Hartley guinea pigs (Harlan Heathfield, UK), weighing 500 – 800 g, were used in this study. The animals were group-housed in individual cages in climate-controlled animal quarters and given water and food ad libitum, while a 12-h on/12-h off light cycle was maintained.
All protocols described in this study were approved by the University of Groningen Committee for Animal Experimentation.
Tissue preparation
The guinea pigs were sacrificed by a sharp blow on the head. After exsanguination, the trachea was removed from the larynx to the bronchi and rapidly placed in a Krebs-Henseleit (KH) buffer solution of 37°C, gassed with 95% O2 and 5% CO2. The composition of the KH-solution in mM was: NaCl 117.50; KCl 5.60; MgSO4 1.18; CaCl2 2.50; NaH2PO4 1.28; NaHCO3 25.0 and D-glucose 5.50; pH 7.4. The trachea was prepared free of serosal connective tissue. Twelve single proximal tracheal open-ring preparations were mounted for isotonic recording (0.3 g preload) between two parallel platinum point-electrodes in water-jacketed (37°C) organ baths containing 20.0 ml of gassed KH-solution and indomethacin (3 μM), which remained present during the whole experiment to eliminate any influence of prostanoids.
Electrical field stimulation-induced relaxation experiments
After a 30 min equilibration period, tracheal preparations were relaxed with isoprenaline (0.1 μM) to establish basal tone. After a washout period of 30 min with three washes with fresh KH solution, maximal contraction of the tracheal preparations to histamine was determined with cumulative additions of the agonist (0.1, 1, 10 and 100 μM). After washout (30 min), the tracheal preparations were precontracted with histamine to 30% of the maximal histamine-induced tone in the presence of atropine (1 μM) to prevent EFS-induced cholinergic airway contraction. On the plateau, biphasic EFS (150 mA, 4 ms, 4 s, 0.5 – 16 Hz) was applied and frequency response curves (0.5 – 16 Hz in doubling steps) were recorded. Per preparation, one frequency response curve was performed. When used, the nonselective NOS inhibitor Nω-nitro-L-arginine (L-NNA; 100 μM), the specific arginase inhibitor Nω-hydroxy-nor-L-arginine (nor-NOHA; 10 μM), a combination of both inhibitors, or L-arginine (0.3, 1.0 or 5.0 mM) were applied 30 min prior to histamine-addition. In line with previous observations [21], neither the NOS inhibitor, nor the arginase inhibitor and L-arginine affected agonist-induced tone in the open-ring preparations. All measurements were performed in triplicate. After the final EFS-induced relaxation, followed by washout, isoprenaline (10 μM) was added to establish basal tone.
Data analysis
All individual relaxations elicited by EFS were estimated as peak height of the EFS-induced response, and were expressed as a percentage of maximal relaxation as established in the presence of isoprenaline. The contribution of NO to the EFS-induced relaxation was determined by the effect of the NOS inhibitor L-NNA. Similarly, the role of arginase activity in the modulation of EFS-induced airway relaxation was determined by the effect of the arginase inhibitor nor-NOHA. The role of substrate availability in EFS-induced airway relaxation was assessed by measuring the responses in the presence of various concentrations of exogenous L-arginine.
All data are expressed as means ± s.e.m. Statistical significance of differences was evaluated using a paired or unpaired two-tailed Student's t-test as appropriate, and significance was accepted when P < 0.05.
Chemicals
Histamine dihydrochloride, indomethacin, atropine sulphate, Nω-nitro-L-arginine, (-)-isoprenaline hydrochloride and L-arginine hydrochloride were obtained from Sigma Chemical Co. (St. Louis, MO, USA). Nω-hydroxy-nor-L-arginine was kindly provided by Dr J.-L. Boucher (Université Paris V).
Results
In guinea pig tracheal open-ring preparations, EFS induced a frequency-dependent relaxation of histamine-induced tone ranging from 6.6 ± 0.8% at 0.5 Hz to 74.6 ± 1.2% at 16 Hz. Incubation with the NOS inhibitor L-NNA caused a significant inhibition of the EFS-induced relaxation at 0.5 to 8 Hz, particularly at the lower frequencies. The effect of L-NNA ranged from 78.0 ± 10.5% inhibition at 0.5 Hz to 26.7 ± 7.7% inhibition at 8 Hz (P < 0.01 all; Fig. 1).
Figure 1 Role of NO and arginase in iNANC nerve-induced relaxation of guinea pig tracheal smooth muscle. Electrical field stimulation-induced relaxation of precontracted guinea pig tracheal open-ring preparations in the absence and presence of the NOS inhibitor L-NNA (100 μM), the arginase inhibitor nor-NOHA (10 μM) or a combination of both inhibitors. Results are means ± s.e.m. of 8 experiments. *P < 0.05 and **P < 0.01 compared to control, †P < 0.05 and ‡P < 0.01 compared to nor-NOHA-treated.
In contrast, incubation with the arginase inhibitor nor-NOHA significantly enhanced EFS-induced relaxation by 3.3 ± 1.2-fold at 0.5 Hz to 1.2 ± 0.1-fold at 4 Hz (P < 0.05 all; Fig. 1), that is, at the frequencies most sensitive to L-NNA. The increased relaxation in the presence of nor-NOHA was fully reverted by L-NNA (P < 0.05 all), to the level of control preparations in the presence of L-NNA alone (Fig. 1).
Incubation with L-arginine caused a dose-dependent increase of total EFS-induced relaxation, which was maximal at 5.0 mM L-arginine (data not shown). In the presence of 5.0 mM L-arginine, a significant increase in EFS-induced relaxation was observed at all frequencies compared to untreated preparations (P < 0.05 all, Fig. 2). At the lower frequencies, this increase was similar to the increase in EFS-induced relaxation observed after incubation with nor-NOHA (Fig. 2).
Figure 2 Role of L-arginine availability and arginase in iNANC nerve-induced relaxation of guinea pig tracheal smooth muscle. Electrical field stimulation-induced relaxation of precontracted guinea pig tracheal open-ring preparations in the absence and presence of exogenous L-arginine (5.0 mM) or the arginase inhibitor nor-NOHA (10 μM). Results are means ± s.e.m. of 5–13 experiments. *P < 0.05 and **P < 0.01 compared to control.
Discussion
Using perfused tracheal preparations, we have previously demonstrated that endogenous arginase activity is involved in the regulation of agonist-induced airway constriction by inhibition of NO production, presumably by competition with cNOS for L-arginine [16]. In the present study, we demonstrated that endogenous arginase activity is also involved in the regulation of iNANC nerve-mediated airway smooth muscle relaxation.
In line with previous observations [1], it was demonstrated that the NOS inhibitor L-NNA inhibited EFS-induced iNANC relaxation of guinea pig tracheal preparations. This inhibition was most pronounced at the lower frequencies, indicating a prominent role of nNOS-derived NO at these frequencies. By contrast, inhibition of arginase activity by nor-NOHA caused a considerable (up to 3.3-fold) increase in EFS-induced relaxation at low frequencies, indicating that endogenous arginase activity restricts iNANC nerve-mediated airway smooth muscle relaxation. The increased relaxation after arginase inhibition was completely reverted by L-NNA, clearly indicating that arginase activity attenuates iNANC nerve-mediated airway smooth muscle relaxation by limiting NO production, presumably by competition with nNOS for their common substrate, L-arginine.
The observation that exogenous L-arginine significantly enhanced the EFS-induced airway smooth muscle relaxation confirms that L-arginine is indeed a limiting factor in EFS-induced, NO-mediated airway smooth muscle relaxation under basal conditions. Remarkably, the effect of nor-NOHA was similar to that observed in the presence of the maximally effective L-arginine concentration, indicating that endogenous arginase activity is a major factor in regulating the neural NO-mediated airway smooth muscle relaxation.
Recently, we discovered that increased arginase activity is importantly involved in the pathophysiology of asthma by contributing to the allergen-induced NO-deficiency and subsequent airway hyperresponsiveness to methacholine after the early asthmatic reaction, by limiting the availability of L-arginine for cNOS to produce bronchodilating NO [22]. Arginase activity as well as expression was also considerably increased in two mouse models of allergic asthma, irrespective whether the animals were challenged with ovalbumin or with Aspergillus fumigatus [23]. Moreover, enhanced mRNA or protein expression of arginase I was observed in human asthmatic lung tissue, particularly in inflammatory cells and in the airway epithelium [23], while increased arginase activity was measured in asthmatic serum [24]. In guinea pig tracheal strips, it has previously been demonstrated that EFS-induced iNANC relaxation is reduced after ovalbumin-challenge, due to a deficiency of iNANC nerve-derived NO [25]. Thus, it is tempting to speculate that increased arginase activity could similarly be involved in allergen-induced reduced iNANC activity.
A role for arginase by restricting the L-arginine availability for nNOS in iNANC nerves has also been proposed in the pathophysiology of erectile dysfunction [19]. In support, increased expression and activity of arginase II contributing to reduced NO production has been demonstrated in diabetic cavernosal tissue [26]. Neuronal arginase may also be involved in gastrointestinal motility disorders, by reducing nNOS-mediated iNANC relaxation in the internal anal sphincter [18].
Conclusion
This is the first demonstration that endogenous arginase activity is functionally involved in iNANC nerve activity in the airways, by attenuating the generation of nNOS-derived NO. Disturbance of this novel regulation mechanism of airway responsiveness might be involved in the pathophysiology of allergic asthma.
Abbreviations
cNOS, constitutive nitric oxide synthase; EFS, electrical field stimulation; eNOS, endothelial nitric oxide synthase; iNANC, inhibitory nonadrenergic noncholinergic; iNOS, inducible nitric oxide synthase; KH, Krebs-Henseleit; L-NNA, Nω-nitro-L-arginine; NADPH, nicotinamide adenine dinucleotide phosphate; nNOS, neuronal nitric oxide synthase; nor-NOHA, Nω-hydroxy-nor-L-arginine; VIP, vasoactive intestinal polypeptide
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HMa designed and coordinated the study, performed a major part of the experiments, performed the statistical analysis and drafted the manuscript. MAT assisted substantially in performing the experiments. JZ participated in the design of the study, interpretation of results and final revision of the manuscript. HMe conceived of the study, participated in its design and direction, as well as in preparing the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors wish to thank Sijtze Blaauw for technical assistance. We thank the Netherlands Asthma Foundation for financial support (grant 00.24).
==== Refs
Tucker JF Brave SR Charalambous L Hobbs AJ Gibson A L-NG-nitro arginine inhibits non-adrenergic, non-cholinergic relaxations of guinea-pig isolated tracheal smooth muscle Br J Pharmacol 1990 100 663 664 2207492
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| 15743525 | PMC555586 | CC BY | 2021-01-04 16:36:40 | no | Retrovirology. 2005 Mar 2; 2:15 | latin-1 | Retrovirology | 2,005 | 10.1186/1742-4690-2-15 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-2-171574352610.1186/1742-4690-2-17ReviewThe discovery of the first human retrovirus: HTLV-1 and HTLV-2 Gallo Robert C [email protected] Institute of Human Virology University of Maryland Biotechnology Institute 725W, Lombard Street, Baltimore, MD, 21201, USA2 Department of Microbiology and Immunology University of Maryland School of Medicine 655 W. Baltimore Street Baltimore, MD 21201, USA2005 2 3 2005 2 17 17 18 2 2005 2 3 2005 Copyright © 2005 Gallo; licensee BioMed Central Ltd.2005Gallo; 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.
I describe here the history leading up to and including my laboratory's discovery of the first human retrovirus, HTLV-I, and its close relative, HTLV-II. My efforts were inspired by early work showing a retroviral etiology for leukemias in various animals, including non-human primates. My two main approaches were to develop criteria for and methods for detection of viral reverse transcriptase and to identify growth factors that could support the growth of hematopoietic cells. These efforts finally yielded success following the discovery of IL-2 and its use to culture adult T cell lymphoma/leukemia cells.
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Background
After arriving at NIH in 1965 I spent my first year as a young physician caring and treating (mostly unsuccessfully) acute leukemias in children: a vivid experience and one which made me absolute in a decision to be fully involved in laboratory research and not return to clinical medicine. My research interest almost from the very start was in the biology of blood cells, and I focused on comparisons of human leukemic cells with normal leukocytes. This was mainly limited to comparative biochemistry. Specifically, I studied enzymes of pyrimidine nucleoside and nucleotide metabolism, tRNA species and their corresponding amino acyl-tRNA synthetases, and finally DNA polymerases [see refs.[1-5] as examples] – though now this approach would seem to be empirical in the extreme, because we have so many obvious rational things in cancer research today. However, at that time "fishing expeditions" were "where things were at". I hoped to uncover clues that might help us better understand the nature of leukemic cells and also their origin.
Discovery that human T cells made cytokines ("lymphokines") and early hints of human retroviruses
The main leukemia I worked on was acute lymphocytic leukemias (ALL). After all, these were the most common of the acute leukemias and gram quantities of these cells were available from my clinical colleagues at NCI. Importantly, these were the only leukemias for which reasonably similar normal control cells were available, namely, normal human lymphoblasts. Scientists in Philadelphia had just discovered that a plant lectin, phytohemagglutinin (PHA), could induce human lymphocytes to become activated and go through a mitotic cycle. These normal lymphoblasts looked like ALL cells, but these were days before most of us would or could know of the great complexity of subtypes of lumphocytes. Functional discriminatory assays were barely available and monoclonal antibodies with their capacity to provide surface markers were yet to come. Thus, we did not then sub-classify lymphocytic leukemias. Herb Cooper of NIH had learned how to purify lymphocytes from columns packed with nylon; myeloid cells would adhere, but lymphocytes passed through. Cooper generously provided this technique to me. During this period (1968–1970) I became very impressed by the studies of Leo Sachs in Israel and later also of Don Metcalf in Australia who where showing that, like some lymphocytes, myeloid cells could also be grown in the laboratory but not in liquid culture. Instead, they used the technique previously applied to virus transformed cells of cell growth on a methylcellulose solid surface in the form of cell colonies. However, growth was transient and the amount of cells quite limited, precluding many types of biochemical, molecular biological, and virological experiments. Nonetheless, from this system, Sachs and his colleagues and Metcalf and co-workers made seminal discoveries, including a growth/differentiation factor, granulocyte macrophage colony-stimulating factor (GM-CSF), which was specific for the myeloid lineage. Sachs logically believed the main production of GM-CSF would be from myeloid cells, i.e., a feedback regulation – granulopoietic progenitors proliferated and formed "dead end" granulocytes, which should produce their own granulopoietic factor [see refs. [6,7] for reviews].
Meanwhile, while comparing ALL cells to normal lymphocytes, I decided to test the conditioned medium of the PHA-stimulated normal cells for growth factor. The late Alan Wu had just joined me from the laboratories of Till and McCulloch in Toronto shortly after the publication of their famous paper describing hematopoietic stem cell assays (in mice) for the first time. Alan and Joan Prival, a post-doctoral fellow, joined me in reporting the then surprising finding that lymphocytes (T cells) made GM-CSF [8]. This would be the start of my long involvement with "conditioned medium" from PHA-stimulated lymphocytes. Dane Boggs, F. Ruscetti, and co-workers in Pittsburgh had described the same phenomena at almost exactly the same time. These papers were likely among the first to describe lymphokines (lymphocyte-derived cytokines).
In this period (early 1970s) I began to study animal retroviruses because in several animals these kinds of viruses caused leukemias. Thus, no matter whether human retroviruses (leukemia-causing or otherwise) existed or not, a study of animal retroviruses, especially focused on learning their mechanisms of leukemia causation, might provide insights into the mechanisms involved in human leukemias. However, my co-workers and I also decided to search for human retroviruses, an unpopular goal at this time, considering the decades of attempts and failures. I was, nonetheless, encouraged by discussions with William Jarrett, the Scottish veterinarian who discovered feline leukemia virus, and by the work of the late Howard Temin. Temin, of course, had predicted that retroviruses of animals replicated by having their RNA genome transcribed into a DNA form, which would then integrate into the DNA of the target cell. He referred to this integrated form as provirus, the name given to his theory. In 1970 Temin and his colleague Mizutani, and separately, David Baltimore, gave credence to the theory with their discovery of the DNA polymerase carried by all retroviruses, reverse transcriptase (RT)[9,10]. For me it also meant a convenient, inexpensive, and extremely sensitive assay for a retrovirus. (This would be one of two technologies that would be key for later discoveries of all human retroviruses).
RT forms in virions only upon budding from the cell. Consequently, finding this enzyme in media of cultured cells implied release of retrovirus particles, and finding RT from extracts of cells implied the presence of a cell associated virus particles, as for example, virions associated with the cell surface membrane. We found rare cases of leukemia that scored positive in RT assays. The problem, however, was that RT might be a product of a normal cellular gene. We needed to develop the assay not only as a very sensitive one but also one that would distinguish RT from all of the then known cellular DNA polymerases (alpha, beta, and gamma). This became a major objective [see refs. [5] and [11-14] for examples].
Armed with these RT assays we did find a few cases of adult lymphocytic leukemias with RT showing all the characteristics of RT from a retrovirus (we had by then purified and characterized RT from many different animal retroviruses). We published on the one best characterized in Nature New Biology in 1972 [15]. We believed this was a "footprint" of a human retrovirus, but we failed to isolate virus from this patient. (Though we will never know, it is interesting to speculate whether this young adult had ATL because of some clinical similarities to ATL). We also thought it would attract wide interest and excitement in the field. It did not. It was clear that we had to isolate a replicating virus, one we could study, perpetuate, and give to others.
The obvious and easiest approach to virus isolation was by using cell lines. Cell culture technology had become widely available by the 1960s, and many cell lines from different species were available. The approach is generally to co-culture the primary cells (in our case the leukemic cells) with a wide variety of such lines and hope virus will take in one or more. This, of course, would be after scoring positive in the RT assay. However, by this period, there was increasing antagonism to research directed toward the finding of human tumor viruses and especially of retroviruses. The NCI had created the heavily funded Virus Cancer Program which was under attack for failing to find clear evidence of tumor viruses. Moreover, by the mid-1970s there had been not only decades of failure to find human retroviruses, there had been many false starts by many investigators utilizing the co-culture system that involved cell lines, including one by me. The usual problem was a cross contamination with an animal retrovirus. For this reason I became convinced that we had to find ways to grow primary blood cells, but not with the systems of Sachs and Metcalf. These methylcellulose colonies of leukocytes provided too few cells and growth of these cells were limited in number and in time. When we had our next hint of an RT positive leukemic sample it turned out to be from a patient with a myeloid leukemia, so we searched for a growth factor that would maintain and promote growth of human myeloid leukemic cells in liquid suspension culture. This had not been achieved before. From an early-term (first few weeks) abortion, we obtained some human embryonic cells that produced a factor that led to the first successful routine growth of these human leukemic cells in liquid suspension(16). We called these HL (human leukemia) cells, with a given sequential number of the samples we had studied. One of these cultured cell populations became an immortalized cell line, HL-60. It was the first human leukemic myeloid cell line [17] and like almost all the others, the HL-60 cells showed no evidence of virus. However, one growing human leukemic cell culture (not immortalized) did yield virus, and it was anxiously propagated. Unfortunately, the embryonic factor needed to keep these cells alive and growing was lost when the freezer in which they were stored broke down over a long holiday weekend, which was not recognized for some time. (My first lesson in never storing a divisible valuable all in one place!). This led us to a frantic search to find another source. We screened conditioned medium from a wide variety of cell lines and cell strains, including many more fetal cells – all to no avail.
One approach was to culture many different types of cells from many different tissue sources (including human embryos) for several days, collect the media (conditioned media or CM), and add it to leukocytes from normal human cord blood, samples of human bone marrow, and myeloid leukemic cells. In this period (early mid-1970s), a post-doctoral fellow, Doris Morgan, joined our group and took part in the search. As would be expected, CM from PHA-stimulated lymphocytes was one of the cell sources I asked to be screened. Doris was succeeding in growing cells from human bone marrow, and was intensely nursing them daily for months. But they were lymphocytes, not myeloid cells. It was neither unique nor interesting to grow human B cells. Even at this time Epstein-Barr virus (EBV) immortalized cell lines were well known to grow often from normal blood or a bone marrow mixed cell population. Indeed, they were the only kind of blood cells that could be routinely grown in long-term culture, but analyses of the cells revealed that they were T cells, which at that time had only recently been clearly delineated from B cells by certain functional assays (the E rosette assay, for example). The factor we had found in the PHA-CM was a new growth factor. Francis Ruscetti had then joined our group and carried out a set of experiments that demonstrated this more fully, and we reported these results in 1976–1977[18,19] and they were to be the first reports of what we termed a T cell mitogenic factor, later called TCGF, and finally interleukin-2 (IL-2). The purification was later [20]. IL-2 was among the first well-defined cytokines. The combination of IL-2 growth of T cells with sensitive RT assays would be (and still is) the key to the discoveries of human retroviruses in T cell leukemias and AIDS.
The debate about the possible existence of human retroviruses
In this same period the pressure against attempts to find human retroviruses intensified. It was not only the prevailing atmosphere of failure but also reasonable scientific arguments. For examples: (1) there was little evidence for leukemia viruses in primates. (2) When retroviruses were found in animals they were not difficult to find. Extensive viremia preceded disease, therefore, if they infected humans, they would be easy to find and would have been discovered much earlier. (3) Human sera in the presence of complement lysed animal retroviruses, thereby providing a rational mechanism for the conclusion that humans were protected.
Finally, there were technical difficulties such as the ability to culture primary human cells (see Table 1).
Table 1 Factors that led to consensus that human retroviruses did not exist
1. Failure to discover them after an extensive survey by many investigators in the 1950s, 1960s, and 1970s.
2. Ease of detection in animal models because of extensive virema.
3. Difficulties in growing primary human cells.
4. Results showing human sera with complement lysed animal retroviruses.
We reasoned otherwise. Kawakami and colleagues had just discovered gibbon ape leukemia virus, and linked it to chronic myeloid leukemia in that species [21]. Later, we discovered a variant of that virus which caused T cell leukemia [22]. Bovine leukemia virus (BLV) was discovered [23,24], and it was noted that BLV replicated at very low levels thus putting to rest the notion of "extensive viremia always precedes animal retrovirus induced leukemias". The biased view came from the fact that the earlier small animal models were naturally selected for their utility. Consequently, models in which virus is difficult to detect would be selected against. As for human sera lysing retroviruses, unfortunately those studies were limited to tests of retroviruses from non-primates. Later, we would learn that many primate retroviruses, including the retroviruses of many, are not susceptible.
Our ultimate focus on T cell leukemias was dictated by several factors. First, most animal leukemias caused by retroviruses are lymphocytic leukemias and of these T cell leukemias predominate. Second, the first and to this date only leukemia of non-human primates is caused by a retrovirus [21], and a particular strain of this virus which we isolated caused T cell leukemia [22]. Third, fortune dictated that we would end up focusing on human T cell malignancies because of our discovery of IL-2 which allowed us to grow significant numbers of such cells in many but not all instances (not all T cell leukemias or lymphomas respond to IL-2).
One other development also influenced our continuation of the pursuit of human retroviruses. This was a documented interspecies transmission of a gibbon ape leukemia virus (GaLV) from a pet old world Gibbon ape to a new world Wooly monkey. It was well known that retroviruses could move from one species to another, but in all cases these were very ancient events only discovered by analyses of cellular DNA of many animals. But in this case the event occurred "right before our eyes", giving rise to the virus from the Wooly monkey known as simian sarcoma virus [25]. We felt humans could not be excluded, and indeed later we would learn that the first human retrovirus discovered (HTLV-1) has close relatives among many old world primates and may have arisen from an ancient transmission from monkey to man. A more relevant example, of course, is HIV. There is much evidence that it came into humans as a much more recent infection from African primates (see Table 2).
Table 2 Factors encouraging us to continue searching for human retroviruses
1. The discovery of bovine leukemia virus (minimally replicates, difficult to find)
2. Technological advances – A. A sensitive specific assay for a footprint of a retrovirus, namely, reverse transcriptase. B. Capacity to grow significant numbers of primary human T cells in liquid suspension culture giving us access to virus detection and isolation, namely by using IL-2.
3. Discovery of a retrovirus causing leukemias in a species close to man, namely GaLV.
4. A documented example of a retrovirus transmission from one species of primates to another, namely GaLV from a gibbon ape to a wooly monkey [26].
5. Purification and characterization of reverse transcriptase from a patient with an adult lymphocytic leukemia (type unknown) 1972 [15].
Discoveries of HTLV-1 and HTLV-2
The first detection and isolation of HTLV-1 was in 1979, and the first detection came from the analysis of a T cell line established by J. Minna and A. Gazdar from a patient these clinicians called a cutaneous T cell lymphoma. Alternatively, such patients were also called mycosis fungoides or Sezary T cell leukemia depending upon clinical nuances. Though IL-2 was supplied by us for them to use in their initial culturing of these cells, the cells rapidly immortalized. An outstanding post-doctoral fellow, Bernard Poiesz, carried out RT assays on these cells with positive results, and we soon arranged for electron microscopic analysis of concentrated RT plus cultures and found retrovirus particles. Because putative human retroviruses viruses had been found many times before by several investigators in established cell lines, only to be subsequently shown to be accidental laboratory contaminants, by the late 1970s I was well aware that much more had to be done before this work was presentable. For instance, we had to (1) show that the same virus could be isolated from primary tissue samples of the same patient by culturing primary T cells with IL-2; (2) demonstrate that the virus was novel, i.e., not any of the known animal retroviruses; (3) show it could infect human T cells in vitro; (4) demonstrate specific antibodies to the virus in the serum of the patient; (5) demonstrate that proviral DNA could be found integrated in the DNA of the cells from which the virus was isolated; (6) provide evidence that this was not a one-time affair by showing serological evidence of specific antibodies not only in the patient but in others as well. These results were successfully obtained in 1979–1980 and available by the time we submitted and published our first report in 1980 [27], enabling us to follow quickly with several other essential reports [28-33], also including independent isolates from other patients [29,34]. One of these patients was a black woman from the Caribbean, and the second was a white merchant marine who acknowledged sexual contacts in southern Japan and the Caribbean. These and all subsequent isolates of HTLV-1 in our laboratory were from primary cells cultured with IL-2. After an initial struggle to publish in the J. of Virology, fortunately, we were soon able to publish the original report in PNAS, and this opened the door. It soon became clear that HTLV-1 was specifically associated with adult T cell malignancy (usually CD4+ cells) in which the patients frequently had cutaneous abnormalities and hypercalcemia. Clinicians in the United States had not at that time made any distinction of HTLV-1-associated T cell malignancies from other neoplasms, and as noted above collectively referred to these patients with others (non-HTLV associated) as cutaneous T cell leukemia-lyumphomas. However, a few years earlier Kiyoshi Takatsuki and his co-workers Junji Yodoi and Takashi Uchiyama defined clusters of leukemia in southwest Japan with special clinical features and cellular morphology, which when coupled with the geographic clustering, led him to propose in 1977 that this was a distinct form of leukemia. He named it adult T cell leukemia (ATL) [35].
Two events significantly catalyzed the further development of our work and of our understanding of HTLV-1 and its role in T cell malignancies. The first of these (in the summer of 1980) was information from Drs. Tom Waldmann and H. Uchiyama, who had come to NIH as a visit scientist. They brought to our attention the ATL cluster in Japan so in the fall of 1980 I contacted two Japanese friends, the late Yohei Ito, then Chair of Microbiology at Kyoto University and Tad Aoki for more information and for sera from such patients to test for antibodies to HTLV. This specific clinical entity had been described as early as 1977 by Takatsuki and his co-workers Yodoi and Uchiyama, and was called adult T cell leukemia by him. Aoki and Ito sent sera from such patients to me in 1980, and these sera scored positive for antibodies to HTLV-1. Based on these results Ito organized a small meeting at Lake Miwa outside of Kyoto attended by a few co-workers and myself from the U.S. and Aoki, Ito, and several other Japanese scientists most notably Takatsuki, Y. Hinuma, and T. Miyoshi. The meeting was held in March 1981. Several of my colleagues and I presented our results in detail. This included description of several isolates of HTLV-1, characteristics of purified HTLV-1 p24 as well as reverse transcriptase proteins, evidence of integrated HTLV-1 provirus T cell malignancies and healthy volunteers which provided clear evidence for the linkage of HTLV-1 to certain T cell malignancies, and the positive serological results in Japanese ATL patients. In organizing this meeting the intention of Ito was to foster wide collaboration in Japan with me and my co-workers on this disease. The meeting summary was published in Cancer Research in November 1981 [36].
It was only at the end of the meeting when we were summarizing and planning for this collaboration with the Japanese investigators, that Dr. Yorio Hinuma "announced" he too had a retrovirus. He presented EM pictures of virus particles from a cell line established by Dr. Miyoshi by co-cultivation of ATL cells and normal human T cells. These results of Miyoshi were the first indication of the transforming capability of HTLV-l because the cell line that was immortalized was from the normal donor [37]. Later, my colleague M. Popovic was able to make this a routine, that is, we would show that HTLV-1 could routinely immortalize normal human T cells [34]. It was obvious to all that the virus pictures shown by Hinuma were HTLV-1. By the time of this meeting we had already published a few papers on HTLV-1. Hinuma called his isolate ATLV (adult T cell leukemia virus), but argued against collaboration claiming it was not possible to provide human sera from Japan for "cultural reasons". In June 1982 Hinuma and colleagues published on their isolate of ATLV [38]. After comparative analyses of isolates of ATLV and HTLV were performed we published with Japanese colleagues M. Yoshida, T. Miyoshi and Y. Ito that HTLV-1 and ATLV were the same virus [39]. Consequently, we agreed that the virus name should be HTLV to recognize the priority of our virus work, and the disease would be referred to as ATL in recognition of the Japanese priority in distinguishing this malignancy as a specific identity which had been "lumped" with other T cell leukemias/lymphomas in western countries and elsewhere as cutaneous T cell lymphomas [40]. Yoshida was soon to make many of the major advances in the molecular biology of HTLV-1 but this is another story.
The second meeting of considerable importance was in London chaired by the late hematologist Sir John Dacie and attended by Dacie, Drs. Daniel Catovsky, Robin Weiss, Mel Greaves, and William Jarrett among others from Great Britain and by my collaborator in epidemiological studies, Dr. William Blattner, and myself. It was Catvosky who called for this meeting because he noted that we had found HTLV-1 mainly in African Americans and black persons in the Caribbean and he had found an unusual frequency of adult T cell malignancies in Caribbean immigrants to England. He recognized the similarities of their disease to Takatsuki's ATL. Thus, he postulated they were one and the same disease and HTLV-1 would be present in all. He was right. Promptly, Blattner accelerated his studies in the Caribbean and documented that HTLV-1 was endemic in some islands. He and Guy de Thé of France would then show that this result depended upon the particular tribes in Africa from which the individuals descended.
Some of these experiences would be a precursor of a persistent pattern, i.e., HTLVs are not easy to transmit, remain within families and regions over long periods of time, and have old-world linkage. Ultimately, related viruses would be found in old-world primates and more distantly related viruses in some ungulates. The modes of transmission would soon be forthcoming as sexual contact, blood, and mother to child via breast feeding. Later in 1981 we isolated HTLV-2 from a leukemia described as "a hairy cell T cell leukemia" [41], but this strain is far less pathogenic that HTLV-1. Many of the features of these viruses coupled with CD4 T cell tropism would prove to be remarkably similar to those of the virus about to enter our work, HIV.
A companion article in Retrovirology by Kiyoshi Takatsuki recounts the events surrounding the discovery of the disease, adult T-cell leukemia [41].
Acknowledgements
I would like to thank the past and present members of my laboratory, without whom the studies described in this article would not have been possible. My special thanks (in no particular order) goes to Bernie Poiesz, Frank Ruscetti, Doris Morgan, Marvin Reitz, Phil Markham, Prem Sarin, Flossie Wong-Staal, Veffe Franchini, Marjorie Robert-Guroff, M.G. Sarngadharan, V.S. Kalyanaraman, and Bill Blattner.
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Takatsuki K Discovery of adult T-cell leukemia Retrovirology 2 16 15743528 10.1186/1742-4690-2-16
| 15743526 | PMC555587 | CC BY | 2021-01-04 16:36:40 | no | Retrovirology. 2005 Mar 2; 2:17 | utf-8 | Retrovirology | 2,005 | 10.1186/1742-4690-2-17 | oa_comm |
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J NanobiotechnologyJournal of Nanobiotechnology1477-3155BioMed Central London 1477-3155-3-31574352110.1186/1477-3155-3-3ResearchNanopores: maltoporin channel as a sensor for maltodextrin and lambda-phage Berkane E [email protected] F [email protected] A [email protected] C [email protected] D [email protected] R [email protected] M [email protected] Institut Pharmacologie & Biologie Structurale-CNRS UMR5089, 205, rte de Narbonne, F-31077 Toulouse, France2 Lehrstuhl für Biotechnologie, Biozentrum, Am Hubland, D-97074 Würzburg, Germany3 Inserm U-570, CHU Necker-Enfants Malades, 156, rue de Vaugirard, F- 75730 Paris Cedex 15, France4 International University Bremen, School of Engineering and Science, D-28727 Bremen, Germany2005 2 3 2005 3 3 3 18 9 2004 2 3 2005 Copyright © 2005 Berkane et al; licensee BioMed Central Ltd.2005Berkane 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 harvest nutrition from the outside bacteria e.g. E. coli developed in the outer cell wall a number of sophisticated channels called porins. One of them, maltoporin, is a passive specific channel for the maltodextrin uptake. This channel was also named LamB as the bacterial virus phage Lambda mis-uses this channel to recognise the bacteria. The first step is a reversible binding followed after a lag phase by DNA injection. To date little is known about the binding capacity and less on the DNA injection mechanism. To elucidate the mechanism and to show the sensitivity of our method we reconstituted maltoporin in planar lipid membranes. Application of an external transmembrane electric field causes an ion current across the channel. Maltoporin channel diameter is around a few Angstroem. At this size the ion current is extremely sensitive to any modification of the channels surface. Protein conformational changes, substrate binding etc will cause fluctuations reflecting the molecular interactions with the channel wall. The recent improvement in ion current fluctuation analysis allows now studying the interaction of solutes with the channel on a single molecular level.
Results
We could demonstrate the asymmetry of the bacterial phage Lambda binding to its natural receptor maltoporin.
Conclusion
We suggest that this type of measurement can be used as a new type of biosensors.
Single molecule detectionNanobiotechnologyElectrophysiologyNanopore conceptporin
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Nature created and optimized proteins for specific tasks which makes them often interesting in material science. For example, membrane transporters could control the permeability of artificial nanometer sized container. A typical application could be to control the enzymatic activity in a liposome [1]. Another possible application is to reconstitute channels into planar lipid bilayer and use time dependent conductance as a signal [2,3]. Application of an external electric field drives the ions through the nano (and subnano) meter sized channel. Any larger molecule that diffuses into and temporarily sticks to the channel interior will cause typical fluctuations of the ion current which allow to conclude on its mode of translocation. Such studies were used to follow sugar translocation through maltoporin [4]. Similar types of measurements were done to investigate the translocation of antibiotics like ampicillin [5]. Subtle changes in the channel size or small conformational changes can be recorded and this technique could be developed towards an instrument to probe very soft forces.
Porins are attractive candidates for applications because they are very stable. Moreover, recombinant technology permits production of porins in E. coli with high yields [6]. A third advantage is the availability of the high resolution 3-D crystal structure showing details of substrate binding sites which facilitates enormously a rational engineering of modified proteins.
The outer cell wall of Gram-negative bacteria from E. coli is fairly permeable to smaller solutes below a molecular weight of about 400 Da [6]. Such substances can freely permeate under a concentration gradient through general diffusion porins in the outer cell wall. Under stress, e.g. in case of lack of nutrition, the pure diffusion process is too slow and the bacteria need to improve the efficiency of the translocation. For those cases, nature has created a series of rather specific and highly sophisticated membrane channels. The most extensively studied examples of specific porins are the maltooligosaccharide-specific channel Maltoporin of E. coli [4,7,8]. Maltoporin forms ion-conducting channels when reconstituted into lipid bilayers [9,10]. The 3D structure of Maltoporin revealed that the monomer of Maltoporin of E. coli consists of an 18 stranded β-barrel with short turns at the periplasmic side and large irregular loops at the outside of the cell [11].
The bacteriophage Lambda is a virus recognizing Maltoporin at the outer cell surface [12]. In absence of this membrane channel, phage Lambda does not recognize the bacteria. Or, even minor mutations allow the bacteria to defend themselves against virus attacks. The virus itself can, in turn mutate to restore binding ability. According to the high resolution X-ray structure the water filled channel is far too small to permit the translocation of the double strain DNA (about 20 Å) [11]. The infection mechanism thus must involve one of the following processes: Phage binding will cause a strong conformational change within the Maltoporin or, after binding the phage releases a DNA translocation machinery to bring its DNA across the hydrophobic membrane. To date none of these intermediate steps has been observed so far and the underlying process remains unclear. Recently, gpJ, a protein in the phage terminal was identified to be involved in the Maltoporin recognition process [13].
A typical set-up for conductance measurements is shown in figure 1. The measurement cell consists of two chambers separated by a hole (less than 0.1 mm diameter) in a thin poly(tetrafluorethylene) film sandwiched between two half-cells made of Teflon (Goodfellow, Cambridge, UK). Prior to each measurement this hole has to be pretreated to render it lipophilic by coating it with a hexadecane/hexane (1:100 v:v) droplet. After allowing for hexane evaporation, each chamber is filled with 1.5 ml buffer (for example, 1 M KCl, unbuffered, about pH 6). Black lipid bilayers were formed according to the classical Montal-Mueller technique by spreading lipids in hexane/chloroform (9:1) across the aqueous buffer [14]. For sake of stability we used diphytanoyl-phosphocholine (DphPC, Avanti Polar Lipids). After 20 min allowing for evaporation, the buffer level is lowered below the hole level and rose again. Typically after the first or second trial a stable unilamellar membrane is formed. In order to insert single porin trimers within reasonable time, but to avoid insertion of their multiples, a careful balance between the concentration of the protein solution, detergent concentration and buffer volume has to be found. One single porin trimer has to find the membrane and to insert while all others must be inactivated, e.g. by precipitation. Maltoporin from the stock (1 mg/ml in 1% OPOE) was diluted 102-105 times in the buffer containing 1% OPOE. From our own experience in our laboratory the insertion was optimal if smallest amounts (less than 1 μl) were injected. In a second measurement we used painted membranes as described previously [15]. Here the Teflon chamber consists of a larger hole (diameter 800 μm and larger). Membranes were formed by painting 1 μl of a 1% solution of DphPC in n-decane across the hole. This type of membrane facilitates multichannel insertion.
Figure 1 Schematic representation of a typical planar bilayer set-up for ion current recording. 1.a) Two half cells made of Delrine separated by a 25 μm Teflon foil with a hole in the center. Both parts are clamped together. 1.b) Below a microscope picture of the Teflon septum containing a hole. 1.c) Schema of a lipid bilayer with a reconstituted trimeric porin. The Cl- ions are attracted to the positive electrode and K+ to the negative one. Ions are permeating the channel in the MHz range which is beyond the current time resolution. 1.d) The insertion of a single channel will give raise to a jump in conductance. Any objects diffusing in the channel may reduce the permeation time of ions and may be detected either in conductance fluctuations or an averaged reduced conductance.
Membrane current was measured via homemade Ag/AgCl electrodes. One electrode was used as ground and the other connected to the headstage of an Axopatch 200B amplifier (Axon Instruments, USA), allowing the application of adjustable potentials (typically, 100 mV) across the membrane. A similar set-up was used in the second measurement.
We recently investigated the sugar penetration on a single molecular level [4]. We were able to reconstitute a single Maltoporin trimer into the lipid bilayer. Addition of sugar into the bulk phase resulted in a blocking of the channel in a concentration dependent manner. At low sugar concentration individual closure of the channel could be observed. Maltohexaose induces higher frequencies of closure and longer closing times than a smaller sugar like maltose. The analysis of the time-resolved conductance as a function of sugar concentration yielded the binding constant as well as the "on" and "off" rates for the sugar binding. Here we used a modified sugar through covalent binding of an ANDS (3-amino-naphtalene-2,7-disulfonic acid) molecule to the reducing end of a Maltoheptaose as schematically shown in fig. 2A (for details, see [16]). The crystal structure suggests that the maltose molecule enter the channel only with the nonreducing end from the outside (or reducing end from the periplasmic side). Subsequently this molecule can only enter from the cis-side in our setup. In fig. 2B we see that addition on the periplasmic side (trans side) inhibit the entry whereas addition to the outer side (cis side) caused blocking. A good control experiment in order to test the activity is to add unmodified sugar molecules to the previous experiment. In fig. 2C we clearly observe the ability to translocate unmodified sugars. Addition of small amounts of unmodified sugar to the trans-side caused the expected number of events. Further addition of unmodified sugar to the opposite site enhances the sugar induced blocking. These data can be used for a fundamental analysis to probe e.g. the individual energy barrier and it seems that nature has optimized this channel to have the best turnover number. On the other hand these channels can potentially serve to discriminate sucrose from maltose.
Figure 2 Typical recordings of ion current through a single Maltoporin trimer in presence of modified maltohexaose (see [16] for details). (A) Shows the unmodified maltohexaose and on the right hand side the modified sugar molecule. We designed this molecule according the crystal structure to guarantee the low penetration ability from one side. (B) M6-ANDS was added to trans (left) and then to cis (right). Sugar analogue modulates ion current only to the cis-side, the side of Maltoporin addition. The average residence time is 5.0 ms. (C) First, M6-ANDS was injected to the trans-side and no variation in the ion current occurs. As control, maltohexaose was added to the same side (left). The natural substrate is translocated demonstrating that it enters the channel from trans with the reducing end first. Then, M6-ANDS was added additionally to the cis-side (right) generating long current interruptions superimposed to maltohexaose blockade events seen in the figure of the left side. The dashed lines corresponding to zero current. Membrane bathing solution was 1 M KCl, 10 mM Tris, 1 mM CaCl2, pH 7.4, the applied voltage was + 150 mV.
In a second series of experiments we were interested to probe for Lambda phage binding. In principle this should be possible despite the enormous size (about 100 nm size in comparison to 4 nm sized channels). However in a preliminary step we have produced larger quantities of the phage endterminal protein gpJ fused to Maltose Binding Protein (MBP). We reconstituted a larger number of maltoporin in solvent containing membranes and titrated small quantities of the fusion construct MBP-gpJ. We know from the experiments described above that most of the channels are oriented the same direction during the reconstitution. In fig. 3 we show a first result that titration of gpJ to the opposite side of protein addition had no effect. In contrast, addition of gpJ to the side of porin addition caused rapid blocking of the channel. This observation suggest that the porin inserts with the short turns first and that the protein part exposed to the extracellular side is naturally accessible to Lambda phages. These first results are promising and we currently work on improving the resolution. Here we have to note that this observation is in clear contrast by a report on phage lambda binding in a multichannel preparation [17]. The origin of this discrepancy might be simultaneous multiple insertion. Our observation here is in agreement with other reports showing the same orientation [4,5,18]. However, reason why porins inserts in artificial membranes differently than in natural ones remains unclear. One may speculate that the strong asymmetry of natural membranes or unknown chaperons will facilitate the entry with the long loops first.
Figure 3 Here we show the ability to recognize bacterial phage Lambda by blocking the ion conductance through the natural receptor Maltoporin. We first reconstituted about 300 Maltoporin channel in a solvent containing planar lipid bilayer. This leads to a stable conductance after about 30 min with no further protein insertion. Titration of 7 and 42 nM of the fusion protein MBP-gpJ from the bacterial virus Lambda to the compartment corresponding the intracellular side of the channel showed no effect. However, titration to the opposite side corresponding to the extracellular side caused a significant reduction of the ion conductance. Membrane bathing solution was unbuffered 1 M KCl giving a pH of about 6. The applied voltage was + 20 mV.
Sensing with membrane channel is a new way in screening for solute molecules and several promising examples are already shown [2,3,16,19,20]. The actual bottleneck is the complexity in membrane channel assembly. However, the current development in automatized patch-clamping will open a wide range of possibilities [21,22]. We plan to reduce the volume on each side of the membrane and the size of the lipid patch. We currently work with pore diameters of about 1 μm with less background capacitance and thus a better time resolution and to simplify the channel assembly.
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| 15743521 | PMC555588 | CC BY | 2021-01-04 16:38:05 | no | J Nanobiotechnology. 2005 Mar 2; 3:3 | utf-8 | J Nanobiotechnology | 2,005 | 10.1186/1477-3155-3-3 | oa_comm |
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Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-4-121572370010.1186/1475-925X-4-12ResearchA comparative study of reaction times between type II diabetics and non-diabetics Richerson Samantha J [email protected] Charles J [email protected] Judy [email protected] Biomedical Engineering Department, Bucknell University, 1 Derr Dr. Lewisburg, Pa 17837 USA2 Center for Biomedical Engineering and Rehabilitation Science, Louisiana Tech University, 711 S. Vienna St, Ruston, LA 71270 USA3 Electrical Engineering Department, Bucknell University, 1 Derr Dr. Lewisubrg, Pa 17837 USA2005 21 2 2005 4 12 12 3 1 2005 21 2 2005 Copyright © 2005 Richerson et al; licensee BioMed Central Ltd.2005Richerson 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
Aging has been shown to slow reflexes and increase reaction time to varied stimuli. However, the effect of Type II diabetes on these same reaction times has not been reported. Diabetes affects peripheral nerves in the somatosensory and auditory system, slows psychomotor responses, and has cognitive effects on those individuals without proper metabolic control, all of which may affect reaction times. The additional slowing of reaction times may affect every-day tasks such as balance, increasing the probability of a slip or fall.
Methods
Reaction times to a plantar touch, a pure tone auditory stimulus, and rightward whole-body lateral movement of 4 mm at 100 mm/s2 on a platform upon which a subject stood, were measured in 37 adults over 50 yrs old. Thirteen (mean age = 60.6 ± 6.5 years) had a clinical diagnosis of type II diabetes and 24 (mean age = 59.4 ± 8.0 years) did not. Group averages were compared to averages obtained from nine healthy younger adult group (mean age = 22.7 ± 1.2 years).
Results
Average reaction times for plantar touch were significantly longer in diabetic adults than the other two groups, while auditory reaction times were not significantly different among groups. Whole body reaction times were significantly different among all three groups with diabetic adults having the longest reaction times, followed by age-matched adults, and then younger adults.
Conclusion
Whole body reaction time has been shown to be a sensitive indicator of differences between young adults, healthy mature adults, and mature diabetic adults. Additionally, the increased reaction time seen in this modality for subjects with diabetes may be one cause of increased slips and falls in this group.
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Background
Aging slows reflexes and increases the time to react to a number of external stimuli of different modalities [1-4]. What has escaped extensive examination has been the effect of Type II diabetes on these same reaction times and the comparison of modalities across the various sensory inputs. Only two studies have tested older individuals with diabetes. These have demonstrated increased reaction times to visual and auditory stimuli [5,6]. Mohan, et al. [5] found a 30 ms difference in auditory reaction times between those with diabetes (approximately 210 ms) and a control group (180 ms). Dobrzanski, et al. [6] found a doubling of visual reaction time in diabetics (473 ms) versus that measured in healthy individuals (216 ms). In addition to the measured effects in these two studies, diabetes has also been shown to affect peripheral nerves in the somatosensory [7] and auditory system [8], slows psychomotor responses [9], and has cognitive effects on those individuals without proper metabolic control [10-13], all of which may affect reaction times.
One of the largest implications that an increased reaction time may have is in the area of slips and falls. Falls are incurred by one third of the elderly population and are a common source of morbidity and mortality[14]. Evidence that older subjects have an increased incidence of slips and falls when compared to healthy young adults have been attributed to increase in sway as seen by center-of-pressure or -of-gravity (COP, COG), or head and hip variability [15,16]. Although no age related changes have been found in the rms distance of the anterior-posterior (AP) COP, changes in both mean velocity and range of AP and medial-lateral (ML) COP have been seen, with stronger changes in the former [17-20]. Diabetics have been shown to have a higher incidence of postural instability [21-26], and reduced peripheral sensations thus leading to an even higher incidence of falls resulting from slips than their healthy elder counterparts. These changes in balance metrics due to both normal aging and diabetes have been well measured, but never accurately explained. It is our contention that the postural instability may be due to slower input of information to the central nervous system, which does not allow the nervous system to react to stimuli as quickly, producing a higher incidence of slips and falls.
The aim of this study was to measure and compare reaction times to plantar touch, auditory tone, and whole body lateral movement in subjects over 50 years old with and without diabetes, as well as a group of healthy younger adults under 25 years of age. Subjects with diabetes were expected to have reaction times longer than those of the age-matched controls, while the aged controls were in turn expected to have reaction times greater than those seen in the younger adult group. The implications of the changes in reaction time will be discussed with respect to the central and peripheral nervous system.
Methods
Subjects
Subjects included 37 mature adults over 50 yrs old. Thirteen had a clinical diagnosis of type II diabetes made by their primary physician (group PN, mean = 60.6 ± 6.5 yrs, 7 Female/ 6 Male) and 23 did not (group NI, mean = 59.4 ± 8.0 yrs, 11 Female / 12 Make). The majority of the subjects were recruited from within the Veterans Administration (VA) population at the Overton Brooks VA Medical Center. Reaction times from these groups were compared to a younger adult group (age <25, N = 9, mean = 22.9 yrs, 4 Female/ 5 Male) that were recruited through advertising at Louisiana Tech University, and tested at the VA Medical Center. The recruiting, screening, testing and informed consent procedures were reviewed and approved by the local VA Institutional Review Board.
Screening
Subjects recruited for this study were relatively healthy individuals with no current or past history of severe heart, circulation, or breathing problems; chronic lower back pain or spasms; deformities of the spine, bones or joints (including advanced arthritis); cerebral stroke, spinal cord injuries or other damage to the nervous system; non-healing skin ulcers; advanced diabetes; current drug or alcohol dependence; or repeated falls. Individuals taking any prescription medicine to prevent dizziness were also excluded.
Diabetic individuals targeted for this study were those with very early and mild Type II Diabetes. The subject's primary care physician undertook the diagnosis of diabetes. Targeted recruits had all been diagnosed within the last 10 years. All subjects with diabetes were using either diet or oral medication to manage blood sugar levels.
Visual, vestibular, muscoskeletal, and cognitive screening was also done to ensure that no undiagnosed problem existed that would prevent subjects from completing the study.
Plantar sensory tactile threshold were measured on each sole for all subjects using graded Semmes-Weinstein Monofilaments, which, upon bending, exerted a known force that depends on the filament diameter. Tactile force perception thresholds on the glabrous skin of the feet were determined for the right and left feet using these monofilaments according to standard clinical testing protocol. Stimuli were presented randomly three times at a given location, and, if two of the three presentations were detected, a threshold force was considered determined. Although force is a ratio metric (a measure in which an absolute zero is present and meaningful fractions or ratios can be constructed), the measurement of force by this method is still an ordinal (or rank ordered) type of data. Therefore, non-parametric statistics were used to compare tactile thresholds among test locations on the foot, and between right and left feet, as well as to compare among groups.
A certified audiologist at the Overton Brooks VA Medical Center carried out air conduction auditory threshold testing on all mature subjects (but none of the younger adults due to their health). Both mature adult groups underwent testing at 1, 2, 4, and 8 kHz in both ears. Average threshold level was recorded in decibels. Using a One Way ANOVA on Ranks, the threshold in each ear was compared to determine any differences in threshold.
In addition to this screening, all of the mature subjects underwent clinical surface nerve conduction studies of the lower extremity which were performed at the Neurology Service of the Overton Brooks VA Medical Center by a technician under the supervision of a neurologist. Motor (peroneal and tibial nerve) and sensory nerves (sural nerve) were tested bilaterally. F- and M- latency tests that test the entire lower motor loop (sensory nerve -> vertebrae -> motor nerve) were initially performed to ascertain any problems in the Sherrington's final common pathway [27]. However, the first two subjects expressed severe discomfort in undergoing that part of study. Hence the F- and M- latency tests were optional to subsequent subjects. These tests found peripheral neuropathies in all diabetics and none of the remaining mature subjects, who were thus classified as neurologically intact.
Reaction Time Protocol
Reaction time was defined as the time between a stimulus onset and a signaled response of the subject. Three different stimuli were presented – touch, tone and platform movement.
A manually held, miniature single axis force sensor (Sensotec, Inc) with a 2 mm diameter tip was used by the authors to apply a tactile stimulus to the plantar surface of the big toe of each foot. Since the unloaded sensor force could vary over time or with a change in the position of the sensor, the single axis force sensor was calibrated to a zero state prior to each reaction time test series. A force change of more than 0.01 N was determined to be the trigger for an event. Instructions given to the subjects were to "press the button as soon as you feel the sensory touch the bottom of your foot." Subjects signaled detection of the stimulus via hand held bell button press. The latency between the onset of the rise in applied force measured by the force sensor and the resultant bell-press signal was taken to be the reaction time. Reaction times were measured five times and all trials were averaged. For auditory latencies, a bell tone was presented bilaterally via earphones and a subject signaled by pressing the force sensor with the thumb when he heard the signal. A change of approximately 10 times the sensor's resolution (0.01 N) or greater was determined to be the trigger for the detection of the event. Again, reaction time was averaged over all 5 trials.
Finally, a reaction time to a rightward lateral platform movement of 4 mm at 100 mm/s2 was measured, while a subject stood barefoot on the platform with feet in normal stance. The SLIP-FALLS platform [28] was used to induce these movements because it produced smooth, precisely controlled, low vibration translations. Subjects were blindfolded and instructions were presented over a white noise background to the subjects though headphones. Subjects signaled detection of the movement though the use of a hand held push-button remote. Reaction times were averaged over ten trials.
Results
All data sets analyzed failed a normaility test, prompting the authors to use non-parametric tests. In cases where two groups were compared, a Mann-Whitney Rank Sum Test was used. In cases where three or more groups were compared, a Kurskal-Wallis One Way ANOVA was used. The non-normality of the data also precluded the use of two-way ANOVAs. For all tests, the level of significance used was p < 0.05.
Thresholds
Tactile Thresholds from Semmes- Weinstein Monofilament Tests
Table 1 gives the average force necessary for detection of each group tested at each location on the foot sole. None of the diabetics in this study had significant plantar sensory loss. No significant differences were found in thresholds between right and left legs for the metatarsal and toe in any group. Data from the right and left legs were then pooled. A non-paramedic (Kruskal-Wallis) one way ANOVA was used to determine difference in tactile threshold among groups. For both plantar locations, young adults had significantly lower thresholds (median = 0.07 N) than the other groups. The diabetic (median = 3.610 N for metatarsal and median = 3.220 N for toe) and healthy adult groups (median = 3.610 N for both plantar locations) did not differ significantly.
Table 1 Plantar, Auditory, Nerve Conduction and Reaction Time Metrics. Average reaction times to a plantar touch, a pure tone auditory stimulus, and rightward lateral movement of 4 mm at 100 mm/s2 in diabetic (Peripheral Neuropathy), non-diabetic (Neurologically Intact), and young adults. Metrics given in either average ± standard deviation format or median [25% quartile, 75% quartile] format. All metrics for Semmes-Weinstein Monofilament Threshold, Air Conduction Threshold, and Nerve Conduction Studies have been averaged over both the right and left sides of the body. NCV = Nerve Conduction Velocity
Group Peripheral Neuropathy (n = 13) Neurologically Intact (n = 24) Young Adults (n = 9)
Age (yrs) 60.6 ± 6.5 59.4 ± 8.0 22.7 ± 1.2
Tactile Semmes-Weinstein Monofilament Thresholds (N)
Base Metatarsal 3.610 [2.473, 4.000] 3.610 [2.440, 3.610] 0.07 [0.0200, 0.160] †
Big Toe 3.220 [2.220, 4.080] 3.610 [2.440, 3.840] 0.07 [0.0200, 0.160] †
Air Conduction Thresholds (dB)
1 K Hz 20.0 [15.0, 22.5] 15.0 [10.0, 20.0] †
2 K Hz 20.0 [15.0, 30.0] 15.0 [10.0, 22.5] ‡‡
4 K Hz 30.0 [17.5, 55.0] 25.0 [20.0, 35.0] ‡
8 K Hz 35.0 [22.5, 67.5] ‡ 35.0 [20.0, 57.5] ‡
Peak Accel Thresholds (mm/s2) to Platform Lateral Moves
1 mm 122.775 ± 68.964‡ 108.417 ± 59.050 ‡ 60.778 ± 51.832‡†
2 mm 77.407 ± 55.982 ‡† 42.664 ± 37.754 ‡† 10.386 ± 3.091†
4 mm 37.275 ± 30.341 18.572 ± 19.143 13.458 ± 8.343
8 mm 20.297 ± 18.680 14.077 ± 8.122 14.766 ± 8.012
16 mm 18.613 ± 9.790 11.258 ± 7.723 13.590 ± 9.083
Nerve conduction studies
Sensory NCV (m/s) – Sural 41.0 [37.0, 45.0] † 45.0 [42.0, 47.0] †
Motor NCV(m/s) Peroneal 42.0 [39.0, 46.0] † 48.0 [46.0, 49.0] †
Motor NCV(m/s) – Tibial 40.5 [36.0, 45.0] † 45.0 [42.25, 48.75] †
M-wave Latency (ms) – Peroneal 4.7 [4.15, 5.45] 4.6 [4.075, 5.3]
M-wave Latency (ms) – Tibial 5.35 [4.4, 7.0] * 4.8 [4.2, 5.6] *
F-wave Latency (ms) – Peroneal 52.7 [47.625, 60.950] † 49.7 [46.6, 52.325] †
F-wave Latency (ms) – Tibial 58.5 [52.6, 64.7] † 52.5 [50.450, 55.275]†
Reaction Times (ms)
Touch (Big Toe) 353.1 ± 113.6 331.5 ± 140.5 216.0 ± 64.3
Tone (1 kHz) 282.6 ± 65.2 276.9 ± 105.5 218.6 ± 64.3
4 mm Lateral Platform Movement @ 100 mm/s2 777.8 ± 243.0‡† 623.9 ± 191.4‡† 431.5 ± 59.1‡†
†Indicates significant group difference
‡Indicates significant threshold difference
*Indicates a trend to significant threshold difference (0.05 < p < 0.07)
Audiology Thresholds
Because no significant difference (p = 0.481) was found between the right and left ears for all groups, the results of the air conduction testing of both ears were pooled and compared between groups. Averages for each group at each frequency (1, 2, 4, and 8 kHz) can be seen in Table 1. Significant differences were found both between groups and among frequencies. Diabetics had significantly higher thresholds at 8 kHz (median = 35.0 dB) and the healthy adult group had significantly higher thresholds at 4 and 8 kHz (median = 25.0 dB and 35.0 dB respectively). Additionally, there was no significant difference between the diabetics and non-diabetics at 4 and 8 kHz, but there was a significant difference at 1 kHz, and trend toward significance at 2 kHz (p = 0.055).
Thresholds to Rightward Lateral Platform Movements
Determining the minimum acceleration threshold required to detect motion requires special psychophysical test procedures. These procedures, results, and conclusions arising from such testing are complex enough to require treatment in entirely separate papers [29,30]. In summary, peak acceleration values required at threshold are a function of the displacement traveled and the group studied. These values are listed in Table 1. These results show that the lateral perturbation test used in this study (4 mm at 100 mm/s2) is well above the detection threshold of any of the three groups at 4 mm (~ 40.0 mm/s2 for diabetics, and ~ 14.0 mm/s2 for healthy mature and younger adults). Hence we have termed this stimulus a Superthreshold stimulus.
Lower Limb Nerve Conduction Testing
Data was again pooled for both legs because all nerve conduction studies showed no differences between the two legs (values for each group can be seen in Table 1). Significantly slower conduction velocities (p < 0.05) were found for the sural, tibial, and peroneal nerves of the diabetic group. No significant differences was seen in the M latency of the peroneal nerve (p = 0.492) between groups, but a trend towards significance was seen in the tibial nerve (p = 0.07). The F latencies of both the peroneal and tibial nerves of the diabetic group were significantly higher than the healthy adults.
Although there was a significant slowing found within the diabetic group, the deficit present was not judged as severe by a trained neurologist. According to standards set forth by the VA Medical Center, normal motor nerve conduction studies have velocities greater than 44.0 m/s for the peroneal nerve and greater than 41.0 m/s for the tibial nerve. The median conduction velocities for adults with diabetes are 42.0 m/s and 40.5 m/s for the peroneal and tibial nerve respectively. These values are not within the normal range for nerve conduction studies (NCS), yet they do not represent severe slowing. This indicates that those in this study do not have advanced motor deficits, and that the extent of the peripheral neuropathy is significant, yet not severe and debilitating. Also, as expected, sensory nerve conduction velocities were slower than motor nerve conduction velocities, which validate the data.
Reaction Time Measurement
Reaction times to platform movement (4 mm at 100 mm/s2), plantar touch, and a bell tone were measured in all subjects and can be seen in Table 1 and Figure 1 (See Attached File). Measurements for reaction times were taken as the time between the beginning of the stimulus and the button press indicating subjects detected the stimuli. Averages were taken from all trials that were detected.
Figure 1 Reaction Times to Plantar Touch, Auditory Tone, and Whole Body Lateral Perturbation by Group. Average reaction times of each modality for each group are shown. YA = Young Adult; HMA = Healthy Mature Adult; DMA = Diabetic Mature Adult. Note reaction times for platform movement are significantly longer for each group and also increase from young healthy adults to healthy mature adults and diabetic mature adults.
For platform movements at 4 mm at 100 mm/s2, reaction times of all groups are significantly different (p < 0.05) from each other, with reaction times in the adults with diabetes being longest (mean = 777.8 ms), followed by aged matched adults (mean = 623.9 ms). Young adults had the shortest reaction times (mean = 431.0 ms) to movements.
For the touch modality, reaction times for adults with diabetes are significantly (p < 0.05) longer than both other groups (mean = 353.1 ms). However, reaction times to foot sole touch between young (mean = 216.0 ms) and healthy mature adults (mean = 331.5 ms) were not significantly different.
For the tone modality, no significant differences in reaction times were found between groups (diabetic mean = 282.6 ms; healthy adult mean = 276.9 ms; younger adult mean = 218.0 ms). For all groups, movement reaction times were significantly longer than the other two modalities (plantar touch and auditory tone), which did not differ significantly.
Discussion
A reaction time measurement includes the latency in the sensory neural code traversing peripheral and central pathways; perceptive, cognitive and volitional processing; a motor signal again traversing both central and peripheral neuronal structures; and finally, the latency in end effector (e.g., muscle) activation. Unless there is a greatly lessened sensitivity or loss of the sensory receptors for a given modality, a stimulus well above perceptual threshold, will, by its very nature of being superthreshold, produce a strong neuronal signal in the peripheral nerve subserving the location stimulated. And, conversely once the volitional decision is made to signal that an event has been detected, the motor output emanating from the spinal cord to the finger that will press the signaling button should likewise be robust.
Since reaction time measurements have a central component, any decline, including those seen in normal aging, could indicate the presence of a peripheral and/or central neuropathy. Thus, it becomes difficult to tease out peripheral versus central effects when reaction times are slowed. But if the effect of a peripheral neuropathy is known through knowledge of the change in sensory and motor nerve conduction velocities brought about by the neuropathy, the extent to which the neuropathy will slow the reaction time can be estimated.
A lateral platform translational perturbation invokes many senses. These include, but are not limited to pressure and rapidly adapting tactile sensors in the feet and toes, proprioceptive sensors from the ankle and hips (assuming that lateral moves do not affect the knees), kinesthetic sensors in the muscles (muscle force from Golgi tendon organs, reflex activation form spindles, and an ill-defined sense of "perceived exertion", vestibular activation, and certainly visual stimuli (unless occluded via blindfold as we have done in this study), Because of the involvement of all of these "senses," attributing the cause(s) of a slowed platform perturbation reaction time to peripheral and central neuronal changes is difficult. But, if peripheral conduction velocities are known, as are reaction times to more "purer" sensory inputs such as foot sole touch or an auditory tone, then this latter knowledge can be factored into platform reaction time calculations to help tease out peripheral and central effects.
At this point, it is instructive to review the results reported in a preceding section of this paper and in Table 1, and then to call out the various findings to build a specific hypothesis. The key results follow:
1) Subjects with controlled type II diabetes all had mild, but measurable peripheral neuropathies in at least one nerve in the lower limb, while those without diabetes of age >50 had no measurable evidence of neuropathy;
2) Subjects with diabetes had increased reaction times to all three test modalities. Touch and Tone reaction times were slightly, but not significantly, higher, while platform reaction time was significantly higher.
3) Older adults, whether diabetic or not, had longer reaction times to platform moves and to foot sole touch (all locations) than did younger adults, and reaction times to the bell tone did differ between groups, even though those with diabetes had higher auditory air conduction thresholds at every frequency (except 8 kHz) tested than their non-diabetic counterparts.
4) Reaction times to platform movement are 200 to 300% longer in all groups when compared to reaction times to touch and tone;
Implications
Individuals with diabetes often have neurological side effects that affect the peripheral nervous system. However, the increase in whole body movement reaction time seen in adults with diabetes in this study can not solely be related to peripheral nervous system changes due to diabetes. Even when motor nerve conductions slow from 50.0 m/s to 40.0 m/s (as seen in nerve conduction testing here), signal transmission time for a 1 m long nerve increases only 5 ms, which does not account for a 200 ms increase in movement reaction time. An additional slowing has to be occurring in the processing of the signals by the central nervous system.
Deficits in the central nervous system (CNS) of those with diabetes may also be seen in cognitive deficits. Dey, et al. found no correlation between the duration of diabetes and cognitive function in those with non-insulin-dependent diabetes less than 18 years old [31]. They hypothesized that in order to see the decline in cognitive function and other central nervous system effects seen by other researchers [10-13], a longer duration of disease state must be present. However, in our study, diabetics, all with less than 10 years disease duration had a significantly higher reaction time to movement, which could be interpreted to indicate that not only are central effects present, but they manifest themselves early in the disease. These increases in movement reaction times among the mature adults with diabetes may also have an effect on posture and gait. The longer reaction times of a slipping diabetic subject will thus increase the probability of a fall. Diabetics have been shown to have a higher incidence of postural instability [21-26], longer reaction times, and reduced peripheral sensations thus leading to a higher incidence of falls resulting from slips.
Reaction times to plantar surface touch indicate the extent of peripheral neuropathy in the population of diabetics. The fact that the mature adults with diabetes had increased reaction times to plantar touch is another indication that peripheral neuropathy was present in these subjects. However, we can see that the peripheral neuropathy of these adults with diabetes was not severe through the measurements of the Semmes-Weinstein monofilaments and sensory nerve conduction velocities. This increase may also play a role in the reaction time increase seen in the platform movement. If the subjects were unable to sense the movement for an additional 100 ms, then the 200 ms increase seen in adults with diabetes could be attributed to this sensory reaction time deficit, plus an increase in signal transmission through the nerves of approximately 5 ms, and an unknown cognitive slowing.
Auditory reaction times measured here for diabetics and age-matched controls do roughly concur with the one reaction time study that includes diabetics [5]. Although no significant differences in auditory reaction times were seen between mature adults with and without diabetes and their young adult counterparts, a sensorineural hearing loss was seen in the mature adults with diabetes at the mid- and high-frequencies. Controversy over the relationship between diabetes mellitus and sensorineural hearing loss has had a long history. Some authors have concluded that no correlation exists [32-35], yet others find significant correlations between diabetes and loss in the low [36], mid [36,37], and high [37] frequency ranges. This loss has been attributed to changes in the peripheral portion of the auditory pathway, because no change in signal conduction along the central auditory pathway in patients with diabetes has been seen [5]. Mean hearing thresholds tested here were consistent with those published in Tay, et al. [36] for healthy elder subjects. However, subjects with diabetes in this study had slightly higher thresholds than those published in Tay et al, but the higher thresholds were more consistent with those published by Celik, et al. [37]. No auditory evoked potentials were measured, therefore the source of the dysfunction (be it the central or peripheral auditory pathway) cannot be determined.
Conclusion
From this study we can conclude that diabetes does affect reaction times, although the type and severity of the slowing may be related to the difficulty of the task and the prevalence of central and peripheral nerve deficits seen as side effects of diabetes. Auditory reaction times, the simplest of the tasks here with the shortest path between peripheral and central nervous system, did not show any differences in reaction times. When using a test that has a significantly longer path in the peripheral nervous system, such as the reaction time to plantar touch, slightly longer reaction times are seen in the adults with diabetes. When a more complicated task including detecting movement, signal transmission and interpretation, and response was required from the body, as in the platform movement reaction time test, a significant difference in reaction times were seen among all groups. This test takes more fully into account the peripheral nervous system signaling as well as the central nervous system processing and thus is a better overall test to determine deficits in healthy aging and aging individuals with diabetes.
We have presented here, in addition to normal auditory and touch reaction times, lateral whole body reaction time, which has been shown to be the most sensitive indicator of differences between healthy young, healthy mature adults, and mature adults with mild diabetes among the modalities tested here. In other studies, we have found that adults with diabetes have substantially higher thresholds than healthy adults to detecting whole body motion [29]. This, in addition to the increased whole body reaction times, indicate that mild diabetes has profound effects on ability to detect and react to motion, which leads to insights on their ability to detect and prevent slips and falls.
With the data presented here, it is impossible to determine the relative contribution of peripheral and central neural processes to the slowing seen on the whole body reaction time test. To determine this exact relation, authors are currently working on measures of cognitive processing that may provide more insight.
Authors' contributions
SJR aided in the experimental design, carried out the data acquisition, data analysis and data interpretation, and drafted the manuscript. CJR aided in the experimental design, built the set-up, and aided in the final manuscript. JS completed the statistical data analysis and prepared all the tables and figures.
Acknowledgements
Funding from VA Rehabilitation R&D Grant #E2143PC, a VA Senior Rehabilitation Research Career Scientist Award, a Whitaker Foundation Special Opportunity Award and a Louisiana Board of Regents Graduate Fellowship. Thanks to Dr. Anne Hollister for her insight. Thanks to Duanne Redman and Charlotte Eichelberger for their help with clinical data collection.
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| 15723700 | PMC555589 | CC BY | 2021-01-04 16:37:33 | no | Biomed Eng Online. 2005 Feb 21; 4:12 | utf-8 | Biomed Eng Online | 2,005 | 10.1186/1475-925X-4-12 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-401574063410.1186/1471-2105-6-40Methodology ArticleComputational verification of protein-protein interactions by orthologous co-expression Tirosh Itay [email protected] Naama [email protected] Departments of Molecular Genetics and Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel2005 2 3 2005 6 40 40 8 9 2004 2 3 2005 Copyright © 2005 Tirosh and Barkai; 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
High-throughput methods identify an overwhelming number of protein-protein interactions. However, the limited accuracy of these methods results in the false identification of many spurious interactions. Accordingly, the resulting interactions are regarded as hypothetical and computational methods are needed to increase their confidence. Several methods have recently been suggested for this purpose including co-expression as a confidence measure for interacting proteins, but their performance is still quite poor.
Results
We introduce a novel computational method for verification of protein-protein interactions based on the co-expression of orthologs of interacting partners. The performance of our method is analysed using known S. cerevisiae interactions, and is shown to overcome limitations of previous methods. We present specific examples of known and putative interactions that are detected by our method and not by previous methods, and suggest that they represent transient interactions that might have been conserved and stabilized in other species.
Conclusion
Co-expression of orthologous protein-pairs can be used to increase the confidence of hypothetical protein-protein interactions in S. cerevisiae as well as in other species. This approach may be especially useful for species with no available expression profiles and for transient interactions.
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Background
Protein-protein interactions (PPIs) have a central role in most biological processes, and identifying these interactions is an important goal of biological research. PPIs are the subject of extensive experimental studies, but the majority of them remain unknown. In the last few years, high-throughput techniques were developed for the identification of PPIs on a genomic scale. Yeast two-hybrid [1,2] and mass spectrometric analysis of protein complexes [3,4] were used to produce large sets of PPIs. However, these techniques are known to suffer from many false positives and the resulting PPIs are typically regarded as putative [5,6]. Thus, the development of computational methods for assessment and verification of putative PPIs is crucial [7-10]. Two such methods were proposed, that are based on the co-expression [11] and conservation [9] of PPIs, respectively. Here we propose to extend these methods by considering co-expression of orthologous protein pairs. We demonstrate the predictive power of our approach and discuss its advantages.
Results
Verification by mRNA co-expression
It was previously shown that interacting pairs of proteins are often correlated in their expression profiles [11,12]. The correlation of expression profiles was therefore proposed as a confidence measure for putative PPIs [7,10,13]. However, this approach has three major limitations. First, many pairs of non-interacting proteins are also co-expressed (false positives). Second, many pairs of interacting proteins are not co-expressed (false negatives). Third, to properly determine co-expression, mRNA expression profiles from a large and diverse set of conditions are needed, rendering this approach inapplicable for most organisms.
Former studies that used co-expression to identify PPIs did not explicitly examine its predictive power, or did not use a random set of protein-pairs as control for evaluating its performance. We thus carried out an analysis to evaluate the predictive power of this approach for S. cerevisiae, in order to later compare it to our new method. High quality S. cerevisiae expression data is available for many conditions, making it an ideal organism for the use of co-expression for validation of PPIs. We extracted a reference set of 1656 known interaction from the MIPS database [14], and generated a random set by randomly choosing pairs of proteins. Cosine correlation over our entire set of S. cerevisiae conditions was used to compare the levels of co-expression between the reference set and the random set (see methods).
The results of this analysis are summarized in Figure 1. The cumulative distributions of expression correlations in both sets are compared, showing higher degrees of co-expression in the reference set than in the random set (Figure 1a). The resulting predictive power is shown in Figure 1b, where each dot represents a possible correlation threshold for PPIs prediction. The percentages of protein-pairs passing each threshold from the random and reference sets are shown in the horizontal and vertical axes, respectively. For example, the threshold shown in Figure 1 (0.155) which leads to the correct verification of 30% of the reference set (497 true positives), results also in the false verification of approximately 9% of the random set (~149 false positives). Applying this to a set of putative PPIs with 50% false positives (as estimated for the S. cerevisiae yeast two hybrid sets [5,6]) results in a filtered subset with approximately 23% false positives (9% divided by 39%).
We verified that the performance of this method is largely independent of the exact set of conditions that is used, and that filtering the conditions or choosing them specifically for each pair of proteins does not improve the performance (not shown).
Conservation of PPIs
Another approach that was proposed to verify or predict PPIs is based on conservation of interactions [9,15,16]. In this approach (termed "interologs"), pairs of proteins whose orthologs are known to interact in other species are assumed to interact. Such a method can potentially reveal many conserved PPIs, but it is currently limited by the availability and accuracy of interaction data. Without relying on putative interactions, the available set of S. cerevisiae PPIs only correspond to a small fraction of the biologically meaningful interactions, and the situation is much worse for other species. Consequently, this method has so far been based only on S. cerevisiae PPIs, including putative ones, to predict interactions in other organisms. Giot et al. used putative S. cerevisiae PPIs from mass spectrometric analysis to verify Drosophila PPIs found by yeast two-hybrid. Only 65 out of the ~2000 Drosophila putative interactions were identified as having an orthologous interaction in S. cerevisiae. This set was then used to train a statistical model for assignment of confidence scores to putative PPIs. Li et al. used putative S. cerevisiae PPIs gathered from several sources to predict C. elegans PPIs (rather than verify an existing set of putative PPIs). Out of the 5534 predicted C. elegans PPIs, only 949 were identified as having an orthologous interaction in S. cerevisiae [16].
The use of conserved interactions to verify a putative set of PPIs is therefore very limited, since only a small fraction of the putative set would have a known orthologous interaction. Furthermore, using putative PPIs in order to increase the coverage of this approach will decrease its accuracy and introduce many more false positives.
Orthologous co-expression
Motivation
In order to overcome the limitations of the two methods described above, we propose to integrate them and detect PPIs by orthologous co-expression, i.e. co-expression of the orthologs of the interacting partners (Figure 2a). A conserved interaction may be co-expressed only in a subset of the organisms in which it is present, so combining knowledge of co-expression from multiple organisms can be informative.
The use of orthologous co-expression for verification of PPIs is also supported by three previous observations. First, in order to preserve their interaction and functionality, interacting partners should co-evolve [17]. Sequence analysis was previously used to uncover co-evolution at the sequence level [18], but it may also be present at the level of gene expression. Second, as shown in two recent papers, co-expression of functionally linked proteins is more likely to be conserved than the co-expression of random pairs of proteins [19,20]. Hence, orthologous co-expression can replace co-expression, and serve as a better measure to identify functional links in general and PPIs in particular. Third, interacting protein-pairs are more likely to have pairs of orthologs in other species than randomly selected protein-pairs. This observation was made previously for different ascomycota species [21], and can also be seen in our analysis of more distant organisms (Figure 2b). Since orthologous co-expression can only be computed for conserved protein-pairs, the increased conservation of interacting protein-pairs will also increase the percentage of interacting pairs where orthologous co-expression can be computed, and lead to higher percentage of real PPIs out of the total predicted protein-pairs.
Performance
To examine whether orthologous co-expression can indeed be used to predict PPIs, we focused on S. cerevisiae orthologs from five species (C. elegans, E. coli, A. thaliana, D. melanogaster, and H. sapiens). Orthologous pairs of the protein-pairs in the reference and random sets were identified by BLAST [22], and their co-expression was measured using cosine correlation over the entire sets of mRNA expression data (see methods). Co-expression values of the random set orthologs in each organism were used to determine the 5% significance correlation thresholds. The percentage of interactions with significant orthologous co-expression in each organism (out of all the interactions where orthologous co-expression can be computed, i.e. interactions with both orthologs and expression profiles at that organism) is shown in figure 2c. Indeed, for all five organisms we found that orthologous-pairs of known PPIs are more likely to be co-expressed than that of random protein-pairs. Interestingly, the percentages of orthologous-pairs of PPIs with significant co-expression in E. coli and D. melanogaster are even higher than the percentage of PPIs with significant co-expression in S. cerevisiae (Figure 2c). Note, however, that less than 3% of the reference set had orthologous-pairs in E. coli and orthologous co-expression was computed only for 38 PPIs, so the high E. coli value might be a result of insufficient statistics.
The ability to predict PPIs by orthologous co-expression strongly depends on the percentage of interactions where orthologous co-expression can be computed (i.e. where both proteins are conserved and have expression profiles), so the percentages of PPIs that can be predicted by each organism is lower than 7% for all five organisms (Figure 2c). To overcome the lower coverage of each organism we combined the information from all five organisms. We examined the predictive power of this approach by repeating the analysis shown in Figure 1, when the yeast co-expression is replaced by the sum of the orthologous co-expression from the five other species (figure 2d). To avoid over-fitting, we only considered simple summation of the co-expression in different species. Notably, although S. cerevisiae co-expression was omitted from the analysis, the predictive power of this approach was better than that of S. cerevisiae co-expression alone (Figure 2d).
Combining S. cerevisiae and orthologous co-expression
The correlation between S. cerevisiae co-expression and orthologous co-expression of the true interactions in the test set is only 0.34. This means that the two methods are complementary, and that except for detecting interactions between co-expressed proteins, orthologous co-expression can also detect interactions between proteins that are not co-expressed in S. cerevisiae, but their corresponding orthologous are co-expressed in other species. Examples of known interactions from the test set with low co-expression in S. cerevisiae but high orthologous co-expression are shown in Table 1. In these 30 cases, the co-expression in S. cerevisiae is very low or even negative, but the orthologous co-expression is high in at least two species, such that they are easily detected by our approach.
Based on the complementarities of the two methods, namely S. cerevisiae and orthologous co-expression, we proceeded by adding the orthologous co-expression to S. cerevisiae co-expression (figure 2d). The addition significantly improved the results of both methods. Using the same example as mentioned above, the percentage of protein-pairs identified from the random set is reduced from 9% to 5%, while the percentage of proteins-pairs identified from the reference set remained 30%.
Transient interactions
In a previous study relating gene expression to PPIs, Jansen et al. classified protein complexes as 'permanent' and 'transient' [12]. The subunits of permanent complexes were shown to be highly co-expressed, in contrast to transient complexes where co-expression was very low. Transient interactions are therefore harder to detect by co-expression as well as by experimental methods.
To test the performance of our method on transient interactions we examined the nine protein complexes classified as transient: pre-replication complex, replication complex, anaphase promoting complex (APC), TAFIIs, SAGA complex (Spt-Ada-Gcn5-acetyltransferase), CCR4 complex, RSC complex, SRB complex (kornberg's mediator) and SWI/SNF complex. Assuming all pair-wise interactions in these complexes, we compared the percentage of protein-pairs with significant S. cerevisiae or orthologous co-expression for each complex and for the combined set (Figure 3a).
Orthologous co-expression is slightly better than S. cerevisiae co-expression at identifying interactions in the reference set, but the differences in performance increase considerably when transient complexes are examined. In the combined set of 764 transient interactions, orthologous co-expression identifies almost three times (2.68) more interactions than S. cerevisiae co-expression. Moreover, for five out of the nine transient complexes, orthologous co-expression identifies at least three times more interactions than S. cerevisiae co-expression, while the opposite occurs only in one complex – RSC, which is also the smallest complex examined. These results suggest that orthologous co-expression is especially useful for detection of transient interactions.
Specialization of interacting proteins can lead to high orthologous co-expression
Why are there interacting protein-pairs which are not co-expressed in S. cerevisiae, while their corresponding orthologs are co-expressed in other species (Table 1; Figure 3a)? The observation that interacting protein-pairs are co-expressed is believed to be a result of their need to be present in similar amounts at different conditions. However, for transient interactions occurring only in specific processes, this requirement might affect only a small number of conditions, and hence might have a slight influence on the global levels of co-expression. In contrast, the orthologs of such interacting proteins might have adopted a stable interaction, resulting in co-expression at many conditions. Such transient interactions will not be detected by co-expression, and might also be hard to find using experimental methods, but orthologous co-expression may help to identify them. Moreover, one of the interacting proteins may be multifunctional, interacting with several proteins depending on context. The expression of such pleiotropic proteins is likely to be constitutive, and will not show correlation to that of its interacting partners. However, the pleiotropic protein might have several specialized orthologs in other species, each performing distinct functions, and co-expressed with the corresponding orthologs (Figure 3b). Note that in such cases the specialized ortholog may not be the closest one in sequence. However, allowing each protein to have multiple orthologs and choosing the maximal correlation can also increase the orthologous co-expression of false interactions. Consequently, such an approach only reduced the performance of our method (not shown).
Specific examples
To examine if specialization of interacting proteins can account for the high orthologous co-expression of protein pairs in Table 1 and in the transient complexes, we looked in more details at specific examples. Here we provide three examples supporting this notion.
1. CDC28 is the only cyclin-dependent kinase (CDK) in S. cerevisiae involved in cell cycle transitions [23]. CDC28 interacts with different proteins at different stages of the cell cycle, including G1 and B-type cyclins (CLNs and CLBs, respectively) and CDC6. Indeed, no detectable co-expression is found between CDC28 and its interacting partners (Table 1; not shown for CLNs). In contrast, CDC28 has several orthologs in higher eukaryotes (up to five distinct CDKs in mammals), each devoted to specific processes or tissues [23], and the orthologs that were found by our analysis in H. sapiens, D. melanogaster and C. elegans (CDK2, CDC2 and CDK-1, respectively) are highly co-expressed with the corresponding orthologs of CDC6 and the B-type cyclins (Table 1).
2. Yeast TAF5 is a component of at least two transient complexes, the general transcription factor TFIID and the SAGA complex [24]. However, its human ortholog (TAF5) is only known to be a part of the TFIID complex, while a second ortholog (TAF5L) is known to be in both TFIID, and the human equivalent of SAGA [25]. As expected, the co-expression of human TAF5 and the other proteins in human TFIID is higher than that of yeast TAF5 and the other proteins in yeast TFIID (not shown).
3. The opposite case of two S. cerevisiae paralogs with only one ortholog in higher eukaryotes, though less common, may also help to identify PPIs. The nascent polypeptide associated complex (NAC), consists of an alpha subunit (EGD2) and a beta subunit (either EGD1 or BTT1) [26]. BTT1 is not co-expressed with EGD2, presumably since EGD1 and BTT1 are alternating beta subunits that bind both the ribosome and the alpha subunit (EGD2). In contrast, D. melanogaster and C. elegans have only one known orthologous beta subunit, which are co-expressed with the corresponding orthologs of EGD2 (Table 1).
Predictions
Table 2 shows examples of low confidence putative interactions with low co-expression but high orthologous co-expression. These interactions were found by high-throughput yeast two-hybrid [1], and considered low confidence (they had less than 3 interaction sequence tags and were not included in the core data; also not supported by co-expression). However, in light of the high orthologous co-expression from at least two species, we predict that they represent true interactions. In support of that, both proteins in all these examples are localized to the same cellular compartment (according to the MIPS database [14]).
Some of these proposed interactions might also fit the model in Figure 3. For example, SMT3 is the only SUMO gene in S. cerevisiae, which is known to modify TOP2 (DNA Topoisomerase II) and other proteins [27]. However, in vertebrates there are three known SUMO genes: SUMO1, SUMO2, and SUMO3. As suggested by the model in Figure 3, SMT3 is not co-expressed with TOP2, but one of its human orthologs (SUMO1), is highly co-expressed with the human ortholog of TOP2 (TOP2A; see Table 2).
Discussion
We presented here a new computational method for verification of PPIs that is based on the co-expression of orthologous protein-pairs, and demonstrated its predictive power using PPIs identified in S. cerevisiae.
This method extends two of the former methods, namely co-expression of interacting proteins and conservation of interactions (interologs). The first method can only be applied to organisms with expression data and its performance depends on the amount and quality of that data. Our method overcomes this limitation by integrating sequence and expression data from other organisms. It can thus be applied to any sequenced organism, particularly for those without available expression data, thereby replacing the missing data. Moreover, it performs better than the former method even for S. cerevisiae, where many high quality expression data is available, and is especially better in identifying transient interactions. It is difficult to evaluate our approach for other species, since we do not have large representative sets of known interactions, but the success in yeast is promising.
The proposed method also overcomes the limitation of the interologs approach, namely the small fraction of interactions that is known to date. Our method uses expression rather than interaction data, which makes it capable of giving evidence for a much larger number of interactions.
mRNA expression profiles are being generated by many different labs for a wide range of organisms. The improved quality of existing expression profiles as well as the addition of profiles for other organisms will improve the performance of our method. Further improvements can be achieved by giving different weights to the co-expression from different organisms (not shown). A weight can be given to each organism according to the reliability of its expression profiles, or according to its evolutionary distance from the studied organism.
During the writing of this manuscript, a related approach was suggested [28]. Based on the codon adaptation index (CAI) as an estimator for average expression levels, Fraser et al. examined co-evolution of expression levels from four fungi closely related to S. cerevisiae, and used that to predict PPIs in S. cerevisiae. This approach is complementary to the one that we have proposed. Thus, mRNA expression should be used directly when possible, even from relatively distant species (such as D. melanogaster), and CAI should be used from closely related species without available expression data.
Finally, the methods described here are still not accurate enough to verify specific PPIs, but they provide additional evidences and are useful for assessment and filtering of high-throughput PPIs data sets, in order to produce smaller sets of higher confidence, and direct further investigations. Complementary methods should be combined to create a general scheme for verification of putative PPIs, for example by considering only those interactions that are verified by at least two or three methods [7] or using supervised machine learning approaches [29], thus improving the performance of each method alone.
Conclusion
We have shown that expression data from multiple organisms can be used to increase the confidence of hypothetical PPIs by considering co-expression of orthologs of the presumed interacting partners. For organisms such as S. cerevisiae, with highly characterized expression profiles, orthologous co-expression may be combined with co-expression of the actual proteins, whereas for other, less studied organisms, it may replace the missing expression profiles. Notably, this method is especially useful for detection of transient interactions which presents a known weakness of most prediction methods. The success of this method also implies that PPIs tend to be conserved in different organisms, even as distant as yeast and human, further supporting the use of comparative approaches in proteomics.
Methods
Interactions sets – a reference set of S. cerevisiae interactions was extracted from the MIPS (Munich Information Center for Protein Sequences) PPI database [14] at 22/01/04. We excluded genetic interactions, self-interaction, interactions found by high-throughput experiments, interactions without expression data, and redundancies, resulting in a set of 1656 interactions. We did not use larger databases such as the one compiled by von Mering et al. [7] since they are more likely to contain false interactions and are also biased towards co-expression since this information was used in their construction. Randomly generated set of the same size was used as control, and averaged over ten trials. Self-interactions were excluded from the random set. The random set may include real interaction, but their expected frequency is much less than 1%. Transient complexes were taken from Jansen et al. [12]. The transient set was constructed by combining the pair-wise interactions from each transient complexes and removing redundancies (some protein pairs were present in more than one complex).
mRNA expression data – datasets for six organisms were collected from different sources, as described in [19], and can be downloaded from our home page [30]. All datasets were normalized to have a mean of 0 and standard deviation of 1 for each condition.
Expression correlation – cosine correlation over the entire expression data of each organism was used as a measure of co-expression. Former analysis suggested that cosine correlation is the optimal measure of co-expression for the purpose of detecting PPIs [13]. Many genes in all six organisms have missing values in the expression data, so the expression correlations of many orthologous pairs cannot be calculated. To decrease the dependency of our approach in the availability of expression data and to improve its performance, we replace the missing correlations by estimated values. We used the corresponding yeast co-expression when the yeast and orthologous co-expression are combined (green curve in figure 2d). In contrast, when orthologous co-expression is used alone (red curve in figure 2d), the yeast expression data is assumed to be unavailable (in order to show the applicability of the method to organisms without expression data) and an expected correlation is calculated for each species, based on the union of the reference and random sets (average expression correlation of orthologous pairs in a specific species, over the reference and random sets combined with equal weights). The expected correlations are greater than zero for all five species; so putative PPIs are actually given positive scores for the existence of an orthologous pair, corresponding to the notion that PPIs are more likely to have pairs of orthologs [21].
Orthologous proteins – orthologs were found using blastp [22] with a P-value threshold of 10-7, and alignment length threshold of 0.3. The ortholog with the most significant p-value that had available expression data was used to measure co-expression. Other studies had used a reciprocal best-hit BLAST search for finding orthologous; we use a less strict criterion in order to apply the orthologous co-expression method to more protein-pairs.
P-values and Significance – by sampling 100,000 protein pairs we determined p-values for S. cerevisiae and orthologous co-expression as the fraction of pairs with equal or greater correlation of expression profiles; P-values of 0.05 (not corrected for multiple testing) were used as thresholds for significance.
Acknowledgements
This work was supported by the NIH Grant No. A150562 and the Israeli Science Ministry. N. B. is the incumbent of the Soretta and Henry Shapiro career development chair.
Figures and Tables
Figure 1 Higher correlation of expression profiles among interacting protein-pairs. (a) Cumulative distributions of correlations between expression profiles of protein-pairs from a reference set of 1656 known interactions taken from the MIPS database, and a set of randomly chosen pairs of proteins (averaged over ten trials). The dashed line represents a possible correlation threshold (0.155) that can be used for prediction of PPIs. (b) The predictive power of this approach. Each point in this plot represents a specific correlation threshold for the prediction of PPIs. The vertical axes shows the percentage of interaction identified from the reference set (true positives) and the horizontal axes shows the percentage of interaction identified from the random set (false positives). The dashed lines represent the performance of the threshold shown in (a).
Figure 2 Orthologous co-expression can be used to predict PPIs. (a) Schematic representation of the method. (b) Interacting proteins are more likely to have an orthologous pair in other species. The percentage of yeast protein-pairs with an orthologous pair from five species (C. elegans, E. coli, A. thaliana, D. melanogaster, and H. sapiens) is shown for the reference and random sets. This property is seen for the four eukaryotes, but not for E. coli. (c) Orthologous pairs of interacting proteins are more likely to be co-expressed than orthologous pairs of random protein-pairs. The percentage of orthologous pairs having significant (P-value < 0.05) correlation of expression out of the total orthologous pairs with available expression data (conserved+expression), and out of the entire reference set (all interactions) is shown for all organisms (including S. cerevisiae). (d) Orthologous co-expression from five species was added and used to predict S. cerevisiae PPIs (red). The resulting predictive power is shown along with the predictive power of S. cerevisiae co-expression (dashed blue), as shown in Figure 1b. Orthologous co-expression was also added to S. cerevisiae co-expression, resulting in an improved predictive power (green).
Figure 3 Detection of transient interactions. (a) Each circle shows the percentage of protein-pairs in a specific set/complex with a significant level (P-value < 0.05) of S. cerevisiae and orthologous co-expression in the horizontal and vertical axes, respectively. Blue circles represent all pair-wise interactions in a single transient complex; Red circles represent the three sets of protein-pairs (random, reference and transient). The dashed line indicates similar performance of both methods. The table also shows the number of protein-pairs in each set/complex, and the ratio between the percentage of pairs with significant orthologous and S. cerevisiae co-expression, respectively. (b) Proposed model for transient yeast interactions with low co-expression, but high orthologous co-expression. Protein A interacts with protein B, but also performs other functions or interacts with other proteins, such that it is not co-expressed with protein B. However, in higher eukaryotes, a specialized ortholog of A exist, which is co-expressed with the ortholog of B.
Table 1 S. cerevisiae and orthologous co-expression of known Protein interactions
GENE 1 GENE 2 S. cerevisiae Co-expression Orthologous Co-expression
correlation p-value
D. melanogaster
C. elegans
H. sapiens
p-value
CDC28 CLB2 -0.07 0.74 * 0.77 0.59 3.7e-04
CLB4 -0.06 0.71 0.85 0.77 0.59 1.0e-05
CDC6 -0.03 0.60 0.77 0.32 0.37 2.6e-04
CLB3 -0.01 0.52 0.85 0.77 0.59 1.0e-05
CLB1 0.05 0.30 * 0.65 0.59 7.3e-04
CLB5 0.06 0.27 0.87 0.65 0.45 3. 0e-05
DMC1 PDC5 -0.09 0.80 0.59 * 0.17 6.8e-03
PDC1 -0.05 0.67 0.59 * 0.17 6.8e-03
RIS1 -0.02 0.56 * 0.34 0.52 4.3e-03
PRP9 RIS1 0.02 0.41 * 0.28 0.62 3.7e-03
PRP11 0.05 0.30 0.88 0.35 0.33 1.6e-04
NOG2 0.07 0.24 0.41 0.33 0.66 3.5e-04
SSN6 CUS1 -0.06 0.71 0.76 0.32 * 5.4e-04
SNP1 0.08 0.21 0.79 0.21 * 2.1e-03
PAB1 SGN1 -0.17 0.93 0.56 0.19 0.26 2.4e-03
RNA14 -0.06 0.71 0.42 0.17 0.24 5.8e-03
PFS2 RNA14 -0.03 0.60 0.44 0.31 - 8.5e-03
HAT1 HAT2 -0.04 0.33 0.86 0.19 0.22 7.3e-04
SIT4 TAP42 -0.16 0.92 0.54 - 0.36 4.3e-03
TRS23 BET3 -0.10 0.82 * 0.20 0.52 8.1e-03
DNA2 RAD27 -0.01 0.52 * 0.41 0.47 3.9e-03
PRP8 SNU114 0.05 0.30 * 0.60 0.57 8.7e-04
HRB1 MTR10 -0.07 0.74 0.56 0.27 0.27 1.5e-03
BTT1 EGD2 -0.08 0.77 0.42 0.52 0.59 2.0e-04
GPA1 STE11 0.07 0.24 0.55 0.25 0.37 1.1e-03
UBA2 AOS1 0.08 0.21 0.74 0.17 0.41 5.4e-04
SPT15 BRF1 0.09 0.19 0.72 0.21 0.23 1.1e-03
TAF5 TAF9 0.05 0.30 0.86 - 0.18 2.1e-03
LSM5 KEM1 0.04 0.33 - 0.37 0.65 2.3e-03
RPB3 MED7 0.01 0.45 * 0.49 0.32 5.3e-3
* denotes that at least one of the corresponding orthologs did not have expression data.
- denotes that there is no pair of corresponding orthologs.
Table 2 S. cerevisiae and orthologous co-expression of hypothetical Protein interactions
GENE 1 GENE 2 S. cerevisiae Co-expression Orthologous Co-expression
correlation p-value
D. melanogaster
C. elegans
H. sapiens
p-value
TAF5 PIF1 -0.05 0.67 0.74 0.05 0.42 8.7e-04
COR1 XDJ1 -0.01 0.52 * 0.43 0.39 5.0e-03
PUS2 LPD1 -0.06 0.71 * 0.23 0.55 6.1e-03
TAF6 PUB1 0.00 0.49 0.04 0.41 0.50 3.3e-03
PAN3 YNL092W -0.04 0.64 0.77 0.25 * 1.9e-03
SMT3 TOP2 -0.08 0.77 * 0.47 0.69 9.6e-04
* denotes that at least one of the corresponding orthologs did not have expression data.
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Zhang LV Wong SL King OD Roth FP Predicting co-complexed protein pairs using genomic and proteomic data integration BMC Bioinformatics 2004 5 38 15090078 10.1186/1471-2105-5-38
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-5-221575242210.1186/1471-2148-5-22Research ArticlePhylogenomic approaches to common problems encountered in the analysis of low copy repeats: The sulfotransferase 1A gene family example Bradley Michael E [email protected] Steven A [email protected] Department of Chemistry, University of Florida P.O. Box 117200, Gainesville, FL 32611-7200, USA2005 7 3 2005 5 22 22 7 4 2004 7 3 2005 Copyright © 2005 Bradley and Benner; licensee BioMed Central Ltd.2005Bradley and Benner; 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
Blocks of duplicated genomic DNA sequence longer than 1000 base pairs are known as low copy repeats (LCRs). Identified by their sequence similarity, LCRs are abundant in the human genome, and are interesting because they may represent recent adaptive events, or potential future adaptive opportunities within the human lineage. Sequence analysis tools are needed, however, to decide whether these interpretations are likely, whether a particular set of LCRs represents nearly neutral drift creating junk DNA, or whether the appearance of LCRs reflects assembly error. Here we investigate an LCR family containing the sulfotransferase (SULT) 1A genes involved in drug metabolism, cancer, hormone regulation, and neurotransmitter biology as a first step for defining the problems that those tools must manage.
Results
Sequence analysis here identified a fourth sulfotransferase gene, which may be transcriptionally active, located on human chromosome 16. Four regions of genomic sequence containing the four human SULT1A paralogs defined a new LCR family. The stem hominoid SULT1A progenitor locus was identified by comparative genomics involving complete human and rodent genomes, and a draft chimpanzee genome. SULT1A expansion in hominoid genomes was followed by positive selection acting on specific protein sites. This episode of adaptive evolution appears to be responsible for the dopamine sulfonation function of some SULT enzymes. Each of the conclusions that this bioinformatic analysis generated using data that has uncertain reliability (such as that from the chimpanzee genome sequencing project) has been confirmed experimentally or by a "finished" chromosome 16 assembly, both of which were published after the submission of this manuscript.
Conclusion
SULT1A genes expanded from one to four copies in hominoids during intra-chromosomal LCR duplications, including (apparently) one after the divergence of chimpanzees and humans. Thus, LCRs may provide a means for amplifying genes (and other genetic elements) that are adaptively useful. Being located on and among LCRs, however, could make the human SULT1A genes susceptible to further duplications or deletions resulting in 'genomic diseases' for some individuals. Pharmacogenomic studies of SULT1Asingle nucleotide polymorphisms, therefore, should also consider examining SULT1A copy number variability when searching for genotype-phenotype associations. The latest duplication is, however, only a substantiated hypothesis; an alternative explanation, disfavored by the majority of evidence, is that the duplication is an artifact of incorrect genome assembly.
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Background
Experimental and computational results estimate that 5–10% of the human genome has recently duplicated [1-4]. These estimates represent the total proportion of low-copy repeats (LCRs), which are defined as homologous blocks of sequence from two distinct genomic locations (non-allelic) >1000 base pairs in length. LCRs, which are also referred to in the literature as recent segmental duplications, may contain all of the various sequence elements, such as genes, pseudogenes, and high-copy repeats. A set of homologous LCRs make up an LCR family. Non-allelic homologous recombination between members of an LCR family can cause chromosomal rearrangements with health-related consequences [5-7]. While data are not yet available to understand the mechanistic basis of LCR duplication, mechanisms will emerge through the study of individual cases [8].
At the same time, the appearance of LCR duplicates may be an artifact arising from one of a number of problems in the assembly of a genome of interest. Especially when classical repetitive sequences are involved, it is conceivable that mistaken assembly of sequencing contigs might create in a draft sequence of a genome a repeat where none exists. In the post-genomic world, rules have not yet become accepted in the community to decide when the burden of proof favors one interpretation (a true repeat) over another (an artifact of assembly). Again, these rules will emerge over time through the study of individual cases.
Through the assembly of many case studies, more general features of duplication and evolutionary processes that retain duplicates should emerge. Although each LCR family originates from one progenitor locus, no universal features explain why the particular current progenitor loci have been duplicated instead of other genomic regions. From an evolutionary perspective, duplicated material is central to creating new function, and to speciation. One intriguing hypothesis is that genes whose duplication and recruitment have been useful to meet current Darwinian challenges find themselves in regions of the chromosome that favor the generation of LCRs.
Browsing a naturally organized database of biological sequences, we identified human cytosolic sulfotransferase (SULT) 1A as a recently expanded gene family with biomedically related functions. SULT1A enzymes conjugate sulfuryl groups to hydroxyl or amino groups on exogenous substrates (sulfonation), which typically facilitates elimination of the xenobiotic by the excretory system [9]. Sulfonation, however, also bioactivates certain pro-mutagenic and pro-carcinogenic molecules encountered in the diet and air, making it of interest to cancer epidemiologists [10,11]. These enzymes also function physiologically by sulfonating a range of endogenous molecules, such as steroid and thyroid hormones, neurotransmitters, bile salts, and cholesterol [9].
Three human SULT1A genes have been reported [12,13]. The human SULT1A1 and 1A2 enzymes are ~98% identical and recognize many different phenolic compounds such as p-nitrophenol and α-naphthol [14-19]. The human SULT1A3 enzyme is ~93% identical to SULT1A1 and 1A2, but preferentially recognizes dopamine and other catecholamines over other phenolic compounds [19-23]. High resolution crystal structures of SULT1A1 and 1A3 enzymes have been solved [24-26]. Amino acid differences that contribute to the phenolic and dopamine substrate preferences of the SULT1A1 and 1A3 enzymes, respectively, have been localized to the active site [27-30].
Polymorphic alleles of SULT1A1, 1A2, and 1A3 exist in the human population [31-33]. An allele known as SULT1A1*2 contains a non-synonymous polymorphism, displays only ~15% of wild type sulfonation activity in platelets, and is found in ~30% of individuals in some populations [31]. Numerous studies comparing SULT1A1 genotypes in cancer versus control cohorts demonstrate that the low-activity SULT1A1*2 allele is a cancer risk factor [34-36], although other studies have failed to find an association [12]. Ironically, the protection from carcinogens conferred by the high activity SULT1A1*1 allele is counterbalanced by risks associated with its activation of pro-carcinogens. For example, SULT1A enzymes bioactivate the pro-carcinogen 2-amino-α-carboline found in cooked food, cigarette smoke and diesel exhaust [37]. The sulfate conjugates of aromatic parent compounds convert to reactive electrophiles by losing electron-withdrawing sulfate groups. The resulting electrophilic cations form mutagenic DNA adducts leading to cancer.
Recently, it has become widely understood that placing a complex biomolecular system within an evolutionary model helps generate hypotheses concerning function. This process has been termed "phylogenomics" [38]. Through our bioinformatic and phylogenomic efforts on the sulfotransferase 1A system, we detected a previously unidentified human gene that is very similar to SULT1A3, transcriptionally active, and not found in the chimpanzee. In addition, we report that all four human SULT1A genes are located on LCRs in a region of chromosome 16 replete with other LCRs. A model of SULT1A gene family expansion in the hominoid lineage (humans and great apes) is presented, complete with date estimates of three preserved duplication events and identification of the progenitor locus. Positively selected protein sites were identified that might have been central in adapting the SULT1A3 and 1A4 enzymes to their role in sulfonating catecholamines such as dopamine and other structurally related drugs.
Results and Discussion
Four human SULT1A genes on chromosome 16 LCRs
The human SULT1A1 and 1A2 genes are tandemly arranged 10 kilobase pairs (kbp) apart in the pericentromeric region of chromosome 16, while the SULT1A3 gene is located ~1.7 million base pairs (Mbp) away (Figure 1B and 1C). In addition to the three known SULT1A genes, we found a fourth gene, SULT1A4, by searching the human genome with the BLAST-like alignment tool [39]. SULT1A4 was located midway between the SULT1A1/1A2 gene cluster and the SULT1A3 gene (Figure 1B and 1C).
Figure 1 Genomic organization of the SULT1A LCR family. (A) 30 LCRs (red) aligned to the SULT1A3 LCR (blue). Core sequences of SULT1A and LCR16a families are shown between dashed lines. (B) Chromosome 16 positions of 29 SULT1A3-related LCRs. (C) Known genes, bacterial sequencing contigs, and LCRs (outlined in boxes) in three 350 kbp regions of chromosome 16.
The SULT1A4 gene resided on 148 kbp of sequence that was highly identical to 148 kbp of sequence surrounding the SULT1A3 gene (Figure 1A and Table 1). The high sequence identity between the SULT1A3 and 1A4 genomic regions suggested that they were part of a low copy repeat (LCR) family. This suspicion was confirmed by mining the Recent Segmental Duplication Database of human LCR families [40]. In addition to the four-member SULT1A LCR family, the 148 kbp SULT1A3 LCR was related to 27 other LCRs (Figure 1A and Table 1). Many of the SULT1A3-related LCRs are members of the previously identified LCR16a family [41,42]. The SULT1A3-related LCRs mapping to chromosome 16 collectively amounted to 1.4 Mbp of sequence – or 1.5% of chromosome 16.
Table 1 SULT1A3-related LCRs
LCR Name* Chromosome Strand Start End Length % Identity†
A chr18p + 11605429 11633851 28422 97.8
B chr16p + 11985022 12003971 18949 97.6
C chr16p - 14747420 14753000 5580 94.8
D chr16p - 14766628 14792117 25489 96.5
E chr16p - 14805750 14832437 26687 96.6
F chr16p - 14996007 15072649 76642 96.9
G chr16p + 15161625 15185467 23842 95.6
H chr16p - 15417052 15453865 36813 95.9
I chr16p - 16394409 16416404 21995 96.4
J chr16p + 16437719 16461029 23310 96.5
K chr16p + 18371484 18394809 23325 96.4
L chr16p + 18414255 18434928 20673 96.4
M chr16p - 18834216 18904410 70194 96.5
N chr16p + 18962854 18969729 6875 95.2
O chr16p + 21376182 21480283 104101 97.8
P chr16p + 21808293 21910109 101816 98.3
Q chr16p - 22414809 22523008 108199 97.1
R chr16p + 28316465 28341127 24662 97.8
S chr16p + 28427424 28467064 39640 97.3
1A1 chr16p + 28481970 28490644 8674 86.0
1A2 chr16p + 28494950 28502357 7407 86.6
T chr16p - 28621035 28630803 9768 97.7
U chr16p - 28692200 28714506 22306 98.1
V chr16p - 28800873 28828646 27773 97.7
W chr16p - 29084138 29108487 24349 97.8
X chr16p + 29426409 29498137 71728 97.6
1A4 chr16p + 29498152 29644489 146337 99.1
1A3 chr16p + 30236110 30388351 152241 100
Y chr16q + 69784235 69818803 34568 96.2
Z chr16q + 70016088 70061019 44931 97.4
AA chr16q - 74188141 74209430 21289 97.4
*LCR names are as in Figure 1. †Percent identity is relative to the 1A3 LCR.
To determine if other genes in the SULT super family were also recently duplicated during LCR expansions, we searched the Segmental Duplication Database [4] for human reference genes located on LCRs. No other complete cytosolic SULT genes were located on LCRs, but 25% of the SULT2A1 open reading frame (ORF) was located on an LCR (Table 2).
Table 2 Duplication Status of SULT Genes
Accession Gene Chromosome ORF Length ORF Duplicated
NM_001055 SULT1A1, phenol chr16 895 895
NM_001054 SULT1A2, phenol chr16 895 895
NM_003166 SULT1A3, dopamine chr16 895 895
NM_014465 SULT1B1 chr4 804 0
NM_001056 SULT1C1 chr2 898 0
NM_006588 SULT1C2 chr2 916 0
NM_005420 SULT1E1 chr4 892 0
NM_003167 SULT2A1, DHEA chr19 864 210
NM_004605 SULT2B1 chr19 1059 0
NM_014351 SULT4A1 chr22 862 0
The steroid sulfatase gene, which encodes an enzyme that removes sulfate groups from the same biomolecules recognized and sulfonated by SULT enzymes, is frequently deleted in patients with scaly skin (X-linked icthyosis) due to nonallelic homologous recombination between LCRs on chromosome X [43,44]. As demonstrated by the X-linked icthyosis example, SULT1A copy number or activity in the human population could be modified – with health-related consequences – by nonallelic homologous recombination between LCRs on chromosome 16.
SULT1A4: genomic and transcriptional evidence
The sequence of the SULT1A4 gene region from the human reference genome was so similar to that of the SULT1A3 region (>99% identity) that the differences were near those that might arise from sequencing error or allelic variation. It was conceivable, therefore, that some combination of sequence error, allelic variation, and/or faulty genome construction generated the appearance of a SULT1A4 gene that does not actually exist. We therefore searched for additional evidence that the SULT1A4 gene was material.
We asked whether any evidence was consistent with the hypothesis of an artificial SULT1A4 LCR from erroneous genome assembly, as opposed to the existence of a true duplicate region. Here, the quality of the genomic sequencing is important. The junction regions at the ends of the SULT1A4 LCR were sufficiently covered; at least nine sequencing contigs overlapped either junction boundary (Figure 1C). This amount of evidence has been used in other studies to judge the genomic placement of LCRs [45].
As another line of evidence, we compared the nucleotide sequences of the SULT1A4 and 1A3 genomic regions (Table 3). Among the 876 coding positions the only difference was at position 105, where SULT1A4 possessed adenine (A) and SULT1A3 possessed guanine (G). Thus, if two genes do exist, they differ by one silent transition at the third position of codon 35. The untranslated regions, however, contained thirteen nucleotide differences while the introns contained seven additional differences (Table 3). These 21 differences between the SULT1A4 and 1A3 genomic regions disfavor the hypothesis that sequencing errors played a role in the correct/incorrect placement of these LCRs.
Table 3 SULT1A4 and SULT1A3 Genomic Region Differences
Location* Nucleotide SULT1A4 Region SULT1A3 Region
5' UTR -6,246 G C
5' UTR -6,118 C T
5' UTR -6,007 G C
5' UTR -5,246 - T
5' UTR -4,433 - T
Intron 1B -2,775 C T
Intron 1B -2,671 - T
Intron 1B -2,670 - T
Intron 1B -2,594 T G
Intron 1A -91 - A
Exon 2 +105 A G
Intron 4 +853 - A
Intron 4 +1,487 A G
Exon 8 +3,569 - A
Exon 8 +3,570 - A
Exon 8 +3,571 - T
Exon 8 +3,572 - T
3' UTR +5,379 G C
3' UTR +6,438 C -
3' UTR +6,335 C -
3' UTR +6,210 C -
* 21 alignment positions are shown where the nucleotide/gapping (-) of the SULT1A4 region differed from that of the SULT1A3 region. Exon and intron names of the SULT1A3 gene are according to [33]. All nucleotides are numbered relative to the first nucleotide of the start codon, which has a value of +1. There was no position 'zero'. The last nucleotide of the coding sequence occurs at position +3,188. Approximately 3 kb of upstream (5' UTR) and downstream (3' UTR) nucleotides were included in the comparison.
The SULT1A4 gene was located near the junction of two LCRs (Figure 1C). For this reason, it was not clear whether SULT1A4 had a functional promoter. We took a bioinformatic approach to address this question. Expressed sequences ascribed to SULT1A3 were downloaded from the NCBI UniGene website [46]. Each sequence was aligned to SULT1A3 and SULT1A4 genomic regions. Based on the A/G polymorphism at the third position of codon 35, five expressed sequences were assigned to SULT1A4 and nine to SULT1A3 (Table 4). Other expressed sequences were unclassified because they did not overlap codon 35. If the SULT1A4 does exist, there is ample evidence from expressed sequences to make conclusions about its transcriptional activity.
Table 4 Evidence of SULT1A4 Expression
Accession Gene* Tissue† Pos. 105
[Genbank:CB147451] SULT1A4 Liver A
[Genbank:BF087636] SULT1A4 head-neck A
[Genbank:W76361] SULT1A4 fetal heart A
[Genbank:W81033] SULT1A4 fetal heart A
[Genbank:BC014471] SULT1A4 pancreas, epitheliod carcinoma A
[Genbank:F08276] SULT1A3 infant brain G
[Genbank:BF814073] SULT1A3 Colon G
[Genbank:BG819342] SULT1A3 Brain G
[Genbank:BM702343] SULT1A3 optic nerve G
[Genbank:BQ436693] SULT1A3 large cell carcinoma G
[Genbank:AA323148] SULT1A3 cerebellum G
[Genbank:AA325280] SULT1A3 cerebellum G
[Genbank:AA349131] SULT1A3 fetal adrenal gland G
[Genbank:L25275] SULT1A3 placenta G
*Gene classifications made according to the nucleotide at position 105 as described in the text. †Tissue descriptions were taken from GenBank accessions.
The codon 35 A/G polymorphism was reported as allelic variation in SULT1A3 by Thomae et al. [33]. It is conceivable that Thomae et al. sequenced both SULT1A3 and SULT1A4 because of the identical sequences surrounding them. In their study, 89% of CAA (1A4) and 11% of CAG (1A3) codon 35 alleles were detected in one population. Why were the frequencies not more equal, as would be expected if SULT1A4 is always CAA and SULT1A3 is CAG? One hypothesis is that SULT1A3 is indeed CAG/CAA polymorphic as reported, while SULT1A4 is always CAA. Interestingly, in both the chimpanzee and gorilla, codon 35 of SULT1A3 is CAA. This implies that the ancestral SULT1A3 gene (prior to duplication) likely had a CAA codon. An A to G transition might have been fixed in a fraction of SULT1A3 genes after the divergence of humans and great apes. If this scenario is true, some transcripts assigned to SULT1A4 on the basis of codon 35 may actually be from individuals expressing the ancestral CAA version of SULT1A3.
SULT1A progenitor locus
We aligned the coding sequences of all available SULT1A genes and used various nucleotide distance metrics and tree-building algorithms to infer the gene tree without constraints. The unconstrained topology placed platypus as the out group, with the placental mammals ordered (ox,(pig,(dog,(rodents)),(rabbit,(primates)))). This differed from the topology inferred while constraining for the most likely relationships among mammalian orders (platypus,((dog,(ox,pig)), ((rabbit,(rodents)), primates))) [47]. We considered both trees, and found that the conclusions drawn throughout the paper were robust with regard to these different topologies. Therefore, only the tree inferred while constraining for most likely relationships among mammalian orders is discussed (Figure 2).
Figure 2 SULT1A gene tree. TREx upper-limit date estimates of hominoid SULT1A duplications are shown as Ma in red. KA/KS values estimated by PAML are shown above branches. Infinity (8) indicates a non-reliable KA/KS value greater than 100. The 1A3/1A4 branch is dashed. NCBI accession numbers of sequences used: chimpanzee 1A1 [Genbank:BK004887], chimpanzee 1A2 [Genbank:BK004888], chimpanzee 1A3 [Genbank:BK004889], ox [Genbank:U34753], dog [Genbank:AY069922], gorilla 1A1 [Genbank:BK004890], gorilla 1A2 [Genbank:BK004891], gorilla 1A3 [Genbank:BK004892], human 1A1 [Genbank:L19999], human 1A2 [Genbank:U34804], human 1A3 [Genbank:L25275], human 1A4 [Genbank:BK004132], macaque [Genbank:D85514], mouse [Genbank:L02331], pig [Genbank:AY193893], platypus [Genbank:AY044182], rabbit [Genbank:AF360872], rat [Genbank:X52883].
Using the transition redundant exchange (TREx) molecular dating tool [48], we placed upper-limit date estimates at the SULT1A duplication nodes (Figure 2). The SULT1A gene family appears to have expanded ~32, 25, and 3 million years ago (Ma). Therefore, the SULT1A duplications likely occurred after the divergence of hominoids and old world monkeys, with the most recent duplication occurring even after the divergence of humans and great apes.
Mouse, rat, and dog genomes each contained a single SULT1A gene. The simplest evolutionary model, therefore, predicted that one of the four hominoid SULT1A loci was orthologous to the rodent SULT1A1 gene. Syntenic regions have conserved order of genetic elements along a chromosomal segment and evidence of synteny between homologous regions is useful for establishing relationships of orthology and paralogy. Human SULT1A1 is most like rodent Sult1a1 in sequence and function and before the advent of whole genome sequencing it was assumed that they were syntenic and therefore orthologous [49]. Complete genome sequences have since emerged and alignments between them are available in the visualization tool for alignments (VISTA) database of human-rodent genome alignments [50,51]. The VISTA database contains mouse-human pairwise alignments and mouse-rat-human multiple alignments. The multiple alignments were found to be more sensitive for predicting true orthologous regions between rodent and human genomes [51]. We searched the VISTA database for evidence of any human-rodent syntenic regions involving the four SULT1A loci. The more sensitive multiple alignments failed to record any human-rodent syntenic regions involving the SULT1A1, SULT1A2, or SULT1A4 loci but detected synteny involving the SULT1A3 loci and both rodent genomes (Figure 3). These results are indicative of a hominoid specific SULT1A family expansion from a progenitor locus corresponding to the genomic region that now contains SULT1A3. The results from the VISTA database were not as clear when the less sensitive alignment method was employed (Figure 3).
Figure 3 Synteny plots demonstrating SULT1A3 is the progenitor locus of the hominoid SULT1A family. Each box shows a VISTA percent identity plot between a section of the human genome and a section of a rodent genome. Different rodent genomes and alignment methods are indicated as follows: 1 = mouse (Oct. 2003 build) multiple alignment method (MLAGAN); 2 = rat (June 2003 build) multiple alignment method (MLAGAN); 3 = mouse (October 2003 build) pairwise alignment method (LAGAN). Human gene locations are shown above and human chromosome 16 coordinates below.
SULT1A3 and 1A4 LCRs were 99.1% identical overall (Table 1). More careful inspection revealed that the SULT1A3 and 1A4 LCRs were 99.8% identical over the first 120 kbp, but only 98.0% identical over the last 28 kbp (data not shown). This 10-fold difference in percent identities (0.2% vs. 2.0%) suggested that the SULT1A4-containing LCR was produced by two independent duplications. The chimpanzee draft genome assembly aligned with the human genome [52] provides evidence in support of this hypothesis. There is conserved synteny between human and chimp genomes over the last 28 kbp of the 1A4-containing LCR, but no synteny over the first 120 kbp where the SULT1A4 gene is located (data not shown). This finding and the TREx date estimate for the SULT1A3/1A4 duplication event at ~3 Ma indicate that SULT1A4 is a human invention not shared by chimpanzees – our closest living relatives.
It should be noted that the chimpanzee genome assembly is less reliable than the assembly of the human genome. The coverage is significantly lower, and the methods used for assembly are viewed by many as being less reliable, in part because they relied on the human assembly. Other possibilities, less supported the available evidence, should be considered, including deletion of the chimpanzee SULT1A4 gene since the human-chimp divergence, or failure of the draft chimpanzee genome assembly to detect the 120 kbp segment on which the SULT1A4 gene resides.
Adaptive evolution in hominoids
From an analysis of gene sequence change over time, molecular evolutionary theory can generate hypotheses about whether duplication has led to functional redundancy, or whether the duplicates have adopted separate functional roles. If the latter, molecular evolutionary theory can suggest how different the functional roles might be by seeking evidence for positive (adaptive) selection for mutant forms of the native proteins better able to contribute to fitness.
Positive selection of protein function can best be hypothesized when the ratio of non-synonymous (replacement) to synonymous (silent) changes normalized to the number of non-synonymous and synonymous sites throughout the entire gene sequence (KA/KS) is greater than unity. Various models of evolutionary sequence change can be used to calculate these ratios. The simplest assumes a single KA/KS ratio over the entire tree (one-ratio). More complex models assume an independent ratio for each lineage (free-ratios), variable ratios for specific classes of sequence sites (site-specific), or variable ratios for specific classes of sequence sites along specified branches (branch-site specific) [53-57].
Estimating the free parameters in each of these models by the maximum likelihood method [58] enables testing two nested evolutionary models as competing hypotheses, where one model is a special case of another model. The likelihood ratio test (LRT) statistic, which is twice the log likelihood difference between the nested models, is comparable to a χ2 distribution with degrees of freedom equal to the difference in free parameters between the models [59]. Evidence for adaptive evolution typically requires a KA/KS ratio >1 and a statistically significant LRT [60].
We estimated KA/KS ratios for each branch in the 1A gene tree by maximum likelihood with the PAML program [61]. A typical branch in the SULT1A gene tree had a ratio of 0.16, and the ratio was 0.23 on the branch separating extant SULT1A3/1A4 genes from the single SULT1A gene in the last common ancestor of hominoids (Figure 2). Thus, the KA/KS ratio estimated as an average over all sites did not suggest adaptive evolution along the 1A3/1A4 branch.
We then implemented three site-specific and two branch-site evolutionary models that allow KA/KS ratios to vary among sites. Four of the five models estimated that a proportion of sites (2–8%) had KA/KS >1 (Table 5). Each model was statistically better at the 99 or 95% confidence level than the appropriate null model as determined using the LRT statistic (Table 6). Table 6 lists the specific sites that various analyses identified as being potentially involved in positive selection and a subset of these sites that are changing along the SULT1A3/1A4 branch.
Table 5 Likelihood Values and Parameter Estimates for SULT1A Genes
Model f.p.* Log L Parameter Estimates†
One-ratio 39 - 5,047.81 KA/KS = 0.15
Free-ratios 69 - 5,005.18 KA/KS ratios for each branch shown in Figure 2
Site-specific
Neutral 36 - 5,021.14 p0 = 0.48 (p1 = 0.52)
KA/KS 0 = 0 KA/KS 1 = 1
Selection 38 - 4,884.89 p0 = 0.41 p1 = 0.13 (p2 = 0.46)
KA/KS 0 = 0 KA/KS 1 = 1 KA/KS2 = 0.19
Discrete (k = 2) 37 - 4,931.05 p0 = 0.68 p1 = 0.32
KA/KS 0 = 0.06 KA/KS 1 = 0.77
Discrete (k = 3) 40 - 4,880.78 p0 = 0.59 p1 = 0.33 (p2 = 0.08)
KA/KS 0 = 0.02 KA/KS 1 = 0.31 KA/KS2 = 1.24
Beta 37 - 4,884.27 p = 0.27 q = 1.07
Beta+selection 39 - 4,879.97 p = 0.30 q = 1.33
p0 = 0.98 p1 = 0.02 KA/KS = > 2.0
Branch-site specific
Model A 38 - 5,013.29 p0 = 0.48 p1 = 0.49 (p2 = 0.03)
KA/KS 0 = 0 KA/KS 1 = 1 KA/KS2 = > 2.0
Model B 40 - 4,886.52 p0 = 0.68 p1 = 0.30 (p2 = 0.02)
KA/KS 0 = 0.04 KA/KS1 = 0.56 KA/KS2 = > 2.0
*f.p. is the number of free parameters in each model. †Evidence for positive selection is shown in boldface. Proportions of sites in each KA/KS class, p0, p1, and p2, were not free parameters when in parentheses. Neutral site-specific model assumes two site classes having fixed KA/KS ratios of 0 and 1, with the proportion of sites in each class estimated as free parameters. Selection site-specific model assumes a third proportion of sites with KA/KS estimated from the data. Discrete model assumes 2 or 3 site classes (k) with the proportion of sites, and KA/KS ratios for each proportion, estimated as free parameters. Beta model assumes a beta distribution of sites, where the distribution is shaped by the parameters p and q. Beta+selection model assumes an additional class of sites having a KA/KS ratio estimated from the data. Model A, an extension of the neutral model, assumes a third site class on the 1A3/1A4 branch with KA/KS estimated from the data. Model B, an extension of the discrete model with two site classes (k = 2), also assumes a third site class on the 1A3/1A4 branch with KA/KS estimated from the data.
Table 6 Likelihood Ratio Tests for the SULT1A Genes
Selection vs. Neutral Discrete (k = 3) vs. One-ratio Beta+selection vs. Beta Model A vs. Neutral Model B vs. Discrete (k = 2)
Log L1 - 4,884.89 - 4,880.78 - 4,879.97 - 5,013.29 - 4,886.52
Log L0 - 5,021.14 - 5,047.81 - 4,884.27 - 5,021.14 - 4,931.05
2ΔLog L 272.50 334.06 8.60 15.70 89.06
d.f. 2 4 2 2 2
P-value P < 0.001 P < 0.001 0.01 < P < 0.05 P < 0.001 P < 0.001
Positively selected sites*
3 (0.86)
7 (0.63)
30 (0.71)
35 (0.73)
71 (0.88)
77(0.92)
84(0.92)
85(0.95)
86(0.97)
89(0.99) 89 (0.88) 89(0.99) 89(0.99)
93(0.97)
105 (0.72) 105 (0.53)
107 (0.82) 107 (0.75)
132 (0.87) 132 (0.78)
143 (0.51)
146 (0.80) 146(0.97)
222(0.99) 222 (0.58)
236 (0.53)
245 (0.99) 245 (0.99)
261 (0.90)
275 (0.70)
288 (0.89)
290 (0.95)
293 (0.72)
*In parentheses for each positively selected site is the posterior probability that the site belongs to the class with KA/KS >1. Posterior probabilities >90% are bold-face. Positively selected sites also experiencing non-synonymous change on the 1A3/1A4 branch are underlined.
A hypothesis of adaptive change that is based on the use of KA/KSvalues can be strengthened by joining the molecular evolutionary analysis to an analysis based on structural biology [62,63]. Here, we ask whether the sites possibly involved in an episode of sequence evolution are, or are not, randomly distributed in the three dimensional structure. To ask this question, we mapped the sites to the SULT1A structure (Figure 4). Sites holding amino acids whose codons had suffered synonymous replacements were evenly distributed throughout the three-dimensional structure of the enzyme, as expected for silent changes that have no impact on the protein structure and therefore cannot be selected for or against at the protein level (Figure 4). In contrast, sites experiencing non-synonymous replacements during the episode following the duplication that created the new hominoid gene are clustered on the side of the protein near the substrate binding site and the channel through which the substrate gains access to the active site (Figure 4 and Table 7). This strengthens the hypothesis that replacements at the sites are indeed adaptive. The approach employed here based on structural biology does not lend itself easily to evaluation using statistical metrics. Rather, the results are valuable based on the visual impression that they give, and the hypotheses that they generate.
Figure 4 Non-synonymous changes along the 1A3/1A4 branch cluster on the SULT1A1 enzyme structure [PDB: 1LS6] [26]. Red sites experienced non-synonymous changes, green sites experienced synonymous changes. The PAPS donor substrate and p-nitrophenol acceptor substrates are shown in blue. Image was generated using Chimera [86].
Table 7 Non-synonymous Changes on the 1A3/1A4 Branch
Site* Nucleotide Changes/Site Hominoid SULT1A Ancestor Hominoid SULT1A3 Ancestor
Residue PP† Physicochemical Properties Residue PP Physicochemical Properties
44 1 Ser (1.00) tiny polar → Asn (1.00) small polar
71 1 His (0.99) non-polar aromatic positive → Asn (1.00) small polar
76 1 Phe (1.00) non-polar aromatic → Tyr (1.00) aromatic
77 2 Met (0.99) non-polar → Val (1.00) small non-polar aliphatic
84 1 Phe (1.00) non-polar aromatic → Val (1.00) small non-polar aliphatic
85 1 Lys (1.00) Positive → Asn (1.00) small polar
86 2 Val (0.98) small non-polar aliphatic → Asp (1.00) small polar negative
89 3 Ile (0.98) non-polar aliphatic → Glu (1.00) polar negative
93 1 Met (0.00) non-polar → Leu (1.00) non-polar aliphatic
101 1 Ala (1.00) tiny non-polar → Pro (1.00) small
105 1 Leu (1.00) non-polar aliphatic → Ile (1.00) son-polar aliphatic
107 1 Thr (1.00) tiny polar → Ser (1.00) tiny polar
132 1 Ala (1.00) tiny non-polar → Pro (1.00) small
143 1 Tyr (1.00) aromatic → His (1.00) non-polar aromatic positive
144 2 His (0.99) non-polar aromatic positive → Arg (1.00) polar positive
146 2 Ala (1.00) tiny non-polar → Glu (1.00) polar negative
148 1 Val (1.00) small non-polar aliphatic → Ala (1.00) tiny non-polar
222 1 Leu (0.99) non-polar aliphatic → Phe (1.00) non-polar aromatic
* Sites underlined were identified as being positively selected using the branch-site specific models. †Posterior probabilities that the ancestral residues are correct, conditional on the model of sequence evolution used.
We then examined literature where amino acids had been exchanged between SULT1A1 and SULT1A3. One of the sites, at position 146, identified as being involved in adaptive change, is known to control substrate specificity in SULT1A1 and 1A3 [27-30]. The remaining sites identified are nearby.
Conclusion
An interesting question in post-genomic science asks how to create biological hypotheses from various drafts of whole genome sequences. In generating these hypotheses, it is important to remember that a genomic sequence is itself a hypothesis, about the chemical structure of a small number of DNA molecules. In many cases, biologists wish to move from the genomic sequence, as a hypothesis, to create hypotheses about biological function, without first "proving" the genome sequence hypothesis.
This type of process, building hypotheses upon unproven hypotheses, is actually common in science. In fact, very little of what we believe as fact is actually "proven"; formal proof is virtually unknown in science that involves observation, theory, and experiment. Rather, scientists generally accumulate data until a burden of proof is met, with the standards for that burden being determined by experience within a culture. In general, scientists have an idea in an area as to what level of validation is sufficient to avoid making mistakes an unacceptable fraction of the time, and proceed to that level in their ongoing work, until they encounter a situation where they make a mistake (indicating that a higher standard is needed), or encounter enough examples where a lower standard works, and therefore come to accept a lower standard routinely [64].
Genomics has not yet accumulated enough examples for the culture to define the standards for a burden of proof. In the example discussed here, several lines of reasoning would be applied to analyze the sulfotransferase gene family. First, the fact that the draft genome for chimpanzee contains three paralogs, while the draft genome for human contains four, would normally be interpreted (as it is here) as evidence that an additional duplication occurred in the time since chimpanzee and humans diverged. It would also, however, be consistent with the loss of one of four hypothetical genes present in the common ancestor of chimpanzee and humans in the lineage leading to chimpanzee. Another possibility is that the finishing stages of the chimpanzee genome project will uncover a SULT1A4 gene.
Normally, one would resolve this question using an out group taxon, a species that diverged from the lineage leading to chimpanzee and human before chimpanzee and human themselves diverged. The nearest taxa that might serve as an out group today are, however, rat and mouse. As noted above, they diverged so long ago (ca. 150 MY separates contemporary rodents from contemporary primates) that the comparison provides no information. And no closer out group taxon (e.g., orangutan) has had its genome completely sequenced.
Here, the two hypotheses (duplication versus loss after the chimpanzee-human speciation) are distinguished (to favor post-speciation duplication) based on an analysis of the silent nucleotide substitutions using the TREx metric. The very small number of nucleotide differences separating the SULT1A3 and SULT1A4 coding regions favors the generation of the two paralogs after chimpanzee and human diverged.
This comparison, however, potentially suffers from the statistics of small numbers. The number of differences in the coding region (exactly one) is small. By considering ~10 kbp of non-coding sequence, however, additional differences were found. It is possible that in the assembly of the human genome, a mistake was made that led to the generation of a SULT1A4 region that does not actually exist. In this hypothesis, the ~20 nucleotide differences between the SULT1A3 and SULT1A4 paralogs must be the consequence of allelic polymorphism in the only gene that exists. This is indeed how some of the data were initially interpreted.
Does the preponderance of evidence favor the hypothesis of a very recent duplication to generate a pair of paralogs (SULT1A3 and SULT1A4)? Or does the evidence favor the hypothesis that the SULT1A4 gene is an illusion arising from gene assembly error coupled to sequencing errors and/or allelic variation at ca. 20 sites? The culture does not yet have a standard of assigning the burden of proof here, although a choice of hypothesis based simply on the count of the number of mistakes that would need to have been made to generate each hypothesis (none for the first, at least three for the second) would favor the former over the latter. Thus, perhaps naively, the burden of proof now favors the former, and we may proceed to generate the biological hypothesis on top of the genomic hypothesis.
Here, the hypothesis has immediate pharmacogenomic and genomic disease implications due to the specific functional behaviors of SULT1A enzymes. LCR-mediated genomic rearrangements could disrupt or amplify human SULT1A gene copy number. Given our current environmental exposure to many forms of carcinogens and pro-carcinogens that are either eliminated or activated by SULT enzymes, respectively, it is plain to see how SULT1A copy number variability in the human population could underlie cancer susceptibilities and drug or food allergies.
The majority of evidence indicates that a new transcriptionally active human gene, which we refer to as SULT1A4, was created when 120 kbp of chromosome 16 duplicated after humans diverged from great apes. Thus, SULT1A4, or possibly another gene in this region, is likely to contribute to distinguishing humans from their closest living relatives. It is also conceivable that an advantage in gene regulation, as opposed to an advantage from gene duplication, was the driving force behind the duplication of this 120 kbp segment. While cause and effect are difficult to separate, the examples presented here support the hypothesis that genes whose duplication and recruitment are useful to meet current Darwinian challenges find themselves located on LCRs.
The SULT1A4 gene is currently the most obvious feature of the duplicated region and has been preserved for ~3 MY without significant divergence of its coding sequence. One suggestion for the usefulness of SULT1A4 is that it expanded sulfonating enzymes to new tissues. The SULT1A4 gene is located only 10 kbp upstream from the junction boundary of its LCR and 700 kbp away from the SULT1A3 locus. It is possible, therefore, that promoter elements from the new genomic context of the SULT1A4 gene would drive its expression in tissues where SULT1A3 is not expressed – a hypothesis testable by more careful transcriptional profiling.
Multiple SULT1A genes were apparently useful inventions by our stem hominoid ancestor. Following the duplication of an ancestral primate SULT1A gene ~32 Ma, positive selection acted on a small proportion of sites in one of the duplicates to create the dopamine sulfonating SULT1A3 enzyme. In the example presented here, the evidence of adaptive change at certain sites is corroborated by the ad hoc observation that the sites cluster near the active site of the protein. The well known substrate binding differences at the active sites of SULT1A1/1A2 and SULT1A3 (and now SULT1A4) substantiate these findings.
When studying well-characterized proteins as we have done here, episodes of functional change can be identified by piecing together several lines of evidence. It is not immediately possible, unfortunately, to assemble as much evidence for the majority of proteins in the biosphere. Thus, an important goal in bioinformatics is to recognize the signal of functional change from a restricted amount of evidence. Of the three lines of evidence employed here (codon-based metrics, structural biology, and experimental), structural biology, with its obvious connections to protein function and impending growth from structural genomics initiatives, will probably be the most serviceable source of information for most protein families. This should be especially true for protein families not amenable to experimental manipulation, or with deep evolutionary branches where codon-based metrics are unhelpful. If we are to exploit the incontrovertible link between structure and function, however, new structural bioinformatic tools and databases relating protein structure to sequence changes occurring on individual branches are much needed.
This bioinformatic study makes several clear predictions. First, a PCR experiment targeted against the variation between the hypothetical SULT1A3 and SULT1A4 human genes should establish the existence of the two separate genes. Second, a reverse transcription-PCR experiment would be expected to uncover transcriptional activity for the SULT1A3 and SULT1A4 human genes. Since this paper was submitted, these experiments have been done, and indeed confirm our predictions made without the experimental information [65]. Further, after this manuscript and its computationally-based predictions were submitted for publication, a largely finished sequence for chromosome 16 has emerged [66] that confirms our analysis here in every respect.
Methods
SULT1A LCR family organization in the human genome
The July 2003 human reference genome (based on NCBI build 34) was queried with the SULT1A3 coding region using the BLAST-like alignment tool [39], and search results were visualized in the UCSC genome browser [67]. Two distinct locations on chromosome 16 were identified as equally probable. One location was recognized by NCBI Map Viewer [68] as the SULT1A3 locus. The other locus was dubbed SULT1A4 following conventional naming for this family. The coding sequence and genomic location of SULT1A4, as well as expressed sequences derived from SULT1A4, have been deposited with the GenBank Third Party Annotation database under accession [Genbank:BK004132].
To determine the extent of homology between the SULT1A3 and 1A4 genomic locations, ~500 kbp of sequence surrounding SULT1A3 and ~500 kbp of sequence surrounding SULT1A4 were downloaded from NCBI and compared using PIPMAKER [69,70]. Before submitting to PIPMAKER, high-copy repeats in one of the sequences were masked with REPEATMASKER [71].
The Human Recent Segmental Duplication Page [72] was consulted to identify other LCRs related to the SULT1A3-containing LCR. Chromosomal coordinates of 30 SULT1A3-related LCRs were arranged in GFF format and submitted to the UCSC genome browser as a custom track. Sequences corresponding to the chromosomal coordinates of the 30 LCRs were then downloaded from the UCSC genome browser and parsed into separate files. Each LCR was aligned with the SULT1A3-containing LCR using MULTIPIPMAKER [73]. The Segmental Duplication Database [74] was used to examine the duplication status of each gene in the cytosolic SULT super family.
The bacterial artificial chromosome contigs supporting each member of the SULT1A LCR family, and the known genes within each LCR, were inspected with the UCSC genome browser [75]. The DNA sequences of nine bacterial artificial chromosome contigs supporting the SULT1A4 genomic region [NCBI Clone Registry: CTC-446K24, CTC-529P19, CTC-576G12, CTD-2253D5, CTD-2324H19, CTD-2383K24, CTD-2523J12, CTD-3191G16, RP11-28A6] and seven contigs supporting the SULT1A3 region [NCBI Clone Registry: CTD-2548B1, RP11-69O13, RP11-164O24, RP11-455F5, RP11-612G2, RP11-787F23, RP11-828J20] were downloaded from the UCSC genome browser website.
Phylogenetics
The MASTERCATALOG was used for performing initial inspections of the SULT gene family and for delivering a non-redundant collection of SULT1A genes. Additional SULT1A ORFs were extracted from gorilla working draft contigs [Genbank:AC145177] (SULT1A1 and 1A2) and [Genbank:AC145040] (SULT1A3) and chimpanzee whole genome shotgun sequences [Genbank:AACZ01082721] (SULT1A1), [Genbank:AADA01101065] (SULT1A2), and [Genbank:AACZ01241716] (SULT1A3) using PIPMAKER exon analysis. These new SULT1A genes have been deposited with the GenBank Third Party Annotation database under accession numbers [Genbank:BK004887-BK004892]. DNA sequences were aligned with CLUSTAL W [76]. The multiple sequence alignment used in all phylogenetic analyses is presented as supplementary data [see Additional file 1]. Pairwise distances were estimated under various distance metrics (Jukes-Cantor, Kimura 2-parameter, and Tamura-Nei) that account for among-site rate variation using the gamma distribution [77]. Phylogenies were inferred using both neighbor-joining and minimum evolution tree-building algorithms under the following constraints ((((primates), rodents), (artiodactyls, carnivores)), platypus). Phylogenetic analyses were conducted using the MEGA2 v2.1 [78] and PAUP* v4.0 [79] software packages.
Parameter estimates of site class proportions, KA/KS ratios, base frequencies, codon frequencies, branch lengths, and the transition/transversion bias were determined by the maximum likelihood method with the PAML v3.14 program [61]. Positively selected sites, posterior probabilities, and marginal reconstructions of ancestral sequences were also determined using PAML. Sites experiencing synonymous changes along the 1A3/1A4 branch were recorded by hand from an ancestral sequence alignment.
Molecular dating
Starting with aligned DNA sequences, the number (n) of two-fold redundant codons (Lys, Glu, Gln, Cys, Asp, Phe, His, Asn, Tyr) where the amino acid had been conserved in pairs of aligned sequences, and the number of these codons where the third position was identically conserved (c) were counted by the DARWIN bioinformatics platform [80,81]. The pairwise matrix of n and c values for all SULT1A genes is presented as supplementary data [see Additional file 2]. The c/n quotient equals the fraction of identities (f2) in this system, or the transition redundant exchange (TREx) value [48]. TREx values were converted to TREx distances (kt values) by the following equation: kt = - ln [(f2 - Eq.) / (1 - Eq.)], where k is the rate constant of nucleotide substitution, t is the time separating the two sequences, and Eq. is the equilibrium state of the TREx value [48]. The equilibrium state of the TREx value was estimated as 0.54 for primates, and the rate constant at two-fold redundant sites where the amino acid was conserved (k) was estimated as 3.0 × 10 -9 changes/site/year for placental mammals (T. Li, D. Caraco, E. Gaucher, D. Liberles, M. Thomson, and S.A.B., unpublished data). These estimates were determined by sampling all pairs of mouse:rat and mouse:human orthologs in the public databases and following accepted placental mammal phylogenies and divergence times [82,83]. Therefore, the date estimates reported are based on the contentious assumptions that (i) rates are constant at the third position of two-fold redundant codons across the genome, (ii) the fossil calibration points are correct, and (iii) the mammalian phylogeny used is correct. Branch lengths were obtained for the constrained tree topology from the pairwise matrix of TREx distances using PAUP* v4.0. Upper-limit date estimates for nodes corresponding to SULT1A duplication events were obtained by summing the longest path of branches leading to a node and dividing that value by k.
Comparative genomics
Human-chimpanzee genome alignments were inspected at the UCSC Genome Browser. Human-rodent genome alignments were examined with the VISTA Genome Browser [50,51,84]. VISTA default parameters were used for drawing curves. Alignments constructed using both the pairwise method (LAGAN) and the multiple alignment method (MLAGAN) between the human genome builds frozen on April 2003 or July 2003 and both rodent genomes were inspected.
Transcriptional profiling
All expressed sequences ascribed to SULT1A3 were downloaded from NCBI UniGene [85] and aligned with SULT1A3 and SULT1A4 genomic regions using PIPMAKER. Alignments were inspected for the polymorphism in codon 35, as well as any other potential patterns, to determine whether they were derived from SULT1A3 or SULT1A4.
Abbreviations
kbp (kilobase pairs); LCR (low copy repeat); Mbp (million base pairs); Ma (million years ago); ORF (open reading frame); SULT (sulfotransferase); TREx (transition redundant exchange); VISTA (visualization tool for alignments).
Authors' contributions
M.E.B carried out the study and drafted the manuscript. S.A.B participated in designing the study and preparing the manuscript.
Supplementary Material
Additional File 1
Multiple sequence alignment of SULT1A genes.Multiple sequence alignment of SULT1A genes used in all phylogenetic analyses. Characters conserved in all sequences are indicated with asterisks.
Click here for file
Additional File 2
Pairwise n and c values for SULT1A genes. Pairwise n and c values between SULT1A genes. The names of the sequences are the row-headers and the column-headers. Lower triangular matrix contains n values, and upper triangular matrix contains c values.
Click here for file
Acknowledgements
We thank the Foundation for Applied Molecular Evolution for providing computational resources. This work was supported by grant DOD 6402-202-L0-G from the USF Center for Biological Defense, and by an NIH post-doctoral fellowship to M.E.B.
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| 15752422 | PMC555591 | CC BY | 2021-01-04 16:37:17 | no | BMC Evol Biol. 2005 Mar 7; 5:22 | utf-8 | BMC Evol Biol | 2,005 | 10.1186/1471-2148-5-22 | oa_comm |
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BMC Fam PractBMC Family Practice1471-2296BioMed Central London 1471-2296-6-101574829910.1186/1471-2296-6-10Research ArticleHealth care reform and job satisfaction of primary health care physicians in Lithuania Buciuniene Ilona [email protected] Aurelija [email protected] Egle [email protected] International School of Management, Kaunas, Lithuania2 Philosophy and Social Science Department, Kaunas Medical University, Kaunas, Lithuania3 Social Medicine Department, Kaunas Medical University, Kaunas, Lithuania2005 7 3 2005 6 10 10 2 11 2004 7 3 2005 Copyright © 2005 Buciuniene et al; licensee BioMed Central Ltd.2005Buciuniene 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 aim of this research paper is to study job satisfaction of physicians and general practitioners at primary health care institutions during the health care reform in Lithuania.
Methods
Self-administrated anonymous questionnaires were distributed to all physicians and general practitioners (N = 243, response rate – 78.6%), working at Kaunas primary health care level establishments, in October – December 2003.
Results
15 men (7.9%) and 176 women (92.1%) participated in the research, among which 133 (69.6%) were GPs and 58 (30.4%) physicians. Respondents claimed to have chosen to become doctors, as other professions were of no interest to them. Total job satisfaction of the respondents was 4.74 point (on a 7 point scale). Besides 75.5% of the respondents said they would not recommend their children to choose a PHC level doctor's profession. The survey also showed that the respondents were most satisfied with the level of autonomy they get at work – 5.28, relationship with colleagues – 5.06, and management quality – 5.04, while compensation (2.09), social status (3.36), and workload (3.93) turned to be causing the highest dissatisfaction among the respondents. The strongest correlation (Spearmen's ratio) was observed between total job satisfaction and such factors as the level of autonomy – 0.566, workload – 0.452, and GP's social status – 0.458.
Conclusion
Total job satisfaction of doctors working at primary health care establishments in Lithuania is relatively low, and compensation, social status, and workload are among the key factors that condition PHC doctors' dissatisfaction with their job.
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Background
In 1989, after regaining its independence, Lithuania inherited an extremely centralised health care system that mainly conditioned ineffective health care management and resource usage. The above situation was an outcome of the former Soviet health care policy, as it did not encourage people or the state to treasure or safeguard its citizens' health [1]. The Soviet model of medicine that existed in Lithuania was based exclusively on the exaggerated focus and development of the hospital level, whereas the need to develop the primary health care (PHC) level was practically ignored.
The situation has changed a lot though, and the current Lithuanian health policy states that priority shall be given to the development of primary health care [3,2]. Speaking of the very concept of primary health care, it should be stated that it first was offered in 1978 in Alma- Ata, Kazakhstan. Later on the primary health care concept, policy and strategy were further developed in such documents as "Health to Everybody – 2000" and "Health in the 21st Century" [3]. As regards the health care policy, the above documents are considered of utmost importance in Lithuania as well as other EU countries. During the health care reform, the Lithuanian government came to a decision that the greater part of the health care budget should be allocated to primary health care establishments – consolidation of their basis and improvement of respective personnel qualification, which was expected to lead to a rise in the latter's work efficiency. In 1992, the training programme of general practitioners (GPs) was renewed in Lithuanian higher medical schools, and in 1996 the GP Statute was ratified under Lithuanian Health Norm MN 14:1996. District physicians and paediatricians were retrained as GPs, which requires more thorough competence than being a physician, as GPs are supposed to be capable of consulting, diagnosing and providing primary level help in all area. They have to be competent in internal disease treatment and can send patients to secondary level specialists merely in exceptional cases. The number of general practitioners in Lithuania is increasing, and it amounted to 1,150 at the beginning of 2003 [4].
As stated in the announcements of the National Board of Health, the situation in the Lithuanian health system is changing; nevertheless, it faces a number of problems. 78.6 % (1,146 out of 1,458) of GP graduates in 1994–2001 were retrained from district physicians and paediatricians [5]. Another problematic issue is lack of experience in organising their activities. Furthermore, GPs are often delegated many additional functions, such as accounting, work organisation in their establishments, and co-ordination with other institutions. Considerable instability of the legal basis as well as environment makes a serious negative impact too. Besides a great part of those trained as GPs do not follow their professional career, as they are tempted by considerably higher salaries that are offered by pharmaceutical companies and possibilities of working abroad [5]. This tendency may heighten even more now after Lithuania has joined the European Union, as this offers doctors considerably more opportunities to leave the country. The "loss" of doctors has significant economic consequences as well, as training of qualified general practice physicians is rather costly (a two year internship of one GP costs the state about EUR 8,700); therefore, it is essential to retain qualified specialists in the country.
General medicine practice in health care systems of industrialised countries is considered a remarkably well working system that seeks to assess health needs of individuals and to become familiar with their living conditions, taking into consideration their family and social environment [6]. Nevertheless, recent research shows that "the crisis of general medicine" in the West is being widely discussed [6]. In the contemporary era of specialised medicine, the nature of GP's work is a direct opposite of specialisation: in terms of pathology, it covers all areas; in terms of diagnostics, GPs do not have the required equipment to make a diagnosis; working time is unlimited (from 54.1 to 62.2 hours per week); GPs constantly have to negotiate with patients the medicine to be prescribed and are high dependability on patients (calls to homes are not planned). Furthermore, 30 – 60% of cases in GP's work make psychosomatic problems of patients, which requires more psychological rather than medical knowledge. A GP is assessed not only as a specialist, as the above-mentioned criteria tend to be predominant.
One of the ways to look into the existing PHC situation and possible problems is to study job satisfaction of GPs and physicians, as the attitudes people hold toward their jobs are referred to as job satisfaction and this is one of the most widely studied work-related attitudes. Job satisfaction is closely related to the lower level employee turnover and intentions to quit their jobs [7-9]. Since there is a lack of general practice physicians in Lithuania, it is vital to build conditions that would motivate them to stay and work in the Lithuanian health care system, which may be accomplished through the enhancement of job satisfaction at the primary health care level. Job satisfaction is highly important in building up employee motivation and efficiency, as higher job satisfaction determines better employee performance and higher level of patient satisfaction [9].
The aim of this research is to investigate job satisfaction of physicians and general practitioners in primary health care institutions during the health care reform in Lithuania. In 2003, there were 1,608 GPs and physicians working in Lithuanian PHC establishments [4]. The largest number of primary health care institutions were in Kaunas, the second largest city of Lithuania, (27% of all Lithuanian PHC institutions), and GPs provided services to 42% of the city's population. This research studies GPs and physicians who working in Kaunas.
Research tasks:
1. To determine motives for choosing a PHC doctor's profession.
2. To compare total job satisfaction of Lithuanian PHC physicians' and GPs' as well as their intentions to recommend their children to choose a PHC level doctor's profession.
3. To assess factors which determine job satisfaction of PHC physicians, GPs who chose their specialization at higher school, and GPs who were retrained as GPs later in their career.
Review of research on job satisfaction in health care institutions
Job satisfaction comprises positive and/or negative attitudes held by individuals in respect to their job [10]. Employees with higher job satisfaction believe that the organisation will continue satisfying them for a long time; they care about the quality of their work, are more committed to the organisation, stay in it longer, are more productive, feel responsible for the working environment, and strive to make it satisfying [6-8]. Job satisfaction reduces employee turnover, absenteeism, and the number of thefts at work, which in turn reduces organisational costs [10]. Job satisfaction is closely related to the nature of work, the quality of management and working environment. Not only does it influence good employee performance, it also maintains good employee health and longevity. As to the most valued aspects of satisfaction, they comprise such things as compensation, promotion opportunities, fringe benefits, bonuses, management, co-workers, working conditions, nature of work, communication, security [11,12].
Bronsky and Cook (1994) interviewed 334 Ohayo University graduate physicians [13]. For the interviews, they used Job Discriminative Index questionnaire, in which satisfaction is assessed according to five sub-scales ("work", "co-workers", "management", "current reward" and "promotion opportunities"). The research showed that job satisfaction is not directly related to practical aspects of physicians.
In 1989 American Physical Therapeutics Association (APTA) carried out a survey of hiring and retention aspects [14]. This study showed that physicians were satisfied with their work. The highest job satisfaction was gained from "autonomy", whereas the smallest was given by "reward".
Okerlund et all (1994) research revealed that Utah physicians were satisfied with their job [15]. Respondents indicated the followings factors as the most important and having the greatest effect on their job satisfaction: "working freedom", "assistance in skills development", and "salary and fringe benefits". The survey also showed that the key factors of doctors' willingness to leave their practice comprised such things as big clerical workload, dissatisfaction with reforms, high patient expectations and big clinical workload. While young doctors willing to leave practice mainly emphasised communication problems and big clinical workload, older doctors were mainly discontent with changes in the health care system and big clerical workload. Differences between genders were insignificant.
Rozier' et al (1998) found out that intrinsic factors, such as ethical practice, improvement of patient health, and perception of satisfaction were more important than big salary and important title [16]. These groups of factors were significantly affected by the gender factor, i. e. how employees combine work and family responsibilities.
Barnes (1998) conducted research of job satisfaction among rehabilitation physicians, occupational therapy specialists, and speech pathologists. Set apart, there are three factors that describe job satisfaction: external context factors and internal context factors, as well as internal content factors [17]. External factors that affect job satisfaction comprise a competitive salary and additional rewards such as release from work due to family matters, flexible work schedule, and child raising support. The stated internal context factors were "less substantial but inherent to work". These could be factors that are controlled by external forces and directly affect internal employee satisfaction, such as adequate working hours and workload, stable working environment, and support of administration. Barnes also pointed out internal content factors that, first of all, are controlled by the professional himself/herself and that may affect employee's competence and feeling of effectiveness in the organisation. The author concludes that internal factors have more influence on employee career satisfaction than external.
Speakman et al (1996) conducted a study of 106 Texas (USA) doctors' job satisfaction [18]. Respondents stated that their work was "a challenge" in a positive sense: it enabled them to use their capabilities and was stimulating. The doctors also pointed out that they were given sufficient autonomy at work and independence in decision-making, and were able to learn and improve their work. The doctors, however, were dissatisfied with the clerical aspect of their work.
Dowell et al (2000) conducted research, the purpose of which was to examine job satisfaction and stress in the work of GPs in New Zealand. Their job satisfaction level was rather high [19]. However, rural doctors were less satisfied with their job than those working in cities. Besides GPs who worked individually were less satisfied with their work than group practice GPs. 177 GPs (46%) stated that work affected their physical health, and 220 (57%) stated that they had been thinking about quitting general practice. The main causes of stress and dissatisfaction were as follows: too much bureaucracy and restrictions, health care reforms, long working hours, and work "by the telephone".
Methods
Sample
Research was conducted in October – December 2003. Questionnaires were distributed to all Kaunas PHC physicians and GPs (N = 243) in person. The response rate amounted to 78.6 % (191 questionnaire returned).
Questionnaire Development
Total job satisfaction of the respondents was measured on a seven-point scale, where 7 stood for highly satisfied and 1 for highly dissatisfied.
Intentions to recommend one's children to choose a PHC level doctor's profession were measured on a seven-point scale, where 7 stood for "strongly agree" and 1 for "strongly disagree".
The level of satisfaction with different job characteristics was assessed using a job satisfaction questionnaire that embraced questions related to the PHC work specifics. Eight sections of questions were included in order to assess satisfaction with different job characteristics: ability utilisation at work, workload, colleagues, compensation, creativity, autonomy, management, and social status. All statements were measured on a seven-point scale, where 7 stood for highly satisfied and 1 for highly dissatisfied.
The survey data was processed using SPSS version 11.00 statistical package for data analysis. The statistical data reliability was checked according to χ2 criteria, degrees of freedom number (df) and statistical significance. Relationship between two independent variables was assessed relying on Spearmen's rank correlation, taking into consideration the value of the correlation ratio and statistical significance. Reliability notation: p < 0.05 – statistically significant, p < 0.01 – highly significant.
Results
Demographic characteristics of the respondents
15 men (7.9%) and 176 women (92.1%) participated in the research, among which 133 (69.6%) GPs and 58 (30.4%) physicians.
The analysis of the distribution of the respondents according to their age and specialization (see Table 1) revealed that GP and physician distribution according to their age statistically varied: the percentage of GPs in younger age groups (25–34, 35–44, 45–54 years) is higher than that of physicians. The reason for this is that the specialisation of physicians is no longer included into higher school programmes; only GPs are trained there, and increasingly more young doctors retrain from internists to GPs [5]. Comparing the data of January 1998 and December 2002, we can see that the number of GPs increased by 6.6 times, whereas the number of physicians decreased by 42.6%.
Table 1 Characteristics of the respondents
Age group (years) Specialisation Total
GPs* Physicians*
n % n % n %
25–34 23 17.3 1 1.7 24 12.6
35–44 28 21.1 8 13.8 36 18.8
45–54 73 54.9 16 27.6 89 46.6
55–64 9 6.8 26 44.8 35 18.3
65 and more - - 7 12.1 7 3.7
Total 133 100.0 58 100.0 191 100.0
* - χ2 = 63.360; df = 4; p = 0.000
Motives of choosing phc doctor's profession
With the health care reform underway, it is of great importance to determine how GPs became who they are, and what motives made them chose this specialisation. During the survey, the respondents were divided into 3 groups: the first group comprised GPs who had requalified from other specialisations, the second – GPs who chose this specialisation while studying at higher school, and the third one comprised physicians. In all three groups, doctors admitted to have chosen their profession mostly because no other profession was of interest to them. This motive was indicated by 80% of GPs who chose their specialisation at higher school, 63.4% of GPs who changed their qualification, and 77.4% of physicians. The dominating motive of "no other speciality being of interest" can be explained by the fact that the work of a doctor is easiest to imagine and perceive.
Total job satisfaction and intentions to recommend one's children to choose a phc level doctor's profession
Total job satisfaction of the respondents was 4.74 point. Recommending a profession to one's own child makes an important factor that shows if an employee is satisfied with his/her work or not. Most respondents (75.5%) do not intend to recommend their children to choose a PHC level doctor's profession. The assessment of the respondents' general satisfaction with work and their intentions to recommend their children to choose a primary level doctor's profession did not reveal any statistically significant differences between the specialisations (p > 0.05). However, the research revealed a tendency among physicians to be generally more satisfied with their work and more willing to recommend their children to choose a PHC level doctor's profession: total job satisfaction of physicians was 5.03 and intentions to recommend their children to follow their career comprised 2.56, while those of GPs respectively made 4.64 and 2.19.
Satisfaction with different job characteristics
The study of the satisfaction level with different job characteristics shows that respondents were most satisfied with autonomy at work – 5.28, relationship with colleagues – 5.06, and management quality – 5.04 (see Table 2), while compensation (2.09), social status (3.36), and work load (3.93) turned to be causing the highest dissatisfaction among the respondents. Statistically significant differences of satisfaction between the three categories of respondents were determined with two job characteristics, i.e. compensation and social status. GPs who chose specialisation at higher school were more satisfied with compensation (2.68) and social status (4.03) than GPs who changed their qualification (respectively 2.00 and 3.09) and physicians (respectively 1.90 and 3.45) (see Table 2).
Table 2 Means score of satisfaction with job characteristics according to specialization and its choice conditions
Job characteristics Specialisation and its choice Mean score of satisfaction (standard deviation)
GPs who have requalified (standard deviation) GPs who chose specialisation at higher school (standard deviation) Physicians (standard deviation)
Ability utilisation at work 4.79 (1.50) 4.51 (1.60) 5.18 (1.56) 4.84 (1.54)
Volume of work 3.69 (2.00) 4.29 (1.77) 4.24 (2.11) 3.93 (2.00)
Co-workers 5.05 (1.66) 5.26 (1.75) 4.92 (1.68) 5.06 (1.68)
Compensation* 2.00 (1.49) 2.68 (1.80) 1.90 (1.250) 2.09 (1.51)
Creativity 3.96 (1.66) 4.74 (1.80) 4.30 (1.83) 4.19 (1.75)
Autonomy 5.12 (1.65) 5.23 (1.62) 5.65 (1.25) 5.28 (1.57)
Management 4.86 (1.78) 5.17 (1.74) 5.30 (1.84) 5.04 (1.79)
Social status* 3.09 (1.76) 4.03 (2.01) 3.45 (1.85) 3.36 (1.85)
* – p < 0.05
In order to determine the main factors that cause satisfaction and/or dissatisfaction with work, the relationship between total job satisfaction and job characteristics was analysed. Calculations of Spearmen's ratios revealed the strongest correlation between total job satisfaction and such characteristics as autonomy at work (0.566), workload (0.452) and GP's social status (0.458) (see Table 3). When studying the relationship between the intention to recommend one's children to choose a PHC level doctor's profession and averages of satisfaction with job characteristics, the strongest correlation was again found between the intention to recommend one's children to choose a PHC level doctor's profession and autonomy at work (0.371) as well as GP's social status (0.329).
Table 3 Spearmen's ratio between job characteristics and total job satisfaction, and intention to recommend one's children to choose a GP's profession
Job characteristics Total job satisfaction Intention to recommend one's own children to choose a GP's profession
Ability utilisation at work 0.402** 0.241**
Volume of work 0.452** 0.228**
Co-workers 0.349** 0.239**
Compensation 0.240** 0.255**
Creativity 0.383** 0.151*
Autonomy 0.566** 0.371**
Management 0.358** 0.175*
Social status 0.458** 0.329**
** – p < 0.01; * – p < 0.05
Discussions and conclusions
Taking into consideration PHC level doctors' satisfaction with various aspects of work, a conclusion can be drawn that the health care reform that is being implemented meantime and that makes general practice physician the key figure and which should encourage expansion of GP institution is channelled in the right direction. Relying on the data of the conducted research, GPs who chose specialisation while studying at higher school are more satisfied with some aspects of work than physicians and GPs who requalied. However total job satisfaction of doctors who work in the primary health care is relatively low (4.74 on a 7 point scale). Doctors who have worked for 30 – 39 years are most satisfied with their job. Total job satisfaction of GPs and physicians does not have statistically significant difference. Most PHC level doctors (75.5%) do not intend to recommend their children to follow their career.
Such characteristics as autonomy at work, social status, and workload have the biggest impact on total job satisfaction. Stamps and Piedmonte's (2000) research shows that the more autonomy activities possess and the less monotony exists at work, and the more employees are satisfied with their work [20]. The research revealed that compensation, social status, and workload are among the key factors causing PHC doctors' dissatisfaction with their jobs. Thus it can be assumed that the above factors condition low total job satisfaction.
In conclusion it can be stated that the nature of a PHC doctor's work and rather low salaries condition relatively low job satisfaction among Lithuanian PHC doctors.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
These authors contributed equally to this work.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15748299 | PMC555592 | CC BY | 2021-01-04 16:29:12 | no | BMC Fam Pract. 2005 Mar 7; 6:10 | utf-8 | BMC Fam Pract | 2,005 | 10.1186/1471-2296-6-10 | oa_comm |
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BMC Med GenetBMC Medical Genetics1471-2350BioMed Central London 1471-2350-6-101575751910.1186/1471-2350-6-10Research ArticleEvaluation of SLC11A1 as an inflammatory bowel disease candidate gene Crawford Nigel PS [email protected] Maurice R [email protected] Daniel W [email protected] Robert K [email protected] Gary A [email protected] Robert E [email protected] Susan [email protected] Digestive Surgery Research Laboratory, Price Institute for Surgical Research, Department of Surgery, University of Louisville School of Medicine, Louisville, KY 40292, USA2 Department of Biology, University of Louisville, Louisville, KY 40292, USA3 Ameripath Institute of Gastrointestinal Pathology and Digestive Disease, Oakwood Village, OH 44146, USA2005 9 3 2005 6 10 10 8 10 2004 9 3 2005 Copyright © 2005 Crawford 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
Significant evidence suggests that a promoter polymorphism withinthe gene SLC11A1 is involved in susceptibility to both autoimmune and infectious disorders. The aim of this study was to evaluate whether SLC11A1 has a role in the susceptibility to inflammatory bowel disease (IBD) by characterizing a promoter polymorphism within the gene and two short tandem repeat (STR) markers in genetic proximity to SLC11A1.
Methods
The studied population consisted of 484 Caucasians with IBD, 144 population controls, and 348 non-IBD-affected first-degree relatives of IBD patients. IBD subjects were re-categorized at the sub-disease phenotypic level to characterize possible SLC11A1 genotype-phenotype correlations. Polymorphic markers were amplified from germline DNA and typed using gel electrophoresis. Genotype-phenotype correlations were defined using case-control, haplotype, and family-based association studies.
Results
This study did not provide compelling evidence for SLC11A1 disease association; most significantly, there was no apparent evidence of SLC11A1 promoter allele association in the studied Crohn's disease population.
Conclusion
Our results therefore refute previous studies that have shown SLC11A1 promoter polymorphisms are involved in susceptibility to this form of IBD.
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Background
Inflammatory bowel disease (IBD) encompasses a number of disorders that are characterized by chronic inflammation of the gastrointestinal tract. The three specific forms of the disease known as Crohn disease (CD), ulcerative colitis (UC), or the less well-characterized indeterminate colitis (IC), are diagnosed based on the results of clinical observations, histology, radiology, and to a lesser extent, serology. Prominent clinical features include profuse diarrhea, abdominal pain, and increased colorectal cancer risk. IBD affects an estimated one million Americans [1], with UC being the more prevalent form. Current pathogenic models state that IBD develops in a genetically susceptible individual in response to environmental stimuli [2].
The aim of this study was to investigate the role of the gene SLC11A1 in the susceptibility to IBD. SLC11A1 is a proton-coupled bivalent metal antiporter that is crucial in early macrophage activation [3]. SLC11A1 encodes a highly hydrophobic 550 amino acid membrane protein and is located on the long arm of chromosome 2 (2q35), a region that has not been identified as being within an IBD susceptibility locus. Genetic studies have shown that different alleles of a (GT)n dinucleotide repeat polymorphism located within the promoter region of SLC11A1 cause susceptibility to different human diseases. Allele 2 of this polymorphism is associated with susceptibility to intracellular pathogen infection [4], and allele 3 is associated with autoimmune disease susceptibility [3].
SLC11A1 has been implicated in susceptibility to IBD. Previous work from this laboratory [5] has shown that two tetranucleotide short tandem repeat (STR) polymorphisms genetically linked to SLC11A1 are associated with CD [5]. The (GT)n promoter polymorphism, however, was not examined in that study. A recent study by Kojima et al [6] indicated that promoter polymorphism allele 7 was associated with IBD in a Japanese population.
In our current work, we used a case-control association study to define the role of the (GT)n promoter polymorphism in susceptibility to IBD and to further characterize the STR markers previously described [5]. These markers (D2S434 and D2S1323) are 0.67 Mbp proximal and 3.44 Mbp distal of SLC11A1, respectively.
Methods
Population
This prospective study was approved by the University of Louisville Institutional Review Board. Written informed consent was obtained from all subjects. Patients were derived from a University-based colon and rectal surgery practice. Initial IBD diagnoses were determined through radiological, endoscopic, and/or histopathological studies. Histology was available in all cases.
The study population consisted of a total of 628 Caucasians, including 254 unrelated individuals with CD (63% women), 165 with UC (53% women), 65 with IC (70% women), and 144 population controls (74% women). Demographic data for the IBD population is shown in Table 1. For the purposes of more accurate phenotyping, the CD group was subdivided based on the Vienna classification [7]. This categorizes CD patients based on age of onset (A), location of disease (L), and disease behavior (B). With regard to age of onset, 175 of 254 (69%) CD patients were diagnosed at < 40 years of age (A1 group) and 47 of 254 (18%) had disease diagnosed at ≥40 years of age (A2 group). Information regarding age of onset was not available for 32 of 254 patients (13%). Regarding disease location, 70 of 254 (28%) had terminal ileal disease (L1 group), 152 of 254 (60%) had purely colonic and/or ileocolic (ileal and colonic) disease (L2/L3 groups), and 32 of 254 (13%) had disease located proximal to terminal ileum (L4 group). The L2 and L3 groups were combined for the purposes of this study, since colonic disease constitutes the primary focus of our group. Regarding disease behavior, 96 of 254 (38%) had uncomplicated inflammatory disease (B1 group), 28 of 254 (11%) had stricturing disease (B2 group), and 130 of 254 (51%) had penetrating disease (B3 group).
A total of 348 non-affected first-degree relatives of IBD patients were available for the study. There were 63 CD families (29 triads, 30 discordant sibling pairs, 4 triad/discordant sibling pair combinations), 43 UC families (11 triads, 24 discordant sibling pairs, 8 triad/discordant sibling pair combinations), and 14 IC families (5 triads, 5 discordant sibling pairs, 4 triad/discordant sibling pair combinations). In addition, there were 17 CD families, 18 UC families, and 9 IC families who could not be classified as either a triad or discordant sibling pairs.
Markers and genotyping
STRs were amplified from genomic DNA extracted from peripheral leukocytes. Polymerase chain reaction (PCR) primers were custom-synthesized (Proligo, La Jolla, CA). PCR amplification of the SLC11A1 promoter polymorphism was performed using 100 ng template DNA, 2.5 mM MgCl2, 50 mM KCl, 10 mM of tris/HCl, 0.2 μM of each dNTP, 200 pmol of each primer, and 1 unit of Taq polymerase (Promega, Madison, WI). Primer sequences were as follows: 5'GACATGAAGACTCGCATTAG3' & 5'TCAAGTCTCCACCAGCCTAGT3'. The product was amplified using the following conditions: 94°C for 10 min, followed by 25 cycles of 94°C for 30 sec, 55°C for 75 sec, and 72°C for 20 sec. A final extension step was run at 72°C for 6 min. Genotyping was performed using gel electrophoresis (Spreadex EL 300 S-100 gel [Elchrom Scientific, Lake Park, FL]). PCR amplification of the STR markers D2S434 and D2S1323 was performed as described previously [5].
Statistical analyses
Case-control tests of association
Case-control analyses were performed using STR genotype-allele frequencies in cohorts subdivided on the variables outlined in Table 1. Correction for multiple testing was performed by using Storey's q-value method, where p-values were adjusted according to the experimental false discovery rate (FDR) [8]. The global null hypothesis of no difference in genotype-allele frequency between controls and any of the other groups was tested by using Fisher's method [9], which was used to combine all of the control group comparison global p-values obtained for each of the three markers. Q-values were calculated from these global p-values using the QVALUE program [10] with the tuning parameter λ = 0, which dictates the assumption πo = 1, where πo is the proportion of tests in which the null hypothesis is true.
To limit the number of individual disease-control genotype-allele comparisons, further analyses were only performed in those disease groups possessing a global test q-value <0.05. Therefore, 2 × 2 contingency tables were only constructed for genotypes with a global test q-value meeting this condition. Disease versus control comparisons were performed using Fisher's exact test and corrected for multiple testing using the q-value method as described above.
Since IBD affecting the colon is the primary focus of our research, the L2 colonic CD and L3 ileocolonic CD groups were combined, enabling maximization of statistical power. We argue that this approach will not impair the validity of statistical analyses given that the Vienna Classification is somewhat arbitrary and that group genetic homogeneity may be increased by combining all those cases with colonic IBD. Furthermore, owing to the relatively low numbers of individuals with CD proximal to the terminal ileum (L4 group, n = 32), this disease subgroup was excluded from case-control analyses.
Family-based tests of association
Family-based tests used included the pedigree disequilibrium test (PDT) [11,12] and a likelihood ratio test implemented in the computer program TRANSMIT [13]. PDT analysis has distinct advantages over other family-based tests of association. PDT allows for analysis of data from related nuclear families and discordant sibling pairs from extended pedigrees. The likelihood ratio test, as implemented in the computer program TRANSMIT [13], can account for missing parental genetic data by inferring parental genotypes based on the genotypes of their offspring. To maximize power, family-based analyses were not performed at the sub-phenotypic level.
Results
Allele and genotype frequencies
Alleles for the SLC11A1 promoter polymorphism STR D2S434 and STR D2S1323 were named numerically according to molecular weight, with "1" representing a larger allele than "2" and so on. Allele distributions are shown in Table 2.
DNA sequence analysis of the three SLC11A1 promoter polymorphism alleles identified in the study population revealed the following sequences:
Allele 1: T(GT)5AC(GT)5AC(GT)11GGCAGA(G)6
Allele 2: T(GT)5AC(GT)5AC(GT)10GGCAGA(G)6
Allele 3: T(GT)5AC(GT)5AC(GT)9GGCAGA(G)6
These sequences corresponded to alleles 1, 2, and 3 as described by Searle and Blackwell [14]. Alleles 4–7 were not identified in this population.
Case-control association studies
Results from global disease versus control comparisons of the studied markers are shown in Table 3. Analysis of genotype and allele frequencies for the (GT)n promoter polymorphism did not reveal any evidence of association. Additionally, we studied STR markers in genetic proximity to SLC11A1. The STR D2S1323 exhibited evidence of association with the test of the global null hypothesis of homogeneity of all groups exhibiting statistical significance for allele frequencies (q = 0.050, p = 0.008). To limit multiple testing, individual pair-wise disease versus control comparisons were only performed for D2S1323 alleles. These analyses showed over-representation of allele 1 in the UC group (q = 0.022, p = 0.005; UC frequency 78 of 100 alleles [78%] versus control frequency 54 of 92 alleles [59%]). The STR D2S434 did not reveal any evidence of association. Further analyses of genotype and allele data for all markers did not show a correlation with either age of onset or disease behavior.
Family-based association studies
Family-based tests were performed for each of the three markers. Neither the likelihood ratio test nor the PDT revealed significant evidence of association for any of these markers.
Discussion
No association was observed between any SLC11A1 promoter polymorphism alleles and any IBD sub-phenotypic group in this population, which unexpectedly refutes the significant evidence of others [6]. This result was somewhat surprising, given that many studies have shown SLC11A1 promoter polymorphisms to be involved in susceptibility to autoimmune disorders and disorders that are characterized by a high degree of immunological dysregulation, including IBD [3]. Searle and Blackwell [14] investigated the enhancer properties of each of the four identified promoter polymorphism alleles. Allele 3 was found to have endogenous enhancer activity, whereas alleles 1, 2, and 4 were poor promoters in the absence of exogenous stimuli. Stimulation of macrophages with bacterial lipopolysaccharide (LPS) had minimal effects on SLC11A1 expression driven by alleles 1, 2, and 4; however, allele 3-driven SLC11A1 expression was enhanced. It has long been recognized that altered expression of SLC11A1 in response to LPS is a key event in the activation of macrophages upon contact with a variety of pathogens [15].
Based on those observations, we hypothesized that over-representation of promoter allele 3 in CD patients could lead to hyperactivation of bowel wall macrophages that are chronically exposed to high levels of LPS. This could subsequently cause the autoimmune-like phenotype characteristic of IBD. This hypothesis must be rejected in this cohort due to the absence of association with promoter allele 3 in any IBD subgroup. Analysis of intragenic single nucleotide polymorphisms are required to conclusively exclude SLC11A1 as an IBD candidate gene.
D2S1323 was associated with UC in this population. Indeed, this was the only evidence for association in any of the studied STR markers in proximity to SLC11A1. This association was not, however, confirmed upon family-based statistical analysis. The current finding was particularly surprising to us, given that our previous study showed this STR to be associated with CD rather than UC [5]. The significance of this finding is thus uncertain, and the divergent outcomes from the current and earlier studies could be considered somewhat concerning. We do, however, believe that a number of prominent features of the current study render our results of higher validity than those of our previous study. An important feature of our current work is that all histological specimens from our subjects have been reviewed by a single gastrointestinal pathologist. This was not the case in the earlier study, and diagnostic misclassification (i.e., UC and/or IC incorrectly diagnosed as CD and vice versa) may have been a confounding variable that may have in part led to what we now consider the false-positive association observed in the CD population. It should also be noted that the current study contains over two-fold the number of subjects as compared with the previous work, and thus statistical power has been significantly increased. Furthermore, our current statistical methodology is now far superior, and we argue that our stringent means of correcting for the effects of multiple testing effectively minimizes the likelihood of detecting a false-positive association. This is a vast improvement over our earlier study in which compounding of a type 1 error was not taken into account. Finally, genotyping in the earlier study was performed on specimens with a rather heterogeneous nature and consisted of genetic data derived from DNA extracted from both surgical specimens (i.e., somatic DNA) and leukocytes (i.e., germline DNA). In our current study, genetic material has only been isolated from leukocytes, thus reducing the possibility of detecting variations within somatic DNA.
Conclusion
In summary, we have characterized the SLC11A1 (GT)n promoter polymorphism in IBD sub-phenotypic groups and accept the null hypothesis as confirmed by family-based testing. We did, however, identify an association between an allele of the STR D2S1323 and UC upon case-control analysis. This association was not confirmed on family-based and haplotype testing, and its actual significance in IBD remains to be seen.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
NPSC performed genotyping, PDT analyses, and drafted the manuscript; MRE performed genotyping and was responsible for coordination of laboratory work; authors DWC and RKL performed genotyping; GAC performed statistical analyses; REP performed re-review of colonic IBD histology; SG collected all patient samples, participated in study design, coordination, and manuscript preparation and obtained funding for this work. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This project was supported in part by the James and Emmeline Ferguson Research Fund.
Figures and Tables
Table 1 General characteristics of the studied inflammatory bowel disease (IBD) population.
Disease characteristic Crohn's disease (n = 254) Ulcerative Colitis (n = 165) Indeterminate colitis (n = 65)
Average length of clinical follow-up (range, yrs) 4.6 (0–16) 4.3 (0–14) 5.6 (0–12)
Age of IBD onset (range, yrs) 29.2 (6–81) 34.4 (10–75) 29.7 (12–64)
Surgical treatment for IBD complications (% received) 75% 67% 82%
First-degree relative with IBD 36% 20% 22%
Extraintestinal manifestations(a) 43% 36% 49%
(a) Extraintestinal manifestations are defined as IBD-related disease outside the gastrointestinal tract involving joints, eyes, hepatobiliary system, or the skin.
Table 2 Polymorphism allele frequencies.
SLC11A1 Promoter Allele Ulcerative Colitis Indeterminate Colitis Ileocolic Crohn's Disease (L1) Colonic Crohn's Disease (L2/3) Controls
N % N % N % N % N %
1 0 0 0 0 0 0 0 0 2 1
2 40 21 19 21 28 29 44 25 42 23
3 152 79 71 79 68 71 132 75 136 76
Total 192 90 96 176 180
D2S434 Allele N % N % N % N % N %
3 23 30 22 24 22 25 48 12 25 14
4 53 29 23 26 23 26 46 27 52 30
5 43 30 23 27 24 26 48 22 47 27
6 23 19 17 9 8 19 30 12 30 17
Pooled(a) 16 14 5 12 11 6 22 8 20 11
Total 158 90 88 194 174
D2S1323 Allele N % N % N % N % N %
1 78 78 28 70 39 72 83 67 54 59
2 22 22 12 30 15 28 41 33 38 41
Total 100 40 54 124 92
(a) Pooled alleles with frequency <5%
Table 3 Case-control association global q-value and p-values for disease group versus control comparisons.
STR marker Disease versus control comparison global p-value Global test of homogeneity(a)
Ulcerative colitis Indeterminate colitis Terminal ileal Crohn's disease (L1) Colonic Crohn's disease (L2/3) Combined p-value q-value
Promoter (GT)n Genotype 0.553 0.424 0.743 0.768 0.855 0.855
Allele 0.457 0.548 0.473 1.000 0.832 0.855
D2S434 Genotype 0.390 0.345 0.365 0.705 0.562 0.842
Allele 0.930 0.191 0.157 0.153 0.206 0.412
D2S1323 Genotype 0.027 0.424 0.202 0.490 0.093 0.279
Allele 0.005 0.247 0.111 0.253 0.008 0.050
(a) Statistically significant comparisons following correction for multiple testing are shown in bold typeface
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| 15757519 | PMC555593 | CC BY | 2021-01-04 16:03:33 | no | BMC Med Genet. 2005 Mar 9; 6:10 | utf-8 | BMC Med Genet | 2,005 | 10.1186/1471-2350-6-10 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-5-231577177910.1186/1471-2458-5-23Study ProtocolEpidemiology of acute coronary syndromes in a Mediterranean country; aims, design and baseline characteristics of the Greek study of acute coronary syndromes (GREECS) Pitsavos Christos [email protected] Demosthenes B [email protected] Antonis [email protected] Spyros [email protected] Yannis [email protected] Yannis [email protected] Peter [email protected] Georgia [email protected] Christodoulos [email protected] the GREECS Study Investigators 1 First Cardiology Clinic, School of Medicine, University of Athens, Greece2005 16 3 2005 5 23 23 1 2 2005 16 3 2005 Copyright © 2005 Pitsavos et al; licensee BioMed Central Ltd.2005Pitsavos 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 present study GREECS was conducted in order to evaluate the annual incidence of acute coronary syndromes (ACS) and to delineate the role of clinical, biochemical, lifestyle and behavioral characteristics on the severity of disease. In this work we present the design, methodology of the study and various baseline characteristics of people with ACS.
Methods/Design
A sample of 6 hospitals located in Greek urban and rural regions was selected. In these hospitals we recorded almost all admissions due to ACS, from October 2003 to September 2004. Socio-demographic, clinical, dietary, psychological and other lifestyle characteristics were recorded. 2172 patients were included in the study (76% were men and 24% women). The crude annual incidence rate was 22.6 per 10,000 people and the highest frequency of events was observed in winter. The in-hospital mortality rate was 4.3%. The most common discharged diagnosis for men was Q-wave MI, while for women it was unstable angina.
Discussion
This study aims to demonstrate current information about the epidemiology of patients who suffer from ACS, in Greece.
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Background
During the past decades epidemiological investigations have provided a portrait of the potential candidate for acute coronary syndromes, in the US, as well as in many other parts of the world, especially in European countries [1-9]. However, many investigators claimed for differences in the profile, i.e. socio-demographic characteristics, prevalence of risk factors, dietary habits, etc. of people who suffer from coronary heart disease, between populations as well as among individuals within populations [10-12]. A potential explanation can be attributed to the gene-environment interactions, as well as various cultural and behavioural particularities.
The profile of cardiovascular disease patients in Greece has been, mainly, investigated from few large scale, population-based studies, like the Seven Countries Study in the early 1960s [13], the Hellenic study of acute myocardial infarction, which recruited about 7500 patients with myocardial infarction from almost all hospitals countrywidethe in early 1990s [14], the CARDIO2000 case-control study of acute coronary syndromes [15], as well as some small case-control or observational studies that included patients from specific areas [16,17]. However, the prevalence, annual incidence and management of patients with coronary heart disease in Greece is unknown, since all these studies recruited patients during certain time periods, and information relating cardiovascular events with treatment, as well as lifestyle habits, like exercise, diet, and psychological stress and depression, are lacking. Additionally, during the past decade, Greece has experienced marked but uneven socio-economic development, with the average income increasing by about 20-fold [18]. Consequently, the lifestyle of people throughout the country has changed dramatically, as well as the incidence of cardiovascular disease.
The aim of the GREEk study of acute Coronary Syndromes (GREECS) is to evaluate the prevalence and annual incidence of acute coronary syndromes (ACS), as well as the characteristics, and management of these patients, in a sample of six Greek urban and rural regions. Secondary goals are to examine the role of adoption to the Mediterranean diet and other lifestyle habits, as well as various clinical, biochemical, psychological and personal characteristics of these patients on the severity and the short (30 days) – long (6, 12 months) – term prognosis. In this work we present the methodology used, and the baseline characteristics of the studied population.
Methods / Design
Population of the study
Between October 1, 2003 and September 30, 2004 (12 months) we enrolled almost all consecutive patients (participation rate = 98%) that entered in the cardiology clinics or the emergency units of six major General Hospitals, in Greece (Hippokration hospital in Athens, and the general hospitals in Lamia, Karditsa, Halkida, Kalamata and Zakynthos island). By the exception of Athens – where there are several other hospitals -, all the other hospitals cover the whole population of the aforementioned regions, including urban and rural areas. The studied regions were randomly selected from all Greek regions in order to cover a wide range of the county (Figure 1).
Figure 1 Regions covered by the study.
During the study period 2,172 patients were admitted for ACS in the selected hospitals, 1649 (76%) of them were men and 523 (24%) were women. Power analysis showed that the number of enrolled participants is adequate to evaluate two – sided differences between groups of the study and the investigated parameters greater than 20% (± 5%), achieving statistical power greater than 0.80 at 5% probability level (P-value).
Diagnosis of ACS
At entry a 12-lead electrocardiogram was performed and clinical symptoms were evaluated in all patients, by a cardiologist of the Study. Based on the electrocardiographic findings patients were classified as having ST-segment elevations, non-ST segment elevations or other electrocardiographic abnormalities. Moreover, blood tests were performed to detect evidence of myocardial cell death. We measured troponin I levels and the MB fraction of total creatinine posphokinase (CPK). According to the Joint European Society of Cardiology and American College of Cardiology Committee, blood samples were obtained on hospital admission, at 6 to 9 h, and again at 12 to 24 h if earlier samples were negative and the clinical index of suspicion was high [19]. We included only cases with discharge diagnoses of ACS (acute myocardial infarction (MI) or unstable angina (UA)). In particular, acute myocardial infarction was defined by at least two of the following features: (a) electrocardiographic changes (patients with or without ST segment elevations), (b) compatible clinical symptoms, and (c) specific diagnostic sensitive biomarkers elevations (troponin I > 0.4 ng/ml and the MB fraction of CPK > 8.8 ng/ml). UA was defined by the occurrence of one or more angina episodes, at rest, within the preceding 48-hours, corresponding to class III of the Braunwald classification [20].
The study was approved by the Medical Research Ethics Committee of our Institution and was carried out in accordance with the Declaration of Helsinki (1989) of the World Medical Association.
Other clinical and biochemical characteristics
In all patients a detailed medical history was recorded, including previous hospitalization for cardiovascular disease (i.e. coronary heart disease, stroke or other cardiovascular disease), presence and management of hypertension, hypercholesterolemia, renal failure and diabetes mellitus. Moreover, we recorded patients' medical family history. In particular, we asked information concerning first-degree relatives (biological parent, or brother, or sister) about presence of coronary heart disease, hypertension, dyslipidemias and diabetes. Premature history (<55 years old for males and < 65 years old for females) of myocardial infarction, sudden death, coronary arteries bypass grafting procedure or percutaneous coronary angioplasty in first-degree relatives classified the participants in the positive family history group for coronary heart disease [20]. Premature history (<55 years old for males and < 65 years old for females) of hypertension, hypercholesterolemia, hypertriglyceridemia, and diabetes, defined as the use of special medication or known, but untreated, condition classified the participants in the positive family history group for these co-morbidities. In addition to troponin I and the MB fraction of creatinine mentioned above, we also measured white blood cell counts, urea, and uric acid.
Demographic, anthropometric and lifestyle characteristics
Socio-demographic characteristics included: age, sex, marital status and number of children, years of school, type of occupation and occupational skills, which they were evaluated through a ten-point scale from unskilled – hand workers (lower values) to executive – skilled workers (higher values) that has been developed for the purposes of the Study; and mean annual income of the family (through self reports) during the last three years. Regarding people in the family who were not working, we used the average family income, while for unemployed individuals we used the basic monthly allowance they take from the Social Service Office.
Height and weight was measured, to the nearest 0.5 cm and 100 g respectively. Body mass index (BMI) was then calculated as weight (in kilograms) divided by height (in meters) squared. Based on the World Health Organization [21], overweight was defined as BMI between 25 and 29.9 kg/m2, while obesity as BMI greater than 29.9 kg / m2.
To evaluate physical activity status of the patients during the past year we used a modified version of a self-reported questionnaire provided by the American College of Sports Medicine [22]. Based on this questionnaire we assessed the frequency (times per week), duration (in minutes per time) and intensity of sports or occupation related physical activity. Participants who did not report any physical activities were defined as sedentary. For the rest of the participants we calculated a combined score by multiplying the weekly frequency, duration and intensity of physical activity.
Current smokers were defined as those who smoked at least one cigarette per day or have stopped cigarette smoking during the past 12 months. Former smokers were defined as those who had stopped smoking more than one year previously. The rest of them were defined as never smokers or rare smokers. Exposure to environmental cigarette smoke for at least 30 minutes per day and three days per week at workplace, home or other public places was recorded in all patients, too.
Nutritional habits and dietary ascertainment
The evaluation of the nutritional habits was based on a validated semi-quantitative food frequency questionnaire [23]. The consumption of certain food items and the portion size as an average per week, during the past year, was recorded. Then, the frequency of consumption was quantified approximately in terms of the number of times a month the food was consumed. In order to describe total diet composite scores were applied, which are necessary for the evaluation of epidemiological associations. According to a dietary pyramid that has been developed to describe the Mediterranean dietary pattern [24] we calculated a special diet score for each participant that assessed adherence to the Mediterranean diet (range 0 – 55). In particular, for the consumption of 11 items presumed to be close to this pattern (i.e. those suggested on daily basis or more than 4 servings per week) we assigned score 0 when a participant reported no consumption, 1 when reported consumption of 1 to 4 times / month, 2 for 5 to 8 times, 3 for 9 to 12 times / month, 4 for 13 to 18 times / month and 5 for more than 18 times per month. On the other hand, for the consumption of foods presumed to be away from this diet (like meat and meat products) we assigned the opposite scores (i.e. 0 when a participant reported almost daily consumption to 5 for rare or no consumption). Especially for alcohol we assigned score 5 for consumption of less than 3 wineglasses per day, score 0 for consumption of more than 7 wineglasses per day and scores 1 to 4 for consumption of 3, 4 to 5, 6 and 7 wineglasses per day. Higher values of this diet score indicates greater adherence to the Mediterranean diet, while lower values indicate adherence to the "Westernized" diet.
Psychological evaluation
Depressive symptomatology was assessed using a translated and validated version of the Center of Epidemiological Studies Depression Scale (CES-D) [25]. The CES-D is a well known and world-widely used self-rating scale for the measurement of depression. It is a self-reporting instrument and was originally developed in order to assess depression symptoms without the bias of an administrator affecting the results. Higher scores on this scale are indicative of more severe depression [25]. CES-D consists of 20 items that cover affective, psychological, and somatic symptoms. The patient specifies the frequency with which the symptom is experienced (that is: a little = 1, some = 2, a good part of the time = 3, or most of the time = 4). Previous investigations indicate that the CES-D score is a valid and sensitive measure of clinical severity in depressed patients and support its continued use as a research instrument [26].
Clinical symptomatology of occupational stress was determined by a special self-reported questionnaire based on the survey obtainable by the Job Stress Help Line [27]. By this questionnaire the presence of occupational stress and insecurity was evaluated and summarized for every patient by scoring each item of the questionnaire (1 = yes, 0 = no). Total score ranged from 0 to 10.
Statistical analysis
In this, brief, baseline report continuous variables are presented as mean values ± standard deviation. The categorical variables are presented as absolute and relative (%) frequencies. Future analyses will follow with appropriate statistical techniques. Associations between continuous variables and group of patients will be evaluated through the analysis of variance, after controlling for equality of variances (homoscedacity) using the F-test. Due to multiple comparisons we will apply the Bonferroni rule to correct for the inflation of type – I error. Associations between categorical variables will be tested by the use of the chi-squared test, without the correction of continuity. Correlations between continuous variables will be tested by the use of Pearson's correlation coefficient for the normally distributed, and by the use of Sperman's rho coefficient for the ordinal or skewed variables. The association between the investigated socio-demographic, clinical and biochemical characteristics on the short term outcome (i.e. 1 month) will be tested by the development of multiple logistic regression models, while the association of the aforementioned characteristics on the long term outcome (6 and 12 months) will be tested by the use of Cox proportional hazards models. Appropriate tests for goodness-of-fit (i.e. deviance and Pearson's residuals) will be applied in all models. All statistical calculations will be performed on the SPSS version 12.0 software (SPSS Inc, Texas, U.S.A.).
Baseline characteristics
From October 2003 to September 2004, 2172 patients with discharge diagnosis of ACS were enrolled into the study (1649 men, 65 ± 13 years old and 523 women, 62 ± 11 years old, p < 0.001). The men-to-women ratio was 3- to -1. The annual incidence of ACS was 22.6 per 10000 of people (34.0 per 10000 men and 10.9 per 10,000 women). The annual incidence was calculated with the exception of events at Hippokration Hospital in Athens, because it was very difficult to define the referent population since there are many hospitals in the area. The mean number of daily admissions was 6 ± 3 persons per day, averaging 5 ± 4 men and 2 ± 1 women.
Table 1 illustrates the age-sex distribution of the patients. Figure 2 illustrates the series of the monthly number of admissions for ACS in the selected hospitals during the 12 months period. A seasonal variability was observed in the counts of hospital admissions due to ACS (Figure 2).
Table 1 Age-sex distribution of the patients
Age (years) Men (n = 1609) Women (508)
< 30 5 (0.3%) 0 (0.0%)
30–39 28 (1.7%) 5 (1.0%)
40–49 220 (13.3%) 31 (5.9%)
50–59 335 (20.3%) 52 (9.9%)
60–69 388 (23.5%) 115 (22.0%)
>70 673 (40.8%) 320 (61.2%)
Figure 2 Monthly distribution of hospital admissions for ACS in the centers of the study (dotted line represents the smoothed 12 month average)
Of the 2172 patients enrolled into the study, 38% had ST segment elevations, 27% had non-ST segment elevations and the rest of them, i.e. 35% patients, had other electrocardiographic findings. According to the discharge diagnosis, 764 (35%) patients were diagnosed as having unstable angina, 699 (32%) patients as having non-Q-wave MI and 709 (33%) patients as having Q-wave MI.
Table 2 presents the cross-tabulation of electrocardiographic (ECG) findings and discharge diagnosis. The majority of patients with ST segment elevations had Q-wave MI, while about 61% of patients with an undetermined electrocardiographic pattern had UA. It is of interest that a considerable proportion of patients without ST segment elevations or other ECG findings had Q-wave MI. Moreover, 8% of patients with ST segment elevations were defined as having UA at discharge.
Table 2 Electrocardiographic (ECG) findings and discharge diagnosis
Diagnosis at discharge
ECG changes Q-wave MI Non-Q-wave MI Unstable angina
ST-segment elevations 88% 18% 8%
Without ST-segment elevations 5% 46% 31%
Other ECG findings 7% 36% 61%
% values are by diagnosis group
Table 3 illustrates various baseline clinical characteristics of the patients by discharge diagnosis. We also found that 24% of ACS patients had family history of diabetes, 24% of patients had family history of dyslipidemias and 43% of patients reported family history of hypertension.
Table 3 Clinical characteristics by discharge diagnosis
Unstable angina Non-Q-wave MI Q-wave MI
Men
Number 544 (33%) 523 (32%) 582 (35%)
Age (years) 65 ± 12 67 ± 13 63 ± 13
Body mass index (kg/m2) 28 ± 4.7 27 ± 3.5 27 ± 37
Obesity (%) 116 (24%) 86 (20%) 113 (22%)
Hypertension (%) 257 (51%) 215 (41%) 215 (41%)
Hypercholesterolemia (%) 245 (49%) 151 (42%) 212 (46%)
Diabetes mellitus (%) 146 (30%) 145 (34%) 134 (27%)
Renal failure (%) 22 (5%) 31 (8%) 23 (6%)
Prior coronary heart disease 299 (60%) 212 (51%) 153 (29%)
Women
Number 220 (42%) 176 (34%) 127 (24%)
Age (years) 70 ± 10 74 ± 11 72 ± 13
Body mass index (kg/m2) 28 ± 5.1 28 ± 4.2 28 ± 4.2
Obesity (%) 52 (26%) 27 (20%) 37 (31%)
Hypertension (%) 160 (76%) 106 (71%) 76 (63%)
Hypercholesterolemia (%) 105 (50%) 61 (50%) 52 (46%)
Diabetes mellitus (%) 80 (40%) 63 (45%) 32 (28%)
Renal failure (%) 13 (7%) 17 (13%) 3 (3%)
Prior coronary heart disease 115 (56%) 63 (44%) 29 (25%)
P < 0.05 and **P < 0.01 between diagnosis group after correcting for multiple comparisons through the Bonferroni adjustment
Table 4 illustrates various lifestyle and behavioral characteristics of the patients by discharge diagnosis.
Table 4 Lifestyle and behavioral characteristics by discharge diagnosis
Unstable angina Non-Q-wave MI Q-wave MI
Men
Number 544 (33%) 523 (32%) 582 (35%)
Diet score (0–55) 27 ± 2.6 26 ± 2.7 24 ± 2.4
CES-depression score (0–60) 18.5 ± 11 22.5 ± 10 21 ± 10
Physical inactivity (%) 20% 17% 22%
Former smoking (%) 50% 41% 32%
Current smoking (%) 37% 47% 57%
Passive smoking (years) 16 ± 15 17 ± 15 20 ± 16
Financial status
Low 7% 5% 8%
Medium 53% 65% 55%
High 37% 28% 33%
Very high 3% 2% 4%
Years of school 8 ± 4 7.7 ± 4 9.0 ± 4.6
Occupational skills (0–10) 4.2 ± 1.7 4.1 ± 1.4 4.6 ± 1.6
Women
Number 220 (42%) 176 (34%) 127 (24%)
Diet score (0–55) 26 ± 2.6 25 ± 2.4 23 ± 2.3
CES-depression score (0–60) 24 ± 11 23 ± 11 20 ± 9
Physical inactivity (%) 32% 28% 38%
Former smoking (%) 11% 5% 11%
Current smoking (%) 49% 71% 55%
Passive smoking (years) 23 ± 15 22 ± 15 21 ± 16
Financial status
Low 15% 9% 13%
Medium 58% 73% 66%
High 23% 18% 16%
Very high 4% 1% 5%
Years of school 6 ± 4.3 5.5 ± 3.6 6 ± 3.5
Occupational skills (0–10) 4.0 ± 1.3 3.5 ± 1.2 3.8 ± 1.2
* P < 0.05 and **P < 0.01 between diagnosis group after correcting for multiple comparisons through the Bonferroni correction
The median (and 25th, 75th percentiles) time between the overt of symptoms and the time medical care was sought, was 4 (2, 10) hours. Based on the discharge diagnosis the duration of hospitalization was 7 (5, 8) days for patients with Q-wave MI, 6 (5, 8) days for non-Q-wave patients and 5 (3, 7) days for UA patients. Furthermore, 60% of patients with ST-elevation received trombolytic therapy.
The in-hospital mortality rate was 36 deaths per 1000 male patients and 63 deaths per 1000 female patients (i.e. overall 82 deaths). The in-hospital mortality rate of patients with ST segment elevation was 74 deaths per 1000 patients, for non-ST segment elevation was 34 deaths per 1000 and for undetermined electrocardiographic findings was 2 deaths per 1000 patients.
Discussion
In this brief report we presented the design, and the aims of an epidemiological study of ACS, that has been conducted in six major general hospitals, in Greece (i.e. the GREECS). We also presented the baseline characteristics of almost all patients who hospitalized in these hospitals for ACS during the study period (i.e. October 2003 to September 2004).
The overall incidence rate of ACS observed in our survey was 22.6 events per 10000 of population. Based on this figure it could be speculated that the prevalence of ACS in the investigated Greek areas is 2.6%. In a similar recent study located only in northwestern Greece, which also included sudden cardiac death before hospital admission, the incidence rate of ACS was much higher (i.e. 39 events per 10000 people) [17]. Based on a review paper by Chimonas [28] that evaluated the prevalence and incidence of ACS in Greece during 1988, we observe that the current rates are slightly higher in men compare to the annual incidence in late 1980s (i.e. 29.7 events per 10000 of people), but the observed incidence rate in women is almost two-fold as compared to 1988 (i.e. 5.2 events per 10000 people). Future analyses of our study will answer to the questions whether the observed difference in incidence rates could attribute to various lifestyle and behavioral changes, like dietary and smoking habits, or other environmental particularities, occurred in Greece the last years.
We also observed that the annual incidence for men was 34 per 10,000 of people, while the incidence for women was significantly lower, i.e. 11 per 10,000 of people. This finding will be tested whether can be attribute to the increased smoking habits observed in women during the past decades, as well as to various other lifestyle habits and work-related conditions, like physical inactivity, unhealthy diet, social insecurity and job stress, observed in female population from late 1960s to present [18].
Furthermore, we observed that the in-hospital mortality rate in was 4.3%, which was similar to the in-hospital mortality rate that has been calculated based on a sample of 25 European countries, i.e. 4.9% [9].
In this work we have also presented various baseline clinical and lifestyle characteristics of the enrolled patients. Future analyses will evaluate the role of the investigated clinical characteristics as well as family history of the common cardiovascular risk factors on the severity and prognosis of the ACS patients. Regarding dietary and other lifestyle related habits, we aim to evaluate whether the adherence to a Mediterranean dietary pattern, the adoption a physically active lifestyle, the abstinence of smoking and the control of psychological factors is associated with less severe disease and a better short and long term outcome. We anticipate that the completion of the follow up and the analysis of the results will provide current, novel and valuable information about the epidemiology of ACS, in Greece, as well as the role of clinical, psychosocial and lifestyle characteristics on the prognosis of cardiac patients.
Abbreviations
ACS = acute coronary syndromes
CPK = creatinine posphokinase
MI = acute myocardial infarction
UA = unstable angina
ECG = electrocardiographic
CES-D = Center of Epidemiological Studies depression
BMI = body mass index
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CP, DBP = are the principal investigators of the study, had the concept and the design of the study, and wrote the paper, AA, YK, YM, SZ, PS, CS = contributed to the design of the study, GK = contributed to the data management and analysis
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors would like to present and thank the field investigators of the "GREECS" study: Dr. George Giannopoulos, Dr. Sophia Arapi, Dr. Theodoros Gialernios, Dr. Constandina Massoura, Dr. George Papanagnou, Dr. Antonis Karanasios, Dr. Lambros Rizos, Dr. Michalis Mparmparoussis, Dr. George Kassimatis, Dr. Skevos Sideris, Dr. Nick Daskalopoulos for their support in the clinical evaluation and Mr. Alexander Chalamandaris for the database management.
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| 15771779 | PMC555594 | CC BY | 2021-01-04 16:28:54 | no | BMC Public Health. 2005 Mar 16; 5:23 | utf-8 | BMC Public Health | 2,005 | 10.1186/1471-2458-5-23 | oa_comm |
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BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-6-121574829710.1186/1471-2474-6-12Research ArticleCross-cultural adaptation of the VISA-A questionnaire, an index of clinical severity for patients with Achilles tendinopathy, with reliability, validity and structure evaluations Silbernagel Karin Grävare [email protected]é Roland [email protected] Jon [email protected] Lundberg Laboratory of Orthopaedic Research, Dept of Orthopaedics, Göteborg University, Sahlgrenska University Hospital, Göteborg, Sweden2 Sportrehab – Physical Therapy & Sports Medicine Clinic, Göteborg, Sweden3 Dept of Orthopaedics, Sahlgrenska University Hospital, Göteborg, Sweden2005 6 3 2005 6 12 12 24 11 2004 6 3 2005 Copyright © 2005 Silbernagel et al; licensee BioMed Central Ltd.2005Silbernagel 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
Achilles tendinopathy is considered to be one of the most common overuse injuries in elite and recreational athletes and the recommended treatment varies. One factor that has been stressed in the literature is the lack of standardized outcome measures that can be used in all countries. One such standardized outcome measure is the Victorian Institute of Sports Assessment – Achilles (VISA-A) questionnaire, which is designed to evaluate the clinical severity for patients with Achilles tendinopathy. The purpose of this study was to cross-culturally adapt the VISA-A questionnaire to Swedish, and to perform reliability, validity and structure evaluations.
Methods
Cross-cultural adaptation was performed in several steps including translations, synthesis of translations, back translations, expert committee review and pre-testing. The final Swedish version, the VISA-A Swedish version (VISA-A-S) was tested for reliability on healthy individuals (n = 15), and patients (n = 22). Tests for internal consistency, validity and structure were performed on 51 patients.
Results
The VISA-A-S had good reliability for patients (r = 0.89, ICC = 0.89) and healthy individuals (r = 0.89–0.99, ICC = 0.88–0.99). The internal consistency was 0.77 (Cronbach's alpha). The mean [95% confidence interval] VISA-A-S score in the 51 patients (50 [44–56]) was significantly lower than in the healthy individuals (96 [94–99]). The VISA-A-S score correlated significantly (Spearman's r = -0.68) with another tendon grading system. Criterion validity was considered good when comparing the scores of the Swedish version with the English version in both healthy individuals and patients. The factor analysis gave the factors pain/symptoms and physical activity
Conclusion
The VISA-A-S questionnaire is a reliable and valid instrument and comparable to the original version. It measures two factors: pain/symptoms and physical activity, and can be used in both research and the clinical setting.
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Background
Achilles tendinopathy is a common overuse injury, especially among athletes involved in activities that include running and jumping [1-4]. Several studies report the incidence of Achilles tendon disorders in runners to be 6–18% of all injuries [3,5,6]. Most commonly afflicted are middle-aged men, but Achilles tendinopathy occurs in both men and women at various ages [2,4,7-9]. Common complaints are pain during and after physical activity, tenderness on palpation and morning stiffness [7-10]. Symptoms usually subside with decreased physical activity, but tend to return as soon as physical activity is increased [2]. With increased severity patients may also have pain during daily functional activities [7,10]. Achilles tendinopathy causes many patients to significantly decrease their physical activity level, with a potentially negative impact on their overall health and general well-being [2,3,7].
Despite the high incidence of Achilles tendon disorders there have been few randomized treatment studies in patients with Achilles tendinopathy [8,11-16]. It is also difficult to compare the results of studies as outcome measures vary widely. A universally used clinical outcome measure of the symptoms and function would help comparisons between treatments in various clinics and research studies, and could also be the basis of criteria for various treatments.
Robinson et al. [17] developed a questionnaire as an index of clinical severity of Achilles tendinopathy; the Victorian Institute of Sports Assessment – Achilles questionnaire (VISA-A). The VISA-A questionnaire is an easily self-administered questionnaire that evaluates symptoms and their effect on physical activity. It can be used to compare different populations with Achilles tendinopathy, and facilitate comparisons between studies. The VISA-A score has already been used to evaluate the outcome of treatment [16]. In the clinic, the VISA-A questionnaire can be used to determine the patient's clinical severity and provide a guideline for treatment as well as for monitoring the effect of treatment. In order to use the VISA-A questionnaire for non-english speaking patients it needs to be translated, culturally adapted and properly evaluated [18].
Therefore, the purpose of this study was to translate and culturally adapt the English VISA-A questionnaire to Swedish, to perform reliability and validity evaluations of the Swedish version, and to analyze the factor structure of the questionnaire.
Methods
To establish good face validity and content validity, the translation and cultural adaptation of the VISA-A questionnaire into Swedish was performed in several steps [18]. The English version was translated into Swedish independently by three people. All three were working in the medical field and had English as a second language. The three translations were synthesized into one Swedish version by a panel of experts consisting of four physical therapists who specialize in musculoskeletal disorders. The back translations of the Swedish version into English were performed by another three people. Two of the back translators were in the medical field (a sports medicine doctor and a physical therapist), and the third person was Swedish but has lived in the USA for many years. The panel of experts (same as above) then compared the original version with the back translations.
The panel of experts consolidated the various versions into one pre-final version of the VISA-A questionnaire – Swedish version (VISA-A-S). The pre-VISA-A-S was pilot tested on five patients and five healthy individuals. After pilot testing question 1 was made clearer by entering the minutes in the boxes.
The final version of the VISA-A-S (Additional file 1) was tested on both healthy individuals and patients with a diagnosis of Achilles tendinopathy. All subjects were given written information about the purpose and procedure of the study, and informed consent was obtained. Ethics approval was obtained from the Ethics Committee at the Medical Faculty, Gothenburg University, Sweden.
For test-retest evaluation, we recruited a convenience sample of 15 healthy individuals (Table 1), age 20–40 years. They completed the VISA-A-S questionnaire three times within two weeks. The questions were answered with respect to their right Achilles tendon.
Table 1 Summary of study populations Mean, standard deviation (SD) and 95 % confidence interval (CI) for age, duration of symptoms and VISA-A-S score for the study populations.
Age (years) Duration of symptoms (months) VISA-A-S Score
Mean SD 95% CI Mean SD 95% CI Mean SD 95% CI
Healthy (n = 15, 3 F, 12 M) 29.5 4.3 27.1 – 31.9 N/A N/A N/A 96 4 94 – 99
Reliability Group
Patients (n = 22, 8 F, 14 M) 45.4 15.5 38.6 – 52.3 55.5 134.7 6.3 – 57.4 50 24 40 – 61
Validity Group
Patients (n = 51, 19 F, 32 M) 43.1 14.5 39.0 – 47.2 31.8 90.8 6.3 – 57.4 50 23 44 – 56
(N/A = not applicable, F = females, M = men)
Fifty-one patients (Table 1), 39 to 47 years old, with Achilles tendinopathy, were included in the reliability evaluation for internal consistency and the validity evaluations. Twenty-two (Table 1) of the 51 patients also participated in a test-retest evaluation. Bilateral symptoms were reported in 15 of 51 patients (7 of the 22 in test-retest group). The patients were recruited from 11 physical therapy clinics throughout Sweden. The inclusion criteria were the same as in the original study [17]. The subjects had to be older than 18 and be able to give written consent. The subjects had to have a diagnosis of Achilles tendinosis, paratendinitis, or partial rupture with or without a retrocalcaneal or Achilles bursitis. The diagnosis was based on patient history and the physical therapists' clinical findings. Subjects with total Achilles tendon rupture and pregnant or nursing women were excluded. At their first physical therapy visit, all patients completed questionnaires regarding their injury, physical activity [19], tendon injury according to Stanish [1], and one VISA-A-S questionnaire for each leg. For the patients with bilateral symptoms, the side with the lowest VISA-A-S score, or if the score was equal, the side with the longest duration of symptoms was chosen for the evaluations. The 22 patients that participated in the test-retest evaluation completed the VISA-A-S questionnaire a second time within a week of the first visit.
Construct validity of the VISA-A-S was tested according to the original article on the VISA-A English version [17]. The results from the 51 patients who completed the VISA-A-S questionnaire were compared with the results from the tendon grading system by Stanish et al. (1984). The results from the VISA-A-S questionnaire for patients with Achilles tendinopathy were also compared with the results of healthy individuals.
Criterion validity of the VISA-A-S questionnaire was evaluated by comparing the results of our patients (n = 51) with the results of the two patient groups, the non-surgical group (n = 45) and surgical group (n = 14), in the original article by Robinson et al. (2001). The results of the healthy individuals in our study were also compared with the results from the healthy individuals in the original study.
The structure of the VISA-A-S questionnaire was evaluated with a factor analysis.
Statistical analysis
All data were analysed by SPSS 11.5 for Windows. Descriptive data are reported as mean, standard deviation and 95% confidence interval.
Test-retest data was analysed by Pearson's r, as performed for the VISA-A English version [17]. Inter-Class Correlation Coefficient (ICC) and Wilcoxon paired test for non-parametric data was also calculated for test-retest data since the questionnaire presents ordinal data. Internal consistency was assessed by calculation of Cronbach's alpha.
Comparison of VISA-A-S with Stanish et al. (2000) tendon grading system was performed by calculating the Spearman's rank correlation coefficient for non-parametric data. VISA-A-S scores for the healthy group and the patient group were compared using the Mann Whitney U test. For comparison of the VISA-A-S with the VISA-A, a two sample t-test was used since only means and standard deviations, and no raw data, were available from the results in the original study [17]. The level of significance was set at p < 0.05.
A principal axis factoring with varimax rotation, eigenvalue over 1.0, was applied for evaluation of the structure of the questionnaire.
Results
Table 2 summarizes the reliability evaluation of the VISA-A-S questionnaire. The VISA-A-S showed good test-retest reliability for healthy individuals (Pearson's r > 0.88, ICC > 0.88) and for patients (Pearson's r = 0.89, ICC = 0.89). There was no significant difference between the scores on test days 1 and 2. When analyzing each question separately (Table 3) the results showed good reliability. For questions 3 and 6, however, there were significant differences (p = 0.007 and p = 0.03 respectively) between the two test occasions. The internal consistency for the 8 questions in the VISA-A-S was 0.77 as measured with Cronbach's alpha.
Table 2 Summary of reliability tests of VISA-A-S score
Pearson's r ICC Wilcoxon Cronbach's alpha
Healthy (n = 15)
Test-retest 1–2 0.88 0.88 0.07
2–3 0.99 0.99 0.32
1–3 0.90 0.90 0.07
Patients (n = 22)
Test-retest 1 week 0.89 0.89 0.051
Patients (n = 51)
Internal consistency 0.77
Table 3 Test-retest scores for the 8 questions in the VISA-A-S score (patients, n = 22)
Pearson's r ICC Wilcoxon
Question 1 0.74 0.72 0.184
Question 2 0.81 0.81 0.308
Question 3 0.89 0.86 0.007
Question 4 0.78 0.78 0.269
Question 5 0.71 0.71 0.793
Question 6 0.68 0.66 0.032
Question 7 0.87 0.86 0.577
Question 8 0.79 0.79 0.721
The VISA-A-S score correlated significantly with the tendon grading system by Stanish et al. (2000) (Spearman's r = -0.68; p < 0.01). The patients with Achilles tendinopathy had a significantly lower score (p < 0.0001) compared with the healthy individuals. The mean VISA-A-S score for patients in the present study was significantly (p < 0.01) lower than the mean VISA-A score for the non-surgical group in the original article by Robinson et al. (2001). When comparing the VISA-A-S score for patients in the present study with the VISA-A score of the surgical group in the original study [17] there was no significant difference (p > 0.2). There was no significant (p > 0.2) difference between the healthy individuals score on the VISA-A-S when comparing with the healthy individuals in the original article [17].
The factor analysis revealed two factors of importance (eigenvalue over 1.0): pain/symptoms (questions 1–6) and physical activity (questions 7 and 8).
Discussion
A widely-used clinical outcome measure for patients with Achilles tendinopathy would help comparisons between treatments in various clinics and research studies. The VISA-A questionnaire is an easily self-administered questionnaire. Since research is performed in various countries it is important to properly translate, culturally adapt and evaluate instruments like a questionnaire in order to be able to compare the results [18].
This study demonstrates that the Swedish version of the VISA-A questionnaire has good reliability and validity. With careful translation and cultural adaptation, we established good face validity and content validity. The test-retest reliability and the internal consistency were considered good. A significant and strong correlation between the VISA-A-S and the tendon grading system by Stanish et al. (2000) indicates good construct validity. The comparison of the results of the patients and healthy individuals in the present study with the results of the non-surgical group, the surgical group and the healthy individuals, as reported in the original article by Robinson et al. (2001), indicates good criterion validity.
The factor analysis gave the two factors: pain/symptoms (questions 1–6) and physical activity (questions 7 and 8), strongly confirming that the questionnaire is valid for evaluating the patient's symptoms and its effect on physical activity. The factor analysis and an internal consistency of 0.77 as measured by Cronbach's alpha indicate that no question should be excluded.
We did not include a separate group of pre-surgical patients as in the original study by Robinsson et al. (2001). This is because the advances in the non-surgical rehabilitation of patients with Achilles tendinopathy during recent years have resulted in markedly reduced number of patients awaiting surgery. The patients in the present study can therefore be viewed as representing patients from both groups (surgical and non-surgical) of patients used in the original study [17]. This would explain why the patient group in the present study had a significantly lower score when compared to the non-surgical group in the original article [17].
The one week duration between the two tests in the test-retest reliability part was somewhat long. This duration was chosen because this is the usual time that lapses between a patient's first and second visit with the physical therapist. This may explain why two of the questions differed significantly between test-days. During this week the patients have met with their physical therapist, which may have caused the patients to change their view on their symptoms and physical ability.
Good criterion validity indicates that the Swedish version and the English version of the VISA-A questionnaire evaluate the same aspects of clinical severity in patients with Achilles tendinopathy. It can, thus, be expected that similar scores in the two versions indicate the same index of severity in patients with Achilles tendinopathy.
The 11 physical therapy clinics throughout Sweden, which participated in this study, all reported that the questionnaire was easily administered, and required a minimum of communication between the physical therapist and the patient. The physical therapists perceived the questionnaire as a good clinical tool and useful when treating patients with Achilles tendinopathy.
A review of the literature in regards to treatment of Achilles tendinopathy yielded only a few randomized treatment trials [8,11-16]. There are however prospective and retrospective cohort studies as well as case studies [4,20-25]. Comparing the results of all these studies is difficult since the outcome measures vary. A questionnaire like the VISA-A and VISA-A-S which gives an index of the clinical severity for patients with Achilles tendinopathy, and also is easily administered and easy to fill out, could be very helpful in the future. Paavola et al. (2002) noted that few randomized intervention studies had a follow-up longer than twelve months. The VISA-A and VISA-A-S questionnaire can be filled out easily and quickly and require a minimum of assistance during follow-up and could therefore be very helpful for long-term follow-ups. The VISA-A questionnaire has successfully been used as an outcome measure in a randomized double-blind, placebo-controlled treatment trial [16]. Currently we are evaluating the VISA-A-S questionnaires responsiveness over time in a randomized treatment study for patients with Achilles tendinopathy.
Conclusion
This study has carefully performed the recommended steps for cross-cultural adaptation and has performed reliability and validity evaluations of the new version. The factor analysis, measuring the two factors: pain/symptoms and physical activity, reinforces that the VISA-A questionnaire can be used as an index of clinical severity. The present study culturally adapts and validates the VISA-A-S questionnaire (Additional file 1) for the Scandinavian countries and it is comparable to the original version.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
KGS conceived of the study, participated in its design, performed data acquisition, analyzed and interpreted the data and drafted the manuscript
RT conceived the study, participated in its design, interpreted the data and helped to draft the manuscript.
JK participated in the study design, interpreted the data and helped to draft the manuscript.
All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
VISA-A-S questionnaire The Swedish version of the VISA-A questionnaire in MICROSOFT WORD format.
Click here for file
Acknowledgements
The authors wish to thank the participating physical therapists for their help in collecting the data. This study was supported by grants from the Swedish National Centre for Research in Sports.
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| 15748297 | PMC555595 | CC BY | 2021-01-04 16:32:04 | no | BMC Musculoskelet Disord. 2005 Mar 6; 6:12 | utf-8 | BMC Musculoskelet Disord | 2,005 | 10.1186/1471-2474-6-12 | oa_comm |
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BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-6-161576928710.1186/1471-2474-6-16Research ArticleA model of impairment and functional limitation in rheumatoid arthritis Escalante Agustín [email protected] Roy W [email protected] Rincón Inmaculada [email protected] Division of Rheumatology and Clinical Immunology, Department of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA2005 15 3 2005 6 16 16 17 8 2004 15 3 2005 Copyright © 2005 Escalante et al; licensee BioMed Central Ltd.2005Escalante 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
We have previously proposed a theoretical model for studying physical disability and other outcomes in rheumatoid arthritis (RA). The purpose of this paper is to test a model of impairment and functional limitation in (RA), using empirical data from a sample of RA patients. We based the model on the disablement process framework.
Methods
We posited two distinct types of impairment in RA: 1) Joint inflammation, measured by the tender, painful and swollen joint counts; and 2) Joint deformity, measured by the deformed joint count. We hypothesized direct paths from the two impairments to functional limitation, measured by the shirt-button speed, grip strength and walking velocity. We used structural equation modeling to test the hypothetical relationships, using empirical data from a sample of RA patients recruited from six rheumatology clinics.
Results
The RA sample was comprised of 779 RA patients. In the structural equation model, the joint inflammation impairment displayed a strong significant path toward the measured variables of joint pain, tenderness and swelling (standardized regression coefficients 0.758, 0.872 and 0.512, P ≤ 0.001 for each). The joint deformity impairment likewise displayed significant paths toward the measured upper limb, lower limb, and other deformed joint counts (standardized regression coefficients 0.849, 0.785, 0.308, P ≤ 0.001 for each). Both the joint inflammation and joint deformity impairments displayed strong direct paths toward functional limitation (standardized regression coefficients of -0.576 and -0.564, respectively, P ≤ 0.001 for each), and explained 65% of its variance. Model fit to data was fair to good, as evidenced by a comparative fit index of 0.975, and the root mean square error of approximation = 0.058.
Conclusion
This evidence supports the occurrence of two distinct impairments in RA, joint inflammation and joint deformity, that together, contribute strongly to functional limitations in this disease. These findings may have implications for investigators aiming to measure outcome in RA.
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Background
Physical disability is an important outcome of rheumatoid arthritis (RA) [1,2]. The American College of Rheumatology preliminary definition of improvement in RA, used primarily to assess the short term response to medical interventions, includes disability as one of its seven key outcomes [3]. An understanding of disability in RA requires an appreciation of the interrelationship between the biology of the disease, the person and his or her psychology, and the social environment [4-7].
Inquiries into physical disability in RA, needing to weigh the influence of numerous variables interacting over time in complex ways, benefit from a conceptual framework, or model [8]. A model informs research by clarifying the relationships between variables, and facilitates communication of ideas related to the research in question [9]. In studying the development of disability in RA, we proposed a theoretical framework [10], which we based on the disablement process that occurs with aging [8]. Initially based on purely theoretical grounds, our model proposed strategies to quantify the four sequential stages of the main disease-disability pathway in RA: pathology → impairment → functional limitation → physical disability [10]. A useful device to facilitate the understanding of these stages of disablement, is to think of them in terms of the level at which they occur, and can be quantified. Thus, pathology occurs at the level of molecules, cells, or tissues, and is measured using tests such as the erythrocyte sedimentation rate, the C-reactive protein concentration, cytokine expression patterns, or images of the joints obtained with X-ray or MRI. Impairments are dysfunctions or structural abnormalities that occur at the level of organs or organ systems. They include signs and symptoms of disease such as pain, morning stiffness, joint tenderness, swelling and deformity. Functional limitations are restrictions in basic physical or mental actions, and they involve the whole person. Although they can be measured in a number of different ways, we have chosen to use performance-based functional tests such as the grip strength, walking velocity and the timed shirt-button test to measure functional limitations [11]. Disability involves difficulty with a physical or mental activity, within a social context. The measurement level therefore should include the person and the societal environment. We have used self report measures of physical disability such as the Health Assessment Questionnaire, or the physical function scale of the SF-36, to measure physical disability [12].
As noted above, we based our initial model and its proposed measurement strategies on purely theoretical grounds [10]. Since its publication, however, we have provided initial empirical evidence to support two of the model's main disease-disability pathway stages, and our approach to measuring them, using data from a clinical sample of RA patients. Those two stages are functional limitation and physical disability [11-14]. In the present report, we show additional data to support our definition of impairment in RA.
Methods
Patients
From 1996 to 2000, we enrolled consecutive patients meeting the 1987 RA criteria [15], into a study of the disablement process in RA We have described our sample in previous publications [13,14]. Here, we will show cross-sectional results obtained during the recruitment evaluation of each participant.
Settings
We recruited patients from six outpatient rheumatology clinics in San Antonio, Texas: 1) An Army Medical Center, 2) An Air Force Medical Center, 3) A private, university-based clinic, 4) A community-based, seven-rheumatologist private practice, 5) A county-funded clinic and, 6) A Veterans Administration clinic. All evaluations were done on location in these facilities.
Data collection procedures
Our study was approved by the Institutional Review Board of each of the clinical facilities were we went to recruit patients, and all patients gave written, informed consent. A physician or a research nurse, assisted by a trained research associate, conducted evaluations at the clinic where the patient was recruited. The evaluation lasted approximately 90 minutes, and consisted of a comprehensive interview, physical examination, review of medical records, and laboratory and X-ray tests. Interviews were conducted in either English or Spanish, as preferred by patients.
Data elements
Impairments
We measured impairments using self-report and physical examination. We used a validated, one-page joint mannequin for patients to mark the joints that were painful or swollen [16]. This variable is expressed as the painful joint count. A physician or research nurse, trained in joint examination techniques, assessed 48 joints in each patient for the presence or absence of tenderness or pain on motion, swelling or deformity. Each of these variables is expressed as a count for the number of affected joints [17].
Functional limitations
We measured functional limitations using the following performance-based rheumatology function tests: 1. Grip strength. We measured grip strength with a hand held JAMAR® Dynamometer (Sammons Preston, Bolingbrook, Illinois). In a sitting position, with the elbow held at 90 degrees, and the forearm supported on a flat horizontal surface, patients were asked to squeeze the handle with as much as strength as possible. Three repetitions from each hand were recorded, in kilograms. The mean value of all repetitions for both hands is shown; 2. Walking velocity. Starting from a standing position, patients were asked to walk at their usual pace, for a distance of 50 feet, or 25 feet if they had difficulty covering the full distance. No effort was made to conceal the stopwatch used to time the patients. Results are expressed in feet per second. Patients unable to walk were assigned a velocity of 0 feet per minute; 3. Timed button test. Patients were asked to don and fasten the front buttons of a standard eight-button, men or women's extra-large, denim shirt (Wal-Mart, San Antonio, Texas). A stop watch was activated when the patient took the shirt as it was offered by the examiner, and stopped when the last button was fastened. This test quantifies the performance and large and small upper extremity joints. Results are expressed as buttons per minute. Patient unable to don the shirt were assigned a value of 0 buttons per minute.
Analysis
We began the analysis by inspecting summary statistics and histograms of all study variables. Skewed variables were square root transformed toward normality. We specified a structural equation model (SEM) of impairment and functional limitation in RA [18]. We hypothesized that impairment in RA is represented by two distinct constructs. The first of these is characterized by joint inflammation & pain, the second by joint deformity. Each of these two constructs is represented by a latent variable in the model. The inflammation-pain construct is measured by a physical examination joint count for tenderness and swelling [17], and a self-reported joint count of painful joints [16]. The joint deformity construct is measured by the deformed joint count [17]. Because a latent variable is more reliably measured by two or more measures, we disaggregated the deformed joint count into upper and lower extremities counts, and a count for other joints. The latter included the temporo-mandibular, acromio-clavicular and sterno-clavicular joints. We assessed the influence of joint impairment on functional limitation, the next stage in the RA disablement process, by positing a direct path from the former to the later in the SEM. As we have shown previously [11], we defined functional limitation as a latent variable measured by three rheumatology function tests: grip strength, shirt-button speed and walking velocity over 50 feet [11]. We used a maximum likelihood procedure to estimate the model parameters. Once these were estimated, we examined modification indices seeking parameters not estimated in the initial model, that may increase model fit if added to the model. Here, it is important to keep in mind that structural equation modeling is not meant to be a data-driven technique, rather, it should be a theory-driven one [18]. We therefore only considered parameters that would not substantially change the basic structure of the model, i.e. one with two impairment latent variables and one functional limitation latent variable. Also after specifying the initial model, we evaluated a the effect of adding a direct path from the joint inflammation to the joint deformity latent variables. We quantified the degree of fit of the hypothetical model to our empirical data using the comparative fit index (CFI), the normed fit index (NFI), and the root mean square error of approximation (RMSEA). Interpretation of these fit indices is subjective, and there are no universally accepted guidelines. Generally, fit index values ≥ 0.95 are considered to indicate acceptable fit of a model to data [18]. RMSEA values ≤ 0.05 indicate close fit, values ≤ 0.08 indicate reasonable error of approximation [19]. We used the parameter estimates for the latent variables to compute their values and plot frequency distributions for each one. We used the Amos 5.0 statistical pathway package to specify and test the SEM. (SmallWaters Corporation, Chicago, Illinois).
Results
We recruited 779 patients from 1996 to 2000. We have described the clinical characteristics of the study sample in earlier publications [13,14]. Briefly, from 1996 to 2000, we recruited consecutive patients who met the 1987 criteria for the classification of RA [15], from six rheumatology clinics in San Antonio, Texas. In addition to having RA, patients had to be 18 years of age or older. No other inclusion or exclusion criteria were applied. The median age of the patients was 57 years (min to max 19 to 90), 70% were women and 56% were Hispanic. The median number of years of formal education was 12 (min to max 0 to > 16), 21% were working full- or part-time, 27% were disabled from work. The median disease duration was 8 years (min to max 0 to 52). Mean joint counts were 15 for tender, 7 for swollen and 10 for deformed. Subcutaneous nodules were present in 30%, and rheumatoid factor in 89%. The joint counts displayed skewed distributions. Square root transformation reduced skewness from tender, swollen and painful joint counts, but not from the deformed joint counts. We used the transformed values for the former three variables, but used the unstransformed deformed joint count in the SEM.
A graphical display of the proposed model is shown in Figure 1. We hypothesize that two distinct impairments take place in RA, each represented by a latent variable in the SEM. The two impairments, joint inflammation and joint deformity, are shown as ovals on the left side of Figure 1. Joint inflammation is measured by the extent of joint tenderness, joint swelling and joint pain, shown in boxes in Figure 1. Table 1 lists the path coefficients from joint inflammation to the measured variables. All three were large, and statistically significant. The standardized coefficient was > 0.5 for each measured variable, suggesting the a standard deviation change in the latent variable of joint inflammation, is associated with a change in the measured variables of at least one half standard deviation (Table 1).
Figure 1 Identification diagram of a structural equation model of the relationship between the stages of impairment and functional limitation in rheumatoid arthritis. Two types of impairment, joint inflammation and joint deformity are shown as ovals on the left. Measurements for these latent variables include joint tenderness (JT), joint swelling (JS) and joint pain (JP), for joint inflammation; and joint deformities. We disaggregated joint deformities into upper limb (DUL), lower limb (DLL) joints, and other joints (DOJ). Several of the parameters were constrained to enable estimation. Circles represent residuals or disturbance terms, for each variable. See Table 1 for parameter estimates.
Table 1 Parameter estimates from a structural equation model of joint impairment on functional limitations in RA.
Parameter estimates
Direct Paths Unstandardized SE P-value Standardized
Joint inflammation → Functional limitation -.377 0.029 ≤ 0.001 -0.576
Joint deformity → Functional limitation -0.088 0.007 ≤ 0.001 -0.564
Joint tenderness → Joint inflammation 1.000 0.872
Joint pain → Joint inflammation 0.837 0.046 ≤ 0.001 0.758
Joint swelling → Joint inflammation 0.445 0.034 ≤ 0.001 0.513
Upper limb joint deformity → Joint deformity 1.000 0.849
Lower limb joint deformity → Joint deformity 0.478 0.029 ≤ 0.001 0.785
Other joint deformity → Joint deformity 0.011 0.001 ≤ 0.001 0.308
Functional limitation → Grip strength 1.000 0.773
Functional limitation → Walking velocity 0.958 0.051 ≤ 0.001 0.740
Functional limitation → Button speed 0.046 0.002 ≤ 0.001 0.757
Correlations*
Upper extremity deformity ←→ Other joint deformity -0.22 --- 0.004 ---
Walking velocity ←→ Shirt button speed 0.274 --- ≤ 0.001 ---
Parameters estimated using maximum likelihood with Amos 4.0. See Figure 1 for a diagram of the specification model.
* Correlation shown are between the residuals of the measured variables.
The latent variable of joint deformity is measured by the deformed joint count, which we disaggregated into counts for the joints in the upper and lower extremities, and other joints. The path coefficients for upper and lower extremity deformities were stronger than that of the other joints (i.e. temporo-mandibular, acromio-clavicular and sterno-clavicular joints), but all three remained significant Table 1).
To obtain a better understanding of the properties of the latent variables, we used the path coefficients from the two impairment latent variables to compute their estimated values, generating a new variable for each one. Figures 2 and 3 show the distribution of the two impairment latent variables, after rescaling them variables to vary from 0 to 100. Joint inflammation displayed a characteristic Gaussian distribution (Figure 2). This was not the case for joint deformity, however, which remained skewed by the a substantial number of patients who lacked deformities on physical examination (Figure 3).
Figure 2 Frequency distributions of the joint inflammation (JI) impairment latent variable. This was computed from JI = JT1/2 + JS1/2 × 0.445 + JP1/2 × 0.837, where JT = joint tenderness, JS = joint swelling, and JP = joint pain. Weights were estimated using maximum likelihood with Amos, after constraining the coefficients for JT and DUL to 1. The latent variable was then rescaled to vary from 0 to 100.
Figure 3 Frequency distributions of the joint deformity (JD) impairment latent variable. This was computed from JD = DUL + DLL × 0.478 + DOJ × 0.011, where DUL = deformity upper limb, DLL = deformity lower limb, and DOJ = deformity other joints. The weights for the equation were estimated using maximum likelihood with Amos, after constraining the coefficients for JT and DUL to 1. The latent variable was then rescaled to vary from 0 to 100.
In the structural equation model, both of the impairment latent variables displayed strong direct paths toward functional limitations. The standardized coefficients were -0.5 or less, representing a change of more than half a standard deviation in the functional limitations for every standard deviation change in the impairments (Table 1). The squared multiple correlation of functional limitation was 0.65, suggesting that impairments explain 65% of functional limitation's variance.
The initial CFI and NFI of the model were both 0.95, suggesting a close fit of the model to the data. The initial RMSEA was 0.07, suggesting reasonable fit [19]. Modification indices suggested a number of potential parameters that could increase model fit if added to the estimation model. Most of these did not make clinical sense, or ran counter to the a priori model we were testing, and we therefore did not specify them. However, we noted two covariances that would increase model fit without altering the overall structure of the model. The first of these was between the residuals of the measured variables for deformities in the upper extremity and deformities in other joints; the second, between residuals for walking velocity and the timed button test. After specifying these two covariances, CFI increased to 0.975, NFI to 0.966, and RMSEA decreased to 0.058. Also post hoc, we tested a direct path between joint inflammation and joint deformity, but the resulting coefficient was small and did not reach statistical significance. We therefore omitted an inflammation → deformity path from the final model.
Discussion
Several models have been proposed to study disability in the general population [8,9,20], and can be applied to study RA and other types or arthritis [10,21,22]. The World Health Organization (WHO) has developed the International Classification of Functioning, Disability and Health (ICF), with a corresponding set of core measures for a number of chronic conditions [21]. One set of ICF core measures has been proposed for RA [21]. It includes a comprehensive range of body structures and functions, activities and participation that can be assessed in studies of disability in RA [21]. The ICF classifies its components into categories that are analogous to the disablement process stages: body structures and functions in the ICF are analogous to the stages of pathology and impairment, while activities and participation correspond to functional limitations and disability in the disablement process [22].
We chose the disablement process over other disability models, among other reasons, because it considers the stages in disablement as an explicit sequence of linked events, one leading to the next. Rather than suggesting specific functions or activities to measure, it offers broad definitions of each stage, leaving it up to investigators to find ways to test them. Several of the variables we include in the present analysis are represented in the ICF classification, including joint pain and deformity, and walking. Variables not represented in the ICF, but which we did obtain, include joint tenderness and swelling, and grip strength. Our measurement of the stage of disability, described elsewhere, has considerable homology with that of the ICF [12]. The most important difference between our RA disablement model and the ICF classification, is that the latter does not contemplate the stage of pathology with sufficient detail for our aim, to map pathways from disease to disability in RA.
We used the disablement process framework to build a model of impairment and functional limitation in RA [8]. Impairments occur when pathology at the level of the molecule, cell or tissue, crosses the clinical horizon causing symptoms or signs of disease. They represent derangement of structure or function at the organ level. Consistent with this framework, we used the articular manifestations of RA, joint pain, tenderness, swelling and deformity, to measure impairment. It should be noted that we consider impairment to be theoretical construct that cannot be directly quantified. We studied it as a latent variable, the articular signs and symptoms listed above serving as the tools we used to tap into the impairment construct.
The concept of impairment as a stage in the disablement process is not intended to oversimplify the anatomical or physiological derangements that occur within that stage. In fact, the derangements can be quite complex, depending on the nature of the initial pathology, and the organ system under study. In the case of rheumatoid joints, the initial pathology can be broadly classified into two discrete, but related groups: inflammation and damage [10]. It should be noted that we did not include measures of these two pathological processes here. However, because impairments are tied to their underlying pathology, we posited two types of impairments, one for each type of joint pathology. The first type is related to inflammation in the joints, and the other to damage. The former, we measured using tender, swollen and painful joint counts, the latter, using the deformed joint count.
Both impairment latent variables displayed strong, statistically significant path coefficients toward the measured variables, providing evidence that the measures we chose adequately tap into the proposed impairments. In the diagram of our model, these paths are shown as arrows from the latent to the measured variables, to indicate that it is the joint inflammation or damage (both of them latent variables), that are "causing" the joint signs and symptoms that we are able to measure. Not included in our final model because it did not reach statistical significance, was a path from joint inflammation to deformity. This is likely because we restricted the present analysis to the stages of impairment and functional limitation. Although there is considerable evidence that inflammation leads to damage in RA joints, the link between the two processes occurs during the stage of pathology, not impairment. We expect to find a strong link between inflammation and damage when we extend our analyses to include pathology measures such as the erythrocyte sedimentation rate, C-reactive protein, joint erosions and joint space narrowing.
According to disablement theory, impairments lead to functional limitations [8,10]. We expected that this should translate into a link between variables representing these two stages. We thus posited direct paths from each type of impairment to a latent variable representing functional limitations. We have shown previously that functional limitations can also be represented as a latent variable [12], and that it can be measured satisfactorily using the performance-based rheumatology function tests, grip strength, walking velocity and the shirt-button test [12]. We found strong path coefficients from both impairment latent variables to the functional limitation latent variable (Figure 1). Moreover, the impairments accounted for 65% of the variance in functional limitations. Both these findings provide additional support for our definition of rheumatoid impairments and functional limitations.
The disablement process was proposed as a framework to aid investigators in their efforts to understand the development of disability in aging, and in specific disease states [8]. The framework's acknowledgement of the sequential nature of a disease's manifestations make it especially informative to inquiries into disability in chronic diseases. It is worthy of attention that the disablement model can also be applied advantageously to outcomes other than disability.
One of our goals with this and earlier efforts to model RA's stages [10-12], is to improve the current interpretation of the disease's outcome. Current systems used to assess RA's outcome mix stages of the disease process, without regard to their sequential nature, or omit some stages altogether [3,23]. For example, the improvement criteria of American College of Rheumatology (ACR), define response on the basis of the ACR's core set of RA disease activity measures [24]. These measures, although empirically tested in clinical trials, were adopted without reference to an explicit model of the disease. The improvement criteria include measures of pathology (i.e. the erythrocyte sedimentation rate or the C-reactive protein), impairment (i.e. the tender and swollen joint counts, global assessment of disease activity, pain scale), and disability (i.e. the Health Assessment Questionnaire). They do not include measures of functional limitation [3].
The success with which the ACR and similar response measurement systems have been used in clinical trials, does not preclude the possibility that they could be improved [25]. Mixing or omitting disease stages may dilute a response measurement system's ability to detect the effect of treatments targeted at the early stages of the disease process. Although treatments that are primarily anti-inflammatory may indeed affect late disease stages such as physical disability, their effect is indirect, mediated through their primary effect on the inflammatory process. We have proposed what we believe would be a more rational approach to outcome assessment, using the stages of the disablement model to inform the selection of outcome measures [10]. Thus, measures of pathology and impairment would best capture response to anti-inflammatory therapies, while measures of functional limitation would best capture response to joint surgery or other rehabilitation interventions [10]. Empirical data to test our proposal would be of great interest.
Certain constraints apply to the interpretation of our findings. The maximum likelihood estimator that we used for SEM assumes multivariate normality, a requirement that is not strictly met by some of the variables we used in this analysis. Non-normality may affect standard errors, and thus significance testing, about the parameter estimates, albeit not the value of the parameters themselves. The overall structure of the model we propose, i.e. two distinct impairments linked to functional limitations, is thus likely to be unaffected by this deviation from assumptions. The patient sample we studied is sufficiently large that the potential deleterious effect of non-normality on the significance of the path coefficients may be offset. It should also be noted that data we used here to test the impairment → functional limitation relationship are cross-sectional. The sequential link between the two stages we propose has face validity in that joint tenderness, swelling and deformity are causes, not consequences, of diminished grip strength, walking velocity and shirt button speed. Nevertheless, confirmation of our findings in a longitudinal dataset would strengthen the evidence for the model.
Conclusion
We conclude that two distinct impairments occur in RA, one characterized by signs and symptoms of joint inflammation, the other by joint deformity. Both of these contribute substantially to the functional limitations that occur in this disease.
List of abbreviations
RA = Rheumatoid arthritis;
ÓRALE = Outcome of rheumatoid arthritis longitudinal evaluation;
CFI = Comparative fit index;
NFI = Normed fit index;
RMSEA: Root mean square error of approximation;
SEM: Structural equation model;
ACR = American College of Rheumatology
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
AE designed and obtained funding for the study, directed the statistical analysis, and drafted initial and final versions of the manuscript; RWH performed the statistical analysis and edited the manuscript; IDR designed and obtained funding for the study, supervised its implementation, and edited the manuscript.
Table 2 Squared multiple correlations of impairments and functional limitations
VARIABLE R2
Joint Deformity‡ 0.000
Joint Inflammation‡ 0.000
Functional Limitation‡ 0.650
Upper limb joint deformities 0.617
Lower Limb Joint Deformities 0.722
Other joint Deformities 0.095
Shirt-button time 0.574
Walking velocity 0.547
Grip strength1/2 0.598
Tender joint count1/2 0.761
Swollen Joint Count1/2 0.263
Painful Joint Count1/2 0.574
Posited relationships between variables are shown graphically in Figure 1.
‡ Identifies latent variables
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This research was supported by an Arthritis Investigator Award and a Clinical Science Grant from the Arthritis Foundation; and NIH grants RO1-HD37151, K24-AR47530, K23-HL004481, and grant M01-RR01346 for the Frederic C. Bartter General Clinical Research Center.
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| 15769287 | PMC555596 | CC BY | 2021-01-04 16:32:05 | no | BMC Musculoskelet Disord. 2005 Mar 15; 6:16 | utf-8 | BMC Musculoskelet Disord | 2,005 | 10.1186/1471-2474-6-16 | oa_comm |
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BMC Med Inform Decis MakBMC Medical Informatics and Decision Making1472-6947BioMed Central London 1472-6947-5-41574353510.1186/1472-6947-5-4Research ArticleModeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance Brillman Judith C [email protected] Tom [email protected] David [email protected] Edward [email protected] Rick [email protected] Edith [email protected] Department of Emergency Medicine, MSC10 5560, 1 University of New Mexico, Albuquerque NM 87131-0001, USA2 Mail Stop F600, Los Alamos National Labs, Los Alamos, New Mexico 87545, USA3 Mail Stop T006, Los Alamos National Labs, Los Alamos, New Mexico 87545, USA4 Mail Stop F607, Los Alamos National Labs, Los Alamos, New Mexico 87545, USA2005 2 3 2005 5 4 4 22 9 2004 2 3 2005 Copyright © 2005 Brillman et al; licensee BioMed Central Ltd.2005Brillman 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
Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC).
Methods
We collected ED CCs from 2/1/94 – 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 – 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real).
Results
Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets.
Conclusion
We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results.
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Background
Traditional public health surveillance for specific conditions is unlikely to quickly identify a disease outbreak. Emergency department (ED) data appears to have the potential for more timely disease surveillance [1-6]. In non-traditional surveillance, signs of an outbreak might include an increase in ED visits for respiratory, gastrointestinal or neurologic chief complaints (CC).
Crucial to any surveillance is an understanding of normal patterns in the data. Utilization patterns in the ED are thought to be difficult to predict, due to large variability that arises in part because EDs are required to medically evaluate and stabilize everyone who requests care. Therefore, ED visit rates cannot be controlled by insurers, institutions or policies.
Many "drop in" surveillance systems have been hampered by a lack of knowledge of baseline activity [7]. Other systems have used short-term moving averages of the recent past to predict current usage [8], but this does not provide optimal performance when day-of-the-week or seasonal effects are smoothed away by the averaging.
Some "active" ED surveillance systems provide on-going data collection. The EMERGEncy ID NET [9] and RSVP [10] function as sentinel surveillance systems where data from a small sample used may not represent the overall occurrence of disease. If data collection is inconsistent, it does not provide reliable information about syndrome incidence.
This paper reviews our experience with an operational near-real-time surveillance system, the Bio-Surveillance Analysis, Feedback, Evaluation and Response (B-SAFER) system. B-SAFER is the result of collaboration between the Los Alamos National Laboratory, the University of New Mexico Health Sciences Center, and the New Mexico Department of Health. Medical surveillance systems such as B-SAFER require considerable expertise in computer science and systems integration for their design and architecture, to comply with security and privacy issues, and to ensure timely flow of information [11,12]. These systems also require medical and epidemiological expertise to identify appropriate items to monitor for anomalous events and to understand their significance.
Methods
Setting
This observational study uses data from the Emergency Center of the University Hospital, Albuquerque NM (UH), a tertiary-care county-university health sciences center. The Emergency Center includes pediatric and adult emergency departments, an urgent care center, a trauma center and an observation unit. There are roughly 200 patient visits per day, or 73,000 per year, representing 36 % of Emergency Department visits in Albuquerque. This study was approved by the Institutional Review Boards of the University of New Mexico Health Sciences Center and Los Alamos National Laboratory.
Data stream
The data is from the computerized ED patient tracking system in place since 1994. Data in the system includes: date and time of arrival and discharge, age, sex, chief complaints, discharge diagnoses and disposition. CCs are recorded by the nurse at the time of triage and entered into the system by a clerk. The clerk may select from a drop-down menu of complaints or may enter the complaints as free text. The menu option is rarely used because clerks find free-text entry more flexible and convenient.
We group daily CC counts into seven categories: respiratory, gastrointestinal (GI), undifferentiated infection (UDI), lymphatic, skin, neurological, and "other" (Table 1). The "other" category includes all visits except those in the first six categories. These grouping categories are also used by other surveillance programs, such as ESSENCE (Walter Reed Army Institute of Research) and the Real-time Outbreak Detection System ((RODS, University of Pittsburgh) [11,13,14]. Our grouping scheme is provided in Table 1.
Table 1 B-SAFER dictionary for matching chief complaints to body systems
Respiratory Gastro-intestinal Neurologic Skin Lympatic UDI (undifferentiated infection)
Breath
Bronchiolitis
Chest congestion
Chest pain
Cold Congested
Congestion
Cough
Croup
Flu
Headache
Laryngitis
Pneumonia
Respiratory
Sinus
Stuffy nose
Throat Abdominal Pain
Abdomen/back pain
Abdomen pain
Abdominal cramps
Abdominal pain
Blood in stool
Diarrhoea
Food poisoning
Hepatitis
Jaundice
Stomach pain
Vomit
Nausea
Non responsive Altered mental status
Anxious
Confusion
Difficulty Talking
Difficulty thinking
Difficulty Walking
Disoriented
Drowsy
Facial droop
Facial weakness
Hyper
Loss of consciousness
Mental
Nervous
Numbness
Paralysis
Seizure
Slurred speech
Sores
Stroke
Swallowing
Syncope
Thinking slow
Tingling
Trouble talking
Trouble thinking
Trouble walking
Unresponsive Weak Abscess
Abnormal Skin
Blisters
Bug Bites
Cellulitis
Chicken pox
Dermatitis
Insect bite
Itching
Pox Rash
Skin redness
Skin swelling
Tick bite Arm pit
Glands
Lumps
Lumps in neck
Neck
Nodes
Red streaks
Skin streaks
Weak Achy
Body aches
Body sores
Fatigue
Fever
Fussy
Infection
Tired
To obtain this scheme, we reviewed a frequency table of all CCs which occurred at least 5 times over nine years and assigned relevant CCs to groups, as was done for example in [15] and [16]. Key words were selected to capture multiple chief complaints containing that word. For instance "breath" captures "shortness of breath", "trouble breathing", "I can't breathe", "can't catch breath", "breathing problems", etc. Common abbreviations were also included as key words. The groupings were then reviewed by the project medical advisory board which included epidemiologists, infectious disease, emergency and occupational health physicians.
Each CC is assigned to a group when the first match was made between a word in the CC and a word in our CC dictionary. There were no examples of negative chief complaints such as "no cough", in our nine-year database, so we did not develop a system for handling these. Any negative complaint would have been classified into the category of the key word.
First order model
The training set for model development was retrospective cohort data from 2/1/94 through 5/31/02. We used least squares regression in started log scale to fit trends, seasonal effects, and day-of-week effects. The "started log" is the logarithm of one plus the number of daily CCs. We add one before taking the logarithm to avoid problems with taking the logarithm of zero counts. The started log scale results in more symmetrically distributed forecast errors with variance that is much less dependent on the mean count. Results are back-transformed to natural scale for display. Error bars behave as desired (widening when the CC count increases) and skewing is commensurate with the magnitudes of the baseline values.
A model that incorporates the above effects is
S(d) = [Σi ci × Ii(d)] + [c8 + c9 × d] + [c10 × cos(2πd / 365.25) + c11 × sin(2πd / 365.25)]
where
a) S(d) denotes the started log of the number of chief complaints) for day d, where "Day 1" is taken as February 1, 1994,
b) [Σi ci × Ii(d)] captures the day-of-the-week effect, where the sum is over the indices i = 1 to 7 and Ii(d) denotes the indicator function for day d, i.e., Ii(d) equals 1 when day d is the i-th day of the week and equals zero otherwise, and the seven model coefficients {ci} are constrained to sum to zero,
c) [c8 + c9 × d] captures a long term linear effect,
d) [c10 × cos(2πd / 365.25) + c11 × sin(2πd / 365.25)] captures the seasonal component, where the average number of days per year is 365.25, with the model coefficients c10 and c11 dictating the time and amplitude of the seasonal effect.
This model arises from transforming the counts to started log scale and adding day-of-the-week effects to the regressor variables in a cyclical regression model [16,17]. Application of the model allows for coefficients to be set to zero when the corresponding constituent effects are not statistically apparent. For example, the coefficient c9 is zero for complaint categories that do not exhibit linear long term trends, and the coefficients c10 and c11 are zero for categories which do not exhibit seasonality. A plot of the predicted respiratory complaints and corresponding upper confidence limits from this model is given in Figure 1, illustrating how the constituent effects interact to yield baseline predictions. The predictions were obtained using ordinary least squares for parameter estimation and then back transforming from started logs to natural scale.
Figure 1 Predicted Respiratory Complaints. The predicted respiratory complaints and corresponding upper confidence limits illustrates seasonality and day-of-week effects.
Evaluation of goodness of fit
As with all statistical models, it is important to assess goodness of fit. A careful residual analysis reveals trends in the forecast errors, the most important of which follow from the one-size-fits-all character of the model. That is, the first order model postulates that complaint activity peaks with the same magnitude and at exactly the same time of year from season to season. Such postulated behavior is only approximately true, and the season-to-season differences lead to the trends in residuals from the model.
For example, if a peak of respiratory complaints occurs later than average in the year, then the baseline will initially over-predict (in anticipation of an average peak time) and then under-predict (when the season's peak actually occurs). Similarly, if the amplitude of a season's peak is higher or lower than the historical average amplitude, predictions will be consistently too low or too high near the peak. We return to this subject in the section on hierarchical modeling.
Near real time monitoring: Page's test
By comparing CC counts to predictions and confidence limits, anomalies can be identified [18,19]. Extra counts could arrive all on one day, or appear as a gradual increase starting at some particular time, or arrive sporadically, persist at a constant level for a fixed duration, etc. The best statistical test for detecting extra counts depends on the pattern of extra counts, so there cannot be a single best test for detecting all possible anomalous patterns.
A particular test, based on Page's statistic, is optimal for detecting a constant excess above baseline when the start time and duration of the excess is unknown [20]. This test also has competitive power compared to other sequential tests to detect other anomalous patterns. For these reasons, Page's statistic is widely used in statistical process control and has been proposed in the context of disease surveillance [21]. We recommend Page's statistic unless specific anomalous patterns are suspected, in which case a specialized test could be developed.
Page's statistic is a type of cumulative sum, which we denote as P(d). On each day d, the forecast error εd between the started log of the observed number of complaints and the started log of the baseline prediction is computed for each complaint category. The standard deviation sd of εd is computed as well. Then Page's statistic is calculated for day d as
P(d) = maximum of 0 and [P(d-1) + εd/sd - 1/2].
If P(d) becomes too large, then the observed complaint levels are significantly greater than the baseline predictions and complaint levels are deemed anomalously high. Here, the phrase "too large" is formally defined in terms of the desired false positive rate for monitoring, and the threshold value for P(d) is calibrated using empirical data to account for model prediction errors.
Comparison to other data streams
A comprehensive comparison to other data streams is beyond our scope. Briefly, we compared our respiratory CC counts to existing influenza data for the 2002–2003 influenza season. We evaluated New Mexico (NM) Department of Health sentinel influenza surveillance data reported weekly by approximately 20 clinics. When the week-long monitoring period is combined with the time needed for compilation and dissemination, notification of an outbreak early in one week is often not formally received until two weeks after it occurred. We obtained virology laboratory data from routine clinical and surveillance testing of respiratory specimens reported by three laboratories that conduct at least 75% of the clinical virology testing for NM. Such data streams have their own timeliness issues, due in part to culturing of samples.
Results
ED data from 2/1/94 – 5/31/02 is used as training data for least squares fitting to establish control limits; data from 6/1/02 – 53/31/03 is then used in near real time surveillance. Approximately, 17% of the complaints fall into the respiratory category, 10% gastrointestinal, 6% undifferentiated infection, 3% skin, 3% neurological, 1% lymphatic and 60% "other."
Day-of-week effects
For all systemic complaint categories there are day-of-week differences. See Figure 2. For five of the seven categories, there are more visits on Monday than on any other day; UDI and skin peak on Sunday. Weekly minimums occur later in the week: on Thursday for skin, Friday for GI and UDI, and Saturday for respiratory, neurologic and lymphatic. In some cases, there is a high-to-low trend as the week progresses.
Figure 2 Day-of-week effects for each CC category. The average day-of-week effect with corresponding error bars for six CC categories.
For respiratory and UDI complaints, there is an average difference of 7 cases per week between Monday and Friday, and the weekly differences conform to a bell-shaped statistical distribution. While day-of-week effects are statistically significant in all categories owing to the size of the data set, in some categories there are so few complaints that the difference is of no practical consequence. For example, in the lymphatic and neurologic categories, the average difference between the weekly peak (Monday) and weekly minimum (Saturday) is less than one case per day. Certain other daily effects may exist, e.g. holiday effects [22], but sample sizes for UH data are not large enough to detect them.
Seasonal effects
As is well known, there are annual cycles of respiratory complaints with peaks in January or February. Figure 3 demonstrates these cycles in our data for respiratory complaints; the cycles are similar but less pronounced for UDI complaints.
Figure 3 Annual cycles in Respiratory Complaints. Annual cycles in respiratory complaints (by week) for the past three flu seasons, from 2000–1 through 2002–3.
Long term trends
Total ED visits increased over the study period by 4%, in part reflecting the population increase of about 1.5 % per year for the metropolitan area. Rates for most of the complaint categories have changed over the eight years that data has been collected. Respiratory complaints show a decrease of roughly 20% over the monitoring period (Figure 4), illustrating that long term averages need not reflect current or future behavior. Skin-related complaints also show a slight decrease, while increases are observed in nearly all other categories. Only lymphatic complaints do not appear to change over the monitoring period. Had there been a complaint category where a nonlinear trend were clearly present, either the c9 component would have been modified periodically, or a nonlinear model would have been used.
Figure 4 Average daily number of respiratory complaints by calendar year. The average daily number of respiratory CCs decreases over the training data.
Surveillance
Patterns observed in the Test Year
We focus on respiratory CCs because they have the strongest season-to-season variation, which makes them the most challenging to predict, and because respiratory is thought to be one of the most important bioterrorist categories. Overlaying the data streams on the baseline plot in real time allows for visual inspection of the results, similar to that for a standard control chart. Figure 5a shows the daily counts, the baseline prediction, and the upper control limit (for a 2.5% false alarm rate) for the respiratory category. Figure 5b shows the scaled forecast errors (in the started log scale) for the prospective data. Both figures reveal a later-than-average flu season, as does Figure 5c, showing large values of Page's statistic P(d). These plots illustrate how a misfit to the one-size-fits-all model can produce systematic trends in surveillance data. This subject is revisited in the section on hierarchical modeling.
Figure 5 Results on validation data for Respiratory Complaints. Prospective data (June 1, 2002 through May 31, 2003 (a) Daily, predicted, and upper control limit for respiratory counts ; (b) Scaled forecast errors for respiratory counts; (c) Page's statistic applied to the same forecast errors. A control value of 3.3 bfor Page's statistic results in an approximate theoretical 2.5% false alarm rate when forecast errors are Gaussian.
The peak in respiratory CCs and the elevated P(d) preceded a rise in reports from the state-wide influenza sentinel surveillance system. A similar pattern, delayed by several weeks, was found in the rise of requests for laboratory tests for influenza. ED CCs also preceded New Mexico reporting of deaths from pneumonia and influenza. We conclude that surveillance using the first order model is sufficiently sensitive to mild departures from baseline activity and that it can provide timely notification relative to traditional surveillance.
Simulated outbreaks
In a simulation study we injected K extra respiratory CC counts beginning at random days during the test year, with the simulated outbreak lasting from 1 to 10 days, from 2 to 10 days, and exactly 1 day. Generally, departures of approximately 3 or more standard deviations from the baseline model should be detected with high probability. The simulated per-day shift above the baseline prediction ranged from 1 to 5 standard deviations in our simulations, so testing one day at a time could fail to detect those outbreak having small per-day shifts. Also, because of the pattern in the residuals near each seasonal peak, we considered EWMA (exponentially weighted moving average, see the Discussion) as one way to modify the current forecast on the basis of errors in the recent past.
In Table 2 we give the fraction of simulations (out of 1000) in which the Page statistic exceeded its threshold of 3.3 for the null model, baseline model, and for the same models modified by the EWMA procedure. For comparison to one-day-at-a-time testing, we also give the fraction of simulations in which the maximum forecast error that occurred during the outbreak exceeded its 2.5% false alarm rate threshold of 1.96. We see that Page's test outperforms the one-at-a-time test and that the EWMA modification does not improve anomaly detection because of its tendency to underestimate the size of multiple-day outbreaks. However, if we restrict attention to those outbreaks that last only one day, then one-at-a-time testing is better (for each of the models), as we would expect. Compare the baseline model results to the null model (which uses the average CC count in the training data to predict the test data) results to gauge the benefit of fitting the baseline model. Of course the null model is not acceptable regardless of its performance in this context because it ignores the trend (which causes the null model to be biased high for the respiratory CCs), day-of-week effects, and seasonality.
Table 2 The fraction of simulations (out of 1000, so the 95% confidence limit is approximately ± 0.03) in which the Page statistic (or the one-at-a-time statistic) exceeded its 2.5% false alarm threshold during the simulated outbreak for the baseline model, the baseline model with residuals modified by EWMA, the null model, and the null model with residuals modified by EWMA.
Outbreak duration Test Baseline Baseline + EWMA Null Null + EWMA
1–10 days Page 0.46 0.21 0.28 0.17
1–10 days One-at-a-time 0.37 0.31 0.17 0.31
>1 day Page 0.42 0.20 0.30 0.14
>1 day One-at-a-time 0.31 0.25 0.13 0.26
1 day Page 0.44 0.36 0.30 0.45
1 day One-at-a-time 0.71 0.70 0.47 0.68
Discussion
Models
Long-term trends can occur in surveillance data for multiple reasons. Changes may occur in: local resources (more or specialty EDs), access and reimbursement practices (facilities change which insurance plans with which they are associated, major shifts in insurers drives patients to other facilities), changes in the underlying population (shifts in population size or age), and changes in the local economy. Moving averages were not used because although they generate visually pleasing curves they smooth over day-of-week and seasonal effects that are important for developing baselines.
Concerning model quality, one useful test is whether the forecast error variance in the testing data is approximately the same as that in the training data. Upon dividing the forecast errors in the test data by their standard deviations in the training data, the scaled forecast error variances range from 0.87 to 1.07 for the seven CC categories (ideally, these ratios should be near 1). Further, the fraction of scaled forecast errors that exceed 1.96 ranged from 0.0 to 0.033 (when the model holds and residuals are Gaussian, the portion of one-sided residuals exceeding 1.96σ is 2.5%). Thus, departures from stationarity in the time series are mild enough that the forecast errors show that future complaints can be reasonably well predicted using a single baseline model for each category.
When monitoring complaint levels over multi-year time frames, it is necessary to periodically update baseline model coefficients in order to minimize the extrapolation in forecasting. One approach to choosing an update frequency is to do a planned update every year, but also monitor residuals for patterns, including shifting variance, that have not been observed previously to check whether additional updates are needed.
Hierarchical modeling to capture season-to-season differences
The first-order model is useful for routine monitoring. It has the obvious shortcoming, however, of describing each season in a one-size-fits-all fashion. As noted above in evaluation of the model's goodness-of-fit, forecast errors reflect modelling imperfections as well as random variability, limiting somewhat the sensitivity of surveillance to detect smaller anomalies. Improving on this situation requires more refined baselining.
Hierarchical methods [23] can overcome the one-size-fits-all shortcoming, or, at a minimum, provide information that is valuable in assessing the quality of one-size-fits-all modelling assumptions. In the hierarchical approach, each season is allowed to have its own time of peak activity, its own seasonal duration, and its own peak magnitude. For practical purposes, the hierarchical model shares the global characteristics of the first order cyclical regression model. The seasonal component is modelled with a scalable Gaussian function, in contrast with the fixed-width sine and cosine harmonics previously. And the underlying baseline changes linearly within a season, as opposed to behaving linearly over a longer time period.
Applying the hierarchical model to respiratory CC data illustrates the season-specific nature of chief complaints. On the average, our respiratory complaints peak on January 22, with a season-to-season standard deviation in the day of the peak of 12 days. The durations of individual seasons, defined in terms of the standard deviations for the Gaussian-shaped peaks, vary by factor of two over the monitoring period. And there is no apparent relation between the time that the peak occurs and the magnitude of the flu season.
Use of hierarchical models for real time syndromic monitoring could be considered, but at a significant computational cost. In order to capture the peak time and magnitude of an ongoing season, the model must be updated on a frequent (e.g., weekly) basis, involving lengthy runs of Markov chain Monte Carlo software. Because the first order cyclical regression model fits the data sufficiently well to detect anomalies of interest, we have used the first order model for routine monitoring. A similar first order cyclical regression model is used by the Centers for Disease Control to monitor pneumonia and influenza related mortality data [24], also with success.
Related efforts
Influenza surveillance basing alerts on comparison to historical data were described by Irvine [25]. Daily counts were compared to historical averages and standard deviations. Their data demonstrated a peak in CCs during influenza season.
Lazarus et. al. [26] use a generalized linear mixed model based on four years of data from ambulatory health encounters. They find that indicators for day-of-week, month, holiday effects as well as a secular trend term contribute significantly to their model fit. There may be ED data from other hospitals where month-to-month effects exist but are not part of a longer seasonal trend, but we don't see them in our data. Logistic regression [26] is useful for scaling over census tracts of different population sizes and, when complaint counts behave proportional to underlying census populations, is also useful in modeling overall complaint levels.
Reis and Mandl [27] used CCs for their time series models (autoregressive integrated moving average, ARIMA, models) for total and respiratory visits. After fitting a day-of-week effect and a seasonal effect, there remained positive autocorrelation in the forecast errors, which they modelled using a particular time series model. In our CC data, there is negligible autocorrelation in the errors after fitting our model, except that due to the variation in when the seasonal peak occurs. For example, if a peak occurs early, then we observe a sequence of positive errors, which leads to positive autocorrelation of the type reported. For our CC data, the best-fitting ARIMA-type model applied to the residuals after fitting the trend, seasonality, and day-of-week effect was the EWMA (equivalent to a moving average fit to the first differences, also denoted ARIMA(0,1,1) for the particular autoregressive integrated moving average model that it corresponds to). Reis and Mandl [27] note that the ARIMA model adjusts to multi-day outbreaks and so it reduces the error on days 2, 3, ... of a multi-day outbreak. Therefore they suggest using both the original errors (containing serial correlation) and the ARIMA-model-adjusted errors in two monitoring schemes.
The EWMA also adjusts to multi-day outbreaks and therefore suffers from signal loss if the outbreak persists for multiple days (the results in Table 2 illustrate this effect). Therefore, the Reis and Mandl [27] suggestion to monitor two residual series is relevant if we use EWMA or any approach (including the hierarchical model) that uses the very recent past (in addition to the trend, seasonality, and day-of-week effects) to modify the current forecast (leading to two or more forecast methods). Also, sequential tests were not used in [27] for their 7-day simulated outbreaks. Each simulated outbreak added simulated additional counts to the daily CC data. Forecasts were made (on the basis of a model that used the overall mean, the day-of-week means, and the trimmed day-of-year means) and were modified using the ARIMA-modeling of the residuals. If any single-day forecast error exceeded a threshold, then the simulated outbreak was said to be detected. Sequential tests are ideal for multi-day outbreaks so the performance (the false negative rate for a fixed false positive rate) of Page's statistic or a moving window such as in [28] or the scan statistic such as in [29] would be better than the performance of one-at-a-time tests in the case where all simulated outbreaks lasted 7 days. Reis et al. [28] applied several sliding detection windows, each of at most 7 days to ED visit daily counts in which simulated outbreaks (in the form of additional ED visits) lasting 3, 7, and 14 days were added to the real data in a simulation study. On the other hand, if each outbreak lasted only one day, then monitoring single-day errors would be optimal. In summary, we concur with [30] regarding the robustness and simplicity of Page's test. Alternatively, there are occasions when using a modest number of specific tests is effective as was done in [29].
Use of free text chief complaints
Many surveillance systems report the use of CCs or discharge diagnoses based on ICD-9 codes. Most of these use discharge diagnosis ICD-9 codes in specialized settings such as the Military [13] or in HMOs [23]. ED ICD-9 codes were also used when processing data retrospectively [4]. In most EDs, however, ICD-9 discharge diagnosis coding is not performed on a "near-real" time basis and would not be available for "near-real time" surveillance.
By contrast, free text chief complaints are obtained at the time of patient entry into all Emergency Departments and free text discharge diagnoses are determined at or close to the time of ED discharge. Therefore, use of our grouping scheme is relevant to the majority of EDs in which chief complaints and discharge diagnoses are recorded as free text and are available for "near-real time" surveillance.
Because of their timeliness, CCs are used in our "near-real time" surveillance system B-SAFER [31,32]. We considered using CoCo, a naive Bayesian free-text classifier developed by the University of Pittsburgh [33], but this was not made available to us. Another automated classification based on weighted key words system is used by ESSENCE [34]. The New York City Department of Health uses a key word and key phrase SAS-based coding system [35]. A comparison of the performance of expert based classification systems such as ours, and automated classification systems has not been done.
There are other potential limitations in using ICD-9 codes for surveillance. It is to be expected that early cases of unusual diseases will be misdiagnosed. Assigned ICD-9 diagnostic codes may be more reflective of the diagnostic bias or practice patterns of the provider, than of the true incidence of disease. Furthermore, ICD-9 diagnosis code assignment is potentially subject to billing bias: codes which garner the highest reimbursement may be used, rather then those that most accurately represent the disease process. Use of ICD-9 codes for chief complaints is also problematic. Because ICD-9 codes were developed for classification of diagnoses, the dictionary for chief complaints is not robust. Therefore, the use of free text chief complaints may result in increased sensitivity, although the B-SAFER team believes that some types of coding standards would be beneficial [36].
Results and applications
Day-of-week patterns in EDs have been previously reported in the literature [4,26,27,37,38]. Although the magnitudes of the day-of-week effects vary depending on the setting, the first day of the work week typically exhibits the greatest number of events. And, as we have shown, there is day-of-week variability within infectious disease syndromes which can be obscured by the failure to consider each body system's pattern individually. It is also important to understand utilization patterns on weekends, which ED data provides, as the effects of bio-terrorist or natural outbreaks are unlikely to be limited to weekdays.
Seasonal effects in infectious diseases are best known for respiratory infections (e.g. influenza and respiratory syncytial virus). This seasonality is in large part due to the yearly winter influenza epidemics. Our data is quite consistent with these findings. However, we provide an analysis of the pattern of respiratory related chief complaints based on the longest (8.6 years) historical data base. Furthermore, the seasonality of infectious disease complaints for other body systems, or for non-ID related complaints has not previously been reported. Seasonality in specific GI infections has been noted in other settings. Some gastrointestinal infections are more common in the winter (e.g. rotavirus) while others (e.g. Campylobacter, Cryptosporidium, enterovirus) are more common in the warm months from outdoor cooking and recreational water exposures.
Seasonality is also important because the signal-to-noise ratio in complaint counts maychange depending on time of year. As illustrated in Figure 1, the error bars are larger during the height of flu season than at other times of the year, leading to reduced sensitivity for detecting an increase in respiratory complaints at that time.
It is unclear why, in our data, the number of respiratory complaints fell with time while the number of fever complaints rose. This may reflect the relative mildness of recent influenza seasons. Alternatively, rather than actual differences in patient presentations, this may represent changes in the practices of choosing or recording chief complaints. This would require further investigation. Implementation of a standard drop-down menu for chief complaints might prevent some bias over time in the selection of chief complaints. Use of a new system, however, would likely change the distribution of chief complaints and thus not allow for the creation of baselines based on historic data.
Surveillance
Using the models described above we successfully identified a respiratory outbreak in advance of the traditional flu-reporting data streams described in the Methods section. Incoming B-SAFER reports were monitored at least once daily, seven days a week, by the project epidemiologist. This allowed for prompt handling of events indicating a condition reportable by statute to the NM Department of Health. Because there is approximately a 2-week delay for traditional flu-related data sources, provided our respiratory CC captures some of the NM flu cases, we expected to, and did, identify a flu-related respiratory peak in advance of these other sources.
Limitations
This analysis is based on data from one ED and patterns identified may be somewhat specific to metropolitan Albuuerque. Indigent or Hispanic populations may be over-represented in the ED studied as compared to other EDs. Visit patterns may differ by the local health care infrastructure, population insurance status, access to care, or local climate. We wewere fortunate that electronic ED data was available for the previous eight years. Other institutions may lack the source data for a similar analysis.
CCs are determined by a nurse and recorded in free text by a clerk. This process may conceivably distort patients' literal CCs. Free text CCs are quite variable and require extensive processing. These CCs may also have varied had they been recorded by a physician. Although the rationale for using CCs rather than discharge diagnoses was provided above, there is a tradeoff between the better timeliness of CC data and the better sensitivity of discharge diagnoses [39]. Note that our modelling is as easily applied to diagnoses codes as to chief complaints.
Any approach to disease surveillance using either CCs or discharge diagnoses requires large numbers of symptomatic patients. Analyses based on such large-scale counts are unlikely to discover a small and geographically dispersed event such as the anthrax Anthrax outbreak of October 2001.
As we work more with our data, we will understand it better. Opportunities exist for performing sensitivity analyses, comparing the baseline patterns for CCs to those for discharge diagnoses, and more thoroughly evaluating the performance of our signals as compared to existing standards.
Conclusion
We have demonstrated a robust statistical approach to characterize baseline data for ED visits. We demonstrated day-of-week, seasonal and long-term effects by infectious disease in grouped chief complaint categories. ED data provides information on daily visit patterns, rather than just 5-day-a-week patterns. Using respiratory complaints as an example, we have shown that these models when applied to "near real-time" surveillance data provide an early indicator of an anomaly. This increase in respiratory visits was identified early by a rise in Page's statistic. This anomaly corresponded to events detected later by more traditional methods. Understanding baseline patterns in ED data provides the ability to distinguish expected versus unexpected events during infectious disease surveillance.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JB and EU provided medical advice from the ED and public health perspectives. They were responsible for choice and groupings of chief complaints. JB obtained IRB approvals and recruited sites. TB and RP are responsible for choice and implementation of statistical tests. DF provided computer systems, interface and architecture. EJ provided technical input, review and project direction. JB, EU, TB and RP collaboratively wrote 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
Supported by: Los Alamos National Laboratory directed research funding
New Mexico Department of Heath #457631
University of New Mexico Emerging Infectious Disease Center – CDC – E11/CCE620225-01
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| 15743535 | PMC555597 | CC BY | 2021-01-04 23:52:12 | no | BMC Med Inform Decis Mak. 2005 Mar 2; 5:4 | utf-8 | BMC Med Inform Decis Mak | 2,005 | 10.1186/1472-6947-5-4 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-5-91575242110.1186/1472-6920-5-9Technical AdvanceUsing item response theory to explore the psychometric properties of extended matching questions examination in undergraduate medical education Bhakta Bipin [email protected] Alan [email protected] Mike [email protected] Gemma [email protected] David [email protected] Academic Unit of Musculoskeletal and Rehabilitation Medicine, University of Leeds, UK2 School of Education, Murdoch University, Western Australia2005 7 3 2005 5 9 9 16 3 2004 7 3 2005 Copyright © 2005 Bhakta et al; licensee BioMed Central Ltd.2005Bhakta 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
As assessment has been shown to direct learning, it is critical that the examinations developed to test clinical competence in medical undergraduates are valid and reliable. The use of extended matching questions (EMQ) has been advocated to overcome some of the criticisms of using multiple-choice questions to test factual and applied knowledge.
Methods
We analysed the results from the Extended Matching Questions Examination taken by 4th year undergraduate medical students in the academic year 2001 to 2002. Rasch analysis was used to examine whether the set of questions used in the examination mapped on to a unidimensional scale, the degree of difficulty of questions within and between the various medical and surgical specialties and the pattern of responses within individual questions to assess the impact of the distractor options.
Results
Analysis of a subset of items and of the full examination demonstrated internal construct validity and the absence of bias on the majority of questions. Three main patterns of response selection were identified.
Conclusion
Modern psychometric methods based upon the work of Rasch provide a useful approach to the calibration and analysis of EMQ undergraduate medical assessments. The approach allows for a formal test of the unidimensionality of the questions and thus the validity of the summed score. Given the metric calibration which follows fit to the model, it also allows for the establishment of items banks to facilitate continuity and equity in exam standards.
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Background
It is acknowledged from medical student learning behaviour that assessment often drives learning [1]. Therefore, if students are learning what is being assessed then it is vital that the content of the assessment reflects the learning objectives. This process, known as blueprinting, maps the content of assessments against the clinical competencies (knowledge, skills and attitudes) that the student is expected to acquire [2]. The pyramid of competence developed by Miller provides a conceptual framework for ensuring that student assessments are valid and cover core aspects of factual knowledge and problem solving (e.g. Extended Matching Questions – EMQ), performance assessment in "vitro" (e.g. Objective Structured Clinical Examinations – OSCE) and performance in "vivo" (e.g. case presentations, log books) [3].
At the University of Leeds the undergraduate medical course includes an integrated medical and surgical specialities program (Rheumatology, Orthopaedics, Rehabilitation, Anaesthetics, Dermatology, Infectious diseases, Oncology and Palliative Medicine, Genitourinary Medicine and Accident and Emergency medicine). Acknowledging that no single assessment format can adequately assess all the learning objectives within the course blueprint, a combination of assessments (including OSCE, EMQ, slides with problem solving, reflective learning log books and case presentations) are currently used to assess the student's competence. Although a combined score is used to assess the competence of the students, test scores within the individual assessments reflect a richer profile of the individual, allowing an understanding of strengths and weaknesses that can result in improvement in the individual and the educational programme. Analysis of the quality of individual assessments is essential to this process. The focus of this paper is the use of item response theory to examine the validity of the typical use of a single score obtained from the summative EMQ examination, to characterise each student and their individual differences, in short the investigation of the relative unidimensionality of the EMQ examination
The EMQ is a form of multiple-choice type question [4] designed to test the student's knowledge. EMQs are written by experts from each of the medical specialties. EMQs have four components; a theme (e.g. leg or cancer pain), the lead-in for the questions that gives the students instructions on what to do (e.g. "for each patient select the most likely diagnosis"); the questions in the form of vignettes giving the pertinent information based on which the student is to select the correct answer; and finally the potential answers (e.g. a list of potential diagnoses relevant to the theme) (Figure 1). The response option includes one correct answer for each question, and other possible responses as distractors, a reasonably plausible response if the student does not know the correct response for whatever reason.
Figure 1 Example of Extended Matching Question (EMQ) Format.
The use of EMQs has been advocated to overcome some of the criticisms levelled at the use of multiple-choice questions to test factual and applied knowledge. There are advantages to using EMQs [4]:
• The format of themes aid the organisation of the examination, and the use of blueprinting is a natural aid to the process of writing EMQs
• As questions are written in themes or general topic it allows the teacher to write many questions for that theme and then share these questions out randomly to create more than one examination paper
• Good questions provide a structure designed to assess application of knowledge rather than purely recall of isolated facts
• The approach to writing these questions is systematic, which is very important when several people are contributing questions to one exam
• The extended list of options allows the inclusion of all relevant options, and reduces the opportunity for students to 'guess' the correct answer as in MCQs
• EMQs were found to be more discriminating than two and five option versions of the same questions resulting in a greater spread of scores, and reliability was higher as a consequence of this [5,6].
The importance of ensuring validity and reliability of the EMQ procedure is crucial. Current evidence for this is limited but does support good reliability and validity [1,6,7]. This study considers EMQs as used in the medical education at two levels. Within the individual EMQ it explores the operation of the distractors, and across EMQs it explores whether (a) summary scores are justified; (b) how EMQs vary in difficulty across specialities and (c) whether the EMQ scores taken together discriminate students of different abilities.
Traditionally, methods of analysis based on classical test theory have been used to evaluate such tests. The focus of the analysis is on the total test score; frequency of correct responses (to indicate question difficulty); frequency of responses (to examine distractors); reliability of the test and item-total correlation (to evaluate discrimination at the item level) [8-11]. Although these statistics have been widely used, one limitation is that they relate to the sample under scrutiny and thus all the statistics that describe items and questions are sample dependent [12]. This critique may not be particularly relevant where successive samples are reasonably representative and do not vary across time, but this will need to be confirmed and complex strategies have been proposed to overcome this limitation.
Developments of the Classical Test Theory can be found in modern test theory and, in particular, the Rasch model [13] of Item Response Theory (IRT). This too uses the total test score, but in this instance from a theoretical basis. It is also concerned with reliability (in the form of a person separation statistic which is similar to Cronbach's alpha). However, in addition it provides a mechanism for testing the invariance of items which allows the construction of a bank of calibrated questions that facilitates a direct comparison over different administrations of the test [14]. From such an item bank, different combinations of questions can be incorporated into an examination to ensure that the difficulty of the exam remains consistent for successive cohorts of students.
The use of the Rasch model entails a different perspective, or paradigm, from IRT approaches in general [15]. Where data do not conform to the expectations of the Rasch model, the main challenge is not to find a model that better accounts for the data, but to understand statistical misfit as substantive anomalies that need to be understood, and by being understood, to lead to the construction of more valid and reliable tests. This is the approach taken in this study. That is, analysis of data based on existing items will be considered closely both statistically and substantively with a view to guiding better question construction. Thus the aim of this paper is to explore the use of Rasch analysis to determine the validity of the EMQ examination currently taken by 4th year medical undergraduates.
Methods
Data collection
Responses to the EMQ examination taken by one hundred and ninety three 4th year medical students were used as the source data. The examination is designed to test factual and applied knowledge taught in the Medical and Surgical Specialties course and is taken by the students at the end of the 16 weeks course. The course is run three times per year and rotates with two other courses (Paediatrics / Obstetrics / Gynaecology and Primary Care / Public Health / Psychiatry). All questions were devised by the lead educational supervisor within each specialty. Training in EMQ writing was provided to the medical specialty supervisors. The examination consisted of 98 EMQs distributed across eight specialties and 27 themes, each with two to four EMQs. Each themed group of EMQs had eight to 15 possible response options (e.g. see example in Figure 1). There were 12 Oncology, 14 Anaesthetics, 12 Dermatology, 12 A&E, 12 Infectious Diseases, 16 Orthopaedics, 8 Rheumatology and 12 Rehabilitation EMQs. The final exam mark is the sum of correct answers to all themes, summed across specialties, giving an indication of the applied knowledge across the range of medical and surgical specialties which comprised the Medical and Surgical Specialities module.
No other information was collected about the students other than which term they had sat the EMQ examination. The students take this examination at the end of the course and the medical and surgical specialties course is repeated three times a year. Differential Item Functioning (see below) was used to determine the impact of the term in which the examination was taken on student performance.
Parameter estimation
The Rasch model is a probabilistic unidimensional model which asserts that (1) the easier the question the more likely the student will respond correctly to it, and (2) the more able the student, the more likely he/she will pass the question compared to a less able student. The model assumes that the probability that a student will correctly answer a question is a logistic function of the difference between the student's ability [θ] and the difficulty of the question [β] (i.e. the ability required to answer the question correctly), and only a function of that difference
From this, the expected pattern of responses to questions can be determined given the estimated θ and β. Even though each response to each question must depend upon the students' ability and the questions' difficulty, in the data analysis, it is possible to condition out or eliminate the student's abilities (by taking all students at the same score level) in order to estimate the relative question difficulties [14,16]. Thus, when data fit the model, the relative difficulties of the questions are independent of the relative abilities of the students, and vice versa [17]. The further consequence of this invariance is that it justifies the use of the total score [18,19]. In the current analysis this estimation is done through a pair-wise conditional maximum likelihood algorithm, which underlies the RUMM2020 Rasch measurement software [20,21]
If the above assumptions hold true then the relationship between the performance of students on an individual question and the underlying trait (applied knowledge within the medical and surgical specialties course) can be described by an S shaped curve (item response function). Thus the probability of answering the question correctly consistently increases as the location on the trait (knowledge) increases (Figure 2). The steepness of the curve indicates the rapidity with which the probability that a student responding to the question correctly, changes as a function of this location (ability). The location of the curve along the horizontal axis (defined by the point at which the 0.5 probability level bisects the horizontal scale) indicates the difficulty of the question. The location of the student on the same axis indicates their level (of knowledge, ability etc.) on the trait.
Figure 2 An Item Response Function (Item Characteristic Curve).
When the observed response pattern does not deviate significantly from the expected response pattern then the questions constitute a true measurement or Rasch scale [22]. Taken with confirmation of local independence of questions, that is, no residual associations in the data after the person ability (first factor) has been removed, this supports the unidimensionality of the scale [23,24].
General tests of fit
In this analysis, responses to the EMQ are analysed as dichotomous options, that is, one correct answer and all of the other options are analysed together as one incorrect response. To determine how well each question fits the model, and so contributes to a single trait, a set of 'fit' statistics are used which test how far the observed data match those expected by the model. The trait refers to the required knowledge base that the student must acquire within the medical and surgical specialties course. The Item – Trait Interaction Statistic (denoted by the chi-square value), reflects the degree of invariance across the trait. A significant chi-square value indicates that the relative location of the question difficulty is not constant across the trait. In addition, question fit statistics are examined as residuals (a summation of the deviations of individual students responses from the expected response for the question). An estimate of the internal consistency reliability of the examination is based on the Person Separation Index where the estimates on the logit scale for each person are used to calculate reliability.
Misfit of a question indicates a lack of the expected probabilistic relationship between the question and other questions in the examination. This may indicate that the question does not contribute to the trait under consideration. In the current study students are divided into three ability groups (upper third, middle third and lower third) denoting each Class interval with approximately 65 students in each. Furthermore, significance levels of fit to the model are adjusted to take account of multiple testing (e.g. for 24 items the level would be 0.002 and for 98 the level would be 0.0005) [25].
As well as invariance across the trait, questions should display the same relative difficulty, irrespective of which externally defined group is being assessed. Thus, the probability of correctly answering a question should be the same between groups given the same ability level of the student. For example, given the same ability, the students should not be more likely to answer a question correctly simply because they sat the exam in the third term instead of the first or second term. This type of analysis is called Differential Item Functioning (DIF) [26]. The basis of DIF analysis lies in the item response function, and the proportion of students at the same ability level who correctly answer the question. If the question measures the same ability across groups of students then, except for random variations, the same response curve is found irrespective of the group for whom the function is plotted [26]. Thus DIF refers to questions that do not yield the same response function for two or more groups (e.g. gender or the cohort of students).
DIF is identified by two way analysis of variance (ANOVA) of the residuals with the term in which the examination was taken by the student as one factor and the class interval as the other [27]. Two types of DIF are identified: (a) uniform DIF demonstrating that the effect of the term in which the exam was taken are the same across all class intervals (main effect), and (b) non-uniform DIF which demonstrates that the effect of which term the student sat the exam in is different across class intervals (interaction effect). Where there are more than two levels of a factor, Tukey's post hoc test is used to indicate which groups are contributing to the significant difference.
Although EMQ are analysed as though they have dichotomous response categories (correct or incorrect), it is possible to examine how the separate incorrect options within an individual EMQ are contributing to the student's response. This procedure is very similar to the technique of Graphical Item Analysis(GIA) [28], though in this case the RUMM 2020 programme [21] produces the analysis with no extra user effort. The proportions of students in each class interval who have selected the various response categories, including the correct option, are plotted on a graph of the item response function. This visually illustrates how often the various response options are being selected by the students in relation to one and other, and can be compared across themes given that different options are likely to have different response patterns for different questions within a theme. This is particularly useful in improving the quality of the distractor responses.
In view of our limited sample size (and particularly the ratio of students to items) we elected in the first instance to examine in detail the psychometric properties of the musculoskeletal component of the EMQ examination, acknowledging the limitations associated with the accuracy of the person estimate based upon 24 items (29). Subsequent analysis of the whole examination is reported to demonstrate the potential benefits of using Rasch analysis, but again acknowledging the limited conclusions that can be drawn on student ability and question difficulty estimates for the whole examination as a result of looking at 98 items with 193 students.
Results
Data were collected from 193 students (Term 1 = 61, Term 2 = 64, and Term 3 = 68). Total scores ranged from 36 to 78 out of a maximum mark of 98 (mean = 60.3, median = 61). Initially, analysis of data was undertaken from the combined specialties of rheumatology and orthopaedic questions, which consisted of 24 EMQs'.
Analysis of the musculoskeletal component of the EMQ examination
To estimate individual question difficulty using the Rasch model, all the incorrect response options were treated together as one incorrect option. The fit of the 24 questions to the Rasch model was acceptable, both in terms of individual item fit (Table 1) and over all Item-Trait Interaction (χ2 = 79.73, p = 0.003). This suggests that the musculoskeletal questions mapped on to a single dimension of applied knowledge in this case and within the power of the test of fit. This was further supported by a principal components analysis of the residuals identifying a first residual factor accounting for just 8% of the variation. However, the Person Separation Index was low, 0.50, indicating a low reliability. This, however, can be ascribed to the intrinsic homogeneity of the students who are selected under rigorous criteria and who are all studying for the same exam.
Table 1 Individual Item difficulty (location) and Tests of Fit (residuals and chi-square and its probability) for the 25 musculoskeletal EMQs.
Question Location SE Residual ChiSq DF Prob
OR63 -2.68 0.47 -0.60 0.06 1 0.81
OR64 -2.32 0.40 -0.67 0.43 1 0.51
OR65 -2.03 0.35 -1.02 0.96 1 0.33
OR66 1.03 0.15 2.48 1.85 1 0.17
OR67 -2.27 0.39 -1.12 2.32 1 0.13
OR68 1.74 0.16 0.20 0.02 1 0.88
OR69 -0.29 0.19 -0.79 0.52 1 0.47
OR70 -3.49 0.68 -0.25 0.59 1 0.44
OR71 0.81 0.16 1.62 1.19 1 0.28
OR72 -1.66 0.30 -0.52 0.44 1 0.51
OR73 -0.48 0.20 0.21 0.47 1 0.49
OR74 1.79 0.16 1.64 7.80 1 0.01
OR75 0.12 0.17 -0.64 0.33 1 0.57
OR76 1.70 0.16 -0.71 3.90 1 0.05
OR77 1.12 0.15 -0.52 2.95 1 0.09
OR78 1.20 0.15 1.76 0.08 1 0.78
RH79 -1.47 0.28 -0.61 1.00 1 0.32
RH80 1.57 0.15 -1.00 2.01 1 0.16
RH81 0.22 0.17 1.52 4.22 1 0.04
RH82 1.11 0.15 1.01 0.00 1 0.96
RH83 -1.08 0.24 -0.58 0.43 1 0.51
RH84 2.08 0.16 -0.18 0.09 1 0.76
RH85 1.50 0.15 -0.12 0.00 1 0.99
RH86 1.78 0.16 -0.88 3.28 1 0.07
OR** representing Orthopaedic EMQ
RH** representing Rheumatology EMQ
ChiSq Chi – squared statistic
SE Standard error
Location Value identifies question difficulty on logit scale
Residual Fit of question to underlying trait
DF Degrees of freedom
None of the items displayed DIF by the term in which the examination was taken. The location (a logit scale – Table 1) measures the item difficulty of the musculoskeletal EMQs and shows the range of difficulties from easy to hard (negative values indicating easier questions and positive values indicating harder questions).
Analysis of the whole EMQ examination (all eight specialties)
An initial exploration of the overall fit of the 98 questions to the Rasch model was poor with a significant question – trait interaction (χ2 = 291.4, p < 0.0001). Three out of the 98 EMQs showed significant misfit to the model at the individual level; one Infectious Diseases question, one Oncology question and one Rehabilitation Medicine question. Once these misfitting questions were omitted from the analysis, the overall fit of the remaining 95 EMQs to the Rasch model improved, and showed no significant Item – Trait Interaction. The questions from each of the component specialties within the course had reasonable spread of difficulty across the logit scale (Figure 3). Overall the students were more likely to answer the A&E EMQs correctly than other themes. Five questions (Oncology2, Oncology3, A&E41, Rheumatology84 and Rehabilitation93) displayed DIF by term in which the examination was taken (p < 0.01) indicating that the student's responses to these questions were influenced by the term in which the students sat the examination. Note that when the overall trait from all specialties is considered, Rheumatology84 shows DIF, but not when just the musculoskeletal-related trait was considered.
Figure 3 Map of question difficulty and student ability on Rasch transformed logit scale. Right hand side shows questions in order of difficulty and the left hand side shows the distribution of the students abilities based on their total examination score.
Response option analysis
The analysis of response options is descriptive. The proportions of students who have selected the various response categories, including the correct option, are plotted on a graph of the item response function. This visually illustrates student behaviour in their response to questions.
Three main patterns of response to the questions were identified.
a) the incorrect response option selected more frequently than the correct answer
The majority of the students are selecting the same wrong answer, irrespective of their ability level (Figure 4). In contrast, a more typical pattern of responses to a hard question would be students choosing a range of incorrect response options randomly, rather a single option.
Figure 4 A question where the vast majority of the students are selecting the same incorrect response option (0 – blue).
b) the distractor response option is selected more frequently than right answer in some ability groups
Responses to this EMQ show that students with lower ability are likely to select the incorrect answer, while the more able students select the correct response (Figure 5). Response option 0 is considered by most to be wrong, but options 1 and 2 clearly attract students of lesser ability.
Figure 5 Distractor response option (2 – green) selected more often than the correct answer (3**) by lower ability students, but not higher ability students.
c) the correct answer too obvious
The majority of the students in all ability groups select the correct answer (Figure 6). An "easy" question is not in itself undesirable as it may test the student on a critical piece of knowledge.
Figure 6 Correct answer is too obvious (2** – green).
Discussion
In this study, Rasch methodology was used to analyse the Extended Matching Questions examination taken by our fourth year medical students in 2001. Analysis of the musculoskeletal subset of questions showed that they mapped on to the trait under investigation (assessment of the musculoskeletal applied knowledge content of the course) and thus supported the notion of a valid unidimensional scale. Exploratory analysis of the whole examination showed that only three out of the 98 EMQs displayed significant misfit to the measurement model. These EMQs should be reviewed for ambiguities in question format and relationship to the course blueprint. DIF was identified in five of the 98 questions (these were different to the items displaying misfit) suggesting that the pattern of student responses to these EMQs were dictated by the term in which the exam was undertaken. The reason for this apparent variation needs to be understood before such questions are included into the bank of EMQs used for future examinations. In the present analysis only 'term' was considered for DIF, but DIF by gender also needs to be considered as gender can influence the response students make to the distractors [30].
Rasch analysis revealed the variety of response patterns made by the students. In some questions the incorrect response is selected more frequently than correct response, suggesting that the question appears extremely hard in that the majority of students are consistently selecting the same wrong option. This may relate to the stem being ambiguous, poor delivery of the teaching with students having experienced difficulty in understanding the topic such that the chosen response seems plausible to everyone.
In those questions where the incorrect response is selected more frequently than the correct response but in only some ability groups (distractor), this suggests that the distractor option creates a strongly discriminating question. In this case less able students are more likely to select a distractor response while the more able students are likely to select the correct option.
Where most students select the correct answer, regardless of their overall ability level, the question provides little information to distinguish the ability of the students. Kehoe [31] has suggested that "questions that virtually everyone gets right are useless for discriminating between students and should be replaced by more difficult items", particularly in the case where the pass mark of the exam is criterion referenced. However as the question might test an essential component of the course that is important for all students to know it may be reasonable for such questions to be included in the examination even though they may be poor discriminators of low and high ability students. The content of the examination needs to have a mixture of questions that are discriminatory as well as those that define the pass standard (which would include questions that appear to be too easy but test an essential component of the course). If a question is too easy and does not test an essential component of the course then it needs to be revised.
The data presented in this paper on the analysis of response pattern within individual questions is purely descriptive. Although visual inspection of the response patterns is informative, Wang [32] is currently developing a quantitative Rasch model based on the analytical approach traditionally used in Classical Test Theory. This alternative would appear to provide a more statistical, as opposed to descriptive analysis about how the response options are working within the context of the Rasch measurement model.
The use of the extended matching format with up to 14 response options reduces the impact of guessing on the student's overall mark compared with standard multiple choice questions. However, it could also be argued that setting questions within themes puts the test at risk of a breach of the local independence assumption, in that responses to questions on the same theme may be locally dependent. The PCA of the residuals reject this and support the assumption of local independence.
Rasch analysis also allows for the development of a bank of questions that have been calibrated with one another in terms of their difficulty. This allows different examinations to be constructed (from combinations of the calibrated questions) while retaining the same level of overall difficulty. This will reduce the likelihood of students in consecutive years taking harder or easier exams when the standard they have to attain is unchanged.
Classical test theories (including Generalisability theory), widely used to investigate the quality of student assessment, make few assumptions about the characteristics of the questions such as whether they form a unidimensional construct. Therefore this approach can be used in a variety of measurement situations. A comparison between the classical approach and the Rasch approach with regard to discrimination is given in Figure 7. However the statistics obtained from classical analysis only apply to the specific group of students who took the test (i.e. the analysis is sample dependent). This analysis cannot separate the attributes of the questions from the attributes of student (e.g. ability) making it difficult to compare the performance of different sets of students who take the same format examinations with year on year content variations.
Figure 7 Graph of a misfitting EMQ (classic test theory and Rasch); EMQ 3.
In contrast, the Rasch measurement model checks two important assumptions: (a) the probability of answering one question correctly does not increase the probability of answering another question correctly within the examination (local independence) and (b) all questions in the examination map on to one construct (unidimensionality). With respect to a) above, questions that incorporate sections whose answers influence the response to other sections within the same question cannot be analysed using this approach and, to b), unidimensionality is a requirement for the summation of any set of items (33).
In addition, arguments have been made to use the two parameter and three parameter logistic models (the latter which adds a guessing parameter) [34] as these much better reflect the type of curve derived from educational data. Unfortunately, apart from sample size requirements which are very high for these type of model, it is known that almost 100% of the time their parameters violate interval scaling [35]. Thus these models do not provide the invariance or quality of measurement which is required for summative unidimensional scales. The property of the invariance of the ratio of difficulty across items (that is this ratio between any two items is constant, irrespective of the ability of the students) is again an essential requirement for measurement.
Finally, a consistent problem with criterion related tests is the apparent low reliability, as expressed by a low person separation index. This is to be expected, as traditional tests of reliability are not appropriate for criterion-referenced tests [36] where the score distribution is likely to be very narrow.
Conclusion
Case and Swanson [4,5] have set out clear guidelines on how to write an EMQ. The Rasch measurement model, and the associated analysis used in this study, will ideally be the next stage in the process of EMQ writing. It can be used to give feedback to the question writers on how to revise the problem questions. The analysis clearly shows how students use options, and this information coupled with expert knowledge and understanding of the questions will help questions writers to create and improve the quality of EMQs. It allows for a formal test of the unidimensionality of the questions and thus the validity of the summed score. Given the metric calibration which follows fit to the model, it also allows for the establishment of items banks to facilitate continuity and equity in exam standards. Thus modern psychometric methods based upon the work of Rasch provide a useful approach to the calibration and analysis of EMQ undergraduate medical assessments.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors have contributed to the analysis and writing of this paper and its revisions, and all have read approved the final version.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors are grateful for financial support from HEFCE Teaching Quality Enhancement Funds to undertake this project.
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| 15752421 | PMC555598 | CC BY | 2021-01-04 16:30:56 | no | BMC Med Educ. 2005 Mar 7; 5:9 | utf-8 | BMC Med Educ | 2,005 | 10.1186/1472-6920-5-9 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-451575243210.1186/1471-2105-6-45DatabasewFleaBase: the Daphnia genome database Colbourne John K [email protected] Vasanth R [email protected] Don G [email protected] Center for Genomics and Bioinformatics, Indiana University, Bloomington, Indiana USA2 School of Informatics, Indiana University, Bloomington, Indiana USA3 Department of Biology, Indiana University, Bloomington, Indiana USA2005 7 3 2005 6 45 45 1 10 2004 7 3 2005 Copyright © 2005 Colbourne 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
wFleaBase is a database with the necessary infrastructure to curate, archive and share genetic, molecular and functional genomic data and protocols for an emerging model organism, the microcrustacean Daphnia. Commonly known as the water-flea, Daphnia's ecological merit is unequaled among metazoans, largely because of its sentinel role within freshwater ecosystems and over 200 years of biological investigations. By consequence, the Daphnia Genomics Consortium (DGC) has launched an interdisciplinary research program to create the resources needed to study genes that affect ecological and evolutionary success in natural environments.
Discussion
These tools include the genome database wFleaBase, which currently contains functions to search and extract information from expressed sequenced tags, genome survey sequences and full genome sequencing projects. This new database is built primarily from core components of the Generic Model Organism Database project, and related bioinformatics tools.
Summary
Over the coming year, preliminary genetic maps and the nearly complete genomic sequence of Daphnia pulex will be integrated into wFleaBase, including gene predictions and ortholog assignments based on sequence similarities with eukaryote genes of known function. wFleaBase aims to serve a large ecological and evolutionary research community. Our challenge is to rapidly expand its content and to ultimately integrate genetic and functional genomic information with population-level responses to environmental challenges. URL: .
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Background
The micro-crustacean Daphnia is a ubiquitous resident of inland waters within all continents of the globe and is the subject of study for numerous biological disciplines including limnology, ecology, physiology, toxicology, population genetics and evolution. Many attributes make this organism an ideal model for ecological and evolutionary genomics research. As the principal grazers of algae and the primary forage of fish, Daphnia are key members of aquatic food webs and are easily sampled in great numbers. These animals inhabit remarkably diverse environments and show striking patterns of convergent evolution linked to specific habitat transitions [1]. Their mode of reproduction (cyclical parthenogenesis) is convenient for experimental genetics, providing both long-term clonal lineages and controlled outbred populations by manipulating the environmental cues required for the induction of male production and for mating [2]. Yet most notably, Daphnia offer unprecedented opportunities to study historical responses to environmental change, by harvesting, dating and resurrecting annually sedimented diapausing eggs within lacustrine basins and by competing past products of evolution against their modern descendents [3,4]. For these reasons, an international network of investigators is creating community resources, already proven to effectively promote genomic-scale investigations in other disciplines (molecular, cell and developmental biology), with a goal to understand connections between genome structure, gene expression, individual fitness and population-level responses to environmental challenges.
wFleaBase is a project of the Daphnia Genomics Consortium [5] and is designed to be a resource where users can search and retrieve sequence data for genes of ecological importance, or find putative genes modulating traits of interest based on their homologies to functionally characterized genes in other model organisms. Therefore, wFleaBase is an organized repository of Daphnia specific sequences with standard bioinformatic tools to facilitate gene discovery. This function includes BLAST analyses and links to gene reports for other eukaryotic genomic models via euGenes [6]. However, for most of these other model species, characterized genes are ineluctably biased toward those sets whose phenotypic effects are observed in the benign settings of a laboratory. With the additional goal of elucidating the function of novel genes with environment-specific expression patterns, wFleaBase is also designed to help locate genes with no known functions. For this purpose, a pipeline of bioinformatic tools is created to supply DNA markers from raw sequence trace files. Genetic map information on the location of variable DNA markers will soon be presented, allowing researchers to systematically screen genomic regions for the presence of quantitative trait loci (QTL) by using the available markers in their studies. Finally, to facilitate subsequent gene-specific capture by positional cloning, catalogues of available arrayed DNA libraries are displayed within the DGC web pages.
The main sources of data for wFleaBase are direct submissions from DGC members and from research at large genome sequencing centers. The latest data can be accessed by web browser at and Internet file transfer at .
Construction and content
Generic genome database
This service is built using tested genome database components and open source software that are shared in common with several other databases. Middleware in Perl and Java are added to bring together BLAST, sequence reports, searches and other bioinformatics programs for web access. The Indiana University Genome Informatics Laboratory houses wFleaBase, along with related genome databases FlyBase [7] and euGenes [8]. In the last two years, this work is coalescing with sister organism database projects under the umbrella of Generic Model Organism Database project (GMOD [9]). The relational database from GMOD [10] used for FlyBase and wFleaBase is named Chado (after "the Way of Tea" ceremony). It includes a schema for structuring a growing range of genome information, works with the free PostgreSQL database package (among others), and includes a Chado XML exchange format and tools. Significantly, a community of bioinformaticians is sharing development and use of these components. Another project that has made wFleaBase simple to start is Argos (D.G. Gilbert et al., in preparation [11]), a framework for building and distributing genome databases, with pre-configured core components listed in Table 1. A third basic GMOD component of wFleaBase is LuceGene (D.G. Gilbert et al., in preparation), which provides rapid, data object-oriented searches, with data and document retrieval of a wide range of genome information.
To start wFleaBase, we copied a genome database/web server template from Argos infrastructure, including Chado database and genome informatics tools, loaded the database with a first set of sequences, and produced BLAST comparisons of these against 10 other eukaryote genomes from the euGenes project. The euGenes project provides a standard summary of gene and genome information from eukaryotic organisms, and includes over 200,000 named genes and their functions, with 900,000 genome features. This eukaryote genome collection allows new genomes (like Daphnia's) to be matched by sequence similarity, then annotated with reference gene information. When Daphnia or other new organism sequences are matched to this data, it suggests their gene function and provides starting points for experiments into their ecological and evolutionary genetic significance.
wFleaBase records and accession numbers
Sequences are assigned unique and stable accession numbers upon entry into the Chado database, which are organized into seven divisions according to whether they are derived from genome survey sequences (GSS), expressed sequence tags (EST) or high-throughput genomic (HG) and cDNA (HC) projects. Daphnia sequences from other public databases (PB), mitochondrial sequences from molecular systematic studies of the genus (MT) and amplicons of microsatellite DNA markers (MS) are also categorized. To date, wFleaBase contains 14,451 records, including EST (WFes0000001-WFes0012408), GSS (WFgs0000001-WFgs0001495) and MS (WFms0000001-WFms0000548) sequences. Each sequence header provides a short description on the type of sequence, the species and strain from which the sequence was obtained, the library identification code for the cloned fragment with synonyms and contact name. Full contact information is provided elsewhere [12]. For convenience, sequences can be downloaded in fasta format from each division via the FTP service or by navigating to specific Data sub-directories of the Genomics hyperlink, which is printed on the side menu of the web pages. At this moment, researchers are requested to send new Daphnia sequences to the corresponding author, for processing and archiving the data into wFleaBase. Submissions undergo quality assurance checks for vector contamination and correct taxonomy before they enter the database.
Utility
Gene searching and discovery
As an information and gene discovery system, wFleaBase focuses on providing efficient tools for searching and retrieving records of interest. Its current features are best highlighted by co-navigating the web pages along with a user interested in locating ecologically relevant genes, for example, genes that confer resistance to elevated levels of ultra-violet radiation encountered by closely related species to D. pulex. Beginning at the welcome page, the user can navigate via the hyperlink located at the top menu towards the Blast page of wFleaBase to perform sequence-similarity searches on the archived data using the BLAST family of programs. The user enters a nucleotide sequence, whose gene function is well characterized and evolutionarily conserved, with a goal to find the homologous gene in Daphnia. For example, a Drosophila melanogaster mRNA sequence obtained from GenBank (NM 165564) or from FlyBase (FBgn0003082) for the gene photorepair is used to query all Daphnia sequences using the default settings of the tblastx program. Alternatively, the user can select to query species-specific GSS or EST databases. This search retrieves record WFgs0000440, which is a 917 nucleotide sequence with a best match score of 83 bits and an E-value of 5e-45. Using this information, the user can then download the Daphnia sequence onto their personal computers as a text file, design primers using their own software to probe the arrayed Daphnia cosmid library by the Polymerase Chain Reaction (PCR), identify bacterial clones containing the gene, and characterize the entire locus by sequencing. Indeed, this specific exercise identifies at least three cosmids (out of 37,000) containing a likely homologue to photorepair from Drosophila [13].
Returning to the welcome page, the user can instead choose to explore tables containing data extracted from automated BLAST searches against the euGenes database, which includes annotated genome sequences from 10 eukaryotic model organisms. Although this option for gene searching is more tedious, it does allow users to focus precisely on the data currently available in wFleaBase. Four tables of BLAST results are offered at by following the "Genomics" hyperlinks located at the top and side menus. At present, Daphnia EST and GSS sequences are each compared to the protein coding genes and to genomic sequences in euGenes. Many options exist for sorting the BLAST tables. The user can specify what BLAST result columns to show, and can sort these columns based on the ascending or descending order of their entries. The tables can also include BLAST results against all organisms within euGenes or the tables can be filtered to include results from comparisons against a single taxon. For example, the same user, now looking to find a Daphnia homologue to genes known to confer salt-resistance to species inhabiting saline environments, begins by searching for names or euGenes accession numbers of functionally related genes within the Blast tables using the wFleaBase search function located in the top menu of the Blast tables. If the user chooses to search for "ATPα", which is a sodium/potassium-exchanging ATPase shown to be under positive selection in brine shrimp populations adapted to ultra-saline waters [14], 11 EST records that match ATPα in fly are discovered with bit scores and E-values ranging from 42.36 and 0.002 to 327.0 and 2.2e-89. The user can retrieve the Daphnia sequences via hyperlinks located in the first column of the search results, or further uncover the extent of evolutionary conservation for this gene by examining the euGene Reports, also via hyperlinks located in the last column. Alternatively, if the user chooses to use the FlyBase accession number for this gene (FBgn0002921) to retrieve Daphnia homologues using the search function, the same 11 records are obtained.
Tools for hunting unknown genes
Although effective, the candidate gene approach to finding Daphnia genes of ecological interest is limited by the levels of sequence and functional conservation among characterized genes in other model organisms. Work is underway by the DGC to create the required tools for identifying ecologically relevant genes by positional mapping using microsatellite markers. wFleaBase presently archives 528 microsatellite markers [15]. Yet, to generate additional loci for genetic mapping in D. pulex and D. magna, wFleaBase integrates a suite of computational programs that (i) identifies microsatellites from raw DNA sequencer trace files, (ii) designs optimal primers for amplifying the markers and (iii) indexes the amplicon, microsatellite motifs and primer information into the Microsat database [16]. The Microsat database will rapidly grow by applying this pipeline to trace files emerging from the Daphnia genome sequencing project.
The wFleaBase search function
wFleaBase uses LuceGene to support rapid search and retrieval of the sequence database, of Blast table entries, of Daphnia Medline references and of Daphnia web documents. LuceGene [17], based on the Lucene [18] search system, is an open-source part of the GMOD project. A major benefit of LuceGene is the large variety of data formats that can be added to the search system with minimal work. For instance, currently supported formats used in wFleaBase include Simple text, XML (Medline abstracts and Gene sequence annotation), HTML, Tabular data, Bio-formats (Fasta, GenBank, EMBL) and Gene object data used by euGenes. Search terms such as "magna" to retrieve all sequences from this species, can be entered in a Search box at the head of all web pages. The search is refined at the main wFleaBase search page by specifying the search library (sequences, references, documents or Blast tables) and the library fields containing the queried term. Options are also available to detail the output format, and each result is hyperlinked to the source document for easy access to the data. On a separate web page (Batch download), users can recover multiple records obtained from complex queries and save the results to a file.
Discussion and conclusions
The wFleaBase project is young. Its future success is linked to the DGC and its goal to develop Daphnia into a functional genomics research model, with the added advantage of interpreting observations in the context of natural ecological challenges. wFleaBase is currently designed to facilitate gene discovery for immediate use in research projects. However, the functionality of this service will grow in equal pace with the rapid accumulation of genomics data. Within the next year, this site will host the full genome sequence for D. pulex – a collaborative project involving the U.S. Department of Energy Joint Genome Institute, the Environmental Protection Agency and the DGC. Genetic maps for D. pulex and D. magna are also under construction. Therefore, wFleaBase will soon be enhanced by implementing the CMap module of GMOD and allow users to compare the Daphnia genetic and physical maps emerging from current research and to choose the most appropriate set of markers for quantitative trait locus (QTL) mapping projects. Simultaneously, wFleaBase will also assemble cDNA sequences from another large DGC sequencing effort aimed to document most of the Daphnia transcriptome. Daphnia gene reports will be created in accordance to standards set by current model organism databases [19]. Gbrowse genome browser and Apollo annotation editor will be used for viewing genome features when they are available. Informatics efforts focus on implementing existing database tools rather than development of new ones, providing a cost-effective genome database for these species. In this way, the current format linking Daphnia genomic information to other model species will be reinforced, allowing greater opportunities to apply the candidate gene approach for identifying genes of ecological importance.
Availability and requirements
wFleaBase is publicly available and can be accessed at using web browsers and at by using internet file transfer protocols.
Authors' contributions
JC contributed data, web documentation and aided in the overall design and functionality of this database. VS contributed programming for both the database and Blast searches, and contributed to its design and development. DG contributed the generic database framework design, the overall web structure, the euGenes data and built GMOD database and Blast tools. All authors read and approved the final manuscript.
Acknowledgements
The bioinformatics work here has been supported by the NSF grant 0090782 and the NIH grant 1R01HG002733-01 to D. Gilbert. This project was also financed by grants to M. Lynch (Indiana University), J. Hamilton (Dartmouth College), and J. Colbourne from the National Science Foundation (FIBR, DEB) and by seed funds from the Center for Genomics and Bioinformatics, supported in part by the Indiana Genomics Initiative (INGEN) under the Lilly Endowment. We thank T. Crease (University of Guelph) and H. Watanabe (Okazaki National Research Institutes) for contributing data. S. Lourido (Tulane University) created the elegant logo.
Figures and Tables
Table 1 GMOD and Argos components used in wFleaBase
Section Components
wFleaBase Data, database files, documents, web tools specific to Daphnia
Java Chado database tools, genome sequence reports, LuceGene search, Ant build system, database interfaces, XML tools, Tomcat web server, Axis web services
Perl BioPerl, GBrowse, Chado database tools, Cmap comparative maps, database interfaces, Web tools, XML tools
Servers BLAST (NCBI), Apache web server, PostgreSQL, and BerkeleyDB databases
Systems Compiled portions for supported operating systems
Install & Root Common configurations, web server, installation scripts and instructions
==== Refs
Colbourne JK Hebert PDN Taylor DJ Givnish TJ and Sytsma KJ Evolutionary origins of phenotypic diversity in Daphnia Molecular Evolution and Adaptive Radiation 1997 Cambridge, Cambridge University Press 163 188
Pfrender ME Lynch M Quantitative genetic variation in Daphnia: temporal changes in genetic architecture Evolution 2000 54 1502 1509 11108579
Hairston NG Holtmeier CL Lampert W Weider LJ Post DM Fischer JM Caceres CE Fox JA Gaedke U Natural selection for grazer resistance to toxic cyanobacteria: Evolution of phenotypic plasticity? Evolution 2001 55 2203 2214 11794781
Cousyn C De Meester L Colbourne JK Brendonck L Verschuren D Volckaert F Rapid, local adaptation of zooplankton behavior to changes in predation pressure in the absence of neutral genetic changes Proceedings of the National Academy of Sciences 2001 98 6256 6260 10.1073/pnas.111606798
DGC: Daphnia Genomics Consortium
euGenes: A eukaryote organism genome information service
The FlyBase Consortium The FlyBase database of the Drosophila Genome Projects and community literature Nucleic Acids Research 1999 27 85 88 9847148 10.1093/nar/27.1.85
Gilbert DG euGenes, a eukaryote organism genome information service Nucleic Acids Research 2002 30 145 148 11752277 10.1093/nar/30.1.145
Stein LD Mungall C Shu S Caudy M Mangone M Day A Nickerson E Stajich JE Harris TW Arva A Lewis S The generic genome browser: a building block for a model organism system database Genome Research 2002 12 1599 1610 12368253 10.1101/gr.403602
GMOD: Generic Model Organism Database
Argos: A Replicable Genome infOrmation System
DGC: People database
DGC: Cosmid library resource
Saez AG Escalante R Sastre L High DNA sequence variability at the alpha 1 Na/K-ATPase locus of Artemia franciscana (brine shrimp): Polymorphism in a gene for salt-resistance in a salt-resistant organism Molecular Biology and Evolution 2000 17 235 250 10677846
Colbourne JK Robison B Bogart K Lynch M Five hundred and twenty eight microsatellite markers for ecological genomic investigations using Daphnia Molecular Ecology Notes 2004 4 485 490 10.1111/j.1471-8286.2004.00721.x
DGC: wFleaBase software
GMOD: Document/object search and retrieval for genome databases
AJP: The Apache Jakarta Project, Lucene
Stein LD Integrating biological databases Nature Reviews Genetics 2003 4 337 345 12728276 10.1038/nrg1065
| 15752432 | PMC555599 | CC BY | 2021-01-04 16:02:52 | no | BMC Bioinformatics. 2005 Mar 7; 6:45 | utf-8 | BMC Bioinformatics | 2,005 | 10.1186/1471-2105-6-45 | oa_comm |
==== Front
BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-5-251574354010.1186/1471-2407-5-25Research ArticleAngiogenesis in cancer of unknown primary: clinicopathological study of CD34, VEGF and TSP-1 Karavasilis Vasilis [email protected] Vasiliki [email protected] Evangelos [email protected] Elena [email protected] Evangelia [email protected] Haralambos [email protected] George [email protected] Theodore [email protected] Nicholas [email protected] Medical Oncology Department, Ioannina University Hospital, Ioannina, Greece2 Sarcoma Unit, Royal Marsden Hospital, London, UK3 Department of Pathology, Ioannina University Hospital, Ioannina, Greece4 Medical Oncology Department, Patras University Hospital, Rion, Greece5 Medical Oncology Department, School of Medicine, Aristotle's University of Thessaloniki, Greece6 Laboratory of Biochemistry, School of Medicine, University of Ioannina, Ioannina, Greece7 Biomedical Research Institute-Foundation for Research and technology (BRI-FORTH), Ioannina, Greece2005 3 3 2005 5 25 25 17 12 2004 3 3 2005 Copyright © 2005 Karavasilis 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
Cancer of unknown primary remains a mallignancy of elusive biology and grim prognosis that lacks effective therapeutic options. We investigated angiogenesis in cancer of unknown primary to expand our knowledge on the biology of these tumors and identify potential therapeutic targets.
Methods
Paraffin embedded archival material from 81 patients diagnosed with CUP was used. Tumor histology was adenocarcinoma (77%), undifferentiated carcinoma (18%) and squamous cell carcinoma (5%). The tissue expression of CD34, VEGF and TSP-1 was assessed immunohistochemically by use of specific monoclonal antibodies and was analyzed against clinicopathological data.
Results
VEGF expression was detected in all cases and was strong in 83%. Stromal expression of TSP-1 was seen in 80% of cases and was strong in 20%. The expression of both proteins was not associated with any clinical or pathological parameters. Tumor MVD was higher in tumors classified as unfavorable compared to more favorable and was positively associated with VEGF and negatively with TSP-1.
Conclusion
Angiogenesis is very active and expression of VEGF is almost universal in cancers of unknown primary. These findings support the clinical investigation of VEGF targeted therapy in this clinical setting.
==== Body
Background
Cancer of unknown primary (CUP) is a unique clinical entity that accounts for an approximately 3% of human cancers[1]. Patients with CUP present with metastases for which the site of origin cannot be identified at initial workup. Early dissemination, unpredictability of metastatic pattern and aggressiveness constitute fundamental characteristics of these tumors. Although the clinical characteristics of CUP have been established, little is known about the underlying biology of these tumors [2,3].
Angiogenesis, the formation of new vessels, is essential for tumor growth and the development of metastases. It evolves though a complex multifactor process that involves interaction of pro-angiogenic and anti-angiogenic signals from tumor, endothelial and stromal cells. The angiogenic activity is reflected in the development of novel microvessels in tumor tissue that is quantified by the intratumoral microvessel density (MVD). Among several molecules implicated, Vascular Endothelial Growth Factor (VEGF) and Thrombospondin-1 (TSP-1) appear to be most relevant. Much evidence indicates that VEGF is a key activator of angiogenesis[4,5] and TSP-1 a primary endogenous inhibitor of angiogenesis[6] Up to now, no useful prognostic factors have been established other than the classic pathologic and laboratory ones and immunohistochemical detection of various factors did not add prognostic value in CUP.[7,8] Moreover, investigation of the expression of crucial angiogenesis factors that can be therapeutically targeted is today of great interest for the oncologists who deal with CUP clinical research.[9]
We were prompted to investigate angiogenesis in unknown primary cancer in an attempt to enrich our understanding of the biology of these tumors. We studied by immunohistochemistry the tissue expression of VEGF and TSP-1 in CUP and correlated with MVD and clinicopathological parameters. In a recently published study vascular endothelial growth factor, and CD34, factors were not found to be of prognostic value in adenocarcinoma of unknown primary.[8]
Methods
A total of 81 patients diagnosed with CUP and treated in three University Medical Oncology Settings (Ioannina, Patras and AHEPA, Thessaloniki, Greece) between January 1997 and December 2002 were selected on the basis of availability of archival tumor tissues and accessibility to medical notes. Pathology diagnosis was reviewed by two pathologists blinded to written pathology report and representative paraffin blocks were selected for immunohistochemistry.
Subgroup definition
Eligible cases categorized into unfavorable and more favorable subgroups (tables 1 and 2). Patients with poorly differentiated carcinoma with midline distribution, papillary adenocarcinoma of peritoneal cavity and adenocarcinoma involving only axillary lymph nodes in women, squamous cell carcinoma involving cervical lymph nodes and poorly differentiated neuroendocrine carcinomas were assigned to favorable CUP subsets. Patients with adenocarcinoma metastatic to the liver, multiple visceral involvement and extensive metastatic bone disease were considered as unfavorable.
Systemic chemotherapy was given in 64 patients (78%); four patients with cerebral metastases received whole brain irradiation. Chemotherapy was consisted of a platinum based combination. Objective response to chemotherapy was observed in 34 patients (53%) while one patient with brain metastases responded to radiotherapy. Median survival for all patients was 10.5 months (Figure 1). Patients belonging to favorable subsets had a significantly higher response rate to treatment (Fisher's t-test, p = 0.04) and a longer survival, 11.5 vs 8.5 months (p = 0.01).
Immunohistochemistry
Immunostaining was performed on formalin-fixed, paraffin-embedded tissue sections by the labeled streptavidin avidin biotin (LSAB) method. In brief, tissue sections were deparaffinised in xylene and dehydrated. They were immersed in citrate buffer (0,1 m, pH 0,6) and subjected to microwave twice for 15 min. Subsequently, all sections were treated for 30 min with 0,3% hydrogen peroxide in methanol to quench endogenous peroxidase activity. Mouse monoclonal antibodies directed against human CD34 antigen (M 7165, Dako) in dilution 1/50 β) VEGF Ab-3 (isoform 121, clone Jh121, Neomarkers) in dilution 1/50 and c) thrombospondin (Mob 315, DBS) in dilution1/50 were used. Positive control slides were included in all cases. All dilutions were made in TBS-1% BSA solution and were followed by overnight incubation.
The assessment of immunostaining was made by two experienced pathologists using light microscope. Tumor specimens too small to provide sufficient sections for all the immunoassaying procedures were disregarded from the study.
Immunostaining evaluation
Staining of endothelial cells for CD34 was used to evaluate the MVD. Any CD34 positive endothelial cell clusters clearly separated from each other were considered as single countable microvessels. A lumen was not required to identify a vessel. Larger vessels with muscular walls were excluded from counting. In each sample three areas of most prominent vascular density (hot spots) were identified at ×40 power field and microvessel counting was done under ×400 magnification. Counting was performed by two independent observers blinded to clinical information. The median count was used to make distinction between low and high MVD.
Immunoreactivity for VEGF was observed in stromal and epithelial cells. Only staining of tumor cells was considered for analysis. To evaluate the expression of VEGF protein, we devised a combined score that corresponds to the sum of staining intensity (0 = negative, 1 = weak, 2 = intermediate, 3 = strong staining) and percentile quadrants of positive cells (0 = 0%, 1 = 1–25%, 2 = 26–50%, 3 = >50%). The maximum score was 6. Score 2 was regarded to represent weak expression, score 3 intermediate and score 4–6 strong expression.
Staining for TSP-1 was only considered in regard to extracellular matrix. The expression of TSP-1 was characterized according to the extent and the intensity of staining classified as negative, +1: mild, focal, +2: intermediate, multifocal, +3: strong, diffuse reactivity.
Statistics
Staining results were analyzed against clinical subgroup, histological differentiation, response to treatment and survival. The association between MVD and clinical subgroup, histological differentiation and response to treatment was assessed by an unpaired t test. A Fisher's exact test was used to determine associations between VEGF and TSP-1 and the clinical subgroup, histological differentiation and response to treatment. Spearman non parametric correlation test was used for associations between MVD, VEGF and TSP-1.
Survival was calculated by Kaplan-Meier method and comparison of survival curves was performed by the log-rank test. For statistical significance a two-tailed p value was considered. The Graphpad Instat version 3.05 (Graphpad Software, Inc, San Diego, CA) and Prism version 4 (Graphpad Software, Inc, San Diego, CA) software programs were used for statistical analysis and graphing.
Results
Demographics of studied cases are depicted in Tables 1 and 2. Overall survival of patients included in this study is shown in Figure 1. (Median survival 10 months).
Immunohistochemistry
Microvessel density
Widespread staining for CD34 was seen in all tumor specimens. Within the most prominent vascular areas of the tumors the recorded mean MVD was 59 microvessels/mm2 (range, 16 to 300 microvessels/mm2) (Table 3, figure 2). A positive association was observed between VEGF expression and MVD (Spearman r = 0.36, p = 0.0016) and negative with TSP-1.
VEGF expression
Positive staining of tumor cells for VEGF, both membranic and cytoplasmic was observed in all cases. This was strong in 83% of the cases and moderate in 17%. VEGF staining was heterogeneous within tumors comprising of areas of intense and also weak staining and demonstrated a characteristic granular cytoplasmic staining pattern. (Figure 3) A weak and focal positive reaction of intervening stromal cells (fibroblasts or/and macrophage) and endothelial cells was also seen in some cases. This staining was excluded from VEGF analysis. (Table 3)
TSP-1 expression
Stromal TSP-1 staining was detected in 80% of the cases, while in 20% it was absent (figure 4). Strong (score = 3)and intermediate (score = 2) TSP-1 staining was observed in 50% of cases and weak (score = 1) in 30% (Table 3). A negative association was observed between TSP-1 expression and MVD, (Spearman r = -0.3426, p = 0.003) while there was no association between TSP-1 and VEGF expression.
Association between immunostaining and clinicopathological variables
MVD was found statistically higher in unfavorable CUP cases compared to more favorable ones (70 vs 46 microvessels/mm2, t test, p = 0.034) (table 4). This was the only correlation detected between angiogenesis related tissue markers studied and clinicopathological variables. No association was detected between VEGF and TSP-1 and tumor differentiation, response to treatment, clinical subgroups and survival (Table 4).
Discussion
The investigation of the biological profile of CUP and the understanding of molecular pathways underlining these tumors has been limited. We have worked on these issues and found several oncoproteins overexpressed, but failed to establish any clinically relevant correlations[10,11]. We now investigated neo-angiogenesis by assessing MVD and the tissue expression of two representative molecules involved in angiogenesis; the major stimulator of angiogenesis VEGF (A), and the intrinsic angiogenic inhibitor TSP-1. Overall, we demonstrated that a high angiogenetic activity occurs in CUP tumors, which is higher in unfavorable when compared with more favorable subsets.
VEGF is known to play a key role and MVD is considered to reflect the final result of the tumor angiogenesis cascade. In the present study, all cases were found to be VEGF-positive and in the majority VEGF was overexpressed. Tumor VEGF and MVD were strongly correlated that is in line with findings in solid tumors[12,13]. We failed to demonstrate any significant correlations of angiogenic activity with regard to clinical outcome, but this was obviously due to universal expression of both CD34 and VEGF in our cases.
We also demonstrated that in our series TSP-1 was overexpressed in 50% and was absent or weak in approximately half of the cases. TSP-1 correlated inversely with microvessel counts. The role of TSP-1 in epithelial tumor growth and metastases remains controversial. In vitro studies suggest that TSP-1 may promote tumor cell adhesion and invasion by up-regulating urokinase plasminogen activator and its receptor[14] but in clinical studies overexpression has been associated with a lower MVD score and a better clinical outcome in several carcinomas[15]. Moreover, other studies suggest that TSP-1 inhibits tumor progression and may serve as an indicator of less aggressive potential and of favorable prognosis in solid tumors.[16] We consider that low TSP-1 in our material reflects a suppression of anti-angiogenic mechanism of TSP-1 that possibly contributes to the aggressiveness of these tumors.
CUP patients have in general a brief life expectancy with a median survival approximately of 3–9 months.[17,18] It must be emphasized that CUP diagnosis applies to a heterogeneous group of patients who are usually grouped together in biological and therapeutic studies to obtain statistically meaningful results. However several patients fare better and enjoy longer survival and within this more favorable prognostic subgroup, unique subsets, such us young patients with midline tumors and women with peritoneal carcinomatosis or isolated axillary adenocarcinoma, have a distinct clinical biology compared to others also classified as unknown primary cancer. [19-23]. In our study MVD score were found low in the group of more favorable tumors compared to unfavorable, but neither MVD nor VEGF or TSP-1 were associated with known prognostic factors.[24] Similarly, Hillen et al, in a small study, evaluated MVD as a prognostic factor for patients with liver metastases of unknown primary and found that MVD score correlated with marginally shorter survival.[25]
Conclusion
In conclusion, we found that angiogenesis is very active and VEGF expression is universal in cancer of unknown primary, which supports the clinical investigation of VEGF targeted therapy in this clinical setting.[9] To identify additional druggable molecular targets in cancer of unknown primary we need to advance our knowledge on the biology of these tumors and validate novel molecular therapeutics.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
NP conceived, coordinated and designed the study, interpreted the data and drafted the manuscript; VK, EB, designed and carried out the study, performed the statistical analysis, interpreted the data and drafted the manuscript; VMM carried out the pathological and immunohistochemical study, interpreted data and drafted the manuscript; ET, EK carried out the pathological and immunohistochemical study, interpreted data and drafted the manuscript; HK, GF, TF participated in designing the study, acquisition and interpretation of data and revising critically the manuscript. All of the authors have 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 Survival curve of 81 CUP patients analyzed
Figure 2 High-density of neoplastic vessels highlighted by stained anti-CD34 antibody in a case of a poorly differentiated adenocarcinoma of unknown primary (Original magnification 200, counterstained with hematoxylin).
Figure 3 Strong cytoplasmic immunohistochemical staining of tumor cells for VEGF in a CUP case. It is shown the characteristic granular cytoplasmic staining pattern and the occasionally weak positive reaction of intervening stromal and endothelial cells (original magnification ×200, counterstained with hematoxylin)
Figure 4 Diffuse strong immunoreactivity of extracellular matrix for TSP-1 adjacent to neoplastic cell population in a CUP case. (Original magnification ×400, counterstained with hematoxylin)
Table 1 Demographics.
Characteristic No (%)
Patients 81
Sex
Male 39 48
Female 42 52
Age (Years)
Median 66
Range 37–84
PS
Median 1
Range 0–3
Histology
Adenocarcinoma 62 77
Well- and moderately differentiated 27 43
Poorly differentiated 35 57
Undifferentiated carcinoma 15 18
With neuroendocrine features 5 33
Other undifferentiated neoplasms 11 67
Squamous cell carcinoma 4 5
Metastatic sites at presentation
Liver and/or multiple visceral involvement 17 21
Lymph nodes 23 29
Mediastinal-retroperitoneal 17 74
Axillary 2 9
Cervical 4 17
Peritoneal cavity 19 23
Peritoneal adenocarcinomatosis in females 14 74
Malignant ascites and other site 5 26
Lung 5 6
Bones 5 6
Brain 4 5
Neuroendocrine tumors 4 5
Multiple 4 5
Table 2 CUP subsets and outcome.
Subgroups (#) (%)
Favorable 50 62
Unfavorable 31 38
Treatment (#) (%)
Chemotherapy 64 78
Radiotherapy 4 6
Median survival (months)
All cases 10.5
favourable cases 11.5
unfavorable cases 8.5
Table 3 Assessment of tissue expression of VEGF, TSP-1 and CD34.
VEGF TSP-1 MVD (CD34)
Immunostaining score
Median 5 2 59 microvessels/mm2
(range) 2–6 0–3 16–300
Expression intensity (% of cases)
Negative 0 20
Weak 0 30
Intermediate 17 30
Strong 83 20
Table 4 Immunohistochemical expression of CD34 (MVD). VEGF, and TSP-1 and correlations.
Variable MVD (counts) VEGF (%) TSP-1 (%)
Median CD 34 P value Low (<4) High (≥4) P value Low (<2) High (≥2) P value
Age ns ns ns
< 65 62 20 80 35 27
> 65 58 15 85 22 16
Tumor differentiation ns ns ns
Moderate and well 55 3 28 12 20
Poorly and undifferentiated 59 14 55 32 36
Clinical subgroups 0.034 ns ns
More favorable 46 6 46 28 23
Unfavorable 70 11 37 22 27
Treatment outcome ns ns ns
Objective response 58 11 39 22 28
No response 59 4 46 25 25
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| 15743540 | PMC555600 | CC BY | 2021-01-04 16:03:04 | no | BMC Cancer. 2005 Mar 3; 5:25 | utf-8 | BMC Cancer | 2,005 | 10.1186/1471-2407-5-25 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-491575752110.1186/1471-2105-6-49Research ArticleA configuration space of homologous proteins conserving mutual information and allowing a phylogeny inference based on pair-wise Z-score probabilities Bastien Olivier [email protected] Philippe [email protected] Sylvaine [email protected]échal Eric [email protected] UMR 5019 CNRS-CEA-INRA-Université Joseph Fourier, Laboratoire de Physiologie Cellulaire Végétale; Département Réponse et Dynamique Cellulaire; CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France2 Gene-IT, 147 avenue Paul Doumer, F-92500 Rueil-Malmaison, France3 Département d'Ecophysiologie Végétale et de Microbiologie; CEA Cadarache, F-13108 Saint Paul-lez-Durance, France4 Laboratoire de Biologie, Informatique et Mathématiques; Département Réponse et Dynamique Cellulaire, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France2005 10 3 2005 6 49 49 25 11 2004 10 3 2005 Copyright © 2005 Bastien 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
Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons) and be the basis for a novel method of consistent and stable phylogenetic reconstruction.
Results
We have built up a spatial representation of protein sequences using concepts from particle physics (configuration space) and respecting a frame of constraints deduced from pair-wise alignment score properties in information theory. The obtained configuration space of homologous proteins (CSHP) allows the representation of real and shuffled sequences, and thereupon an expression of the TULIP theorem for Z-score probabilities. Based on the CSHP, we propose a phylogeny reconstruction using Z-scores. Deduced trees, called TULIP trees, are consistent with multiple-alignment based trees. Furthermore, the TULIP tree reconstruction method provides a solution for some previously reported incongruent results, such as the apicomplexan enolase phylogeny.
Conclusion
The CSHP is a unified model that conserves mutual information between proteins in the way physical models conserve energy. Applications include the reconstruction of evolutionary consistent and robust trees, the topology of which is based on a spatial representation that is not reordered after addition or removal of sequences. The CSHP and its assigned phylogenetic topology, provide a powerful and easily updated representation for massive pair-wise genome comparisons based on Z-score computations.
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Background
Past events that gave birth to biological entities can be tentatively reconstructed based on collections of descriptors traced in ancient or present-day creatures. Using genomic sequences, an estimate of the relative time separating branching events, previously supported by geological records, could be formalized using mathematical models. The use of proteins for evolutionary reconstructions was vastly explored as soon as the first amino acid sequences were made available [1-9]. The rich biological information contained in protein sequences stems from their being, on the one hand, translation of genes that reflect the history of genetic events to which the species has been subjected, and on the other hand, effectors of the functions constituting a living creature [10] Since protein sequences are encoded in a 20-amino acid alphabet, they are also considered to embody more information-per-site than DNA or RNA [11]; they also exhibit smaller compositional trends [12,13]. When compared, sequences that share substantial features are considered as possible homologues [14], based on the fundamental postulate that can be simply stated as "the closer in the evolution, the more alike and conversely, the more alike, probably the closer in the evolution".
As summarized by Otu and Sayood [15], the techniques of molecular phylogenetic analyses can be divided into two groups. In the first case, a matrix representing the distance between each pair of sequences is calculated and then transformed into a tree. In the second case, a tree is found that can best explain the observed sequences under evolutionary assumptions, after evaluation of the fitness of different topologies. Some of the approaches in the first category utilize distance measures [16-19] with different models of nucleotide substitution or amino acid replacement. The second category can further be divided into two groups based on the optimality criterion used in tree evaluation: parsimony [20,21] and maximum likelihood methods [22,23]. For a detailed comparison of these methods see [24].
In phylogeny inference based on distance methods, features separating related proteins are used to estimate an observed distance, also called the p-distance, the simplest measure of which is just the number of different sites between proteins. Divergence time (t), also called genetic distance or evolutionary time, is calculated from the p-distance, depending on assumptions derived from evolutionary models [11,24]. For example, the assumption that mutational events happen with equal probability at each site of any sequence leads to the molecular clock model [2]. Although widely used, it is well-known to be unrealistic and numerous corrections have been proposed to refine it [19,25,26]. By definition, the distance matrix is given as T = (tab) where a and b represent the homologous sequences from the analyzed dataset. Tree reconstruction algorithms are then applied to these matrices [11,24]. Eventually, phylogenetic trees corresponding to the classified sequences are statistically evaluated with bootstrap methods and, when available, calibrated using dated fossils [25,26].
Doolittle et al. [27] have proposed methods for converting amino acid alignment scores into measures of evolutionary time. Similarity between amino acids [28-30] provides a way to weight and score alignments [31]. In practice the optimal alignment of two sequences (a and b) is determined from the optimal score s(a,b) [25,27], computed with a dynamic programming procedure [32,33]. In aligned sequences, conservation is measured at identical sites, whereas variation is scaled at substituted sites. To estimate the variation/conservation balance, the p-distance can be given as a function of fid, the fraction of identical residues: p-distance = 1 - fid. To take into account that multiple mutations can happen at the same site, an expression of fid was proposed by Doolittle et al. [27] using s(a*,b*), the score obtained from randomized a and b sequences [34] and sid, the average score of the sequences compared with themselves [19,25,27]:
To connect pair-wise alignments and phylogeny, divergence time has been approximated:
t(a,b) = -λ log[fid (a,b)] (2)
introducing a Poisson correction [2] as a reasonable stochastic law relating amino acid changes and elapsed time. As mentioned earlier, adjustments and corrections of equation (2) were proposed to fit more realistically the complexity of evolution [11,25,35]. This attempt of unification helped reconstructing phylogeny of major lineages [27]. However, detailed phylogenic trees obtained from evolutionary close sequences are not satisfactory. In practice, phylogenies are reconstructed based on multiple alignments. Multiple alignment based (MAB) trees are re-calculated when incremented with additional sequences; although MAB methods are usually considered accurate, numerous cases of inconsistencies (incongruence) between observed data and deduced MAB trees are recorded (see [15,36]).
Here, we re-examine the estimate of the p-distance between two homologous sequences, based on fid, as a source for geometric positioning, divergence time calculations and evolutionary reconstruction. We based our model on mathematical properties that alignment scores should respect; i) information theory [37,38] applied to sequence similarity, ii) algorithmic theory applied to alignment optimization [28] and iii) alignment probability, particularly in conformity with the TULIP theorem [39]. We used these properties as a framework of constraints to build a geometric representation of a space of probably homologous proteins and define a theoretically explicit measure of protein proximity. This unified model conserves information in the way physical models conserve matter or energy. The obtained representation of protein sequences is unaltered by adding or removing sequences. Applications include therefore the reconstruction of evolutionary consistent and robust trees, the topology of which is based on a spatial representation that is not reordered after addition or removal of sequences.
Results and discussion
Pair-wise sequence alignment scores in information theory
Criteria to measure the variation/conservation balance between proteins should embody as much as possible the structural and functional potentiality within sequences of amino acids. In the absence of explicit physical criteria, amino acid similarity was solved empirically by measuring amino acid substitution frequencies in alignments of homologous sequences [30,40]. Given two amino acids i and j, the similarity function s(i,j) was set as:
where is the observed frequency of substitution of i by j or j by i, and πi and πj are the frequencies of i and j in the two aligned sequences. The frequency is the estimate of the probability of substitution of i by j in real alignments; whereas πiπj is the estimate of the probability of substitution under the independency hypothesis. The similarity function gives a 20 × 20 similarity matrix usable to score protein sequence alignments, that can be interpreted in the information theory [37,38] according to the following proposition.
Proposition 1
Amino acid substitution matrix values are estimates of the mutual information between amino acids in the sense of Hartley [37,38]. Consequently, the optimal alignment score computed between two biological sequences is an estimate of the optimal mutual information between these sequences.
Proof
Given a probability law P that characterizes a random variable, the Hartley self-information h is defined as the amount of information one gains when an event i occurred, or equivalently the amount of uncertainty one loses after learning that i happened:
h(i) = -log(P(i)) (4)
The less likely an event i, the more we learn about the system when i happens. The mutual information I between two events, is the reduction of the uncertainty of one event i due to the knowledge of the other j:
Ij→i = h(i) - h(i/j) (5)
Mutual information is symmetrical, i.e. Ij→i = Ii→j, and in the following will be expressed by I(i;j). The self and mutual information of two events i and j are related:
h(i ∩ j) = h(i) + h(j) - I(i;j) (6)
If the occurrence of one of the two events makes the second impossible, then the mutual information is equal to - ∞. If the two events are fully independent, mutual information is null. The empirical measure of the similarity between two amino acids described in equation (3) can therefore be expressed in probabilistic terms:
where Pϖ is the joint probability to have i and j aligned in a given alignment and Pπ the measure of probability that amino acids occur in a given sequence. From equations (4) and (6), equation (7) becomes:
s(i, j) = h(i) + h(j) - h(i ∩ j) (8)
that is
s(i, j) = I(i; j) (9)
As a consequence, the similarity function (or score) is the mutual information between two amino acids. Additionally, the score between sequences (the sum of elementary scores between amino acids, [32,33,41,42]) is, according to the hypothesis of independence of amino acid positions, the estimated mutual information between the two given biological sequences.
Once two sequences are aligned, we pose the question whether the alignment score is sufficient to assess that the proteins are conceivably alike and thus evolutionarily related? The theorem of the upper limit of a sequence alignment score probability (TULIP theorem, [39]), sets the upper bound of an alignment score probability, under a hypothesis less restrictive than the Karlin-Altschul model [43]. Given two real sequences a and b (a = a1a2...am and b = b1b2...bn), where s = s(a,b) the maximal score of a pair-wise alignment obtained with any alignment method, b* the variable corresponding to the shuffled sequences from b, and given P{S(a,b*)≥s} the probability that an alignment by chance between a and b* has a higher score than s, then whatever the distribution of the random variable S(a,b*) the TULIP theorem states:
with k > 1, μ the mean of and σ its standard deviation. The unique restriction on S(a,b*) is that it has a finite mean and a finite variance. A first corollary of the TULIP theorem is that the Z-score is a statistical test for the probability of a sequence alignment score. We additionally state the following new corollary.
TULIP corollary 2
Given the TULIP theorem conditions, let be the Z-score [44]. Then, z(a,b*) is the greatest possible value for k (k∈]1,+∞[), which holds relation (10) true. In consequence, with k = z(a,b*), then
The best upper bound value for P{S(a,b*)≥s} is termed . From the TULIP theorem and corollaries, the comparison of a protein to a given reference a, weighed by an alignment score, is characterized by a bounded probability that the alignment is fortuitous.
Question of the proximity between protein sequences in the light of information theory
Since the optimized alignment score of two protein sequences allows an access to both the mutual information between proteins and an upper bound that the alignment is not fortuitous, one would expect that it is an accurate way to spatially organize proteins sets. A simple relation would be "the higher the mutual information, the nearest". There are three ways to assess the proximity between two objects a and b in a given space E [41]. The first is dissimilarity, a function f(a,b): E × E → + such that f(a,b) = 0 ⇔ a = b and f(a,b) = f(b,a); the second is the distance per se, that is a dissimilarity such that the triangle inequality is respected: ∀ a,b,c ∈ E, f(a,c) ≤ f(a,b) + f(b,c); and the third is the similarity defined as a function f(a,b): E × E → such that and f(a,b) = f(b,a). Representing objects in a space is convenient using the notion of distance. When the optimal alignment is global, i.e. requiring that it extends from the beginning to the end of each sequence [32], it is theoretically possible to define a distance per se, that is to spatially organize the compared sequences [41]. However, from a biological point of view, global alignment algorithms are not reliable to assess homology of protein domains. Local alignments are better suited, using scoring matrices to find the optimum local alignment and maximizing the sum of the scores of aligned residues [28,31]. In contrast with global alignments, local alignments do not allow any trivial definition of distances [41].
Although amino acid similarity is a function f(i, j): E × E → , owing to the local alignment optimization algorithms, the computed score is a function f(a,b): E × E → +, requiring the existence of at least one positive score in the similarity matrices. Thus, when constructing an alignment with the Smith and Waterman [33] method, the constraint that s(a,b)>0 (i.e. I(a;b) > 0) is imposed. This condition is consistent with proposition 1: if two sequences are homologous, knowledge about the first has to bring information about the second, that is to say, the mutual information between the two sequences cannot decrease below zero: I(a;b) > 0 (i.e. s(a,b) > 0). As a consequence, in the following geometric construction we sought a refined expression for the proximity of proteins.
Geometric construction of a configuration space of homologous proteins (CSHP) conserving mutual information
In a set of homologous proteins, any sequence a can be selected as a reference, noted aref, in respect to which the others are compared. A geometric representation of objects relatively to a fixed frame is known as a configuration space (CS). In physics, a CS is a convenient way to represent systems of particles, defined by their positional vectors in some reference frame. Here, given n similar sequences, it is therefore possible to consider n references of the CSHP. In a given (CSHP, aref), each amino acid position aligned with a position in the aref sequence, corresponds to a comparison dimension (CS dimension). Proteins are simply positioned by a vector, the coordinates of which are given by the scores of aligned amino acids. Gaps are additional dimensions of the CS. When considering that local algorithms identify the space of biological interest, i.e. a CSHP, the gap penalty is a parameter that maximizes the shared informative dimensions. Thus, given the amino acids mutual information, alignment optimization methods define the relative positions of proteins.
At this point in our construction, a first important property of the CSHP can be deduced. Since mutual information with aref is sufficient for the full positioning, then positioning of proteins in a given (CSHP, aref) is unambiguous, unique, and is not altered when proteins are added or removed. In other words, a (CSHP, aref) is a univocal space.
Given two sequences a and b, if b occurs in (CSHP, aref), then a also occurs in (CSHP, bref). The pair-wise alignment of a and b having no order (symmetry of the mutual information), the positions of b in (CSHP, aref) is dependent of the position of a in (CSHP, bref). Thus, once a (CSHP, aref) has been built, ∀b ∈ (CSHP, aref), part of the geometry of (CSHP, bref) is learnt. Thus, in a CSHP, information needed for the position of n sequences is totally contained in the geometry of the n (CSHP, aref). This geometric stability is not observed with multiple alignments, which can be deeply modified by addition or removal of sequences. In the CSHP, protein position is unaltered by additions or removals of other proteins. In practice, the construction of CSHP is therefore completely deduced from any all-by-all protein sequence comparison [45,46] and can be easily updated.
The q-dissimilarity, a proximity notion for a geometric representation of the CSHP
In the CSHP, the definition of a distance per se based on mutual information is reduced ad absurdum (For demonstration, see methods). To define a proximity function i) sharing properties of distance, i.e. increasing when objects are further apart, ii) deriving from similarity and iii) relying on mutual information, particularly the property "f(a,a) ≠ f(b,b) is possible", we introduce a fourth notion of proximity. Such proximity was called q-dissimilarity (for quasi-dissimilarity), a function f(a,b): E × E → + is defined such that
∀ a ∈ E, ∀ b ∈ E, f(a,b) = f(b,a) (13)
Let s be a similarity, then q = e-s is a q-dissimilarity, named the 'canonical q-dissimilarity' associated to s. Accordingly, the TULIP theorem allows a statistical characterization of q(a,b) the canonical q-dissimilarity between two sequences a and b.
TULIP corollary 3
From the TULIP corollary 2, relation (14) is simply deduced:
with Q(a,b*) being the random q-dissimilarity variable associated with S(a,b*). Given a (CSHP, aref), each sequence b aligned with a is characterized by a q-dissimilarity q(a,b). In geometric terms, b can be represented as a point contained in a hyper-sphere B of radius q(a,b).
The representation of a (CSHP, aref) shown in Figure 1 is therefore in conformity with all constraints listed earlier and can also serve as a Venn diagram for the setting of events realized following a continuous random variable Q(a,b*). When a is compared to itself, it is set on a hyper-sphere A of radius q(a,a), which is not reduced to one point. In the context of information theory, it is therefore possible to express that the proximity respects the property "q(a,a) ≠ q(b,b) is possible". Considering Figure 1, is the probability for a random sequence b* to be in the hyper-sphere B. In conclusion, the q-dissimilarity is therefore a proximity notion that allows a rigorous geometric description of the configuration space of homologous proteins, real or simulated, (CSHP, aref, q).
Unification of pair-wise alignments theory, information theory, p-distance and q-dissimilarity in the CSHP model
A geometric space is a topological space when endowed with characterized paths that link its elements. Here, paths can be defined as the underlying evolutionary history separating sequences [11]. Given u the common unknown ancestor, then the divergence time t(a,b) is theoretically the summed elapsed times separating u to a and to b. Without any empirical knowledge of u, the simplest approximation for t(a,b) was sought as a function of the fraction of identical residues fid, thus of the p-distance. With the hypothesis of the molecular clock, this function can be given as equation (2), where the transmutation of a and b is a consequence of a Poisson process. By using relation (9) on the equivalence between score similarity and mutual information, then the fundamental postulate "the closer in the evolution, the more alike and conversely, the more alike, probably the closer in the evolution" can be reformulated:
Fundamental postulate
Given two homologous proteins a and b, the closer in the evolution, the greater the mutual information between a and b (i.e. the optimal computed score s(a,b)) and conversely, the greater the mutual information between a and b, probably the closer in the evolution.
Whereas the first part of the postulate is a consequence of the conservational pressure on mutual information, the second assertion founds the historical reconstruction underlying a set of biological sequences on statistical concepts. A corollary is that evolution of two homologous proteins is characterized by a loss of mutual information.
In the CSHP, this formulation of the fundamental postulate allows a novel mathematical formalization of the p-distance in probabilistic terms. Basically, the p-distance is the divergence observed between two sequences knowing that they share some features (the observed sequences a and b) and that they were identical before the speciation event (sequence u).
Looking back to equation (1), we can re-formulate fid in probabilistic terms, considering the fraction of shared features (identical sites) knowing the observed data and the existence of a common ancestor. Given two proteins a and b, let us consider the random variable Q(a,b*), defined in TULIP corollary 3. In (CSHP, aref), shown in Figure 1, one can define the probability law P{Q(a,b*)≤ρ} as the probability that the q-dissimilarity between b* and aref is lower than ρ. The hyper-sphere of radius ρ contains therefore the b* random sequences sharing informative features with a accordingly. The probability pid/a that b* shares identity with a, knowing that the q-dissimilarity between b* and a is lower than that between the real sequences b and a, is:
pid/a (b*) = P{Q(a,b*) ≤ q(a,a) / Q(a,b*) ≤ q(a,b)} (15)
which is a probabilistic expression of fid in respect to the reference aref. According to the Venn diagram in Figure 1: pid/a (b*) = P(A/B)
Using the Bayes theorem, equation (15) can be expressed as:
In consequence:
which can be expressed as
Assuming that substitution rates are independent of lineages [35], then random sequence models a* and b* are equivalent, that is to say Q(a,a*) ≈ Q(a,b*) and
Thus pid/a, and symmetrically pid/b, provide a probabilistic expression of fid knowing the data, i.e. the observed mutual information between a and b expressed as Q(a,b).
Given two homologous sequences a and b, when their optimal score is s(a,b) ≥ μ + ψ with ψ being a critical threshold value depending on the score distribution law (See Methods for the demonstration for the critical threshold), owing to the TULIP corollary 2, we can state that pid/a is bounded above:
This expression can also be developed as:
where μ1, σ1, μ2 and σ2 are the mean and the standard deviation of S(a,b*) and S(a,a*) respectively. The right term in relation (21) exhibits analogies with fid given by equation (1), showing that the pragmatic approach by Feng and Doolittle [19] could be supported and generalized in a theoretical elaboration.
Using the Poisson correction, an expression of t(a,b) is given as the linear combination of the two corrections of the p-distance deduced from pid/a and pid/b :
t(a,b) = -[log(pid/a (b*)) +log(pid/b (a*))] (22)
with a* and b* the random variables corresponding to the shuffled sequences of a and b respectively. The sum of the logarithms corresponds to the product of the two probabilities, an expression of the hypothesis of independence of lineage. Interestingly, equation (22) provides an expression of the symmetric effect of time on the variations that independently affected a and b.
From relation (20), t(a,b) appears as a function of Z-score ratios. For any set of homologous proteins, it is therefore possible to measure a table of pair-wise divergence times and build phylogenetic trees using distance methods.
Reconstruction of protein phylogeny: first example, case study of the glucose-6-phosphate isomerase phylogeny
We compared the trees we obtained, called TULIP trees, to phylogenetic trees built using classical methods, for instance the popular PHYLIP [47] or PUZZLE-based [48] methods, termed here MAB trees (for multiple alignment-based trees). Firstly, because MAB trees are constructed from multiple alignments, removals or additions of proteins modify the multiple alignments. Inclusion of sequences is considered as a way to improve the quality of multiple alignments and to increase the sensitivity of the comparison of distant sequences [49,50]. By contrast, the protein space used to build TULIP trees is not reordered when data sets are incremented or decremented (drawing of the TULIP tree may apparently change due to the tree graphic representation methods; nevertheless the absolute tree topology is not reordered). This remarkable property is due to both the geometrical construction by pair-wise comparison and the convergence of the distance matrix elements estimated by equation (21). Indeed, the estimate of the right-hand term of equation (21) relies on a Monte Carlo method, after randomization of the biological sequences [39,44,51] and is therefore dependent on the sequence randomization model [52] and convergent in respect to the weak law of large numbers [53]. Convergence is proportional to , where numbrand is the number of randomizations. In the case studies presented here, we set numrand = 2000 (see Methods). By contrast, stability of MAB trees is sought by bootstrapping approaches and consensus tree reconstruction. MAB trees appear as the result of a complex learning process including possible re-adjustment of the multiple alignments after eye inspection pragmatically applied to assist the reconstruction. Alternatively, Bayesian analyses have been recently proposed for phylogenetic inference [54], estimating posterior probability of each clades to assess most likely trees. Still, in a recent comparative study, Suzuki et al. [55] and Simmons et al. [56] provided evidence supporting the use of relatively conservative bootstrap and jacknife approaches rather than the more extreme overestimates provided by the Markov Chain Monte Carlo-based Bayesian methods. In the absence of any decisive methods to assess the validity of the trees obtained after such different approaches, no absolute comparison with the TULIP classification trees can be rigorously provided.
Whenever a TULIP classification was achieved on a dataset that led to a consensual MAB tree, both were always consistent. For example, Figure 2 shows the phylogenetic PHYLIP [47] and TULIP trees obtained for glucose-6-phosphate isomerases (G6PI). Phylogeny of the G6PI enzyme has been studied by Huang et al. [57] in order to demonstrate the horizontal transfer of this enzyme in the apicomplexan phylum due to a past endosymbiosis [57]. Owing to the neighbor-joining analysis used by Huang et al. [57] (see methods) Figure 2A shows that apicomplexan G6PI is "plant-like". The TULIP tree shown in Figure 2B is consistent with this conclusion. Interestingly, differences between the two trees are found only when the bootstrap values on the MAB tree are not strong enough to unambiguously assess branching topology.
Reconstruction of protein phylogeny: second example, case study of the enolase phylogenic incongruence
TULIP classification tree further helps in solving apparent conflicting results obtained with MAB methods. In a comprehensive study from Keeling and Palmer [36] the PUZZLE-based reconstruction of the enolase phylogeny led to incongruent conclusions. Enolase proteins from a wide spectrum of organisms were examined to understand the evolutionary scenario that might explain that enolases from land plants and alveolates shared two short insertions. Alveolates comprise apicomplexan parasites, known to contain typical plant features as mentioned above, particularly a plastid relic. In this context, the shared insertion in apicomplexan and plant enolases (Figure 3) has been interpreted as a possible signature for some evolutionary relationship between apicomplexans and plants [58,59] and a likely sign of a lateral transfer. From the distribution of this insertion in enolases from several key eukaryotic groups, Keeling and Palmer [36] postulated that lateral transfer had been an important force in the evolution of eukaryotic enolases, being responsible for their origin in cryptomonads, Chlorarachnion and Arabidopsis. However, they could not conclude about alveolates, finding a conflict between the distribution of the insertion and the MAB phylogenetic position (Figure 4A). The authors had to admit that lateral gene transfers failed to explain apicomplexa enolases, and were compelled to suppose that the lack of congruence between insertion and phylogeny could be because of a parallel loss of insertions in lineages, or to more complex transfers of gene portions.
Based on our theoretical model, we constructed the corresponding TULIP tree. TULIP trees given with BLOSUM 62 or PAM 250 matrices, Fitch-Margoliash or neighbor-joining methods led indistinctly to a unique tree topology (Figure 4B). Separation of great phyla (Archaebacteria, Eubacteria, Diplomonads, Trypanasomes, Animals, Fungi and Amoeba) is recovered. A plant-like cluster is additionally reconstructed, in which a distinct separation occurred between {Rhodophytes ; Cryptomonads} and {Land Plants ; Charophytes ; Chlorarachnion ; Alveolates} main clusters. It is remarkable that this latter cluster is that characterized by the enolase insertion.
This topology corresponds to the observed distribution of the enolase short insertions and provides therefore a solution to the apparent enolase phylogeny incongruence: the phylogenetic position of alveolates is not in conflict with the distribution of enolase insertion and the apicomplexa enolase is possibly a consequence of a lateral transfer, like in cryptomonads.
Large scale phylogeny based on a CSHP built from massive genomic pair-wise comparisons
A CSHP containing large sets of protein sequences can be built after any all-by-all massive comparison providing Z-score statistics. Because the space elaboration is explicit, then quality of the mutual information conservation depends on the choice of the scoring matrix, the geometric positioning depends on the local alignment method, the homology assessment depends on the alignment score and probabilistic cutoffs and the phylogenetic topology on the choice of the stochastic law correction. Eventually, genome-scale pair-wise comparisons [39,36] find in the present CSHP a robust, evolutionary consistent and easily updatable representation.
Methods
Glucose-6-Phosphate Isomerase sequences
The 41 Glucose-6-phosphate isomerase (EC 5.3.1.9) sequences studied in the paper are taken from several representative groups, as provided from the Swiss-prot database. Group I: Archae ([Swiss-prot:G6PI_HALN1], Halobacterium sp.; [Swiss-prot:G6PI_METJA], Methanococcus jannaschii). Group II: Bacteria Actinobacteria ([Swiss-prot:G6P1_STRCO], Streptomyces coelicolor; [Swiss-prot:G6PI_COREF, Corynebacterium efficiens; [Swiss-prot:G6PI_MYCTU], Mycobacterium tuberculosis). Group III: Bacteria Cyanobacteria ([Swiss-prot:G6PI_ANASP], Anabaena sp.; [Swiss-prot:G6PI_SYNEL], Synechococcus elongates). Group III: Bacteria Bacillus ([Swiss-prot:G6PI_LACFE], Lactobacillus fermentum; [Swiss-prot:G6PI_BACHD], Bacillus halodurans; [Swiss-prot:G6PI_BACSU], Bacillus subtilis; [Swiss-prot:G6PI_CLOPE], Clostridium perfringens). Group IV: Bacteria Proteobacteria ([Swiss-prot:G6PI_BIFLO], Bifidobacterium longum; [Swiss-prot:G6PI_ECOLI], Escherichia coli). Group V: Bacteria Chlamydiae ([Swiss-prot:G6PI_CHLTR], Chlamydia trachomatis; [Swiss-prot:G6PI_CHLCV], Chlamydophila caviae; [Swiss-prot:G6PI_CHLMU], Chlamydia muridarum). Group VI: Others Bacteria ([Swiss-prot:G6PI_CHLTE], Chlorobium tepidum; [Swiss-prot:G6PI_DEIRA], Deinococcus radiodurans; [Swiss-prot:G6PI_BORBU], Borrelia burgdorferi; [Swiss-prot:G6PI_THEMA], Thermotoga maritime). Group VII: Fungi ([Swiss-prot:G6PI_SCHPO], Schizosaccharomyces pombe; [Swiss-prot:G6PI_YEAST], Saccharomyces cerevisiae; [Swiss-prot:G6PI_NEUCR], Neurospora crassa; [Swiss-prot:G6PI_ASPOR], Aspergillus oryzae). Group VII: Eukaryota Viridiplantae ([Swiss-prot:G6PI_ARATH], Arabidopsis thaliana; [Swiss-prot:G6PI_MAIZE], Zea mays; [Swiss-prot:G6PI_SPIOL, Spinacia oleracea; [Swiss-prot:G6PA_ORYSA], Oryza sativa). Group VIII: Eukaryota Alveolata Apicomplexa ([Swiss-prot:G6PI_PLAFA], Plasmodium falciparum; [Swiss-prot:Q9XY88], Toxoplasma Gondii; [Swiss-prot:269_185], Cryptosporidium parvum). Group IX: Animals ([Swiss-prot:G6PI_DROME, Drosophila melanogaster; [Swiss-prot:G6PI_MOUSE], Mus musculus; [Swiss-prot:G6PI_HUMAN], Homo sapiens; [Swiss-prot:G6PI_PIG], Sus scrofa; [Swiss-prot:G6PI_RABIT], Oryctolagus cuniculus; [Swiss-prot:G6PI_TRYBB], Trypanosoma brucei brucei). Group X: Other Eukaryota ([Swiss-prot:AY581147], Entamoeba histolytica; [Swiss-prot:G6PI_LEIME], Leishmania mexicana; [Swiss-prot:AY581146], Dictyostelium discoideum; [Swiss-prot:Q968V7], Giardia intestinalis).
Enolase sequences
Enolase sequences used for the case-study presented in this paper were taken from eight major groups previously studied by [36]. Group I: Land Plant, Charophytes, Chlorophytes, Rhodophytes and Cryptomonads ([Swiss-prot:CAA39454], Zea mays; [Swiss-prot:Q42971], Oryza sativa; [Swiss-prot:Q43130], Mesembryanthemum crystallinum; [Swiss-prot:P42896], Ricinus communis; [Swiss-prot:Q43321], Alnus glutinosa; [Swiss-prot:Q9LEJ0], Hevea brasiliensis 1; [Swiss-prot:P25696], Arabidopsis thaliana; [Swiss-prot:P26300], Lycopersicon esculentum; [Swiss-prot:AF348914], Chara corallina; [Swiss-prot:AF348915], Nitella opaca; [Swiss-prot:AF348916], Nitellopsis obtusa; [Swiss-prot:AF348918], Pycnococcus provasolii 2; [Swiss-prot:AF348919], Bigelowiella natans – Chlorarachnion -; [Swiss-prot:AF348920], Mastocarpus papillatus 1; [Swiss-prot:AF348923], Prionitis lanceolata 1; [Swiss-prot:AF348931], Rhodomonas salina 1; [Swiss-prot:AF348933], Guillardia theta; [Swiss-prot:AF348935], Pedinomonas minor). Group II : Animals and Fungi ([Swiss-prot:P04764], Rattus norvegicus; [Swiss-prot:P51913], Gallus gallus A; [Swiss-prot:P07322], Gallus gallus B; [Swiss-prot:Q9PVK2], Alligator mississippiensis; [Swiss-prot:P06733], Homo sapiens A; [Swiss-prot:P13929, Homo sapiens B; [Swiss-prot:P15007], Drosophila melanogaster; [Swiss-prot:AF025805], Drosophila pseudoobscura; [Swiss-prot:O02654], Loligo pealeii; [Swiss-prot:AF100985], Penaeus monodon; [Swiss-prot:Q27527], Caenorhabditis elegans; [Swiss-prot:Q27877], Schistosoma mansoni; [Swiss-prot:P33676], Schistosoma japonicum; [Swiss-prot:Q27655], Fasciola hepatica; [Swiss-prot:P00924], Saccharomyces cerevisiae 1; [Swiss-prot:Q12560], Aspergillus oryzae; [Swiss-prot:P42040], Cladosporium herbarum; [Swiss-prot:P40370], Schizosaccharomyces pombe 1; [Swiss-prot:AF063247], Pneumocystis carinii f.). Group III: Amoebae ([Swiss-prot:P51555], Entamoeba histolytica; [Swiss-prot:Q9U615], Mastigamoeba balamuthi). Group IV: Alveolates ([Swiss-prot:AF348926], Paramecium multimicronucleatum; [Swiss-prot:AF348927], Paramecium tetraurelia; [Swiss-prot:AF348928], Colpidium aqueous; [Swiss-prot:AF348929], Tetrahymena thermophila I; [Swiss-prot:AF348930], Tetrahymena bergeri; [Swiss-prot:Q27727], Plasmodium falciparum; [Swiss-prot:AF051910], Toxoplasma gondii). Group V: Trypanosomatidae ([Swiss-prot:AF159530], Trypanosoma cruzi eno1 partial; [Swiss-prot:AF152348], Trypanosoma brucei complete). Group VI: Hexamitidae ([Swiss-prot:AF159519], Hexamita inflata eno1 partial; [Swiss-prot:AF159517], Spironucleus vortens partial). Group VII: Archaebacteria ([Swiss-prot:Q9UXZ0], Pyrococcus abyssi; [Swiss-prot:O59605], Pyrococcus horikoshii; [Swiss-prot:Q60173], Methanococcus jannaschii; [Swiss-prot:Q9Y927], Aeropyrum pernix). Group VII: Eubacteria ([Swiss-prot:O66778], Aquifex aeolicus; [Swiss-prot:P37869],Bacillus subtilis; [Swiss-prot:Q9K717], Bacillus halodurans; [Swiss-prot:P77972], Synechocystis sp.; [Swiss-prot:P33675], Zymomonas mobilis; [Swiss-prot:P08324], Escherichia coli; [Swiss-prot:P47647], Mycoplasma genitalium; [Swiss-prot:Q8EW32, Mycoplasma penetrans; [Swiss-prot:P74934], Treponema pallidum).
Demonstration that distance of a protein to itself cannot be defined in the CSHP
In the simplest case, building a distance between amino acids (that would lead to distance between sequences) on the basis of computed similarity values would have to respect the condition:
∀i ∈ E, ∀j ∈ E,d(i,j) = 0 ⇒ i = j (a)
for i and j, two given amino acids and d the distance function. Using this condition in the proposition, any organization of the CSHP with a geometric distance is reduced ad absurdum.
Proposition
Building a distance between amino acids derived from the composed function d(i,j) = (φ ○ s)(i,j), where s is a similarity function and φ a bijection, is impossible without a loss of mutual information. Moreover, two proteins from distinct organisms can have the same configuration, being like "twins", and d(i,j) = 0 does not imply i = j.
Proof
Condition (a) implies that φ (s(i,i)) = φ (s(j,j)) = 0. This equality imposes that s(i,i) = s (j,j) and, following equation (7) of main text, that I(i;i) = I(j;j). Considering for example tryptophan (W) and glutamic acid (E), if W occurs in a sequence, the mutual information gained about the occurrence of W at the aligned position would be the same as that gained in the case of E about the occurrence of E at the aligned position in the homologous protein. This statement is easily rejected on the basis of biochemical concerns. On one hand, aspartic acid (D) shares common biochemical properties with E, particularly a carboxylic acid, and easily substitutes in homologous sequences. By contrast W, exhibiting a unique biochemical feature, is less substitutable without altering the function. Thus the mutual information I(E;E) is necessarily lower than I(W;W). This that can be checked in scoring matrices such as BLOSUM 62 [30] where I(E;E) = 5, I(D;D) = 6 and I(W;W) = 11. Condition d(i,i) = 0 leads to an obvious loss of information. The second assertion of the proposition is obvious.
Determination of the threshold value ψ, for topological reconstructions in the CHSP based on pair-wise alignment score probabilities
An important basis of the reconstruction of a probabilistic evolutionary topology in the CSHP is based on the demonstration that, given S the random variable corresponding to the alignment scores of pairs of shuffled sequences and μ the mean of S, given two homologous sequences a and b, when their optimal score is s(a,b) ≥ μ + ψ (with ψ a critical threshold value depending on the score distribution law), owing to the TULIP corollary 2, we can state that pid/a is bounded above
To the purpose of this demonstration, we considered the cumulative distribution function F(s) = P(S ≤ s), its derivative f(s) known as the probability density function defined as dF(s) = f(s)ds, and the positive delta function δ (s) = (s - μ)2(1 - F(s)). Since δ (s) = (s - μ)2(1 - F(s)) is null for s = μ and , the Rolle's theorem implies that ∃s0 ∈]μ,+∞[ such as [60]; s0 corresponds to a maximum of δ (s) and is therefore the solution of the equation
2(1 - F(s)) - (s - μ) f(s) = 0 (b)
one can express as
The term corresponds to a continuous function. Interestingly, is known as the hazard function [61], that is the probability of s, per score unit (i.e. mutual information), conditional to the fact that the pair-wise alignment score is at least equal to s. The hazard function is also defined by . A critical hypothesis is that φ (x) function is strictly increasing and conversely that is strictly decreasing. Considering , equation 2 has only one solution s0 and this solution is bounded above:
In consequence, δ (s) reaches its maximum for a s0 (s0 ≤ μ + ψ) and it is strictly decreasing on ]μ + ψ ,+∞[.
The estimation of s0 is not trivial because it depends on the knowledge of the cumulative distribution function. Extensive studies provided experimental and theoretical supports for an extreme value distribution of alignment scores [31,43,44]. Using the extreme value distribution of type I, i.e. the Gumbel distribution [62], the cumulative distribution is given by
with θ and β (β > 0) the location and scale parameters. The probability density function g(s) is defined by dG(s) = g(s)ds. We observe with that . Using the Taylor's polynomial formula, i.e. :
In consequence, for a Gumbel score probability distribution:
A graphical determination of ψ from a Gumbel distribution is illustrated in Figure 5.
If a pair-wise alignment score of two sequences a and b is relatively high, that is s(a,b) ≥ μ + ψ, then the trivial inequality s(a,a) ≥ s(a,b) implies
(s(a,b) - μ)2(1 - F(s(a,b))) ≥ (s(a,a) - μ)2(1 - F(s(a,a))) (h)
that is to say
From inequality (i), we deduce that pid/a is bounded above.
Construction of PHYLIP multiple alignment based trees and pair-wise alignment based TULIP trees
To build PHYLIP trees, multiple sequence alignments were created with ClustalW [63]. PHYLIP trees where constructed using the protpars and neighbor modules from the PHYLIP package [47] and the BLOSUM 62 substitution matrix. Bootstrap support was estimated using 1000 replicates. To build TULIP trees, for each couple of sequences a and b, alignment was achieved with the Smith-Waterman method and the BLOSUM 62 scoring matrices, using the BIOFACET package from Gene-IT, France [64]. We computed estimated z-scores z(a,b*), z(a,a*), z(a*,b), z(b*,b*), with 2000 sequence shuffling. For all computations, an estimation of the Gumbel parameters θ and β was made using the computed μ and σ of any S(a,b*) and the formula and θ = μ - βΓ'(1), where Γ'(1) ≈ 0.577216 is the Euler constant. In all computations, both Gumbel parameters were very close (in the case of enolases, mean(θ) = 35.04, SD(θ) = 0.12, mean(β) = 3.92, SD(β) = 0.08). As a consequence, the assumption Q(a,a*) ≈ Q(a,b*) was verified for any pair of sequences. We used the parameters to estimate μ = θ + βΓ '(1) (in the case of enolases, μ = 37.33), and μ + ψ ≈ μ + 10.5178 ≈ 47.85. As any pairs of computed scores are higher than this critical threshold, we used relation [20]. Estimation of evolutionary time was achieved according to equations [20] and [22]. Trees were constructed using Fitch-Margoliash and Neighbor-Joining methods [47].
List of abbreviations
CSHP, configuration space of homologous proteins, TULIP, theorem of the upper limit of a score probability
Authors' contributions
OB conceived the main theoretical model, designed and developed the method to build phylogenetic trees and drafted the manuscript. SR and PO participated in the theoretical model refinement and in the design and development of computational methods to build TULIP trees. EM contributed to the conception of this study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank John Dunlop for copy editing and Joël Bleuse and Jacques Demongeot for critical reading of this manuscript. We are indebted to Jean-Paul Eynard, Jean-Michel Pabiot and Jean-Jacques Codani for supportive help. Part of this work was financed by Ministère de la Recherche et de la Technologie and by Agence Nationale de la Valorisation de la Recherche Rhône-Alpes.
Figures and Tables
Figure 1 Geometric and probabilistic representation of the configuration space of homologous proteins (CSHP). For any sequence a taken as a reference (aref), one can build a configuration space (CSHP, aref) where all sequences that are homologous to a can be set. When two sequences a and b are aligned with a score s(a,b), then b is positioned in (CSHP, aref) and a in (CSHP, bref). The sequence alignment length determines the number of configuration dimensions; pair-wise amino acid scores determine the unique solution for its positioning. The q-dissimilarity (q = e-s) defines a proximity between sequences allowing a geometric representation (CSHP, q). Remarkable properties are i) the conservation of mutual information, [I(a;b) = I(b;a) ⇒ q(a,b) = q(b,a)], between (CSHP, aref) and (CSHP, bref), ii) a probabilistic representation of homologies based on q-dissimilarities by Venn diagrams (A and B) and iii) the assignment of a topology relying on protein evolution assumptions. Evolutionary paths for a and b lineages, sharing an unknown ancestor u, have a probabilistic expression, bounded above (see text), supporting a phylogenetic topology (TULIP trees).
Figure 2 Glucose-6-phosphate isomerase phylogeny. (A) Multiple alignment based (MAB) tree. (B) TULIP tree. Both trees were constructed using the BLOSUM 62 similarity matrix. For MAB tree construction, bootstrap support was estimated using 1000 replicates. To build TULIP trees, Z-scores were estimated with 2000 sequence shuffling. Topology supported by high bootstrap results in the MAB tree (figures in black), are consistently recovered in the corresponding pair-wise alignement based TULIP tree.
Figure 3 Enolase phylogenic incongruence. When aligned, the enolase region corresponding to amino acids 73–118 of the Oryza sativa gene, exhibit two insertions (red boxes) that are only present in land plants, charophytes and alveolates. In alveolates, these insertions are consistent with a horizontal gene transfer. However, to date, evolutionary reconstructions based on enolase sequences did not allow any phylogenetic branch gathering for these clades [36].
Figure 4 Solution of the enolase phylogenic incongruence. (A) Multiple alignment based (MAB) tree. (B) TULIP tree. Both trees were constructed using the BLOSUM 62 similarity matrix. For MAB tree construction, bootstrap support was estimated using 1000 replicates. To build TULIP trees, Z-scores were estimated with 2000 sequence shuffling. Clades that contain a unique insertional signature (Land plants, green box; Charophytes and Chlorarachnion, blue box; Alveolates; yellow box) are not gathered in the MAB tree, as previously reported [36]. By contrast, in the TULIP tree, the phylogeny of enolase proteins is reconciled with the insertional signature detection in Land plants, Charophytes, Chlorarachnion and Alveolates.
Figure 5 (A) Gumbel score distribution simulated for enolases used in the present paper (B) graphical determination of ψ
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| 15757521 | PMC555736 | CC BY | 2021-01-04 16:02:51 | no | BMC Bioinformatics. 2005 Mar 10; 6:49 | utf-8 | BMC Bioinformatics | 2,005 | 10.1186/1471-2105-6-49 | oa_comm |
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BMC ImmunolBMC Immunology1471-2172BioMed Central London 1471-2172-6-51574827810.1186/1471-2172-6-5Methodology ArticleCyProQuant-PCR: a real time RT-PCR technique for profiling human cytokines, based on external RNA standards, readily automatable for clinical use Boeuf Philippe [email protected] Inès [email protected] Delphine [email protected] Séverine [email protected] Jean-Christophe [email protected] Bartholomew Dicky [email protected] Odile [email protected] Charlotte [email protected] Unité d'Immunologie Moléculaire des Parasites, URA CNRS 2581, Département de Parasitologie et de Médecine Moléculaire, Institut Pasteur, 28 rue du Dr. Roux, 75724 Paris Cedex 15, France2 Unité d'Immunophysiopathologie Infectieuse, URA CNRS 1961, Département d'Immunologie, Institut Pasteur, 28 rue du Dr. Roux, 75724 Paris Cedex 15, France3 Immunology Unit, Noguchi Memorial Institute for Medical Research, PO box LG 581, Legon, Ghana2005 4 3 2005 6 5 5 27 8 2004 4 3 2005 Copyright © 2005 Boeuf 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
Real-time PCR is becoming a common tool for detecting and quantifying expression profiling of selected genes. Cytokines mRNA quantification is widely used in immunological research to dissect the early steps of immune responses or pathophysiological pathways. It is also growing to be of clinical relevancy to immuno-monitoring and evaluation of the disease status of patients. The techniques currently used for "absolute quantification" of cytokine mRNA are based on a DNA standard curve and do not take into account the critical impact of RT efficiency.
Results
To overcome this pitfall, we designed a strategy using external RNA as standard in the RT-PCR. Use of synthetic RNA standards, by comparison with the corresponding DNA standard, showed significant variations in the yield of retro-transcription depending the target amplified and the experiment. We then developed primers to be used under one single experimental condition for the specific amplification of human IL-1β, IL-4, IL-10, IL-12p40, IL-13, IL-15, IL-18, IFN-γ, MIF, TGF-β1 and TNF-α mRNA. We showed that the beta-2 microglobulin (β2-MG) gene was suitable for data normalisation since the level of β2-MG transcripts in naïve PBMC varied less than 5 times between individuals and was not affected by LPS or PHA stimulation. The technique, we named CyProQuant-PCR (Cytokine Profiling Quantitative PCR) was validated using a kinetic measurement of cytokine transcripts under in vitro stimulation of human PBMC by lipopolysaccharide (LPS) or Staphylococcus aureus strain Cowan (SAC). Results obtained show that CyProQuant-PCR is powerful enough to precociously detect slight cytokine induction. Finally, having demonstrated the reproducibility of the method, it was applied to malaria patients and asymptomatic controls for the quantification of TGF-β1 transcripts and showed an increased capacity of cells from malaria patients to accumulate TGF-β1 mRNA in response to LPS.
Conclusion
The real-time RT-PCR technique based on a RNA standard curve, CyProQuant-PCR, outlined here, allows for a genuine absolute quantification and a simultaneous analysis of a large panel of human cytokine mRNA. It represents a potent and attractive tool for immunomonitoring, lending itself readily to automation and with a high throughput. This opens the possibility of an easy and reliable cytokine profiling for clinical applications.
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Background
Cytokines are a family of low-molecular weight proteins secreted by various cell types, with pleiotropic functions and constitute a tightly regulated network that plays a central role in the immune system. Cytokines, classified into different groups such as interleukins (IL), interferons (IFN), colony-stimulating factors (CSF), tumour necrosis factors (TNF), tumour growth factors (TGF) and chemokines are implicated in the differentiation, proliferation, migration and effector functions of immune cells. Interacting one with the others, they have polarizing effects on the target cells and are pivotal in tuning immune responses [1]. Therefore, it is rather the make-up of cytokines milieu that influences the immune response rather than the action of a single cytokine. Numerous studies indicate that the clinical and/or immunological status depends on the balance between pro-inflammatory cytokines and their regulatory counterparts [2]. Thus, cytokine profiling should be achieved through analysis of simultaneous quantification of a pattern of cytokines including pro and anti-inflammatory cytokines [2,3]. Moreover, recent reports have highlighted the need for clinical immuno-monitoring of patients to adapt treatment or prevent relapses [4-6]. Thus, analysis of the cytokine pattern is central not only in the definition of the immunological status of patients but also in the study of the pathophysiological pathways as well as the cellular subpopulations involved [7,8].
Cytokines are often produced locally so that the concentration of circulating cytokines in the plasma is usually low. Their half-life and turnover may vary complicating the delineation of informative cytokine profiles. Although transcription of messenger RNA is not strictly correlated to protein secretion and activity, detection of cytokine RNA by real time PCR is now considered a reference technique for analysis of small-size samples with high sensitivity [9]. It can be used on its own or to validate and complement information obtained with other techniques such as micro-arrays [10,11].
The already available techniques, which offer a so-called "absolute quantification" of the target cytokine mRNA, achieve quantification by reference to an external standard curve based on serial dilutions of a known amount of the corresponding cDNA [12]. Moreover, to allow for comparison between experiments, data are normalized by reference to an internal standard, which is an endogenous gene for which the number of copy per cell is supposed constant under different experimental conditions [13,14]. The term of "absolute" quantification is not completely appropriate since these techniques neither control for the variable efficiency of the RT step nor take it into account in their measurements [15,16].
In the present study, we first show that the efficiency of the RT step depends on the target mRNA and on the experiments and that these variations have critical impact on the reliability of mRNA quantification. To overcome this, we describe here CyProQuant-PCR, a new technique for absolute measurement of cytokine mRNA based on an external RNA standard curve. Primer pairs have been designed for allowing amplification of a set of cytokine mRNA using the same conditions both in terms of thermocycling parameters and master mix components, a prerequisite for multiple cytokine mRNA measurements with high throughput.
In the present paper, we describe i) the construction of the synthetic RNA standard, ii) the primer pairs specific for the following human cytokines IL-1β, IL-4, IL-10, IL-12p40, IL-13, IL-15, IL-18, IFN-γ, and for the tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ), the β2-microglobulin (β2-MG) and the ubiquitin-C (UBC) to be used as internal standards and iii) the conditions for efficient real time amplification of multiple cytokine specific mRNA. The technique was validated using in vitro stimulated PBMC and its intra and inter-experimental variability were assessed. Finally, CyProQuant-PCR was used to quantify TGF-β1 transcripts in small blood samples from children with acute Plasmodium falciparum malaria.
Results
Primer design and validation
Primer pairs were designed from published genomic sequences using Primer Express software (Applied Biosystems), except for the UBC and the YWHAZ genes for which the primers had already been described [17]. When possible, the following criteria were applied. The percent of G+C content was kept in the 20–80% range and runs of an identical nucleotide were avoided. The five nucleotides at the 3' end had no more than two G and/or C bases and the melting temperature was kept between 58 and 60°C. Among the primers proposed by Primer Express software, we selected forward and reverse primers amplifying a product spanning one intron and not leading to the amplification of pseudogenes or other related genes to secure primer specificity for target cDNA. The specificity of the amplification was assessed for each gene by electrophoresis and dissociation curve analysis as shown in Figure 1A and 1B. The absence of any contaminating bands corresponding to genomic DNA amplification on agarose gel as well as the presence of a unique peak on the dissociation curve validated the specificity of the primer pair for the target cDNA. PCR products were systematically sequenced after cloning and showed more than 98% identity with the expected sequence (data not shown).
Primer concentrations were optimised to determine the minimum primer concentrations giving the lowest threshold cycle (CT) and the maximum signal-to-noise fluorescence ratio (ΔRn) while minimising non-specific amplification (data not shown). Among the final concentrations of 50, 300 and 900 nM tested for each primer, the optimal final concentration was set at 900 nM for every primer. The primer pairs are shown in Table I.
Standard curve design
Generation of the RNA standard curve
To generate the standard RNA corresponding to each target sequence, gene specific primers were fused in their 5' end to the RNA polymerase T7 promoter sequence. The PCR performed with these modified primers pairs lead to a larger PCR product with the T7 promoter sequences upstream and downstream from the specific amplicon. The in vitro transcription gave a synthetic RNA, which was assessed for its integrity and clonality by electrophoresis (data not shown). The molecular mass of each RNA standard was calculated on the basis of its sequence and solutions ranging from 101 to 1012 copies of standard RNA were made.
These serial diluted solutions were reverse transcribed and the cDNA amplified in duplicate to generate a standard curve by plotting the threshold cycle (CT) against the logarithmic value of the starting RNA copy number for each dilution. Figure 1C shows an example of these curves for β2-MG. Every curve generated a dynamic range of a least 6 orders of magnitude. This allowed for a reliable and reproducible quantification of cellular mRNA sample.
Comparison between CyProQuant-PCR and DNA standard based approach
CyProQuant-PCR was compared to the classical approach based on a DNA standard curve for the quantification of TNF-α, IL-1β and IFN-γ transcripts. DNA templates for RNA transcription of TNF-α, IL-1β and IFN-γ were used to generate a range of concentration from 10 to 1012 copies/μl. We thus disposed of DNA and RNA ranges stemming from the same sequence. In parallel, a range of cDNA was also generated from the RNA standards. Ranges of RNA standard were reverse transcribed and then amplified in parallel to the cDNA and DNA ranges by real time PCR under the same conditions (same mix, same final volume, same PCR plate). The slope of the standard curves generated by these three ranges as well as the corresponding efficiency are shown in Table 2. The data show that the RT-PCR efficiencies from CyProQuant-PCR are lower than the PCR efficiencies from cDNA or DNA ranges for the three genes. Since the PCR efficiencies of the cDNA and DNA ranges are similar for each gene, this discrepancy is due to the RT efficiency, which is lower than 100% (from 75% for IFN-γ to 98% for TNF-α). In addition, data show that RT efficiency displays intergenic variation with a lower efficiency for IFN-γ compared to the two other cytokines (Table 2).
In order to show that these intergenic differences were not due to interassay variation, we compared amplification efficiencies of TNF-α, β2-MG, MIF and UBC from i) a single cellular RNA sample, ii) a pooled RNA sample containing standard RNA for each of the targets and iii) a pooled DNA sample corresponding to the cDNA obtained from the pooled RNA sample containing standard RNA for each of the targets. Results obtained are summarised in Table 3 and show that RNA amplification efficiencies vary from one gene to the other but are not affected by the molecular context since they do not differ between the single cellular RNA sample and the pooled RNA sample. Moreover, since the amplification efficiencies of the pooled DNA sample have been normalised to 100%, the difference of amplification efficiency between the RNA samples and the pooled DNA corresponds to differences in RT efficiency.
Altogether, these data demonstrate that RT efficiency displays intergenic variations that are not due to interassay differences.
Impact of RT efficiency variability on transcripts quantification reliability
RT efficiency has critical impact on transcript quantification as shown in Table 4. Indeed, if we compare CyProQuant-PCR or DNA standard based approach for quantification of a known number of TNF-α, IL-1β and IFN-γ transcripts, results show that DNA standard based approach underestimates of around two logs the number of transcripts. Moreover, this underestimation varies according to the transcript studied and the experiment. CyProQuant-PCR is thus more precise and more reliable than the classically used DNA-based approaches.
Taken together, these results support the use of RNA as external standard for reliable and reproducible quantification of transcripts.
RT-PCR efficiency is similar for sample and RNA standard
Since RT-PCR efficiency varies, absolute mRNA quantification can only be reliably obtained if the external RNA standard and the cellular RNA are retro-transcribed and amplified with the same efficiency. This was secured by comparing the standard curves obtained after amplification of a range of 10X serial dilution of cellular RNA and β2-MG external RNA standard. The slopes obtained were of -3,307 and -3,305 for the cellular RNA and for the external RNA standard, respectively. This corresponds to efficiencies of 100,6% and 100,7% respectively (data not shown).
Endogenous standard, normalization and reliability of the technique
The choice of a stable expressed endogenous gene to be used as an internal standard is a prerequisite for accurate RT-PCR expression profiling. This has to be adapted to the clinical situation and the tissue of origin of the samples. Since our purpose was to establish a technique to be used with peripheral blood leukocytes, we tested three genes reported in stable amounts in leukocytes: the β2-MG, the UBC and the YWHAZ [17]. We compared the stability of the amount of transcript under different conditions of activation. Total cellular RNA was extracted on two different days from the same two aliquots of PBMC stimulated for 3 hours by LPS. Thus, reverse transcription was realised on RNA extracted from the same number of cells. The three endogenous gene transcripts were amplified by CyProQuant-PCR in the same plate in duplicate using the same PCR master mix. Table 5 shows the mean of the results obtained for the two extractions. The data show that β2-MG transcripts are stable with less than 12% of variation whereas UBC and YWHAZ transcript levels showed up to 47% variability depending on the in vitro conditions. Moreover, based on the approach recently described by Pachot et al. [18], we assessed the inter-individual variability of the β2-MG basal level using whole blood samples from 6 different healthy donors. All CT values were within 19 and 21,86 cycles, which corresponds to a variation of less than 5 times in gene expression for an overall RT-PCR reaction efficiency of 1,81 (slope = -3,867). This is considered as acceptable for a reference gene [18]. β2-MG was thus chosen as an internal standard gene for future experiments. The same conclusions were drawn using PHA (phytohemagglutinin) as a stimulant (data not shown). In addition, these data validate the reliability and reproducibility of our RNA extraction protocol.
Validation of the CyProQuant-PCR technique
Human TNF-a transcript quantification: comparison of CyProQuant-PCR to TaqMan®
Since TaqMan® technology developed by Applied Biosystems is considered as the "gold standard", we compared CyProQuant-PCR to TaqMan® for the quantification of TNF-α transcripts in isolated monocytes stimulated for 6 hours with LPS. Total RNA was extracted from 106 cells and 10X serial dilutions were prepared and reverse transcribed. The resulting cDNA were amplified in duplicate on the same plate in the same thermocycling conditions using either the TaqMan® commercial kit (Applied Biosystems) or CyProQuant-PCR primers with SYBR Green PCR master mix (Applied Biosystems). Figure 2 shows the curves obtained using the two techniques on the same samples. This indicates that CyProQuant-PCR is as good as TaqMan® in terms of sensitivity and even more efficient: 105% (slope -3,198) for CyProQuant-PCR versus 80 % (slope -3,908) for TaqMan®.
Quantification of TNF-α and MIF by CyProQuant-PCR and ELISA
To validate our approach, we measured the levels of TNF-α and MIF transcripts and secreted proteins by CyProQuant-PCR and ELISA respectively. PBMC from healthy individuals were stimulated by LPS or SAC for 3, 9 and 18 hours. Figure 3 shows an increase of TNF-α transcripts after 3 hours of stimulation that precedes protein secretion. In contrast, MIF transcripts were constitutively present at the steady state and LPS or SAC stimulation did not significantly modify the level of transcripts although it induces the release of substantial amounts of MIF proteins. This is in agreement with reports showing that MIF exists within cells under homeostasis both as a preformed protein ready to be secreted and as a messenger RNA, which can then be rapidly translated in the absence of induced transcription [19].
Early cytokine profiling of PBMC from healthy donors stimulated with LPS or SAC
To validate the panel of primers designed for CyProQuant-PCR, we assessed the early cytokine response as well as the kinetics of expression for PBMC from two healthy donors under stimulation with LPS or SAC. Figure 4 illustrates CyProQuant-PCR detection of IL-1β, IL-4, IL-10, IL-12p40, IL-13, IL-15, IL-18, MIF, TGF-β1 and TNF-α transcripts for one donor. No significant increase in IL-15, IL-18, MIF or TGF-β1 transcripts was detected. Both stimulants induce a similar kinetics of transcript accumulation for IL-1β and IL-10. In contrast, IL-4 and IL-13 transcripts peaked very early (2 hours) under LPS stimulation but not under SAC stimulation. IL-12p40 transcripts also accumulated earlier under LPS stimulation compared to SAC but was detected later on. The amount of TNF-α transcript was sustained under SAC stimulation compared to LPS. Detection of the corresponding proteins for TNF-α, IL-10 and IL-12p40 confirmed the mRNA profile observed (data not shown). The amplitude of the response differed somewhat between the two donors but the kinetics obtained for each cytokine were similar (data not shown).
Application: Use of CyProQuant-PCR to quantify TGF-β1 transcripts in malaria patients
Before the application to clinical samples, the experimental reproducibility of CyProQuant-PCR was evaluated using a range of TGF-β1 and β2-MG RNA standards. Inter-experimental variability was assessed from eight independent experiments. The coefficients of variation were satisfactory whatever the starting copy number of RNA standard, ranging from 0,39 to 1,07% and from 0,91 to 1,2% for TGF-β1 and β2-MG respectively (Table 6). TGF-β1 transcripts were then quantified after LPS stimulation of PBMC from malaria patients and asymptomatic controls. Figure 5 shows a significant difference in the amount of TGF-β1 transcripts between patients and asymptomatic controls only after LPS stimulation (p = 0,036).
Discussion
In this paper we have described a new technique, CyProQuant-PCR, for absolute quantitative profiling of human cytokine mRNA using real time RT-PCR. Although real time PCR is now becoming a popular technique, it still requires improvement for proper quantification. All the techniques for absolute quantification available so far use a DNA standard curve assuming that RT efficiency is constant and approaches 100% [12]. We show here that RT is not 100% efficient, and more importantly, that its efficiency changes from one gene to another and from one experiment to another. We thus designed primers and standard RNA to be used in such a way that all cytokine mRNA of interest as well as three housekeeping genes could be amplified using the same conditions (thermocycling parameters and buffer). This technique is rapid, reliable and reproducible. When compared to the TaqMan® commercial kit, which represents the "gold standard", CyProQuant-PCR was as sensitive but less expensive and more flexible. Indeed, for a given gene, the design of the probe might be quite difficult and, with judicious selection of primer pairs, comparable sensitivity can be achieved with the use of SYBR Green [20].
A major reason for not using an RNA standard curve is its poor stability due to its sensitivity to RNase degradation. In our hands, we did not find any detectable degradation when keeping the standard in concentrated aliquots (stock solution at 1000 μg/mL) in RNase-free water at minus 80°C and avoiding freeze-thawing cycles. However, for a greater stability, we incorporated modified dNTP, 2'-Fluoro-dCTP and 2'-Fluoro-dUTP, which have been reported to decrease the sensitivity of the in vitro transcribed RNA to specific RNases [21,22], and tested the effect of this incorporation on the RT-PCR efficiency. We did not find significant difference in the efficiencies (100% for the non-modified IL-4 standard versus 104% for the 2'-Fluoro-standard) (data not shown).
The methodology described here was easily and successfully applied to the quantification of several cytokine genes. The quantification is reliable on 7 to 8 logs with a sensitivity ranging from 1000 to 100 copies depending the cytokine. It was first used to determine the magnitude and the kinetics of early induction of cytokines mRNA upon PBMC stimulation using bacterial derived materials. This showed that CyProQuant-PCR is powerful enough to detect early on modest cytokine induction such as IL-4. Such a tool is useful to decipher the kinetics of cytokine response involved in physiopathological pathways but also as a read-out to measure minor immune responses to specific antigens [23].
We finally applied CyProQuant-PCR to measure the level of TGF-β1 transcripts in asymptomatic controls and malaria patients after LPS stimulation. Interestingly, we observed that cells from malaria patients have a significant higher capacity to respond to LPS compared to controls. The increased TGF-β1 transcript accumulation by patients' cells, suggests that an inadequate production of TGF-β1 might play a role in malaria pathogenesis as already proposed [24]. Further work is needed to elaborate on this finding. This first study provided the proof that CyProQuant-PCR is readily applicable to small clinical samples from paediatric cases. This opens the possibility to further quantify the cytokine imbalance associated with malaria pathogenesis and generate a disease cytokine signature, a prerequisite for novel therapeutic interventions targeting cytokine gene expression. Areas of application include both infectious and non-infectious diseases, as well as chronic inflammatory diseases such as rheumatoid arthritis or sarcoidosis and acute diseases such as sepsis or malaria. Beside its importance in patient immuno-monitoring, cytokine profiling is also a major tool to study specific immune response against antigens for design and testing of immuno-modulatory drugs or vaccines.
Conclusion
In conclusion, we provide here CyProQuant-PCR, a simple technique for genuine absolute quantification of cytokine mRNA using SYBR Green® which is as sensitive as the TaqMan® technique. Because the parameters of amplification are identical for all the cytokines developed, CyProQuant-PCR is readily automatable notably for 384-well plates and might allow multiple cytokine profiling of samples of very limited size at a relatively high throughput. CyProQuant-PCR opens the possibility to use cytokine mRNA measurements in clinical studies not only for an increased knowledge but also to help clinicians in patients' stratification and treatment decision.
Methods
Healthy human PBMC: isolation and in vitro stimulation
Blood was collected from healthy donors at the French Blood Bank. Peripheral blood mononuclear cells (PBMC) were isolated by density separation over Ficoll Hypaque and washed two times in RPMI 1640 (Gibco BRL, Invitrogen, Cergy Pontoise, France). Cells were re-suspended in RPMI 1640 (Gibco BRL, Invitrogen, Cergy Pontoise, France) supplemented with 2 mM glutamine (Gibco BRL, Invitrogen, Cergy Pontoise, France) and 10% AB+ human serum (French Blood Bank) at 2.106 cells/mL and either used directly for RNA extraction or cultured in duplicate with or without LPS (10 ng/mL, E. Coli O111: B7, Sigma, L'Isle d'Abeau Chesnes, France) or SAC (0,0075%, PanSorbine Cells, Calbiochem, La Jolla, CA, USA). After incubation, cells were washed with PBS and re-suspended in RNA-PLUS (Q-Biogene, Illkirch, France) for RNA isolation.
Malaria patients and asymptomatic controls
Twenty children admitted during the high malaria transmission season of 2001 to the emergency room at the Department of Child Health, Korle-Bu Teaching Hospital, Ghana were included. Five asymptomatic controls matched to patients for age, residence location and time of sample collection were enrolled. The general inclusion and exclusion criteria were as described by Kurtzhals et al. [25]. Parents or guardians signed informed consent forms. The study received ethical clearance from The Ethics and Protocol Review Committee at the university of Ghana Medical School and the Ministry of Health. Total cellular RNA was extracted from PBMC recovered from 500 μL of blood following supplier's instructions (RNA PLUS, Q-Biogene, Illkirch, France) after 22 hours of incubation at 37°C, 5% CO2 with or without LPS (10 ng/mL, E. Coli O111: B7, Sigma, L'Isle d'Abeau Chesnes, France).
RNA isolation
RNA was extracted following supplier's instructions, re-suspended in 60 μL of RNase-free water (Ambion, Huntingdon, UK) and quantified spectrophotometrically at 260 nm.
Primers
Oligonucleotide primers were synthesized at Eurogentec (Saraing, Belgium). To validate primers, a pool of cDNA from healthy human PBMC stimulated for 6 and 12 hours with LPS (10 ng/mL, E. Coli O111: B7, Sigma, L'Isle d'Abeau Chesnes, France) and PHA-L (10 μg/mL, Sigma-Aldrich, Lyon, France) was used. Analysis of the amplicons was assessed by 4% agarose gel electrophoresis and dissociation curve studies using Dissociation Curve Software (Applied Biosystems, Foster City, CA, USA). PCR products were cloned into pCR2.1 vector using Original TA cloning kit (InVitrogen, Cergy Pontoise, France) and sequenced (Genome Express, Meylan, France).
Construction of the external DNA and RNA standards
PCR products generated by each primer pairs were column-purified (Nucleospin, Macherey-Nagel, Hoerdt, France) and quantified spectrophotometrically at 260 nm. The molecular weight of the standard DNA was calculated by N*487-[(N-1)*175] were N is the number of bases composing the standard DNA. Stock solution of 1012 copies of standard DNA /3,85 μL were made in Tris-EDTA buffer (Ambion, Huntingdon, UK), split in single-use aliquots and stored at -80°C in safe-lock tubes. External DNA standard range was made extemporaneously by 1:10 serial dilutions in water.
Gene specific primers were fused on their 5' end to the sequence of the RNA polymerase T7 promoter to generate modified primers. These primers were used to amplify a gene specific PCR product flanked by transcription initiation sites. Five hundred nanograms of this construct were in vitro transcribed (MegaShortScript, Ambion, Huntingdon, UK). The standard RNA generated was purified (MegaClear, Ambion, Huntingdon, UK) and loaded on a 4% agarose gel for electrophoresis. The concentration of the standard RNA was determined spectrophotometrically at 260 nm. The molecular weight of the transcript was calculated by N*500-[(N-1)*175] were N is the number of bases composing the standard RNA. Stock solution of 1012 copies of standard RNA /3,85 μL were made in RNA storage solution (Ambion, Huntingdon, UK), split in single-use aliquots and stored at -80°C in safe-lock tubes. External RNA standard range was made extemporaneously by 1:10 serial dilutions in water.
Reverse transcription
For CyProQuant-PCR assays, 100 ng of total cellular RNA from PBMC and serial dilution of external RNA standard were reverse transcribed simultaneously in a parallel procedure using Reverse Transcription TaqMan reagents (Applied Biosystems, Foster City, CA, USA) on a MasterCycler Gradient (Eppendorf, Le Pecq, France). The final volumes were set at 100 μL for the cellular RNA samples and 50 μL for the external RNA standard range. The thermocycling parameters were as follows: 25°C, 10 min.; 48°C, 60 min. and 95°C, 5 min. cDNA were immediately used for PCR amplification.
Real-time RT-PCR and quantification of transcripts
Reverse-transcribed standard RNA and cellular RNA were amplified simultaneously on the same PCR plate on an ABI Prism 7700 (Applied Biosystems, Foster City, CA, USA). An aliquot of 5 μL of the RT reaction was amplified in duplicate in a final volume of 30 μL of SYBR Green PCR Master mix (Applied Biosystems, Foster City, CA, USA). Thermocycling conditions were 50°C for 2 min., 95°C for 10 min. and 40 cycles of [95°C/15 sec.; 60°C, 1 min]. The sample target RNA copy numbers were calculated using SDS 1.9 Software (Applied Biosystems, Foster City, CA, USA). The baseline fluorescence was set manually to correct for differences in initial cDNA concentration and the threshold was positioned at a fluorescence level that was 10 times higher than the background signal. Target mRNA copy numbers in cellular samples were calculated based on a standard curve generated by SDS 1.9 Software (Applied Biosystems, Foster City, CA, USA) by plotting cycles at threshold (CT) against the logarithmic values of the starting RNA standard copy number.
ELISA tests
TNF-α and MIF secreted proteins were quantified by sandwich ELISA following supplier's instructions (Bio-Source, Clinisciences, Montrouge, France and R&D, Lille, France respectively). Results are expressed as pg/mL for one million living cells.
Statistical analysis
Tests for significance were done using Stata software (Stata Corporation, College Station, Texas, 77845 USA) by Kruskal-Wallis rank test.
Patent application
Results disclosed in this manuscript have been protected in French patent application FR0408645.
Authors' contributions
PB developed the entire technique. IV did the in vitro stimulation experiments. DJ gave technical assistance. SL helped in RNA extraction of malaria samples. JCB introduced PB to molecular biology techniques and provided critical advices. BDA designed and conducted the study that yielded the malaria samples. OMP revised the manuscript and supported the work. CB conceived the strategy and coordinated the study. PB and CB drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by the European Commission program INCO-DC (Grant n°IC18-CT-980370), by the WHO/TDR/MIM project 980037 and by the program DVPI of the Pasteur Institute. Philippe Boeuf was supported by a PhD fellowship from the PAL-PLUS program (French Ministry of Technology, Research and National Education) and from CANAM (Caisse Nationale d'Assurance Maladie et Maternité des Travailleurs non Salariés des Professions non Agricoles).
Figures and Tables
Figure 1 Primer validation. A. Agarose gel electrophoresis of several CyProQuant-PCR products generated by amplification of a pool of cDNA from PBMC stimulated in vitro by LPS or PHA. Scale is shown in base pairs (bp). B. Dissociation curve analysis of these CyProQuant-PCR products. Negative derivative of the fluorescence is plotted against temperature. The single peak shows that SYBR Green fluorescence detects only the specific CyProQuant-PCR product. C. Amplification plots and standard curve resulting from the amplification of a range of β2-MG external RNA standard using CyProQuant-PCR.
Figure 2 Comparison to TaqMan® technology. Total RNA was extracted from isolated monocytes stimulated for 6 hours by LPS, serial-diluted 1:10 and retro-transcribed to generate standard curves by plotting the CT against the concentration of this cellular RNA in arbitrary units. TNF-α transcripts were amplified by real time PCR using either (A) the TaqMan® commercial kit and the TaqMan® universal PCR master mix or (B) our primers and the SYBR Green PCR master mix. Data represent the standard curves obtained for the two techniques, their slopes and the deducted efficiencies.
Figure 3 TNF and MIF transcription and secretion kinetics. PBMC from 2 donors were stimulated for 3, 9 or 18 hours (T3, T9 and T18 respectively) with LPS or SAC. TNF-α and MIF transcripts were quantified using CyProQuant-PCR and the corresponding secreted proteins were measured by ELISA. Transcript numbers are normalised for one million copies of β2-MG RNA relative to the level of transcripts in un-stimulated cells (fold increase). Secreted proteins are expressed as pg/mL for one million living cells.
Figure 4 Early cytokine production kinetics by PBMC stimulated in vitro with LPS or SAC. Total cellular RNA was extracted from PBMC from 2 donors stimulated for 0.5, 1, 2, 3, 6 or 9 hours with LPS (blue bars) or SAC (red bars). Cytokine transcripts were quantified using CyProQuant-PCR. The results are shown for one donor and were expressed in mRNA copy numbers calculated relative to un-stimulated cells, after normalisation against β2-MG (fold increase).
Figure 5 TGF-β1 transcripts quantification in malaria patients and asymptomatic controls TGF-β1 transcripts were quantified without stimulation and after 22 hours of LPS stimulation of PBMC harvested from asymptomatic controls (n = 5) and from patients suffering from acute malaria (n = 20). Results are normalised for one million copies of β2-microglobulin. Whiskers indicate data range, boxes extend from the 25th to the 75th percentile and the horizontal lines show the median. Asterisk indicates significant higher value in malaria patients compared to asymptomatic controls (p = 0.036) in the capacity of response to LPS.
Table 1 Primer sequences used for Cyproquant assays Table shows position of amplification product within cDNA sequence (upper line) and within genomic sequence (lower line). GenBank accession numbers are indicated.
Gene name 5'-3' primer sequence Position cDNA-gene Accesion Number
IL-1β FW CCTGTCCTGCGTGTTGAAAGA 639–788 NM_000576
RW GGGAACTGGGCAGACTCAAA Int. Span. M15840
IL-4 FW AACAGCCTCACAGAGCAGAAGAC 177–278 BC070123
RW GCCCTGCAGAAGGTTTCCTT Int. Span. AF395008
IL-10 FW GCTGGAGGACTTTAAGGGTTACCT 210–318 AY029171
RW CTTGATGTCTGGGTCTTGGTTCT Int. Span. AF418271
IL-12p40 FW CATGGTGGATGCCGTTCA 636–767 AF180563
RW ACCTCCACCTGCCGAGAAT Int. Span. AY008847
IL-13 FW ACAGCCCTCAGGGAGCTCAT 135–231 NM_002188
RW TCAGGTTGATGCTCCATACCAT Int. Span. AF377331
IL-15 FW CCATCCAGTGCTACTTGTGTTTACTT 351–466 U14407
RW CCAGTTGGCTTCTGTTTTAGGAA Int. Span. X91233
IL-18 FW GACGCATGCCCTCAATCC 966–1071 NM_001562
RW CTAGAGCGCAATGGTGCAATC Exon E17138
IFN-γ FW GTTTTGGGTTCTCTTGGCTGTTA 151–263 NM_000619
RW AAAAGAGTTCCATTATCCGCTACATC Int. Span. AF375790
MIF FW GCCCGGACAGGGTCTACA 322–399 M25639
RW CTTAGGCGAAGGTGGAGTTGTT Int. Span. L19686
TGF-β1 FW CAAGGGCTACCATGCCAACT 1786–1869 NM_000660
RW AGGGCCAGGACCTTGCTG Int. Span. X05839
TNF-α FW CCCCAGGGACCTCTCTCTAATC 358–455 NM_000594
RW GGTTTGCTACAACATGGGCTACA Int. Span. AB088112
β2-MG FW AATTGAAAAAGTGGAGCATTCAGA 255–389 NM_004048
RW GGCTGTGACAAAGTCACATGGTT Exon M17987
UBC FW ATTTGGGTCGCGGTTCTTG 20–152 BC039193
RW TGCCTTGACATTCTCGATGGT Int. Span. D63791
YWHAZ FW ACTTTTGGTACATTGTGGCTTCAA 1090–1183 NM_003406
RW CCGCCAGGACAAACCAGTAT Exon NC_000008
Note: FW = Forward primer; RW = Reverse primer; Int. Span. = Intron spanning, i.e. Primers derived from different exons; Exon = both primers derived from the same exon. β2-MG = β2-microglobulin. UBC = Ubiquitin C. YWHAZ = tyrosine 3-monooxygenase tryptophan 5-monooxygenase activation protein, zeta polypeptide.
Table 2 Comparison between CyProQuant-PCR and DNA standard-based approaches Efficiencies (E) are deducted from the slopes (S) of the standard curves based on E = 100*(10-1/S - 1). The deducted RT efficiency from the CyProQuant-PCR is calculated by dividing the RT-PCR efficiency from the RNA range (ERT-PCR) by the PCR efficiency from the cDNA range (EPCR).
Target gene CyProQuant-PCR DNA standard-based PCR
RNA range cDNA range Deducted RT efficiency DNA range
Slope ERT-PCR Slope EPCR Slope EPCR
TNF-α -3.610 89 -3.556 91 98 -3.503 93
IL-1β -3.479 94 -3.197 105 89 -3.067 112
IFN-γ -3.843 82 -3.116 109 75 -2.944 119
Table 3 Intergenic difference of RT efficiencies is not due to interassay variation. Serial 1:10 dilutions of a single cellular RNA sample, of a pooled RNA sample and of a pooled DNA sample were used to generate standard curves for different targets (TNF-α, β2-MG, MIF and UBC). Slopes were indicative of amplification efficiencies, which were normalised to 100% for the pooled DNA sample for easier comparison to amplification efficiencies of the two other samples.
Single cellular RNA sample Pooled RNA sample Pooled DNA sample
TNF-α 95 95 100
β2-MG 90 90 100
MIF 88 88 100
UBC 81 80 100
Table 4 RNA and DNA standard-based quantification of TNF-a, IL-1β and IFN-γ transcripts Fifty million copies of TNF-α, IL-1β and IFN-γ standard RNA were amplified in parallel of a range of RNA standard (CyProQuant-PCR) and DNA standard. Copy numbers were directly deduced from the cycle at threshold. *For TNF-α, the experiment was repeated at two days interval.
Target gene Actual copy number Calculated copy number
CyProQuant-PCR DNA standard approach
TNF-α* 5 107 5.2 107 7.7 105
5 107 5.0 107 3.6 105
IL-1β 5 107 4.3 107 6.7 105
IFN-γ 5 107 5.5 107 7.8 105
Table 5 Expression stability of endogenous standard genes under non-normalised conditions After 3 hours of stimulation with LPS, PBMC from healthy donors were split in 2 identical aliquots and total cellular RNA was extracted at 2 days interval. The same volume of RNA was reverse transcribed and β2-MG, UBC and YWHAZ transcripts were amplified by real time PCR using CyProQuant-PCR technique. Results show the mean values obtained for the 2 extractions in arbitrary units.
Stimulation conditions Molecules detected (arbitrary units)
T0 LPS 3 h
β2-MG 1.24 107 1.48 107
(1.00 107 – 1.49 107) (1.17 107 – 1.79 107)
UBC 2.04 105 2.39 105
(0.62 105 – 3.46 105) (1.51 105 – 3.28 105)
YWHAZ 1.89 105 2.80 105
(0.78 105 – 3.01 105) (2.25 105 – 3.34 105)
Table 6 Inter-experimental reproducibility of the quantification of a range of TGF-β1 and β2-MG RNA standards A set of TGF-β1 and β2-MG RNA standards ranging from 103 to 1010 copies was amplified by RT-PCR. Coefficients of inter-experimental variation were determined from eight different experiments and calculated for threshold cycles.
Absolute copy number Coefficients of inter-experimental variations (%)
TGF-β1 β2-MG
1.00E+03 1.04 1.20
1.00E+04 1.07 1.19
1.00E+05 0.94 1.13
1.00E+06 0.90 1.08
1.00E+07 0.79 0.95
1.00E+08 0.71 0.91
1.00E+09 0.54 0.93
1.00E+10 0.39 1.18
Average 0.80 1.07
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| 15748278 | PMC555737 | CC BY | 2021-01-04 16:03:32 | no | BMC Immunol. 2005 Mar 4; 6:5 | utf-8 | BMC Immunol | 2,005 | 10.1186/1471-2172-6-5 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-5-281576299010.1186/1471-2407-5-28Research ArticleRole of axillary sentinel lymph node biopsy in patients with pure ductal carcinoma in situ of the breast Zavagno Giorgio [email protected] Paolo [email protected] Renato [email protected] Zeno [email protected] Giuliano [email protected] Paolo [email protected] Paolo [email protected] Mario [email protected] Roberto [email protected] Giovanni [email protected] Andrea [email protected] Maria Elena [email protected] Giorgia [email protected] Donato [email protected] Clinica Chirurgica II, University of Padova, Via Giustiniani 2, 35128 Padova, Italy2 Chirurgia Generale, University of Ferrara, Corso Giovecca 203, 44100 Ferrara, Italy3 Chirurgia Generale, Hospital of Venezia, Castello 6776, 30122 Venezia, Italy4 Chirurgia Generale I, Hospital "Borgo Trento", Piazzale Stefani 1, 37126 Verona, Italy5 Chirurgia Generale II, Hospital of Vicenza, Via Rodolfi 6, 36100 Vicenza, Italy6 Chirurgia Generale, Hospital of Conegliano, Via Bisagno 4, 31015 Conegliano, Italy7 Chirurgia Generale II, Hospital "Borgo Trento", Piazzale Stefani 1, 37126 Verona, Italy8 Anatomia Patologica, University of Padova, Via Gabelli 61, 35128 Padova, Italy9 Anatomia Patologica, Hospital of Venezia, Castello 6776, 30122 Venezia, Italy2005 11 3 2005 5 28 28 13 11 2004 11 3 2005 Copyright © 2005 Zavagno 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
Sentinel lymph node (SLN) biopsy is an effective tool for axillary staging in patients with invasive breast cancer. This procedure has been recently proposed as part of the treatment for patients with ductal carcinoma in situ (DCIS), because cases of undetected invasive foci and nodal metastases occasionally occur. However, the indications for SLN biopsy in DCIS patients are controversial.
The aim of the present study was therefore to assess the incidence of SLN metastases in a series of patients with a diagnosis of pure DCIS.
Methods
A retrospective evaluation was made of a series of 102 patients who underwent SLN biopsy, and had a final histologic diagnosis of pure DCIS. Patients with microinvasion were excluded from the analysis. The patients were operated on in five Institutions between 1999 and 2004.
Subdermal or subareolar injection of 30–50 MBq of 99 m-Tc colloidal albumin was used for SLN identification. All sentinel nodes were evaluated with serial sectioning, haematoxylin and eosin staining, and immunohistochemical analysis for cytocheratin.
Results
Only one patient (0.98%) was SLN positive. The primary tumour was a small micropapillary intermediate-grade DCIS and the SLN harboured a micrometastasis. At pathologic revision of the specimen, no detectable focus of microinvasion was found.
Conclusion
Our findings indicate that SLN metastases in pure DCIS are a very rare occurrence. SLN biopsy should not therefore be routinely performed in patients who undergo resection for DCIS. SLN mapping can be performed, as a second operation, in cases in which an invasive component is identified in the specimen. Only DCIS patients who require a mastectomy should have SLN biopsy performed at the time of breast operation, since in these cases subsequent node mapping is not feasible.
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Background
Ductal carcinoma in situ (DCIS) is defined as the proliferation of malignant epithelial cells in the mammary ductal system, with no evidence of invasion of the basement membrane. By definition, the disease is localized to the breast, with no spread to regional nodes or distant sites.
In the pre-mammography era, DCIS was rarely diagnosed, accounting for only 1 to 2% of all breast cancers. The increasing use of screening mammography in recent years has resulted in a dramatic increase in the diagnosis of DCIS, which now accounts for roughly 20% of all mammographically detected cancers [1].
Axillary treatment in patients with a diagnosis of pure DCIS is controversial. By definition, pure DCIS is not invasive and cannot spread to the regional nodes, so axillary staging should not be indicated in DCIS patients. In practice, however, nodal metastases are found in 1 to 2% of patients with a diagnosis of pure DCIS, and this paradoxical condition usually depends on a focus of invasion being missed by the pathologist because of a sampling bias [2]. Before the introduction of sentinel lymph node (SLN) biopsy, it was widely agreed that axillary lymph node dissection (ALND) should be avoided in patients with DCIS, because of the low yield of positive findings in these cases and the significant morbidity associated with the procedure [3]. The introduction of SLN biopsy as a minimally invasive tool for staging the axilla led to a renewed interest in axillary staging for these patients, for two reasons. First, this procedure is far less invasive than ALND and has minimal morbidity and complications, so the cost/benefit ratio may be positive even if the likelihood of finding metastatic nodes is low. Second, SLN biopsy allows more accurate axillary staging than ALND specimen examination, because SLN can be thoroughly evaluated by serial sectioning and immunohistochemical methods. This has led to the increased detection of nodal micrometastases in patients with invasive breast cancer, and has raised the expectation that the rate of positive nodal findings in patients with DCIS could also be increased. Currently, the indications for SLN biopsy in patients with a diagnosis of pure DCIS are controversial.
The aim of the present study was to determine the prevalence of SLN metastasis in a multi-institutional series of patients with a diagnosis of pure DCIS in order to determine the usefulness of routine SLN biopsy in such cases.
Methods
Patients
Our series consisted of 102 patients with a final histopathological diagnosis of pure DCIS who underwent SLN biopsy in five Institutions between January 1999 and January 2004. Patients with DCIS with microinvasion were excluded from the study.
All patients underwent SLN biopsy after they had given their written informed consent, and had decided whether they would undergo a completion ALND if SLN were found to be positive.
Lymphoscintigraphy
On the day before surgery, all the patients received an injection of 30–50 MBq of 99 m-Tc-nanocolloidal albumin (Nanocoll, Nicomed-Italia, Saluggia, Italy) in 0.2 cc of saline.
In patients with palpable tumours, the radiocolloid was injected subdermally into the cutaneous projection of the tumour. In those with non-palpable tumours, the lesion was localized by stereotactic or ultrasonographic placement of a self-retaining wire and the cutaneous projection was marked by an ink spot on the skin, which served as a guide for subdermal radiocolloid injection. Patients with extensive microcalcifications or multifocal tumours were given subareolar radiotracer injection. Finally, patients who underwent delayed SLN biopsy after excision of the primary lesion were given subdermal radiotracer injection in two fractions, at the sides of the surgical scar.
Twenty minutes and 2 hours after injection, scintigraphic images were obtained using a large-field-of-view gamma camera (Orbiter, Siemens, IL, USA) equipped with a parallel hole, low energy and a high resolution collimator.
Surgical procedure
Primary tumour resection with simultaneous SLN biopsy was performed in patients with a preoperative diagnosis of DCIS made by stereotactically-guided directional vacuum-assisted biopsy and in those with mammographical finding pathognomonic of DCIS, with or without a preoperative cytologic diagnosis of malignancy. A diagnostic excisional biopsy was performed in patients with a doubtful pre-operative diagnosis, and SLN biopsy was performed as a second operation in those with a final histologic diagnosis of DCIS.
As a rule, in patients undergoing conservative surgery for non-palpable lesions, an intraoperative x-ray examination of the resected specimen was performed, in order to confirm that complete tumour excision had been achieved.
SLN biopsy was performed 16 to 24 hours after radiocolloid injection. When conservative surgery was performed, the SLN was excised through the incision used for resecting the primary tumour, if located in the upper outer quadrant, whereas a separate axillary incision was performed if the tumour was located in another breast site. A gamma-ray-detecting probe was used for intraoperative SLN identification. All lymph nodes with a probe-detected radioactive count >10% of that of the hottest node were excised.
Histopathology
The excised breast lesions were sampled with serial cuts and the margins were identified by ink.
The histopathologic diagnosis and classification of DCIS was made according to the Holland grading system [4]. The Van Nuys prognostic index [5] was used only in some of the cases. The search for microinvasive foci was performed in selected cases both with haematoxylin-eosin serial sections and immunostains to smooth muscle actin and CD10 for the detection of myoepithelial cells. In all cases, the tumour size and margin status were specified in the histopathological report. The SLN examination was performed as described elsewhere [6]. Briefly, for frozen-section examination, nodes with diameters of ≤ 0.5 cm were bisected, while nodes measuring >0.5 cm were sectioned each 2 to 3 mm. For each sample, two frozen sections made at 40 μm intervals were examined. The frozen tissue was then thawed, fixed and embedded to obtain permanent sections.
For definitive histological examination, two consecutive 5 μm thick tissue sections were cut from a paraffin block at two levels, 40 μm apart from each other. The sections were then stained with haematoxylin-eosin and immunostained with monoclonal antibody to cytokeratin.
Results
The 102 patients with pure DCIS had a median age of 59.4 years (range 37 to 85). The histologic characteristics of the tumours are reported in Table 1.
Of the 102 patients, 20 (19.6%) had palpable, and 82 (80.4%) non-palpable breast tumours.
Seventy-four patients (72.5%) were treated with conservative surgery (quadrantectomy or lumpectomy) and 28 (27.5%) with mastectomy.
Ninety-one patients underwent primary tumour resection with simultaneous SLN biopsy: 16 on the basis of a preoperative histologic diagnosis of DCIS obtained by a vacuum-assisted large core biopsy, 63 with a preoperative cytology suggestive of malignancy and 12 only on the basis of the mammographical findings alone.
Patients with a doubtful preoperative diagnosis underwent excisional biopsy of the breast lesion, and the 11 patients with pure DCIS at definitive histology, underwent SLN biopsy and, if required, definitive treatment of the primary tumour as a second operation.
A total of 147 SLNs were identified and excised from the 102 patients: a single SLN was found in 61 cases (59.8%), two SLNs in 37 (36.3%), and three SLNs in 4 (3.9%).
A positive SLN was found in one (0.98%) patient, who had one micrometastases (0.6 mm) detected by haematoxylin-eosin staining. The pathologic review of the surgical specimen confirmed that the tumour had been completely excised, and no microinfiltration was detected. In this SLN positive case, the primary tumour was a micropapillary DCIS, G2, with a diameter of 16 mm. The patient did not undergone previous microbiopsy or FNAC, and SLN biopsy was performed at the time of the primary tumour excision. Completion ALND was not performed.
Discussion
Indications for routine SLN biopsy in patients with a diagnosis of pure DCIS are still controversial. The first study proposing SLN biopsy for DCIS patients came from the H. Lee Moffitt Cancer Center in the year 2000 [7]: in a study of 87 DCIS patients, 5 (5.7%) presented SLN metastases, and the authors concluded that SLN biopsy should be routinely used in DCIS patients in order to identify and correctly stage patients with undetected invasive disease. In a later report on 195 DCIS patients from the same Institution, 26 (13%) patients were SLN positive and this finding strengthened the recommendation that SLN biopsy should become a routine part of surgical treatment for all DCIS patients [8].
The Memorial Sloan-Kettering Cancer Center group reviewed patients with "high risk" DCIS and found that 9/76 (12%) patients had SLNs positive for metastases. The authors suggested that SLN biopsy should be performed in all DCIS patients with one or more of the following characteristics: palpable or mammographic mass, suspicion of microinvasion, multicentric disease, high nuclear grade or necrosis [9].
On the other hand, in subsequent studies negligible rates of nodal involvement were found in patients with pure DCIS: Kelly et al. [10] found nodal metastases in only 3/134 (2.2%) patients, Intra et al. reported SLN metastases in 7/223 (3.1%) patients [11], Farkas et al. found no cases of SLN metastasis among 44 patients [12]. All these investigators concluded that SLN biopsy should not be routinely performed in patients with DCIS, with the exception of those undergoing mastectomy, because this operation precludes the possibility of performing a subsequent SLN biopsy if invasive foci are found at histology of the mastectomy specimen. Our results are consistent with the latter series of reports, since we found only one case of SLN micrometastasis among 102 patients with a diagnosis of pure DCIS (0.98%).
The variability in the reported rates of nodal metastases probably reflects differences in the accuracy of pathological evaluation of the primary breast tumour: extensive sampling and a thorough histological examination of the DCIS are of crucial importance in ruling out microinvasive foci. Microinvasive DCIS is a different pathological entity, with a well-defined metastatic potential, and should be excluded from studies evaluating the role of axillary staging in non-invasive cancer.
Our results support the view that the presence of axillary nodal metastases in patients with a final histopathological diagnosis of pure DCIS is a very unusual phenomenon, if the primary breast tumour has been completely excised and thoroughly examined by the pathologist. We, therefore, believe that routine SLN biopsy in all DCIS patients represents an over-treatment and should be avoided.
Enthusiasm for SLN biopsy in DCIS patients is partly due to the low morbidity of the procedure. However, complications such as lymphedema, seroma, infection and sensory neuropathy have been reported after SLN biopsy [13-15]. Moreover, performing SLN biopsy in patients with a small DCIS treated with a conservative operation precludes the possibility of using this procedure in patients with a subsequent homolateral invasive tumour, which is not infrequent in this setting [1].
Finally, the policy of performing primary tumour excision with simultaneous SLN biopsy in all patients with a preoperative diagnosis of DCIS may incur a risk of performing axillary biopsy in patients with benign breast lesions, since intraoperative frozen section histology is usually unreliable in patients with small areas of microcalcifications.
We therefore believe that this procedure should be used cautiously, being reserved for cases in which a real advantage can be expected.
Another factor that has prompted interest in SLN biopsy for DCIS patients is the widespread use of image-guided core needle biopsy for the diagnosis of mammographically-detected abnormalities, a diagnostic technique that often fails to identify invasive foci: 14 to 29% of patients with a preoperative core needle biopsy of DCIS are found to have invasive cancer at surgical excision [16,17], and require a second operation for axillary staging. With vacuum-assisted large core biopsy the under-diagnosis rate is lower, but it is still not negligible. Supporters of routine SLN biopsy claim that this procedure is not reliable after primary tumour excision. They therefore believe that all patients with a preoperative diagnosis of DCIS who undergo definitive surgery and are upstaged because the pathologist finds foci of invasion on the specimen, must undergo an ALND if a SLN biopsy was not performed at the time of the first operation [18]. We disagree with this view, because it has been clearly demonstrated that SLN biopsy can be safely performed as a second procedure after primary tumour excision [19,20]. The only exceptions to this are patients who undergo mastectomy and those who require a wide quadrantectomy of the upper outer quadrant, which can disrupt lymphatic pathways toward the axilla. We therefore believe that only patients with a preoperative diagnosis of DCIS who need mastectomy or wide excision close to the axilla should undergo a concomitant SLN biopsy. In all other cases, only the primary tumour excision should be performed and SLN biopsy should be reserved, as a second procedure, for patients found to have infiltration at the histologic examination of the primary tumour.
It has been claimed that some features of DCIS (large dimensions, high grade, comedo-type, mass forming lesions) are associated with a higher risk of invasive disease and nodal metastases. Therefore, some authors suggest that SLN biopsy should be reserved for all patients with these "high- risk" DCIS [9,21,22]. However, several investigators fail to correlate any histopathologic parameter with lymph node metastases [8,10,11].
The majority of patients in our series presented with low risk DCIS: most of the tumours were small (<1 cm) and only 20/102 patients had a palpable breast mass. Therefore, we cannot rule out that a higher incidence of microinvasion and nodal metastases might be found in a patient population with more aggressive forms of DCIS. However, in our series, the one DCIS patient with SLN metastasis had a small micropapillary, G2, non-palpable tumour.
In our opinion, clinical judgement should be used in patients with large solid tumours or diffuse comedo-type DCIS, bearing in mind that most of these patients require a mastectomy and are therefore candidates for SLN biopsy.
Conclusion
Our results confirm that the finding of SLN metastasis in pure breast DCIS is a very rare occurence, if the primary tumour has been completely excised and microinvasion has been ruled out by a thorough histologic examination. Therefore, our current policy is to avoid routine SLN biopsy at the time of primary tumour resection in the presence of a preoperative diagnosis of DCIS. We reserve this procedure, as a second operation, for cases in which an invasive component is identified at the histologic examination of the surgical specimen. Only patients with a diagnosis of DCIS requiring a mastectomy or a wide resection close to the axilla should undergo concomitant SLN biopsy.
However, particularly if a large high-grade tumour is found, patients should be informed of the risk that invasive disease may be found, and that a second procedure for SLN biopsy may be required. In these cases, the patient can decide whether to undergo a potentially unnecessary SLN biopsy at the time of the first operation or whether to run the risk of requiring a second operation.
Competing interests
The author(s) declare that they have no competing interests
Authors' contributions
GZ planned the study and drafted the manuscript
PC, RM, ZF, GS, PB, PP, AB contributed with their own cases to the final multi-institutional series
RM and GC reviewed the pathologic specimens of selected cases and drafted the histopatology section of the manuscript
MEP and GM were in charge of data collection
ML and DN co-ordinated the study
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The present investigation was financially supported by the Fondazione della Cassa di Risparmio di Padova e Rovigo
Figures and Tables
Table 1 Characteristics of pure DCIS diagnosed in 102 patients
PATIENTS
N %
HISTOLOGIC SUBTYPE
Comedo 42 41.2
Cribriform 16 15.7
Solid 17 16.7
Papillary 7 6.9
Micropapillary 6 5.9
Comedo and solid 5 4.9
Others 9 8.8
TUMOUR SIZE
0–5 mm 14 13.7
5–10 mm 40 39.2
10–15 mm 19 18.6
15–20 mm 14 13.7
20–30 mm 10 9.8
>30 mm 2 2.0
Unknown 3 2.9
TUMOUR GRADE
G1 21 20.6
G2 37 36.3
G3 44 43.1
TYPE OF SURGERY
Lumpectomy/quadrantectomy 74 72.5
Mastectomy 28 27.5
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| 15762990 | PMC555738 | CC BY | 2021-01-04 16:03:07 | no | BMC Cancer. 2005 Mar 11; 5:28 | utf-8 | BMC Cancer | 2,005 | 10.1186/1471-2407-5-28 | oa_comm |
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CytojournalCytoJournal1742-6413BioMed Central London 1742-6413-2-61577748010.1186/1742-6413-2-6Case ReportDesmoplastic small round cell tumour: Cytological and immunocytochemical features Granja Nara M [email protected] Maria D [email protected] Jeni [email protected] Adhemar Longatto [email protected] Fernando C [email protected] Pathology Department, School of Medicine, São Paulo University, São Paulo, Brazil2 Department of Pathology and Treatment and Research Center of A.C. Camargo Hospital, São Paulo, Brazil3 Life and Health Sciences Research Institute, Health Sciences School, University of Minho, Braga, Portugal4 Pathology Division, Adolfo Lutz Institute, São Paulo, Brazil5 Medical Faculty, Department of Pathology, University of Porto, Porto, Portugal6 IPATIMUP – Institute of Molecular Pathology and Immunology of University of Porto, Porto, Portugal2005 18 3 2005 2 6 6 17 2 2005 18 3 2005 Copyright © 2005 Granja et al; licensee BioMed Central Ltd.2005Granja 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
Desmoplastic small round cell tumor (DSRCT) is a rare and highly aggressive neoplasm. The cytological diagnosis of these tumors can be difficult because they show morphological features quite similar to other small round blue cells tumors. We described four cases of DSRCT with cytological sampling: one obtained by fine needle aspiration biopsy (FNAB) and three from serous effusions. The corresponding immunocytochemical panel was also reviewed.
Methods
Papanicolaou stained samples from FNAB and effusions were morphologically described. Immunoreaction with WT1 antibody was performed in all cytological samples. An immunohistochemical panel including the following antibodies was performed in the corresponding biopsies: 34BE12, AE1/AE3, Chromogranin A, CK20, CK7, CK8, Desmin, EMA, NSE, Vimentin and WT1.
Results
The smears showed high cellularity with minor size alteration. Nuclei were round to oval, some of them with inconspicuous nucleoli. Tumor cells are clustered, showing rosette-like feature. Tumor cells in effusions and FNA were positive to WT1 in 3 of 4 cytology specimens (2 out 3 effusions and one FNA). Immunohistochemical reactions for vimentin, NSE, AE1/AE3 and WT1 were positive in all cases in tissue sections.
Conclusion
The use of an adjunct immunocytochemical panel coupled with the cytomorphological characteristics allows the diagnosis of DSRCT in cytological specimens.
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Introduction
Desmoplastic small round cell tumor (DSRCT) is a rare and highly aggressive neoplasm described as a distinct clinicopathologic entity in 1989 by Gerald and Rosai [1]. Usually affecting young males and presenting as an abdominal mass, the tumor grows along serosal membranes with multiple nodules attached to the peritoneal surface [2]. Other primary sites have been reported as pleura [3], paratesticular region [4], bone and soft tissues [5] and ovary [6,7].
Histologically, a typical feature of DSRCT is the presence of clusters of tumor cells distributed within a cellular stroma. The shape of clusters varies from round to elongate. Tumor cells are small to medium-sized with round to oval hyperchromatic nuclei, with inconspicuous nucleoli. Necrotic cells and mitosis are common features. Cytoplasm is usually scanty, and cell borders are indistinct. Intracytoplasmic eosinophilic rhabdoid inclusions may be found in larger cells with nuclear pleomorphism [8]. The immunohistochemical profile shows divergent differentiation, a striking feature of this tumor. DSRCT may present a problem in the differential diagnosis with other round cell tumors. Tumor cells are immunoreactive for epithelial, neural and myogenic markers [2]. Cytogenetical studies have demonstrated a reciprocal chromosome translocation between the Ewing's sarcoma gene on chromosome 22 and the Wilms' tumour gene WT1 on chromosome 11, which is distinct from the translocation observed in Ewing sarcoma/peripheral neuroectodermal tumor (PNET) [9].
The cytological smears of DSRCT obtained by FNAB are moderately cellular. Tumor cells show round to oval nuclei with fine chromatin and inconspicuous nucleoli. Cytoplasm is scanty to moderate, with variable number of vacuoles. Tumor cells are arranged in loose clusters. Occasionally, spindle fibroblast-like cells are observed. Stromal fragments may be detected [10]. Effusion samples show cohesive cell clusters and similar cytological features. Mitoses or individual necrotic cells may be present, as nuclear molding [3]. In the current study, we describe the morphological and immunocytochemical features of four cytologic specimens, one of them obtained by FNAB and three from serous effusions (2 peritoneal fluid samples and one pleural effusion), from 3 patients with a diagnosis of DSRCT.
Materials and methods
We retrieved from the cytological files of Hospital do Cancer – A. C. Camargo four cytological specimens from 3 patients diagnosed with DSRCT, including one fine-needle aspiration sample and 3 fluid samples, during the last five years (2000–2004). FNA was performed on an inguinal mass of one patient. Alcohol-fixed smears were stained with Papanicolaou technique. Serous effusions were prepared with Cytospin (Shandon, Pittsburgh, Pennsylvania, USA). We evaluated two peritoneal fluid samples and one pleural fluid sample. One case (patient 1, peritoneal fluid) had a cellblock available. All cases were confirmed by histological analysis and immunohistochemical reactions. The histological sections were cut in sections of 4 μm and stained with H&E and immunohistochemistry. Immunocytochemical study was also performed on all cases.
Immunohistochemical and immunocytochemical reactions were performed using streptavidin-biotin peroxidase technique with positive and negative controls. Diaminobenzidine was the chromogen. Table 1 shows the antibodies used and dilutions. All antibodies were from DAKO Corporation, Capinteria, CA, U.S.A.
Table 1 Antibodies and dilutions used in this study
Marker Antibody clone Dilution
34BE12 34BE12 1:100
AE1/AE3 AE1/AE3 1:500
Chromogranin A DAK-A3 1:100
CK20 KS20.8 1:50
CK7 OV-TL 12/30 1:100
CK8 35BH11 1:100
Desmin D33 1:100
EMA E29 1:2000
NSE BBS/NC/V1-H14 1:1500
Vimentin Vim 3B4 1:200
WT1 6F-H2 1:400
Cases
Patient 1
22-year-old white female, with abdominal pain. Video-laparoscopy showed a liver mass and multiple peritoneal implants diagnosed as DSRCT. Six months after the diagnosis, she started chemotherapy for four months, and reduction of tumor mass was observed. One month after the end of chemotherapy, the tumor was removed. Macroscopically, tumor mass measured 5.0 × 4.0 × 3.8 cm and was involving uterus, pericolic tissue, and vagina. Histological analysis shows also involvement of both ovaries and large bowel wall. Ten out of 13 lymph nodes showed metastasis of DSRCT. The peritoneal fluid colleted during surgery was negative for neoplastic cells. Eight months after the first surgery, she presented with a recurrence in the abdominal cavity and a new resection of the tumor mass showed involvement of cecal appendix. Peritoneal fluid sample collected at that time was positive for malignant cells. In the follow up examination, seven months after the second surgery, it was found an inguinal tumor mass of 15 mm. FNA was performed and showed DSRCT metastasis. After the diagnosis, this patient was transferred to another institution.
Patient 2
Seven year-old male with back pain and fever. CT scan showed pleural effusion and a mediastinal mass measuring 16.0 × 9.0 cm. Tumour mass showed involvement of soft tissues. Surgical biopsy and pleural drainage were performed. The patient was treated with radiotherapy and chemotherapy, but died 8 months after the diagnosis.
Patient 3
Male, 15-year-old had acute abdominal pain and was submitted to an exploratory laparotomy that disclosed a large pelvic mass, involving epiplon and sigmoid, cecum, liver and peri-aortic lymph nodes. This patient had multiple nodules on peritoneal surface. The biopsy of tumor was performed. One month after the diagnosis, chemotherapy was initiated. The patient was submitted to chemotherapy during 8 months, with reduction of more than 50% of tumor mass. A second laparotomy was done to excise retroperitoneal and retrovesical mass. At this time peritoneal fluid sample was collected. After surgery, chemotherapy was continued. The patient is alive, with residual disease.
Results
Cytological findings
Case 1 (Fine needle aspiration)
The smears showed high cellularity. The tumor cells exhibited a slight variation in size. Nuclei were round to oval, some of them with small nucleoli. The cytoplasm was scanty. Tumor cells are clustered, with rare clusters showing rosette-like features. The background of the smears showed lymphocytes. (Figure 1).
Figure 1 Clusters of small round tumor cells showing rosette-like features in smear of fine needle aspiration specimen of DSRCT.
Cases 1, 2 and 3
All fluid samples showed similar features. The samples showed high cellularity. Tumor cells were more frequently arranged in tridimentional clusters, but occasionally, isolated cells are also seen. Additionally, clusters showing rosette-like features are rarely observed. Nuclei were round to oval, some of them with small nucleoli. The cytoplasm was scanty (Figure 2).
Figure 2 Effusion from patient with DSRCT exhibiting high cellularity. Observe tridimentional clusters of neoplastic cells. Nuclei were round to oval, some of them with small nucleoli and the cytoplasm is scanty.
Immunohistochemical and Immunocytochemical findings
The distribution of immunoreactivity in histological and cytological samples from the patients are summarized in Table 2. Tumor cells in effusions from patients 1 and 2 and, the smear obtained by FNA (Patient 1) were positive to WT1 (Figure 3).
Table 2 Distribution of immunoreactions in patients 1, 2 and 3 histological and cytological samples.
Marker Patient 1 Biopsy Patient 2 Biopsy Patient 3 Biopsy Patient 1 Cytology Patient 2 Cytology Patient 3 Cytology
34BE12 - - ND ND ND ND
AE1/AE3 + + + ND ND ND
Chromogranin A + + ND ND ND ND
CK20 - - ND ND ND ND
CK7 - - ND ND ND ND
CK8 + - ND ND ND ND
Desmin + - ND ND ND ND
EMA + + ND ND ND ND
NSE + + + ND ND ND
Vimentin + + + ND ND ND
WT1 + + + + + -
(Effusion & FNA)
N.D. = not done.
Figure 3 DSRCT tumor cells in effusion showing nuclear positive reaction to WT1.
Patient 1
the histological sample collected before chemotherapy, exhibited immunohistochemical positivity for vimentin, epithelial membrane antigen (EMA), neuron specific enolase (NSE), chomogranin A, and desmin in dot-like perinuclear pattern. Cytokeratin expression was observed with anti-cytokeratin cocktail (AE1/AE3) and Cytokeratin 8. Tumor cells also expressed WT1 protein.
Patient 2
tumor cells exhibited positivity for vimentin, EMA, NSE, chomogranin A, AE1/AE3 and WT1. Desmin and cytokeratins 7, 20 and 34BE12 were negative.
Patient 3
tumor cells exhibited positivity for vimentin, NSE, AE1/AE3 and WT1. The immunohistochemical study was performed before chemotherapy.
Discussion
DSRCT is a rare neoplasm that affects young patients. It may present a problem in the differential diagnosis with other small round cell tumors. The diagnosis of DSRCT however can be established with correlation of clinical, cytological and immunocytochemical features. The cytological features that we found in the smears obtained by FNA are similar to other descriptions in the literature. Similar to reports of Zeppa et al [11], we did not detect in our smears fragments of fibrosis or cytoplasmic granules or vacuoles. The finding of stromal fragments, frequently seen in FNA is not a common finding in liquid based preparations [12].
One of the characteristics of DSRCTs is its dissemination along serous surfaces. Due to this fact, development of serous effusions is a common clinical finding in DSRCTs patients, with detection of tumor cells in the fluid. In effusions, tumor cells may be present in aggregates but no obviously architectural arrangement is seem.
Demonstration of a divergent phenotype and the reciprocal translocation characteristic of DSRCT are critical to the diagnosis.
In a reported series of 32 cases of DSRCTs [13], 88% of cases were immunoreactive for AE1/AE3, 84% for NSE, 81% for desmin. These results were similar to other previous studies [2]. Lae et al [13] detected positivity to WT1 antibody in 29 out of 32 cases. Our immunohistochemical results are in agreement with other previous studies. Strong membrane expression of HER2/neu and immunoreactivity to c-kit protein are not common findings [14].
The establishment of a specific reciprocal translocation t (11; 22)(p13;12) as diagnostic in DSRCT was based on the results of Sawyer et al [9]. Shen et al [15] and Roberts et al [16] described variants of with other chromosome involved in addition to chromosome 11 and 22. The translocation t (11; 22)(p13;12) involve the EWS gene in 22q24 and WT1 gene in 11p13. This translocation produces the chimeric transcript EWS/WT1 and the related WT1 protein, which can be detected by immunohistochemical method.
EWS gene encodes a protein which the precise function and normal role has not yet been elucidated. Recently, Thomas et al [17] proposed that the protein product of the EWS gene interacts with Brn-3a cellular transcription factor via a direct protein-protein interaction. Native WT1 protein function has not completely known, but it represses transcription in vitro of many genes. WT1 is a tumor-suppressor gene that encodes a protein, which mediates transcriptional repression and interacts with p53 protein [18], product of another tumor suppressor gene, TP53, frequently deleted or mutated in many human tumors. In absence of intact p53 protein, WT1 acts as a transcriptional activator [19]. Normal WT1 protein is expressed in tissues, which undergo mesenchymal-epithelial conversion derived from mesoderm, in a specific period of development [20] and it plays a role in mesothelial formation in embryonic development [21]. Immunohistochemical detection of WT1 in DSRCTs is predictive of the translocation and it also demonstrates that the chimeric protein is expressed in significant amount in tumour cells 22, 23. In addiction to consistent WT1 expression, the typical serosal involvement in DSRCT has raised the possibility that this tumor might be a blastematous tumour derived of primitive mesothelium [24]. Mesothelin is a glycoprotein of unknown function strongly expressed in mesothelial cells. Although lack of specificity of expression of mesothelin for mesothelial origin, the expression of this protein in DSRCT may have some significance on histogenisis of this tumor [25].
We detected WT1 immunoreactivity in all tumors tissues and in 2 out of 3 serous effusions with malignant cells, as well as on FNAB smears. The high frequency of DSRCTs with WT1 protein expression suggests that in consensus with clinical tomographic and cytological findings, this antibody may be used to confirm the diagnosis of DSRCT in cytological samples. We observed a negative WT1 reaction in the cytological sample of patient 3. This sample was collected 10 months after the end of chemotherapy protocol. We can hypothesize if chemotherapy hampered a different antigenic pattern in malignant cells, and influenced this result.
Among other small round cell tumors, most of cases of rhabdomyosarcomas and neuroblastomas do not disclose nuclear WT1 staining [26,27]. Comparing DSRCT and Ewing Sarcoma/PNET, Hill et al. [28] detected WT1 nuclear immunoreactivity in all 13 DSRCT cases studied; conversely, all 11 cases of Ewing Sarcoma/PNET were negative. Additionally, Wilm's tumor was demonstrated to present a high percentage of cases with nuclear WT1 staining; for this reason, correlation with clinical findings is necessary to do a differential diagnosis between Wilm's tumour and DSCRT in effusions [26]. On the other hand, it is important to emphasize that malignant mesothelioma should also be considered in the differential diagnosis, since it can show varied histological appearances including sarcomatoid differentiation with desmoplastic areas, or even resembling undifferentiated sarcomas [29]. WT1 might also decorate nuclei of both epithelioid or biphasic mesothelioma but in general, WT1 stain most frequently epithelioid mesotheliomas [30]. The use of a panel of markers can also help in the differential diagnosis.
In conclusion, cytological and immunophenotypical findings in an appropriate clinical context is sufficient to suggest DRSTC, what sounds highly contributive for us, considering the high aggressiveness of this tumor.
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| 15777480 | PMC555739 | CC BY | 2021-01-04 16:36:23 | no | Cytojournal. 2005 Mar 18; 2:6 | utf-8 | Cytojournal | 2,005 | 10.1186/1742-6413-2-6 | oa_comm |
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Theor Biol Med ModelTheoretical Biology & Medical Modelling1742-4682BioMed Central London 1742-4682-2-71576047410.1186/1742-4682-2-7ResearchHigh-Temperature unfolding of a trp-Cage mini-protein: a molecular dynamics simulation study Seshasayee Aswin Sai Narain [email protected] Centre for Biotechnology, Anna University, Chennai 600025, India2005 11 3 2005 2 7 7 9 10 2004 11 3 2005 Copyright © 2005 Seshasayee; licensee BioMed Central Ltd.2005Seshasayee; 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
Trp cage is a recently-constructed fast-folding miniprotein. It consists of a short helix, a 3,10 helix and a C-terminal poly-proline that packs against a Trp in the alpha helix. It is known to fold within 4 ns.
Results
High-temperature unfolding molecular dynamics simulations of the Trp cage miniprotein have been carried out in explicit water using the OPLS-AA force-field incorporated in the program GROMACS. The radius of gyration (Rg) and Root Mean Square Deviation (RMSD) have been used as order parameters to follow the unfolding process. Distributions of Rg were used to identify ensembles.
Conclusion
Three ensembles could be identified. While the native-state ensemble shows an Rg distribution that is slightly skewed, the second ensemble, which is presumably the Transition State Ensemble (TSE), shows an excellent fit. The denatured ensemble shows large fluctuations, but a Gaussian curve could be fitted. This means that the unfolding process is two-state. Representative structures from each of these ensembles are presented here.
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Background
Understanding the mechanisms behind protein folding, which is one of the most fundamental biochemical processes, is proving to be a challenging task for biochemists and biophysicists. Recent developments in instrumentation and methodology have enabled us to take major steps forward in comprehending the dynamics of proteins and peptides at the molecular level. Protein engineering methods such as Phi-value analysis [1] and various spectroscopic techniques such as NMR have made the task more practicable.
Proteins are composed of two major secondary structural elements, helices and sheets, which, along with loops, pack together to form super-secondary and tertiary structures. Trp cage is a novel, and a highly stable, mini-protein fold. A 20-residue Trp-cage miniprotein has been designed [2]. It has the sequence NLYIQWLKDGGPSSGRPPPS. While residues 1–9 form an alpha helix, residues 10–15 form a 3,10 helix. W6 is caged by the C-terminal poly-proline stretch. D9 and R16 are involved in a stabilizing salt-bridge interaction.
Molecular dynamics simulations, which make use of classical Newton mechanics to generate trajectories, are playing an ever-expanding role in biochemistry and biophysics due to substantial increases in computational power and concomitant improvements in force fields. In particular, the contribution of such studies to protein folding is immense [1]. As pointed out by Fersht and Dagget, molecular dynamics simulations are capable of unraveling whole protein folding / unfolding pathways [1]. Indeed, simulation techniques have been widely used for studying helices and sheets. Today, folding simulations of more-than-model peptides are being carried out on high-power computers.
Despite being a new mini-protein construct, the Trp cage motif has attracted considerable computational analysis. Folding simulations of this protein in explicit water have been carried out using what is known as the Replica Exchange Method. A two-state folding mechanism has been proposed and free energy surfaces have been determined [3]. Moreover, a few folding simulations of have been carried out using implicit solvation models [4-6]. In this article, the results of a high-temperature unfolding simulation of the Trp-cage mini-construct are presented. Three separate structural clusters are identified: the close-to-native-state cluster, the intermediate cluster and the denatured ensemble. These clusters, considered in terms of their radii of gyration, are shown to be Gaussian ensembles. Structural features representing each of these ensembles are also illustrated.
Results and Discussion
Molecular dynamics simulations of the Trp-cage mini-protein construct (PDB ID: 1L2Y) were carried out using the OPLS-AA force-field incorporated in the freely available program, GROMACS. The simulations were carried out at 498 K, at which temperature the unfolding process is favored. This temperature provides a good description of the unfolding process, at least in respect of CI2 and the homeodomain of engrailed [7]. It is also much higher than the melting temperature determined by experiment (315 K) or through replica-exchange simulations (400 K) [3].
It can be seen that the RMSD (figure 1) of the evolving structure with reference to the starting structure increases rapidly in the first 40 ps, during which time the only structural change observed is denaturation of the 3,10 helix. This is followed by rapid unwinding of the second and third turns of the helix. While the third turn unwinds within 200 ps, the second turn remains intact for a little longer and remains visible until 250 ps. The first helical turn remains stable until about 800 ps after which it also denatures. During this time period W6 begins to move out of the cage that is formed by the prolines. The above listed processes are not adequately reflected by the time-evolution of the Rg (figure 2) and are all categorized as close-to-native-state ensemble. Representatives from this ensemble are shown in figure 3a and 3b.
Figure 1 Time evolution of the root mean square deviation (nm) with reference to the starting structure.
Figure 2 Time evolution of the radius of gyration (nm)
After 800 ps, there is a jump in the values of both RMSD and Rg. The new value remains constant until about 3200 ps. This state is characterized by complete annihilation of the cage. The W6 is released from the Pro cage and becomes completely "solvent-exposed". It must be noted that the use of the term "solvent exposed" is not entirely appropriate in this context as there is no real change in the solvent-accessible surface area of the W side-chain. However, the point is that, this W is no longer protected by the proline cage. Native contacts are retained in the form of a salt-bridge between D9 and R16. Representatives of this ensemble are shown in figure 3c, d. In fact, the folding simulations carried out by Ruhong Zhou [3] point to an intermediate state characterized by the single salt-bridge interaction. This state, which is the only intermediate state observable, may be the transition state ensemble (TSE). This would mean that the unfolding process is two-stage and is the reversal of the folding process. In order to assess whether this state is indeed the TSE, lower temperature simulations at 293 K were performed. Eight structures were randomly obtained from this ensemble and the simulations were carried out for 5 ns on each of these structures. The progress of each simulation was monitored using Rg. The idea is that, at temperatures favoring the folding process, structures from the TSE roll down towards the native state with a probability of approximately 0.5, assuming a two-state process [1]. Of the eight simulations, three simulations showed a drastic fall in the Rg, indicating a collapse towards the native state. In a fourth simulation, there was a slight decrease in the Rg, which was not drastic, but still implying a fall towards the native state. In the other four simulations, a significant jump in the Rg was observed, indicating a tendency towards the unfolded conformation. These observations show that this ensemble is, most probably, the TSE.
Figure 3 Representative structures from the folding pathway obtained after (A) 0 ps (B) 700 ps (C) 1000 ps (D) 2500 ps (E) 4000 ps (F) 5000 ps. Structures A and B belong to the first ensemble; C and D to the second and E and F to the third. Color code: Pro: Red; Trp: Blue; Asp: Green; Arg: Yellow
After 3200 ps, a further jump in RMSD and Rg is observed leading to a state where these values fluctuate markedly. This highly disordered state, showing a measure of heterogeneity, is the denatured ensemble, in which the salt-bridge interaction that characterized the intermediate state is also lost. There is a significant jump in the distance between the Asp9 and Arg 16 sidechains after this time. As a result, there are no native contacts in this state. This is represented by structures in figures 3e and 3f.
In this manuscript, I also discuss a new method for identifying sufficiently populated states during the course of an MD simulation. The idea is that each state is to a large extent topologically different from any other state and can be characterized by an approximately Gaussian distribution of the radius of gyration. This is to be expected because each state lies at a defined height in the free-energy well. In this simulation it can be observed that transitions from one state to another are characterized by a significant jump in the radius of gyration. The distribution of the radius of gyration was determined for each of the three states and for the entire time-evolving system. For each of the three ensembles and for the entire time duration, the distribution was calculated over the ranges of values shown in table 1. It was found that Gaussian-like curves could be fitted for the three ensembles taken separately, while the distribution for the entire system was highly skewed (figure 4). The slight skew in the curve for the close-to-native state ensemble might be due to the inability to sufficiently demarcate the helix unwinding stages in the plot.
Table 1 Rg range and time corresponding to each state seen in the simulation
Ensemble Time (ps) Rg range (nm)
Native 0–800 0.7 – 0.8
TSE 800–3200 0.72 – 1
Unfolded 3200–5000 0.8 – 1.4
Entire range 0–5000 0.7 – 1.4
Figure 4 Distributions of Radius of gyration for (A) Ensemble 1 (B) Ensemble 2 (C) Ensemble 3 (D) Entire range of structures.
Conclusion
High-temperature unfolding molecular dynamics simulations of a Trp cage miniprotein construct have been carried out. This has shown that the process is two-stage, akin to the folding process results [3]. The three ensembles, including the TSE, are shown to be Gaussian with respect to their Rg values.
Methods
The starting structures for the simulations were obtained from PDB 1L2Y [3]. The first three models were used to carry out the 5 ns simulations and similar results were obtained with each. Results presented here correspond to model 1. All simulations were carried out using GROMACS 3.2 [8,9], running on a single Fedora Linux system. The OPLS-AA force field was used. The peptide was solvated in a box containing approx. 500 water molecules [10]. Periodic boundary conditions were employed to eliminate surface effects. Energy minimization with a tolerance of 2000 kJ/mol/nm was carried out using the Steepest Descent method. All bonds were constrained using LINCS [11]. The system was loosely coupled to a temperature bath (at 498 K or 293 K) using Berendsen's method [12]. Berendsen's pressure coupling was used. Long-range electrostatics was handled using the PME method [13]. All potential cut-offs were set at 1 nm. The final MD simulations were carried out with a time-step of 2 fs and without any position restraints. All analyses were conducted using programs built within GROMACS. The RMSD values were obtained from a least square fit of the respective non-hydrogen atoms (main-chain and side-chain). The radius of gyration was also calculated for the whole protein minus hydrogens as an indicator of the compactness of the overall structure. The compiled DSSP [14], which was downloaded separately and run from GROMACS, was used to calculate secondary structure formation.
Competing Interests
The author(s) declare that they have no competing interests.
Acknowledgements
I would like to thank Prof. P. Gautam of Centre for Biotechnology, Anna University for being a constant source of inspiration and encouragement. I also thank Mr. Mahesh Viswanathan for helping me with drawing the graphs. I also thank the anonymous reviewers for their comments.
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| 15760474 | PMC555740 | CC BY | 2021-01-04 16:39:26 | no | Theor Biol Med Model. 2005 Mar 11; 2:7 | utf-8 | Theor Biol Med Model | 2,005 | 10.1186/1742-4682-2-7 | oa_comm |
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BMC Clin PharmacolBMC Clinical Pharmacology1472-6904BioMed Central London 1472-6904-5-21574353710.1186/1472-6904-5-2Research ArticlePharmacokinetics of isoflavones, daidzein and genistein, after ingestion of soy beverage compared with soy extract capsules in postmenopausal Thai women Anupongsanugool Ekasin [email protected] Supanimit [email protected] Noppamas [email protected] Saipin [email protected] Chaichan [email protected] Department of Pharmacology, Faculty of Medicine, Chiang Mai University, Thailand2 Department of Obstetrics and Gynecology, Faculty of Medicine, Chiang Mai University, Thailand2005 3 3 2005 5 2 2 27 8 2004 3 3 2005 Copyright © 2005 Anupongsanugool et al; licensee BioMed Central Ltd.2005Anupongsanugool 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
Isoflavones from soybeans may provide some beneficial impacts on postmenopausal health. The purpose of this study was to compare the pharmacokinetics and bioavailability of plasma isoflavones (daidzein and genistein) after a single dose of orally administered soy beverage and soy extract capsules in postmenopausal Thai women.
Methods
We conducted a randomized two-phase crossover pharmacokinetic study in 12 postmenopausal Thai women. In the first phase, each subject randomly received either 2 soy extract capsules (containing daidzin : genistin = 7.79 : 22.57 mg), or soy beverage prepared from 15 g of soy flour (containing daidzin : genistin = 9.27 : 10.51 mg). In the second phase, the subjects received an alternative preparation in the same manner after a washout period of at least 1 week. Blood samples were collected immediately before and at 0.5, 1, 2, 4, 6, 8, 10, 12, 24 and 32 h after administration of the soy preparation in each phase. Plasma daidzein and genistein concentrations were determined by using high performance liquid chromatography (HPLC). The pharmacokinetic parameters of daidzein and genistein, i.e. maximal plasma concentration (Cmax), time to maximal plasma concentration (Tmax), area under the plasma concentration-time curve (AUC) and half-life (t1/2), were estimated using the TopFit version 2.0 software with noncompartmental model analysis.
Results
There were no significant differences in the mean values of Cmax/dose, AUC0–32/dose, AUC0-∝/dose, Tmax, and t1/2 of genistein between both preparations. For pharmacokinetic parameters of daidzein, the mean values of Cmax/dose, Tmax, and t1/2 did not significantly differ between both preparations. Nonetheless, the mean AUC0–32/dose and AUC0-∝/dose after administration of soy extract capsules were slightly (but significantly, p < 0.05) higher than those of soy beverage.
Conclusion
The bioavailability of daidzein, which was adjusted for the administered dose (AUC/dose), following a single oral administration of soy beverage was slightly (but significantly) less than that of soy extract capsules, whereas, the bioavailability adjusted for administered dose of genistein from both soy preparations were comparable. The other pharmacokinetic parameters of daidzein and genistein, including Cmax adjusted for the dose, Tmax and t1/2, were not different between both soy preparations.
==== Body
Background
Menopause is associated with estrogen deficiency and its accompanying symptoms such as accelerated bone loss and atherosclerosis. Hormone replacement therapy (HRT) has traditionally been used for treatment of menopausal disorders. Estrogen helps to maintain bone density, relieve menopausal symptoms, as well as influence emotional state [1]. Estrogen replacement therapy after menopause therefore improves the health and quality of life for women. However, the Women's Health Initiative (WHI) randomized controlled trial has recently found that although estrogen-alone hormone therapy reduces the risk of hip and other fractures in healthy postmenopausal women with prior hysterectomy, it significantly increases the risk of stroke (but has no significant effect on the risk of coronary heart disease, breast or colorectal cancer) [2]. In addition, long-term estrogen replacement therapy in postmenopausal women who have a uterus might has the disadvantage of being tissue agonists for endometrial tissue, which increases the incidence of endometrial cancer [3]. Although adding progestin to estrogen can be used to prevent the development of endometrial cancer, this combination may cause some unwanted side effects, i.e. breast cancer, venous thromboembolism, stroke and coronary heart disease [3]. Thus, alternative therapies, which include natural products such as phytoestrogens and herbs, offer attractive options because they may protect against breast and endometrial cancer, have fewer side effects and still provide health benefits [4].
The most common forms of phytoestrogens are the isoflavones. Among the food consumed by humans, soybeans contain the highest concentration of isoflavones. Isoflavones have a chemical structure resembling that of estrodiol-17β, the most potent mammalian estrogen. The major isoflavones, namely, genistein and daidzein, have several features in common with estradiol-17β, including an aromatic A ring with hydroxyl group in the same plane at a distance similar to that in estradiol (Figure 1) [5]. Indeed, isoflavones appear to have both estrogenic and antiestrogenic effects, like selective estrogen receptor modulators (SERMs), depending on the target tissue [6]. Therefore, rather than classifying soy isoflavones as "estrogens", they should be judged more correctly as natural SERMs. Reproductive cells, especially those of the breast and uterus, are rich in estrogen receptor α (ERα), whereas other cells (such as those in the bone) have greater amounts of estrogen receptor β (ERβ) than ERα. This differential distribution of the two types of estrogen receptors, and the greater affinity of the isoflavones for ERβ in relation to ERα, suggests that the isoflavones have different effects in different tissue [7].
Figure 1 Structures of isoflavones.
Despite the beneficial effects of isoflavones on postmenopausal health are still controversial, there are some researches, including epidemiological studies, suggest that isoflavones may help to alleviate postmenopausal symptoms and protect against chronic diseases such as hormone-dependent cancer (e.g. breast and endometrial cancer), cardiovascular diseases and osteoporosis [8-11]. Thus, in terms of both health promotion and chronic disease prevention, the potential public health impact of daily soy consumption could be important, especially in postmenopausal women.
Although many commercial soy capsules containing isoflavone extract are now available in many Western countries, soybeans and soy food (such as tofu, soy flour, soy milk, etc.), which provide the main sources of isoflavones, are consumed in significant amounts in Asian countries because they are inexpensive and high in quality protein. The purpose of this trial was to compare the pharmacokinetics of plasma isoflavones, daidzein and genistein, in postmenopausal Thai women after a single dose of orally administered commercial soy extract capsules and soy beverage.
Methods
Study design
This study was a single dose, randomized two-phase crossover study with a washout period of at least one week. It was approved by the Medical Ethics Committee of the Faculty of Medicine, Chiang Mai University and was in compliance with the Helsinki Declaration.
Subjects
A total of 12 postmenopausal Thai women, who ranged in age from 46–61 years (average 52.83 ± 3.88 years), participated in this study. Their mean weight and height was 52.23 ± 6.38 kg and 1.54 ± 0.06 cm, respectively. The body mass index (BMI) of each subject was within 18–24 kg/m2 (22.06 ± 1.83 kg/m2). Their serum follicle-stimulating hormone concentrations were more than 20 IU/l and the average level was 69.60 ± 31.21 IU/l. All had to be in good health on the basis of medical history and physical examination. Routine blood tests including complete blood count (CBC) with differential, blood urea nitrogen (BUN), creatinine (Cr) and liver function test (LFT) had to be within the normal limit. Subjects had to give both verbal and written information regarding the study. Signed informed consent was obtained prior to entry. Exclusion criteria included subjects with known premenopausal status (<12 months since the last spontaneous menstrual bleeding and a serum follicle-stimulating hormone concentration ≤ 20 IU/l) as well as those with a known history of chronic renal, liver, pulmonary or cardiovascular diseases, recent cigarette smoking, substance abuse or addiction, use of antibiotics within the previous 6 weeks, consumption of more than 2 alcoholic drinks/day, regular use (more than 1 dose/week) of over-the-counter or prescribed medications, and malignancy.
Isoflavone preparations
The isoflavone preparations used in this study were commercial soy extract capsules and soy beverage. Soy extract capsules were purchased from Canada, whereas, the soy beverage was prepared from mixing 15 g of commercial soy flour (Doi Khum®, Chiang Mai, Thailand, lot no. 19GODE) with 300 ml of hot water. The concentration of isoflavones in each preparation was measured as aglycones (daidzein and genistein) and β-glycoside conjugates of isoflavones (daidzin and genistin) that were analyzed by high performance liquid chromatography (HPLC) as described below.
Quantification of isoflavones in soy preparations
Two soy preparations, soy flour and soy extract capsules, were chosen as isoflavone sources. The sample extractions and concentration determinations were modified from the method described by Nakamura et al. [12]. Two hundred mg of soy flour or 300 mg of powder from the soy capsule (1 capsule) was placed in a centrifuge tube. Ten ml of 80% methanol in water was added to the centrifuged tubes, and sonicated for 30 min. Isoflavonoids were extracted for 24 h at an ambient temperature. One ml of the mixture was centrifuged, and 10 μl of clear supernatant was diluted with mobile phase (100 times for soy flour and 400 times for soy extract capsule) and spiked with 20 μl of internal standard (IS, 100,000 ng/ml fluorescein and 50,000 ng/ml chloramphenicol for quantification of aglycones and β-glycosides, respectively). Five μl of the mixture was injected into the HPLC system. Separation was performed isocratically at 50°C. The flow rate of the mobile phase was maintained at 1 ml/min and the analytes were detected by UV absorption at 259 nm. The mobile phase for the quantification of aglycones consisted of 5 mM phosphoric acid in methanol/acetonitrile (100:85, v/v), whereas, that for quantification of β-glycoside conjugates comprised 5 mM phosphoric acid in methanol/acetonitrile (80:20, v/v). The Isoflavone contents of unknown samples were determined by using a calibration curve of the peak height ratios of isoflavones and IS versus respective isoflavone concentrations with the use of linear regression. All samples were analyzed within the same day, in which intraday assay validation was performed.
Dosage and drug administration
Subjects were admitted to the Clinical Pharmacology Unit of the Faculty of Medicine, Chiang Mai University at 6:30 a.m. after an overnight fast of at least 8 h. They were randomized to receive either 2 soy extract capsules with 300 ml of water, or 300 ml of soy beverage at 7:00 a.m. They remained upright and fasted for 2 h after soy product administration. Water and lunch were served at 2 h and 4 h, respectively after dosing. Blood samples were collected at different time points (see below). After the blood sample collection at 12 h postdose, the subjects were discharged from the Clinical Pharmacology Unit and asked to come back again on the next day to give blood samples at 24 and 32 h postdose. While waiting for blood sample collections, the subjects were allowed to perform all of their daily activities, except moderate to high degrees of exercises. After a washout period of at least 1 week, the subjects received the alternative preparation and the blood samples were collected in the same manner. Identical food and fluid were served during the 2 study periods. The subjects were required to refrain from drinking caffeine containing beverages and alcohol, and instructed to consume no soy products (except those given in this study) from the time of screening until the end of the research.
Blood sample collection
Venous blood samples (7 ml/each) for determination of soy isoflavones were collected predose and then exactly 0.5, 1, 2, 4, 6, 8, 10, 12, 24 and 32 h after administration. Samples were obtained from the forearm by venipuncture through an indwelling intravenous catheter (BD Insyte®) and collected in a heparinized vacutainer (BD Insyte®). The blood collecting tubes were centrifuged at 2,500 rpm for 20 min and the plasma samples were separated and frozen at -20°C until analyzed.
Determination of plasma isoflavone concentrations
The assay was modified from the solid phase extraction procedure and HPLC technique as previously described by Thomas et al [13]. Briefly, an aliquot (125 μl) of plasma was transferred to a 1.5 ml plastic vial and treated with 0.25 ml of β-glucuronidase/sulfatase mixture from Helix pomatia (Sigma G-0876) to hydrolyze glucuronide and sulfate conjugates of genistein and daidzein. The enzyme mixture was made up freshly and contained 0.15 g of ascorbic acid in 10 ml of 0.2 M acetate buffer, pH 4.0, and 500 μl of β-glucuronidase/sulfatase from Helix pomatia. To allow for complete hydrolysis in the plasma samples, 0.75 ml of 0.2 M ammonium acetate buffer was added, and the tubes were capped and heated overnight in a water bath (15–18 h, 37°C). The tubes were removed from the water bath and allowed to cool to room temperature. After enzymatic hydrolysis, plasma samples were spiked with 5 μl of internal standard (IS, 100,000 ng/ml fluorescein in 80% methanol). After vortex mixing for 30 sec and centrifugation at 13,000 rpm for 5 min, sample purification was performed by using a solid phase extraction cartridge placed in the vacuum box. The cartridges were preconditioned with 2.50 ml each of methanol, water and 175 mM phosphate buffer, respectively. The samples were loaded into the cartridges and allowed to flow freely, then the cartridges were washed with 0.1 ml of 20% methanol in water, 0.1 ml of 175 mM phosphate buffer and 0.1 ml of 20% methanol in water, consecutively. After drying the cartridge by slow suction, isoflavones and IS were eluted with 2.0 ml of 20% methanol in ethyl acetate. The eluents were dried by a SpeedVac concentrator. The residues were dissolved in 50 μl of the mobile phase, and 10 μl of samples were injected into a HPLC system. The mobile phase consisted of 4 mM perchloric acid in methanol/acetonitrile (115:85, v/v). The flow rate was maintained at 1 ml/min and the analytes were detected by UV absorption at 259 nm, and the column was maintained at 40°C. The retention time for daidzein and genistein was 7.699 and 13.031 minutes, respectively. The lower limit of quantitation was 5 ng/ml. Plasma concentrations of daidzein and genistein were determined using a calibration curve and linear regression of the seven known isoflavone concentrations versus the peak height ratios of isoflavones and IS.
The %CV of intraday precision for plasma daidzein concentrations ranged from 0.85–9.45%, whereas, the %CV of interday ranged from 3.90–9.96%. The %CV of intraday and interday precision for plasma genistein concentrations ranged from 1.29–3.65%, and 4.37–6.84%, respectively. The %deviation in intraday and interday assay for plasma daidzein concentrations ranged from 4.35–5.84% and -6.11–4.22%, respectively. On the other hand, the %deviation in intraday and interday assay for plasma genistein concentrations ranged from -5.00–1.98% and -7.79%–1.52%, respectively.
Data analysis and statistical methods
Pharmacokinetic parameters
The maximal plasma concentration (Cmax, ng/ml) and time to maximal plasma concentration (Tmax, h) were obtained directly by the visual inspection of each subject's plasma concentration-time profile. The areas under the plasma concentration-time curve from time 0–32 (AUC0–32, ng.h/ml) and 0-∝ (AUC0-∞, ng.h/ml) as well as half-life (t1/2, h) were determined by non-compartmental analysis. The slope of the terminal log-linear portion of the concentration-time profile was determined by least-squares regression analysis and used as the elimination rate constant (Ke). The elimination t1/2 was calculated as 0.693/Ke. The AUC from time zero to the last quantifiable point (AUC0–32) was calculated using the trapezoidal rule. Extrapolated AUC from time t to infinity (AUCt-∞) was determined as Ct/Ke. Total AUC was the sum of AUC0–32+ AUC32-∞. In this study, the sampling time was continued for more than 3 half-lives, therefore, the AUC0-32 was sufficient to cover at least 80% of the total AUC. The calculation was performed by using the TopFit software version 2.0 for personal computer.
Statistical analysis
The pharmacokinetic parameters were presented as mean ± SD. The differences between the mean values of Cmax/dose, Tmax, t1/2, AUC0–32/dose and AUC0-∝/dose of the two isoflavone preparations were statistically analyzed by using the paired t-test. However, the results from the analysis were not different regardless of whether the statistical comparison was performed by using the paired t-test (parametric method) or Wilcoxon's sign rank test (non-parametric method).
Results
Isoflavone contents in soy preparations
The quantification of isoflavone contents demonstrated that both soy flour and soy extract capsule preparations contained predominantly daidzin and genistin (the form of β-glycoside conjugates), whereas, aglycones were rarely found and isoflavones in other forms were not measured. Isoflavone contents in both soy preparations are shown in Table 1.
Table 1 Isoflavone contents in soy preparations used in this study.
Daidzin Genistin Total1
Soy flour
Mg/g 0.62 ± 0.005 0.70 ± 0.01 1.32 ± 0.01
% 47 53 100
Soy extract capsule
Mg/capsule 3.90 ± 0.04 11.29 ± 0.17 15.18 ± 0.21
% 26 74 100
1Summation of daidzin and genistin contents.
Pharmacokinetics of daidzein and genistein in healthy postmenopausal Thai women
The Mean plasma daidzein and genistein concentration-time curves after a single dose of both orally administered soy preparations are shown in Figure 2, 3. The individual and mean pharmacokinetic parameters of daidzein and genistein following oral administration of soy beverage and soy extract capsules are shown in Tables 2, 3.
Figure 2 Mean plasma daidzein concentration-time curves after a single dose of orally administered soy beverage and soy extract capsules.
Figure 3 Mean plasma genistein concentration-time curves after a single dose of orally administered soy beverage and soy extract capsules. (Note: the orally administered dose of genistein from soy extract capsules was approximately two times higher than that from soy beverage).
Table 2 Individual and mean pharmacokinetic parameters of daidzein following a single dose of orally administered soy beverage (B) and soy extract capsules (C).
Subj No. Cmax (ng/ml) Cmax/Dose1 AUC0–32 (ng.h/ml) AUC0–32/Dose1 AUC0-∝ (ng.h/ml) AUC0-∝/Dose1 Tmax (h) t1/2 (h)
B C B C B C B C B C B C B C B C
1 67.42 60.73 7.27 7.80 354.62 650.40 38.25 83.49 496.21 722.51 53.53 92.75 6 6 6.06 8.04
2 142.17 56.33 15.34 7.23 1142.38 931.84 123.23 119.62 1206.09 1024.24 130.11 131.48 6 1 6.74 6.02
3 64.95 76.70 7.01 9.85 1118.40 1117.20 120.65 143.41 1704.60 1171.78 183.88 150.42 8 6 19.80 6.85
4 132.30 124.43 14.27 15.97 1336.29 1356.49 144.15 174.13 1402.26 1427.11 151.27 183.20 6 6 6.70 6.91
5 77.66 93.20 8.38 11.96 1293.52 1251.35 139.54 160.64 1483.69 1561.72 160.05 200.48 8 10 9.94 10.50
6 105.65 142.82 11.40 18.33 1409.24 1378.43 152.02 176.95 1533.26 1450.55 165.40 186.21 10 6 6.21 6.86
7 160.86 125.37 17.35 16.09 1576.65 1669.28 170.08 214.28 1761.36 1721.18 190.01 220.95 4 6 8.48 5.97
8 102.77 74.08 11.09 9.51 1158.07 1090.36 124.93 139.97 1227.07 1149.04 132.37 147.50 6 6 6.85 6.76
9 45.34 105.77 4.89 13.58 300.89 849.39 32.46 109.04 340.35 876.40 36.72 112.50 4 6 3.46 4.40
10 52.98 84.74 5.72 10.88 401.82 579.95 43.35 74.45 533.03 761.42 57.50 97.74 1 6 4.82 4.38
11 99.98 87.38 10.79 11.22 1291.80 1546.66 139.35 198.54 1455.80 1705.46 157.04 218.93 8 10 6.59 7.63
12 103.60 120.64 11.18 15.49 578.60 909.87 62.42 116.80 665.06 971.69 71.74 124.74 4 6 6.49 5.71
Mean 96.31 96.02ND 10.39 12.33 996.86 1110.94ND 107.54 142.61* 1150.73 1211.93ND 124.14 155.57* 5.92 6.25 7.68 6.67
SD 36.18 27.71 3.90 3.56 455.69 342.26 49.16 43.94 504.94 354.46 54.47 45.50 2.43 2.26 4.14 1.65
1Pharmacokinetic parameters adjusted for oral administered dose. ND = statistical analysis was not performed due to differences in oral administered doses between both preparations, *Statistical significance (p < 0.05) compared to corresponding values of soy extract capsules.
Table 3 Individual and mean pharmacokinetic parameters of genistein following a single dose of orally administered soy beverage (B) and soy extract capsules (C).
Subj No. Cmax (ng/ml) Cmax/Dose1 AUC0–32 (ng.h/ml) AUC0–32/Dose1 AUC0-∝ (ng.h/ml) AUC0-∝/Dose1 Tmax (h) t1/2 (h)
B C B C B C B C B C B C B C B C
1 90.41 151.49 8.60 6.71 954.82 1950.40 90.85 86.42 1040.69 2248.09 99.02 99.61 6 6 8.46 9.92
2 68.08 124.90 6.48 5.53 693.31 1821.17 65.97 80.69 739.89 1941.50 70.40 86.02 1 8 7.62 7.42
3 149.53 301.34 14.23 13.35 1801.34 4392.34 171.39 194.61 1989.66 5048.92 189.31 223.70 8 8 8.25 9.37
4 212.94 516.77 20.26 22.90 1979.53 5541.88 188.35 245.54 2140.25 6175.55 203.64 273.62 6 6 8.10 8.93
5 96.95 220.38 9.22 9.76 1281.58 2478.42 121.94 109.81 1423.11 3072.33 135.41 136.12 8 10 9.73 12.40
6 96.57 301.62 9.19 13.36 1277.75 2776.28 121.57 123.01 1483.87 2972.50 141.19 131.70 10 6 10.10 7.74
7 223.77 371.31 21.29 16.45 3184.25 5260.83 302.97 233.09 4022.02 6003.94 382.69 266.01 4 8 12.60 9.58
8 110.60 193.01 10.52 8.55 948.75 2217.44 90.27 98.25 998.31 2370.24 94.99 105.02 6 6 5.07 8.41
9 54.86 183.88 5.22 8.15 425.80 1481.47 40.51 65.64 527.69 1500.60 50.21 66.49 4 6 4.41 4.80
10 69.95 214.54 6.66 9.51 773.90 1856.73 73.63 82.27 789.07 1940.05 75.08 85.96 6 6 5.34 6.63
11 105.13 217.57 10.00 9.64 1128.81 3121.71 107.40 138.31 1177.74 3208.73 112.06 142.17 6 8 6.32 5.27
12 117.61 345.31 11.19 15.30 871.60 2591.36 82.93 114.81 914.46 2631.99 87.01 116.61 4 6 5.35 5.05
Mean 116.37 261.84ND 11.07 11.60 1276.79 2957.50ND 121.48 131.04 1437.23 3259.54ND 136.75 144.42 5.75 7.00 7.61 7.96
SD 53.80 110.68 5.12 4.90 746.04 1372.13 70.98 60.79 949.02 1599.34 90.30 70.86 2.34 1.35 2.44 2.28
1Pharmacokinetic parameters adjusted for oral administered dose. ND = statistical analysis was not performed due to differences in oral administered doses between both preparations.
The mean plasma daidzein concentration-time curve after a single dose of both orally administered soy preparations revealed a biphasic pattern. The first peak of plasma daidzein concentration was reached approximately 1 h after ingestion of both preparations, whereas, the second peak attained higher plasma concentrations at 5.92 ± 2.43 h for soy beverage, and 6.25 ± 2.26 h for soy extract capsules. The mean maximal plasma daidzein concentrations (Cmax) were 96.31 ± 36.18 ng/ml and 96.02 ± 27.71 ng/ml for soy beverage and soy extract capsules, respectively. Pharmacokinetic analysis of the plasma concentration-time curves showed that the elimination t1/2 was 7.68 ± 4.14 h for soy beverage and 6.67 ± 1.65 h for soy extract capsules. The AUC0–32 was 996.86 ± 455.69 and 1110.94 ± 342.26 ng.h/ml for soy beverage and soy extract capsules, respectively, whereas, the AUC0-∝ was 1,150.73 ± 504.94 ng.h/ml for soy beverage and 1,211 ± 354.46 ng.h/ml for soy extract capsules.
The mean plasma genistein concentration-time curve after a single dose of both orally administered soy preparations also demonstrated a biphasic pattern, but the first peak of plasma genistein concentration after ingestion of soy extract capsules was less evident. The mean Tmax for the second peak of plasma concentration was 5.75 ± 2.34 h for soy beverage and 7.00 ± 1.35 h for soy extract capsules. The mean Cmax was 116.37 ± 53.80 ng/ml for soy beverage and 261.84 ± 110.68 ng/ml for soy extract capsules. Pharmacokinetic analysis of the plasma concentration-time curves showed the elimination t1/2 of 7.61 ± 2.44 h for soy beverage and 7.96 ± 2.28 h for soy extract capsules. The AUC0–32 was 1276.79 ± 746.04 and 2957.50 ± 1372.13 ng.h/ml for soy beverage and soy extract capsules, respectively, whereas, the AUC0-∝ was 1,437.23 ± 949.02 ng.h/ml for soy beverage and 3,259.54 ± 1,599.34 ng.h/ml for soy extract capsules.
After oral administration of both soy preparations, the pharmacokinetic parameters of daidzein were statistically compared, and the mean values of Cmax/dose, Tmax, and t1/2 did not significantly differ between both preparations. Nonetheless, the mean values of AUC0–32/dose and AUC0-∝/dose after administration of soy extract capsules were slightly (but significantly, p < 0.05) higher than those after soy beverage intake (Table 2). For pharmacokinetic parameters of genistein, there were no significant differences in the mean values of Cmax/dose, AUC0–32/dose, AUC0-∝/dose, Tmax, and t1/2 between both preparations (Table 3).
Discussion
In this study, the pharmacokinetics of plasma daidzein and genistein were evaluated in 12 postmenopausal Thai women after a single dose of orally administered soy beverage and soy extract capsules. These women were enrolled from a pool of volunteers after they had been screened for medical history, BMI, serum follicle-stimulating hormone concentration and blood tests. Since the design of this study was similar to that of the bioequivalence testing, 12 subjects were enrolled according to the minimum number of subjects stipulated by the Canadian and European guidelines for bioequivalence testing.
The soy extract product available in Thailand was not selected as the study preparation because only minimum isoflavone content was labeled without any details about the proportion of daidzin and genistin content. The Canadian soy extract preparation was used as an alternative because the exact total of isoflavone content (18.2 mg/capsule) as well as daidzin and genistin content (9.1 mg/capsule, each) were declared. Nonetheless, our quantification of isoflavones in this preparation revealed that the amount of daidzin and genistin in each capsule was 3.90 ± 0.04 and 11.29 ± 0.17, respectively. Therefore, the orally administered dose in this study was calculated according to our quantification, but not by the amount declared.
Initially, we tried to measure the total isoflavone contents in each preparation by using the acid hydrolysis method [14,15]. Briefly, either 1 g of soy flour or powder from the soy extract capsules was dispersed in a mixture of 10 ml of 10 M HCl and 40 ml of 96% ethanol (containing 0.05% butylated hydroxy toluene, BHT) followed by refluxing at 100°C for 2 h. The mixture was cooled to room temperature and the ethanol lost during the refluxing was replaced. One ml of this mixture was centrifuged. Ten μl of clear supernatant was diluted 100 times and determined by the HPLC method. The principle of this assay is to hydrolyze β-glycoside conjugates of isoflavones to aglycones, and the detection of total aglycones reflects the total isoflavones. Unfortunately, we failed to measure total isoflavones as aglycones after acid hydrolysis. The recovery of daidzein and genistein was very low (approximately 20–30%) as compared to the other published data, even though we had tried to vary many factors such as the concentration of HCl, duration of refluxing time, temperature during reflux, amount of BHT added, etc. However, since isoflavones are present predominantly as β-glycoside conjugates (e.g. daidzin and genistin) in most commercially available soy products such as soybean or soy flour (with the exception of fermented soy products) [14], we used a specific HPLC condition for measuring daidzin and genistin, and another condition for measuring aglycones (daidzein and genistein) without acid hydrolysis. Isoflavones in other forms (malonyl glycoside and acetyl glycoside conjugates) were not determined because their commercial standards were not available. Glycitein and its derivatives were also not determined, due to a much smaller amount found in soybeans [16].
Since both soy preparations consist of different proportions of daidzin : genistin (approximately 1:1 for soy flour, 1:3 for soy extract capsules), the appropriate amount of daidzin content in each preparation was therefore considered first for pharmacokinetic comparison. In this study, each volunteer was assigned to receive 2 capsules of soy extract (daidzin : genistin = 7.79 : 22.57 mg) to compare with soy beverage prepared from 15 g of soy flour (9.27 : 10.51 mg). These dosages caused an approximately equal amount of daidzin between the two preparations, whereas, the genistin content in soy extract capsules was approximately two-fold higher than that of soy beverage. The oral administration of these dosages resulted in the plasma concentrations of daidzein and genistein being high enough and convenient for measurement by the HPLC method. In addition, preparing soy beverage from 15 g of soy flour in 300 ml of water was practical and created an acceptable concentration for consumption.
From previous studies, the bioavailability of isoflavones was investigated and compared among various soy food and beverages. So far, there has been no study that compares bioavailability of isoflavones from soy extract versus natural soy food or beverages. Our purpose was to investigate pharmacokinetics of daidzein and genistein after ingestion of soy beverage compared to soy extract capsules in postmenopausal Thai women. Daidzein and genistein contents in soy food can vary and depend on the raw material and processing conditions used to produce a particular food product. Furthermore, in each type of soy food, there are different forms of isoflavones in differing amounts. However, based on the equivalent dose of isoflavones, the administration of different soy food has shown no difference in isoflavone bioavailability [17]. In our study, the bioavailability, adjusted for dosage (determined by AUC0–32/dose and AUC0-∝/dose) of genistein after ingestion of both soy preparations, was not significantly different. In contrast, the bioavailability of daidzein following ingestion of soy extract capsules was significantly greater than that following ingestion of soy beverage. This might be the result of at least 2 possibilities. Firstly, the food matrix of soy flour may alter the bioavailability of daidzein. Dietary factors such as fiber and carbohydrate have been associated with differences in the metabolism of daidzein to equol [18-20]. Urinary recovery of equol is higher following the ingestion of tempeh when compared with homogeneous soymilk and textured vegetable protein. This suggests that the combination of a food matrix might protect daidzein from degradation until it reaches the large intestine where it can be metabolized to equol by the microflora [20]. Secondly, in this study, we only determined the isoflavone contents in both soy preparations as the forms of β-glycoside conjugates (daidzin and genistin) as well as aglycones (daidzein and genistein). However, soy flour and soy extract capsules might have contained some malonyl glycoside and acetyl glycoside conjugates of isoflavones, which were not measured in this study. We postulate that the proportions of malonyl glycoside and acetyl glycoside conjugates of daidzein in soy extract capsules may be greater than those in soy flour, resulting in better bioavailability after these conjugates are converted and absorbed as daidzein from the gastrointestinal tract.
The mean Tmax of daidzein and genistein from both soy preparations in this study was shorter than the values of 8–11 h (after ingestion of β-glycoside conjugates) as reported in previous studies [21,22]. This difference might result from variation in age, race, uptake rates, hydrolysis of glycosides by gut bacteria or gut wall enzymes, further metabolism (for example glucuronides within the liver), etc. The second peak demonstrated in the plasma concentration-time curves of daidzein and genistein possibly resulted from enterohepatic recirculation of the glucuronide and sulfate conjugates of isoflavones excreted in bile [22]. The elimination t1/2 of daidzein and genistein in this study was comparable to the values of 6–8 h from other studies [20-22].
It has been suggested that a daily intake of soy isoflavone extract containing 50/50 mg of genistin and daidzin [23] or 76 mg of isoflavones [24] can significantly decrease hot flushes in the group treated with soy products over the placebo. A meta-analysis of 38 clinical trials, which examined the relationship between soy protein intake and serum lipids, have shown that the consumption of soy in men and women is associated with a significant decrease in serum cholesterol, LDL and triglyceride levels [25]. In a randomized, double-blind, placebo-controlled trial, which examined the effects of dietary soy supplements containing 118 mg of isoflavones on the lipid profiles of men and postmenopausal women with relatively normal cholesterol levels, the LDL/HDL ratio decreases in the isoflavone treatment groups without any change in total cholesterol [26]. In addition, it has been found that those postmenopausal women with greatest phytoestrogen consumption have the highest bone mineral density (BMD) at the hip and spine. Subjects with the highest intake of isoflavones also have significantly lower levels of serum PTH, osteocalcin and urinary N-telopeptide [27]. Besides, isoflavone supplementation (61.8 mg of isoflavones) for four weeks shows potentially beneficial effects on bone metabolism and serum lipids in perimenopausal women in a randomized controlled trial [28]. Another trial has demonstrated that continuous dietary intake of isoflavones (37.3 mg/day) for ten weeks may inhibit postmenopausal osteoporosis [29]. Therefore, in our opinion, the total dose of isoflavones that benefits menopausal health is up to approximately 100 mg/day. Since our study revealed that the bioavailability of genistein from soy beverage and soy extract capsules was similar, and the bioavailability of daidzein was slightly (although statistically significant) lower than that of soy extract capsules, the amount of soy beverage, which provides the isoflavone bioavailability equivalent to soy isoflavone capsules containing 50/50 mg of daidzein and genistein, should be equal to approximately 5 cups/day (15 g of soy flour/cup). If one consumes other soy food or prepares a soy beverage in a higher concentration, the daily volume of consumption would be reduced. This inexpensive soy beverage is an appropriate alternative food supplementation compared to the more expensive soy extract capsules and HRT for postmenopausal Thai women, and is in line with Thailand's present socioeconomic status. However, the different proportion of daidzein and genistein in various soy preparations might affect beneficial outcomes for postmenopausal women. In this aspect, clinical studies should be investigated further.
Conclusion
The bioavailability of daidzein, which was adjusted for the administered dose (AUC/dose) following a single oral administration of soy beverage, was slightly (but significantly) less than that of soy extract capsules, whereas, that of genistein from both soy preparations was comparable. There was also no difference in other pharmacokinetic parameters of daidzein and genistein, including Cmax adjusted for dose, Tmax and t1/2 between both soy preparations.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
EA performed the quantification of isoflavones and statistical analysis. ST supervised data collection and analysis, and drafted the manuscript. NR supervised the quantification of isoflavones. SP initiated the research question and participated in the selection of patients eligible for the study. CS participated in the design of the study and drafted 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
This work was supported by the Faculty of Medicine, Chiang Mai University, Thailand.
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| 15743537 | PMC555741 | CC BY | 2021-01-04 16:27:54 | no | BMC Clin Pharmacol. 2005 Mar 3; 5:2 | utf-8 | BMC Clin Pharmacol | 2,005 | 10.1186/1472-6904-5-2 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-3-111574828510.1186/1479-5876-3-11ResearchHLA class I and II genotype of the NCI-60 cell lines Adams Sharon [email protected] Fu-Meei [email protected] Deborah [email protected] Devika [email protected] Susan L [email protected] Herbert C [email protected] David [email protected] Francesco M [email protected] Immunogenetics Section, Department of Transfusion Medicine, Clinical Center, National Institutes of Health, Bethesda, Maryland, 20892, USA2 Developmental Therapeutics Program, Information Technology Branch, National Cancer Institute, Bethesda, Maryland, 20892, USA3 Laboratory of Immunopathology, National Institute of Allergy and Infectious Diseases, Rockville, USA2005 4 3 2005 3 11 11 24 1 2005 4 3 2005 Copyright © 2005 Adams et al; licensee BioMed Central Ltd.2005Adams 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.
Sixty cancer cell lines have been extensively characterized and used by the National Cancer Institute's Developmental Therapeutics Program (NCI-60) since the early 90's as screening tools for anti-cancer drug development. An extensive database has been accumulated that could be used to select individual cells lines for specific experimental designs based on their global genetic and biological profile. However, information on the human leukocyte antigen (HLA) genotype of these cell lines is scant and mostly antiquated since it was derived from serological typing. We, therefore, re-typed the NCI-60 panel of cell lines by high-resolution sequence-based typing. This information may be used to: 1) identify and verify the identity of the same cell lines at various institutions; 2) check for possible contaminant cell lines in culture; 3) adopt individual cell lines for experiments in which knowledge of HLA molecule expression is relevant. Since genome-based typing does not guarantee actual surface protein expression, further characterization of relevant cell lines should be entertained to verify surface expression in experiments requiring correct antigen presentation.
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Background
A panel of sixty cancer cell lines of diverse lineage (lung, renal, colorectal, ovarian, breast, prostate, central nervous system, melanoma and hematological malignancies) was developed, characterized and extensively used by the National Cancer Institute's Developmental Therapeutics Program (NCI-60) since the early 90's as a screening tool for anti-cancer drug development [1]. This strategy [2-9]. yielded data about drug-related cytotoxicity for about 100,000 compounds. In addition, extensive functional characterization of the NCI-60 response to diverse biological or chemical stimulation has been accumulated [10-15]. Although originally developed for chemo-sensitivity testing, with the development of high-throughput analyses the NCI-60 panel has been broadly characterized for other biological applications [16-25]. Thus, patterns incidentally identified provided platforms for further investigations of mechanisms of tumorigenesis and cancer progression [5,6,26-30]. More recently, genomic DNA [24] and proteomics analyses have further characterized the profile of these cell lines [31]. The combined database provides the most comprehensive phenotyping of commonly accessible cancer cell lines offering correlative information about genetic, transcriptional and post-translational qualities. With growing interest in the identification of novel tumor antigens recognized by T cells as targets for antigen-specific immunization ([32], the NCI-60 could become an ideal tool for in silico discovery [33] ([34] and for tumor cell-specific T-cell reactivity testing [35]. For this purpose, accurate information about the extended human leukocyte antigen (HLA) phenotype of each cell line is necessary for the definition and validation of specific HLA/epitope combinations. Although antiquated and partial information about the HLA phenotype of some of the NCI-60 cell lines is available through the American Type Culture Collection (ATCC), Rockville, MD, no high-resolution information obtained by definitive sequence-based typing (SBT) has ever been published. Since T cell recognition of HLA-epitope complexes is narrowly restricted to unique combinations [36], this information is critical to select reasonable candidates for antigen-discovery choosing cell lines bearing HLA phenotypes most relevant to the disease population studied [37]. Accurate information about the HLA genotype of each cell line may, in addition, help their identification, validation and qualification among different laboratories excluding possible errors related to switching of cell lines or culture contamination. Therefore, we provide high-resolution SBT of the complete NCI-60 panel obtained from their original source: the National Cancer Institute's Developmental Therapeutics Program.
Results and Discussion
Previous knowledge of the HLA phenotype of NCI-60 cell lines
We reviewed and collected available information about the HLA phenotype of the NCI-60 cell lines, performed according to serological testing before submission to the ATCC (Table 1). The information was collected through the ATCC website: . Most cell lines had not been previously typed; the large majority of the cell lines from which such information is available had been developed from Caucasian patients. HLA typing was reported according to the old serologic nomenclature at a very low level of resolution. In addition, several reported typings did not match the present typing as shown in Table 2 and 3. This was the case for the colon carcinoma cell line HT29 that maintained a correct haplotype (with the exclusion of the HLA-Cw locus) but had a completely different second haplotype. The melanoma cell line SK-MEL-5 had an almost identical haplotype with the exception of one HLA-B allele originally typed as Bw16 (inclusive of the molecularly-defined alleles: B*38 and B*39), while the present typing was HLA-B*07. Another melanoma cell line SK-MEL-28 maintained a haplotype similar to the previously reported HLA-A11, -B40 but appeared to have lost an HLA-A allele (HLA-A26) compared with the original ATCC description. Finally, the multiple myeloma cell line RPMI 8226 was matched at one haplotype (HLA-A19, -B15 and -Cw2) but was totally discrepant at the second haplotype (HLA-A*6802, -B*1510 and -Cw*0304). The HLA typing of the other two previously typed cell lines was confirmed in the present study. Overall, in spite of the discrepancies in HLA typing observed between the previous and the present analyses, a resemblance was noted in the cell line genotype suggesting that mis-typing related to the low accuracy of serological methods might have been at the basis of the discrepancy rather than contamination or switching of the cell lines.
Table 1 Available information from the ATCC about the NCI-60 panel
Name ATCC no. Sex Race Tumor Type ATCC HLA typing Discrepant
BT-549 HTB-122 F C Breast CA
HS 578T HTB-126 F C Breast CA
MCF7 HTB-22 F C Breast CA
MDA-MB-231 HTB-26 F C Breast CA
MDA-MB-435 HTB-129 F C Breast CA
T-47D HTB-133 F Breast CA
SF-268 CNS CA
SF-295 CNS CA
SF-539 CNS CA
SNB-19 CNS CA
SNB-75 CNS CA
U251 CNS CA
COLO 205 CCL-222 M C Colon CA
HCC-2998 Colon CA
HCT-116 CCL-247 M Colon CA
HCT-15 CCL-225 M Colon CA
HT29 HTB-38 F C Colon CA A1,3,B12,17 Cw5 Yes
KM12 Colon CA
SW-620 CCL-227 M Colon CA
MOLT-4 CRL-1582 M Leukemia, ALL
CCRF-CEM CCL-119 F C Leukemia, ALL
HL-60 CCL-240 F C Leukemia, APL
K-562 CCL-243 F Leukemia, CML
SR CRL-2262 M C Leukemia, LCIL
LOX IMVI Melanoma
M 14 Melanoma
SK-MEL-2 HTB-68 M C Melanoma
SK-MEL-5 HTB-70 F C Melanoma A2,11, B40,Bw16 Yes
SK-MEL-28 HTB-72 M Melanoma A11,26, B40,DRw4 Yes
UACC-62 Melanoma
UACC-257 Melanoma
RPMI 8226 CCL-155 M MM Aw19, B15,37, Cw2 Yes
A549/ATCC CCL-185 M C NSCLC
EKVX NSCLC
HOP-62 NSCLC
HOP-92 NSCLC
NCI-H23 CRL-5800 M AA NSCLC
NCI-H226 CRL-5826 M NSCLC
NCI-H322M NSCLC
NCI-H460 HTB-177 M NSCLC
NCI-H522 CRL-5810 M C NSCLC
IGROV1 Ovarian CA
OVCAR-3 HTB-161 Ovarian CA
OVCAR-4 Ovarian CA
OVCAR-5 Ovarian CA
OVCAR-8 Ovarian CA
NCI/ADR-RES Ovarian CA
SK-OV-3 HTB-77 F C Ovarian CA
DU-145 HTB-81 M C Prostate CA
PC-3 CRL-1435 M C Prostate CA A1,9 No
786-O Renal CA
A498 HTB-44 F Renal CA
ACHN CRL-1611 M C Renal CA
CAK1-1 HTB-46 M C Renal CA A9,B12,35 No
SN12C Renal CA
TK-10 Renal CA
UO-31 Renal CA
RXF-393 Renal CA
AA = African American; ALL = Acute Lymphoblastic Leukemia; APL = Acute promyelocytic leukemia; C = Caucasian; CA = Carcinoma; CML = Chronic Myelogenous Leukemia; CNS = Central Nervous System; F = Female; LCIL = Large Cell Immunoblastic Lymphoma; M = Male; MM = Multiple Myeloma; NA = Not Available; NSCLC = Non Small Cell Lung Cancer.
The information about the ATCC cell lines (Cell Lines with ATCC no.) was obtained accessing the following URL: . Additional information was obtained through the National Cancer Institute's Developmental Therapeutics Program URL: .
Table 2 Sequence-based typing of NCI-60 HLA class I Loci
Cell Line ID Tissue A locus B Locus Cw Locus
BT-549 41292-D Breast CA N.R. 151701, 5501 030301, 07a
HS 578T 41293-D Breast CA 03a, 24a 35a, 40a 030401, 04a
MCF7 41294-D Breast CA 020101 18a, 44a 05a
MDA-MB-231 41296-D Breast CA 0201, 0217 4002, 4101 020202, 17a
MDA-MB435 41297-D Breast CA 110101, 240201 15a, 35a 030301, 04a
T47D 41298-D Breast CA 3301 1402 0802
SF-268 41286-D CNS CA 010101, 3201 0801, 4002 020202, 07a
SF-295 41287-D CNS CA 010101, 2601 070201, 5501 03a, 07a
SF-539 41288-D CNS CA 020101 08a, 35a 04a, 07a
SNB-19 41289-D CNS CA 020101 18a 05a
SNB-75 41290-D CNS CA 020101, 110101 35a, 39a 04a, 120301
U251 41291-D CNS CA 020101 18a 05a
COLO 205 41299-D Colon CA 01a, 02a 07a, 08a 070201, 07a
HCC-2998 41300-D Colon CA 02a, 24a 3701, 400601 04a, 0602
HCT-116 41301-D Colon CA 01a, 02a 18a, 4501 05a, 07a
HCT-15 41302-D Colon CA 02a, 24a 08new, 350101 04a, 07a
HT29 41303-D Colon CA 01a, 24a 35a, 440301 04a
KM12 41304-D Colon CA 02new 70201 70201
SW-620 41305-D Colon CA 02a, 24a 07a, 15a 070201, 07a
MOLT 4 41281-D Leukemia, ALL 010101, 2501 18a, 570101 0602, 120301
CCRF-CEM 41282-D Leukemia, ALL N.R. 08a, 40a 030401, 07a
HL-60 41284-D Leukemia, APL 10101 570101 0602
K-562 41280-D Leukemia, CML 110101, 310102 18a, 40a 03a, N.R.
SR 41285-D Leukemia, LCIL 02a, 03a 3701, 3901 0602, 120301
LOX IMVI 41315-D Melanoma 110101, 2902 070201, 440301 070201, 1601
M 14 41316-D Melanoma 110101, 240201 15a, 35a 030301, 04a
SK-MEL-2 41317-D Melanoma 03a, 26a 35a, 38a 04a, 120301
SK-MEL-5 41319-D Melanoma 020101, 110101 07a, 40a 030401, 070201
SK-MEL-28 41318-D Melanoma 110101 4001 030401
UACC-62 41321-D Melanoma 02a, 32a 39a, 44a 05a, 12a
UACC-257 41320-D Melanoma 020101 18a, 44a 05a, 07a
RPMI-8226 41283-D MM 3001, 6802 1503, 1510 020204, 030402
A549/ATCC 41306-D NSCLC 2501, 3001 18a, 440301 120301, 1601
EKVX 41307-D NSCLC 010101 3701 0602
HOP-62 41308-D NSCLC 030101 07a, 44a 05a, 070201
HOP-92 41309-D NSCLC 03a, 24a 27a, 470101 01a, 06a
NCI-H23 41312-D NSCLC 8001 5001 0602
NCI-H226 41311-D NSCLC 010101, 240201 07a, 39a 070201, 120301
NCI-H322M 41310-D NSCLC 2902 440301 1601
NCI-H460 41313-D NSCLC 24a, 68a 35a, 51a 03a, 15a
NCI-H522 41314-D NSCLC 020101 44a, 5501 030301, 05a
IGROV1 41322-D Ovarian CA 240201, 3301 4901 07a
OVCAR-3 41323-D Ovarian CA 020101, 2902 070201, 5801 070201, 07a
OVCAR-4 41324-D Ovarian CA 010101, 3201 0801, 4002 07a, 15a
OVCAR-5 41325-D Ovarian CA 01a, 02a 08a, 44a 05a, 07a
OVCAR-8 41326-D Ovarian CA 010101, 2501 570101 0602
NCI/ADR-RES 41295-D Ovarian CA 010101, 2501 570101 0602
SK-OV-3 41327-D Ovarian CA 03a, 68a 18a, 35a 04a, 05a
DU-145 41328-D Prostate CA 030101, 3303 5001, 570101 0602
PC-3 41329-D Prostate CA 010101, 240201 1302, 5501 01a, 06a
786-O 41330-D Renal CA 030101 07a, 44a 05a, 070201
A498 41331-D Renal CA 020101 0801 07a
ACHN 41332-D Renal CA 2601 4901 07a
CAKI-1 41333-D Renal CA 2301, 240201 3502, 440301 04a, 04new
SN12C 41334-D Renal CA 03, 24new 07a, 44a 05a, 070201
TK-10 41335-D Renal CA 3301 1402 0802
UO-31 41336-D Renal CA 010101, 030101 07a, 14a 07a, 08a
RXF-393 41337-D Renal CA 02a, 24a 1401, 44a 05a, 0802
Sequence-based typing for the HLA class I loci are reported with the highest degree of resolution. Non-resolved ambiguities are reported as two digit denominations with a superscript a as previously described 43. HLA typings divergent from those originally described in the ATCC database are reported in red. ID# refers to the HLA laboratory reference number. New alleles are indicated by the suffix new following the allele. N.R. – Ambiguity not resolved at the lower level of resolution.
Table 3 Sequence-based typing of NCI-60 HLA class II Loci
Cell Line ID Tissue DRβ1 Locus DQB1 Locus DPB1 Locus
BT-549 41292-D Breast CA 11a, 13a 030101, 060401 020102, 0401
HS 578T 41293-D Breast CA 01a, 150101 050101, 0602 0401, 7801
MCF7 41294-D Breast CA 03a, 15a 0201, 0602 020102, 0401
MDA-MB-231 41296-D Breast CA 0701, 1305 0202, 030101 020102, 1701
MDA-MB435 41297-D Breast CA 040501, 130101 0302, 0603 1301, 1901
T47D 41298-D Breast CA 010201 050101 020102, 0401
SF-268 41286-D CNS CA 03a, 04a 0201, 0302 0401, 0601
SF-295 41287-D CNS CA 14a, 15a 050301, 0602 0401
SF-539 41288-D CNS CA 030101, 12a 0201, 030101 010101, 0401
SNB-19 41289-D CNS CA 030101 0201 0402
SNB-75 41290-D CNS CA 0103, 11a 03a, 050101 0401, 0402
U251 41291-D CNS CA 030101 0201 0402
COLO 205 41299-D Colon CA 040101, 130101 0603 0401
HCC-2998 41300-D Colon CA 11a, 16a 030101, 050201 0401
HCT-116 41301-D Colon CA N.R. 02new, 03new 030101, 0402
HCT-15 41302-D Colon CA 03a, 14a 02a, 050301 010101, 0401
HT29 41303-D Colon CA 0402, 0701 02a, 0302 0401
KM12 41304-D Colon CA 040101 0302 1301
SW-620 41305-D Colon CA 0103, 130101 050101, 0603 010101, 0401
MOLT 4 41281-D Leukemia, ALL 07new, 12new 0202, 030101 20102
CCRF-CEM 41282-D Leukemia, ALL 030101, 0701 0201, 0202 0401, 1301
HL-60 41284-D Leukemia, APL N.R. 030302 0401, 1301
K-562 41280-D Leukemia, CML 03a, 04a 0201, 0302 0401, 0402
SR 41285-D Leukemia, LCIL 01a, 160101 050101, 050201 0401
LOX IMVI 41315-D Melanoma 0701, 150101 0202, 0602 0401, 110101
M 14 41316-D Melanoma 040501, 130101 0302, 0603 1301, 1901
SK-MEL-2 41317-D Melanoma 0402, 130101 030101, 0603 020102, 0401
SK-MEL-5 41319-D Melanoma 040101, 130101 0302, 0603 030101, 1601
SK-MEL-28 41318-D Melanoma 0404 0302 030101
UACC-62 41321-D Melanoma 12a, 130101 030101, 0603 0401, 1401
UACC-257 41320-D Melanoma 040101 030101, 0302 0401
RPMI-8226 41283-D MM 030101, 0701 0201, 0202 010102, 1301
A549/ATCC 41306-D NSCLC 0701, 110401 0202, 030101 N.R.
EKVX 41307-D NSCLC 150101 0602 0401
HOP-62 41308-D NSCLC 13a, 15a 06a, 06a 0402
HOP-92 41309-D NSCLC 01a, 150101 050101, 0602 0401, 0402
NCI-H23 41312-D NSCLC 130101 0603 1901
NCI-H226 41311-D NSCLC 150101, 160101 050201, 0602 020102, 0401
NCI-H322M 41310-D NSCLC 0701 0202 0401
NCI-H460 41313-D NSCLC 01a, 04a 030101, 050101 N.R.
NCI-H522 41314-D NSCLC 040101, 150101 03a, 0602 0401
IGROV1 41322-D Ovarian CA 11a, 11a 03new new, 0501
OVCAR-3 41323-D Ovarian CA 080101, 080401 0402 020102, 0401
OVCAR-4 41324-D Ovarian CA 030101, 040101 0201, 030101 0401, 1301
OVCAR-5 41325-D Ovarian CA 030101, 040101 0201, 030101 0401
OVCAR-8 41326-D Ovarian CA 0701, 150101 030302, 0602 020102, 1301
NCI/ADR-RES 41295-D Ovarian CA 0701, 150101 030302, 0602 020102, 1301
SK-OV-3 41327-D Ovarian CA 01a, 030101 0201, 050101 020102, 0401
DU-145 41328-D Prostate CA N.R. 030302, 050101 0401
PC-3 41329-D Prostate CA 0701, 130101 0202, 0603 0401
786-O 41330-D Renal CA 13a, 15a 06a, 06a 0402
A498 41331-D Renal CA 030101 0201 010101
ACHN 41332-D Renal CA 160101 050201 020102
CAKI-1 41333-D Renal CA 0701, 110401 0202, 03a 020102, 1001
SN12C 41334-D Renal CA 040101, 150101 03a, 0602 N.R.
TK-10 41335-D Renal CA 010201 050101 0402
UO-31 41336-D Renal CA 130201, 150101 0602, 0609 0402, 0501
RXF-393 41337-D Renal CA 110101, 150101 030101, 0602 010101, 0401
Sequence-based typing for the HLA class II loci are reported with the highest degree of resolution. Non-resolved ambiguities are reported as two digit denominations with a superscript a as previously described [43]. HLA typings divergent from those originally described in the ATCC database are reported in red. ID# refers to the HLA laboratory reference number. New alleles are indicated by the suffix new following the allele. N.R. = Ambiguity not resolved at the lower level of resolution.
Overall, there was no evidence of contamination among the cell lines tested with clean homozygous or heterozygous combinations observed in all loci analyzed. SBT of HLA class I and HLA class II loci are reported in Table 2 and 3 respectively. Information about the HLA typing of the cell lines is also available through the Molecular Targets URL: . Approximately 17% of the cell lines (10 out of 58 including: T47D, SNB-19, U251, KM12, RPMI-8226, EKVX, NCI-H23, NCI-H322M, A498, ACHN and TK-10) exhibited a pseudo-homozygous pattern suggestive of complete loss of heterozygosity encompassing the HLA class I and HLA class II regions. This frequency is close to the loss of haplotype that we originally described for melanoma cell lines generated at the National Cancer Institute (Bethesda, MD) [38,39] and subsequently observed in other cancers [40,41]. We conclude that this is an unlikely representative of patients' homozygosity because complete HLA class I and II homozygosity is exceedingly rare in the population at large. To corroborate this statement, we analyzed 554 genomic DNA specimens from normal donors recently typed with the same technology in our laboratory. Genomic DNA for the normal donors was obtained from whole blood samples. Only 5 individuals were found to be truly homozygous for all HLA class I and class II loci for a frequency of 0.9%.
Overall, discrepancies between ATCC typings and the present typing or the unbalanced frequency of homozygosity could be related to accumulated genetic alterations between the cell lines since the time of their original expansion from the patient and should not be surprising.
A particular case was represented by the NCI/ADR-RES cell line which was previously believed to be an adriamycin derivative of the breast cancer cell line MCF-7. Subsequently, it was discovered not to be related to MCF-7, but it's derivation was unclear [42]. Karyotyping analysis suggested it was related to the ovarian cell line OVCAR-8. Subsequent DNA fingerprinting confirmed that both cell lines were generated from the same individual. HLA genotyping confirms this since the cell lines are indeed identical.
To avoid possible misinterpretations, a large number of alleles are not presented here with their definitive nomenclature but rather at a two digits level of resolution because some of the ambiguities could not be completely resolved by SBT as previously described [43]. However, more detailed information about individual cell lines can be obtained by contacting Sharon Adams directly at the HLA laboratory, Department of Transfusion Medicine, Bethesda, MD. As previously described [43], it is possible to resolve most of these ambiguities using various methods including sequence-specific primer PCR or pyro-sequencing [44]. If necessary in the future, the NIH HLA laboratory may assist in further characterization of individual HLA alleles. Another caveat is that the identification of HLA alleles at the genomic level does not necessarily correspond to surface expression of their protein products since various abnormalities in transcription, translation and assembling could influence the surface expression of HLA molecules [39,45,46].
Finally, several new alleles were identified (referred to in the tables as new, for which a nomenclature is pending; in detail KM12 HLA-A*02new = Genebank Accession # AY918166; SN12C HLA-A*24new = # AY918167; CAKI-1 HLA-Cw04new = # AY918170). Information regarding the sequence of these alleles could be obtained by directly contacting the HLA laboratory, Department of Transfusion Medicine, Bethesda, MD.
Materials and Methods
Cell Lines
Genomic DNA from the NCI-60 cell line anticancer drug discovery panel was obtained from SH of the National Cancer Institute Developmental Therapeutics Program (Bethesda, MD). Cells were grown in RPMI 1640 supplemented with 10% fetal bovine serum and 5 mM L-glutamine.
DNA Isolation
Genomic DNA was isolated from peripheral blood using the Gentra PUREGENE isolation kit (Gentra Systems, Minneapolis, MN, USA). The DNA was re-suspended in Tris HCl buffer (pH 8.5) and the concentration was measured using a Pharmacia Gene Quant II Spectrophotometer. The DNA was then stored at -70°C until testing.
Sequence-Based Typing (SBT)
HLA class I loci sequence-based typing (SBT) was performed as previously described ([43]. The primary PCR amplification reaction produced a 1.5 kb amplicon encompassing exon 1 through intron 3 of the HLA class I locus. All reagents necessary for primary amplification and sequencing were included in the HLA-A, HLA-B and HLA-C alleleSEQR Sequenced Based Typing Kits (Atria Genetics, Hayward, CA, U.S.A.). The primary amplification PCR products were purified from excess primers, dNTPs and genomic DNA using ExoSAP-IT (American Life Science, Cleveland, OH, U.S.A.). Each template was sequenced in the forward and reverse sequence orientation for exon 2 and exon 3 according to protocols supplied with the SBT kits. Excess dye terminators were removed from the sequencing products utilizing an ethanol precipitation method with absolute ethanol. The reaction products were reconstituted with 15 μl of Hi-Di™ Formamide (PE Applied Biosystems / Perkin-Elmer, Foster City, CA, U.S.A.) and analyzed on the ABI Prism* 3700 DNA Analyzer with Dye Set file: Z and mobility file: DT3700POP6 [ET].
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| 15748285 | PMC555742 | CC BY | 2021-01-04 16:39:28 | no | J Transl Med. 2005 Mar 4; 3:11 | utf-8 | J Transl Med | 2,005 | 10.1186/1479-5876-3-11 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-501576046510.1186/1471-2105-6-50Research ArticleUnbiased descriptor and parameter selection confirms the potential of proteochemometric modelling Freyhult Eva [email protected] Peteris [email protected] Maris [email protected] Jarl ES [email protected] Vincent [email protected] Mats G [email protected] The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden2 Department of Pharmaceutical Biosciences, Uppsala University, Box 591, S-751 24 Uppsala, Sweden3 Department of Engineering Sciences, Uppsala University, Box 528, S-751 20 Uppsala, Sweden2005 10 3 2005 6 50 50 20 9 2004 10 3 2005 Copyright © 2005 Freyhult 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
Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis.
Results
A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small.
Conclusion
The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here.
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Background
Current computational methods for prediction of protein function rely to a large extent on predictions based on the amino acid sequence similarity with proteins having known functions. The accuracy of such predictions depends on how much information about function is embedded in the sequence similarity and on how well the computational methods are able to extract that information. Other computational methods for prediction of protein function include structural similarity comparisons and molecular dynamics simulations (e.g. molecular docking). Although these latter methods are powerful and may in general offer important 3D mechanistic explanations of interaction and function, they require access to protein 3D structure. Computational determination of a 3D structure is well known to be resource demanding, error prone, and generally requires prior knowledge, such as the 3D structure of a homologous protein. This bottleneck makes it important to develop new methods for prediction of protein function when a 3D model is not available.
Recently a new bioinformatic approach to prediction of protein function called proteochemometrics was introduced that has several useful features [1-4]. In proteochemometrics the physico-chemical properties of the interacting molecules are used to characterize protein interaction and classify the proteins into different categories using multivariate statistical techniques. One major strength of proteochemometrics is that the results are obtained directly from real interaction measurement data and do not require access to any 3D protein structure model to provide quite specific information about interaction.
Proteochemometrics has its roots in chemometrics, the subfield of chemistry associated with statistical planning, modelling and analysis of chemical experiments [5]. In particular it is closely related to quantitative-structure activity relationship (QSAR) modelling, a branch of chemometrics used in computer based drug discovery. Modern computer based drug discovery is based on modelling interactions between small drug candidates (ligands) and proteins. The standard approach is to predict the affinity of a ligand by means of numerical calculations from first principles using molecular dynamics or quantum mechanics. QSAR modelling is an alternative approach where experimental observations are used to design a multivariate regression model.
With xi denoting descriptor i among D different descriptors and y denoting the biological activity, (linear) QSAR modelling aims at a linear multivariate model
y = wT x = w0 + w1x1 + w2x2 + ... + wDxD (1)
where w = [w0, w1, w2,..., wD]T are the regression coefficients and x = [1, x1, x2,..., xD]T. The activity y, may be the binding affinity to a receptor but may also be any biological activity e.g., the growth inhibition of cancer cells. In comparison with numerical calculations from first principles and similar approaches, the main advantages of QSAR modelling are that it does not require access to the molecular details of the biological subsystem of interest and that information can be obtained directly from relatively cheap measurements.
The joint perturbation of both the ligand and protein in proteochemometrics yields additional information about the different combinations of ligand and protein properties for an interaction than can be obtained in conventional QSAR modelling where only the ligand is perturbed. In recent years, various other bioinformatic modifications of conventional QSAR modelling have been reported. These include simultaneous modifications of the ligand and the chemical environment (buffer composition and/or temperature) in which the interaction take place [6-8], and three-dimensional QSAR modelling of protein-protein interactions that directly yields valuable stereo-chemical information [9].
Although proteochemometrics has already proven to be an useful methodology for improved understanding of bio-chemical interactions directly from measurement data, the quantitative proteochemometric models designed so far have not yet been subject to a detailed and unbiased statistical evaluation.
A key issue in this evaluation is the problem of overfitting. Since the number of ligand and protein properties available is usually very large, to avoid overfitting, one has to constrain the fitting of the regression coefficients. For example, in ridge regression [10], a penalty parameter is tuned based on data to avoid overfitting, and in partial least squares (PLS) regression [11-13] the overfitting is controlled by tuning the number of latent variables employed. In proteochemometrics as well as in many QSAR studies reported, the performance estimates reported are obtained as follows: 1) Perform a K-fold CV for different regression parameters, 2) Select the parameter value that yields the largest estimated performance value, and 3) Report the most promising model found and the associated performance estimate. Although this procedure may seem intuitive and may yield predictive models (as we in fact demonstrate below) the performance estimates obtained in this way may be heavily biased. Interestingly, this problem was recently addressed in the context of conventional QSAR modelling [14], and has also been discussed in earlier work, see [15,16].
As an alternative or complement to constraining the regression coefficients, one may also reduce the variance by means of variable subset selection (VSS). In QSAR modelling, many algorithms for VSS have been proposed based on various methodologies, for example optimal experimental design [17,18], sequential refinements [19], and global optimization [20]. VSS is used to exclude variables that are not important for the response variable, in the process of model building. Variables that are not important receive low weights in both a PLS and a ridge regression model, however if the fraction of unimportant variables is very large [21] the overall predictive power of the model is reduced. In this case VSS can improve the predictivity. However, if the fraction of unimportant variables is rather small, the quality of the model will not be improved by using VSS, it might on the contrary be slightly reduced. However, the interpretability of the model will in both cases be improved.
Although many of the advanced algorithms for VSS are powerful, they are all computationally demanding. Therefore, in order to keep the computing time down in our use of the double loop cross validation procedure employed here, conceptually and computationally simple algorithms for VSS were used instead of the more advanced ones presented, e.g. in [17-20]. Most likely, the more advanced algorithms would yield more reliable models with even higher predictive power than for the models designed here. However, the main issue of interest in this paper is to confirm the potential of proteochemometrics.
In previous reported proteochemometrics modelling, all available examples were used in the VSS. These were split into K separate parts and a conventional K-fold cross validation (CV) was performed. However, since all available examples were used, there were no longer any completely independent test examples available for model evaluation. Interestingly, this problem of introducing an optimistic selection bias via VSS was recently also pointed out in the supervised classification of gene expression microarray data [22].
In this paper we employ a procedure that can be used to perform unbiased statistical evaluations of proteochemometric and other QSAR modelling approaches. An overview of this so-called double loop CV procedure is presented in Figure 1, and may be regarded as a refinement of the current practice in proteochemometrics in the following respects:
1. K1 different variable subset selections are performed, one for each step in the outer CV loop. This avoids optimistic selection bias.
2. The best performance estimates (Q2) found in the inner loop by means of K2-fold CV are computed, but not reported as the model's performance estimate. This avoids the second optimistic selection bias mentioned above.
3. An unbiased performance estimate, P2, is computed in the outer loop and is reported as the performance estimate of the modelling approach defined by the procedure in the inner loop (the methods of VSS, regression, and model selection employed). P2 is the result of different models that are designed and selected in the inner loop. It reflects the performance that one should expect on average.
4. Repeated K1-fold CVs which yield information about the robustness in the results obtained (presented as confidence intervals).
In addition to these refinements, this work also demonstrates the potential of fast and straight forward alternative methods for VSS and regression in the inner loop. Moreover, it indicates that the performance estimates reported by certain software packages for QSAR may be quite misleading.
We reanalyzed two of the largest proteochemometric data sets yet reported. The first data set is presented in [2] and contains information about the interactions between 332 combinations of 23 different compounds with 21 different human and rat amine G-protein coupled receptors. In total, there are 23 × 21 = 483 possible interactions and the basic task is to fill in the 483-332 = 151 missing values. The second data is presented in [23] and contains information about the binding of 12 different compounds (4-piperidyl oxazole antagonists) to 18 human α1-adrenoreceptor variants (wild-types, chimeric, and point mutated). As for the first data set, there are not interaction data available for all the 12 × 18 = 216 possible interactions, but for 131, see [24] for more details about this data set. Below these two data sets are referred to as the amine data set and the alpha data set, respectively.
Results
Software
Computer programs were written in MATLAB (Mathworks Inc., USA) to integrate the double loop procedure in Figure 1 with robust multivariate linear regression using partial least squares (PLS) regression and ridge regression. These programs also contained two simple and fast methods for variable ranking called corrfilter and PLSfilter. For details, see the Methods section.
Parameters
The joint variable selection and PLS tuning performed in the inner K2-fold CV loop was performed with K2 = 5. The different values of ND (the number of molecular descriptors) evaluated were 10, 20, 50, 100, 200, ..., 1000, 1500, 2000, ..., 6000 for the amine dataset and 10, 20, 50, 100, 200, ..., 1000, 1500, 2000 for the alpha data set. The values of NL considered were either the number of latent variables 1, 2, ..., 8, for both the amine and alpha data set or the degree of RR penalty 0, 0.5, 1.0, ..., 3.0 for the amine data set and 10, 50, 100, 150, 200 for the alpha data set. In the outer K1-fold CV loop, the same number of splits (K1 = 5) was used as in the inner loop. On the global level, the complete experiments were performed 100 times using different random partitions of the complete data sets.
Unbiased predictive power
In Table 1 a summary of the results from 100 randomly selected partitions of the complete amine data set are presented in the form of the mean values and standard deviations obtained. The number of molecular descriptors and latent variables selected in the inner loop are summarized in Table 2. The average values of the biased Q2 obtained in the inner loops look quite promising for the PLSfilter method (Q2 = 0.90 for both PLS and RR) and is even higher than the value reported in earlier studies [2]. However, the corresponding unbiased performance estimate P2 is much smaller (P2 = 0.52 or 0.51 for PLS and RR, respectively). The Q2 values for the models obtained after variable selection using corrfilter are significantly lower than when using PLSfilter, but the P2 values are almost on the same level for the two variable selection methods when no variable selection at all is used. corrfilter reduces the number of descriptors to about one third of the initial number, but corrfilter still selects more than twice as many descriptors than PLSfilter (see Table 2). Since the main reason for variable selection is improving the interpretation of the model by reducing the number of descriptors, this indicates that one should select PLSfilter instead of corrfilter. In Figure 2, the external (unbiased) predictions used to compute P2 for the PLS model using PLSfilter show that there is useful predictive power, but only for examples with mid-range pKi values. The model has poor predictability for both low and high pKi values, indicating that the standard design procedure used in the inner CV loop does not always yield reliable models. This confirms earlier findings [14], that maximization of the unbiased performance estimate Q2 is not always reliable, and also indicates that unreliable designs can be detected by means of the outer CV loop employed in this work.
The estimated performances of the models for the alpha data sets are presented in Table 3. Here both the Q2 and P2 values are high and the difference between the two measures is smaller than for the amine data set. This indicates a lower level of overfitting. The number of descriptors selected in the variable selection is much lower for the alpha data set (see Table 4) than for the amine data set. Both the high P2 values and the display of the external prediction in Figure 3 show that the models have high predictive power. Also, the predictive power is significantly higher after variable selection than without. This is an example when variable selection does not only improve the model interpretability, but also the the model predictivity. The above results indicate, for example, that a combination of PLSfilter, PLS regression and model selection by maximization of Q2 produces individual models with predictive power. The relative standard deviation of the predictive power is less than 5% for the two data sets considered. However, the number of variables selected has a relative standard deviation of 455/1933 = 25% and 69/199 = 35%, respectively. Moreover, the standard deviation in the number of latent variables (an implicit constraint on the regression coefficients) is approximately one (1.4 and 0.8) or 15%. In conclusion, the individual models are quite different but essentially all of them yield useful predictions.
Comparisons to other programs
To verify that our computations using MATLAB are comparable to computations by other programs, such as SIMCA, GOLPE and UNSCRAMBLER, models without variable selection were performed with all four approaches. In the comparison we have compared Q2 values for models based on all descriptors built with PLS using between one and ten latent variables for the amine data set. All the Q2 values were computed using the leave out CV method with five random groups and are presented in Figure 4.
Remarkably, the Q2 values obtained with SIMCA 7.0 are much higher than for the other methods. This is due to the fact that SIMCA does not use the standard formula (Eq. 3) to compute Q2 (personal communication with Umetrics), for some general information see [25].
Robustness and interpretability
To study the robustness and interpretability of the set of models obtained using the two data sets considered, two different levels of information were computed and presented. The first level of information consists of two histograms displaying, for each ligand block (L1–L6 for the amine data set, and L1–L3 for the alpha data set (for the alpha data set the three ligand blocks correspond to the three positions of modification in the ligand)), and for each transmembrane region (TM1–TM7), how often different kinds of descriptors are selected. The histograms are based on the 500 observations obtained in the 5-fold cross validation performed 100 times using different, randomly selected, partitions of the data set. The descriptors are divided into receptor descriptors and ligand descriptors that are further subdivided into original descriptors, cross term descriptors, and absolute valued cross terms. In Figure 5, hit rates for receptor and ligand blocks in the 100 different 5-fold cross validations performed are presented, both for the original, the cross term, and the absolute valued cross term descriptors. In Figure 5 A and 5B, the results for the amine data set are presented. Figure 5 C and 5D displays the corresponding results for the alpha data set.
The second level of information displays the average and the standard deviation of the contribution of the different TM regions in the receptors for creation of receptor-ligand affinity according to the 500 different models designed. The contributions were calculated exactly as described in [2] for a single model, and then the average and standard deviation were calculated. Therefore, the results presented in Figure 5 corresponds to Fig. 3 in [2] where the results for a single proteochemometric model were presented. As before, for each TM region, the contributions to the affinity by different ligands is displayed, this time the variance (uncertainty) information is added. The top of Figure 6 shows the detailed contributions of TM regions to affinity, for each possible combination of ligand and receptor, according to the 500 different proteochemometric models designed using the amine data set, PLS regression and the PLSfilter VSS algorithm. The bottom part displays the corresponding results for the alpha data set when employing ridge regression and the PLSfilter VSS algorithm.
Discussion
In summary, the results reported here confirm earlier reports on the potential of proteochemometrics modelling for prediction of biological activity. It is interesting to note that the VSS did increase the predictivity of the models for the alpha data set, but not for the amine data set. The VSS for the alpha data set did also reduce the number of variables to approximately 4% of the original variables, while for the amine data set 15–38% of the variables remained after VSS. This indicates that for models where many variables receive low weights (as for the alpha data set) the VSS can significantly improve the model, whereas for a data set like the amine data set, with less low weighted variables, the VSS does not improve the model even though it can improve the interpret ability of the model.
The basic goal of proteochemometric modelling is to obtain a single quantitative model that can predict biological activities accurately and which can be easily interpreted biochemically. In this context, it is important to stress that the only role of the outer loop employed in this work is to obtain unbiased estimates of the average performance of the design procedure considered in the inner loop. The additional random splitting of the data sets is used on top of this to gain information about the stability in the performance estimates. Thus, for procedures in the inner loop that yield small variances around a high average of P2, there is statistical support that a single design will yield useful predictions. In order for a single model to be chemically interpretable as well, all the models selected in the inner loop should yield approximately the same number (same set) of variables and the constraints on the regression coefficients (e.g., the number of latent variable in PLS regression) in all models should be approximately equal. With this in mind, the results presented in this work indicate that it is possible to design single proteochemometric models with predictive power based on the two data sets considered but that there is a relatively large variance (from one design set to another) in the variables selected and the constraints put on the regression coefficients. This indicates that although a single proteochemometric model would be useful for predictions, a detailed chemical analysis of such a model would be uncertain. More reliable information should be gained from a careful joint analysis of all the models (and their variables) selected in the inner loops of the different evaluations performed. For example, as briefly discussed in [9], the variables selected with the highest frequency should be of great interest. Thus, systematic and simultaneous biochemical analyses of all the models selected in the inner loops of this kind are required. For illustrative purposes of the complexity and potential of such analyses, here we have presented frequency distributions indicating which variable blocks are selected frequently in the two modelling problems considered.
Moreover, we have also presented estimates of the variability (uncertainty) in estimating the contributions to affinity, between various combinations of ligands and receptors, from different transmembrane regions. In Figure 5 (top), histograms display how often different kinds of descriptors were selected in the 500 models designed for the amine data set. One conclusions is that for corrfilter, the absolute valued cross terms are selected three times as often as ordinary cross terms. Another conclusion is that for PLSfilter, fewer variables are selected and there is no obvious preference for one of the two types of cross terms. For the alpha data set it is obvious from Figure 5 C that only TM2 and TM5 are important to the model. From Figure 6 C and 6D, it is also obvious that the cross terms (and also the absolute valued cross terms) are selected less often than the ordinary descriptors.
Figure 6 A and 6B displays contributions to affinity decomposed separately for each TM region and each drug/receptor combination. One conclusion here is that there is substantial variance in the estimates of the contributions which now is revealed and should dampen the risk of over-interpretations. Another conclusion is that the different regression and variable selection methods employed give similar results. Therefore, only one result each for the amine and the alpha data sets are presented in Figure 6. A third conclusion is that a more clear and more reliable pattern of contributions can be identified in the present study than from the estimated contributions in [2] which were based on a single model only. For example, a pattern of consistently negative average contribution is found from TM3 and the receptors 5HT1B to 5HT1F, but this pattern does not appear in Fig. 3 of [2]. A fourth conclusion is that for the alpha data set, there seem to be no significant contributions to affinity from TM1, TM3, TM4, TM6 and TM7. This result agree with previous results for this data set [2].
Although earlier findings have been confirmed, one should note that there are a number of differences between the present and earlier studies which makes detailed comparisons difficult: 1) In earlier work different variable subset selection methods were employed and in some attempts there were no subset selection at all. 2) The normalization and use of nonlinear cross terms differ between the present and earlier studies of the alpha data set. 3) The limited forms of external predictions attempted earlier e.g., in [2] are not directly comparable with the present results. 4) Different software packages have been employed for model selection and performance estimation.
Conclusion
This work employs a methodology for unbiased statistical evaluation of proteochemometric modelling and confirms that proteochemometric modelling is a new bioinformatic methodology of great potential. The statistical evaluation performed on two of the largest proteochemometric data sets yet reported indicates that detailed chemical analyses of single proteochemometric models may be unreliable and that a systematic analysis of the set of different proteochemometric models produced in the statistical evaluation should yield more reliable information. Finally, although this work has focused on confirming the potential of proteochemometrics, the kind of systematic unbiased performance estimation employed here is of course also relevant for closely related areas of bioinformatics like microarray gene expression analysis and protein classification.
Methods
Data sets
In the amine data set, each of the 23 compounds was described by means of 236 different GRid INdependent Descriptors (GRIND) [26] computed for the lowest energy conformation found and organized into 6 different blocks associated with different kinds of physical interactions. In addition, each receptor was split into seven separate transmembrane regions by means of an alignment procedure and then each amino acid was described by means of five physico-chemical descriptors (z-scales). In total, 159 trans-membrane amino acids were translated into 795 physico-chemical descriptors organized into 7 different blocks (regions). In the alpha data set each of the 12 different compounds was described by means of 24 binary descriptors indicating the presence of different functional groups at three positions in the compound. Moreover, 52 amino acids in the trans-membrane regions of the receptors were identified to have varying properties between receptors and each of them were also coded into five or two physico-chemical properties each, yielding totally 96 descriptor values.
Before the proteochemometric modelling step, the amine data set was subjected to preprocessing in order to reduce the dimensionality of the original descriptors. This step should be part of the design procedure, leaving external examples outside. However, this issue is not expected to be critical and was therefore ignored in this study. For the compounds in the amine data set, after mean centering (no normalization), principal component analysis (PCA) was employed separately to each of six different blocks of GRIND descriptors, each block representing a particular kind of physical interaction. Similarly, each of the seven trans-membrane receptor block descriptors was subjected to PCA. This resulted in 6 × 10 = 60 compound descriptors and 7 × 15 = 105 receptor descriptors. Finally, 12,600 additional "cross-term" descriptors were produced by combining the compound and receptor descriptors nonlinearly. The cross-terms were added to account for non-linearities and they are shown to significantly improve the model predictivity. For each pair of compound and receptor descriptor blocks (totally 6 × 7 = 42 pairs), the 150 possible products between a compound and receptor descriptor value were computed. In addition, the absolute value of the deviation of each product from the average of the product over the data set available was computed. This resulted in 300 descriptor values for each of the 42 block pairs i.e., 42 × 300 = 12,600 values. For the alpha data set, the cross terms formed were the 2 × 24 × 96 = 4,608 possible products between the descriptors of ligands and receptors. No block-wise PCA was employed to reduce the dimensionality.
As a final step before entering the modelling phase, all descriptor values were mean centered and normalized to have unit variance.
Robust PLS and ridge regression
In PLS regression, first a latent variable model
x = t1b1 + t2b2 + ... + tMbM (2)
of the vector x of descriptor values is created where tm is latent variable and bm is the corresponding basis (loading) vector. As few uncorrelated latent variables as possible which have the largest covariances with the response variable y, are selected. Then, a linear model y = a0 + a1t1 + ... + aMtM is obtained from ordinary least squares fitting. Usually, this predictor is transformed back into the original variables yielding y = wT x as in (1). The robustness of PLS comes from the latent variable modelling which eliminates problems caused by strongly correlated variables and few examples. Ridge regression achieves its robustness by adding a penalty term (or, equivalently, a Bayesian prior) to the ordinary least squares criterion that reduces the variances in the regression coefficients. In the experiments considered below, the degree of penalty used in the RR and the number of latent variables used in the PLS regression were tuned in the inner CV loop to maximize their corresponding inner K2-fold cross validation performance estimates.
Variable ranking algorithms
In the PLS modelling, the subsets of molecular descriptors used were selected jointly with the latent variables. Before the joint selection was performed, the molecular descriptors were ranked using two simple and fast methods: A bottom-up algorithm, which we call corrfilter, and a top-down algorithm which we call PLSfilter, corrfilter ranks the molecular descriptors according to the Pearson correlation coefficient between the descriptor and the response variable (the affinity). PLSfilter first builds a PLS model using all available descriptors and between one and L latent variables, where L is the number of latent variables associated with the model in (2) that explain 99% of the observed variance in y. Then each descriptor is ranked according to the corresponding mean of the squared coefficients, wi, in the regression models (1) from the L different models. For the alpha data set below only PLSfilter is applicable. This is due to the discrete nature of the ligand descriptors.
Inner loop: joint VSS and regression parameter selection
After completing the variable ranking, the most promising combination of the number of top-ranked variables and the number of latent variables in the PLS regression modelling or the degree of penalty in the ridge regression modelling was selected as judged by a K2-fold CV performance estimate. The performance estimates for different combinations of values of ND, the number of top-ranked molecular descriptors, and values of NL, the number of latent variables (PLS) or degree of penalty (RR), were considered. Finally, the pair (, ) of numbers yielding the highest estimated predictive power was selected.
The predictive power of the models was measured by the commonly used dimensionless quantity Q2 defined as
where n is the number of examples, yi is the measured biological activity of example i, is the corresponding prediction, and is the arithmetic mean value of all the measured activities. Hence, Q2 is a CV estimate of the fraction of the variance of the response variable explained by the model. In the case of ordinary least squares fitting, Q2 is also a CV estimate of the squared Pearson correlation coefficient between the true (y) and the predicted () response values. Thus, a value of Q2 close to one is traditionally interpreted as a good (valid) model.
Outer loop: external K1-fold CV
As already mentioned, selection of a QSAR model that maximizes a K2-fold CV performance estimate is common in conventional chemometrics and is also applied in proteochemometrics. This method of tuning is more complicated and therefore slower than simpler alternatives (such as tuning to maximize a single conventional hold out performance estimate) but is expected to be less sensitive to overfitting. Although parameter tuning based on CV is attractive, overfitting may still occur and the performance estimate obtained may be too optimistic. Some aspects of this danger were recently pointed out [14] and has also been discussed in much earlier work [15]. In conclusion, it is important to employ a second external CV as in Figure 1 to estimate the true performance also of sophisticated design procedures that employ CV for parameter tuning.
For each step in the external K1-fold CV loop, one of the K1 subsets of the whole data set was kept for validation and the rest were used for design of a regression model. The predictions obtained in this outer CV loop were finally used in the formula for Q2 in (3). However, since the predictions used for calculating Q2 were kept outside the whole design procedure, as in earlier work [9,16], we denote the computed quantity by P2 to indicate that this is an unbiased performance estimate based on external predictions.
Repeated K1-fold CVs
The results obtained from a single K1-fold CV are interesting but are sometimes heavily influenced by the particular data partitioning used. In the work reported here, we therefore performed repeated K1-fold CV in the outer loop. For each partitioning selected randomly, the corresponding value of P2 was computed using the procedures described above. Thus, a set of different values of P2 were obtained and used for determination of the variability in the results obtained.
Computations
The main body of programming and computations were performed using MATLAB on standard processors (900 MHz). For comparisons, we also employed the program packages SIMCA (Umetrics, Sweden), GOLPE [17] and UNSCRAMBLER (CAMO, Norway).
Authors' contributions
E.F. and M.G. devised and implemented the proposed double CV loop procedure, the feature selection algorithms, and a numerically efficient version of ridge regression required. P.P., M.L., J.E.S.W. provided the data sets studied together with experience and insights gained from their earlier work on proteochemometrics. V.M. and M.G. supervised the project. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by the Swedish Research Council (621-2001-2083, 621-2002-4711), Carl Tryggers stiftelse (Stockholm), the Göran Gustafsson foundation (Stockholm), and the faculty of science and technology (Uppsala University).
Figures and Tables
Figure 1 Overview of double loop. An overview of the double loop CV procedure used to obtain the desired unbiased performance estimate P2. In the embedded inner CV loop, the most promising model is selected which yields the largest unbiased performance estimate, Q2. In the outer CV loop, external test examples are kept outside the inner loop and are only used to test the most promising model found in the inner loop. Note that the estimate P2 reflects the average performance of the modelling procedure employed in the inner loop and that the estimate is based on many different models designed in the inner loop.
Figure 2 External predictions for amine data set. External predictions for amine data set sorted according to growing values of pKi. The figure shows the experimental pKi values (dashed line) and the mean value of the predicted pKi values (solid line) with a 95 % confidence interval (dotted lines). The predictions shown in the figure are from the PLS modelling after variable selection using PLSfilter. The results indicate that the high and low pKi values are hard to predict.
Figure 3 External prediction for alpha data set. External predictions for alpha data set sorted according to growing values of pKi. The figure shows the experimental pKi values (dashed line) and the mean value of the predicted pKi values (solid line) with a 95 % confidence interval (dotted lines). The predictions shown in the figure are from the PLS modelling after variable selection using PLSfilter.
Figure 4 Comparison of software. Q2 values obtained using different software for the prediction of affinities based on PLS models, without variable selection, for the amine data set. Between one and ten latent variables were used and SIMCA (dashed line), UNSCRAMBLER (dash-dotted line), GOLPE (dotted line) and MATLAB (solid line) were used to both build the models and evaluate them by computing Q2 values. The SIMCA Q2 values are much higher than the other Q2 values.
Figure 5 Hit rates. A The hit rates for the receptor blocks in the amine data set. The figure shows for each transmembrane region the hit rates for the original receptor descriptors, the cross term descriptors and the absolute valued cross term descriptors involving that transmembrane region. B The hit rates for the ligand blocks in the amine data set. The figure shows for each ligand descriptor block the hit rates for the original receptor descriptors, the cross term descriptors and the absolute valued cross term descriptors involving that ligand descriptor block. C and D The corresponding hit rates for the alpha data set. The blue bars show the hit rates computed for the PLS models using PLSfilter, the cyan bars show the hit rates computed for the PLS models using corrfilter, the red bars show the hit rates computed for the RR models using PLSfilter, and the yellow bars show the hit rates computed for the RR models using corrfilter
Figure 6 Detailed contributions to affinity. A Contributions of TM regions in amine GPCRs to the ligand affinity according to the proteochemometrics models created using PLS in combination with the variable selection method PLSfilter. The contributions are shown for all the 21 receptors, for each receptor 23 bars corresponding to the 23 ligands are shown (in alphabetical order i.e., Amperozide, Clozapine, Fluparoxan, Fluspirilene, GGR218231, Haloperidol, L741626, MDL100,907, ORG5222, Ocaperidone, Olanzapine, Pipamperone, Raclopride, Risperidone, S16924, S18327, S33084, Seroquel, Sertindole, Tiospirone, Yohimbine, Ziprasidone, Zotepine). The blue bars show the average contribution and the height of the green bars show one standard deviation. The average value and standard deviation were computed using all the 500 models designed (100 repeats and five blocks for each repeat). B Contributions of TM regions in α1-adrenoreceptors to the ligand affinity according to the proteochemometrics models created using RR in combination with the variable selection method PLSfilter. The contributions are shown for all the 18 receptors, for each receptor 12 bars corresponding to the 12 ligands are shown (in numerical order 1–12). The blue bars show the average contribution and the height of the green bars show one standard deviation. The average value and standard deviation were computed using all the 500 models (100 repeats and five blocks for each repeat).
Table 1 Q2 and P2 values for amine data set. The mean and standard deviations for the P2 and Q2 values obtained with the amine data set using two different variable selection methods (corrfilter and PLSfilter) and two different regression methods (PLS and RR). The 5-fold cross validation procedure was repeated 100 times, using 100 different random partitions of the data. ND and NL values were selected in an inner 5-fold cross validation loop by optimizing the Q2 value. For one random partition of the amine data into five cross validation groups, one P2 and five Q2 values were obtained. For every random partition the mean Q2 is computed. The mean and standard deviations were computed based on the 100 P2 values and the 100 mean Q2 values.
Filter Regression P2 (mean ± std) mean Q2 (mean ± std)
no filter PLS 0.52 ± 0.021 0.49 ± 0.011
no filter RR 0.53 ± 0.022 0.49 ± 0.012
corrfilter PLS 0.49 ± 0.028 0.76 ± 0.0057
corrfilter RR 0.44 ± 0.038 0.76 ± 0.0085
PLSfilter PLS 0.52 ± 0.025 0.90 ± 0.0026
PLSfilter RR 0.51 ± 0.027 0.90 ± 0.0056
Table 2 NL and ND for amine data set. The mean and standard deviation of the number of latent variables or degree of RR penalty (NL) and the number of molecular descriptors (ND) used to build the models for the the amine data set. The values of NL and ND are tuned by optimizing the Q2 value in an inner cross validation loop.
Filter Regression NL (mean ± std) ND (mean ± std)
no filter PLS 6.49 ± 1.48 12765 ± 0
no filter RR 1.13 ± 1.45 12765 ± 0
corrfilter PLS 7.00 ± 0.88 4748 ± 730
corrfilter RR 1.83 ± 1.18 4871 ± 640
PLSfilter PLS 6.18 ± 1.40 1933 ± 455
PLSfilter RR 2.10 ± 1.17 2136 ± 349
Table 3 Q2 and P2 values for alpha data. set The mean and standard deviations for the values of Q2 and P2 obtained for the alpha data set using the variable selection method PLSfilter and the regression methods PLS and RR.
Filter Regression P2 (mean ± std) mean Q2 (mean ± std)
no filter PLS 0.55 ± 0.045 0.50 ± 0.066
no filter RR 0.65 ± 0.037 0.59 ± 0.027
PLSfilter PLS 0.77 ± 0.033 0.83 ± 0.0095
PLSfilter RR 0.76 ± 0.043 0.83 ± 0.010
Table 4 NL and ND for alpha data set. The mean and standard deviation of the number of latent variables or degree of penalty (NL) and the number of molecular descriptors (ND) used to build the models for the the alpha data set.
Filter Regression NL (mean ± std) ND (mean ± std)
no filter PLS 7.01 ± 1.15 4728 ± 0
no filter RR 93.64 ± 60.34 4728 ± 0
PLSfilter PLS 7.39 ± 0.80 199 ± 69
PLSfilter RR 26 ± 22 192 ± 85
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| 15760465 | PMC555743 | CC BY | 2021-01-04 16:02:49 | no | BMC Bioinformatics. 2005 Mar 10; 6:50 | utf-8 | BMC Bioinformatics | 2,005 | 10.1186/1471-2105-6-50 | oa_comm |
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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-5-101575532310.1186/1471-230X-5-10Research ArticlePersistent demographic differences in colorectal cancer screening utilization despite Medicare reimbursement Ko Cynthia W [email protected] William [email protected] Laura-Mae [email protected] Department of Medicine, Division of Gastroenterology, Box 356424, University of Washington, Seattle, Washington, USA2 Department of Health Services, Center for Cost and Outcomes Research, Box 359736, University of Washington, Seattle, Washington, USA3 Department of Family Medicine, Box 354982, University of Washington, Seattle, Washington, USA2005 8 3 2005 5 10 10 11 1 2005 8 3 2005 Copyright © 2005 Ko 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
Colorectal cancer screening is widely recommended, but often under-utilized. In addition, significant demographic differences in screening utilization exist. Insurance coverage may be one factor influencing utilization of colorectal cancer screening tests.
Methods
We conducted a retrospective analysis of claims for outpatient services for Washington state Medicare beneficiaries in calendar year 2000. We determined the proportion of beneficiaries utilizing screening fecal occult blood tests, flexible sigmoidoscopy, colonoscopy, or double contrast barium enema in the overall population and various demographic subgroups. Multiple logistic regression analysis was used to determine the relative odds of screening in different demographic groups.
Results
Approximately 9.2% of beneficiaries had fecal occult blood tests, 7.2% had any colonoscopy, flexible sigmoidoscopy, or barium enema (invasive) colon tests, and 3.5% had invasive tests for screening indications. Colonoscopy accounted for 41% of all invasive tests for screening indications. Women were more likely to receive fecal occult blood test screening (OR 1.18; 95%CI 1.15, 1.21) and less likely to receive invasive tests for screening indications than men (OR 0.80, 95%CI 0.77, 0.83). Whites were more likely than other racial groups to receive any type of screening. Rural residents were more likely than urban residents to have fecal occult blood tests (OR 1.20, 95%CI 1.17, 1.23) but less likely to receive invasive tests for screening indications (OR 0.89; 95%CI 0.85, 0.93).
Conclusion
Reported use of fecal occult blood testing remains modest. Overall use of the more invasive tests for screening indications remains essentially unchanged, but there has been a shift toward increased use of screening colonoscopy. Significant demographic differences in screening utilization persist despite consistent insurance coverage.
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Background
Screening for colorectal cancer is now recommended by several organizations [1-5], and insurance coverage of screening tests is becoming more widespread. For example, the Centers for Medicare and Medicaid Services began reimbursement for the commonly used screening tests in 1998, covering 100% of charges for fecal occult blood tests and 80% of charges for flexible sigmoidoscopy, colonoscopy for high risk individuals, and barium enema. Coverage was extended to include colonoscopy for average risk individuals in July, 2001. Despite existing guidelines, many eligible people are not receiving screening tests according to current recommendations [6-9]. In 2001, only 23.5% of surveyed adults over the age of 50 had received fecal occult blood testing in the previous year, and 43.4% had received lower endoscopy in the previous 10 years [7]. However, use of screening colonoscopy may be increasing [10]. Age, race, insurance coverage, and place of residence, have all been associated with utilization [7,9,11-15]. Although lack of insurance coverage may be one reason for under-utilization, we recently showed that the proportion of Medicare beneficiaries receiving invasive colorectal screening tests (defined as colonoscopy, flexible sigmoidoscopy, or barium enema) did not increase in 1998, immediately after introduction of Medicare coverage for these tests [16]. In a 9-month period during this year, only 6.3% of Washington state Medicare beneficiaries received fecal occult blood testing, 6.3% had any type of invasive tests, and 3.2% had invasive screening tests. The purpose of this study was to examine the effect of insurance coverage on overall utilization of screening tests and on demographic differences in screening utilization.
Methods
Data source
The study was approved by the University of Washington Institutional Review Board. We used the calendar year 2000 Physician/Supplier Part B Standard Analytic File and the Denominator File, which are administrative databases covering Medicare beneficiaries and maintained by the Centers for Medicare and Medicaid Services. The Denominator File contains information about date of birth, gender, race, place of residence, vital status, and enrollment in Medicare Part A, Medicare Part B or capitated health plans. The Physician/Supplier Part B Standard Analytic File contains claims data for outpatient physician and supplier services, including the date of the visit, associated diagnoses (coded as International Classification of Diseases [ICD9] codes), and procedures performed (coded as Current Procedural Terminology [CPT] or common procedure [HCPCS] codes).
Patient selection
All Medicare beneficiaries listed as Washington State residents in the Denominator File in calendar year 2000 were eligible for inclusion (n = 772,153). Beneficiaries who were less than 65 years old (n = 132,711), who died during the study year (n = 32,678), or who were not enrolled in both parts A and B throughout the study year (n = 33,263) were excluded. We excluded patients enrolled in capitated health plans during any part of the study year (n = 170,232) because they may have received screening tests while in these plans for which claims were not submitted. Based on ICD9 codes in the Physician/Supplier Standard Analytical File, we also excluded patients with a diagnosis code for a personal history of colon polyps (V12.72, n = 683), colon or rectal cancer (V10.05 or V10.06, n = 398), or inflammatory bowel disease (555.x, 556.x, n = 227) in this calendar year, since these patients are at increased risk of colorectal cancer and may need more frequent surveillance. Patients without one of these diagnoses were analyzed as average risk. However, if patients did have a history of one of these conditions, but this code was not listed in calendar year 2000, they could have been misclassified as being at average risk. We did not exclude patients with a family history of colorectal cancer, as we felt they could not be reliably identified from the available ICD9 diagnosis codes. We had 401,961 eligible beneficiaries for analysis.
Identification of screening tests
We identified screening fecal occult blood tests using the HCPCS code assigned by the Centers for Medicare and Medicaid Services for this test (G0107). We also examined the use of invasive colon tests (flexible sigmoidoscopy, colonoscopy, and double contrast barium enema). However, using ICD9 codes, it can be difficult to designate a given invasive colon test as screening or diagnostic [17]. To define tests as screening indication or diagnostic indication, we used the following algorithm, similar to our previous study [16]. We first identified these procedures using all CPT or HCPCS codes for colonoscopy, flexible sigmoidoscopy, and barium enema (colonoscopy – 44388, 44389, 44392, 44393, 44394, 45378, 45380, 45383, 45384, 45385, G0105, G0121; sigmoidoscopy – 45300, 45305, 45308, 45309, 45315, 45320, 45330, 45331, 45333, 45338, 45339, G0104; barium enema – 74270, 74280, G0106, G0120, G0122). We then defined an invasive procedure as performed for a screening indication if: 1) the procedure was coded using the relevant HCPCS codes for screening tests; 2) ICD9 codes V76.51 (screening-malignant neoplasm-colon) or V76.41 (screening-malignant neoplasm-rectum) were associated with the procedure; or 3) there were no ICD9 diagnosis codes of gastrointestinal tract symptoms, weight loss, or anemia associated with any physician visits within the previous 3 months (abdominal pain – 787.3, 789.0x, 789.6x; altered bowel habits – 564.0, 787.x; gastrointestinal bleeding – 578.x; positive fecal occult blood test – 792.1; weight loss – 783.2; iron deficiency anemia – 280.x; anemia, unspecified – 285.9). Because of this 3-month exclusion rule, we analyzed only claims submitted between April 1, 2000 and December 31, 2000. We analyzed only the first test performed, as later tests may have been performed to evaluate abnormalities found on the initial test.
Data analysis
We determined the proportion of average risk beneficiaries who received screening fecal occult blood tests or who underwent flexible sigmoidoscopy, colonoscopy, or double contrast barium enema. For these tests, proportions were calculated using all tests identified by all CPT and/or HCPCS codes (all invasive tests) or only tests identified by the algorithm described above (invasive tests for screening indications).
We also analyzed screening test utilization in population subgroups as defined by age, sex, race, and place of residence (urban vs. rural). We compared differences in proportions of beneficiaries undergoing screening using chi-square tests. Place of residence was defined as urban or rural depending on the health service area in which the patient lived. Rural health service areas include all ZIP codes that are closest to a rural hospital, as defined by the Washington State Department of Health. Multiple logistic regression analysis was used to determine the relative odds of screening in different demographic groups (Stata 8.0, Stata Corp., College Station, TX). Significance of the regression models was tested using the log-likelihood statistic, and the method of Hosmer and Lemeshow was used to assess goodness of fit of the regression models.
Results
Beneficiaries were predominantly white, and there were more females than males (Table 1). In the nine-month study period, 9.2% of Washington State Medicare beneficiaries had a claim submitted for screening fecal occult blood tests (Table 1). Fecal occult blood testing was more common in women than in men, in beneficiaries aged 70 to 74 than in other age groups, and in rural residents than in urban residents. Whites were the most likely to receive screening fecal occult blood tests, and Hispanics the least likely. These differences were all statistically significant (p < 0.001).
Overall, 7.2% had any invasive test (colonoscopy, flexible sigmoidoscopy, or barium enema for diagnostic or screening indications) in the 9-month study period (Table 1). Utilization of invasive tests for screening indications was uncommon, occurring in only 3.5% during the nine-month study period. With all invasive tests combined, men, beneficiaries aged 70 to 74, whites, and urban residents were more likely to utilize tests than women, other age groups, other racial groups, and rural residents, respectively. With all invasive tests for screening indications combined, similar demographic variation in utilization was found. Fifty-eight percent of all invasive tests and 41% of invasive tests for screening indications were colonoscopies.
However, when examining utilization of colonoscopy, sigmoidoscopy, and barium enema separately, some interesting demographic differences were seen (Table 1). Men, beneficiaries aged 70 to 74, whites, and urban residents were more likely to undergo colonoscopy. Flexible sigmoidoscopy was more common in men, beneficiaries age 65 to 69, whites, and urban residents. These differences were still present, but less pronounced when looking at colonoscopy and flexible sigmoidoscopy for screening indications. Use of barium enema for screening was infrequent in both rural and urban patients. Although Hispanics were less likely to utilize colonoscopy and sigmoidoscopy, they were more likely to undergo barium enema than whites.
We developed multiple logistic regression models to determine the relative odds of receiving screening tests in different population subgroups (Table 2). Parallel, previously published data from 1994–98 are presented for comparison [16]. These models show that women were more likely to receive screening fecal occult blood tests (odds ratio 1.18; 95% confidence interval 1.15, 1.21), but less like to receive invasive tests for screening indications (odds ratio 0.80; 95% confidence interval 0.77, 0.83). Beneficiaries aged 75 and over were less likely to be screened than younger beneficiaries. For example, compared with beneficiaries aged 65–69, those aged 75–79 were less likely to be screened with either fecal occult blood tests (odds ratio 0.94; 95% confidence interval 0.91, 0.96) or with invasive tests (odds ratio 0.82; 95% confidence interval 0.78, 0.86). Screening utilization was also significantly lower in beneficiaries aged 80 years or older compared with those aged 65–69. Hispanics were less likely than whites to be screened with either fecal occult blood tests (odds ratio 0.30; 95% confidence interval 0.23, 0.38) or with the invasive tests (odds ratio 0.40; 95% confidence interval 0.28, 0.56). Rural residents were more likely to be screened with fecal occult blood tests (odds ratio 1.20, 95% confidence interval 1.17, 1.23), but less likely to receive invasive tests for screening indications (odds ratio 0.89; 95% confidence interval 0.85, 0.93).
We developed similar multiple logistic regression models to look individually at utilization of colonoscopy, sigmoidoscopy, or barium enema for diagnostic or screening indications (Table 3). Utilization of colonoscopy and flexible sigmoidoscopy was less common in women than in men, while women were more likely to undergo barium enema (odds ratio 1.13, 95% confidence interval 1.04, 1.24). The odds of beneficiaries undergoing colonoscopy initially increased slightly with age, but then decreased at age 80 and over. The odds of undergoing sigmoidoscopy decreased with age, while the odds of undergoing barium enema increased with age. Hispanics were less likely than whites to undergo colonoscopy or sigmoidoscopy, but more likely to undergo barium enema (odds ratio 1.84, 95% confidence interval 1.22, 2.78). Other racial groups were less likely than whites to utilize colonoscopy, flexible sigmoidoscopy or barium enema. With inclusion of only screening tests (Table 4), women again utilized colonoscopy and sigmoidoscopy less often than men, but utilized barium enema similarly (odds ratio for barium enema 0.98, 95% confidence interval 0.84, 1.13). Utilization of colonoscopy was relatively constant until age 80, but then declined. Again, Hispanics were less likely than whites to utilize colonoscopy or sigmoidoscopy for screening indications (odds ratio for colonoscopy 0.36; 95% confidence interval 0.21, 0.62). Urban residents were more likely than rural residents to receive colonoscopy or sigmoidoscopy for screening indications (odds ratio for colonoscopy 1.10; 95% confidence interval 1.03, 1.18), but utilized barium enema similarly.
Discussion
We previously showed that colorectal cancer screening tests are under-utilized and that utilization did not increase shortly after introduction of the Medicare screening benefit [16]. In this study, we extend these findings and examine the effect on utilization after 2 to 3 years of insurance coverage. Although utilization of fecal occult blood testing increased moderately between 1998 and 2000 (6.30% vs. 9.15% over 9 months, respectively), utilization of more invasive tests remained infrequent (6.26% in 1998 vs. 7.19% in 2000 receiving any invasive test; 3.17% in 1998 vs. 3.48% in 2000 receiving invasive tests for screening indications over 9 months). This was true for all demographic subgroups examined. However, there was some shift in the type of invasive procedure done, with increasing use of colonoscopy compared with flexible sigmoidoscopy and barium enema. In 2000, 58% of all invasive tests and 41% of invasive tests for screening indications were colonoscopies, compared to 47% and 35% in 1998, respectively. Medicare coverage for screening colonoscopy in average risk beneficiaries did not begin until July 2001, and therefore most colonoscopy exams during our study were likely done in high risk patients. Utilization of screening colonoscopy may have increased even further after this change in reimbursement policies to cover average risk individuals.
In addition, we show that insurance coverage for screening does not eliminate disparities in screening utilization. In fact, disparities actually increased over time in some instances. Compared to 1994–1998 [16], the relative odds of any invasive testing for Hispanics versus whites actually decreased in 2000, while the effect for invasive tests for screening indications in different racial groups was mixed (Table 2). Disparities related to gender and place of residence were essentially unchanged between 1994–8 and 2000. These findings extend those of other studies in the general population [12,14], where universal insurance coverage of screening was not present, and studies of previous years in Medicare beneficiaries [13,15].
The precise reasons for the observed demographic disparities in screening are unknown. The sex and race-related disparities are consistent with other data showing differential use of medical services in general in these population subgroups. Screening in general was most common in beneficiaries age 65 to 74. As the potential benefit of screening decreases with age and shorter life expectancy, the age-related decrement in screening after age 75 may be clinically appropriate. Regarding geographic differences, the availability of screening services, especially for the invasive and more resource intensive tests such as colonoscopy and sigmoidoscopy, may be greater in urban than in rural areas. Fecal occult blood testing is less resource intensive and is likely to be more available in rural areas, potentially explaining some of the geographic differences in screening.
Projecting these data out over a longer period gives a more complete picture of utilization differences. For example, 3.5% of whites, 2.8% of blacks, and 1.6% of Hispanics had invasive screening tests done over the 9-month study period. Assuming constant screening rates, over a 5-year period, 23% of whites would undergo invasive tests, compared to only 19% of blacks and 11% of Hispanics. These differences are further magnified if fecal occult blood test utilization is also considered. These screening disparities may contribute to the known differences in colorectal cancer incidence and survival in different racial groups [18,19].
This study has several limitations. First, we cannot clearly separate the effects of Medicare coverage from secular trends towards increasing utilization of screening tests. Second, we used administrative claims databases to assess health services utilization. Although the accuracy of coding for the diagnoses and procedures studied here is not established, claims coding surgical services and procedures is fairly reliable and accurate [20-24]. Third, we analyzed data from only one state, and these results may not necessarily be generalizable to other regions. In particular, the number of minorities in this study was relatively small, and the confidence intervals for minority groups in the multiple logistic regression models were wide. However, patterns of utilization of colorectal cancer screening tests were similar in Kansas Medicare beneficiaries [9,12]. Another study looking at national trends in colorectal tests in Medicare beneficiaries found that use of sigmoidoscopy, barium enema, and fecal occult blood testing declined over a similar period, while colonoscopy utilization increased [25].
Our study only included patients age 65 and older, and we did not assess utilization in patients younger than this. This was a cross-sectional study, and patients who may have previously been screened with procedures that are not recommended annually would have been classified as unscreened in our analysis. Utilization of colonoscopy and colonoscopy practice patterns may have changed substantially since 2000 [26-28]. Lastly, we excluded patients who enrolled in capitated health plans, where health services utilization patterns may differ from those in traditional fee-for-service plans [29,30]. Screening frequency should also be studied within these health plans.
Although we developed an algorithm to distinguish screening from diagnostic tests, we cannot be certain that tests we designated as screening were truly intended as screening tests. We excluded patients with physician visits for gastrointestinal symptoms over the prior 3 months, but it may take longer to have a colonoscopy scheduled for these indications. This may influence our estimates of screening frequency. In our previous study, we found that 82% of procedures designated as screening from the HCPCS codes would have been classified as screening from our algorithm [16]. However, even if all colonoscopies, flexible sigmoidoscopies, or barium enemas done were intended as screening, only 7.2% of the study population would have had an invasive screening test during the 9-month study period, or 9.6% per year. Since not all invasive tests done are intended for screening, we believe that less than 7% of our study population had invasive screening tests during the 9-month study period.
This study extends our previous work and shows that provision of insurance coverage of screening tests does not necessarily increase utilization of such tests in the medium term. Even 2 to 3 years after beginning universal coverage and widespread publicity about colorectal cancer screening [31], screening rates changed only modestly, and demographic differences in screening utilization remained. Thus, insurance coverage may be only one small factor affecting patients' decisions to undergo colorectal cancer screening [32]. We did find a moderate shift towards use of the most expensive test, colonoscopy, over time. It may be that the more ready availability of colonoscopy services in urban areas influences patients' or providers' decisions to use this form of screening. Conversely, female or non-white beneficiaries may be more reluctant to undergo invasive screening tests, or providers may be less likely to offer invasive tests to these subgroups. In addition, out-of-pocket costs for screening tests may still be prohibitive for some populations, affecting screening utilization. We did not have information about private insurance or indirect costs which could influence decisions about screening. These aspects of disparities in screening utilization cannot be addressed using administrative claims data. Therefore, further efforts should be made to identify and address additional barriers to and preferences about colorectal cancer screening in the general Medicare population, and especially in underserved subgroups.
Conclusion
Overall utilization of colorectal cancer screening tests increased only modestly 2 to 3 years after institution of Medicare coverage, but there was a shift towards screening colonoscopy and away from less invasive tests. Demographic differences in screening persisted despite consistent insurance coverage.
List of abbreviations
CPT: Current Procedural Terminology
HCPCS: Health Care Common Procedures Coding System
ICD9: International Classification of Diseases – 9
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CK conceived the study, participated in data analysis and drafted the manuscript. WK participated in design and analysis of the study. LMB participated in design of the study and data analysis. 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
Table 1 Demographic characteristics of beneficiaries and proportion receiving colon tests
Any invasive test * Invasive screening test †
Demographic characteristics (n, %) Fecal occult blood test (%) All (%) Colono- scopy (%) Flexible sigmoid-oscopy (%) Barium enema (%) All (%) Colon-oscopy (%) Flexible sigmoid-oscopy (%) Barium enema (%)
All subjects 401961 (100) 9.15 7.19 4.19 2.41 0.59 3.48 1.44 1.86 0.18
Sex
Male 168206 (41.8) 8.57* 7.48* 4.31* 2.64* 0.54* 4.04* 1.73* 2.12* 0.18
Female 233755 (58.2) 9.57 6.97 4.11 2.25 0.62 3.08 1.23 1.66 0.18
Age
65–69 100754 (25.1) 10.27* 7.87* 4.35* 3.05* 0.47* 4.39* 1.71* 2.51* 0.18
70–74 100454 (25.0) 10.39 8.04 4.59 2.87 0.59 4.19 1.73 2.27 0.19
75–79 90975 (22.6) 9.77 7.56 4.53 2.39 0.64 3.60 1.58 1.82 0.20
≥ 80 109778 (27.3) 6.48 5.46 3.39 1.41 0.65 1.89 0.83 0.90 0.16
Race
White 380492 (94.7) 9.36* 7.27* 4.23* 2.45* 0.59* 3.53* 1.46* 1.89* 0.18
Black 5649 (1.4) 4.66 6.30 3.77 2.05 0.48 2.81 1.26 1.49 0.07
Hispanic 2179 (0.5) 3.12 4.68 2.75 0.87 1.06 1.61 0.60 0.69 0.32
Asian 6946 (1.7) 5.83 5.64 3.43 1.86 0.30 2.58 1.04 1.41 0.13
Other 6595 (1.6) 6.60 5.78 3.71 1.77 0.29 2.56 1.12 1.35 0.09
Residence
Rural 73041 (18.2) 10.64* 6.85* 4.04 2.28 0.54 3.26* 1.37 1.72* 0.17
Urban 328920 (81.8) 8.82 7.26 4.22 2.44 0.60 3.53 1.46 1.89 0.18
* Statistically significant difference (p < 0.005).
* All flexible sigmoidoscopy, colonoscopy, or barium enema exams
† Invasive tests (flexible sigmoidoscopy, colonoscopy, or barium enema) without an associated exclusion diagnosis such as abdominal pain or anemia
Table 2 Multivariable models of characteristics associated with utilization of colon tests
OR (95% CI) for fecal occult blood test, year 2000 OR (95% CI) for any invasive test*, 1994–1998 OR (95% CI) for any invasive test, year 2000 OR (95% CI) for screening colon test*, 1994–1998 OR (95% CI) for screening colon test year 2000
Sex
Male 1.00 1.00 1.00 1.00 1.00
Female 1.18 (1.15, 1.21) 0.99 (0.98, 1.01) 0.96 (0.93, 0.98) 0.84 (0.82, 0.86) 0.80 (0.77, 0.83)
Age
65–69 yrs 1.00 1.00 1.00 1.00 1.00
70–74 yrs 1.01 (0.98. 1.04) 1.12 (1.10, 1.14) 1.09 (0.99, 1.06) 1.05 (1.02, 1.08) 0.96 (0.92, 1.00)
75–79 yrs 0.94 (0.91, 0.96) 1.14 (1.12, 1.17) 0.96 (0.92, 0.99) 0.99 (0.96, 1.01) 0.82 (0.78, 0.86)
≥ 80 yrs 0.59 (0.57, 0.61) 0.83 (0.82, 0.85) 0.68 (0.65, 0.70) 0.60 (0.58, 0.61) 0.43 (0.41, 0.45)
Race
White 1.00 1.00 1.00 1.00 1.00
Black 0.47 (0.42, 0.54) 0.83 (0.78, 0.89) 0.83 (0.74, 0.92) 0.68 (0.62, 0.76) 0.74 (0.63, 0.87)
Hispanic 0.30 (0.23, 0.38) 0.65 (0.56, 0.76) 0.59 (0.48, 0.72) 0.48 (0.38, 0.61) 0.40 (0.28, 0.56)
Asian 0.60 (0.54, 0.66) 0.72 (0.66, 0.78) 0.74 (0.67, 0.82) 0.65 (0.58, 0.72) 0.70 (0.60, 0.81)
Other 0.67 (0.61, 0.74) 0.71 (0.68, 0.75) 0.77 (0.69, 0.85) 0.66 (0.62, 0.71) 0.40 (0.28, 0.56)
Residence
Urban 1.00 1.00 1.00 1.00 1.00
Rural 1.20 (1.17, 1.23) 0.94 (0.92, 0.96) 0.92 (0.89, 0.95) 0.92 (0.89, 0.94) 0.89 (0.85, 0.93)
* Data from 1994–98 was published previously [16].
Table 3 Multivariable models of characteristics associated with utilization of any colonoscopy, flexible sigmoidoscopy, or barium enema
Colonoscopy Flexible sigmoidoscopy Barium enema
Odds ratio 95% confidence interval Odds ratio 95% confidence interval Odds ratio 95% confidence interval
Sex
Male 1.00 1.00 1.00
Female 0.97 0.97, 1.00 0.89 0.86, 0.93 1.13 1.04, 1.24
Age
65–69 1.00 1.00 1.00
70–74 1.06 1.01, 1.10 0.94 0.89, 0.99 1.25 1.10, 1.41
75–79 1.04 1.00, 1.08 0.78 0.74, 0.82 1.35 1.19, 1.52
≥ 80 0.77 0.74, 0.81 0.46 0.43, 0.49 1.38 1.22, 1.55
Race
White 1.00 1.00 1.00
Black 0.87 0.76, 0.99 0.79 0.66, 0.95 0.81 0.55, 1.18
Hispanic 0.61 0.48, 0.80 0.32 0.20, 0.50 1.84 1.22, 2.78
Asian 0.78 0.69, 0.89 0.73 0.62, 0.87 0.59 0.40, 0.87
Other 0.87 0.76, 0.99 0.69 0.58, 0.83 0.49 0.31, 0.77
Residence
Rural 1.00 1.00 1.00
Urban 1.06 1.02, 1.11 1.10 1.05, 1.16 1.11 0.99, 1.24
Table 4 Multivariable models of characteristics associated with utilization of screening colonoscopy, flexible sigmoidoscopy, or barium enema
Colonoscopy Flexible sigmoidoscopy Barium enema
Odds ratio 95% confidence interval Odds ratio 95% confidence interval Odds ratio 95% confidence interval
Sex
Male 1.00 1.00 1.00
Female 0.74 0.71, 0.78 0.84 0.80, 0.88 0.98 0.84, 1.13
Age
65–69 1.00 1.00 1.00
70–74 1.02 0.96, 1.09 0.91 0.86, 0.96 1.06 0.86, 1.30
75–79 0.94 0.87, 1.01 0.73 0.68, 0.77 1.13 0.92, 1.40
≥ 80 0.49 0.46, 0.54 0.36 0.33, 0.39 0.90 0.73, 1.11
Race
White 1.00 1.00 1.00
Black 0.81 0.64, 1.03 0.73 0.59, 0.91 0.38 0.14, 1.02
Hispanic 0.36 0.21, 0.62 0.32 0.19, 0.53 1.72 0.82, 3.63
Asian 0.69 0.54, 0.86 0.72 0.59, 0.88 0.69 0.36, 1.34
Other 0.75 0.60, 0.95 0.68 0.55, 0.89 0.50 0.22, 1.11
Residence
Rural 1.10 1.00 1.00
Urban 1.10 1.03, 1.18 1.14 1.05, 1.18 1.09 0.90, 1.32
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| 15755323 | PMC555744 | CC BY | 2021-01-04 16:03:27 | no | BMC Gastroenterol. 2005 Mar 8; 5:10 | utf-8 | BMC Gastroenterol | 2,005 | 10.1186/1471-230X-5-10 | oa_comm |
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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-5-61574545610.1186/1471-230X-5-6Research ArticleA murine model of ulcerative colitis: induced with sinusitis-derived superantigen and food allergen Yang Ping-Chang [email protected] Chang-Sheng [email protected] Zi-Yuan [email protected] Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada2 Department of Otolaryngology, Shanxi Medical University, the First Hospital, Taiyuan, Shanxi, China3 Division of Gastroenterology, Department of Internal Medicine, Shanxi Medical University, the First Hospital, Taiyuan, Shanxi, China2005 3 3 2005 5 6 6 3 9 2004 3 3 2005 Copyright © 2005 Yang et al; licensee BioMed Central Ltd.2005Yang 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 etiology of ulcerative colitis (UC) is to be understood. The basic pathological feature of UC is intestinal chronic inflammation. Superantigen, such as Staphylococcus enterotoxin B (SEB), is reported to compromise intestinal barrier function by increasing epithelial permeability and initiate inflammation in the intestinal mucosa. Inasmuch as anatomic position of the sinus, chronic sinusitis-derived SEB may follow the secretion and to be swallowed down to the gastrointestinal tract and induce lesions to the intestinal mucosa.
Methods
Sinus wash fluid (SWF, containing SEB) was collected from a group of patients with both chronic sinusitis (CS) and UC. A group of mice were sensitized to ovalbumin (OVA) in the presence of SWF. The sensitized mice were challenged with the specific antigen OVA. The inflammatory status of the colonic tissue was determined with histology, serology and electron microscopy. Using horseradish peroxidase (HRP) as a tracer, another group of mice was stimulated with SWF for 2 hours. The HRP activity was detected in the colonic tissue with enzymatic approaches and electron microscopy.
Results
Epithelial hyperpermeability in colonic epithelium was induced by stimulating with SWF. The HRP activity in the colonic mucosa was almost 11 times more in the SWF treated group (3.2 ± 0.6 μg/g tissue) than the control group (0.3 ± 0.1 μg/g tissue). Mice were sensitized using a mixture of SWF and OVA (serum OVA-specific IgE was detected with a highest titer as 1:64). Challenge with OVA induced extensive inflammation in the colonic mucosa by showing (1) marked degranulation in mast cells (MC, 46.3 ± 4.5%) and eosinophils (Eo, 55.7 ± 4.2%); (2) inflammatory cell infiltration (MC = 145.2 ± 11.4; Eo = 215.8 ± 12.5; mononuclear cell = 258.4 ± 15.3/mm2 tissue); (3) increased MPO activity (12.9 ± 3.2 U/g tissue) and inflammatory scores (1.8 ± 0.3); (4) mucosal surface ulcers; (5) edema in the lamina propria; (6) bacterial translocation and abscess formation in the subepithelial region.
Conclusion
Introducing Sinusitis-derived SEB-containing SWF to the gastrointestinal tract compromised colonic mucosal barrier function increasing epithelial permeability to luminal macromolecular protein in mice. The SWF facilitated colonic mucosal sensitization to luminal antigen. Multiple challenging the sensitized colonic mucosa with specific antigen OVA induced inflammation, induced a condition similar to human ulcerative colitis.
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Background
Ulcerative colitis (UC) is a disease characterized by inflammation and ulcers in the mucosa of the large intestine with unknown etiology. The inflammation usually occurs in the rectum and lower part of the colon, but it may affect the entire colon. The inflammation speeds up colonic motility and causes diarrhea. Ulcers form in places where the inflammation has killed the cells lining the colon; the ulcers bleed and produce pus. Theories about what causes ulcerative colitis abound, but none have been proven. The most popular theory is that the body's immune system reacts to a virus or a bacterium causing ongoing inflammation in the intestinal mucosa; others include genetic predisposition, autoimmune disorders and impaired immune regulation [1-3].
over the past 15 years, more than 2000 patients with chronic sinusitis (CS) including some patients with both CS and UC visited our clinic and were treated with different remedies, including medical treatment and functional sinus endoscopic surgery (FESS). Apart from improvement of chronic sinusitis, those patients with both CS and UC showed great improvement of UC as well (data not shown) that couldn't be explained with the specific treatment alone. Therefore, we postulated that there might be an association between CS and UC in these patients. Microbial infection is the most common cause of CS. The microbial products, such as lipopolysaccharide (LPS), staphylococcus aureus enterotoxin B (SEB) [4,5] can be discharged into the nasal cavity through the natural ostia going backward to the pharynx, and then be swallowed, entering the gastrointestinal tract to affect mucosal physiological functions [6,7].
Staphylococcus enterotoxin B is an extracellular toxin produced by certain strains of Staphylococcus aureus (S. aureus) [8]. Many cases of food poisoning worldwide involve S. aureus enterotoxins [9]. In addition, enterotoxins can be found in cases of toxic shock syndrome [10] and have been implicated in the autoimmune disease rheumatoid arthritis [11]. SEB is synthesized as a precursor protein of 266 amino acids. This precursor is then activated during excretion by cleavage of the N-terminal portion of the molecule. The active enterotoxin B is a single 239 amino acid chain of molecular weight 28,000 daltons and isoelectric point of 8.6 [12]. SEB is a superantigen and possesses powerful immune regulatory capability that results in increased T cell activation and proliferation. SEB-treated Balb/c mice display a dose-dependent colonic inflammation [13]. SEB can also induce colonic epithelial barrier dysfunction [14] that may promote uptake of exogenous antigens, microbial products and other noxious substances into the intestinal tissue to contact immune cells and initiate inappropriate immune reactions.
The nasal cavity and sinus are primary sites of colonization by S. aureus, and a quantity of SEB was detected in the nasal cavity of the patients with allergic rhinitis [15,16]. Based on clinical observations, we hypothesized that sinusitis-derived SEB plays a certain role in the pathogenesis of chronic inflammation in the intestinal mucosa via impairing the epithelial barrier function and inducing inappropriate immune reactions. In this study, we aimed to investigate if (1) sinusitis-derived SEB increases intestinal permeability in the mice; (2) the presence of sinusitis-derived SEB facilitates intestinal sensitization to luminal antigen; (3) oral challenge with specific antigen induces intestinal allergic inflammation in the sensitized animals. Accordingly, we developed a murine model of ulcerative colitis with oral allergen in the presence of SEB-containing SWF. The animal model showed intestinal inflammation associated with intestinal mucosal eosinophilia, mastocytosis, acute diarrhea, bacterial translocation and micro-ulcer formation on the surface of the colonic mucosa.
Methods
Sinus-wash fluids collected from the patients with CS and UC
Sinus-wash fluids (SWF) were collected from 32 patients with both CS and UC (18 male and 14 female; aged from 26 to 58, with an average of 35.82). We diagnosed CS in the patients as an inflammation of the sinus mucosa with a persistent mucoid or mucopurulent nasal discharge for longer than 3 months that resisted antimicrobial therapy and antral irrigation. Diagnosis was made on the basis of clinical history, rhinoscopic findings, and computed tomographic scan of paranasal sinuses. CS was confirmed by computed tomographic examination that showed diffuse mucosal thickening in the ethmoid or/and maxillary sinuses bilaterally with scores higher than 12 by the Lund-Mackay staging system [17]. Twenty-five healthy medical students were enrolled in this study, their nasal wash fluids were used as controls.
The diagnosis of UC was based on clinical history, colonoscopy and histology. The history included persistent bloody diarrhea, rectal urgency, or tenesmus. Examinations and sigmoidoscopy and biopsy were performed to confirm the presence of colitis and to exclude the presence of infectious etiologies.
Every patient underwent maxillary sinus puncturing and washing. The SWF was collected prior to other procedures. Five ml saline was injected into the sinus cavities and re-collected and stored at -70°C for further use. SEB content in SWF was evaluated with ELISA (All the reagents used in this study were purchased from Sigma unless otherwise mentioned). None of the subjects had recent upper respiratory acute infections. This study was approved by the Ethical Committee of the First Hospital of Shanxi Medical University.
Animals
For the purpose of verifying our hypothesis of an association between sinus pathology and colitis, an animal model of ulcerative colitis was developed. Mice were sensitized to a model food antigen, ovalbumin (OVA) with or without the presence of SEB-containing SWF. Animal experiments were approved by Animal Use and Care Committee at Shanxi Medical University. Male Balb/c mice (10 week old) were purchased from Beijing Animal Research Institute and maintained in the animal center at Shanxi Medical University. All mice were housed according to guidelines of the animal center. Water was available continuously through automatic ports, and a commercial mouse diet was provided ad libitum.
Effect of SEB containing SWF on colonic epithelial permeability in the mice
A group of mice was introduced 0.2 ml SWF (containing SEB 50 μg and 10 mg horseradish peroxidase, HRP. SWF was concentrated with the method of ammonium sulfate precipitation for higher content of SEB) via intragastric gavage under a light anesthetization. Mice were killed by cervical dislocation 2 hours later (based on preliminary results; data not shown). Control groups were designed as: a naïve control group, treated with HRP 10 mg in 0.2 ml PBS via intragastric gavage; an inactivated-SWF control group, the SWF was pretreated with anti-SEB (100 μg in 0.2 ml) for 30 min, then introduced to the mice in gavage with HRP. Colon was removed; a piece of colon (3 cm) was opened; the contents were collected and dissolved in 1 ml PBS for HRP assay; the colon tissue was snap frozen and stored at -70°C for HRP assay; another piece of colon (2 × 2 × 4 mm) was fixed with 2.5% glutaraldehyde for 2 hours; then rinsed in sodium cacodylate buffer, incubated in 3,3'-diaminobenzidine tetrahydrochlorine and H2O2 (pH 7.6) for 30 min, and postfixed with 2% osmium tetroxide for 60 minutes, followed by staining en bloc with 4% uranyl acetate for 30 minutes. Tissue samples were dehydrated through a graded series of ethanol, cleared in propylene oxide, and embedded in Epoxy embedding medium. Thin sections were prepared and stained with 4% uranyl acetate for 5 min and subsequently with 2.5% lead citrate for 2 min and examined at 80 kV with a JEM 1200 electron microscope. HRP containing endosomes were photographed randomly for further analysis.
Frozen colon tissue was weighed and immersed into lysis buffer (20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM Na2EDTA, 1 mM EGTA, 1% Triton, 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na3VO4 (sodium ortho-vanadate), 1 μg/ml leupeptin) and homogenized on ice; the homogenates and colon content were centrifuged at 1,500 g for 20 minutes at 4°C respectively, the supernatants were collected for HRP assay. HRP amount in colon content and colon tissue was determined by assaying enzyme activity in the collected supernatant according to the previous report [18]. Briefly, a 0.2-ml sample of supernatant was mixed with 2.8 ml phosphate buffer (0.1 M, pH 6.0) containing 0.003% H2O2 and 0.025 ml of a solution of o-dianisidine di-HCl (10 mg/ml). The optical density was determined at 460 nm with a spectrophotometer.
Intestinal sensitization to luminal OVA with the presence of SEB-containing SWF
Ten mice were used for each group. A group of mice was sensitized by means of intragastric gavage with 50 μg OVA in 0.2 ml alum-SWF (the adjuvant, prepared with SWF instead of distilled water that contains 50 μg SEB). Another group was also treated with the same protocol, but the SWF in the adjuvant was pretreated with an anti-SEB antibody (100 μg in 0.2 ml). Control groups were designed as: mice were only exposed to OVA without the presence of SWF, or were only exposed to SWF without OVA; naïve controls were treated with 0.2 ml saline. Each mouse was injected with 50 ng pertussis toxin peritoneally.
Mice were challenged with specific antigen OVA
From the 14th day after sensitization, all mice were challenged with 50 mg OVA in 0.3 ml saline by means of intragastric gavage 3 times, 48 h apart. Diarrhea was determined by visually monitoring mice for up to 1 hour following intragastric challenge. Mice demonstrating profuse liquid stool were recorded as diarrhea-positive animals. Two days after the last challenge, mice were sacrificed by decapitation; blood samples were collected, serum was separated and stored at -70°C for further analysis. Colon was removed, one piece was fixed with 4% paraformaldehyde for histology; one piece was snap frozen for MPO analysis; one piece was fixed with Carnoy solution for mast cell count; one piece was fixed with 2% glutaraldehyde for electron microscopy.
Myeloperoxidase activity evaluation
Myeloperoxidase (MPO) activity was determined according to the method of Bradley et al [19]. Tissue samples were homogenized in hexadecyltrimethylammonium bromide buffer in a glass homogenizer on ice. The homogenates were centrifuged and MPO activity in the supernatants was determined. One unit of MPO activity was defined as the amount required to degrade 1 mM H2O2 in 1 minute at 25°C.
Colonic mucosal inflammatory score
The morphology of the epithelium, villi, and subepithelial layer of colon mucosa were assessed, and the numbers of eosinophils and mononuclear cells were counted in 10 randomly selected fields (magnification, ×400) for each mouse (100/group). To determine mast cell numbers, tissues were fixed in Carnoy's fixative and paraffin sections were stained with 0.5% toluidine blue. Mast cells were counted in 10 fields for each mouse (100/group). Cell numbers were expressed per mm2 of mucosa. All sections were coded to avoid observer bias. The degree of inflammation on microscopic tissue sections was scored as follows: (from 0 to 4, 0 indicates normal, 4 indicates severe condition): 0, no leukocyte infiltration; 1, low level of leukocyte infiltration; 2, moderate level of leukocyte infiltration; 3, high vascular density and thickening of the colon wall; and 4, transmural leukocyte infiltration, loss of goblet cells, high vascular density, and thickening of the colon wall. Grading was done in a blinded manner [20].
Observation of mast cell and eosinophil activation
Colon tissue was processed with routine procedures and observed with an electron microscope. Activation of mast cell and eosinophil in colonic mucosa was determined by the phenomenon of degranulation [21].
Serum specific IgE measurement
Passive cutaneous anaphylaxis (PCA) was employed to determine serum OVA-specific IgE level. The serum was diluted in phosphate-buffered saline (PBS) from 1:4 to 1:128. The mice were injected intradermally with 50 μl serum into each of 4 dorsal shaved skin sites. The sites were outlined with a water-insoluble red marker. Forty-eight hours later each mouse received an injection of 0.1 ml OVA (1 mg/ml) containing 4% Evans blue via the tail vein. The results were recorded thirty minutes after the challenge; the diameter of the blue spot on the injection sites larger than 6 mm was recorded as positive reaction.
Statistics
Data were expressed as mean ± SD; Student t test was used to compare the difference between groups. p < 0.05 was accepted as significant criteria.
Results
Staphylococcus enterotoxin B was detected in SWF from the patients with both CS and UC
The content of SEB was significantly higher in the SWF of patients with both UC and CS (from 30.5 to 565.6, with an average of 154.5 ± 81.7 pg/ml) than in the nasal wash fluids (from 0 to 18.6, with an average of 8.5 ± 4.3 pg/ml) of healthy control subjects.
Stimulation with SWF increased mouse colonic mucosal permeability to luminal macromolecular protein HRP
Horseradish peroxisase activity was detected in colonic content in both control mice (group of HRP-only and group of anti-SEB treated SWF) and SWF treated mice (Fig 1A). The HRP activity was significantly higher in the colonic tissue of the mice treated with SWF compared to controls (Fig 1B). Observation with EM revealed that few HRP endosomes of small size were observed in the upper region (above the nucleus) of cytoplasm in the colonic epithelium of control mice. Large size HRP endosomes were observed in both upper and lower (below the upper edge of the nucleus) regions of the mice treated SWF. Image analysis of HRP endosome showed that the HRP endosome area was significantly larger in the mice treated with SWF comparing with those control mice (Fig 2).
Figure 1 Mouse colonic epithelial permeability increased by stimulation of SWF. Bars stand for HRP activity in colonic content (Fig 1A) and colonic tissue (Fig 1B). Each group consists of 10 mice. *, p < 0.05, compared with controls.
Figure 2 HRP endosomes in the colonic mucosa. Representative EM photomicrographs were taken from colonic mucosa of control (Fig 2A) and sensitized mice (Fig 2B). Bars stand for HRP endosome area in 300 μm2 cell area (Fig 2C). *, p < 0.05, compared with controls.
Serum specific anti-OVA IgE was detected in the mice treated with SWF and luminal OVA
Passive cutaneous anaphylaxis (PCA) is the gold standard method to measure allergen-specific IgE antibody levels in mouse models of allergy. The present study demonstrated that only the group treated with both SWF and OVA showed positive results with the highest titer 1:64. No specific IgE was detected in the serum of mice in control groups.
Activation of mast cells and eosinophils in the colonic mucosa of the sensitized mice after challenging with specific antigen
Activation of mast cells and eosinophils was observed as degranulation of the granules in the cytoplasm. Piecemeal type degranulation was observed more often than the anaphylactic type in the mast cells in the colonic mucosa of the sensitized mice with EM. Crystal core degranulation and matrix degranulation were observed in eosinophils of the sensitized mice. Granules were categorized intact or degranulated for each mast cell or eosinophil. Each type granule was numerated with EM. After challenge with specific antigen OVA, degranulation was observed in both mast cells and eosinophils of the sensitized mice. The ratio of degranulation was much higher in the sensitized mice compared with controls (Fig 3).
Figure 3 Activation of mast cell and eosinophil in the colonic mucosa. Representative photomicrographs (10 for each mouse) were taken from colonic mucosa of naïve mice (Fig 3A, 3C) and sensitized mice (Fig 3B, 3D). Bars stand for ratio of degranulation that was calculated with the numbers of degranulated granules divided by the numbers of total granules of mast cells (Fig 3E) and eosinophils (Fig 3F). *, p < 0.05, compared with naïve controls.
Histopathology of colonic mucosa of the sensitized mice after challenge with specific antigen
Challenge with specific antigen OVA resulted in inflammatory cell infiltration in the lamina propria and subepithelial regions. The infiltrate consisted of mainly mast cells, eosinophils and mononuclear cells. Compared with control, the sensitized group showed significantly more inflammatory cell infiltration, most of them might be activated immune cells (Fig 4). Capillary or small vein dilation was observed in the subepithelial region of the intestinal mucosa after OVA challenge; profound edema in the tissue of the same area was frequently observed (Fig 6B, 6F).
Figure 4 Inflammatory cell infiltration in the colonic mucosa. Bars stand for numbers of mast cell (Fig 4A), eosinophil (Fig 4B) and mononuclear cell (Fig 4C). Cell numbers are expressed as cells/mm2 tissue. *, p < 0.05, compared with naïve controls.
Figure 6 Ultrapathology of the colonic mucosa of the sensitized mice after challenge with OVA. Representative EM photomicrographs are taken from the colonic mucosa of the sensitized mice after challenge with OVA and show (A) epithelial cell (epi) necrosis (EN) and dilation of the blood vessel (BV) in the subepithelial region (×3,000); (B) epithelium destruction, basement membrane (BM) hyperplasia and blood vessel dilation (×2,000); (C) bacteria (arrows) adhering to and penetrating the epithelial cells (×3,000); (D) abscess (Ab) formation in subepithelial region with a colony of bacteria (arrows) and a red blood cell (RBC) in it (×2,000); (E) micro-ulcer (empty arrow) formation on the surface of colonic mucosa with bacteria (arrow) adherence (×2,500); (F) edema and blood vessel (BV) dilation in the lamina propria (LP) (×2,500).
Inflammatory status in the colonic mucosa was assessed by colonic tissue MPO measurement, inflammatory cell infiltration and mucosal surface condition observation. MPO is a critical parameter that indicates activation of neutrophils. We observed a significant increase in MPO and inflammatory score in colonic mucosa after challenge with OVA in the mice treated with SWF and OVA (Fig 5).
Figure 5 MPO activity and inflammatory scores of the colonic mucosa. Colonic tissues show an increased MPO activity (A) and increased inflammatory scores (B) after challenge with OVA. *, p < 0.05, compared with naïve controls.
Electron microscopy revealed ultrapathology in the sensitized and OVA-challenged colonic mucosa that included: (i) epithelial cell destruction (Fig 6A, B); (ii) bacteria adherent to or penetrating the epithelial cells; (iii) bacteria translocated to the subepithelial region and abscess formation (Fig 6C, D); (iv) micro-ulcer formation on the surface of the colonic mucosa (Fig 6E); (v) edema in lamina propria (Fig 6F).
Challenge with specific antigen OVA induced diarrhea in the sensitized mice
Following the protocol of challenge with specific antigen OVA, the sensitized mice developed diarrhea 15–30 minutes after each challenge that lasted up to 1 hour. The number of diarrhea episodes (3 to 7 times) increased in parallel with the number of OVA challenges. No diarrhea was noted in control mice although those mice also received the same number of OVA challenges. Diarrhea was also noted by direct observation of the colon and cecum; the liquid stool observed following OVA challenge-induced diarrhea in the sensitized mice contrasts with the solid pellets seen in the distal colon of other mice in control groups.
Discussion
Our knowledge about the etiology of ulcerative colitis is still limited. Although some theories about its origins have been advanced, such as genetic predisposition, autoimmune disorders, infection, and so on [23,24], the precise pathogenesis needs to be further understood. In clinical practice, we noted a close association between CS and UC in some patients and their UC was significantly improved after having removed sinus pathology (data not shown). The results of animal experiments verified our speculation: superantigen SEB from sinusitis cooperated with ingested antigen to induce intestinal sensitization. Challenge with the obligate antigen initiated colonic mucosal inflammation as well as the clinical symptom diarrhea. Book DT et al [25] also noted the same phenomenon and suggested that IBD was more prevalent in those people with chronic sinusitis than in other populations.
Rhinosinuses are empty cavities lined with mucosa. The anatomic feature, only having a small ostium, makes them very easily to be blocked and subsequently infected. Infection with S. aureus in sinuses is frequently encountered [4,5]. Thus, chronically infected sinuses may be a source of SEB that is released to nasal cavity frequently. A mucus blanket on the surface of nasal mucosa naturally traps small particles from air and the secretions from sinuses and removes them subsequently. Since the direction of the locomotion of the mucus blanket is backward, people sometimes swallow the secretions into the gastrointestinal tract (e.g., during sleep).
There are many toxic substances in the secretions from chronic sinusitis. SEB is one that has been well characterized. The unique feature of SEB is that it can down regulate intestinal barrier function [6,13], activate T lymphocytes without the help from antigen presenting cells to activate T cells. Superantigens bind directly to MHC class II molecules and to a subset of T-cell receptor (TCR) Vβ chains [26,27]. Unlike conventional antigens, superantigens do not require processing by antigen-presenting cells to activate immune cells [31]. Administration of superantigen results in initial selective expansion of T cells that bear specific Vβ chains that recognize the superantigen, followed by their deletion [29]. Another unique feature of superantigen is that it mutes T suppression cell function and promotes Th1/Th2 skewing [30]. It primes an environment to develop sensitization in local tissue. The results in the present animal experiments are consistent with previous studies. Mice treated with SEB-containing SWF and OVA developed intestinal sensitization, but not in those mice treated with only OVA, or SEB-depleted SWF plus OVA. This finding demonstrates that SEB plays a crucial role in the sensitization of the intestinal mucosa to luminal antigen in these mice. Louini D et al [31] reported that SEB also directly sensitized skin and caused Th2 pattern inflammation in the local skin.
Intestinal epithelial cells form a barrier between the luminal contents and the subepithelial region. The barrier restricts substances to be absorbed. It only allows some small molecules such as water to pass it freely. Antigens are macromolecular proteins that are not allowed to be absorbed before being digested to small peptides or amino acids under normal physiological conditions. But in reality, intact antigens do pass the intestinal barrier to reach lamina propria to induce inappropriate immune reactions under certain circumstance. How antigens cross the intestinal epithelial barrier is still a mystery. By introducing both SWF and HRP to mouse gastrointestinal tract, intact HRP in the colonic tissue was increased nearly 11 times compared to control. The results implicate that superantigen SEB is one of the factors that facilitate antigens to be absorbed without destroying their antigenicity. Lu et al also reported that SEB significantly increased colonic mucosal permeability in a mouse study [13].
We have begun to appreciate that food allergy plays a role in the inflammation of intestinal mucosa [32]. The results of this animal model support that inappropriate immune reactions initiate intestinal inflammation. Simply delivering OVA to gastrointestinal tract did not show sensitization in the mice while the combination of SEB-containing SWF and OVA induced sensitization in the colonic mucosa. Based on these data, we suggest that SEB facilitate sensitization of the intestinal mucosa to OVA. The mechanism behind this phenomenon might be that SEB increases permeability of the intestinal mucosa [6,13]. Thus OVA in the intestinal lumen can be transported to deep region of the mucosa. This exogenous protein then contacts the local immune cells to initiate inappropriate immune reactions and sensitizes the mucosa subsequently. The results of challenge with OVA show extensive inflammation in the colonic mucosa in this study. The inflammatory changes may be a result of local mast cell degranulation in response to OVA challenge. Mast cells release chemical mediators such as histamine that is able to increase vascular permeability and to induce edema in the tissue that was noted in the present study and also reported elsewhere [33].
In chronic allergic diseases such as asthma, during continuous antigen exposure, eosinophils are primed by IL-5 and attracted by chemokines, infiltrating the local tissue [34]. We also observed extensive eosinophil infiltration in the colonic tissue after three challenges with specific antigen OVA in the present study. These eosinophils are believed to be responsible for the late phase of the allergic reaction, producing the major basic protein which is toxic to the epithelium [34]. The micro-ulcers on the surface of colonic mucosa may be caused by eosinophil activation. Supportive evidence acquired from EM observation demonstrates that most eosinophils have been activated by showing extensive degranulation. We also noted this phenomenon in the jejunal mucosa in the late phase reaction in a rat model of food allergy [32].
Marked mast cell hyperplasia in the colonic tissue was observed in this study. In general, mast cell numbers in tissues are relatively constant, even though mast cell hyperplasia is observed in both the inflammatory and in repair/remodeling stage of various inflammatory disorders [35]. The functional significance of the accumulation of mast cells in these processes is largely unknown. In allergy, apart from their classical role in eliciting the early phase, mast cells also have an important role in late and chronic stages as we observed in a previous study [32]. In these stages they may interact with and be activated by infiltrated inflammatory cells and by resident structural cells such as epithelial cells, smooth muscle cells and fibroblasts. In the case of allergic reaction, mast cells are mainly activated by the mechanism of IgE mediated FcεRI bridging that accounts for the mast cell activation in the present study. The roles of mast cells in the late phase reactions may be amplified by eosinophils, platelets and neutrophils [36]. If a sensitized patient frequently ingests an obligate antigen unconsciously whereas the allergic reaction only reaches subclinical level, an early inflammation may progress without being noticed until reaching the advanced stage. The data also show that the degranulation type of mast cells and eosinophils in this study is mainly piecemeal. It indicates that the nature of the degranulation type belongs to a chronic process [37]. The local inflammation may progress to chronic status in the tissue without any further medical intervention.
Striking epithelial damage of colonic mucosa of the sensitized mice after challenge with OVA was observed in this study. This phenomenon has been well-documented in airway allergy [38]. Although ulcers are one of the main clinical signs in inflammatory bowel diseases, the etiology is not clear. There have not been many studies considering an association between the ulcers and allergic reactions in the gastrointestinal tract. Eosinophil released major basic protein is suggested to be the major offender to cause epithelial cell exfoliation in the airway mucosa [38]. The erosion and prominent damage to the epithelial barrier can explain the phenomenon of bacterial adhering to and penetrating to colonic mucosa in this study. These bacteria are commensal bacteria. They usually are not considered pathogenic. The damaged epithelium may provide an entry port for the colonizing bacteria to invade the colonic mucosa and to establish an infection.
Conclusion
In summary, we reported a murine model of ulcerative colitis in this paper that was induced with sinusitis-derived SEB and OVA sensitization followed by repeat challenges with specific antigen OVA. The histopathology of the colonic mucosa included inflammatory cell infiltration, activation of mast cell/eosinophil, epithelial barrier damage, micro-ulcer formation on the surface of colonic mucosa, bacteria translocation and abscesses formation in the subepithelial region.
List of abbreviations
UC, ulcerative colitis; CS, chronic sinusitis; FESS: functional endoscopic sinus surgery; OVA, ovalbumin; SEB, Staphylococcus enterotoxin B; MPO, myeloperoxidase; HRP, horseradish peroxidase.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PCY was involved in study design, histology, EM observation, data analysis and manuscript preparation; CSW and AZY were involved in SWF collection and animal model.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by a grant of National Foundation of Natural Science of China (9200412, to PC Yang and CS Wang)
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-2-191576637910.1186/1742-4690-2-19Short ReportFunctional characterization of two newly identified Human Endogenous Retrovirus coding envelope genes Blaise Sandra [email protected] Parseval Nathalie [email protected] Thierry [email protected] Unité des Rétrovirus Endogènes et Eléments Rétroïdes des Eucaryotes Supérieurs, UMR 8122 CNRS, Institut Gustave Roussy, 39 rue Camille Desmoulins, 94805 Villejuif Cedex, France2 Unité de Biologie des Rétrovirus, Département de Virologie, Institut Pasteur, 25 rue du Dr Roux, 75724 Paris cedex 15, France2005 14 3 2005 2 19 19 27 1 2005 14 3 2005 Copyright © 2005 Blaise et al; licensee BioMed Central Ltd.2005Blaise 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.
A recent in silico search for coding sequences of retroviral origin present in the human genome has unraveled two new envelope genes that add to the 16 genes previously identified. A systematic search among the latter for a fusogenic activity had led to the identification of two bona fide genes, named syncytin-1 and syncytin-2, most probably co-opted by primate genomes for a placental function related to the formation of the syncytiotrophoblast by cell-cell fusion. Here, we show that one of the newly identified envelope gene, named envP(b), is fusogenic in an ex vivo assay, but that its expression – as quantified by real-time RT-PCR on a large panel of human tissues – is ubiquitous, albeit with a rather low value in most tissues. Conversely, the second envelope gene, named envV, discloses a placenta-specific expression, but is not fusogenic in any of the cells tested. Altogether, these results suggest that at least one of these env genes may play a role in placentation, but most probably through a process different from that of the two previously identified syncytins.
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Findings
Endogenous retroviral sequences represent approximately 8% of the human genome. These sequences (called HERVs for Human Endogenous Retroviruses) share strong similarities with present-day retroviruses, and are the proviral remnants of ancestral germ-line infections by active retroviruses, which have thereafter been transmitted in a Mendelian manner (reviewed in [1-3]). The 30,000 HERV elements have been grouped according to sequence homologies into more than 80 distinct families (each originating from the same founder element), based on a systematic listing of human repeats in the Repbase database [4]. Most of these elements are non-coding due to the accumulation of mutations, deletions, and/or truncations. A screening of the human genome for retroviral envelope genes with coding capacity, based on a specific envelope protein motif and on the HERV families described in Repbase, has revealed 16 fully coding envelope genes, transcribed in several healthy tissues [5,6], among which two (syncytin-1 and syncytin-2) possess a fusogenic activity [7,8]. Using another approach, based on BLAST searches with various retroviral sequences as queries, a recent elegant study has analyzed the coding potential of human retroviral sequences and two additional fully coding envelope genes have emerged from this screen [9]. These two envelope genes do not belong to the HERV families listed in Repbase. The first one was designated "HERV-W/FRD-like" env, due to partial homology with syncytin-1 and syncytin-2, encoded by proviruses of the HERV-W and HERV-FRD families, respectively [7,8]. The second one was designated "ZFERV-like" env, due to its homology with the envelope protein encoded by a provirus recently discovered in the zebrafish genome [10]. The sequences and predicted hydrophobic profiles of the two proteins (renamed here EnvV and EnvP(b) respectively, see below), disclose the characteristic signature of retroviral envelope proteins, with a putative proteolytic cleavage site between the SUrface (SU) and TransMembrane (TM) moieties, and a hydrophobic transmembrane domain within the TM subunit which permits its anchorage to the membrane (Figure 1A).
Figure 1 A) Hydrophobicity profile and predicted features of the EnvV (formerly W/FRD-like env) and EnvP(b) (formerly ZFERV-like env) proteins. The SU (Surface Unit) and TM (TransMembrane) moieties of the envelopes are delineated, with the position of the putative proteolytic cleavage site (consensus, R/K-X-R/K-R) between the two subunits and the « CWLC » motif (consensus, C-X-X-C) indicated. The hydrophobic regions associated with the fusion peptide and the transmembrane region are shaded in light gray, and the putative immunosuppressive domain (ISU) in dark gray. B) Phylogenetic tree of retroviral envelopes and position of the newly identified genes. The tree is based on an alignment of approximately 180 amino acids corresponding to the extracellular and transmembrane domains of the TM subunit of envelope proteins. The protein alignment, phylogenetic tree and bootstrap analysis were performed with the ClustalW program (neighbour joining option). The tree was viewed by using the TreeView program. The scale bar indicates 10% aa sequence difference. The phylogenetic tree determined by the parsimony method was congruent with the neighbour joining tree (data not shown). The two "new" V and P(b) env genes are represented in red, ERV env genes from other species and exogenous retroviruses in blue. The sequences used for the alignments were those of the consensus element of each family, or the coding env gene when present. The consensus sequences of the HERVK(HML-9), HERVFXA21B1 and HERVFXA21B2 families, which are not listed in Repbase, were each inferred from the comparison of 3–6 sequences. Abbreviations: MoMLV, Moloney Murine Leukemia Virus; FeLVA, Feline Leukemia Virus strain A; PERVC, Pig Endogenous Retrovirus strain C; GALV, Gibbon Ape Leukemia Virus; MPMV, Mazon-Pfizer Monkey Virus; MMTV, Mouse Mammary Tumor Virus; JSRV, Jaaksiekte Sheep Retrovirus; HTLV, Human T cell leukemia Virus; BLV, Bovine Leukemia Virus; HIV, Human Immunodeficiency Virus. C) Genomic organization of the envV and envP(b) loci. The envelope ORF (open box) with gag- and pol- related sequences (hatched boxes) and long terminal repeats (black boxes), Alu (dark gray boxes) and MER51B (light gray boxes) retroelements are indicated. Consensus PBS sequences (obtained from two sequences for the HERV-V family and from four sequences for the HERV-P(b) family) are indicated above the corresponding provirus, together with the PBS for the Val and Pro-tRNA, respectively. D) envV and envP(b) mRNA expression in a panel of 19 healthy human tissues, as determined by real-time quantitative RT-PCR. RNAs from human tissues were prepared as described in [6]. The reaction was performed using Sybr Green Master Mix (Applied Biosystems). PCR was developed using an ABI PRISM 7000 sequence detection system. Primer sequences (5'-3') were as follows: (CATGACTTTGGAAAAGGAGG) and (GCCAAAGAGGAAAAGTAAGAGT) for envV; (CAAGATTGGGTCCCCTCAC) and (CCTATGGGGTCTTTCCCTC) for envP(b). The transcript levels were normalized relative to the amount of 18S mRNA (as determined with the primers and TaqMan probe from Applied Biosystems). Samples were assayed in duplicate. PBL, peripheral blood lymphocytes. E) Assay for fusogenicity of envV and envP(b). XhoI containing primer sequences (5'-3') were as follows: (ATCACCTCGAGACACTCCATCGAACCACTTCAT) and (ATCACCTCGAGGGCTGTTCTAGGATGGGTTATT) for envV; (ATCACCTCGAGAGAAGAGAAACTTGAACCGTCC) and (ATCACCTCGAGGGGCTGATAGATGAATGGGTAT) for envP(b). The PCR products were cloned into the phCMV-G vector, opened with XhoI, and the constructs were verified by partial sequencing. Cell lines and fusion assays are as described in [12], except for the SH-SY5Y neuroblastoma cell line (ATCC number CRL-2266).
Since these genes belong to previously uncharacterized HERV families, we first analyzed their phylogenetic relationship with known HERV families and animal retroviruses. We generated a phylogenetic tree of endogenous and exogenous retroviruses based on the env gene, namely on the alignment of a conserved domain of the transmembrane (TM) subunit [3,5]. In this tree (Figure 1B), the "HERV-W/FRD-like" env gene is closely related to that of MER66, MER84 and Z69907 families. This gene seems to be part of a very degenerate proviral structure, with only the LTR being identifiable (see below and Figure 1C). As mentioned in [9], a highly homologous gene (95.7% identity at the nucleotide level) encoding an envelope protein truncated due to a frameshift can be found 40 kb downstream. This cognate env gene is unambiguously part of a proviral structure, displaying just upstream of it the 1.6 kb open reading frame of a gag gene, followed by a pol-like non coding region (data not shown. The flanking sequences of both proviruses are distinct. No other provirus or env gene belonging to this "family" can be found in the human genome by a BLAST search on the Ensembl database. Approximately 4 kb upstream of each of these two env genes, as expected, the RepeatMasker program that screens DNA sequences for interspersed repeats present in mammalian genomes identifies 5' LTR sequences (or fragments of LTR sequences). 3' LTRs are also found just downstream of the envelope genes (see Figure 1B for the map of the fully coding env gene locus). The analysis of the PBS (Primer Binding Site) region located downstream of the two 5' LTRs of this family reveals a high degree of homology to the PBS for Val-tRNA (Figure 1C), so we propose to name this new family HERV-V.
The "ZFERV-like" env gene clusters, in the TM-based tree, with the "HERV-I superfamily", which indeed also includes the ZFERV env from zebrafish (see Figure 1B). As indicated in the retrosearch database , this envelope gene is part of an identifiable provirus (see Figure 1C). A BLAST query on the Ensembl database using the provirus sequence showed that this new HERV family contains three additional members. All four HERV elements, harbouring a proviral LTR-gag-pol-env-LTR structure (although the only coding gene is the env gene described in [9]), are close to – but yet unambiguously distinct from – the HERV-IP family. The analysis of the PBS region of these four proviruses reveals a high degree of homology to the PBS for Pro-tRNA (see Figure 1C), so we propose to name this new family HERV-P(b) (since the HERV-P family already exists, [11]).
To determine whether these two genes could play a role in human placentation, we then characterized their expression pattern and fusogenic properties, as previously performed for the 16 coding envelope genes already identified [6,8]. To get insight into their expression profile, we used a Real-Time RT-PCR strategy as described in [6]. In this study, specific primers had been designed for Sybr Green amplification in such a way that only env genes with an open reading frame would be amplified among all the envelope genes of a given family, by positioning them within domains of maximal divergence between the coding and the non-coding copies. For the HERV-V coding envelope, the primer pair was designed in the 3' part of the gene, where the two envV genes are the most divergent (79% identity in the last 200 nt). An additional primer pair was also designed to monitore the expression of the truncated HERV-V env gene. To assess the specificity of each primer pair for the corresponding env gene, the PCR products obtained upon amplification of genomic DNA were cloned into a pGEM-T vector and 6 clones per amplicon were sequenced. In each case, the 6 sequences corresponded to the expected env gene. Analysis of the expression level of the coding envP(b) and envV genes was achieved on a series of 19 healthy human tissues, and the results are represented in Figure 1D. The expression pattern of envV was found to be placenta-specific. Interestingly, the truncated envelope of the HERV-V family is highly expressed in the placenta as well, but poorly in other tissues (data not shown). EnvP(b) expression, on the other hand, was observed at a rather low level in almost all the tissues tested, without any specificity for the placenta.
Among the 16 coding env genes of the human genome tested in [8], only two, namely envW (syncytin-1) and envFRD (syncytin-2), had been found to be fusogenic in an ex vivo assay. As these two env genes were highly and specifically expressed in the placenta, it was suggested that they are involved in a major physiological process within this organ, namely fusion of the cytotrophoblast cells to form the syncytiotrophoblast layer. The two newly identified env genes were therefore similarly tested. To do so, they were first cloned and introduced into a eukaryotic expression vector. The envP(b) gene was PCR-amplified from the DNA of BAC RP11-828K24 by using a proofreading DNA polymerase and running a 15-cycle PCR reaction, whereas the envV gene -not available as BAC DNA- was PCR amplified from the genomic DNA of a Caucasian individual using the Expand long template enzyme mix (Roche Applied Science). Both env genes were then assayed for cell-cell fusion on a large panel of mammalian cells (known to express on the whole the receptors for all retroviral envelopes identified to date) using a transient transfection assay and two clones from each construct. As shown in Figure 1E, cell-cell fusion was observed in five out of nine cell lines tested for envP(b), and in none of them for envV. The truncated envelope protein member of the HERV-V family was also tested and, as expected, was not fusogenic (data not shown). In some respect, these results are surprising. Indeed, the putative protein encoded by envP(b) is fusogenic despite the absence of a canonical fusion peptide, i.e. of a hydrophobic region located at the N-terminus of the putative TM subunit, just downstream of the SU-TM cleavage site (see Figure 1A). Conversely, the envV gene product, notwithstanding its canonical sequence, is not fusogenic (at least in the panel of cells tested). To check that the lack of fusogenicity of the latter gene is not due to a fortuitous gene polymorphism of the envV gene from the selected individual, we PCR-amplified, cloned and assayed the envV gene from two other individuals (for both the complete and the truncated envV genes): no cell-cell fusion was observed either (data not shown). Finally, we identified and cloned the chimpanzee orthologous envV gene (which is fully coding as well): neither did it display any fusogenic activity in our assay (data not shown).
In conclusion, the present analysis shows, rather paradoxically, that the envelope protein with fusogenic properties is not placenta-specific, whereas the one which is exclusively expressed in the placenta -a characteristic pattern of the two previously described fusogenic syncytin-1 and syncytin-2 gene products- is not fusogenic. In this respect, these results suggest that the two newly identified envV and envP(b) genes are most probably not "syncytin-like" genes, sensu stricto. Additional experiments should now be devised (e.g. search for conservation among primates, search for Single Nucleotide Polymorphisms) to assess their role -if any- in human physiology.
List of abbreviations
HERV, human endogenous retrovirus; TM, transmembrane; LTR, Long Terminal Repeat; PBS, Primer Binding Site.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
SB carried out the cloning of the env genes and the cell-cell fusion assays.
NdP analyzed the sequences, constructed the phylogenetic tree, designed and carried out the Real-Time RT-PCR experiments, and drafted the manuscript.
TH conceived the study.
Acknowledgements
This work was supported by the CNRS and by grants from the Ligue Nationale contre le Cancer (Equipe Labellisée). We thank Christian Lavialle for critical reading of the manuscript.
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| 15766379 | PMC555746 | CC BY | 2021-01-04 16:36:40 | no | Retrovirology. 2005 Mar 14; 2:19 | utf-8 | Retrovirology | 2,005 | 10.1186/1742-4690-2-19 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-5-271575532910.1186/1471-2407-5-27Research ArticleDifferences between men with screening-detected versus clinically diagnosed prostate cancers in the USA Hoffman Richard M [email protected] S Noell [email protected] David [email protected] Arnold L [email protected] Medicine Service, New Mexico VA Health Care System, Albuquerque, New Mexico, USA2 New Mexico Tumor Registry, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA3 Non-communicable Disease Epidemiology Unit, London School of Hygiene and Tropical Medicine, London, UK4 CDC Division of Cancer Prevention and Control and Indian Health Service National Epidemiology Program, Albuquerque, New Mexico, USA5 Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA2005 8 3 2005 5 27 27 4 10 2004 8 3 2005 Copyright © 2005 Hoffman 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 advent of prostate specific antigen (PSA) testing in the United States of America (USA) has led to a dramatic increase in the incidence of prostate cancer in the United States as well as the number of men undergoing aggressive treatment with radical prostatectomy and radiation therapy. We compared patient characteristics and treatment selection between American men with screening-detected versus clinically diagnosed prostate cancers.
Methods
We evaluated 3,173 men with prostate cancer in the USA. Surveys and medical records provided information on demographics, socioeconomic status, comorbidities, symptoms, tumor characteristics, and treatment. We classified men presenting with symptoms of advanced cancer – bone pain, weight loss, or hematuria – as "clinically diagnosed"; asymptomatic men and those with only lower urinary tract symptoms were considered "screening-detected." We used multivariate analyses to determine whether screening predicted receiving aggressive treatment for a clinically localized cancer.
Results
We classified 11% of cancers as being clinically diagnosed. Men with screening-detected cancers were more often non-Hispanic white (77% vs. 65%, P < 0.01), younger (36% < 65 years vs. 25%, P ≤ 0.01), better educated (80% ≥ high school vs. 67%, P < 0.01), healthier (18% excellent health vs. 10%, P < 0.01), and diagnosed with localized disease (90% vs. 75%, P < 0.01). Men with screening-detected localized cancers more often underwent aggressive treatment, 76% vs. 70%, P = 0.05.
Conclusion
Most cancers were detected by screening in this American cohort. Appropriately, younger, healthier men were more likely to be diagnosed by screening. Minority status and lower socio-economic status appeared to be screening barriers. Screening detected earlier-stage cancers and was associated with receiving aggressive treatment.
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Background
Prostate-specific antigen (PSA) testing was introduced in the United States of America (USA) in the late 1980s with Federal Drug Administration (FDA) approval for prostate cancer surveillance [1]. However, the test indications were soon expanded to include prostate cancer screening. By the early 1990s, the American Urologic Association and the American Cancer Society were recommending PSA testing, along with digital rectal examination (DRE), as part of annual prostate cancer screening [2,3]. The advent of PSA testing led to a dramatic increase in the incidence of prostate cancer in the USA, with the number of new cases rising from 152,811 in 1990 to over 230,000 in 1992 [4,5]. During the past decade, the number of American men undergoing aggressive treatment with radical prostatectomy and radiation therapy also increased substantially [6,7].
Urologic screening studies provide the most comprehensive information about the men undergoing PSA screening [8-10]. Several trials have taken place in both Europe and the USA. In general, study subjects usually were recruited through advertisements and they were screened with combinations of PSA, DRE, and transrectal ultrasound. The average age of these study participants was in the mid-60s, and minority subjects were not well represented. Minimal data were provided on symptoms, comorbidity, or socioeconomic status. Among American men diagnosed with clinically localized prostate cancers, approximately 90% underwent treatment with radical prostatectomy or radiation therapy.
Population-based data on PSA screening are largely unavailable, including information on the proportion of prostate cancers diagnosed by screening, the demographic, socioeconomic, and clinical characteristics of men with screening-detected cancers, and the association of screening detection with treatment decisions. We used data from the United States-based Prostate Cancer Outcomes Study (PCOS) to 1) determine the proportion of screening-detected prostate cancers in a population-based cohort, 2) compare baseline demographic, socioeconomic, and clinical characteristics between men with screening-detected versus clinically diagnosed cancers, and 3) determine whether men with screening-detected clinically-localized prostate cancers were more likely to undergo aggressive treatment (radical prostatectomy or radiation therapy).
Methods
Study population
The American National Cancer Institute instituted the PCOS in 1994 to measure practice patterns and health-related quality of life among men diagnosed with prostate cancer in the United States. Methods for this multi-site, longitudinal project are described elsewhere [11]. Briefly, PCOS subjects were men histologically diagnosed with prostate cancer between October 1, 1994 and October 31, 1995. Subjects were identified using a rapid case ascertainment system by the six participating National Cancer Institute Surveillance, Epidemiology and End Results (SEER) cancer registries (Atlanta, Georgia metropolitan area; Los Angeles County California; King County, Washington; Connecticut; Utah; and New Mexico). Eligible subjects were residents of the areas covered by these registries at the time of diagnosis and were between the ages of 39 and 89 years, except in King County, where only men over 60 years were eligible. The institutional review board of each participating institution approved the study.
Eligible patients were sampled within strata of age, race/ethnicity, and tumor registry to approximate a sample representative of the United States population of prostate cancer patients. The PCOS oversampled younger men and minorities and excluded patients with race/ethnicity other than non-Hispanic white, African American, or Hispanic, because their sample sizes were small.
A total of 11,137 men with prostate cancer comprised the eligible patient population for the study and the PCOS randomly selected 5,672 of these men. Among these selected patients, 3173 (55.9%) completed a health-related quality-of-life survey questionnaire 6 months after initial diagnosis. We used survey and medical record data collected from these subjects to evaluate differences in patient characteristics and treatments between men with screening-detected cancers and those who were diagnosed clinically. Responders to the PCOS survey were younger than non-responders and more likely to be non-Hispanic white and have a higher socioeconomic status. A substantial proportion of the responders had regional stage and moderately differentiated cancers, while non-responders had a greater proportion of distant stage and poorly differentiated cancers. Responders also were more likely to receive radical prostatectomy [11].
Data collection
Investigators contacted eligible subjects by mail and/or telephone requesting them to sign a release form allowing review of all medical records from any physicians and facilities diagnosing and/or providing care for prostate cancer. Records were obtained from private and public hospitals, freestanding radiological or surgical centers, Veterans Administration hospitals, Health Maintenance Organizations, and private physician offices. Certified Tumor Registry abstractors collected baseline information on demographics, clinical symptoms before diagnosis (systemic and urinary), comorbidity, diagnostic procedures and results (including PSA levels and digital rectal examination findings), clinical staging, tumor characteristics, and treatment details. The PCOS re-abstracted a random sample of 5% of records to assess and correct any systematic coding errors.
The PCOS also collected data on general and disease-specific measures of health-related quality of life, symptoms, comorbidity, and specific treatments received for prostate cancer using a mailed self-administered questionnaire. Most respondents completed the self-administered questionnaire (91%); those who did not return the questionnaire were contacted by telephone and asked to complete the survey by telephone or in person. Subjects were asked to recall their health-related quality of life and symptoms, including the domains of urinary, bowel, and sexual function, just before their prostate cancer was diagnosed. Demographic and socioeconomic questions from this survey were used to determine race/ethnicity, employment status, educational level, household income, insurance status, and marital status. A question assessing comorbidity asked about 12 medical conditions that were likely to affect prostate cancer treatment decisions and long-term quality of life. The conditions were derived from the Charlson index as well as the expert opinion of the PCOS investigators [12]. If the patient reported being told by a doctor that he had cerebrovascular disease, inflammatory bowel disease, liver disease, or ulcers, he received one point on his comorbidity score for each condition. If the patient reported that any of eight conditions – arthritis, diabetes, depression, hypertension, chest pain, heart attack, heart failure, or chronic lung disease – limited his activity or required prescription medications, he received 1 additional point for each of these conditions. In the analyses, comorbidity scores were divided into the categories of 0, 1, 2, and greater than or equal to 3 points.
We assigned screening status using information from the medical record abstract and the patient questionnaire. We considered men presenting with symptoms consistent with advanced prostate cancer, including bone pain, weight loss or hematuria, to be "clinically diagnosed." We initially created separate categories for men with only irritative or obstructive symptoms consistent with benign prostatic hyperplasia and an asymptomatic group who had neither prostate cancer nor lower urinary tract symptoms.
Clinical cancer stage was based on an algorithm using information abstracted from medical records. The algorithm was necessary because the community-based medical records were not detailed enough to classify cases by TNM (tumor, node, metastases) staging [13]. The algorithm defined T1 tumors as confined to the prostate with a normal digital rectal examination and no positive scans (magnetic resonance imaging, computed tomography, bone scan) or evidence of metastases. T2 tumors were defined as confined to the prostate, with abnormal or suspicious digital rectal examinations, but no positive scans or evidence of metastases. We defined clinically localized cancers as either T1 or T2 tumors. Initial treatment, based on medical record abstractions, was defined as treatment received within the first six months after diagnosis. We defined aggressive treatment as either radical prostatectomy or radiation therapy. We defined conservative management as androgen deprivation, either surgical or chemical, or watchful waiting.
Statistical analyses
Descriptive statistics were calculated for ethnicity/race, age, stage at diagnosis, education, marital status, employment, income, digital rectal exam and PSA results, Gleason score from biopsy or transurethral resection of the prostate, comorbid conditions and self-reported general health status. We used contingency tables to compare men presenting without any symptoms, those with lower urinary tract symptoms alone, and those with prostate cancer symptoms. Although screening is defined as applying a diagnostic test to asymptomatic people [14], the prevalence of benign prostatic hyperplasia is very high among men at risk for prostate cancer [15]. We found that the men with only lower urinary tract symptoms were much more similar to asymptomatic men than to men we classified as having clinically diagnosed cancers. Therefore, we also considered cancers diagnosed in men who reported only lower urinary tract symptoms at the time of PSA testing to be "screening-detected." We used this combined screening-detected group to compare baseline characteristics against clinically diagnosed cases and in modeling treatment selection for clinically localized cancers. Logistic regression analyses were used to determine whether screening history was independently associated with selecting aggressive treatment versus conservative management among men with clinically localized prostate cancer. Covariates for this multivariate model, based on previous literature, included age, race/ethnicity, marital status, study site, education, insurance status, annual income, comorbidity, health status, and tumor characteristics [16,17]. We also examined interactions between screening status with age, comorbidity, PSA level, and Gleason score.
The results of the logistic regression models are shown as percentages receiving the treatment of interest, adjusting for the independent variables included in the model. These percentages were directly adjusted to the distribution of the variables among the weighted sample used in each model [18]. The probability of receiving the treatment of interest can then be directly compared across levels of the variables included in the model.
All analyses were performed with the Survey Data Analysis statistical package (Research Trial Institute, Research Triangle Park, North Carolina, 1997) to account for the complex survey design. We obtained unbiased estimates of parameters for all eligible prostate cancer patients in the PCOS areas by using the Horvitz-Thompson weight, which is the inverse of the sampling proportion for each sampling stratum (defined by age, race/ethnicity, and study area). A two-tailed P-value of < .05 was considered statistically significant.
Results
The baseline demographic, socioeconomic, and clinical characteristics of the PCOS subjects are shown in Tables 1 and 2. The majority of subjects were non-Hispanic white men, older than sixty-five, and married at the time of diagnosis. Socioeconomic status was relatively high; a majority had more than a high school education, and a substantial proportion of subjects had private insurance. Among the study subjects, 10.7% presented with symptoms consistent with prostate cancer and were considered to be clinically diagnosed cases. Nearly two-thirds of subjects had lower urinary tract symptoms, while 30.9% were completely asymptomatic. Overall, 83.1% of men rated their general health at "good" or "excellent" before their cancer diagnosis.
We compared baseline characteristics of asymptomatic men, those with lower urinary tract symptoms alone, and men with clinically diagnosed cancers in Table 3. We found that the characteristics of men with lower urinary tract symptoms alone were closer to the asymptomatic men than to the clinically diagnosed cancer cases for race/ethnicity, socioeconomic status, health status, and cancer grade and stage. When we combined these two groups into a single category of screening-detected cases, we found significant differences between the screening-detected and clinically diagnosed cases. Men with screening-detected cancers were more likely to be non-Hispanic white, were younger age, and had a higher socioeconomic status. They also reported being healthier and were more likely to have early stage disease.
We then evaluated whether screening status independently predicted receiving aggressive treatment among the 2796 men who were diagnosed with clinically localized cancer. The primary treatment for these men was radical prostatectomy for 1535 (53.4%), while 518 (20.6%) underwent radiation therapy, 671 (26.0%) were treated conservatively; we had no treatment information for 72 subjects (2.5%). The results of the multivariate analysis are shown in Table 4. After adjusting for age, race/ethnicity, marital status, area of the country, education, insurance coverage, annual income, comorbidity, self-reported health status, and tumor characteristics, we found that men with screening-detected cancers were more likely to receive aggressive treatment. The adjusted percentage of men with screening-detected cancers undergoing aggressive treatment was 76% (95% CI 0.74, 0.78) vs. 70% (95% CI 0.64, 0.76), in men with clinically diagnosed cancers, OR = 1.5 (95% CI 1.1, 2.3), P = 0.05. Other factors that were significantly associated with aggressive treatment included geographic area, ethnicity, age, marital status, comorbidity, health status, and tumor characteristics. We found no significant interactions for treatment selection between screening status with age, comorbidity, PSA level, or Gleason score.
Discussion
We found that the majority of cancers (89.3%) in a population-based PCOS cohort were detected by screening. Compared to men with clinically diagnosed prostate cancer, men with screening-detected cancers were younger, more likely to be married, less likely to be a member of a minority group, and in better health. The cancers detected by screening were more likely to be clinically localized and less likely to be poorly differentiated. Among men with clinically localized prostate cancers, those with screening-detected cancers were significantly more likely to undergo aggressive treatment, even after adjusting for demographics, comorbidity, and tumor characteristics
Our finding that a high proportion of prostate cancers diagnosed in 1994 and 1995 were detected by screening is consistent with the temporal correlation between the increased use of PSA testing and the increased incidence of prostate cancer in the USA beginning during the early 1990s [4,5]. Although prostate cancer incidence rates decreased for several years in the mid 1990s, more recent data show that incidence rates are again increasing [5,19,20] and survey results from the Centers for Disease Control's Behavioural Risk Factor Surveillance System (BRFSS) show that a high proportion of American men continue to undergo PSA testing [20]. These data suggest that our findings are still relevant for prostate cancers being diagnosed in the USA. We also found that men with screening detected cancers were more likely to have early stage cancers, again mirroring the epidemiologic data showing an increased incidence of early stage cancers and a decreased incidence of advanced stage cancers [4,5]. The majority of screening-detected tumors were moderately to poorly differentiated; however, a significantly higher proportion of clinically diagnosed cancers were poorly differentiated.
Previous data, including an analysis of the PCOS cohort, have shown African Americans to be twice as likely as non-Hispanic whites to present with advanced stage cancers [4,5,21]. In the current analysis, we found a greater prevalence of ethnic/racial minorities in the clinically diagnosed versus screening-detected cancers. This disparity may reflect ethnic/racial differences in accessing preventive health care services, particularly arising from socioeconomic barriers. This in turn could contribute to disparities in cancer stage at diagnosis [22-24]. However, African Americans also have been reported to demonstrate more skeptical attitudes towards screening [25] and the stage disparity could be due to racial differences in tumor aggressiveness [26].
Men with screening-detected clinically localized cancers were more likely to undergo aggressive treatment with radical prostatectomy or radiation therapy than men with clinically diagnosed cancers. The odds ratio for receiving aggressive treatment was statistically significant at 1.5, but the adjusted absolute difference between screening-detected and clinically diagnosed cases was only 6 percentage points. This modest association between screening status and treatment selection suggests that clinical practice may be only partly consistent with the American College of Physicians' view that "aggressive treatment is necessary to realize any benefit from the discovery of a tumor [27]." Our findings may reflect the scientific uncertainty about whether and how to treat screening-detected prostate cancers [28].
Our study has some potential limitations. We classified men presenting with symptoms of advanced cancer as being clinically diagnosed. We do not know that these symptoms actually prompted diagnostic PSA testing. However, the tumor registry medical record abstractors are trained to identify the events leading to a cancer diagnosis; they would attempt to record only symptoms consistent with cancer. Classifying PSA as a screening test is also difficult given the high prevalence of lower urinary tract symptoms in older men [15]. Few members of our study cohort were truly asymptomatic because nearly two-thirds reported lower urinary tract symptoms. However, our classifications for clinical diagnosis and screening detection were internally valid because men diagnosed with symptoms of advanced cancer were significantly more likely to present with advanced stage and more aggressive cancers than the combined group of men who were either asymptomatic or had only lower urinary tract symptoms. Additionally, when we compared demographic and socioeconomic characteristics across groups, we generally found that the men with lower urinary tract symptoms alone most closely resembled the asymptomatic men.
Selection bias may have occurred because 44% of the sampled patients did not complete the 6-month survey. Responders were younger than non-responders, more likely to be non-Hispanic white, had higher socioeconomic status, had earlier stage disease, and were more likely to receive radical prostatectomy. Results may be less generalizable to older men, those with lower socioeconomic status, or members of racial/ethnic groups other than non-Hispanic white. However, these were also the groups who were less likely to have screening-detected cancers. We do not believe that including these non-responders would have altered our findings on the differences between screening-detected and clinically diagnosed cancers. However, based on their demographics, socioeconomic status, and advanced disease stage, the non-responders were not likely to have a high proportion of screening-detected cancers and thus we may have overestimated the proportion of screening-detected cancers. Another potential limitation arose from asking subjects to recall their baseline symptoms 6 months after diagnosis. Recall errors could lead us to misclassify screening status. However, Legler and colleagues prospectively studied a subset of PCOS subjects and found high concordance for symptom recall at 6-months after diagnosis compared with reports at the time of diagnosis [29]. Finally, we may have had incomplete symptom data, particularly for questions appearing only in the medical record abstract. The abstracts would report a symptom if it appeared in the medical records; the absence of a symptom could be due to either the patient being asymptomatic or the physician's failure to ask about or record the symptom. We performed a sensitivity analysis by using only subject reported symptoms from the survey, then only symptoms reported on the medical record abstract, and then ultimately using a combination of both sources. The results for all analyses were essentially the same.
Conclusion
The great majority of prostate cancers diagnosed in our study cohort were detected by screening. Appropriately, younger and healthier men were more likely to be diagnosed by screening. Minority status and lower socioeconomic status appeared to be screening barriers. Screening detected earlier stage and less histologically aggressive prostate cancers. After adjusting for baseline demographic, socioeconomic, clinical, and tumor factors, men with a screening-detected clinically localized cancer were slightly more likely to receive aggressive treatment, either radical prostatectomy or radiation therapy, than men with clinically diagnosed cancers.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
RMH and ALP initiated the project. SNS co-ordinated the data collection and was responsible for the data analyses. RMH, SNS, DE and ALP prepared 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
This study was supported in part by the U.S Centers for Disease Control and Prevention, Contract U48/CCU610818-06-4; National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Public Health Services contracts N01PC67007, N01CN67009, N01PC67010, N01PC67006, N01PC67005, N01PC67000; and the New Mexico VA Health Care System, Albuquerque, New Mexico.
We thank the men who participated in the Prostate Cancer Outcomes Study and their physicians. We also thank the study teams at each of the research centers for their contributions.
Presented in part at the 26th Annual Meeting of the Society of General Internal Medicine, Vancouver, Canada, May 2, 2003.
Figures and Tables
Table 1 Baseline demographic and socioeconomic characteristics.
Variable Number of subjects (Sample size = 3173) Weighted percentages
SEER registry
Atlanta 316 22.5
Connecticut 669 35.8
Los Angeles 938 13.5
New Mexico 342 11.5
Seattle 325 6.0
Utah 583 10.8
Ethnicity
Non-Hispanic white 2187 75.7
Non-Hispanic black 539 13.8
Hispanic 447 10.5
Age
< 49 102 2.3
50–64 1137 32.3
65–74 1336 44.5
75+ 598 20.9
Current marital status
Married 2499 78.6
Unmarried 637 20.3
Unknown 37 1.1
Education
< High school degree 695 20.9
High school/some college 1419 43.6
≥ College degree 1014 34.0
Unknown 45 1.5
Insurance
Private 2595 82.1
Public/Medicare 309 10.0
Unknown 269 7.9
Income (annual)
< $20,000 792 23.3
$20 – 40,000 921 28.8
$40,000 + 1128 36.3
Unknown 332 11.6
Legend: SEER = Surveillance, Epidemiology, and End Results
Table 2 Baseline clinical characteristics.
Variable Number of subjects (sample size = 3173) Weighted percentages
Symptoms
Asymptomatic 1001 30.9
Lower urinary alone 1832 58.4
Systemic 340 10.7
Comorbid conditions
None 1211 37.2
1 1008 32.5
2 520 16.3
3+ 434 13.9
Health status
Excellent 534 16.9
Good 2104 66.2
Fair or poor 500 15.9
Unknown 35 1.0
PSA (ng/ml)
<4 294 8.8
≥ 4 2675 84.5
Unknown 204 6.7
Digital rectal examination
Abnormal 1741 54.8
Normal 1033 33.2
Unknown 399 12.0
Gleason score
2 – 4 481 13.9
5 – 7 2029 65.1
8 – 10 373 11.6
Unknown 290 9.4
Tumor stage
Local 2796 88.7
Regional 126 3.7
Advanced 251 7.6
Legend: PSA = prostate-specific antigen
Table 3 Distribution of baseline demographic, socioeconomic, and clinical characteristics by screening status.
Variable Clinically diagnosed (weighted %) Asymptomatic* (weighted %) LUTS** (weighted %) Asymptomatic or LUTS*** (weighted %)
SEER registry P = 0.16* P = 0.31** P = 0.24***
Atlanta 14.2 14.2 12.9 13.3
Connecticut 19.1 24.7 21.9 22.9
Los Angeles 38.1 34.5 36.0 35.5
New Mexico 13.7 9.8 12.0 11.3
Seattle 4.3 5.9 6.4 6.2
Utah 10.5 10.9 10.8 6.8
Ethnicity P < 0.01 P < 0.01 P < 0.01
Non-Hispanic white 65.1 79.9 75.5 77.0
Non-Hispanic black 20.8 12.4 13.3 13.0
Hispanic 14.1 7.8 11.2 10.0
Age P < 0.01 P = 0.38 P ≤ 0.01
< 49 2.2 4.1 1.4 2.3
50–64 22.8 40.5 29.7 33.4
65–74 50.7 38.7 46.5 43.8
75+ 24.4 16.7 22.4 20.5
Current marital status P < 0.01 P = 0.03 P = 0.01
Married 72.9 81.4 79.7 80.3
Unmarried 27.1 18.6 20.3 19.7
Education P < 0.01 P < 0.01 P < 0.01
< High school degree 33.3 16.2 21.7 19.8
High school/college 37.6 45.5 44.8 45.0
≥ College degree 29.7 38.3 33.5 35.2
Income (annual) P < 0.01 P = 0.01 P < 0.01
< $20,000 36.8 19.0 28.4 25.1
$20 – 40,000 35.9 30.3 33.2 32.2
$40,000 + 27.3 50.7 38.4 42.7
Insurance P < 0.01 P = 0.23 P = 0.01
Private 39.7 48.9 44.0 44.3
Public/Medicare 53.1 42.7 48.2 48.2
Unknown 6.4 8.4 7.9 8.1
Comorbid conditions P ≤ 0.01 P = 0.41 P = 0.05
None 32.2 41.8 35.8 37.8
1 30.3 34.0 32.2 32.8
2 17.6 16.4 16.1 16.2
3+ 19.8 7.8 16.0 13.2
Health status P < 0.01 P < 0.01 P < 0.01
Excellent 9.9 23.0 15.2 17.9
Good 63.5 68.9 66.5 67.3
Fair or poor 26.6 8.1 18.3 14.8
PSA (ng/ml) P = 0.01 P = 0.20 P = 0.05
<4 11.7 7.0 9.3 8.5
≥ 4 78.5 87.9 83.7 85.2
Unknown 9.8 5.1 7.0 6.4
Digital rectal examination P = 0.10 P = 0.07 P = 0.06
Abnormal 59.4 54.0 54.3 54.2
Normal 27.0 34.0 33.9 33.9
Unknown 13.6 12.0 11.7 11.9
Gleason score P < 0.01 P < 0.01 P < 0.01
2 – 4 9.6 13.6 14.9 14.4
5 – 7 53.4 69.9 64.6 66.5
8 – 10 22.3 8.3 11.4 10.3
Unknown 14.7 8.2 9.1 8.8
Tumor stage P < 0.01 P < 0.01 P < 0.01
Local 74.6 92.8 89.1 90.4
Regional 4.0 3.2 4.0 3.7
Advanced 21.4 4.0 7.0 5.9
*P value comparing asymptomatic cases with clinically diagnosed cases.
** P value comparing lower urinary tract symptoms alone cases with clinically diagnosed cases.
***P value comparing asymptomatic and lower urinary tract symptoms alone cases with clinically diagnosed cases.
Legend: LUTS = lower urinary tract symptoms, SEER = Surveillance, Epidemiology, and End Results; PSA = prostate-specific antigen
Table 4 Multivariate model of factors associated with undergoing aggressivea treatment for clinically localized cancer (n = 2796).
Variable Received aggressive treatment Wald F P-value
Adjusted percentages (95% CI) Odds ratio (95% CI)
Screening history 0.05
Not-screened 76 (74, 78) 1.0
Screened 70 (64, 76) 1.5 (1.1 – 2.3)
SEER registry < 0.01
Los Angeles 71 (67, 75) 1.0
Atlanta 89 (85, 93) 4.8 (2.7 – 8.5)
Connecticut 77 (73, 81) 1.5 (1.1 – 2.2)
New Mexico 72 (66, 78) 1.1 (0.7 – 1.7)
Seattle 73 (67, 79) 1.2 (0.8 – 1.8)
Utah 77 (73, 81) 1.5 (1.0 – 2.3)
Ethnicity < 0.01
Non-Hispanic white 76 (74, 78) 1.0
Non-Hispanic black 69 (63, 75) 0.6 (0.4 – 0.9)
Hispanic 79 (75, 83) 1.2 (0.8 – 1.9)
Age < 0.01
< 49 95 (92, 100) 1.0
50–64 89 (87, 91) 0.4 (0.2 – 1.2)
65–74 79 (77, 81) 0.2 (0.1 – 0.5)
75+ 41 (35, 47) 0.03 (0.01 – 0.08)
Current marital status < 0.01
Married 77 (75, 79) 1.0
Unmarried 71 (67, 75) 0.6 (0.5 – 0.8)
Education 0.95
< High school degree 76 (72, 80) 1.0
High school/college 76 (74, 78) 1.0 (0.7 – 1.3)
College degree 76 (72, 80) 0.9 (0.7 – 1.4)
Insurance 0.25
Private 76 (74, 78) 1.0
Medicare/Public 74 (68, 80) 0.9 (0.6 – 1.4)
Unknown 81 (75, 87) 1.6 (0.9 – 2.8)
Income (annual) 0.98
< $20,000 76 (72, 80) 1.0
$20 – 40,000 76 (72, 80) 1.0 (0.7 – 1.4)
$40,000 + 76 (72, 80) 1.0 (0.7 – 1.5)
Comorbid conditions 0.01
None 78 (74, 82) 1.0
1 77 (73, 81) 0.9 (0.7 – 1.2)
2 77 (73, 81) 0.9 (0.6 – 1.3)
3+ 68 (62, 74) 0.5 (0.3 – 0.7)
Health status < 0.01
Excellent 80 (76, 84) 1.0
Good 77 (75, 79) 0.8 (0.5 – 1.2)
Fair or poor 67 (61, 73) 0.4 (0.3 – 0.7)
PSA (ng/ml) < 0.01
<4 69 (63, 75) 1.0
≥ 4 77 (75, 79) 1.7 (1.1 – 2.6)
Unknown 68 (60, 76) 0.9 (0.5 – 1.9)
Gleason score < 0.01
2 – 4 68 (62, 74) 1.0
5 – 7 78 (76, 80) 2.0 (1.4 – 2.8)
8 – 10 71 (65, 77) 1.2 (0.7 – 2.0)
Unknown 80 (74, 86) 2.3 (1.3 – 4.2)
aAggressive treatment was defined as radical prostatectomy or radiation therapy.
Legend: SEER = Surveillance, Epidemiology, and End Results; PSA = prostate-specific antigen
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| 15755329 | PMC555747 | CC BY | 2021-01-04 16:03:07 | no | BMC Cancer. 2005 Mar 8; 5:27 | utf-8 | BMC Cancer | 2,005 | 10.1186/1471-2407-5-27 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-111574828710.1186/1477-7525-3-11ResearchAre decisions using cost-utility analyses robust to choice of SF-36/SF-12 preference-based algorithm? Pickard A Simon [email protected] Zhixiao [email protected] Surrey M [email protected] Todd A [email protected] Center for Pharmacoeconomic Research, College of Pharmacy, Room 164, 833 S. Wood St (MC886), University of Illinois at Chicago, Chicago, IL, 60612 USA2 Midwest Center for Health Services and Policy Research, Hines VA Hospital, Hines, Illinois, USA3 Center for Healthcare Studies and Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA2005 4 3 2005 3 11 11 3 1 2005 4 3 2005 Copyright © 2005 Pickard et al; licensee BioMed Central Ltd.2005Pickard 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
Cost utility analysis (CUA) using SF-36/SF-12 data has been facilitated by the development of several preference-based algorithms. The purpose of this study was to illustrate how decision-making could be affected by the choice of preference-based algorithms for the SF-36 and SF-12, and provide some guidance on selecting an appropriate algorithm.
Methods
Two sets of data were used: (1) a clinical trial of adult asthma patients; and (2) a longitudinal study of post-stroke patients. Incremental costs were assumed to be $2000 per year over standard treatment, and QALY gains realized over a 1-year period. Ten published algorithms were identified, denoted by first author: Brazier (SF-36), Brazier (SF-12), Shmueli, Fryback, Lundberg, Nichol, Franks (3 algorithms), and Lawrence. Incremental cost-utility ratios (ICURs) for each algorithm, stated in dollars per quality-adjusted life year ($/QALY), were ranked and compared between datasets.
Results
In the asthma patients, estimated ICURs ranged from Lawrence's SF-12 algorithm at $30,769/QALY (95% CI: 26,316 to 36,697) to Brazier's SF-36 algorithm at $63,492/QALY (95% CI: 48,780 to 83,333). ICURs for the stroke cohort varied slightly more dramatically. The MEPS-based algorithm by Franks et al. provided the lowest ICUR at $27,972/QALY (95% CI: 20,942 to 41,667). The Fryback and Shmueli algorithms provided ICURs that were greater than $50,000/QALY and did not have confidence intervals that overlapped with most of the other algorithms. The ICUR-based ranking of algorithms was strongly correlated between the asthma and stroke datasets (r = 0.60).
Conclusion
SF-36/SF-12 preference-based algorithms produced a wide range of ICURs that could potentially lead to different reimbursement decisions. Brazier's SF-36 and SF-12 algorithms have a strong methodological and theoretical basis and tended to generate relatively higher ICUR estimates, considerations that support a preference for these algorithms over the alternatives. The "second-generation" algorithms developed from scores mapped from other indirect preference-based measures tended to generate lower ICURs that would promote greater adoption of new technology. There remains a need for an SF-36/SF-12 preference-based algorithm based on the US general population that has strong theoretical and methodological foundations.
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Background
Health-related quality of life (HRQL) measures have many applications, including the measurement of population health status and outcomes of medical interventions that subsequently can be applied to economic evaluations of health care interventions. One such method of economic evaluation, cost utility analysis (CUA), is a special form of cost effectiveness analysis that evaluates incremental costs and effects of an intervention by assessing health effects using quality-adjusted life years (QALYs) [1]. QALYs incorporate both length of life and quality of life into a single metric, and are calculated by summing the time periods individuals spend in different health states, weighted by the qualities of the health states [2]. Because new therapies are typically more expensive than standard therapies, CUA has gained prominence as a method to inform decision makers who seek to compare the tradeoff in incremental costs and gains in health conferred by new treatment choices within and across disease states.
Optimally, CUA is used to guide the allocation of resources on a societal level. The Panel on Cost Effectiveness in Health and Medicine recommended that community preferences for health states collected from a representative sample of the US general population should be "the most appropriate ones for use in a Reference Case analysis" for US decision makers [2]. Such an approach is facilitated by indirect preference-based generic measures of health-related quality of life (HRQL) such as the Quality of Well-Being Scale [3], Health Utilities Index [4,5], and EQ-5D [6,7], as opposed to elicitation of preferences directly from patients using techniques such as the standard gamble, rating scale, and the time trade-off. Indirect preference-based HRQL measures typically generate index-based single summary scores for health states described by the instrument's classification system using an algorithm based on preferences of the community or general population.
An important development in health services research has been the emergence of algorithms that generate single preference-based summary scores based on items, domain scores, or summary scores from the Short Form 36 (SF-36) [8] and SF-12, a 12-item subset of the SF-36 [9]. While many SF-12 and SF-36 datasets are available due to the widespread use of this family of health assessment measures in clinical trials and population health surveys, their value for application to economic evaluations has been previously limited due to an absence of a scoring algorithm that could generate QALYs from SF-12 and SF-36 response sets. The preference-based algorithms provide an opportunity to use SF-36 and SF-12 data in CUA. As of 2004, 10 published algorithms were identified in the literature that were based on SF-36 or SF-12 items, subscale scores, or summary scores [10-18]. Each preference-based algorithm is unique, derived from different modeling approaches, items/domains, data and/or sources of preferences. Several of these algorithms have been compared in studies, and found to differ from one another and from valuations directly elicited from patients [19-22]. Studies have used some of the algorithms to conduct CUA [23-27], which may be used to inform health care resource allocation. Although the algorithms are known to produce different results, their impact on incremental cost-utility ratios (ICURs) and related decision-making in health care have not been clearly demonstrated.
The purpose of this study was to examine how choice of algorithm for the SF-36/SF-12 might affect decision-making. The specific objectives for the study were to calculate ICURs by applying each algorithm to data from 2 different studies that included longitudinal assessments of the SF-36, to compare the ranking of each algorithm-based ICUR across conditions, and finally to interpret whether differences in ICURs generated by each algorithm had the potential to affect decision making. There were two specific hypotheses. First, ICURs calculated from different algorithms were expected to differ because preferences derived from those algorithms had been found to be different [19]. Second, the rank ordering of ICURs was expected to be similar between the conditions, stroke and asthma, examined in the CUA simulations.
Methods
Data sources
To illustrate the outcomes of CUA using the different SF-36 algorithms, data with empiric responses to the SF-36 from patients were used from two different sources and conditions: (1) a clinical trial of adults with asthma [19]; and (2) a longitudinal study of health-related quality of life (HRQL) after stroke [28]. The study of asthma patients was a 12-month randomized controlled trial conducted in inhaled corticosteroid naïve adult patients with mild persistent to moderate persistent asthma that compared two inhaled corticosteroid treatments, triamcinolone acetonide hydrofluoroalkane and fluticasone propionate. Patient included in this trial were ≥ 18 years old, had had a forced expiratory volume in 1 second ≥ 60% of their predicted value after withholding inhaled β-agonists, and had had airway reversibility of ≥ 15% following the administration of an inhaled β-agonist. For the purpose of this analysis, responses to the SF-36 at baseline and 12 months were used.
The second source of data was a natural history of HRQL after stroke. Stroke patients who were hospitalized with a confirmed ischemic stroke and consented to participate were included. Patients were excluded if they were ≤ 18 years old, could not comprehend English-based questionnaire, lived > 150 kilometers from Edmonton, Alberta, had hemorrhagic or lower brain stem stroke, coma, global or Wernicke's aphasia, or life expectancy was less than 6 months for any medical reason. Patients were enrolled in the study within two weeks of stroke and no later than 3 weeks after stroke. Health status measures, including the SF-36, were self-assessed by patients. For this analysis, responses to the SF-36 at baseline and 6 months were used. Both the stroke and asthma studies used version 1 of the SF-36.
Measures
The SF-36 has been traditionally described as a psychometrically-derived generic health status profile, with 8 subscales and two summary scores, the physical component summary (PCS-36) score and the mental component summary (MCS-36) score. The eight domains include physical functioning (PF), role limitations-physical (RP), bodily pain (BP), general health (GH), mental health (MH), role limitations-emotional (RE), vitality (VT), and social functioning (SF). The SF-12 is a shorter, 12-item version of the SF-36 that does not generate domain scores but provides summary scores, the PCS-12 and MCS-12, that are highly predictive of the PCS-36 and MCS-36 [9]. Scores of the 8 subscales range from 0 to 100. The summary scores (i.e. PCS-36, MCS-36) have a mean score of 50 and a standard deviation of 10. Similarly, the PCS-12 and MCS-12 summary scores have a mean of 50 and a standard deviation of 10.
Preference-Based Algorithms for SF-36 and SF-12
Nine publications that derived 10 unique preference-based algorithms for the SF-36 or SF-12 were identified (Table 1) [10-18]. Four algorithms were identified that mapped scores for the SF-36, and 6 algorithms mapped scores for the SF-12. The mapping approach was described as 1st generation if the algorithm was derived from directly elicited preferences, and denoted as 2nd generation mapping if the SF-12/SF-36 algorithm was based upon scores from an indirect preference-based HRQL measure, such as the EQ-5D. Note that these algorithms relate to the most recently advocated algorithms, as several authors published earlier algorithms and subsequently published updates (e.g. Shmueli) [16]. For brevity, each published algorithm is identified by the name of the first author.
Table 1 Summary of SF-12/SF-36 preference-based algorithms
Theoretical Range*
Algorithm Minimum Maximum Original source of Preferences Source of value (country) Source of sample (country) Sample Size
Brazier (SF-12) 0.35 1.00 1st generation – SG UK UK 836
Lundberg (SF-12) 0.27 0.97 1st generation – VAS Sweden Sweden 4,180
Franks (SF-12) -0.24 0.92 2nd generation – EQ-5D UK US 240
Franks (SF-12) -0.09 0.96 2nd generation – HUI3 Canada US 240
Franks (SF-12) -0.07 0.98 2nd generation – EQ-5D UK US 12,998
Lawrence (SF-12) 0.15 1.01 2nd generation – EQ-5D UK US 14,580
Shmueli (SF-36) 0.23 1.00 1st generation – VAS Israel Israel 2,505
Brazier (SF-36) 0.30 1.00 1st generation – SG UK UK 836
Fryback (SF-36) 0.59 0.84 2nd generation – QWB US US 1,356
Nichol (SF-36) 0.24 1.05 2nd generation – HUI2 Canada US 6,921
*Maximum and minimum scores are based on best and worst responses to all items on the SF-36 and SF-12. For the Lundberg algorithm, minimum obtained is based on male, ≥ 80 years of age, while maximum is based on female, <30 years of age. For the Nichol algorithm, the minimum is based on 100 years of age, while maximum is based on 0 years of age.
Brazier and colleagues constructed an econometric model for predicting health state valuations by first revising the SF-36 into a health status measure with 6 domains called the SF-6D [10]. Using a variant on the standard gamble, 249 health states defined by the SF-6D were valued by a representative sample of the UK general population. Ordinary least squares (OLS) models were estimated to predict all 18,000 SF-6D health states. The Brazier (SF-36) algorithm used for the present study was based on the parsimonious consistent model, the preferred specification for model 10. The same data and a similar approach was used to estimate an algorithm based on the SF-12 [17].
Fryback and colleagues predicted Quality of Well-being Index (QWB) scores from SF-36 domain scores using data collected from the Beaver Dam Health Outcomes Study [12]. A six-variable regression model with three main effects (PF, MH, and BP) and three interaction terms (GH*RP, PF*BP, and MH*BP) is used to estimate preferences.
Nichol and colleagues mapped the SF-36 to the preference-based Health Utility Index Mark 2 (HUI2). They estimated HUI2 scores from SF-36 domain scores and sociodemographic variables from a sample of Southern California Kaiser Permanente members [15]. The Nichol method used OLS models, retaining statistically significant parameter estimates that included all eight domains of the SF-36 and age of the respondent.
Shmueli updated an examination of the relationship between Visual Analog Scale (VAS) ratings and SF-36 domains provided in a population health survey in Israel by predicting VAS values from SF-36 domains using linear and non-linear regression models [16]. The model was anchored such that scores of 100 on all 8 SF-36 domains would result in a VAS score of 100. The present study used the anchored algorithm that included statistically significant coefficients for PF, MH, VT, and GH.
Franks and colleagues mapped the SF-12 to the EQ-5D Index and HUI3 using a convenience sample of 240 low-income, predominantly Latino and black patients visiting a community health center in New York [11]. Two equations were separately developed that mapped the PCS-12 and MCS-12 onto EQ-5D and HUI3 scores using OLS models. Described as a pilot, the authors observed that the level of explained variance was consistent with the Fryback and Nichol studies (between 50–60%). Franks led a second investigation, again mapping the SF-12 to EQ-5D scores, using data from the Medical Expenditure Panel Survey (MEPS) [18]. The algorithm based upon SF-12 responses that did not include demographic variables was utilized for the present study. In a similar analysis, Lawrence and colleagues predicted the EQ-5D scores from the SF-12 using MEPS data [13]. A series of 2-variable, 3-variable, and 6-variable models, based on functional variations on, and interactions between, the PCS-12 and MCS-12 were developed. The 2-variable model was advocated for its simplicity and predictive ability across a diverse set of subgroups in the validation set.
Finally, Lundberg and colleagues investigated the relationship of preference-based measures and the SF-12 based on self-assessed HRQL from a random sample of residents in Uppsala County of Sweden [14]. Linear regression models were used to predict valuations from 11 of the 12 items on the SF-12 (excluding the global health item), age, and gender. When using proportion explained variance as a criterion, the reduced VAS-based model that retained only significant coefficients was recommended, with 50% of variance explained by the model.
Data analysis
Empiric data for stroke and asthma were used to ensure that actual health state changes were represented. The present analysis was based on patients who completed both pre- and post- assessments and had no missing items. After the scoring algorithms were applied to SF-36 responses using the 10 algorithms [10-18], the change in utility was transformed into QALYs, with the assumption that the incremental gain/lose in health state utility was realized for a 1-year period. QALYs were calculated using the area under the curve (AUC) approach. We assumed incremental costs associated with the intervention were $2000 per year greater than standard treatment in both the stroke and asthma patients. Such costs over standard treatment were considered reasonable approximations for the costs of an innovative treatment in asthma and stroke, and although distributions of costs could have been used to further simulate a "realistic CUA", but would further complicate the paper without contributing to the main purpose of this study. The incremental cost utility ratio (ICUR) between the intervention and control groups was calculated by dividing incremental costs by gain in QALYs. The algorithms were ranked based on ICURs for each condition.
The pre/post domain and preference-based scores were described for both study groups visit using means and standard deviations. The 95% confidence intervals (CIs) for ICURs were based on the CIs for the preference scores. The pre/post change scores were evaluated with paired t-tests. The rank order of the ICURs was compared between the asthma and stroke groups using Spearman's correlation coefficient (rs). P-values < 0.05 were considered statistically significant.
Results
Of the 304 patients enrolled in the asthma study, 220 (72.4%) completed both the baseline and final SF-36 assessment. The stroke study had 81 of 124 initial respondents (65.3%) complete the SF-36 at baseline and final follow-up. In comparison to the patients in the asthma study, patients in the stroke study were older (mean age 67.4 years vs. 39.1 years) and had much lower mean average PF, RP, SF, and PCS scores (Table 2). Positive change was observed on all 8 domains of the SF-36 in the asthma patients from the baseline to the end of the study (all p-values < 0.01). Stroke patients showed trend towards improvement on all 8 domains, with significant improvement on all domains (p-values < 0.01) with the exception of GH and BP (p-values > 0.05).
Table 2 Demographics Characteristics and SF-36 Scores
Asthma Patients (n = 220) Stroke Patients (n = 81)
Baseline Assessment Final Assessment Baseline Assessment Final Assessment
Mean (SD) Mean (SD) Mean (SD) Mean (SD)
Age 39.1 (12.6) 67.4 (14.4)
Female (%) 55 49
GH 59.4 (18.8) 69.4‡ (19.0) 54.4 (18.4) 56.8 (22.2)
BP 66.4 (23.2) 75.5‡ (21.8) 62.3 (27.4) 68.8 (30.8)
PF 63.1 (21.9) 81.3‡ (21.4) 17.8 (25.9) 41.6‡ (33.0)
RE 63.3 (41.4) 79.6‡ (34.5) 47.3 (44.7) 68.3† (44.1)
RP 38.1 (40.0) 73.3‡ (37.4) 8.3 (23.7) 32.1‡ (40.2)
MH 71.2 (17.9) 75.9‡ (16.6) 67.2 (19.2) 77.9‡ (17.2)
SF 72.6 (22.0) 83.1‡ (19.8) 42.7 (26.4) 60.8‡ (31.8)
VT 48.8 (20.7) 60.0‡ (21.6) 41.5 (17.8) 50.5† (22.8)
PCS 40.1 (9.0) 48.2‡ (9.1) 28.9 (8.52) 34.5‡ (12.8)
MCS 48.1 (11.1) 50.5† (10.3) 46.4 (11.2) 51.7† (10.8)
†p-value < 0.01; ‡p-value < 0.001, based on t-test for dependent samples
According to the preference-based summary scores, all patients in both studies demonstrated statistically significant improvement from baseline to the end of the study (p-value < 0.001) (Table 3). In the asthma study, the mean (SD) change in preference scores ranged from 0.063 (0.117) to 0.130 (0.159). In the stroke study, change scores ranged between 0.055 (0.124) and 0.143 (0.215).
Table 3 Preference-Based Scores for Asthma and Stroke Samples using SF-36 Algorithms
Baseline Assessment (Ti) Final Assessment (Tf) Difference (Tf-Ti) 95% CI
Asthma (n = 220) Mean (SD) Mean (SD) Mean (SD) Lower Upper
Brazier (SF-36, SG) 0.694 (0.101) 0.757 (0.113) 0.063‡ (0.117) 0.048 0.082
Brazier (SF-12, SG) 0.724 (0.116) 0.789 (0.119) 0.065‡ (0.125) 0.047 0.078
Fryback (SF-36, QWB) 0.655 (0.063) 0.721 (0.072) 0.066‡ (0.070) 0.057 0.075
Nichol (SF-36, HUI2) 0.765 (0.123) 0.840 (0.118) 0.075‡ (0.114) 0.060 0.090
Shmueli (SF-36, VAS) 0.683 (0.124) 0.766 (0.130) 0.084‡ (0.111) 0.069 0.098
Lundberg (SF-12, VAS) 0.667 (0.113) 0.759 (0.119) 0.091‡ (0.117) 0.076 0.107
Franks (SF-12, EQ-5D) 0.699 (0.181) 0.814 (0.152) 0.115‡ (0.169) 0.093 0.138
Franks (SF-12, HUI3) 0.643 (0.170) 0.764 (0.173) 0.121‡ (0.176) 0.098 0.144
Franks (SF-12, EQ-5D, MEPS) 0.667 (0.174) 0.797 (0.163) 0.129‡ (0.167) 0.107 0.151
Lawrence (SF-12, EQ-5D) 0.667 (0.158) 0.798 (0.159) 0.130‡ (0.159) 0.109 0.152
Stroke (n = 81)
Shmueli (SF-36, VAS) 0.602 (0.115) 0.656 (0.155) 0.055‡ (0.124) 0.027 0.082
Fryback (SF-36, QWB) 0.548 (0.060) 0.616 (0.100) 0.069‡ (0.094) 0.048 0.089
Lundberg (SF-12, VAS) 0.512 (0.108) 0.592 (0.155) 0.080‡ (0.156) 0.045 0.114
Brazier (SF-12, SG) 0.609 (0.099) 0.696 (0.145) 0.087‡ (0.152) 0.054 0.121
Nichol (SF-36, HUI2) 0.656 (0.110) 0.745 (0.147) 0.089‡ (0.143) 0.058 0.121
Brazier (SF-36, SG) 0.552 (0.087) 0.669 (0.139) 0.116‡ (0.137) 0.086 0.147
Franks (SF-12, HUI3) 0.482 (0.150) 0.615 (0.200) 0.133‡ (0.200) 0.089 0.177
Lawrence (SF-12, EQ-5D) 0.491 (0.132) 0.626 (0.204) 0.134‡ (0.194) 0.091 0.177
Franks (SF-12, EQ-5D) 0.478 (0.199) 0.618 (0.232) 0.139‡ (0.233) 0.088 0.191
Franks (SF-12, EQ-5D, MEPS) 0.472 (0.165) 0.615 (0.219) 0.143‡ (0.215) 0.096 0.191
‡p-value < 0.001, based on t-test for dependent samples
NB: algorithms are ordered from smallest to largest difference score for each condition
Table 4 shows the results from the two sets of CUA simulations, and the rank order of the algorithms. As the incremental cost of $2000 is held constant across the algorithms, the differences in QALYs are mirrored by the differences in ICURs. In the asthma patients, estimated ICURs ranged from Lawrence's SF-12 algorithm at $30,769/QALY (95% CI: 26,316 to 36,697) to Brazier's SF-36 algorithm at $63,492 (95% CI: 48,780 to 83,333). ICURs for the stroke cohort varied slightly more dramatically. The MEPS-based algorithm by Franks et al. provided the lowest ICUR at $27,972/QALY (95% CI: 20,942 to 41,667). The Fryback and Shmueli algorithms provided ICURs that were greater $50,000/QALY and did not have confidence intervals that overlapped with most of the other algorithms. The rank order of algorithms based on ICUR was similar across the two conditions, with rs = 0.60 (p-value < 0.10).
Table 4 Ranking of SF-36/SF-12 Algorithm by Estimated Incremental Cost Utility Ratio
Incremental Cost 1 year QALYs Gained ICUR ($/QALY) [95% CI] Rank
Asthma
Lawrence (SF-12, EQ-5D) $2000 0.065 30 769 [26 316, 36 697] 1
Franks (SF-12, EQ-5D, MEPS) $2000 0.065 31 008 [26 490, 37 383] 2
Franks (SF-12, HUI3) $2000 0.061 33 058 [27 778, 40 816] 3
Franks (SF-12, EQ-5D) $2000 0.058 34 783 [28 986, 43 011] 4
Lundberg (SF-12, VAS) $2000 0.046 43 956 [37 383, 52 632] 5
Shmueli (SF-36, VAS) $2000 0.042 47 619 [40 816, 57 971] 6
Nichol (SF-36, HUI2) $2000 0.038 53 333 [44 444, 66 667] 7
Fryback (SF-36, QWB) $2000 0.033 60 606 [53 333, 70 175] 8
Brazier (SF-12, SG) $2000 0.033 61 538 [51 282, 85 106] 9
Brazier (SF-36, SG) $2000 0.032 63 492 [48 780, 83 333] 10
Stroke
Lawrence (SF-12, EQ-5D) $2000 0.067 29 851 [22 599, 43 956] 3
Franks (SF-12, EQ-5D, MEPS) $2000 0.072 27 972 [20 942, 41 667] 1
Franks (SF-12, HUI3) $2000 0.067 30 075 [22 599, 44 944] 4
Franks (SF-12, EQ-5D) $2000 0.070 28 777 [20 942, 45 455] 2
Lundberg (SF-12, VAS) $2000 0.040 50 000 [35 088, 88 889] 8
Shmueli (SF-36, VAS) $2000 0.028 72 727 [48 780, 148 148] 10
Nichol (SF-36, HUI2) $2000 0.045 44 944 [33 058, 68 966] 6
Fryback (SF-36, QWB) $2000 0.035 57 971 [44 944, 83 333] 9
Brazier (SF-12, SG) $2000 0.044 45 977 [33 058, 74 074] 7
Brazier (SF-36, SG) $2000 0.058 34 483 [27 211, 46 512] 5
NB: algorithms are ordered from lowest to highest ICUR in the asthma patients
Discussion
The development of preference-based algorithms for the SF-36 and SF-12 to facilitate CUA has fostered studies that recognized these preference-based scores can differ from each other and from directly elicited valuations in patients with asthma, hypertension, lung transplantation, and osteoporosis [19,20,29,30]. However, the extent to which the differences might lead to different decisions on implementing or reimbursing for a new technology has been unclear. Using actual health states self-assessed by patients and imputing what might be considered conservative costs for an innovative treatment, our analysis demonstrated that ICURs based on the derivation algorithms can vary dramatically. The 10 algorithms produced a wide range of ICURs that varied more than 2-fold in magnitude for the asthma cohort and almost 3-fold in the stroke study.
Although guidelines or thresholds for decision making based on cost per QALY are contentious, cost-effectiveness thresholds that health care decision makers are willing to accept in health care reimbursement decisions exist, if not explicitly, then implicitly. Some guidance has been published. The National Institute of Clinical Effectiveness (NICE) in the UK has indicated they do not have an explicit threshold [31], while a threshold of around £20,000 to £30,000 per QALY gained (about $37,000 to $55,000 in 2004 US dollars) [32,33] or slightly higher [34] has been cited as the value used in making decisions. Laupacis et al (1992) suggested that a treatment costing less than $20,000/QALY could be considered very cost-effective, a treatment costing between $20,000/QALY and $100,000/QALY was judged acceptable, while a treatment costing more than $100,000/QALY was deemed not likely to be cost-effective [35]. Other studies have suggested that $50,000/QALY provides a threshold for judging cost effectiveness [36,37]. Although arbitrary criteria, the application of any of the cited guidelines to the CUAs illustrated in the present study convey that the choice of algorithm can dictate whether the intervention is considered cost-effective or unacceptable. The choice of algorithm could determine whether a drug is considered for formulary listing, particularly if an emphasis is placed on cost-effectiveness as a criterion by the decision-making committee, as is often done by publicly funded health care systems.
The CUA simulations illustrated how selection of a specific algorithm could lead to a different interpretation of the cost-effectiveness of an intervention. In the asthma cohort, algorithms by Lawrence [13], and the three equations by Franks [11,18] generated relatively smaller ICURs close to a level that may be considered very cost-effective, i.e. $20,000, with 95% confidence intervals that did not bound the $50,000/QALY threshold. In contrast, the Nichol, Fryback, and both Brazier methods produced ICUR point estimates above $50,000/QALYs that would be unacceptable by most guidelines. In stroke, the Lawrence and Franks methods again generated ICURs that would indicate the technology of interest was relatively cost-effective, at $30,000/QALY or less, while the algorithms by Shmueli [16] and Fryback [12] produced ICURs over $50,000/QALY. In examining the robustness of the results, all algorithms produced ICURs below $20,000/QALY when incremental costs for the hypothetical intervention are reduced to less than $500, but algorithm selection becomes critical as incremental costs increase and thresholds such as $20,000/QALYs or $50,000/QALY are crossed.
Changes in the rank order of algorithms between conditions can be explained, not only by differences in the preference-based weighting assigned to each of the domain/summary scores, but several additional factors. There are differences in the SF-36 items or subscales retained by some of the methods. For instance, Brazier's SF-6D based on the SF-36 does not include GH, while the score generated by the Shmueli algorithm is largely influenced by the GH domain. The responsiveness/sensitivity of algorithms appears to be somewhat related to the scale range. It was not surprising that Fryback's method produced relatively larger ICURs that implied the intervention was less cost effective, as Fryback's method had a much smaller range of scale relative to the other algorithms (Table 1). Algorithms that incorporate demographic characteristics, such as the Nichol and Lundberg methods, provide estimates that are influenced by age of the cohort and could contribute to changes in their rank order.
In order to provide some guidance in the selection of preference-based algorithms for the SF-36 and SF-12 the algorithms were appraised in the context of their theoretical and methodological foundations, source of community-based preferences, and their relative potential to enhance or deter the uptake of new technology. The study results clearly illustrated that choice of algorithm can affect the estimated ICUR, and that there was a tendency for the Fryback and Shmueli methods to generate higher estimates of ICURs relative to the other algorithms. From a third-party payer perspective, algorithms generating higher ICURs would appeal to third-party reimbursement decision makers with short-term budget constraints, as algorithms that generate higher ICURs provide less encouraging evidence in the adoption of new technology when considered in the context of the $/QALY benchmarks previously discussed. The Panel on Cost Effectiveness of Health and Medicine [2] recommended that preference-based measures have a theoretical basis and represent community-based preferences. The Brazier algorithms are arguably most favorable on a theoretical basis. Only the Brazier, Lundberg, and Shmueli algorithms were based on preferences directly elicited from the general populace, i.e. first generation. The Lawrence, Franks, Nichol, and Fryback methods mapped the SF-12/SF-36 onto scores obtained from indirect utility-based measures, e.g. EQ-5D, HUI2, to derive what we termed "second generation" preference-based algorithms. Such an approach is limited by differences in the descriptive systems [17]. Interestingly, algorithms derived from directly elicited valuations of health states (i.e. first-generation mapping) tended to generate smaller magnitudes of change compared to the algorithms that mapped the SF-36/12 using other indirect utility measures (i.e. second-generation mapping). One explanation for the second-generation algorithms producing larger change scores is that several of them were derived from the utility scores of the HUI3 and EQ-5D, which have broader scale ranges compared to the SF-6D [38].
A further consideration is the theoretical foundation for the elicitation technique used in the valuation study. Only Brazier employed methodology using the SG technique for first-generation mapping. The SG has the most appeal in economic theory due to its foundations in Expected Utility Theory (EUT), although it has been suggested that the axioms of EUT are empirically flawed [39], and requires the respondent possess a rudimentary understanding of probabilities. Scores generated by Lundberg, Shmueli, and Fryback methods were based on first or second generation mapping of the SF-12/SF-36 onto scores from rating scales. Rating scales have been criticized for lack of theoretical basis in economics [39,40], as a rating scale is not a choice-based technique and its ability to represent preferences on a cardinal scale is debatable. In contrast, the TTO and SG are choice-based techniques that generate utilities [2]. Lundberg utilized a variant of the TTO in addition to the VAS, but the complexity of the TTO task does not lend itself to a mail survey design. Lundberg observed that the TTO models did not perform as well as the VAS. Most of the algorithms were developed using self-assessed preferences for health status from a general population where severe states are rare, rather increasing the representation of more severe health states by statistical design, as done by Brazier [10,17] and by other developers of preference-based measures [4,7].
Given that HRQL may be valued differently between countries [41], an algorithm based on the preferences of a representative sample of the general population for the country of interest would be most desirable for resource allocation decision-making on the societal level, e.g. when the payer is the national ministry of health. The algorithms for the SF-36 find their preference-based origins from a diverse range of national sources. The algorithm by Shmueli was based on valuations obtained from representative samples of the Israeli Jewish population [16], while Lundberg's algorithm was based on valuations from the Sweden populace [14]. Brazier's utilities for health states were elicited from respondents in the United Kingdom [10]. The preferences for algorithms derived by Franks (EQ-5D) [11] and Lawrence [13] were mapped from the EQ-5D scoring function derived from the general population in the United Kingdom [7]. Fryback [12] mapped scores from the QWB that were based on community-based preferences from San Diego, California, USA [3]. The Nichol [11,15] and Franks [11] algorithms were mapped from the utility-based scores of the HUI2 and HUI3 systems, respectively, that were originally elicited from respondents in Ontario, Canada [4,5]. Nichol and Lundberg algorithms may not be considered as representing general population values because they include demographic variables such as age and/or gender. At present, only the Fryback algorithm has preferences originating from a community in the US, albeit a second generation mapping of those preferences.
Among the algorithms presently available, Brazier's algorithms for the SF-12 and SF-36 appear to be most favorable because of their methodological and theoretical basis. From the perspective of the third party reimbursement decision maker, the Brazier algorithms are not among those that tend to encourage adoption of new technology, tending to provide relatively higher estimates of ICUR. For those decision-makers, those algorithms would appear to be more fiscally conservative in the sense that they would not promote the adoption new technology any more than the other methods according to the results of this study. Although similar estimates are obtained using the Brazier SF-36 and SF-12 algorithms [17], it would be preferable to utilize the Brazier SF-36 algorithm rather than the SF-12-based algorithm if responses to the SF-36 are available because of the richer information afforded by the descriptive system. If alternative algorithms are used for CUA, it may be suggested to test robustness of conclusions by sensitivity analysis using Brazier's SF-36 or SF-12-based algorithms. At present, no SF-36/SF-12 algorithm has been published based on the first generation preferences of the US general population. As there is evidence to support health states valuations by the general US population differ from other countries[41], this represents an opportunity for future research leading to the development of an algorithm specific to the US as well as for other countries.
Note that there are several limitations and assumptions to the CUAs simulated in this paper. The primary purpose was to determine whether choice of preference-based algorithm applied to SF-36/SF-12 data has the potential to change the conclusion of a CUA; hence, aspects of the CUA not central to the purpose were simplified. For example, incremental costs were assumed to be constant, whereas in reality, considerable cost variance would be observed across patients. CUAs were performed in two patient populations, i.e., asthma patients and stroke patients, rather than using a single data set, to enhance the generalizability of the results. The rank order of the algorithms is limited to the datasets examined in this study, however, and comparisons of the algorithms across more diseases/conditions and persons with different demographic characteristics may provide stronger evidence of the rank order "stability". Change scores and thus ICUR estimates depend on baseline health status and the impact of an intervention on the various domains of health as captured by changes in responses to the SF-12/SF-36 items. Note that baseline domain scores for asthma and stroke cohorts were lower than US-based population norms. Several algorithm developers provide caveats for the application of their algorithm, including concerns about profiles severely limited by ceiling or floor effects [12], and inconsistent estimates and overprediction of poorest health states [10]. For instance, the descriptive system of the SF-6D is more concentrated at the milder end of health problems relative to the EQ-5D [42]. These concerns may be particularly relevant to the stroke cohort, where floor effects were observed.
Conclusion
In summary, SF-36/SF-12 preference-based algorithms tend to generate a wide range of ICURs that can potentially lead to different reimbursement decisions. Brazier's algorithms for the SF-36 and SF-12 had an arguably stronger methodological and theoretical basis, and tended to produce higher ICURs. For decision-makers who consider cost-effectiveness in the decision to reimburse for a new medical technology, selection of an algorithm that generates relatively higher ICURs would provide less convincing evidence of the cost-effectiveness of a new technology and consequently, its uptake. The "second-generation" algorithms mapped from other indirect preference-based measures tended to produce lower ICURs. When an alternative algorithm is selected, sensitivity analysis is recommended using the Brazier SF-12/SF-36 algorithm in order to examine the robustness of CUA. There remains a need for an SF-36/SF-12 algorithm developed from U.S.-based general population preferences with strong methodological and theoretical foundations.
Authors' contributions
Drs. Pickard and Lee were responsible for the conception of the study and acquisition of data. Mr. Wang analyzed the data. Dr. Pickard and Mr. Wang were involved in the drafting of the article. All authors contributed to the interpretation of results and revising the article for important intellectual content.
Acknowledgements
Astrazeneca provided funding support for the original stroke study through an unrestricted investigator initiated research grant.
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| 15748287 | PMC555748 | CC BY | 2021-01-04 16:38:15 | no | Health Qual Life Outcomes. 2005 Mar 4; 3:11 | utf-8 | Health Qual Life Outcomes | 2,005 | 10.1186/1477-7525-3-11 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-161577747810.1186/1477-7525-3-16ReviewRole of health-related quality of life measures in the routine care of people with multiple sclerosis Solari Alessandra [email protected] Epidemiology Unit, National Neurological Institute C. Besta, Via Celoria 11, 20133 Milan, Italy2005 18 3 2005 3 16 16 7 2 2005 18 3 2005 Copyright © 2005 Solari; licensee BioMed Central Ltd.2005Solari; 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.
Health-related quality of life instruments are expected to be of particular value in routine care of people with multiple sclerosis (MS), where they may facilitate the detection of disease aspects that would otherwise go unrecognised, help clinicians appreciate patient priorities particularly in terms of treatment goals, facilitate physician-patient communication, and promote shared decision-making. However, it appears that these instruments are little used routine clinical approaches to people with MS. To address this issue, I performed a bibliographic search of studies that evaluated the efficacy of generic or disease-specific health-related quality of life (HRQOL) instruments in MS clinical practice from clinicians' or patients' perspectives. I found only one cross-sectional study, which compared preferences for three instruments, and assessed acceptability in people with MS.
Reasons for lack of transfer of HRQOL measurements to clinical practice may be cultural, methodological, or practical. With regard to MS, the proliferation of instruments seems to constitute a barrier, with no particular instrument having gained wide popularity or consensus. Other barriers are lack of resources for the administration, collection and storage of the data, and inability of clinicians to score, interpret, and use HRQOL instrument to guide clinical care. It is therefore important to refine existing tools, extending clinical validation to wider contexts and cultures. More studies assessing acceptability and clinicians' and patients' preferences for different instruments are also required.
Multiple sclerosishealth-related quality of lifeoutcomes assessmentclinical practice
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Review
Multiple sclerosis (MS) is a demyelinating disease of the central nervous system of unknown etiology and poorly understood pathogenesis. There is a north-south gradient of MS prevalence in the northern hemisphere, with highest levels (over 100 per 100,000) in northern regions [1,2]. It is a chronic disease with a modest effect on life expectancy, but a broad spectrum of consequences, of variable severity, on physical and psychological characteristics, that vary between individuals and within individuals over time. The disease typically strikes women (2:1) in their peak years of career development and family life; commonly there are exacerbations and remissions followed by progression whose rate and extent vary [3]. There is also a benign form of MS, characterised by few relapses, long periods of remission, and mild activity limitations over the long term [4]. The available treatments have at best a modest benefit on the course of the disease [5].
Health-related quality of life measures
Interest in measuring outcomes in MS has increased markedly over the past 20 years. Standardised instruments have been developed, the most-used being the Expanded Disability Status Scale (EDSS) [6] which is a mixed impairment/activity limitations scale based on neurological examination of eight functional systems, plus ambulation/mobility status. Despite major limitations – bias towards locomotor function, variable sensitivity to change according to scale score, and suboptimal inter-rater reliability – the EDSS is widely-used by researchers and clinicians because its scores are readily understood by all.
More recently, the importance of MS outcome assessment from the perspective of the person with the disease has been recognised [7]. After 1992, the number of publications on health-related quality of life (HRQOL) increased steadily, as did those employing MS-specific instruments (see Figure). Generic instruments were applied to MS [7-12], and disease-specific instruments were devised and validated [13-24]. The seven available MS-specific HRQOL instruments are listed in the Table 1; all were published between 1995 and 2001. Three consist of a generic module (SF-36 [13,15] or FACT-G [14]) plus an MS-specific module. In most cases people with MS participated in their development [13,16]. Except for the MS Quality of Life 54 (MSQOL-54), which has been translated into several languages [13,20-23], and the Functional Assessment of MS (FAMS), which is also available in Portuguese [24], these questionnaires are available only their original versions. Aspects of responsiveness were evaluated in four of the seven instruments, but in general sensitivity to change has been insufficiently investigated [18,25-28]
Table 1 Characteristics of MS-specific HRQOL questionnaires
MSQOL-54 FAMS MSQLI RAYS HAQUAMS MSIS-29 LMSQoL
Publication year 1995 1996 1999 2000 2001 2001 2001
Generic module SF-36 (36 items) FACT-G (28 items) SF-36 (36 items) -- -- -- --
MS module 18 items 31 items 9 scales 50 items 38 items 29 items 8 items
People with MS involved in development No Yes -- No Yes Yes Yes
Versions US English [13]
Italian [20]
French [21]
Canadian French [22]
Japanese [23] US English [14]
Portuguese [24]
US English [15] Hebrew [16] German [17] English [18] English [19]
Reliability Alpha Test-retest [13,20–23] Alpha [14,24] Alpha Test-retest [15] Alpha [16] Alpha Test-retest [17] Alpha Test-retest [18] Alpha Test-retest [19]
Responsiveness RCT [25]
RCT [26] -- RCT [27] -- RCT [28] Effect size [18] --
Domains not assessed Vision Vision
Bladder/ bowel
Sexual function -- -- -- Vision
Sexual function Vision
Time period assessed Past 4 weeks
Current time Past week -- Past week Past year
Past 4 weeks
Past week Past 2 weeks Past month
Time to complete 20 minutes 20 minutes -- -- 20 minutes -- --
Publications (no.) 16 10 5 1 3 7 2
Publication period 1995–2004 1996–2004 1999–2003 2000 2001–2004 2001–2004 2001
MSQOL-54 is the MS quality-of-life 54; FAMS is the Functional Assessment of MS; MSQLI is the MS Quality of Life; HAQUAMS is the Hamburg Quality of Life Questionnaire in MS; FACT-G is the Functional Assessment of Cancer Therapy, General version; LMSQoL is the Leeds MS Quality of Life. Alpha is Cronbach's coefficient alpha. RCT aspects of responsiveness assessed in randomized controlled trial.
HRQOL and routine clinical practice
HRQOL studies in MS have drawn attention to the multiplicity of domains that may be compromised by the disease, and the effects of this compromise on ability to cope. As expected, people with MS, especially those with a progressive course, report reduced physical functioning compared to the general population [10,11,29-31]; they are more likely to suffer fatigue [29,32] and depression [32,33] than the general population, and are also more likely to be unemployed [8,10,30,31,34,35]. Unexpectedly, however, it has been reported that the importance attached to compromise in different HRQOL domains may vary considerably between MS sufferers and their neurologists [7].
The ultimate aim of measuring HRQOL is to provide a comprehensive assessment of patients' health status, to serve as a baseline from which to tailor interventions, pharmacological or otherwise, and assess their effectiveness, both in the clinical trial setting and in routine care. HRQOL instruments are expected to be of particular value in routine care, where they may improve the detection of disease aspects that would otherwise go unrecognised, help clinicians appreciate patient priorities particularly in terms of treatment goals, facilitate physician-patient communication, and promote shared decision making. In addition HRQOL data from clinical trials can provide information that clinicians can usefully discuss with their patients [36]. Unfortunately, although recent MS trials include some HRQOL assessment, there is no internationally agreed gold standard for conducting such assessment or reporting outcomes. HRQOL evaluations are not required as endpoints in MS trials by the European Agency for the Evaluation of Medicinal Products [37]. Even when HRQOL endpoints are included, data collection and reporting are often of poor quality [38] with the consequence that cost effectiveness issues, which HRQOL instruments can throw light on, such as preserved function, less work missed, and improved emotional well-being, are not analysed.
Literature survey
It appears that HRQOL instruments are little used in routine clinical approaches to people with MS. To address this issue, I searched MEDLINE (1966–2004), the Cochrane Library (Issue 1, 2005) and the Cochrane MS Group trials register (2004) for studies that evaluated the efficacy of generic or MS-specific HRQOL instruments in clinical practice from the clinicians' or MS patients' perspective, also checking study references. Studies considering patient-reported outcomes other than HRQOL, and domain-specific measures were excluded.
I found only one study, a cross-sectional postal survey conducted in Canada, published in 2004 [39]. This study assessed MS sufferers' preferences regarding two generic instruments (the EuroQol EQ-5D and the SF-36), and an MS-specific instrument (MSQOL-54). Over 90% of 183 participants reported that EuroQol EQ-5D and the SF-36 were acceptable or very acceptable, and 85% did so for MSQOL-54. Surprisingly, over 75% of participants felt that a combination of the three instruments best described their HRQOL.
The reasons for lack of transfer of HRQOL assessment into clinical practice may be cultural, practical, or methodological [40-43]. With regard to cultural factors, patients generally welcome the opportunity to provide clinicians with information regarding their HRQOL [43]. That this is also the case for people with MS is suggested by high participation rates in most postal surveys assessing patient-reported health status [30,35,39,44], and by the good acceptability of HRQOL instruments [39]. By contrast, information on practicing clinicians' perceptions of the utility of HRQOL data is limited and conflicting: studies have uncovered a lack of knowledge of HRQOL as well as concerns that these instruments may be a covert means of assessing physicians' performance [45,46].
Practical considerations be particularly important in clinical settings, where data must be provided promptly and in an understandable manner to be of use. Instruments must be administered, processed, scored, stored and retrieved – all of which have logistic and financial implications [47]. Most HRQOL instruments are lengthy and may be burdensome for patients and clinicians. For most existing instruments, the score is not immediately available, but needs to be calculated, while score interpretation may not be straightforward. For example a recently published study on transplant physicians found that 55% would be more likely to use HRQOL data if it were more comprehensible [48]. In the United States time spent gathering and interpreting HRQOL information as part of the clinical encounter is not built into reimbursement by third-party payers [49]. It is noteworthy, however, that questionnaire length seems not to be a drawback for people with MS since a combination of HRQOL instruments was preferred by over 75% of participants in the only study found [39].
Another factor limiting the dissemination of HRQOL tools in MS clinical practice is likely to be that too many instruments are available, and unlike EDSS, none has emerged as clearly superior to any other.
Conclusion
Existing HRQOL tools for people with MS should be refined and their clinical validation pursued in the widest possible cultural context. More studies assessing instrument acceptability and preferences of clinicians and people with MS are also needed. It would be useful for example to implement computer-based technology (touch-screens and adaptive administration to reduce respondent burden by selecting pertinent items and omitting inappropriate ones) and other alternatives to traditional paper-and-pencil or interview methods, which should of course be evaluated for acceptability and reliability [48]. The objective is not to add HRQOL measurements to the chores of everyday practice, but to incorporate meaningful HRQOL instruments into the care process [50].
Figure 1 Number of publications on HRQOL in people with MS between 1992 and 2004. Blue bars indicate all studies on HRQOL; light blue bars indicate studies employing MS-specific instruments. Studies considering patient-reported outcomes other than HRQOL, or domain-specific measures are excluded.
Acknowledgements
I am indebted to Dr. Barbara Vickrey and Dr. Christoph Heesen for their helpful suggestions for improving the manuscript. Thanks are also due to Dr. Maura Moggia, trial search coordinator of the Cochrane MS Group, for providing the search strategy, and to Mrs. Giusi Ferrari for retrieving the studies, and to Don Ward for help with the English.
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| 15777478 | PMC555749 | CC BY | 2021-01-04 16:38:13 | no | Health Qual Life Outcomes. 2005 Mar 18; 3:16 | utf-8 | Health Qual Life Outcomes | 2,005 | 10.1186/1477-7525-3-16 | oa_comm |
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Int J Equity HealthInternational Journal for Equity in Health1475-9276BioMed Central London 1475-9276-4-41577400310.1186/1475-9276-4-4ResearchStrategies to prevent HIV transmission among heterosexual African-American women Essien E James [email protected] Angela F [email protected] Ronald J [email protected] GO [email protected] Nora I [email protected] The HIV Prevention Research Group. College of Pharmacy, University of Houston, 1441 Moursund Street, Houston, Texas 77030. USA2 Center for AIDS Research, Baylor College of Medicine, Houston, USA3 WHO Center for Health Promotion and Prevention Research, University of Texas School of Public Health, Houston, Texas 77030, USA4 College of Pharmacy. Texas Southern University. Houston, Texas 77004., USA2005 17 3 2005 4 4 4 18 10 2004 17 3 2005 Copyright © 2005 Essien et al; licensee BioMed Central Ltd.2005Essien 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
African-American women are disproportionately affected by HIV, accounting for 60% of all cases among women in the United States. Although their race is not a precursor for HIV, the socioeconomic and cultural disparities associated with being African American may increase their risk of infection. Prior research has shown that interventions designed to reduce HIV infection among African-American women must address the life demands and social problems they encounter. The present study used a qualitative exploratory design to elicit information about strategies to prevent HIV transmission among young, low-income African-American women.
Methods
Twenty five low income African American women, ages 18–29, participated in five focus groups of five women each conducted at a housing project in Houston, Texas, a large demographically diverse metropolitan area that is regarded as one of the HIV/AIDS epicenters in the United States. Each group was audiotaped, transcribed, and analyzed using theme and domain analysis.
Results
The participants revealed that they had most frequently placed themselves at risk for HIV infection through drugs and drinking and they also reported drug and alcohol use as important barriers to practicing safer sex. The women also reported that the need for money and having sex for money to buy food or drugs had placed them at risk for HIV transmission. About one-third of the participants stated that a barrier to their practicing safe sex was their belief that there was no risk based on their being in a monogamous relationship and feeling no need to use protection, but later learning that their mate was unfaithful. Other reasons given were lack of concern, being unprepared, partner's refusal to use a condom, and lack of money to buy condoms. Finally, the women stated that they were motivated to practice safe sex because of fear of contracting sexually transmitted diseases and HIV, desire not to become pregnant, and personal experience with someone who had contracted HIV.
Conclusion
This study offers a foundation for further research that may be used to create culturally relevant HIV prevention programs for African-American women.
African AmericanswomenHIVAIDSRisk behaviorsIntervention.
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Background
Despite the impressive strides that have been made by behavioral scientists in developing culturally sensitive HIV intervention programs for minority populations in the United States, HIV infection continues to be a major public health problem and is increasingly affecting minority populations, persons infected through heterosexual contact, the poor and women [1]. African-American women are one group disproportionately affected by HIV [1,2]. Although they constitute only 13% of the female population, they account for 68% of all HIV cases among women in the United States [3-5] and AIDS is the leading cause of death among African-American women, aged 25 to 44 years [4,6]. The rate of HIV infection among African-American women is estimated to be four times higher than the rate for Latinas and more than nineteen times higher than the rates for Anglo women [7]. The disproportionate rate is further amplified among African-American women who use drugs [2].
The main exposure categories for all women in the United States is heterosexual contact accounting for approximately 40% of all new AIDS cases among this group [2,5]. This is followed by injection drug use that constitutes 25% of all new cases of AIDS among women. Among African-American women, 42% of AIDS cases are attributed to personal injection drug use while 38% are attributable to heterosexual transmission [8]. These behaviors often occur within the same context. From 1995 to 1999, mortality from AIDS has decreased more among men than women and among Whites and people of higher income status. However, the percentage decrease during the same time period was lowest among African-American women and women from the south1, suggesting a need for an effective HIV prevention program for these women.
Aside from heterosexual contact and injection drug use, depression, physical and sexual abuse, and lack of condom negotiation skills are some of the psychosocial determinants of HIV risk behaviors among women [5]. Drug use, violence and depression have been described as a tripartite of risk factors that appear to have a profound influence on HIV risk and HIV infection among African-American women [8]. For example, crack cocaine use has had a devastating effect on the African-American community and appears to increase the likelihood of riskier sexual behavior as the amount of crack use increases [9]. In addition, conditions of poverty and homelessness are closely related to trading sex for drugs, a condition that affects many crack cocaine users and one that increases HIV risk [10].
While African-American women may not be placed at risk of HIV infection because of their race and ethnicity, St. Lawrence, Eldridge, Reitman, Little, Shelby, and Brasfield [11] note that race and ethnicity may be a reflection of the socioeconomic and cultural disparities that are associated with HIV transmission. According to Sanders-Philips [12], an understanding of the socioeconomic dynamics of HIV transmission among African-American women and incorporating this information into HIV prevention programs could significantly enhance HIV prevention efforts for these women.
An array of socioeconomic and cultural factors exacerbate high-risk behaviors that place African-American women at risk for HIV infection [12,13], the most notable being the role of poverty. Although poverty in itself is not a precursor for HIV infection, several studies have established a direct link between low socioeconomic status and AIDS incidence [14-17]. For many African-American women, changing HIV-risk-related behavior is difficult because, daily, they deal with problems of poverty by engaging in sex for drug exchanges, prostitution, violence, and powerlessness in negotiating safer sex practices in relationships with African-American men [12,13]. It has been suggested that HIV risk-reduction programs for African-American women must address the life demands and social problems that these women face including poverty, alcohol and drug use, and other cultural and contextual issues that influence the role of women in safer sex decision making in the African-American community [11,18]. Research evidence has shown that for interventions to be effective among ethnic minority populations, they must be presented in a socio-cultural context as well as have gender specificity and a sound theoretical framework [19-21]. To date, only a limited number of studies have explored the impact of these variables on the decision to practice safe sex among African-American women, and the role that they play in developing culturally relevant HIV prevention interventions.
Individual and small group behavior change techniques have long been used in HIV prevention interventions for women and have resulted in increased condom use by inner-city women in primary health care settings, mental health clinics, and among women living in economically disadvantaged neighborhoods [4]. Community-level interventions have been used less frequently, yet they are needed to disseminate health promotion messages that influence individual behavior change and strengthen social norms to support and reinforce such change. Lauby and colleagues [4] reported that a two-year, large-scale community-based intervention significantly impacted partner communication about condom use and attempts to get a main partner to use condoms. Their research highlights the effectiveness of reaching large numbers of women and changing their condom-use behaviors regarding communication with a main sex partner. Safer sex requires both partners' consent and often the male in risky sexual situations is resistant [2]. Theall, et al [5], examined the factors associated with HIV seropositivity among African-American women, aged 18 to 59 years, who were active crack cocaine users and/or injection drug users and found that an inability to say no to male sex partners was the strongest predictor of positive serostatus and as a result, skills building for negotiating and communicating safer sex practices is needed in prevention programs.
Several HIV interventions have been developed to promote condom use and enhance sexual communication skills among African American women. The most promising interventions have been programs that are based on social cognitive principles. However, Kalichman et al [21] notes that HIV prevention programs that are based on social cognitive principles and proven to be effective in the scientific literature have not been widely utilized in community settings because of their dependence on expert interventionists for implementation in face-to-face formats, making them difficult to transfer to community-based organizations. In contrast, social cognitive theory principles applied to HIV prevention can be delivered effectively by videotapes and community-based organization personnel with minimal training in skills building techniques. The rationale for using videotapes as part of an HIV intervention delivery system is provided by the emerging literature which demonstrates the feasibility of this medium in changing high risk sexual behaviors [21,22].
Using constructs from social cognitive theory, the health belief model and theory of reasoned action, Roye & Hudson [22] conducted a study to assess the impact of a culturally appropriate videotape-based intervention on condom use among urban adolescent women who use contraception. The study showed that the videotape based intervention promoted favorable changes in sexual behaviors. Similarly, Kalichman et al [21] tested the efficacy of a culturally sensitive HIV prevention intervention for African-American women by randomly assigning African-American women to three intervention conditions: a single session public health service videotape intervention that provided HIV information delivered by two white women; a second videotape intervention that provided the same information but delivered by an African-American woman; and a third intervention module that was similar to the second but with the addition of culturally relevant materials. The women who received the intervention that used culturally relevant materials reported in follow-up assessments an increase in antibody testing and requests for condoms. Taken together, these studies demonstrate that culturally sensitive videotape-based HIV prevention interventions may be effective in changing high risk sexual behaviors.
The research presented here results from qualitative studies conducted among African-American women in Houston, Texas, to elicit information that could be used to develop an HIV prevention intervention for similar populations. The research had two overarching purposes: to examine the sociocultural contexts of sexual risk taking among African-American women and to determine how a videotape-based HIV prevention intervention could be tailored so that it is effective in preventing HIV transmission among African-American women.
Methods
Design
This study utilized a qualitative exploratory design to elicit information about strategies for preventing HIV transmission among African-American women. Twenty five low income African-American women, aged 18–29, participated in five focus groups of five women each conducted at a housing project in Houston, Texas. Houston, Texas, a large demographically diverse metropolitan area, was selected as the study site because of its distinction as a leading HIV/AIDS epicenter in the United States [6]. The housing complex was targeted for convenience sampling and because it is located within the same predominantly low-income African-American community as the research institution. Approval for the study was obtained from the relevant university Committee for the Protection of Human Subjects.
Procedure
Focus group participants were recruited by displaying posters and flyers at strategic locations in the housing complex identified by the project manager. The flyer listed the study inclusion criteria: African-American heterosexual female, aged 18 to 29, self-reported unprotected vaginal intercourse with two or more partners or injection drug using partner in the last six months, or having been diagnosed or treated for a sexually transmitted disease in the past year. The flyer listed a university telephone number that prospective participants could call to obtain additional information about the study and/or to schedule their participation in a group. A trained research assistant confirmed the caller's eligibility. The study participants were recruited using a convenience sampling approach and study participants encouraged their friends to enroll in the study. Of the 89 prospective participants who contacted the university, 42 agreed to participate. Of that number, seventeen individuals did not appear on the scheduled day and thus 25 individuals formed the study sample. The groups were conducted at the housing project's clubhouse by a facilitator, slightly older than the target group, trained in focus group methodology and having approximately seven years' experience conducting groups with low-income African-American women. The facilitator was assisted by a trained research assistant who took notes to ensure that pertinent information not captured by the audiotape was obtained and to record major points and items discussed.
Participants were told that their involvement would help researchers and clinicians to learn about what they feel is important and better ways of helping the African-American community to combat HIV and AIDS. They were informed that their involvement was voluntary, that they could refuse to answer any question, and that they could cease participation at any time without penalty. Agreement was also obtained to audiotape the session. Prior to the start of each group, active written informed consent was obtained from each participant. The facilitator discussed with participants the issue of confidentiality of the information discussed at the meeting. Because some of the women were familiar with one another as a result of residency within the same housing complex, the facilitator ensured that all women were aware that what was discussed during the session should not leave the session at its conclusion. They were advised that they would receive a $25 mall gift certificate and condoms as incentives for their participation. Respondents were also advised that the tapes, which were anonymous, would be destroyed following transcription and checking. All questions as well as the informed consent were provided in English.
Data Collection
Semi-structured and open-ended questions were used to elicit information from the participants based on our research interest in determining perceptions of AIDS as a threat to African-American communities, barriers and facilitators to safer sex practices, characteristics of past situations which have placed persons at risk for HIV infection, the role of alcohol and drugs in creating high-risk sexual situations, suggestions on ways to enhance the saliency of AIDS in the African-American community and on how HIV intervention videotapes could be produced to achieve maximum levels of interest [See Table 1].
Table 1 Focus group guide questions
1. Do you perceive AIDS as a threat to the African American community and why?
2. What are the perceived roles of women in heterosexual relationships in the African American community?
3. What are the expectations for personal and sexual responsibilities for contraception and sexually transmitted diseases prevention among African American women?
4. What situations have placed you at risk for HIV infection in the past?
5 How have alcohol and drug use placed you at risk of HIV infection?
6. What are the things that motivate you to practice safe sex?
7. What are the things or barriers that prevent you from practicing safe sex
8. Why do you think that AIDS is spreading so rapidly in the African American community?
9. What information do you think we need to include in a videotape developed to train African Americans about HIV prevention that will encourage them to watch the videotapes?
10. Do you have any other suggestions on how AIDS can be prevented in the African American community?
11. What can we do to get people to sign up for focus groups such as this one and also get them to participate in HIV/AIDS training programs?
12. What can we do to make these training programs most useful to you?
Focus group questions were generated in three stages. The first involved conducting interviews with six key informants [four public health researchers, one pharmacist, and one nurse experienced in HIV/AIDS prevention among African Americans] to generate working hypotheses on facilitators and barriers to HIV prevention and program messages and methods. The hypotheses formulated were then tested in interviews with women similar to members of the target population to reshape the hypotheses and continued until no new information emerged. The resulting hypotheses were used to generate questions that were then field tested with members of the target population. At the end of the first group, additional changes were made as needed to the way in which questions were asked not to elicit different information, but rather to add clarity for the participants.
The resulting focus group interviews from which this research is reported were conducted using a guide consisting of a written list of questions and probes. McCracken [23] has highlighted the advantages of utilizing such a guide including to ensure that all areas of interest are covered and to focus the researcher's attention on listening to the informants, thereby enabling a better understanding of their lines of thought and possibly, unanticipated explanations of the concepts. The duration of each group was about two hours. The focus groups were transcribed at the completion of all groups. Because the questions were carefully crafted and the purpose of the groups was to promote self-disclosure and to generate ideas and perceptions about HIV/AIDS in the African-American community, any idea that emerged was considered valid and not subject to verification by the research team.
Data analysis
Data analysis was performed according to the standard grounded theory approach of Glaser and Strauss [24]. The relatively unclear understanding of the sociocultural contexts of HIV sexual risk taking among African-American women made a qualitative analysis particularly useful. Codes and categories were developed by doing a line-by-line analysis of the participants' transcripts [25] and identifying the emerging themes. The thematic concepts representing ideas expressed by a majority of the members of three or more focus groups were characterized as a domain and are reported below.
Results
Study participants
The study sample consisted of 25 women, ages 18 to 29. The mean age was 24 years. Among the 25 women in the sample, two had completed some post-secondary education and the remainder had completed eighth or ninth grade. Twenty of the women were unemployed and most of these women identified themselves as homemakers. Of the remaining five, the most frequently cited source of employment [n = 4] was the medical field [nursing or medical assistant]. Two identified themselves as HIV positive. None of the respondents had health insurance. The names used for the quotes given in this report are pseudonyms.
Risk situations for HIV transmission
The situational determinants of HIV risk taking and their impact on HIV/AIDS prevention behaviors and education programs were examined. The rate of HIV infection and AIDS is higher among African-American women in Houston and nationally [6] and African-American women are among the poorest of racial/ethnic minorities [26]. Although poverty itself is not a precursor for HIV infection, it does lead to several psychosocial factors that may place African-American women at higher risk for infection. The present population revealed that they had most frequently placed themselves at risk for HIV infection through drugs and drinking and they also reported drug and alcohol use as important barriers to practicing safer sex. Although these behaviors may not be directly linked to poverty, it is reported that people who are oppressed will often turn to substances as a way of coping with daily life [27]. The women also reported that the need for money and having sex for money to buy food or drugs had placed them at risk for HIV transmission.
Comprehension of the situations explicated by low-income African-American women that place them at risk for HIV is of critical importance when developing programs that address HIV risk reduction. Most women had placed themselves at risk of transmission through drug use or needle sharing and having unprotected sex. The sexual activity took place in some instances in exchange for drugs or money and to purchase basic necessities such as food for their children. Other women stated that they had been placed at risk of HIV transmission because they believed they were in monogamous relationships but later learned their partners had been unfaithful.
Edith, a 27 year old volunteer, described the situations that have placed her at risk for HIV in the past year:
When you are on drugs and then you are drinking, it impairs your senses and you don't use common sense or knowledge of what you are doing. You just get caught up in the moment.
Mary, a 26 year old homemaker explains:
Having sex period is a risk. When you can't feed your kids and you need money. When you go sleep around and have sex and they're not using condoms. Cause some of them say they don't use them and some of them say they don't want to use them. They have all kinds of excuses not to use them.
Celia, a 25 year old medical worker, described how she was placed at risk of HIV infection:
I married a man, not really knowing him, and he was sleeping with a lot of women, and sleeping with me unprotected. Yeah, right after we got married, he told me he wasn't going to cheat on me no more and when I found out some of the women he was cheating on me with, I knew that they always stay at the doctor cause something always be wrong with them. I was pregnant and hoping that nothing was wrong with me.
Related to risk for HIV, the participants were asked to discuss why HIV is so prevalent within their [the African-American] community. Unprotected sex, lack of awareness, lack of medical access, and early sexual initiation were most frequently mentioned. When asked why HIV is spreading so rapidly in the African-American community, unprotected sex was stated most often. Lack of knowledge or awareness about prevention was also frequently stated. However, drug use was not highly ranked and was infrequently stated compared to women's beliefs that lack of access to medical care and non-priority of health as well as early sexual initiation and feelings of invincibility among young people were more significant contributors to HIV's rapid spread among African Americans.
Betty, a 25 year old homemaker said:
I think it's spreading around so fast because people that have AIDS just constantly have sex and they pass it on and pass it on and pass it on.
Celia expressed her agreement and added:
Everybody's sleeping with everybody. The women are sleeping with women; the men are sleeping with men and then they sleeping with each other. Ménage á trios or threesomes, foursomes, they popping Ecstasy pills, just partying hard, they smoking marijuana, they speed balling, they taking downers, handlebars. Uh, I don't know, just really not caring and so they're not even going to the doctor to see if anything is wrong with them. They're just continually sleeping and not stopping to take care of their own bodies, to make sure that they are okay.
Kelli, a 24 year old operator stated:
Well, first of all because we're uneducated. And we're just unconcerned. Like they say, you have to do what you have to do to get what you need, and if it's one of them five minute things, they ain't thinking about no condom.
Kim, a 24 year old cashier said:
I don't know – people feel like they can't get it. Maybe they feel like it just can't get to them. They feel like it can get to other people, but it can't get to them.
Although the women related their individual risk for HIV infection to drugs and alcohol, they did not associate drug and alcohol use with the rapid spread of HIV within the overall African-American community.
Barriers to safe sex practices
When asked to name situations that have prevented them from protecting themselves against HIV infection, the reasons given were much the same as when they were asked about situations that have placed them at risk for HIV infection. About one-third of the women named drug and alcohol use as responsible for them not taking needed precautions. Surprisingly, about one-third of women stated that their barrier was their belief that there was no risk based on their being in a monogamous relationship and feeling no need to use protection, but later learning that their mate was unfaithful. Other reasons given were lack of concern, being unprepared, partner's refusal to use a condom, and lack of money to buy condoms.
Chaka discussed how drug and alcohol use were barriers to safe sex practices for her:
Working in the club, you drink and if you intensify that with drugs, you don't' know who you're going home with. You don't know who been to your house and what they have done to you on account of you being high, on account of you being drunk or you done overindulged in either or and it's just not a good feeling.
Charlie stated that she was placed at risk of contracting HIV by her spouse:
I married a man not really knowing him and he was sleeping with a lot of women and then sleeping with me unprotected.
Vicki, an 18 year old high school student said:
Like I said, not having the money to go and buy the protection. That's going to prevent you from preventing it.
In this culture, particularly among poorer uneducated women, men may play a more domineering role over women [28]. There is also a misconception among African-American men and some women that condom use reduces the sensation produced during sexual intercourse [29]. Some of the women in this group reported that their partners had refused to use a condom.
Facilitators to safe sex practices
The women were motivated to practice safe sex because of fear of contracting sexually transmitted diseases and HIV, desire not to become pregnant, and personal experience with someone who had contracted HIV. Over one-third of women acknowledged that fear of contracting sexually transmitted infections including hepatitis and HIV motivated them to practice safe sex. A participant stated:
Every disease that's in the book that's not curable is enough to scare my clothes on.
Mary said:
Syphilis, gonorrhea, HIV, herpes; that ought to want to motivate anybody to practice safe sex.
Other women said that their desire not to become pregnant motivated them to practice safe sex. Sally was motivated by the need to care for her children.
She stated:
Because I have three children and I don't plan on dying until the Lord takes me.
A smaller number of women declared that they were motivated to practice safe sex because they had seen, first hand, the effects of HIV.
Kim said:
Things that motivate me – you see somebody outside on the streets and you see them with it and you see the effect that it's had on them and you look at them and you say that's something that you don't want to do so it motivates you to practice safe sex.
Intervention components
When asked to describe what should be included in a videotape aimed at prevention of HIV within the African-American community, the most prevalent response among participants was to include personal experiences of people affected by HIV and AIDS. They believed that testimonials from those infected with HIV and sensational footage of the ravaging effects HIV and AIDS have on the human body would be most effective. To create a video that addresses HIV prevention, over half of the participants recommended sensationalism to garner the audience's attention. The purpose of the video would be to show the illness, pain, rejection, medication regimen, and years of life lost among those infected.
Fonda, a 24 year old GED preparer with 2 years of college said:
To make them watch it, show the blood, the guts, the pus, the sores, the relationships...
Sally said:
Show them how the body deteriorates from having AIDS. Show them everything else in the world that they are going to miss out on if they don't take care of themselves.
Another participant said:
You need to let them see how these people are just in so much pain and rejection, and not having the finances and things. In order to live like the guy that's an athlete [Magic Johnson], you know to live by the medication, how people are going to other places to get it.
A variety of other suggestions were given including the use of popular culture in the forms of rap and gospel music videos, productions and concerts and creating a Surgeon General's warning against unsafe sex [much like what has been done with tobacco],. Ceah, a 25 year old health care worker said:
Drum them in any way you can. If they like rap, rap it to them. If they like gospel, you sing it to them. If prayer is what it takes, you pray it to them. By any means necessary, you get your message across. Try all ways.
Other recommendations included developing a video in the form of a comedy-drama or a cartoon or using a campaign similar to the one utilized by Mothers Against Drunk Driving in which a person pre-HIV is shown followed by the person who has become ill post-HIV infection.
The participants also offered suggestions for recruiting African Americans into prevention programs where the videotape could be shown. The majority of women suggested the use of financial incentives including air conditioning units, fans, food, and amusement park tickets. The women recommended recruiting participants within the communities in which they live, going door-to-door if needed, and having such programs take place in community settings, such as schools and neighborhood centers. Other suggestions included having family friendly events that men would want to attend, thus ensuring women and children will follow.
Fonda believed that you have to give something to get people to participate. She said:
You know, people want something for something; nobody wants nothing for nothing. Nothing is free in this world.
Shirley, a GED student suggested community involvement:
Fundraisers, cooking things, get the community involved with you as the person that wants to get these things started. Once you get these things started, once you get the community, you got it.
Nilene suggested:
Just get it [word] out, it has to be let out in some kind of way, as far as radio stations, TVs, billboards. People in the communities, the main office, the campus, I mean everywhere, it has to be everywhere. It can't be in one spot, it has to come out, go out.
Veronica, a 25 year old youth coordinator with one year of college says:
If you get the men there, the women will come. A basketball tournament, yes, they like basketball and once you get the men there, the women are gonna come. Seriously, it's bad to say it like that but you're giving something. It's a sex symbol, yes, you understand what I'm saying, but it's a way to get them out there.
Conclusion
Prior research has indicated the need to develop HIV intervention programs that target the socioeconomic contexts of women at risk for infection including cognitive or social cognitive, educational and/or skills building content-specific components. Women in the present study recommended that HIV/AIDS videotaped messages should be developed that highlight the sensational effects of the disease. Contrary to research indicating this method does not work [30-32] and based on the results of the research presented here, it may be worthwhile to field test a videotape that features HIV-positive people with groups of African Americans to evaluate the utility of such a teaching tool as they may have an essential role to play in AIDS prevention. Similar to our findings with African American men [33], the participants also recommended free, confidential testing in a community-based setting with the provision of incentives for testing and participation, findings that offer a first glimpse at what researchers and practitioners can do to create culturally relevant HIV prevention programs for African-American women. Although the findings are limited due to the small sample size, the use of convenience sampling and the location in the southern part of the United States, this research may provide a base for conducting larger studies among low-income African-American women. Before programs are developed, the barriers poor African-American women face on a daily basis should be addressed. Programs are not only needed to help negotiate these barriers, the barriers should also be included in program development. This may necessitate the involvement of various social service agencies as well as health educators and nursing and medical professionals. The women also need skills training to enhance their abilities to negotiate safer sex practices with their partners. If the tide of HIV and AIDS infection among African Americans is to be reduced, programs must incorporate culturally relevant contextual information presented to the target audience in a setting and in a manner that addresses their norms and beliefs and provides them the knowledge and skills needed to make correct decisions2.
Health professionals may wish to learn more about the barriers these women face and work with social service providers to address the issues most salient to the women before developing patient education materials for HIV/AIDS prevention. Methods that appeal to the target audience should be devised but nursing professionals should remember that low-income African-American women are a heterogeneous group. Interventions such as videotapes should be developed to have a wide appeal yet have contextual, cultural, and gender specificity and it is important to remember that it is best to educate women within a community-based setting. Resources are needed to identify, recruit and retain African American women into HIV intervention programs.
Competing interests
The author(s) declare that they have no competing interests.
Contributors
EJE, AFM and GOO conceived and designed the study. EJE, AFM, RJP jointly planned and executed the data analyses. EJE wrote the paper with assistance from AFM, RJP, GOO and NIO.
Acknowledgements
National Institute of Mental Health (NIMH) grant number R01 MH062960-03 supported this research.
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| 15774003 | PMC555750 | CC BY | 2021-01-04 16:39:32 | no | Int J Equity Health. 2005 Mar 17; 4:4 | utf-8 | Int J Equity Health | 2,005 | 10.1186/1475-9276-4-4 | oa_comm |
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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-5-71574544410.1186/1471-230X-5-7Research ArticleAntioxidant effects of insulin-like growth factor-I (IGF-I) in rats with advanced liver cirrhosis García-Fernández María [email protected]ázar Inma [email protected]íaz-Sanchez Matías [email protected] Iñigo [email protected] Juan Enrique [email protected] Alberto [email protected] Amelia Díaz [email protected] Encarna [email protected]ález-Barón Salvador [email protected] Department of Physiology, School of Medicine, University of Navarra, Pamplona, Spain2 Department of Human Physiology. School of Medicine, University of Málaga, Spain3 Department of Physiology, School of Medicine, University of San Pablo-CEU, Madrid, Spain4 Department of Chemistry, School of Medicine, University of Navarra, Pamplona, Spain5 Department of Internal Medicine, Hospital Sierrallana, Torrelavega. Cantabria, Spain6 Department of Microbiology, School of Medicine, University of Málaga, Spain2005 3 3 2005 5 7 7 22 7 2004 3 3 2005 Copyright © 2005 García-Fernández 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 exogenous administration of Insulin-like Growth Factor-I (IGF-I) induces hepatoprotective and antifibrogenic actions in experimental liver cirrhosis. To better understand the possible pathways behind the beneficial effect of IGF-I, the aim of this work was to investigate severe parameters involved in oxidative damage in hepatic tissue from cirrhotic animals treated with IGF-I (2 μg. 100 g-1. day-1). Iron and copper play an important role in oxidative mechanisms, producing the deleterious hydroxyl radical (*OH) that peroxides lipid membranes and damages DNA. Myeloperoxidase (MPO) and nitric oxide (NO) are known sources of free radicals and induce reduction of ferritin-Fe3+ into free Fe2+, contributing to oxidative damage.
Methods
Liver cirrhosis was induced by CCl4 inhalation in Wistar male rats for 30 weeks. Healthy controls were studied in parallel (n = 10). Fe and Cu were assessed by atomic absoption spectrometry and iron content was also evaluated by Perls' staining. MPO was measured by ELISA and transferrin and ferritin by immunoturbidimetry. iNOS expression was studied by immuno-histochemistry.
Results
Liver cirrhosis was histologically proven and ascites was observed in all cirrhotic rats. Compared to controls untreated cirrhotic rats showed increased hepatic levels of iron, ferritin, transferrin (p < 0.01), copper, MPO and iNOS expression (p < 0.01). However, IGF-treatment induced a significant reduction of all these parameters (p < 0.05).
Conclusion
the hepatoprotective and antifibrogenic effects of IGF-I in cirrhosis are associated with a diminution of the hepatic contents of several factors all of them involved in oxidative damage.
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Background
Insulin-like growth factor-I (IGF-I) is an anabolic hormone produced in different tissues in response to growth hormone (GH) stimulation [1]. Liver synthesis of IGF-I accounts for 90% of the circulating peptide. In cirrhosis the reduction of receptors for GH in hepatocytes and the diminished synthesis ability of the hepatic parenchyma cause a progressive fall in serum IGF-I levels. The clinical impact of the decreased in IGF-I production in advanced cirrhosis is largely unknown [2-5]. Recent studies from our laboratory in rats with carbon tetrachloride-induced cirrhosis have demonstrated that short courses of treatment with low doses of IGF-I are able to produce systemic beneficial effects [6-13] and are associated to hepatoprotective [14,15] and antifibrogenic [16] effects.
In order to give a better insight into the pathways by which IGF-I seems to exert its the hepatoprotective and antifibrogenic actions, this study was aimed at analyze several parameters involved in oxidative stress or inflammation in the liver, such as metals ions (iron and copper), iron transport and store proteins (transferrin and ferritin) and enzymes (myeloperoxidase -MPO- and inducible nitric oxide synthase -iNOS-) both in IGF-I treated and untreated cirrhotic rats.
Metal ions, such as iron and copper, exhibit the ability to produce reactive oxygen species, resulting in lipid peroxidation, DNA damage, depletion of sulfhydryls and altered calcium homeostasis [17-19]. Iron-dependent processes play a pivotal role in the development of oxidative-induced cell injury. Specifically, the generation of hydroxyl radicals from hydroperoxide and the formation of aldehydes and lipid peroxy radicals from lipid hydroperoxides are catalyzed by redox-active metals, including iron and copper [17,20,21]. MPO and NO are known sources of free radicals and induce reduction of ferritin-Fe3+ into free Fe2+ contributing to oxidative damage [22,23].
Methods
Induction of liver cirrhosis
Cirrhosis was induced as previously described [9,12]. Briefly, male Wistar rats (3 weeks old, 130–150 g) were subjected to CCl4 inhalation (Merck, Darmstadt, Germany) twice a week for 11 weeks with a progressively increasing exposure time from 1 to 5 minutes. From that time until the 30th week rats were exposed to CCl4 once a week for 3 min. During the whole period of cirrhosis induction animals received Phenobarbital (Luminal, Bayer, Leverkusen, Germany) in the drinking water (400 mg/L). Rats were housed in cages placed in a room with 12-hour light-dark cycle and constant humidity and temperature (20°C). Both food (standard semipurified diet for rodents; B.K. Universal, Sant Vicent del Horts, Spain) and water were given ad libitum. Healthy, age and sex-matched control rats were maintained under the same conditions but receiving neither CCl4 nor Phenobarbital.
All procedures were performed in conformity with The Guiding Principles for Research Involving Animals [24].
Study design
The treatment was administrated the last three weeks (27th -30th) of CCl4 exposure (from day 0 to day 22nd). In the morning of day 0, animals were weight and blood samples were drawn from the retroocular venous plexus from all rats with capillary tubes (Marienfeld, Germany) and stored at -20°C until used for analytical purposes. Cirrhotic rats were randomly assigned to receive either vehicle (saline) (CI, n = 10) or recombinant human IGF-I (Pharmacia-Upshon, Sweden) (2 μg × 100 g bw -1 × day -1 in two divided doses, subcutaneous) (CI+IGF, n = 10) for three weeks. Control rats (CO, n = 10) received saline during the same period. The last dose of IGF-I was administrated the day 21st at 6 p.m.
In the morning of the 22nd day, animals were weight and killed by decapitation. After the abdominal cavity was opened, the liver was dissected and weight. A sample from the left major liver lobe was processed for histological examination (fixed in Bouin's solution). The rest of liver samples were stored at -80°C.
Liver histopathology, Perls'stain and immunohistochemistry
Bouin-fixed tissues were processed and sections (4-μm.) were stained with Haematoxylin and Eosin and Masson's trichrome. Liver cirrhosis was diagnosed according to the criteria previously described [14,16]. Liver sections were stained for iron detection with Perls' Prussian Blue [25,26]. A semiquantitative score was given since 0 to 6 points: 0 when no staining was observed, as it was observed in controls; 6 points were assigned to sections with the maximal staining (full staining), that it was observed in liver macrophages and fibrous septa from cirrhotic rats; 2–5 points when the staining were less extent. Four fields from each preparation (×100 magnification) were evaluated twice by two different observers. The arithmetical mean of the two punctuations was taken as the final score.
Immunohistochemical staining of iNOS in paraffin sections (4 μm) was performed using an avidin-biotin peroxidase technique as described by Shu el al. [27], with some modifications. The primary antibody anti-iNOS (1:500) was obtained from Oxford Biomedical Research, INC, NS 01. The procedure for negative controls was performed by omission of antigen retrieval part of the protocol. The positive staining was estimated blindly in the entire preparation by using a numerical scored from 1 to 8 points attending to the staining area and the intensity of the color. The arithmetical mean of the two evaluations was taken as the final score.
Analytical methods
Sample processing
Hepatic samples were homogenized in a Potter homogenizer in 7 volumes of cold buffer (0.1 M Tris-HCl, 0.25 M sucrose, pH= 7.4) containing 5 mM 2-mercaptoethanol, 0.5 μg/mL Leupeptin, 0.7 μg/mL pepstatin A and 100 μg/mL PMFS. Fibrous parts and unbroken cells debris were eliminated by centrifugion at 500 g for 5 min. Supernatans were used as the whole homogenate.
Analytical determinations on hepatic homogenates
MPO was measured by ELISA, using a commercial kit from BIOXYTECH® (OXIS Int. Portland, OR, USA). Transferrin and ferritin were determined by immunoturbidimetry, using a Hitachi 710 autoanalyzer (Roche Diagnostic, Basilea, Switzerland) and kits for clinical human, from the same laboratory. MDA was assessed after heating samples at 45°C for 60 minutes in acid medium. It was quantitated by a colorimetric assay using LPO-586 (Bioxytech; OXIS International Inc., Portland, OR, USA), which after reacting with MDA, generating a stable chromophore that can be measured at 586 nm (Hitachi U2000 Spectro; Roche). Total proteins were assessed by Bradford's method [28].
Determinations of iron and copper by Atomic Absorption Spectrophotometry
Representative samples (approximately 1 g. of each rat liver) were collected, weighed and later dried in stove (70°C) to constant weight. Iron and copper concentrations were determined by flame atomic absorption spectrophotometry (Perkin Elmer 460, Uberlingen, Germany) [25].
Statistical Analysis
Data were expressed as mean ± SEM. To analyse the homogeneity among groups, Kruskall-Wallis test was used, followed by multiple post-hoc comparisons using Mann-Whitney U tests with Bonferroni adjustment. Any P value < 0.05 was considered to be statistically significant. Calculations were performed with SPSS program version 6.0 (SPSS Inc., Chicago, IL).
Results
Liver cirrhosis was histologically proven and ascites was observed in all rats treated with CCl4.
Table 1 shows the values of parameters involved in oxidative damage in hepatic homogenates. Compared with healthy controls, untreated cirrhotic rats (CI group) showed increased hepatic levels of the following variables: Fe (p < 0.01); transferrin and ferritin (p < 0.01); Cu (p < 0.001); MPO and iNOS expression (p < 0.001). However, cirrhotic animals treated with IGF-I (CI+IGF group) showed significant reductions in hepatic Fe and Cu contents, ferritin, transferrin and MPO levels and iNOS expression (p < 0.05 for all the parameters).
As shown in Figure 1, untreated cirrhotic rats (CI) have significantly greater scores of iron (ferric iron) in the liver using Perls' Prussian blue staining as compared with controls (CO = 0.68 ± 0.11) and cirrhotic rats treated with IGF-I (CI = 5.50 ± 0.22; CI+IGF = 1.70 ± 0.40; AU, p < 0.01). As mentioned before, hepatic levels of iron, assessed by atomic absorption spectrophotometry, were also significantly higher in CI group compared to controls and CI+IGF group (see Table 1). On the other hand, hepatic levels of copper were also increased in untreated cirrhotic rats and returned to normal in CI+IGF group.
Figure 2 shows the immunohistochemical expression of iNOS that was increased in CI group compared both to control and CI+IGF groups.
In order to find a relationship between the studied parameters and oxidative liver damage, MDA levels, an index of lipid peroxidation, were evaluated [29]. Hepatic levels of MDA (nmol/mg protein) were increased in untreated cirrhotic rats compared with control group (CI = 1.741 ± 366; CO = 0.565 ± 0.030; p < 0.05) as it was previously reported in similar protocols [14,16]. This marker of lipid peroxidation was again reduced in CI+IGF (0.99 ± 0.11 nmol/mg protein, p = ns vs controls). A significant direct correlation was found between hepatic iron and hepatic MDA levels (see Figure 3, r = 0.857 p < 0.001). In addition, MPO correlated with hepatic levels of iron (r = 0.719, p < 0.001), iron content with hepatic ferritin (r = 0.656, p < 0.001) and hepatic levels of Cu with MDA (0.649 p < 0.01).
Discussion
These results show that the treatment with low doses of IGF-I induces a reduction of all studied parameters involved in oxidative damage mechanisms in this model of cirrhosis. These findings support the hepatoprotective and antifibrogenic effects previously reported [14,16]. This study also provides evidence for the involvement of oxidative stress in the cell injury occurring in CCl4-induced cirrhosis associated with iron and copper overload and an increase of myeloperoxidase and iNOS expression.
It is well known that iron and copper promote oxidant forces [17,18,21,30]. Oxidant stress is considered present when there is either an overproduction of free radicals or a significant diminution in antioxidant defenses, the result of either being excessive levels of free radicals [29,31]. In both iron and copper storage disorders, generation of free radicals and depletion of antioxidants may be critical factors determining the intensity of liver injury [18,19,30,31]. In a previous work we showed that antioxidant enzymes (superoxide dismutase, SOD, Glutathione peroxidase, GSHPx, and catalase) were reduced in the liver of cirrhotic animals and improved by low doses of IGF-I administration [14]. Of interest, in the present study we demonstrate that hepatic levels of iron and copper metals (both involved in oxidative damage), increased in untreated cirrhotic rats, reverted to normal levels after IGF-I treatment.
Free iron (or low molecular iron or chelatable iron pool) facilitates the decomposition of lipid hydroperoxides resulting in lipid peroxidation and induces the generation of OH radicals and also accelerates the nonenzymatic oxidation of glutathione to form O2*- radicals [18,19,30,32]. The direct and significant correlation between lipid peroxidation and hepatic iron content presented here provides new evidence of the relationship between these parameters.
Most of the body's iron is tightly bound to transferrin, entering cells via receptor-mediated endocytosis. Transferrin avidly binds 2 moles of Fe3+ per mole of protein [32]. Normally the average of transferrin iron saturation is about one third of the full capacity, thereby ensuring that there is virtually no free iron circulating in the extracellular fluids. At pH 7.4, the iron-transferrin complex does not participate in the Fenton reaction. Under more acidic conditions, the complex breaks down with release of iron. This is of important physiological relevance, since the iron-transferrin complex, within endocytotic vesicles, is subjected to an acidic environment (pH 5–6). Intracellular iron released from transferrin is rapidly incorporated into ferritin, minimizing its inherent toxicity [17,30,31]. Iron can be released from the ferritin within the cell by a number of factors that occur in inflammation: acidic pH, proteolysis, myeloperoxidase, NO, O2*-, etc. [33]. Enhanced degradative proteolysis, which also occurs in oxidative stress, may lead to proteolytic modification of ferritin, causing an increase in cellular iron. Although in this study free iron could not be quantified, all of the factors certainly involved in inducing an increase of free iron pool appeared elevated in untreated cirrhotic rats (MPO, iNOS, Cu,...) and returned to normal levels after IGF-I-treatment.
In the present study, we have found that hepatic transferrin and ferritin levels increased in cirrhotic rats with a parallel rise in iron deposition, whereas in cirrhotic rats treated with IGF-I all the above-mentioned parameters appeared diminished (see Table 1 and Figure 1).
High serum ferritin levels and hepatic iron storage have also been reported in hepatitis B virus and hepatitis C virus-related chronic hepatitis and alcoholic liver disease [26,34,35]. It has also been shown that iron induces ferritin biosynthesis [21,22,35-39]. A result here presented shows a direct correlation between hepatic iron and ferritin levels which is consistent with the over-mentioned Authors statements.
In liver cirrhosis the increase in iron content is not a real iron overload as in hemochromatosis, because iron is stored mainly inside the macrophages [40]. In agreement with this data, the present work shows that the iron scores detected in this experimental model of cirrhosis were found in Kupffer cells, as it is shown in Fig. 1.
Transferrin is mainly produced by the liver when hepatic regeneration takes place, as occurs in cirrhosis [37,41,42]. Thus, the reported increase of transferrin in untreated cirrhotic animals could be due to regeneration. However, cellular proliferation does not explain our findings, because in a parallel study in this series we showed that cellular proliferation (assessed by PCNA expression) was higher in IGF-treated cirrhotic animals [15] than those which showed lower hepatic levels of transferrin. Therefore, the hepatoprotective effect of IGF-I in cirrhotic animals could be mediated partly by enhancing the endogenous regenerative response, aimed al the restoration of functional liver mass [14]. In the present work, the described increase of transferrin in untreated cirrhotic animals seems to be a defensive response to the enhanced iron content [17,18,21,22,32].
On the other hand, the mechanisms responsible for the effects of IGF-I described in this article are not fully understood. The beneficial effects of IGF-I could be a result of many properties of this hormone that require further investigation. The well known erythropoietic activity of IGF-I [43,44] could even contribute to an extrahepatic utilization of iron, decreasing its storage in the liver.
Hepatic copper overload leads to progressive liver injury and eventually cirrhosis in Wilson disease and Indian childhood cirrhosis [45]. Copper is absorbed into the intestine and transported by albumin to the liver. Any excess in copper levels is excreted into the bile mainly through a lysosome-to-bile pathway. Hepatic copper accumulation results from a reduction in the bile excretion of copper, as occurs in patients with Wilson disease, biliary obstruction, or other types of cholestasis [45]. Cirrhotic animals included in this protocol showed severe cholestasis after receiving CCl4 for 30 weeks. As previously reported [14] IGF-I-treatment induced a reduction in cholestasis parameters (serum levels of bilirubin, alkaline phosphatase and cholesterol). This may account for explanation of the decreased copper hepatic content revealed in the present work.
After hepatic injury, several kinds of cells (endothelial cells, Kuppfer cells, and circulating platelets, neutrophils and monocytes) are activated in the subsequent inflammatory response [23]. Free radicals produced mainly by macrophages cause local tissue damage in inflammatory conditions [23,31]. Neutrophil and monocyte activation is a critical step in both the host defense system against microorganisms and the inflammatory response. When neutrophils are activated, they begin to produce superoxide radicals (O2*-) and secrete myeloperoxidase (MPO) [23]. The majority of the O2*- formed during this respiratory burst is converted to the bactericidal oxidant hypochlorous acid (HOCl) via a series of reactions catalyzed by superoxide dismutase and MPO [23,29]. Numerous MPO-expressing cells have been detected in fibrous septa of human cirrhotic livers [46]. MPO has been identified as a component of human Kupffer cells [46]. The same authors suggest that the oxidative damage resulting from the action of MPO may contribute to acute liver injury and hepatic fibrogenesis [46]. In our study, the increase of MPO in cirrhotic animals and its decrease in those treated with IGF-I suggests an anti-inflammatory effect of this hormone.
Another result which deserves particular mention is that iNOS expression was significantly lower in cirrhotic rats treated with IGF-I compared to untreated cirrhotic animals. This finding is in accordance with those reported by other groups [47-53]. However the versatility of this molecule, small changes in the experimental conditions or the studied cell line can show results that seem to be an apparently contradiction [54-58]. For example, in our experience, we did not find a similar response in early stage of cirrhosis animals (data not shown). Probably, in early stages of cirrhosis NO induces an improvement in parenchyma irrigation by vasodilatation, but in advanced liver cirrhosis, where exist thick collagen septa, the increase of NO results to lead enhancing oxidative damage by N-derived radicals.
Conclusion
In conclusion, these results show that the hepatoprotective and antifibrogenic effect of IGF-I in rats with liver cirrhosis is associated with a significant reduction of the hepatic levels of several parameters such as Fe, Cu, MPO, iNOS, ferritin and transferring, all of them involved in oxidative damage. In this work, iron and copper overload have been demonstrated in the liver from rats with CCl4-induced cirrhosis. The hepatic levels of both metals diminished in cirrhotic animals treated with IGF-I. MPO content, iNOS immunohistological expression and hepatic ferritin and transferrin levels were increased in untreated animals and returned to normal in cirrhotic animals treated with IGF-I.
The IGF-I effects described in the present study suggest that a therapeutical approach targeted at lowering oxidative stress marker levels could be effective in the chronic liver disease.
Abbreviations
IGF-I, insulin-like growth factor-I; Fe, iron; Cu, cooper; MDA, malondialdehyde; CO, control healthy group; CI, untreated cirrhotic rats; CI + IGF, IGF-treated cirrhotic rats; O2*-, superoxide radicals; MPO, myeloperoxidase; iNOS, inducible nitric oxide synthase; AU, arbitrary units.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
MG: Analytical studies, hypothesis and paper elaboration.
ICC: Experimental design and treatment (induction of liver cirrhosis and IGF-I administration), hypothesis, histopathological study and scores.
MDS: Analytical studies and in vivo assay.
IN: Atomic absorption spectrometry assay.
JEP: In vivo assay.
AC: Hypothesis and experimental design and revision.
ADC: Experimental treatment and documentation.
EC: Histopathological study and measurements.
SGB: Revision.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors wish to express their gratitude to Bruce Scharschmidt (Chiron), for generously granting the rhIGF-I used in this study. We are as well deeply indebted to Mr. J. Celaya and "Fundación Echébano" for their financial collaboration, Dr. Daniela Ceccarelli for the helpful discussion and Mr. Paul Golden for the English manuscript review.
Supported by the Spanish Program I+D, SAF 2001/1672.
Figures and Tables
Figure 1 Perl's Prussian Blue staining for ferric iron (original magnification ×150) in the liver of an untreated cirrhotic rat (CI group) and a cirrhotic animal treated with IGF-I. The CI preparation was scored as 3 points (see Methods) and the section from CI+IGF group was scored with 1 point. No staining was found in control group (CO).
Figure 2 Immunostaining for iNOS in liver from: A, healthy control group (CO); B, untreated cirrhotic group (CI); C, cirrhotic animals treated with IGF-I for three weeks. An increased iNOS immunoreactivity was observed in hepatocytes from CI group, compared to controls and CI+IGF groups. These two pictures (B and C) correspond to two animals from each cirrhotic group that presented the most severe cirrhosis. Although in this section (C, CI+IGF) from a series with decompensated cirrhosis can be observed thick collagen septa, it is also clear the hepatoprotective effect of the IGF-I-therapy versus untreated cirrhotic group (B).
Figure 3 Correlation between hepatic iron content and hepatic MDA levels, a marker of lipid peroxidation (Sperman r = 0.857, p < 0.001, two tails).
Table 1 Hepatic levels of some parameters involved in oxidative damage in the three experimental groups.
Control group (CO, n = 10) Untreated cirrhotic rats (CI, n = 10) Cirrhotic rats treated with IGF-I (CI+IGF, n = 10)
Fe (μg/mg protein) 2.74 ± 0.19 12.87 ± 1.90** 6.80 ± 1.10&
Cu (μg/mg protein) 160 ± 5 1626 ± 678*** 500 ± 258&
Ferritin (ng/mg prot) 32.60 ± 3.80 97.60 ± 12** 67.70 ± 11.30&
Transferrin (μg/mg protein) 8.46 ± 1.05 10.96 ± 0.98** 8.36 ± 0.36&
MPO(ng/mg protein) 1.02 ± 0.02 1.25 ± 0.04** 1.06 ± 0.07&
iNOS (AU) 1.20 ± 0.64 5.53 ± 0.54*** 2.88 ± 0.68&
&p < 0.05 between CI and CI+IGF groups; **p < 0.01 and ***p < 0.001 CI vs CO groups; AU = arbitrary units
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| 15745444 | PMC555751 | CC BY | 2021-01-04 16:03:27 | no | BMC Gastroenterol. 2005 Mar 3; 5:7 | utf-8 | BMC Gastroenterol | 2,005 | 10.1186/1471-230X-5-7 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-131576929410.1186/1477-7525-3-13ResearchEstimating a preference-based index for a menopause specific health quality of life questionnaire Brazier John E [email protected] Jennifer [email protected] Maria [email protected] York F [email protected] Health Economics and Decision Science, School of Health and Related Research, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK2 Institute of General Practice and Primary Care, School of Health and Related Research, The University of Sheffield, Community Sciences Building, Northern General Hospital, Sheffield, UK3 Global Health Economics, Solvay Pharmaceuticals, PO Box 220, D-30002, Hannover, Germany2005 15 3 2005 3 13 13 12 1 2005 15 3 2005 Copyright © 2005 Brazier et al; licensee BioMed Central Ltd.2005Brazier 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 aim of the study was to develop a menopause-specific, preference-based health-related quality-of-life (HRQoL) index reflecting both menopausal symptoms and potential side-effects of Hormone Replacement Therapy (HRT).
Methods
The study had three phases: the development of a health state classification, a prospective valuation survey and the estimation of a model to interpolate HRQoL indices for all remaining health states as defined by the classification. A menopausal health state classification was developed with seven dimensions: hot flushes, aching joints/muscles, anxious/frightened feelings, breast tenderness, bleeding, vaginal dryness and undesirable androgenic signs. Each dimension contains between three and five levels and defines a total of 6,075 health states. A sample of 96 health states was selected for the valuation survey. These states were valued by a sample of 229 women aged 45 to 60, randomly selected from 6 general practice lists in Sheffield, UK. Respondents were asked to complete a time trade-off (TTO) task for nine health states, resulting in an average of 16.5 values for each health state.
Results
Mean health states valued range from 0.48 to 0.98 (where 1.0 is full health and zero is for states regarded as equivalent to death). Symptoms, as described by the classification system, can be rank-ordered in terms of their impact (from high to low) on menopausal HRQoL as follows: aching joints and muscles, bleeding, breast tenderness, anxious or frightened feelings, vaginal dryness, androgenic signs. Hot flushes did not significantly contribute to model fit. The preferred model produced a mean absolute error of 0.053, but suffered from bias at both ends of the scale.
Conclusion
This article presents an attempt to directly value a condition specific health state classification. The overall fit was disappointing, but the results demonstrate that menopausal symptoms are perceived by patients to have a significant impact on utility. The overall effect is modest compared to the more generic health state descriptions such as the EQ-5D. The resultant algorithm generates a preference-based index that can be used economic evaluation and that reflects the impact of this condition.
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Background
The increasing demand for economic evaluation of health care interventions has lead to a corresponding rise in the derived demand for evidence on the key parameter inputs into cost effectiveness models. One of those inputs is the health state utility value used to estimate the quality adjusted life years (QALYs) associated with an intervention. This article is concerned with estimating a preference-based measure for generating utility values for menopausal health states.
Most preference-based measures of health such as the EQ-5D, SF-6D and the Health Utilities Index 3, have a generic health state descriptive system [1-3]. However, general measures of health have been found to be inappropriate or insensitive for some medical conditions [4], and it has been found that these instruments are not sufficiently sensitive to the impact of menopausal symptoms [5,6]. There has been increasing interest in estimating preference-based indices from condition specific measures. These have often involved mapping from condition specific measures onto preference-based measures [4,7-9]. While this approach is useful, it is a second best solution for studies that did not use a generic measure and the aim is to estimate generic preference scores. However, for some conditions generic measures may not be appropriate and in this case a better solution would be to elicit preference weights for the condition specific measure [10]. There have been a number of studies published recently that have estimated conditions specific preference scores, including for Rhinitis [11], Erectile Dysfunction [12], asthma [13] and Prostate symptoms [14]. This is the first attempt to estimate a preference-based measure for menopausal symptoms.
The aim of this study is to quantify the impact of menopause-related health problems on health-related quality of life as indicated by a "strength-of-preference" index. This project had three components. The first was to construct a health state classification system for menopausal symptoms based on work by Zoellner and others [15]; secondly a sample of menopausal health states defined by the latter were then valued by means of the time trade-off; and then modelled the health state values using regression techniques to produce an algorithm for valuing all states described by the menopausal health state system.
Methods
The menopause-specific health state classification
A menopause-specific quality-of-life questionnaire has been developed by Zoellner and colleagues [6,15]. Initially, a pool of 39 menopause-related items – identified as being important on the grounds of two focus group sessions of peri- and postmenopausal women, literature review, and expert opinion – underwent intensive analysis to determine the degree of fulfilment of standard psychometric criteria of re-test reliability, face validity, construct validity and convergent validity.
The application of these criteria resulted in a questionnaire with 22 items grouped into 6 domains, namely (1) psychosocial, (2) physical, (3) vasomotor, (4) sexual, (5) menstrual, and (6) androgenic complaints. In order to derive a health state classification from the former, the most robust item(s) were chosen from each domain. As it was felt important to cover potential side-effects of Hormone Replacement Therapy (HRT), the menstrual domain is represented with two items – 'breast tenderness' and 'vaginal bleeding' – in the classification systems; the latter hence consists of the following seven domains of menopausal health (see table 1): hot flushes, aching joints/muscles, anxious/frightened feelings, breast tenderness, bleeding, vaginal dryness and undesirable androgenic signs. The assignment of the number as well as the descriptor of levels was performed according to the frequency distributions observed in the screening section of the postal survey (n = 785, Table 1). Each dimension contains between three and five levels and defines a total of 6,075 health states.
Table 1 The Menopause health state classification
1. hot flushes
1) You have no hot flushes
2) You get 1–3 hot flushes per day
3) You get 4 or more hot flushes per day
2. aching joints or muscles
1) You have no aching joints or muscles at all.
2) You have 1–3 episodes of aching joints or muscles per week.
3) You have 4 or more episodes of aching joints or muscles per week.
4) You have mild to moderate constant pain in your joints or muscles.
5) You have severe constant pain in your joints or muscles.
3. anxious or frightened feelings
1) You do not have anxious or frightened feelings.
2) You have anxious or frightened feelings 1–3 times per week.
3) You have anxious or frightened feelings 4 or more times per week.
4. breast tenderness
1) You have no breast tenderness.
2) You have mild to moderate breast tenderness.
3) You have severe breast tenderness
5. bleeding
1) You have no bleeding
2) You have mild regular (monthly) bleeding
3) You have mild irregular bleeding
4) You have intense regular (monthly) bleeding
5) You have intense irregular bleeding
6. undesirable cosmetic signs (facial or body hair growth, greasy skin or acne)
1) You have no undesirable cosmetic signs.
2) You have mild to moderate undesirable cosmetic signs
3) You have severe undesirable cosmetic signs.
7. vaginal dryness
1) You have no vaginal dryness.
2) You have mild to moderate vaginal dryness.
3) You have severe vaginal dryness.
The valuation survey
The design of the survey was to elicit values for a sample of states defined by the menopausal health state classification using a variant of the Time Trade-off (TTO) on a sample of women aged 45 to 60. The key design issues were the sample of health states to be valued, the sample of respondents, the valuation technique and the interview.
Selection of health states
It is not possible to value all the states defined by the menopausal specific health state classification. However, there is currently little guidance of the selection of states for valuation [16]. Based on past practice, the states used in this survey were selected using the orthoplan programme in SPSS. This programme generates an orthogonal array of states that need to be valued in order to estimate an additive model. This programme indicated that 49 states were necessary to estimate an additive model. It was decided to enhance these states in order to ensure some degrees of freedom and to permit some examination of interactions. Therefore, the programme selected another 47 as 'hold out' states drawn at random. This resulted in a total of 96 health states valued out of a potential of 6,075 defined by the menopausal health state classification system.
Each respondent was asked to value a sample of eight states. These states were selected from the larger sample of 96 using a stratified sampling technique to ensure that each respondent has a mix of mild, moderate and more severe states. The severity of the states has been assessed by summing the dimension levels. The states were then ranked using this sum score and divided into quartiles to identify four severity groups. Two health states have been selected at random without replacement from each severity group to form a set of eight health states. This was done another 11 times to create 12 sets of states. These 12 sets were used an equal number of times in order to ensure that each of the 96 states would be valued an equal number of times.
The original aim was to interview 150 respondents, where each respondent valued eight health states. This would mean undertaking 24 sessions with between 6–8 respondents at each session and would have resulted in 1200 observations and an average of 12.5 valuations per state.
Selection of respondents
A previous survey was undertaken in Sheffield (UK) with the main aim of validating the new questionnaire designed to assess the health of women in mid -life. One thousand and eighty women aged 45 to 60 were randomly selected from the lists of 6 GPs in Sheffield (180 women per GP List) and sent the postal questionnaire concerning their menopausal symptoms. Of these 790 (73%) were returned. Five were dropped from further analysis due to incomplete response, so the total number was 785 responders. All responders were sent a summary of the study "Women's Health in Mid life" in December 2001 and asked if they would be interested in participating in a second phase of the study. Out of these 417 women replied saying they would like more information of the phase 2 study.
The 6 GP practices signed a consent form agreeing to these women being invited to participate in the valuation survey. Invitation letters were sent by each practice with a Patient information sheet and a Patient consent form. Out of the 417, 229 (55%) attended the interviews and completed a questionnaire.
Valuation technique
Health states were valued using a variant of the time trade-off technique (TTO). This technique asks the respondent to choose between a fixed period of time (t) in the health state to be valued compared to a shorter period in full health (x). The amount of time spent in full health is varied until the respondent is indifferent between the two alternatives. The value of the health state is then x/t for states better than dead.
This study used a self-completed variant of TTO developed by Gudex that uses a titration procedure shown in Table 2, where the respondent is presented with two lists of values [17]. Each row has a value of 25 for t and a declining value for x, where the value of x declines by one year between each row. Twenty-five years was chosen to represent a reasonable life expectancy for this sample of respondents. The respondent is asked to indicate all the cases where they are confident they would choose A (i.e. the health state to be valued), all the cases where they would choose B (i.e. full health) and the put an equals against states where they cannot choose. There was no allowance for states worse than death, but this was felt to be an unlikely scenario for states defined by the menopausal health state classification.
Table 2 The time trade-off question
Choice A ---- Choice B
25 years 25 years
25 years 24 years
25 years 23 years
25 years 22 years
25 years 21 years
25 years 20 years
25 years 19 years
25 years 18 years
25 years 17 years
25 years 16 years
25 years 15 years
25 years 14 years
25 years 13 years
25 years 12 years
25 years 11 years
25 years 10 years
25 years 9 years
25 years 8 years
25 years 7 years
25 years 6 years
25 years 5 years
25 years 4 years
25 years 3 years
25 years 2 years
25 years 1 year
25 years 0 years
Please put an "A" against all cases where you are CONFIDENT that you would choose Choice A.
Please put a "B" against all cases where you are CONFIDENT that you would choose Choice B.
Please put an "=" against the case where you cannot choose between Choice A and Choice B.
Interviews
Respondents were invited to attend interview sessions held in a room at the Institute of General Practice in Sheffield (UK). Two researchers experienced in interviewing patients coordinated these sessions. The interview began with the researchers explaining the purpose of the survey and to explain the TTO task. Patients were asked to complete an example and encouraged to ask questions in order to aid in their understanding. Once the respondents were ready, they were then asked to complete all remaining questions on their own. There were 33 such interview sessions in the survey. Respondents were reimbursed for their time and travel with a voucher for £10.
The questionnaire had questions on age, occupation, education level, general health, whether or not they had stopped menstruating and if so when, and finally whether or not they continued to take HRT. They were then asked to complete the menopausal health state classification. They then undertook a practice TTO question followed by eight TTO questions. The health states were presented in a random order to avoid the risk of an ordering effect. Finally they were asked to value their own health using a TTO question.
Modelling
The overall aim is to construct a model for predicting health state valuations based on the menopausal health state classification. The data generated by the valuation survey described above has a complex structure, as they are skewed and health state valuations are clustered by respondent. Disentangling the respondent effect is a complex task and can only be tackled at the individual level, where each valuation is regarded as a separate observation, rather than using the mean value for each health state. The former has the advantage of greatly increasing the number of degrees of freedom available for the analysis (from 96 to over 1200) and enabling the analysis of respondent background characteristics on health state valuations.
A number of alternative models have been proposed for estimating preference functions from health data [12,14,15]. The general model has been defined elsewhere as [12]:
yij = g (β'xij + θ'rij + δ'zj) + εij (1)
where i = 1, 2, ..., n represents individual health state values and j = 1,2, ..., m represents respondents. The dependent variable, yij, is the TTO score for health state i valued by respondent j. x is a vector of binary dummy variables (xδλ) for each level λ of dimension δ of the classification. Level λ = 1 acts as a baseline for each dimension, so in a simple linear model, the intercept represents state 1111111, and summing the coefficients of the 'on' dummies derives the value of all other states.
The r term is a vector of terms to account for interactions between the levels of different attributes. z is a vector of personal characteristics that may also affect the value an individual gives to a health state, for example, age, sex and education. The role of personal characteristics is not discussed in this paper. g is a function specifying the appropriate functional form. εij is an error term whose autocorrelation structure and distributional properties depend on the assumptions underlying the particular model used.
This is an additive model, which imposes no further restrictions on the relationship between dimension levels of the classification. For example, it does not enforce an interval scale between the levels of each dimension and does not impose ordinality on the levels.
OLS assumes a standard zero mean, constant variance error structure, with independent error terms, that is cov(εijεi'j) = 0, i≠i'. This specification ignores the clustering in the data and assumes that each individual health state value is an independent observation, regardless of whether or not it was valued by the same respondent. An improved specification, which takes account of variation both within and between respondents, is the one-way error components random effects model. This model explicitly recognises that n observations on m individuals is not the same as n × m observations on different individuals. Estimation is via generalised least squares (GLS) or maximum likelihood (MLE).
Analysis of first order interactions alone is problematic, since the large number of possible interactions means there is a risk of finding some are significant purely by chance. We have therefore adopted the approach used in other studies of using summary terms for describing interactions [16,18]. Extreme level dummies were created to represent the number of times a health state contains dimensions at the extreme ends of the scale [18]. Least severe is defined as level 1 on each dimension. Most severe is defined as the bottom level of each dimension. These are used to create dummy variables LEAST and MOST which take a value of 1 if any dimension in the health state is at the least (most) severe level, and 0 otherwise.
Finally we consider alternative functional forms – g in (1) – to account for the skewed distribution of health state valuations. Four functional forms are used. Firstly, a Logit transformation and two complementary log-log transformations suggested by Abdalla and Russell. [19] These are chosen to map the data from the range (-1,1) to the range (-∞,∞) via the unit range (0,1). Secondly, a Tobit transformation which, although designed to deal with truncated data, can approximate for the left skew in this data, where 25% of the values lie between 0.9 and 1. Specifying a Tobit model with upper censoring at 1 does this.
All modelling will be done using STATA 7.0 and SPSSWin.
Results
Respondents
The characteristics of the 229 interviewed women are presented on Table 3. Their mean age was 54 with a range between 46 and 61. Seventy four percent had stopped menstruating and the average time to since they last menstruated was 64 months. A third had taken HRT in the last month. The respondents reported their general health to be in the mid-range of the excellent to poor scale. The seven menopausal symptoms were highly prevalent, with two thirds experiencing aching joints and muscles, nearly half reporting hot flushes and vaginal dryness and around one third experiencing anxiety or fright, breast tenderness and cosmetic signs. The mean valuation of their current health state by the TTO was 22.8 (SD = 4.4) which translated into a health state utility value of 0.91.
Table 3 Characteristics of respondents
Full sample n = 229
Age: mean (s.d) 53 (SD)
Highest qualification %
Degree 26
A levels 11
Other 63
Self-rated general health: %
Excellent 7
Very good 44
Good 31
Fair 14
Poor 4
Reporting the following: %
Hot flushes 45
Aching joints or muscles 74
Anxious or frightened 37
Breast tenderness 31
Bleeding 23
Cosmetic signs 35
Vaginal dryness 45
TTO own valuation 22.8 (4.4)
Stopped menstration 74
Average time since stopped menstruation (months) 64 (72)
Taken HRT in last month 35
Thirty respondents were excluded from the modelling data set, leaving 199. Respondents were excluded due to ambiguity in the responses to the (self-administered) questionnaire. The main sources of ambiguity were the mixing up of responses (e.g. ticks and crosses appearing the wrong way around) and large gaps between the responses with no indication of the appropriate point of indifference. There were also a number of individual responses elicited from 199 respondents that had to be excluded due to similar ambiguities. These exclusions left 1580 health state values across the 96 health states for modelling, a final completion rate of 86% of all questions asked at interview.
Health state values
Descriptive statistics for 50 of the 96 states are presented on Table 4. Each health state is valued on average 16.5 times, which exceeds the original target. Mean health state values range from 0.48 to 0.98 with large standard deviations. The median values usually exceed the mean values, reflecting the highly skewed nature of the data. This skewness is even more apparent at the individual level, as shown in the histogram presented in Figure 1. Very few health states values were 1.0 (32/1580) indicating that that most respondents were willing to trade time for quality of life, however 31% were at the next possible value of 24.5 years. At the other end of the scale, only three had a value of zero where it might have been possible that respondents regarded these states as worse than death.
Table 4 Descriptive Statistics for 50 health state valuations
State Mean n s.d. Median maximum Minimum
1112311 0.93 16 0.08 0.98 0.98 0.78
1112422 0.87 16 0.16 0.90 1.00 0.38
1112433 0.79 15 0.24 0.94 0.98 0.26
1113122 0.88 18 0.16 0.94 0.98 0.42
1113232 0.79 17 0.23 0.82 1.00 0.02
1113512 0.82 15 0.21 0.94 0.98 0.38
1113531 0.80 17 0.22 0.90 0.98 0.38
1121223 0.83 18 0.16 0.88 0.98 0.42
1121331 0.86 16 0.17 0.90 1.00 0.42
1211522 0.87 15 0.19 0.98 1.00 0.38
1322231 0.71 18 0.31 0.84 0.98 0.00
1323123 0.81 14 0.22 0.90 0.98 0.22
1323231 0.72 18 0.27 0.84 0.98 0.22
1331412 0.80 15 0.23 0.90 0.98 0.22
1331432 0.56 13 0.22 0.54 0.94 0.06
1332213 0.77 18 0.23 0.88 0.98 0.26
1333112 0.84 18 0.19 0.92 0.98 0.42
1413211 0.68 13 0.22 0.74 0.90 0.10
1413513 0.78 15 0.22 0.82 0.98 0.22
1423133 0.76 18 0.21 0.80 0.98 0.26
2221511 0.67 13 0.25 0.74 0.98 0.06
2223131 0.83 17 0.14 0.82 1.00 0.54
2231312 0.89 15 0.13 0.98 1.00 0.54
2233333 0.48 13 0.28 0.42 0.98 0.06
2311511 0.87 18 0.16 0.92 1.00 0.42
2313123 0.96 16 0.04 0.98 1.00 0.86
2321122 0.70 13 0.24 0.74 0.98 0.10
2332413 0.54 13 0.26 0.54 0.98 0.06
2421212 0.92 17 0.09 0.98 1.00 0.70
2421322 0.82 18 0.19 0.92 0.98 0.42
2521323 0.65 18 0.30 0.78 0.94 0.00
2523423 0.71 16 0.23 0.74 0.98 0.34
2532323 0.83 18 0.24 0.88 0.98 0.00
2532531 0.71 16 0.26 0.72 0.98 0.14
2533311 0.77 15 0.31 0.94 1.00 0.04
3121111 0.92 17 0.15 0.98 1.00 0.42
3121533 0.81 18 0.20 0.86 0.98 0.34
3132211 0.83 16 0.19 0.90 1.00 0.34
3133412 0.79 17 0.21 0.90 0.98 0.34
3133521 0.67 15 0.26 0.78 0.98 0.14
3232433 0.77 15 0.27 0.90 0.98 0.18
3311433 0.75 17 0.24 0.82 0.98 0.42
3312321 0.89 16 0.14 0.94 0.98 0.46
3412222 0.80 18 0.19 0.86 0.98 0.42
3413111 0.83 18 0.29 0.94 0.98 0.06
3422113 0.82 16 0.14 0.82 0.98 0.56
3422412 0.78 15 0.29 0.90 1.00 0.06
3431133 0.80 16 0.19 0.86 0.98 0.38
3433532 0.71 18 0.32 0.80 0.98 0.02
3511211 0.80 17 0.23 0.86 0.98 0.38
Please put an "A" against all cases where you are CONFIDENT that you would choose Choice A. Please put a "B" against all cases where you are CONFIDENT that you would choose Choice B. Please put an "=" against the case where you cannot choosebetween Choice A and Choice B.
Figure 1 Histogram for TTO values.
Modelling
Basic models: main effects
The Breusch-Pagan test for individual effects suggests these are important (χ2 = 25585.15, P = 0.000) and Hausman's test suggests random rather than fixed effects is the appropriate specification (χ2 = 27.11, P = .035), therefore only Random Effects (RE) and mean models are presented in Table 5. The main effects dummies in each model represent levels of each dimension of the menopausal health state classification. These are expected to have a negative sign and to increase in absolute value within each dimension. It would be inconsistent with the scale for the absolute value to decrease when moving to a worst level within a dimension.
Table 5 Models
Main effects only Interaction effects
(1) (2) (3) (4)
RE Mean RE Mean
c 0.912 0.917 0.925 0.879
HF2 0.007 -0.008 0.006 -0.005
HF3 -0.006 0.008 -0.002 0.013
AJ2 -0.016 -0.013 -0.016 -0.0106
AJ3 -0.026 -0.062 -0.024 -0.062
AJ4 -0.023 -0.022 -0.022 -0.021
AJ5 -0.070 -0.085 -0.066 -0.08
EM2 -0.012 -0.018 -0.012 -0.018
EM3 -0.034 -0.057 -0.029 -0.051
BT2 -0.018 -0.002 -0.018 0.000
BT3 -0.033 -0.039 -0.028 -0.032
BL2 -0.041 -0.026 -0.039 -0.024
BL3 -0.057 -0.025 -0.057 -0.022
BL4 -0.066 -0.058 -0.068 -0.054
BL5 -0.062 -0.043 -0.059 -0.037
COS2 -0.004 0.010 -0.003 0.014
COS3 -0.015 -0.028 -0.011 -0.024
VAG2 0.006 -0.008 0.006 -0.006
VAG3 -0.024 -0.035 -0.02 -0.029
MOST -0.026 -0.013
LEAST 0.002 0.035
N 1580 96 1580 96
adj R2 0.040 0.178 0.039 0.164
inconsistencies 3 2 2 3
MAE 0.056 0.053 0.065 0.0552
No > |0.05| 37 36 47 36
No > |0.10| 14 15 17 15
t(mean = 0) -0.334 † -0.344 †
JBPRED 34.789 20.587 36.089 17.028
LB 214.99 124.92 218.04 150.44
Estimates shown in bold are significant at t0.10; † Mean error is zero by definition.
In the RE model (1), the coefficients have the expected negative sign for all main effect dummies except HF2 and VAG2, but neither of these is significant. There are 13 significant coefficients, including the constant term. There are three inconsistencies involving significant coefficients, AJ3 to AJ4, BL2 to BL3 and BL4 to BL5. The mean model has a better explanatory power than the OLS model (not shown), but has only seven significant coefficients that produce just two inconsistencies.
The ability of the mean and RE main effects models to predict health state valuations within the data set is presented at the bottom of Table 5. The main effects models have similar mean absolute errors, though it is slightly lower for the mean model. The proportion of errors greater than 0.05 and 0.1 is also very similar at 39% and 15% respectively. The JB test found evidence for non-normality of errors for both the models.
An important problem has been identified by the Ljung-Box statistics that reveal significant autocorrelation in the prediction of all the models. Plots of actual against predicted errors reflect a tendency to over predict at the lower end and under predict at the upper end. The model was re-estimated using a Tobit procedure, but this did not improve the predictive performance of the model. Applications of the logit and complementary log functions also did not improve model performance.
Interactions
The RE and mean models in Table 5 include dummy variables for MOST and LEAST, which take the value of 1 if any dimension is at the most or least severe level respectively. The coefficients associated with these dummies suggest a further negative impact when any dimension is at its worst level and a slight positive impact from having any dimension at the least severe level. These coefficients were significant for some models. However, the coefficients on the main effects have been slightly reduced by these additional dummies, particularly the worst levels of AJ5 and BL5. Furthermore, the addition of these variables has not significantly improved either model
Discussion
The paper presents the results of a study aiming to estimate preference functions for the menopausal health state classification. The preference models look credible in terms of the coefficients, though there are a number of problems with the models predictive performance. This paper supports the findings of other studies, that it is the feasible to estimate condition specific preference-based indices [11-14].
It was perhaps more ambitious than other published studies in that it attempted to estimate values for a large health state classification, where it was not possible to directly value all states. It was the first study using statistical inference to model health state values. The explanatory power of the models is not high. This is due to the high variability around the health state means, which may have been a result of the self-completed format of the TTO task. It may also have been due to comparatively low number of observations per state.
The classification describes health states that are mild compared to the full range of states described by descriptive systems such as the EQ-5D or HUI3 that reflects the nature of the conditions. The specific values found for our instrument have tended to be more skewed at the upper end than the generic measures, such as the EQ-5D. This was also found for a number of other conditions, with the lowest value for Erectile Dysfunction being 0.74 [12] and 0.87 for prostate symptoms [14]. However this seems to be a consequence of the comparatively mild impact of this condition, because a preference scale for Asthma had a lowest value of 0.04 [13] and 0.15 for Rhinitis [11]. For milder conditions a valuation technique such as TTO that relies on trading quality with survival may be rather insensitive for some respondents, which is reflected in the higher proportion of people indicting the first response choice down the scale. This might suggest that more effort needs to be made to develop variants of the TTO and SG that allow milder states to be valued with sufficient sensitivity. One approach would be the use of chaining, where each mild state is valued against full health and a lower anchor that is better than being dead, which in turn has been valued against full health [20]. However, this has been shown to produce biased estimates [21,22].
A further problem may have arisen from the descriptive system. The 2 or 3 inconsistencies between coefficients may be due to possible ambiguities in the health state classification. The ranking of AJ3 and AJ4 is ambiguous, since it is possible for 4 or more episodes per week of pain to be worse than mild to moderate constant pain. Also for BL, some people may regard irregular bleeding as better than regular bleeding. Such differences of opinion in the population in the ranking ordering of some levels would reduce the fit of the models.
Of more concern is the evidence for systematic patterns in the residuals resulting in over prediction at the lower end and under predicting at the upper end of the range. The MVH group was able to solve this problem in their valuation of the EQ-5D by the inclusion of an interaction term. The inclusion of interaction terms in this study had little impact on the problem. The application of various transformations to the dependent variable also did not solve this problem.
The models nonetheless provide a basis for valuing menopausal health states using this health state classification. The coefficients are consistent with the cordiality of the health state classification and the size of the mean absolute error of 0.055 to 0.065 is comparable to that achieved in other models [16]. The addition of interaction terms did not improve the model and tended to offset the main effects, therefore it is not proposed to recommend the models with interactions. The choice of models is between the random effects and mean main effects models (i.e. (1) and (2)). Given the mean model is slightly better in terms of fit and numbers of inconsistencies this is the one recommended for use.
The estimation of preference weights for condition specific quality of life has been questioned by some health economists as to its value [23]. The argument for using condition specific descriptive systems is that they are likely to be more sensitive to changes in the condition than generic measures and more relevant to the concerns of patients. On the other hand, condition specific measures often focus on symptoms and it could be argued this concentrates the mind of the respondent on the negative aspects of the conditions. This may have a framing effect that produces lower values because the respondents are not thinking about other aspects of their lives unaffected by the condition. However, the risk of this was reduced by selecting women in the age range of 45–60, most of whom had experienced menopausal symptoms and would have a realistic view of the likely impact of the condition.
The argument for using condition specific descriptive systems is that they are going to better reflect the impact of the condition on a patient's life. However, provided the descriptive system is valued on the same full health – death scale using the same variant of the same valuation technique using a comparable population sample, then the valuations should be comparable. Any remaining differences in values should be a legitimate consequence of the descriptive system. However, this assumes that the value of a dimension is independent of those dimensions outside of the descriptive system and this requires empirical testing. Despite these arguments, there has been increasing interest in estimating condition specific preference measures of health because the analyst often only has condition specific data and wishes to use them to undertake an economic evaluation, or the analyst feels a generic measure is not appropriate for the condition.
Conclusion
The advantages of using a condition specific descriptive system over a generic are that it should be more sensitive to improvements in health. However, the overall fit was disappointing. The results demonstrate that menopausal symptoms are perceived by patients to have a significant impact on utility, but the overall effect is modest compared to the more generic health state descriptions such as the EQ-5D. This research has also demonstrated the problems that can be encountered when trying to value a comparatively mild condition. The resultant algorithm generates a preference-based index that can be used economic evaluation and that reflects the impact of this condition.
Authors' contributions
JB led the project, including the design of the valuation study and undertaking much of the analysis. MP contributed to the design of the valuation survey and undertook the interviews. JR provided a key input into the econometric analyses. YZ designed the menopausal health state classification and contributed to the overall design of the study. All authors contributed to the writing of the paper.
Acknowledgements
The authors are indebted to Solvay Pharmaceuticals for an unrestricted research grant to perform this piece of research.
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| 15769294 | PMC555752 | CC BY | 2021-01-04 16:38:15 | no | Health Qual Life Outcomes. 2005 Mar 15; 3:13 | utf-8 | Health Qual Life Outcomes | 2,005 | 10.1186/1477-7525-3-13 | oa_comm |
==== Front
BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-371576298710.1186/1471-2164-6-37Research ArticleGene expression signature of estrogen receptor α status in breast cancer Abba Martín C [email protected] Yuhui [email protected] Hongxia [email protected] Jeffrey A [email protected] Sally [email protected] Keith [email protected] Aysegul [email protected] C Marcelo [email protected] Department of Carcinogenesis, The University of Texas M.D. Anderson Cancer Center, Science Park-Research Division, Smithville, Texas, USA2 Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA3 Department of Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA2005 11 3 2005 6 37 37 3 11 2004 11 3 2005 Copyright © 2005 Abba et al; licensee BioMed Central Ltd.2005Abba 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
Estrogens are known to regulate the proliferation of breast cancer cells and to modify their phenotypic properties. Identification of estrogen-regulated genes in human breast tumors is an essential step toward understanding the molecular mechanisms of estrogen action in cancer. To this end we generated and compared the Serial Analysis of Gene Expression (SAGE) profiles of 26 human breast carcinomas based on their estrogen receptor α (ER) status. Thus, producing a breast cancer SAGE database of almost 2.5 million tags, representing over 50,000 transcripts.
Results
We identified 520 transcripts differentially expressed between ERα-positive (+) and ERα-negative (-) primary breast tumors (Fold change ≥ 2; p < 0.05). Furthermore, we identified 220 high-affinity Estrogen Responsive Elements (EREs) distributed on the promoter regions of 163 out of the 473 up-modulated genes in ERα (+) breast tumors. In brief, we observed predominantly up-regulation of cell growth related genes, DNA binding and transcription factor activity related genes based on Gene Ontology (GO) biological functional annotation. GO terms over-representation analysis showed a statistically significant enrichment of various transcript families including: metal ion binding related transcripts (p = 0.011), calcium ion binding related transcripts (p = 0.033) and steroid hormone receptor activity related transcripts (p = 0.031). SAGE data associated with ERα status was compared with reported information from breast cancer DNA microarrays studies. A significant proportion of ERα associated gene expression changes was validated by this cross-platform comparison. However, our SAGE study also identified novel sets of genes as highly expressed in ERα (+) invasive breast tumors not previously reported. These observations were further validated in an independent set of human breast tumors by means of real time RT-PCR.
Conclusion
The integration of the breast cancer comparative transcriptome analysis based on ERα status coupled to the genome-wide identification of high-affinity EREs and GO over-representation analysis, provide useful information for validation and discovery of signaling networks related to estrogen response in this malignancy.
==== Body
Background
Estrogen plays essential roles in the development, growth control and differentiation of the normal mammary gland. However, it is well documented that endogenous estrogens are powerful mitogens critical for the initiation and progression of human breast and gynecological cancers [1]. This cell proliferation signal is mediated by the estrogen receptors (ER), members of the nuclear receptor family that function both as signal transducers and transcription factors to modulate expression of target genes [2]. There are two main subtypes of estrogen receptors: ERα and ERβ that generally can form homo- and heterodimers before binding to DNA. Although the DNA binding domains of these receptors are very similar, the overall degree of homology is low [3].
Transcriptional regulation of target genes in response to 17β-estradiol (E2) is mediated by two main mechanisms. In one, the E2-ER complex binds to a specific DNA sequence called the estrogen response element (ERE), this receptor-ligand DNA bounded complex interacts with co-regulatory proteins, promoting chromatin remodeling and bridging with the general gene transcription machinery thus resulting in transcription initiation [4]. Alternatively, the ligand-ER complex can interact with other DNA-bound transcription factors that in turn bind DNA sequences (e.g. via AP1, SP1 complexes) [5,6]. ERα and ERβ have different affinities for different response elements and exhibit distinct transcriptional properties. Additionally, E2 also exerts rapid, non-genomic effects attributed to cell membrane-initiated signaling [7].
Approximately two-thirds of all breast cancers are ERα (+) at the time of diagnosis and expression of this receptor is determinant of a tumor phenotype that is associated with hormone-responsiveness. Patients with tumors that express ERα have a longer disease-free interval and overall survival than patients with tumors that lack ERα expression [8]. However, the association between ERα expression and hormonal responsiveness is not perfect: approximately 30% of ERα-positive tumors are not hormone-responsive while 5–15% of ERα-negative tumors respond to hormonal therapy [9]. The molecular basis for the association between ERα expression, hormonal responsiveness and breast cancer prognosis remains unclear.
Several studies have been carried out using cDNA and oligonucleotide microarrays identifying breast cancer subclasses possessing distinct biological and clinical properties [10-13]. Among the distinctions made to date, the clearest separation was observed between ERα (+) and ERα (-) tumors [10-15]. It has been suggested that there are sets of genes expressed in association with ERα that could play an important role in determining the hormone-responsive breast cancer phenotype [16]. ERα is obviously likely to be important for the E2 induced proliferative response predominantly via the regulation of estradiol-responsive genes. Nevertheless, the expression of additional subsets of genes not necessarily directly regulated by estrogen may also be fundamental in defining the breast cancer hormone-responsive phenotype.
To further elucidate the molecular basis of estrogen-dependent breast carcinogenesis, we here report a comparative transcriptome profiling of invasive breast tumors based on ERα status obtained by SAGE. The SAGE method provides a statistical description of the mRNA population present in a cell without prior selection of the genes to be studied, and this constitutes a major advantage [17]. The breast cancer SAGE comparative analysis was combined with promoter sequence analysis of genes of interest using high-throughput methods of high-affinity ERE identification. In order to have an even more comprehensive picture we also performed a cross-platform comparison between SAGE and DNA microarray studies.
Results and discussion
Biomarkers of ERα status in breast carcinomas
The primary goal of our study was to identify the most commonly deregulated genes in invasive breast carcinomas related to ERα status. To this end SAGE data was obtained from a set of primary breast carcinomas. Thus, a breast cancer SAGE database of almost 2.5 million tags was analyzed, representing over 50,000 tag species. We performed a comprehensive evaluation and comparison of gene expression profiles using a recently developed supervised method [18], to identify the most representative differentially expressed transcripts between tumors groups, i.e. ERα (+) vs. ERα (-) breast tumors.
This statistical analysis revealed 520 genes differentially expressed (Fold change ≥ 2; p < 0.05) between ERα (+) and ERα (-) primary breast carcinomas (see additional data file 1). Among the 520 transcripts, 473 were up-modulated and 47 were down-modulated transcripts in ERα (+) tumors.
The most commonly over-expressed transcripts in ERα (+) tumors were: trefoil factor 1 (TFF1/pS2), synaptotagmin-like 4 (SYTL4), regulating synaptic membrane exocytosis 4 (RIMS4), dual specificity phosphatase 4 (DUSP4), chromosome 1 open reading frame 34 (C1orf34), necdin homolog (NDN), n-acetyltransferase 1 (NAT1) and caspase recruitment domain family 10 (CARD10) (Table 1 and additional data file 1).
Table 1 Most highly up-modulated transcripts in ERα (+) breast carcinomas identified by SAGE.
Gene name Tag Locus Link Fold change (p value) Frequency#
Cell proliferation related
TFF1* (trefoil factor 1) CTGGCCCTCG 7031 51.4 (0.0016) 15/18 (83%)
DUSP4 (dual specificity phosphatase 4) CGGGCAGAAA 1846 14.7 (0.0016) 14/18 (78%)
NDN* (necdin homolog) ACCTTGCTGG 4692 13.3 (0.0026) 11/18 (61%)
HDGFRP3 (hepatoma-derived growth factor) TGTAAAGTTT 50810 9.8 (0.0019) 12/18 (67%)
TSPAN1* (tetraspan 1) GGAACTGTGA 10103 9.5 (0.0017) 15/18 (83%)
SEP6 (septin 6) TCAATTTTCA 23157 7.6 (0.0044) 12/18 (67%)
DHX34* (DEAH box polypeptide 34) GTTGCTCACT 9704 7.1 (0.0129) 9/18 (50%)
Apoptosis related
CARD10* (caspase recruitment domain family) AGAATGTACG 29775 11.1 (0.0030) 15/18 (83%)
Signal transduction related
SYTL4* (synaptotagmin-like 4) TATGTGTGCT 94121 28.0 (0.0003) 15/18 (83%)
ECM1* (extracellular matrix protein 1) ACTGCCCGCT 1893 10.1 (0.0175) 13/18 (72%)
LEPR* (leptin receptor) AAAGTTTGAG 3953 9.8 (0.0302) 10/18 (55%)
PTGES (prostaglandin E synthase) TGAGTCCCTG 9536 8.0 (0.0168) 8/18 (44%)
SCUBE2 (signal peptide, CUB domain EGF-like 2) TCAGCACAGT 57758 7.5 (0.0024) 14/18 (78%)
ADORA2A* (adenosine A2a receptor) TGCTGAGTAG 135 7.1 (0.0460) 11/18 (61%)
ITGBL1 (integrin beta-like 1) CATATTCACA 9358 7.1 (0.0159) 8/18 (44%)
Regulation of transcription related
ESR1 (estrogen receptor 1) AGCAGGTGCC 2099 9.8 (0.0000) 18/18 (100%)
TCEAL1 (transcriptional elongation factor A) AAAGATGTAC 9338 9.8 (0.0014) 13/18 (72%)
ZNF14 (zinc finger protein 14) TAAACAGCCC 7561 8.4 (0.0023) 13/18 (72%)
ZNF38* (zinc finger protein 38) CCAGCATTAC 7589 7.6 (0.0051) 10/18 (55%)
HIF1AN* (hypoxia-inducible factor 1α subunit inhibitor) CCTGAGTGCG 55662 7.1 (0.0094) 10/18 (55%)
HOXC13 (homeo box C13) TTTTTAAAAT 3229 7.1 (0.0157) 9/18 (50%)
Cytoskeleton
MAPT (microtubule-associated protein tau) GTAGACTCGC 4137 9.8 (0.0085) 9/18 (50%)
MYLIP (myosin regulatory light chain interacting) TTTTCCACTC 29116 9.3 (0.0036) 11/18 (61%)
Metabolism and Miscelaneous
RIMS4 (regulating synaptic membrane exocytosis) TTGAAATTAA 140730 24.9 (0.0378) 8/18 (44%)
NAT1 (N-acetyltransferase 1) TATCTTCTGT 9 11.7 (0.0385) 15/18 (83%)
ATP6V1B1* (ATPase, H+ transporting) CCTCCCCCTC 525 10.7 (0.0111) 10/18 (55%)
JDP1 (J domain containing protein 1) TCTGTGAATT 56521 10.0 (0.0035) 12/18 (67%)
CHST11 (carbohydrate sulfotransferase 11) AACCTTCCTC 50515 9.8 (0.0009) 13/18 (72%)
CILP (nucleotide pyrophosphohydrolase) GTTTTGCCCA 8483 9.3 (0.0054) 14/18 (78%)
ABCA3 (ATP-binding cassette sub-family A) GTAGTCACCG 21 8.9 (0.0149) 10/18 (55%)
SEC14L2 GGAAGGCGGC 23541 8.7 (0.0487) 9/18 (50%)
ANXA9* (annexin A9) ACATCCGAGG 8416 8.4 (0.0145) 10/18 (55%)
KCTD3 (K channel tetramerisation domain 3) ATAATTAAAT 51133 8.4 (0.0001) 17/18 (94%)
SFRS7 (splicing factor) TAGCTAATAT 6432 8.0 (0.0031) 12/18 (67%)
SNRPA* (small nuclear ribonucleoprot. polypep. A) AAGATCTCCT 6626 7.6 (0.0009) 15/18 (83%)
NNMT (nicotinamide N-methyltransferase) CCTGCAATTC 4837 7.6 (0.0120) 10/18 (55%)
SLC1A4 (solute carrier family 1 member 4) GACTCACAGG 6509 7.6 (0.0254) 9/18 (50%)
TIPARP (TCDD-inducible polymerase) AAATGGCCAA 25976 7.6 (0.0051) 10/18 (55%)
SLC7A2 (solute carrier family 7 member 2) CACTGACAGC 6542 7.3 (0.0190) 11/18 (61%)
GA* (liver mitochondrial glutaminase) CTGCTGCTAC 27165 7.1 (0.0126) 9/18 (50%)
Function unknown
C1orf34 AGGATGTACA 22996 13.3 (0.0025) 14/18 (78%)
SMILE (hypothetical protein FLJ90492) TAGAGAGTTT 160418 11.1 (0.0004) 15/18 (83%)
RHBDL4 (rhomboid, veinlet-like 4) TTGTTTCTAA 162494 10.7 (0.0099) 9/18 (50%)
KIAA0882 GTCTCATTTC 23158 10.1 (0.0007) 18/18 (100%)
C20orf103* TTTAGTGATT 24141 9.3 (0.0277) 10/18 (55%)
FLJ33387 GCAGGGAGAG 161145 9.3 (0.0118) 10/18 (55%)
TRALPUSH GTTTCCAGAG 116931 8.9 (0.0458) 9/18 (50%)
KIAA0980* TGGTGCTTCC 22981 7.6 (0.0096) 11/18 (61%)
C10orf32 AGTCTGTTGT 119032 7.3 (0.0002) 15/18 (83%)
FLJ13611 TAATCACACT 80006 7.1 (0.0069) 10/18 (55%)
* Genes with known or putative high-affinity EREs mapping in the vicinity of the TSS.
# Transcripts tags changing > 2-fold when compared with the average expression of ER (-) tumors in at least 8 of 18 (44%) ERα (+) invasive carcinomas SAGE libraries.
For the whole list of ERα associated transcripts see additional data file 1.
To validate novel ERα associated genes detected by SAGE not reported in other studies, we performed Real Time RT-PCR analysis of representative transcripts in an independent set of 36 invasive ductal breast carcinomas. In agreement with our SAGE analysis, we detected statistical differences in the over-expression of 8 out of 9 evaluated transcripts in ERα (+) breast tumors including: signal peptide CUB domain EGF-like 2 (SCUBE2) (p = 0.0001), SYTL4 (p = 0.0005), KIAA0882 protein (p = 0.0005), tetraspan 1 (TSPAN1) (p = 0.001), myeloblastosis viral oncogene homolog (C-MYB) (p = 0.002), epidermal growth factor-like 2 (CELSR2) (p = 0.011), nuclear receptor subfamily 4 (NR4A1) (p = 0.029), and enolase 2 (ENO2) (p = 0.033) (Figure 1). A trend of borderline significance was detected for the lectin galactoside-binding protein (LGALS3BP) (p = 0.079) transcript (Figure 1).
Figure 1 Real time RT-PCR validation of nine over-expressed genes in 36 invasive breast carcinomas. a) SCUBE2 (p = 0.0001); b) SYTL4 (p = 0.0005); c) KIAA0882 (p = 0.0005); d) TSPAN1 (p = 0.001); e) CMYB (p = 0.002); f) CELSR2 (p = 0.011); g) NR4A1 (p = 0.029); h) ENO2 (p = 0.033); i) LGALS3BP (p = 0.079). Mean ± 2 Standard Error based on Log2 transformation of real time RT-PCR values of the assayed gene relative to 18S rRNA used as normalizing control.
SCUBE2 (also known as EGF-like 2 or CEGP1) encodes a secreted and cell-surface protein containing EGF and CUB domains that defines a novel gene family [19]. The epidermal growth factor (EGF) motif is found in many extracellular proteins that play an important role during development, functioning as secreted growth factors, transmembrane receptors, signaling molecules, and important components of the extracellular matrix. The CUB domain is found in several proteins implicated in the regulation of extracellular process such as cell-cell communication and adhesion [20]. Expression of SCUBE2 has been detected in vascular endothelium and may play important roles in development, inflammation and perhaps carcinogenesis [19].
The CELSR2 gene (also known EGFL2) encodes a protein member of the nonclassic-type cadherins (flamingo subfamily). These 7-pass transmembrane proteins have nine cadherin domains, seven-epidermal growth factor-like repeats and two laminin A G-type repeats [21]. It is postulated that these proteins are receptors involved in cell adhesion and receptor-ligand interactions [21] playing a role in developmental processes and cell growth/ maintenance in epithelial and neuronal cells [22,23].
SYTL4 (also known as granuphilin-a or SLP4) contains an N-terminal Slp homology domain (SHD) than can specifically and directly bind the GTP-bound form of Rab27A, a small GTP-binding protein involved in granule exocytosis in cytotoxic T lymphocytes [24]. We determined that over-expression of SYTL4 is associated with ERα (+) tumors (Figure 1b). However, the potential role of this gene in breast carcinogenesis remains unknown.
ENO2 (also known as NSE/neuron-specific gamma enolase) encodes one of three enolase isoenzymes found in mammals. This isoenzyme was described to be expressed in cells of neuronal origin. Interestingly, in a recent report Hao et al. (2004) showed high expression of ENO2 transcripts in breast cancer lymph node metastases when compared with primary breast tumors [25].
The TSPAN1 gene (also known as tetraspanin or NET1) encodes a cell-surface protein member of the transmembrane 4 superfamily (TM4SF), involved in the regulation of cell development, activation, growth and motility. A number of tetraspanins were described as tumor-specific antigens, and it was suggested that the function of some TM4SF proteins may be particularly relevant to tumor cell metastasis [26]. Sugiura and Berditchevski (1999) observed that TSPAN1 protein complexes may control the invasive migration of tumor cells and contribute to ECM-induced production of MMP2 in breast cancer cell line [27].
NR4A1, a nuclear receptor subfamily 4, group A gene (also known as steroid receptor TR3 or NUR77) encodes an orphan member of the steroid-thyroid hormone-retinoid receptor superfamily whose members mainly act as transcriptional factors to positively or negatively regulate gene expression and play roles in regulating growth and apoptosis [28,29]. A role for NR4A1 in cell proliferation has been previously reported. It was shown that its expression is rapidly induced by various mitogenic stimuli such as: serum growth factor, epidermal growth factor and fibroblast growth factor [28].
Taken together, the genes that we identified and validated appear to be involved in signaling pathways related to cell proliferation, invasion and metastatic processes, but their exact role in breast carcinogenesis remains to be elucidated.
Gene Ontology analysis
Classification of genes based on Gene Ontology (GO) terms is a powerful bioinformatics tool suited for the analysis of DNA microarray and SAGE data. Analysis of GO annotation allows one to identify families of genes that may play significant roles related to specific molecular or biological processes in expression profiles [30]. We used the Expression Analysis Systematic Explorer software (EASE) [31] to annotate the 520 deregulated genes according to the information provided by the GO Consortium [30]. The GO database provided annotation for 80% (419 out of 520) of the genes identified by SAGE. Results of this analysis are shown in Figure 2 and in detail in additional data file 2.
Figure 2 GO classification of the ERα associated genes identified by SAGE. Percent of coverage representing the percentage of genes annotated with a specific GO term related to Biological Processes (blue bars) and Molecular Function (yellow bars).
We observed that 31% of ERα associated transcripts are involved in biological processes related to cell growth and/or maintenance, 21% are related to cell communication, and 16% are related to regulation of transcription. Approximately 16% of these deregulated genes are related to molecular functions associated with DNA binding and more specifically with transcription factor activity (10%) (Figure 2). Interestingly, using the enrichment GO terms analysis, we identified statistical significant over-representation of specific groups of proteins including: metal ion binding proteins (54 hits out of 419 annotated genes; p = 0.011), calcium ion binding proteins (27 hits out of 419; p = 0.032) and steroid hormone receptor activity related proteins (6 hits out of 419; p = 0.031) (additional data file 2). The GO cluster related to steroid hormone receptor activity proteins includes: estrogen receptor 1 (ESR1, i.e. ERα), androgen receptor (AR), hydroxysteroid 17-β dehydrogenase 4 (HSD17β4), glucocorticoid receptor (NR3C1), oxysterol binding protein (OSBP), and retinoic acid receptor α (RARA). The observation of functionally related groups of genes identified in the SAGE dataset via GO over representation analysis allows the identification of distinct biological pathways directly or indirectly associated to estrogen response related processes and provides the basis for future mechanistic studies.
Identification of high-affinity Estrogen Response Elements
We used a recently reported genome-wide high-affinity ERE database [32] to identify putative EREs in the promoter regions of the SAGE-identified 473 up-modulated genes in ERα (+) breast tumors. We identified 220 EREs distributed on 163 out of the 473 genes (35%) (see additional data file 3). Seventy-two percent of these genes contain one high affinity ERE (117 out of 163) and 28% of them contain two or more EREs in proximity to the transcriptional start sites (TSS) (46 out of 163) (Figure 3a). These EREs can be located in both coding and non-coding sequences such as was described by Bourdeau et al. [32].
Figure 3 High-affinity EREs in ERα (+) up-modulated genes (n = 163). a) Percentage of genes according to number of EREs. b) Distribution of EREs in 5' (blue bars) and 3' (aquamarine bars) regions relative to the TSS (-10 to + 5 kb). Each bar represents an interval width of 500 bp.
The observed frequency of these elements in our study was 220 EREs in 3260 kb (considering a DNA window of 20 kb for each one of the 163 up-modulated genes with EREs). Compared with the expected frequency from random distribution of high-affinity EREs found in the genome (732 EREs in 3,069334 kb 0.8 ERE in 3260 kb) (see material and methods) [32], the number of individual EREs was 270 fold higher than expected by chance (p < 0.00001).
Fifty percent (110 out of 220) of the detected EREs mapped within a 10 kb region 5' of the TSS, while the rest mapped to 3' regions (Figure 3b). Approximately 68% of EREs mapped within the region between -5 to +5 kb from the TSS; in agreement with those observations of Bourdeau et al. [32]. However, it remains to be determined whether distantly located EREs (e.g. -10 kb from the TSS) are functional E2-ER binding sites related to transcriptional activation.
Of the validated transcripts previously discussed (Figure 1), we detected high-affinity EREs on the upstream or downstream regions related to the TSS of SYTL4 (-8384 bp from the TSS: tggacatcatgacct), TSPAN1 (+974 bp and +9384 bp from the TSS: tggtctgaatgaccc and aggtcatttccacct respectively), CELSR2 (+173 bp and +3607 bp from the TSS: tgctcagggtgaccc and aggtcaccatgaccg respectively), and NR4A1 (-3478 bp and +4217 bp from the TSS: tgttcactctgacct).
It is interesting to note that we were unable to identify high-affinity EREs on the majority of deregulated genes (65%) associated with a positive ER α status. The possibility exists that many of these genes are transcriptionally regulated by non-ERE mediated mechanisms such as those involving ER binding to the AP1 or SP1 transcription factors [33]. The AP1 transcription factor is a heterodimer formed by Jun and Fos family member proteins that binds to the phorbol diester (TPA) response element as well as to the AP1 consensus DNA sequence. In this pathway, ER plays a co-activator role for AP1 [6]. The ER/AP-1 complex can confer estrogen responsiveness to additional subset of genes found in our dataset such as: ovalbumin (Fold change: 3; p = 0.033) and c-fos (Fold change: 2.1; p = 0.033); two transcripts detected as over-expressed in ERα (+) breast tumors by SAGE (additional data file 1). Similarly the ER/SP1 complex confers estrogen responsiveness to genes such as: retinoic acid receptor α (RARA) (Fold change: 6.7; p = 0.038), vascular endothelial growth factor (VEGFC) (Fold change: 2.6; p = 0.037), insulin-like growth factor binding protein-4 (IGFBP4) (Fold change: 2; p = 0.01) and heat shock protein 27 (HSPB1) (Fold change: 2; p = 0.045); four transcripts detected as over-expressed in ERα (+) tumors in our study (additional data file 1).
An additional pathway of transcription regulation by estrogen involves the ER-related receptors (ERR), nuclear orphan receptors with significant homology to ERs, which do not bind estrogen and have unknown physiological ligands. ERRs are known to bind to the steroidogenic factor 1 response element (SFRE) and also bind to classic EREs, by means of which they exert constitutive transcriptional activity [34]. We detected over-expression of the nuclear orphan receptor NR4A1 by SAGE and subsequently validated this observation by real time RT-PCR (Figure 1g). Interestingly, and as previously mentioned, the genomic region 5' and 3' to the TSS of NR4A1 contain high-affinity EREs. Interaction between ERs and ERRs has been observed in the transcriptional regulation of certain genes such as the human breast cancer related gene TFF1/pS2, the promoter of which is not only activated by ERs but also by ERRs [35].
As described, ERα can mediate estrogenic response through multiple genomic and non-genomic mechanisms, many of which affect proteins and pathways not necessarily directly or exclusively associated with ERα. Thus it is worth stressing that it will the totality of deregulated proteins the ones that ultimately define the phenotype of ERα (+) breast carcinomas regardless of whether a "direct association" with ER transcriptional regulation exists or not.
In vivo versus in vitro estrogen induced global gene expression findings
The SAGE profiles for E2-responsive genes in MCF-7 cell line, previously reported by us [36], was compared with the ER status genes expression profile found in primary breast carcinomas. Briefly, we detected 199 transcripts differentially expressed (p < 0.01) in MCF-7 treated cells, 124 were up-regulated and 75 were down-regulated transcripts. Basically and as reported Charpentier et al, we observed a general up-regulation cell cycle progression-related genes including: CCT2, CCND1, PES1, RAN/TC4, CALM1, CALM2; and tumor-associated genes such as: RFP, D52L1, TFF1/PS2, CAV1, and NDKA among others [36]. These together could contribute to the stimulation of proliferation and the suppression of apoptosis by E2-ER transcriptional regulation.
By comparing the in vitro (199 differentially expressed transcripts) and in vivo (520 differentially expressed transcripts) gene expression profiles, to our surprise we detect that only few transcripts: TFF1, CCND1, H19, SREBF1 and WWP1 behaved similarly (i.e. up-regulation) in both studies. This is similar to observations made previously by Meltzer and co-workers whom showed that the majority of genes regulated in cell culture do not predict ER status in breast carcinomas [11,37]. This result suggests that the estrogen-responsive pathways affected in vitro represent only a minor portion of the global gene expression profiles characteristic of ERα (+) breast tumors. This maybe in great part the result of the heterogenous nature of bulk tumor tissue but in addition, the in vitro response of a single cell line to E2, in this particular case the widely used MCF-7 cells, may not faithfully reproduce the physiological effects of ER signaling in vivo.
Cross-platform gene expression profiling comparison
In order to identify and validate the most reliable set of genes able to discriminate breast carcinomas based on their ERα status, we performed a cross-platform comparison between the described SAGE dataset with two previously reported breast cancer studies based on DNA microarray methods [12,13]. van't Veer et al. [12] reported the gene expression profile of 97 primary breast tumors based on oligonucleotide microarrays containing 24,479 elements (Agilent Technologies, Palo Alto, CA, USA). In another study, Sotiriou et al. [13] reported the gene expression profile of 99 primary breast tumors using a cDNA microarray containing 7650 elements. Only files containing differentially expressed genes associated to ERα status tumors from both microarrays studies were obtained for cross-platform comparison (see material and methods).
Among the three platforms, a total of 1686 transcripts were identified as over-expressed in ERα (+) breast tumors. One hundred and eighty-three genes were identified by more than one method (Figure 4; additional data file 4). Eleven of these 183 genes were identified by all three methods displaying over-expression in ERα (+) breast carcinomas: estrogen receptor 1 (ESR1), GATA-binding protein 3 (GATA3), mucin 1 (MUC1), v-myb-myeloblastosis viral oncogene homolog (C-MYB) , X-box-binding protein 1 (XBP1), hydroxysteroid 17-β dehydrogenase 4 (HSD17B4), BTG family member 2 (BTG2), transforming growth factor β-3 (TGFB3), member RAS oncogene family (RAB31), START domain containing 10 (STARD10), and KIAA0089 (Table 2).
Figure 4 Cross-platform comparisons of the up-modulated transcripts in ERα (+) breast carcinomas. One hundred and eighty-three genes were identified by more than one study, eleven of which were commonly identified across the three platforms. a) Comparison between SAGE and oligonucleotide microarray platforms [12] showing a highly significant number of overlapping genes (p < 0.001) (see table 2). b) Comparison between SAGE and cDNA microarray platforms [13] (p > 0.05). c) Statistically significant number of overlapping genes identified by both DNA microarrays platforms (p < 0.01).
Table 2 Transcripts identified as over-expressed in ERα (+) breast cancers commonly detected by cross-platforms comparison (SAGE and oligonucleotide microarrays).
Gene name Locus Link ID Fold change Frequency Gene name Locus Link Fold change Frequency#
TFF1* 7031 51.4 15/18 (83%) SULF2 55959 2.9 11/18 (61%)
SYTL4* 94121 28.0 15/18 (83%) THBS4 7060 2.9 8/18 (44%)
DUSP4 1846 14.7 14/18 (78%) AZGP1 563 2.8 9/18 (50%)
NAT1 9 11.7 15/18 (83%) BBC3* 27113 2.8 12/18 (67%)
ECM1* 1893 10.1 13/18 (72%) NET7* 23555 2.8 10/18 (55%)
KIAA0882 23158 10.1 18/18 (100%) NET6 27075 2.8 12/18 (67%)
JDP1 56521 10.0 12/18 (67%) TRAF5 7188 2.8 9/18 (50%)
ESR1 2099 9.8 18/18 (100%) BTG2 7832 2.7 9/18 (50%)
HDGFRP3 50810 9.8 12/18 (67%) RNF123* 63891 2.7 11/18 (61%)
TCEAL1 9338 9.8 13/18 (72%) CHAD* 1101 2.6 12/18 (67%)
TSPAN1* 10103 9.5 15/18 (83%) CSNK1A1 1452 2.6 14/18 (78%)
C20orf103* 24141 9.3 10/18 (55%) EVL 51466 2.6 12/18 (67%)
MYLIP 29116 9.3 11/18 (61%) HIST1H2BD 3017 2.6 10/18 (55%)
ABCA3 21 8.9 10/18 (55%) SUSD3 203328 2.6 9/18 (50%)
SEC14L2 23541 8.7 9/18 (50%) PLAT* 5327 2.6 8/18 (44%)
ANXA9* 8416 8.4 10/18 (55%) RARRES3* 5920 2.6 11/18 (61%)
KCTD3 51133 8.4 17/18 (94%) SH3BGRL* 6451 2.6 8/18 (44%)
SCUBE2 57758 7.5 14/18 (78%) TPBG* 7162 2.6 9/18 (50%)
ITGBL1 9358 7.1 8/18 (44%) UGCG 7357 2.6 11/18 (61%)
C14orf168 83544 6.7 6/18 (33%) CELSR2* 1952 2.5 8/18 (44%)
FBP1 2203 6.7 14/18 (78%) CRIM1 51232 2.5 11/18 (61%)
MYB 4602 6.7 14/18 (78%) FLJ90798* 219654 2.5 9/18 (50%)
RARA* 5914 6.7 12/18 (67%) KIF12 113220 2.5 7/18 (39%)
CaMKIINα 55450 6.3 18/18 (100%) LRIG1 26018 2.5 9/18 (50%)
AR* 367 6.2 10/18 (55%) LRP2* 4036 2.5 10/18 (55%)
ZNF552 79818 6.2 16/18 (89%) PHF15* 23338 2.5 12/18 (67%)
MIPEP* 4285 6.0 14/18 (78%) HSMNP1 55861 2.4 8/18 (44%)
BAI2 576 5.3 15/18 (83%) LOC123169 123169 2.4 12/18 (67%)
DP1L1 92840 5.3 15/18 (83%) PINK1* 65018 2.4 11/18 (61%)
VAV3 10451 5.3 12/18 (67%) PRKAR2B 5577 2.4 7/18 (39%)
KIAA0089 23171 5.2 17/18 (94%) TJP3* 27134 2.4 11/18 (61%)
GATA3 2625 5.1 15/18 (83%) CCND1 595 2.3 9/18 (50%)
QDPR 5860 5.1 11/18 (61%) CYBRD1 79901 2.3 10/18 (55%)
C1orf21 81563 4.9 11/18 (61%) KRT18 3875 2.3 10/18 (55%)
KIAA1143 57456 4.9 7/18 (39%) PURA 5813 2.3 9/18 (50%)
OIP106 22906 4.9 16/18 (89%) SREBF1* 6720 2.3 10/18 (55%)
AGR2 10551 4.6 10/18 (55%) CYB5R1 51706 2.2 6/18 (33%)
MGC4251 84336 4.6 13/18 (72%) DLG3* 1741 2.2 9/18 (50%)
FER1L3 26509 4.4 10/18 (55%) EEF1A2 1917 2.2 11/18 (61%)
C4A 720 4.1 11/18 (61%) GSTZ1 2954 2.2 9/18 (50%)
CRIP2 1397 4.0 15/18 (83%) LOC159090 159090 2.2 6/18 (33%)
NTN4 59277 4.0 10/18 (55%) MGC11242* 79170 2.2 10/18 (55%)
GJA1 2697 3.8 11/18 (61%) MGC18216* 145815 2.2 8/18 (44%)
CGI-111* 51015 3.7 14/18 (78%) NEIL1 79661 2.2 6/18 (33%)
CROT* 54677 3.6 15/18 (83%) XBP1* 7494 2.2 8/18 (44%)
DACH 1602 3.6 13/18 (72%) IRX5 10265 2.1 8/18 (44%)
DKFZP564D172 83989 3.6 10/18 (55%) RAB31 11031 2.1 9/18 (50%)
FGD3 89846 3.6 10/18 (55%) SSBP2 23635 2.1 7/18 (39%)
RNASE4* 6038 3.6 12/18 (67%) TGFB3 7043 2.1 8/18 (44%)
GLUL* 2752 3.3 11/18 (61%) BMPR1B 658 2.0 7/18 (39%)
FOXA1 3169 3.2 10/18 (55%) FLJ21174 79921 2.0 6/18 (33%)
MGC7036 196383 3.2 14/18 (78%) FLJ22386 79641 2.0 7/18 (39%)
MUC1* 4582 3.2 12/18 (67%) HSPB1* 3315 2.0 6/18 (33%)
NAV1 89796 3.1 13/18 (72%) IGFBP4* 3487 2.0 8/18 (44%)
RPLP1* 6176 3.1 12/18 (67%) MGC15737* 85012 2.0 8/18 (44%)
ALCAM 214 2.9 9/18 (50%) SPARCL1 8404 2.0 9/18 (50%)
HSD17B4* 3295 2.9 13/18 (72%) STARD10* 10809 2.0 7/18 (39%)
* Genes with known or putative high-affinity EREs mapping in the vicinity of the TSS.
# Transcripts tags changing > 2-fold when compared with the average expression of ERα (-) tumors. Underlined genes correspond to the transcripts cross-validated among all three compared platforms.
One hundred and fourteen genes were identified as over-expressed by oligonucleotide microarrays [12] and SAGE in ERα (+) tumors, representing a non-random significant number of overlapping genes based on normal approximation to the binomial distribution (p < 0.001) (Figure 4 and Table 2). Sixty-six genes were identified as over-expressed in ERα (+) tumors by both DNA microarrays platforms (p < 0.01). The set of 25 genes overlapping between cDNA microarrays [13] and SAGE were not statistical significant (p > 0.05).
Interestingly, we found a higher number of overlapping genes between the oligonucleotide microarray and SAGE platforms (114 genes), while only 66 genes were observed overlapping when comparing both microarray platforms. It is worth noting that 96% of the 470 genes (Figure 4) identified as overexpressed by the cDNA microarray method [13] were included within the total set of elements in the oligonucleotide microarray platform [12]. In other words, it appears that a better correlation was observed between SAGE and oligonucleotide arrays, than between both DNA microarray methods.
Conclusion
In summary, our comprehensive comparison of overlapping genes across different gene expression platforms provides validation for a significant number of transcripts identified as highly expressed in ERα (+) breast tumors. More importantly this analysis identifies the most promising biomarkers for further evaluation as ERα associated genes in breast cancer. Furthermore, the identified proteins may be of value as breast cancer prognostic indicators analyzed either as a group or individually. It is also likely that groups of co-regulated genes in ERα (+) breast cancers may be associated to the hormonal control of mammary epithelial cells growth and differentiation. Finally, a better understanding of the signaling networks controlled or associated with the estrogen response may lead to the identification of novel breast cancer therapeutic targets.
Methods
SAGE libraries
To perform the comparative breast cancer SAGE analysis based on ERα status, we analyzed 26 Stage I – Stage II invasive breast carcinomas (8 ERα-negative tumors and 18 ERα-positive tumors). To this end, we generated and sequenced 24 breast cancer SAGE libraries at an approximate resolution of 100,000 tags per library, combined with 2 additional breast cancer libraries (ERα-negative tumors) downloaded from the Cancer Genome Anatomy Project – SAGE Genie database (SAGE_Breast_Carcinoma_B_95-259 and B_IDC_4) . For the generation of our SAGE libraries, snap frozen samples were obtained from the M.D. Anderson breast cancer tumor bank, and SAGE analysis was performed as previously described [36,38].
Data processing and statistical analysis of SAGE libraries
SAGE tag extraction from sequencing files was performed by using the SAGE2000 software version 4.0 (a kind gift of Dr. K. Kinzler, John Hopkins University). SAGE data management, tag to gene matching as well as additional gene annotations and links to publicly available resources such as GO, UniGene, LocusLink, were performed using a suite of web-based SAGE library tools developed by us . In our analyses we only considered tags with single tag-to-gene reliable matches. To compare these SAGE libraries, we utilized a modified t-test recently developed by us [18]. This test is based on a beta binomial sampling model that takes into account both, the intra-library and the inter-library variability, thus identifying 'common patterns' of SAGE transcript tag changes systematically occurring across samples [18].
All raw SAGE data reported as Supplementary tables in this manuscript is publicly available at .
Real Time RT-PCR analysis
Template cDNAs were synthesized on mRNAs isolated from an independent set of 36 Stage I – Stage II human breast carcinomas (13 ERα-negative tumors and 23 ERα-positive tumors) obtained from our tumor bank. Primers and probes were obtained from the TaqMan Assays-on-Demand™ Gene Expression Products (Applied Biosystems, Foster City, CA, USA). All the PCR reactions were performed using the TaqMan PCR Core Reagents kit and the ABI Prism® 7700 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). Experiments were performed in duplicate for each data point and 18s rRNA was used as control. Results were expressed as mean ± 2 Standard Error based on Log2 transformation of normalized real time RT-PCR values of the assayed genes. We used t-test to compare the gene expression levels of validated genes between ERα (+) and ERα (-) breast tumors (p < 0.05).
Immunohistochemical determination of ER status
IHC staining and ER status determination was performed by the Pathology Department, MDACC following routine immunohistochemical procedures. Briefly, five micrometer sections of invasive breast carcinomas paraffin embedded tissues were used. Endogenous peroxidase activity was blocked with 3% H2O2 in methanol for 10 min. After pretreatment with Tris-EDTA buffer, in order to block non-specific antibody binding, the slides were incubated with 10% goat serum in PBS for 30 min. Primary monoclonal ERα antibody (ER-6F11, Novocastra, Newcastle, UK) was used at 1:50 dilution and detected following standard immunohistochemical techniques. DAB was used as chromogen and Mayers hematoxylin is used as counterstain. Scoring was performed by breast pathologist (AS). Cuttoff for positivity was determined at 5% of tumor cells staining positively for ER (i.e. < 5% of cells the tumor was considered negative for ERα).
Bioinformatics analysis
For automated functional annotation and classification of genes of interest based on GO terms, we used the EASE [31] available at the Database for Annotation, Visualization and Integrated Discovery (DAVID) at [39]. The EASE software calculates over-representation of specific GO terms with respect to the total number of genes assayed and annotated. Statistical measures of specific enrichment of GO terms are determined by means of an EASE score (p < 0.05). The EASE score is a conservative adjustment of the Fisher exact probability that weights significance in favor of biological themes supported by more genes and is calculated using the Gaussian hypergeometric probability distribution that describes sampling without replacement from a finite population [31]. This allows one to identify biological themes within a specific list of EASE analyzed genes.
High-affinity Estrogen Response Elements (ERE) analysis
To identify the occurrence of EREs within the promoter regions of up-modulated genes in ERα (+) breast tumors, we used a human genome-wide high-affinity ERE database [32]. This public available database contains 71,119 EREs identified across the human genome (related to 17,353 transcriptional start sites), representing the consensus ERE (5'-Pu-GGTCA-NNN-TGACC-Py-3'), and equivalent sequences with only one or two nucleotide variations from such consensus. Based on these restrictions the expected random frequency was calculated as the total number of base pairs in the human genome divided by the frequency of occurrence of a sequence with specified base pairs at 10 positions and two base pair choices at two positions (3,069334246/411 = 732 high-affinity EREs) [32].
Comparison of gene expression patterns identified by different methodologies
ERα status associated genes identified in previous breast cancer studies [12,13] using DNA microarray methods were compared with our SAGE findings.
All over-expressed genes in ERα (+) breast tumors obtained from these studies were downloaded from the corresponding web sites ( and ) [12,13].
These datasets were annotated by LocusLink ID using the EASE software [26], and then compiled into one Excel spreadsheet pivotTable for comparison of overlapping genes between platforms, i.e. SAGE, Oligonucleotide and cDNA arrays. Anonymous ESTs from the microarrays platforms were excluded due to the inability to cross validate the identities between different gene expression profiles. Any combination of two lists was compared for matching gene-identity. The number and identity of genes commonly affected in two platforms (e.g. SAGE study vs. DNA microarray) was determined. We used the normal approximation to the binomial distribution as previously described [40] to calculate whether the number of matching genes derived from each cross-platform comparison was of statistical significance (p < 0.05).
Authors' contributions
M.C.A. conceived the study idea and carried out the real time RT-PCR validations, the biostatistical/ bioinformatics analysis and writing the manuscript. Y.H., H.S. and J.A.D. carried out the breast cancer SAGE libraries and provided practical feedback on aspects of the manuscript. K.B. and S.G. developed the biostatistical and web-page base methodology. A.S. provides the tissue samples and clinical information. C.M.A. is the principal investigator and was involved in the conceptualization, design and writing of the manuscript. All authors read and approved the final manuscript.
Competing interests
The author(s) declare that they have no competing interests.
Supplementary Material
Additional File 1
Differentially expressed genes between ERα (+) vs. ER α (-) breast carcinomas (Fold change ≥2; p < 0.05).
Click here for file
Additional File 2
Gene Ontology overrepresentation analysis.
Click here for file
Additional File 3
High-affinity EREs identified in ERα (+) up-modulated genes.
Click here for file
Additional File 4
Cross-platform comparison of the up-modulated transcripts in ERα (+) breast carcinomas.
Click here for file
Acknowledgements
The authors thank Dr. Michael MacLeod for critical reading of this manuscript. This work was supported by NIH-NCI Grant 1U19 CA84978-1A1 (C. M. Aldaz) and center grant ES-07784.
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| 15762987 | PMC555753 | CC BY | 2021-01-04 16:39:32 | no | BMC Genomics. 2005 Mar 11; 6:37 | utf-8 | BMC Genomics | 2,005 | 10.1186/1471-2164-6-37 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-141577177710.1186/1477-7525-3-14ResearchSubjective impact of osteoarthritis flare-ups on patients' quality of life Majani Giuseppina [email protected] Anna [email protected] Aurelio [email protected] Psychology Unit, Fondazione S. Maugeri, Clinica del Lavoro e della Riabilitazione, IRCCS, Istituto Scientifico di Montescano (PV), Italy2 Scientific Department, Italfarmaco SpA, Milan, Italy2005 16 3 2005 3 14 14 7 1 2005 16 3 2005 Copyright © 2005 Majani et al; licensee BioMed Central Ltd.2005Majani 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
Clinical trials on osteoarthritis (OA) flare-ups treatment usually focus only on objective measures of health status, albeit recent literature suggestions on the importance of patients' subjectivity. Aim of the study was to evaluate the effects of OA and of its different types of medical treatment(s) on Health Related Quality of Life (HRQoL) in terms of both subjective satisfaction and functional status.
Methods
An observational study on prospective data collected from the Evaluation of Quality of life in OA (EQuO) clinical trial (April 1999-November 2000) was conducted; outpatients from 70 participating centers (Orthopedy or Rheumatology Departments in Italy) with a diagnosis of OA of the hip or knee were consecutively enrolled. Patients were observed at OA flare-ups (baseline) and at follow up 4 weeks after treatment. Patients' objective and subjective HRQoL were assessed by means of the SF-36 and the Satisfaction Profile (SAT-P, which focuses on subjective satisfaction); Present Pain at baseline and Pain Relief at follow up were also evaluated.
Results
Among the 1323 patients, 1138 (86%) were prescribed one drug/treatment of osteoarthritis, 169 (13%) 2 drugs/treatments, and 16 (1%) 3 drugs/treatments; most of treatments involved the prescription of NSAIDs; non-coxib, COX2 selective NSAIDs were prescribed in about 50% of patients. Follow-up visits were performed after 29.0 days on average (± 7.69 SD). For all SF-36 domains, all SAT-P items and factors, the differences between baseline and follow up scores resulted statistically significant (p < 0.001), enlighting an improvement both in health status and in subjective HRQoL.
Conclusion
Besides the classic health status measures, the assessment of patients' subjective satisfaction provides important clues on treatments efficacy of OA within the patient-centered medicine model. In clinical practice this could lead to a better doctor-patient communication and to higher levels of treatment adherence.
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Background
The impact of osteoarthritis (OA) on patient's functional levels is well known [1-3]. Pain and physical limitations constitute difficulties patients have to deal with [4,5] and require long term pharmacological treatment and physical therapies.
Usually OA affects elderly people, and is one of the main causes of physical disability. In OA patients, Health Related Quality of Life (HRQoL) and activities of daily living are negatively affected. Significant work disability, reduced ability to deal with household duties and sleep disorders are reported in patients with symptoms of OA flare-ups, together with dysfunctions in the areas of ambulation, body-care and movement (in terms of perceived health status), and emotional behaviour (in terms of perceived psychological functioning) [1,2,4-8].
As a chronic condition, the impact of OA has been studied mainly focusing on its consequences on health status. Similarly, treatment efficacy is assessed within the context of health status and/or symptomatology in many clinical trials [6,7,9-12]. However, health status and symptomatology can be considered only two components of HRQoL [13] and little is known about the impact of OA and its treatments on patient's subjective perspective, in spite of increasing attention on this topic [14-19].
In literature, HRQoL refers to patients' appraisals of their current levels of functioning and satisfaction, compared to what they perceive to be ideal [20]. HRQoL assessment allows a subject to express his or her ability to perform daily activities across many domains which include physical, social and cognitive functioning, role activities and emotional wellbeing. Besides, "...how a subject feels about the performace of each of those activities may be assessed separately by measuring satisfaction for each domain." [21]. The subjective implications of HRQoL, within the context of patient centred medicine, have been already stressed by suggestions from recent reliable scientific literature [15,17-24].
The aim of the present study is to evaluate the effects of OA and of its different types of medical treatment(s) on HRQoL in terms of both subjective satisfaction and functional status.
Methods
Patient population and procedure
Data from collaborating, educated outpatients aged 50–80 years with a diagnosis of OA of the hip or knee according to the criteria of the American College of Rheumatology [25] were collected in this observational, prospective study.
Outpatients (n = 1340) were consecutively enrolled in 70 Italian participating centers (Orthopedy or Rheumatology Departments, listed in Appendix A [see additional file]) from April 1999 to November 2000. 147 patients withdrawn OA treatment before follow-up visit.
All patients signed an informed consent in which the purposes of the study (HRQoL assessment and treatment efficacy, as primary and secondary outcomes respectively) were clearly stated. Approval for this research was obtained by the ethics committee, patients did not receive any remuneration for their participation.
Patients with concomitant osteoarticular disorders, impairment of motor function not due to OA of the hip or knee, concomitant systemic disease(s) affecting HRQoL or requiring NSAIDs/steroids use on a regular basis were not included into the study, in order to avoid biases in the results due to treatments other than OA treatments.
Patients were observed at OA flare-ups, when attending for a visit (baseline) and at follow up 4 weeks after treatment. According to the observational design of this trial, no "study treatments" were assigned to patients, but any drug(s)/medical treatment(s) considered by the physician as adequate to the patient's clinical condition was freely prescribed; therefore patients were not previously randomized to treatment.
During both visits, patients were administered the following: the Visual Analogue Scale (VAS) [26] on Present Pain (baseline) or on Pain Relief (follow up); the Medical Outcomes Study Short-Form 36 Health Status Survey (SF-36) [27] in its validated Italian version [28,29] and the Satisfaction Profile (SAT-P) [30].
Moreover, at follow up, the global assessments of efficacy and tolerability of the medical treatment(s) prescribed for OA flare ups (expressed by the patient and by the physician according to a 4 point semi-quantitative rating scale: excellent – good – moderate – poor) were collected. Side effects to this/these treatment(s), if any, were registered as well.
The assessment procedure was standardized for all the participating centres. During the visit patients were invited to compile alone all the questionnaires and rating scales, only if required patients were assisted by a trained health professional.
Self-reporting bias in HRQoL improvements was kept under control by the assessment procedure and by the adoption of valid and reliable questionnaires.
Measures
Visual Analogue Scale
The VAS is perhaps the most widely used instrument for the measurement of pain intensity. The classic version of the VAS was administered: 10 centimeter line, horizontal. "It is a simple, robust, sensitive, and reproducible instrument that enables a patient to express the severity of his pain in such a way that it can be given a numerical value." [26] Its psychometric properties and its utility in clinical trials have been confirmed [2,8,31,32]. VAS on Present Pain ranged from "no pain" to "the worst pain possible"; VAS on Pain Relief ranged from "no pain relief" to "the maximum pain relief". Scores ranged from 0 to 100.
SF-36
The SF-36 is a well known self-administered and generic health status measure which encompasses 8 domains related to daily life activities: physical functioning, role limitations due to physical problems, role limitations due to emotional problems, vitality, bodily pain, social functioning, mental health and general health perception [33-35]. Each domain scores from 0 (lowest level of functioning) to 100 (highest level of functioning). The instrument has been extensively validated within the Medical Outcome Study [33] and in other settings [34].
Satisfaction Profile
The SAT-P is a self-administered, generic questionnaire which provides a satisfaction profile in daily life and can be considered as an indicator of subjective QoL. Satisfaction can be defined as the cognitive product of the comparison between ideal life and reality, and can therefore be quantitatively measured. The subject is asked to evaluate his/her satisfaction about 32 life aspects with reference to the last month (on 32 10 cm horizontal VAS) independently of his/her objective health status (for example: "How satisfied have you been in the last month with your Resistance to physical fatigue?"; "How satisfied...with your Mood?"; "How satisfied...with your Emotional stability?"). It provides 32 individual scores and 5 factor scores, all ranging from 0 (lowest level of satisfaction) to 100 (highest level of satisfaction). Together with its ability to detect patient's subjective satisfaction, the SAT-P addresses some aspects of daily life which are not included in SF-36 items (i.e. sleep, sexual life, quality of couple relationship, eating, self-confidence, resistance to stress, etc.). Its psychometric properties and clinical utility have been confirmed [30,36,37].
Statistical analyses
Sociodemographic data and clinical values were analysed by means of descriptive statistics. Since the incidence of withdrawals resulted low, analyses were performed on complete cases and no solutions for handling missing data was adopted.
Baseline and follow-up of SF-36 and SAT-P item and factor scores were compared by means of Analusis of Covariance (ANCOVA). Moreover, ANCOVAs were adopted in order to evaluate the impact of clinical variables on SF-36 and SAT-P factor delta scores (calculated subtracting the follow-up scores from baseline scores). The variables included into the models were: age, gender, body weight, OA localization (hip, knee, or both), VAS Present Pain, presence of concomitant disease(s), type of treatment (COX2 selective NSAIDs vs. other treatments). Results were summarized using mean ± SE for continuous variables and frequency (absolute and percent) for categorical variables. All p values are two-tailed and p < .05 was considered statistically significant. All computations were carried-out by resorting to SAS 8.0 procedures.
Results
Patients demographic and clinical characteristics (OA localization (hip/knee/both), VAS Present Pain, type of medical treatment(s) of OA flare-ups, concomitant diseases and treatments are shown in Table 1. Patients' baseline VAS Present Pain resulted consistent with a clinical condition of moderate to severe rheumatic disease.
Table 1 Patients' characteristics
Gender (F/M) 795/528
Age (years, mean ± SD) 64.4 ± 10.3
Marital status:
Single, n (%) 72 (5.4)
Married, n (%) 922 (69.7)
Widowed, n (%) 220 (16.6)
Separated/divorced, n (%) 13 (1.0)
Missing, n(%) 96 (7.3)
Educational level:
Primary school, n (%) 548 (41.4)
Junior high school, n (%) 325 (24.6)
Senior high school, n (%) 280 (21.2)
Degree/Master/PhD, n (%) 103 (7.7)
Missing, n(%) 67 (5.1)
Employment status:
Employed, n (%) 434 (32.8)
Retired, n (%) 550 (41.6)
Housewife, n (%) 288 (21.8)
Missing, n (%) 51 (3.8)
Body weight (kg, mean ± SD) 73.4 ± 11.0
OA localization:
Knee, n (%) 658 (49.7)
Hip, n (%) 463 (35.1)
Knee + hip, n (%) 202 (15.2)
VAS Present Pain (mm, mean ± SD) 67.7 ± 17.0
Concomitant diseases, n (%) 632 (47.8)
Concomitant treatments, n (%) 444 (33.6)
The most frequent concomitant diseases were: hypertension (19.1%), metabolic and nutritional disorders (9.2%), muscoloskeletal, connective tissue and bone disorders (8.2%) and gastrointestinal system disorders (4.3%). The most frequently prescribed concomitant treatments were: cardiologic drugs (9.7%) and antihypertensive (9.4%), antidiabetic drugs (8.4%), antithrombotic agents (4.5%), antiacids (6.7%), sedatives (4.8%). 1138 patients (86%) were prescribed one drug/treatment of OA, 169 patients (13%) 2 drugs/treatments, and 16 patients (1%) received 3 drugs/treatments. Most of treatments involved the prescription of NSAIDs; non-coxib, COX2 selective NSAIDs (nimesulide betadex and nimesulide, the only two COX2 selective NSAIDs available in Italy at the time of this study) were prescribed in about 50% of patients (Table 2).
Table 2 Treatments prescribed for osteoarthritis flare-ups
n % patients
COX2 NON-SELECTIVE NSAIDs
Arylacetic acid derivatives (diclofenac, indomethacin, sulindac, etc.) 221 16.7
Arylpropionic acid derivatives (ibuprofen, naproxen, ketoprofen, etc.) 165 12.5
Oxycams (piroxicam, tenoxicam, etc.) 181 13.7
Others (nabumetone, glucosamine, diacerein, etc.) 107 8.1
COX2 SELECTIVE NSAIDs
Nimesulide betadex (or nimesulide) 689 52.1
OTHER DRUGS/TREATMENTS
Various, systemic (ASA, paracetamol, corticosteroids, centrally acting myorelaxants) 46 3.5
Various, topical (transcutaneous or intraarticular) 44 3.3
Physical treatment (mobilization, iontophoresis, etc.) 60 4.5
Follow-up visits were performed after 29.0 days on average (± 7.69 SD). Only a small number of patients (17; 1.2%) did not attend follow-up visit.
HRQoL assessment: SF-36
For all SF-36 domains, the difference between baseline and follow up scores resulted statistically significant (p < 0.001) (Table 3).
Table 3 SF 36 scores (Mean ± SE). Baseline vs Follow-up scores. At the ANCOVAs: p < 0.001 for all domains
SF-36 domains Baseline Follow up p
Physical Functioning 47.9 ± 0.7 59.3 ± 0.7 <.001
Role Physical 27.8 ± 1.0 48.0 ± 1.1 <.001
Bodily Pain 31.7 ± 0.4 50.5 ± 0.5 <.001
General Health 45.8 ± 0.5 50.0 ± 0.5 <.001
Vitality 46.5 ± 0.5 53.2 ± 0.5 <.001
Social Functioning 44.1 ± 0.6 65.4 ± 0.6 <.001
Role Emotional 46.9 ± 1.2 65.7 ± 1.1 <.001
Mental Health 59.2 ± 0.5 65.4 ± 0.5 <.001
Baseline Present Pain was associated with almost all the SF-36 domains (Table 4). The presence of concomitant disease(s) resulted in a statistically significant association with 4 domains: Role Physical, Bodily Pain, General Health, Social Functioning. The type of OA treatment was associated with Physical Functioning and Bodily Pain. OA localization and age was associated with only one domain: Physical Functioning and Role Physical respectively. Gender and body weight did not correlate with any SF-36 domain.
Table 4 Detected statistical significances on SF-36 delta scores. The p values resulted from the ANCOVAs are indicated.
SF-36 domains Covariates
Age Gender Body weight OA localization Present Pain Concomitant diseases Treatment
Physical Functioning 0.021 0.0001 0.020
Role Physical 0.022 0.007
Bodily Pain 0.0001 0.0001 0.006
General Health 0.0001 0.003
Vitality 0.0001
Social Functioning 0.0001 0.003
Role Emotional 0.007
Mental Health 0.0001
HRQoL assessment: SAT-P factors
All the differences between baseline and follow up SAT-P factor scores were statistically significant (p < 0.001) (Table 5).
Table 5 SAT-P factor scores (M ± SE). Baseline vs Follow-up scores. At the ANCOVAs: p < 0.001 for all Factors.
SAT-P Factors Baseline Follow up p
Psychological functioning 59.3 ± 0.6 65.5 ± 0.5 p <.001
Physical functioning 41.3 ± 0.5 51.9 ± 0.5 p <.001
Work 53.3 ± 0.7 57.8 ± 0.7 p <.001
Sleep/Eating/Leisure 55.4 ± 0.5 60.9 ± 0.5 p <.001
Social functioning 66.0 ± 0.6 70.8 ± 0.5 p <.001
Baseline pain was significantly associated with all SAT-P factors (Table 6). The presence of concomitant disease(s) was in a statistically significant association with 3 out of 5 factors: Psychological functioning, Sleep-Eating-Leisure, Social functioning. OA treatment was associated with the factor Sleep-Eating-Leisure.
Table 6 Detected statistical significances on SAT-P factors. The p values resulted from the ANCOVAs are indicated
SAT-P Factors Covariates
Age Gender Body weight OA localization Present Pain Concomitant diseases Treatment
Psychological functioning 0.0001 0.022
Physical functioning 0.0001
Work 0.007
Sleep/Eating/Leisure 0.0001 0.007 0.026
Social functioning 0.0001 0.018
HRQoL assessment: SAT-P items
Figure 1 shows the graphic representation of baseline and follow up SAT-P item scores. All the differences were statistically significant (p < 0.001).
Figure 1 SAT-P items: mean scores at baseline and at follow-up. For all the differences (ANCOVAs) p < 0.001.
Clinical outcome of OA treatment: Efficacy and Tolerability
At follow-up, mean VAS Pain Relief was 61.1 mm (± 24.3 SD).
In 65% of cases treatment efficacy was evaluated as good or excellent by patients themselves, in 67% of cases it was evaluated as good or excellent by physicians. In 81% of cases treatment tolerability was evaluated as good or excellent by patients themselves, in 84% of cases it was evaluated as good or excellent by physicians. It was evaluated as poor in 7% and 6% of cases respectively (Table 7).
Table 7 OA treatments' evaluations (efficacy and tolerability)
Poor Moderate Good Excellent Missing data
n (%) n (%) n (%) n (%) n (%)
Efficacy – patients 163 (12.3) 288 (21.8) 634 (47.9) 227 (17.2) 11 (0.8)
Efficacy – physicians 129 (9.8) 292 (22.1) 634 (47.9) 257 (19.4) 11 (0.8)
Tolerability – patients 93 (7.1) 147 (11.1) 712 (53.8) 359 (27.1) 12 (0.9)
Tolerability – physicians 79 (6.0) 122 (9.2) 703 (53.1) 407 (30.8) 12 (0.9)
11.1% of patients reported side effects to medical treatment of OA; most of these reactions involved the gastrointestinal system. Poor tolerability led to treatment withdrawal in 6.2% of patients.
Discussion
Our study represents, to our knowledge, the largest observational prospective clinical trial carried out in OA patients' subjective HRQoL. The sample size and the very small number of drop-outs could be considered the strenghts of the study.
A limit of the study could be considered the adoption of the SAT-P which is a new questionnaire, validated on the Italian population [30], but not previously used in clinical trials or in OA patients. Nevertheless, its psychometric properties have been previously confirmed, and moreover it is the only Italian questionnaire specifically aimed at assessing subjective satisfaction in daily life, independently of the presence of a disease. Its user friendly structure and its easily comprehensible graphical representation could be considered substantial methodological facilities both in research and in clinical practice.
Finally, the coherence between the data provided by the two HRQoL instruments could confirm that health status and subjective satisfaction partially overlap, and allows us to study the same phenomenon from two different points of view: the objective and the subjective. This could therefore be considered the added value of the study.
Considering the whole sample, SF-36 results confirm what previous studies have already enlightened in clinical trials: the SF-36 is, according to Kosinski et al. [35], a suitable instrument for assessing health status in OA, and medical treatment improves functionality levels in daily life aspects.
The same conclusions could be drawn for the SAT-P: on the whole sample a general improvement of satisfaction levels can be observed in all the 32 items considered. In other words, pharmacological treatment has a significant positive impact on patients' both objective functioning and subjective well-being [16].
Thanks to the sinergic utility of the two instruments it has been possible to enlight results otherwise left unperceived and whose positive value on patients' life is unquestionable.
Further investigations are needed in order to better clarify the relationships between perceived pain and pain relief and patients' HRQoL. Mastery, self-efficacy and coping abilities could be significant mediators between these two constructs [38,39].
Conclusion
From both an objective and a subjective point of view, OA flare-ups' treatment has proved to have positive effects on HRQoL. The sinergic use of a health status measure (SF-36) and of a tool addressing subjective satisfaction (SAT-P) allows to wider the focus on patients' life.
This methodological approach could help clinicians and researchers in transferring into practice the ICF model issues [40], with special attention on Activity and Participation and on Environmental Factors.
List of abbreviations
ANCOVA Analysis of Covariance
HRQoL Health Related Quality of Life
NSAID Non Steroid Anti Inflammatory Drugs
OA Osteoarthritis
SAT -P Satisfaction Profile
SF-36 Medical Outcome Study Short-Form 36 Health Status Survey
VAS Visual Analogue Scale
Competing interests
The author AS is employee of the company that partially funded the study.
Authors' contributions
GM: responsible for the design of the study, contributed to the statistical evaluation, contributed to the writing of the paper.
AG: responsible for the statistical evaluation, contributed to the writing of the paper.
AS: contributed to the design of the study and data collection, contributed to the statistical evaluation, contributed to revise the manuscript.
Supplementary Material
Additional File 1
Appendix A – Participating Centers
Click here for file
Acknowledgements
This study has been partially funded by Italfarmaco SpA, Milan, Italy. We would gratefully acknowledge the recruitment and data collection of the many physicians and nurses who collaborated to our study (in Appendix A all the participating centers are listed [see additional file]).
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| 15771777 | PMC555754 | CC BY | 2021-01-04 16:38:15 | no | Health Qual Life Outcomes. 2005 Mar 16; 3:14 | utf-8 | Health Qual Life Outcomes | 2,005 | 10.1186/1477-7525-3-14 | oa_comm |
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BMC Mol BiolBMC Molecular Biology1471-2199BioMed Central London 1471-2199-6-61576638710.1186/1471-2199-6-6Research ArticleRecognition and binding of mismatch repair proteins at an oncogenic hot spot Edelbrock Michael [email protected] Huiling [email protected] Allen [email protected] Martha [email protected] Sangeetha [email protected] Kandace J [email protected] Department of Biochemistry & Cancer Biology, Medical College of Ohio, Toledo OH, USA2 Division of Human Cancer Genetics, The Ohio State University, Columbus OH, USA3 Department of Cell Biology and Neuroscience, University of South Alabama, Mobile AL, USA2005 14 3 2005 6 6 6 11 11 2004 14 3 2005 Copyright © 2005 Edelbrock et al; licensee BioMed Central Ltd.2005Edelbrock 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 current investigation was undertaken to determine key steps differentiating G:T and G:A repair at the H-ras oncogenic hot spot within the nuclear environment because of the large difference in repair efficiency of these two mismatches.
Results
Electrophoretic mobility shift (gel shift) experiments demonstrate that DNA containing mismatched bases are recognized and bound equally efficiently by hMutSα in both MMR proficient and MMR deficient (hMLH1-/-) nuclear extracts. Competition experiments demonstrate that while hMutSα predictably binds the G:T mismatch to a much greater extent than G:A, hMutSα demonstrates a surprisingly equal ratio of competitive inhibition for both G:T and G:A mismatch binding reactions at the H-ras hot spot of mutation. Further, mismatch repair assays reveal almost 2-fold higher efficiency of overall G:A repair (5'-nick directed correct MMR to G:C and incorrect repair to T:A), as compared to G:T overall repair. Conversely, correct MMR of G:T → G:C is significantly higher (96%) than that of G:A → G:C (60%).
Conclusion
Combined, these results suggest that initiation of correct MMR requires the contribution of two separate steps; initial recognition by hMutSα followed by subsequent binding. The 'avidity' of the binding step determines the extent of MMR pathway activation, or the activation of a different cellular pathway. Thus, initial recognition by hMutSα in combination with subsequent decreased binding to the G:A mismatch (as compared to G:T) may contribute to the observed increased frequency of incorrect repair of G:A, resulting in the predominant GGC → GTC (Gly → Val) ras-activating mutation found in a high percentage of human tumors.
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Background
Several different DNA repair systems have evolved within all living cells to correct mispaired or damaged nucleotide residues generated either by endogenous events or by exposure to exogenous mutagenic agents [1,2]. The frequency of mutational events varies widely within the genome, and specific sites harboring increased frequency of mutation are now defined as 'hot spots' of mutation. The human ras protooncogene family contains three such hot spots – codons 12, 13, and 61. Factors contributing to these and other hot spots of mutation are still largely unknown, despite much investigation, but now appear to have several different contributions, such as type of DNA damage, genomic location, surrounding sequence, cell cycle position, efficiency of the optimal DNA repair pathway, and involvement of alternate repair and other cellular pathways.
DNA mismatch repair (MMR) is a repair system that corrects mispaired nucleotides and insertion/deletion loops (IDLs), resulting from replication, recombination, or repair errors. Consequences of defects in this DNA repair pathway are evidenced by microsatellite instability (MSI), elevated mutation frequency throughout the genome, enhanced recombination events, as well as tolerance to cytotoxic effects of alkylating agents as evidenced by decreased apoptosis. Deficient MMR is, in turn, associated with hereditary nonpolyposis colorectal cancer (HNPCC), as well as other types of sporadic tumors in humans and animal models [3-6]. Although the contribution of MMR to highly mutable genomic sites other than microsatellite sequences is largely unknown, several specific genetic mutations involved in neoplastic progression, including ras-activating mutations, have been reported as frequent occurrences in HNPCC and other MSI tumors [7,8].
DNA MMR is conserved amongst highly divergent species, reflecting the essential role of this DNA repair process [9,10]. The MMR system in eukaryotes is more complex and has different mismatch-specific repair efficiencies than that of E. coli [11-13]. Several human homologs of bacterial MutS and MutL proteins have now been identified [14-19]. The primary human homologs for MutS that play instrumental roles in MMR include hMSH2, hMSH6, and hMSH3 [20,21]. The hMutSα heterodimer (hMSH2 and hMSH6) has been demonstrated to recognize and bind DNA mispairs and short IDLs [14,21-24]. The hMutSβ heterodimer (hMSH2 and hMSH3) preferentially recognizes and binds IDLs of up to 12 nucleotides. Human cells lacking hMSH2 protein expression also lack its cognate partners, hMSH3 and hMSH6 (due to decreased stability). This lack is associated with defective MMR, microsatellite instability, and is associated with a high percentage of HNPCC [25]. MutL homologs that are most relevant to human MMR are hMLH1 and hPMS2, which form the hMutLα heterodimer [9,26]. Similar to the observed instability of individual MutS homologs, lack of hMLH1 protein expression results in the lack of hPMS2 protein, which in turn results in microsatellite instability, defective MMR, and is also associated with a high percentage of HNPCC [9,10,15,25,27-32]. The MutLα heterodimer is thought to act as a molecular matchmaker between the MutSα-DNA complex and downstream enzyme activities responsible for subsequent identification, excision, and replacement of the incorrect base [9,19]. Biochemical interactions and genetic studies have further implicated proliferating cell nuclear antigen (PCNA), exonuclease I (EXO1), replication protein A (RPA), replication factor C (RFC), and DNA polymerase δ as active participants in the MMR pathway [19,27,29,30,33-35]. Recently, the differential requirement of specific proteins associated with MMR have been identified for both 3'-nick directed and 5'-nick directed MMR by the use of an in vitro model [35,36].
We have previously demonstrated that the efficiency of correct mismatch repair within the cell can differ significantly, depending on exact type of mismatch, site-specific location, phase of cell cycle, or cell type [23,24,37,38]. Within this report, we have focused on a more precise understanding of the differences between MMR protein interactions with a G:A (least repaired) or G:T (best repaired) mismatch located at H-ras codon 12 to better understand molecular events leading to activating mutations at this site. Our current results indicate that initial recognition and subsequent binding for repair signaling by hMutSα may be two separably measurable steps in the MMR pathway that can significantly affect downstream cellular events.
Results
Specificity of hMutSα protein binding to DNA mismatches at H-ras codon 12
To determine that the binding complex recognizing mismatches at codon 12 of H-ras in HCT116 + Ch.3 nuclear extract is composed of hMutsα, DNA binding reactions were conducted using [32P]-G:T oligos incubated with nuclear extracts from these MMR proficient cells. In confirmation of hMutSα binding to a G:T mismatch at this oncogenic site, the hMutSα :DNA complex was efficiently interrupted by goat anti-hMSH6 and with rabbit anti-hMSH2 (Figure 1, lanes 3 and 5), but not by BSA, nonspecific goat IgG, or nonspecific rabbit IgG (lanes 1, 2, and 4, respectively). As further evidence of specific hMutsα:DNA mismatch binding, addition of 1.5 mM ATP completely disrupted the gel shifted band (results not shown) [24,30]. Thus, MMR protein binding to a G:T mismatch located at H-ras codon 12 within the nuclear environment appears to be primarily, if not exclusively, hMutSα.
Comparison of hMutSα binding 'avidity' to a G:T mismatch located at H-ras codon 12 within MMR competent and deficient nuclear extracts
The HCT116 cell line completely lacks both 3' and 5' nick-directed MMR, putatively due to lack of hMLH1 expression, although these cells normally express hMSH2 and hMSH6. To determine if hMutSα recognition and binding of a mismatch at the H-ras codon 12 hot spot might undergo alteration within HCT116 cells, in conjunction with the lack of hMutLα, binding competition experiments were performed. Figure 2 is a comparison of the amount, or 'avidity', of hMutSα binding affinity within MMR proficient (HCT116 + Ch. 3) and within MMR deficient (HCT116) nuclear extracts. These gel shifts demonstrate virtually identical mismatch specific binding avidity of hMutSα to [32P]-G:T-oligo. As well, 50X molar excess of unlabeled G:T-oligo, which does not completely inhibit hMutSα gel shift of the radioactively labeled mismatch, demonstrates a similar degree of competitive inhibition of mismatch specific binding in both MMR proficient and MMR deficient nuclear extracts (Fig. 2, lanes 3 and 5). These results provide strong evidence that there is no discernable difference in hMutSα binding avidity to a G:T mismatch at H-ras codon 12, despite that HCT116 + Ch. 3 is MMR proficient and HCT116 lacks expression of hMutLα and is MMR deficient.
Comparison of competitive inhibition of hMutSα binding avidity to G:T versus G:A mispairs at H-ras codon 12
We have previously determined that the G:A mismatch at H-ras codon 12 is accurately repaired back to G:C much less frequently than G:T [37]. In addition, we have previously demonstrated that recognition and binding of this poorly repaired mismatch is also very weak within HeLa nuclear extracts [23]. We therefore asked if competitive inhibition by several different concentrations of these mismatches would reveal more specific mechanisms of these intriguing differences at this same location. Firstly, using increasing concentrations of unlabeled G:T oligo as a competitor for [32P]-G:T-oligo, we observe the expected highly competitive inhibition of the radioactive G:T mismatch gel shift band in the presence of as little as 25X cold G:T-oligo (Figure 3a; compare cold G:T fold increase lanes "0" up through "100"). In comparison, the radioactive G:T-oligo gel shift band is much less competitively inhibited by increasing concentrations of cold G:A oligo (Figure 3a; compare cold G:A fold increase lanes "0" up through "100"), further confirming the observed preference of hMutSα for a G:T mismatch as compared to a G:A mismatch at the H-ras codon 12 hot spot of mutation. Comparison of the densitometric band intensities reveal that even 25X cold G:T oligo can successfully decrease hMutSα binding to radioactive G:T by 80%, but that 25X cold G:A decreases hMutSα binding to radioactive G:T by only 10%.
An alternate approach to more precisely define the extent of this phenomenon was to measure competitive inhibition of radioactively labeled G:A-oligo (rather than of the avidly bound [32P]-G:T-oligo) by incubation with unlabeled G:A-oligo, to determine if equal ratios of competitive binding might occur for G:T and G:A, despite the very different avidities of hMutSα for each mismatch. This would provide an indication of equal recognition of each type of mismatch by hMutSα, despite unequal binding avidity for these two mismatches. For these experiments, it was necessary to extend incubation periods from 30 min. to 2.5 hr, as sufficient concentrations of gel shifted bands can be detected with the G:A oligo only after extended incubation (unpublished observations). As expected, binding of hMutSα to [32P]-G:A-oligo is approximately 100 fold weaker in intensity than for [32P]-G:T-oligo after the extended incubation (Figure 3b; compare "Intensity" of lanes 1 and 3 on densitometric graph). However, 100X molar excess of cold G:A-oligo competitively inhibits MMR protein binding to [32P]-G:A-oligo by 89%, which is similar to the same concentration of cold G:T-oligo competitive inhibition of MMR protein binding to [32P]-G:T-oligo (97%), as determined by comparison of the densitometric intensity of each band. All of the above experiments have been repeated with similar results.
MMR at H-ras codon 12 hot spot of mutation
Table 1 contains the results of a MMR assay designed to score nick-directed MMR as correct, incorrect or unrepaired [39]. Positive control experiments demonstrate that MMR at the H-ras codon 12 sequence within the pUC19 vector is nick-directed, yielding a high degree of correct repair by MMR proficient E. coli (DH5α); G:T → G:C = 95%, G:A → G:C = 78%, in agreement with our previously published results using a different plasmid vector [23]. Additionally, inserting the same H-ras 69 mer into a pUC18 vector to determine effects of reverse orientation of the oligo and 'nicked' strand resulted in an almost identical frequency of correct repair at codon 12; G:T → G:C = 100%, G:A → G:C = 78%. These results confirm that the mismatch is recognized and repaired by nick-directed bacterial MMR and that there is no strand bias for MMR during high copy number bacterial replication of this plasmid. Background repair after direct transformation into NR9161 E. coli (MMR deficient) was consistently between 41–44% for both mismatches (data not shown), and is favorably comparable to an average of 50 – 60% background repair efficiency by this and other strains of MMR deficient E. coli (personal communication with Roel Schaaper).
Results within Table 1 were determined by incubating plasmids containing a G:T or G:A mismatch at the H-ras codon 12 middle base pair location containing a 5' nick on the "T" or "A" strand (as described in methods) with MMR proficient (HCT116 + Ch. 3) or MMR deficient (HCT116) nuclear extracts. Efficiency of correct, incorrect, and total (correct + incorrect) MMR for each mismatch was subsequently calculated (as described in methods) [39]. Surprisingly, total repair of the G:A mismatch (30%; G:A → G:C + T:A) was almost twice as high as for G:T (18.2%; G:T → G:C + A:T). This phenomenon was consistently observed within the several different experiments required to obtain the total results depicted in Table 1, and was statistically significant (p < 0.05). In direct contrast, correct (nick-directed) repair of G:A → G:C (60%) was significantly low when compared to correct (nick-directed) repair of G:T → G:C (96%) (p < 0.005). Inadvertent nicks in the "G" (correct) strand of the H-ras 69 mer or pUC plasmid during preparation could not have contributed to the increased total, or increased incorrect repair (G:A → T:A) results for the G:A mismatch because the efficiency of incorrect repair would then be similar for both the G:T and the G:A mismatch. Instead, MMR results within Table 1 were found to be consistently different for the two mismatches, and are the compiled results of several different mismatch-containing plasmid preparations and subsequent MMR assays. In addition, MMR proficient E. coli nick-directed repair results are consistently high, for both mismatch-containing plasmid preparation subsequently used for each nuclear extract MMR assay (data not shown). As well, MMR deficient E. coli repair ratios (correct to incorrect) are 1:1 after transformation of unmethylated plasmids (prepared by replication in GM2929; E. coli dam-dcm-). In combination, all of the above control experiments demonstrate a lack of any significant unintentional misrepair events on either strand of DNA, for either site-specific mismatch plasmid preparation. Therefore, the results within Table 1 suggest that increased total repair (correct + incorrect) combined with increased incorrect repair of G:A, as compared to the decreased total repair and increased correct MMR repair of G:T, may result in increased mutational events when a G:A mismatch occurs at this oncogenic site. Somewhat surprisingly, MMR deficient nuclear extracts did not repair either mismatch at codon 12 above background, indicating a complete lack of alternate DNA repair activity that can correct either mismatch at this oncogenic site. Therefore these results indicate that the observed repair efficiencies in MMR proficient nuclear extracts are due solely to MMR activity, rather than to a combination of MMR and other DNA repair pathways. Alternatively, it is possible that the methods used for nuclear extract preparation or the in vitro MMR assay might cause an artifactual decrease in the activity of other DNA repair pathways.
Discussion and conclusions
Previous investigations of ours have revealed significantly decreased nick-directed G:A → G:C repair at codon 12 of H-ras, as compared to G:T → G:C repair at this location [23,37]. Decreased MMR of G:A has also been observed within different sequences by other investigators, although molecular mechanisms for these differences remain obscure [12]. The current investigation was undertaken to determine key steps differentiating G:T and G:A repair pathways at the H-ras oncogenic hot spot within the nuclear environment. Our results suggest that, firstly, the MMR pathway is the primary, if not the only, DNA repair pathway that can recognize and is subsequently responsible for the repair of both mismatches at this oncogenic location within the cell. Secondly, although 'recognition' of either G:T or G:A by hMutSα is likely equal, and is essential for initiation of MMR, the avidity – or strength – of hMutSα binding to either mismatch is not equal, and may play a significant role in the decision of whether to activate nick-directed correct MMR, or the activation of an alternate response pathway. In support of this concept, and in correlation with weak hMutSα binding to G:A, we have observed significantly decreased nick-directed (correct) MMR of G:A → G:C (60%), as compared to nick-directed MMR of G:T → G:C (96%). We have however, consistently measured almost twice the efficiency of total repair of G:A (to either G:C or T:A) as compared to total G:T repair (to G:C or A:T) at this oncogenic site. This phenomenon appears to be due to increased non nick-directed incorrect repair of G:A → T:A (rather than to replication without repair, which results in a mixture of G:C and T:A).
This raises the possibility that (a) initial recognition of a mismatch is essential but separable from (b) differential binding of MMR proteins to specific mismatches, which in turn is directly correlated either with nick-directed correct MMR, or with alternate events other than nick-directed MMR. In support of this concept, Wang, et. al. has recently demonstrated by atomic force microscopy that E. coli MutS-DNA complexes exist in two conformations [40]. The initial recognition of a mismatch by MutS results in a localized kink in the DNA conformation and is termed the initial recognition complex (IRC). The second step is required for MMR and is a further conformational change in which the localized kink in the DNA becomes unbent. This is called the ultimate recognition complex (URC) and may be the conformation required for ATP activation and subsequent MMR activity. It is also possible that either differential binding kinetics (not measurable by gel shift), or very low avidity of binding or a different molecular binding mechanism (undetectable by gel shift) may contribute to subsequent cellular events specific to the G:A mismatch. These possibilities are currently being investigated. An additional hypothesis has been proposed by Junop, et. al., placing E. coli MutS in the role of 'authorizing' different repair events [41]. Also, it is now well documented that while hMutSα recognizes DNA damage other than mismatches, the MMR pathway does not appear to play a direct role in the repair of these damaged bases, but rather is associated with initiation of cell cycle and/or apoptotic events and therefore is described as a "sensor of genetic damage" within this context. The molecular mechanisms contributing to the various cellular activities associated with the well documented hMutSα recognition and differential binding to different DNA structures is not yet clear. Although there does not appear to be a consistent physical size or shape of hMutSα recognizable structures that trigger different pathways of cellular activity, DNA sequence context does appear to play a role [9,12].
Figure 4 is the model summarizing our hypothesis. This concept is compatible with our current set of experimental data, and with the recently described two-step conformational alteration of the E. coli MutS-DNA recognition complex [40]. Briefly, hMutSα appears to equally recognize both G:T and G:A mismatches at the codon 12 hot spot of mutation, but binds more avidly to G:T, or the "URC" conformation. The stronger, or alternate conformational, binding of hMutSα to G:T results in increased accuracy of MMR, but may decrease overall repair efficiency. The less avidly bound G:A mismatch, or the "IRC" conformation, is repaired with increased total efficiency as compared to G:T, but accuracy is sacrificed.
These results agree with, and build upon our previous experimental results, and also agree with the presence of specific mutations predominantly found in human tumors [23,24,37]. It has now been demonstrated by several different investigators that, although any base pair other than G:C at the H-ras codon 12 location is activating, the majority of human tumors containing mutations at this site are G:C → T:A transversions [42-44]. Our observed increased overall MMR of G:A, combined with a high ratio of incorrect MMR of G:A → T:A at this oncogenic location correlates well with the predominant GGC → GTC (Gly → Val) ras-activating mutation found in a high percentage of human tumors [42,44,45]. Thus the demonstration of a significant increase in the frequency of both total repair and incorrect repair of the G:A mismatch at the H-ras codon 12 oncogenic hot spot of mutation, combined with a complete lack of rescue by other DNA repair pathways, is biologically relevant.
Methods
Nuclear extracts, oligonucleotides, and site-specific mismatched plasmids
Human colorectal carcinoma cell lines HCT116 and HCT116 + Ch. 3 were cultured in Iscove's Modified Dulbecco's Medium supplemented with 10% fetal bovine serum (FBS) at 37°C, 5% CO2. HCT116 + Ch. 3 cell line also received 0.4 mg/ml Geneticin (G418). Both HCT cell lines were kind gifts of C. Richard Boland; UCSD. Nuclear extracts were prepared as described previously [46]. Synthetic oligonucleotides of 69 bases containing the coding strand sequence 5'-AATTCACGGAATATAAGCTGGTGGTGGTGGGCGCCGGCG GTTGGGCAAGAGTGCGCTGACCATCCAGG-3', as well as complementary noncoding oligomers, were obtained from Operon (Alameda, CA). The above 69 mer oligo is a portion of the coding sequence of human H-ras DNA. The bolded underlined Grepresents H-ras codon 12 middle G position, plus an additional 30 bases of H-ras sequence both 5' and 3' of codon 12, with an Eco R1 site 5' and a Bam H1 site 3' (restriction enzyme recognition nucleotides in italics). The wild type sequence (coding strand containing codon 12 middle G) was 5'-phosphorylated. Mismatch containing noncoding strand sequences (either T or A opposite codon 12 middle base G) were not phosphorylated, thus providing a 5' nick in the strand containing the incorrect base after ligation into the pUC19 plasmid. All other reagents were purchased from Sigma unless otherwise noted.
Preparation of site-specific mismatched oligonucleotides and plasmids
Complementary 69 mer oligos containing the wild type sequence at codon 12; middle base (coding strand G) and one mismatched base (noncoding strand T or A), as described above, were annealed in an equimolar ratio at a final concentration of 0.2 μg DNA per μl of annealing buffer (1 mM Tris-HCL, 1 mM MgCl2, pH 7.5). Ligation of annealed mismatched oligos to Eco R1/Bam H1 digested pUC19 DNA was accomplished at a 20:1 molar ratio of 69 mer to pUC19 DNA (~3–6 ng DNA per μl of ligation solution), using T4 ligase, per manufacturer's recommendations (Invitrogen Corp.; Carlsbad, CA). The pUC19 vector used for ligation of mismatch-containing H-ras oligo for subsequent measurement of MMR within nuclear extracts was grown in GM2929 (E. coli dam-dcm-). The pUC19 and pUC18 plasmids used as positive controls for correct bacterial mismatch repair were grown in DH5α (E. coli dam+).
Electrophoretic gel mobility shift assays
Gel shift assays were performed using nuclear extracts from either HCT116 or HCT116 + Ch. 3 cells and [32P]-dATP-labeled-69 mer duplexes by a fill-in reaction, using [α-32P]-dATP and Klenow polymerase, per manufacturer's protocol (Invitrogen). Each nuclear protein-DNA binding assay was performed using 0.8 – 1.0 × 105 cpm homoduplex or heteroduplex 69 mer and 5–10 μg protein from nuclear extract solution in an equal volume of 2X gel shift reaction buffer, resulting in a final concentration of 1 mM MgCl2, 0.5 mM EDTA, 0.5 mM DTT, 50 mM NaCl, 10 mM Tris-HCL, pH 7.5, 0.1 mg/ml poly [dI:dC], 5 mg/ml BSA, 6% glycerol, in a final volume of 15 – 20 μl. In addition, each reaction contained 100-fold molar excess (100X) unlabeled homoduplex 69 mer, unless otherwise noted. Incubations were for 30 min. at 37°C, unless otherwise noted. Gel shifts of each reaction were electrophoresed at 20 mA within a 4.8% nondenaturing acrylamide gel in 0.5 × TBE buffer, at room temperature. Radioactively labeled oligomers were visualized by autoradiography of the dried gel.
Site-specific mismatch repair assay within human nuclear extracts
An in vitro mismatch repair assay was performed essentially as described by Thomas et. al. [39], except for modifications as described below. For each reaction, 14 fmols of pUC19 plasmid containing site-specific mismatched H-ras 69 mer, with a nick on the same strand and 36 bases 5' of the incorrect base, was incubated with 50 μg of HCT116 + Ch. 3 or HCT116 nuclear extract in a final volume of 25 μl containing 30 mM HEPES buffer, pH 7.9, 100 μM each of dATP, dCTP, dGTP and dTTP, 200 μM each of CTP, GTP and UTP, 4 mM ATP, 40 mM creatine phosphate, 100 ng/μl creatine phosphokinase, 0.5 mM DTT, and 7 mM MgCl2. Negative control experiments were performed in the above solution without nuclear extract. Each reaction was incubated at 37°C for one hour, as described [39]. Plasmid DNA containing the H-ras insert was recovered using Wizard SV Miniprep System per manufacturer's directions (Promega; Madison, WI), and subsequently used to transform E.coli strain NR9161 (MutL-). In addition, DH5α (MMR proficient E. coli) were transformed directly with the ligated and nicked plasmid as a positive control for correct mismatch repair [37]. Subsequently, plasmid DNA was purified from ampicillin-resistant bacterial colonies and digested with Nae I, which recognizes a single restriction digestion site unique to the wild type H-ras sequence at codon 12 middle base pair (G:C) [23]. After electrophoresis in 1% agarose, banding patterns of resulting DNA fragments were analyzed to score plasmids as having correct, incorrect, or no repair [23,37,38].
MMR efficiency of each human nuclear extract assay, above MMR deficient bacterial background repair, was determined by the following equations [39]:
Total repair efficiency = 100 × (1 - [fraction of unrepaired plasmids incubated with human nuclear extract results / fraction of unrepaired untreated plasmids from direct transformation of NR9161]).
Correct repair efficiency = 100 × {(fraction of correctly repaired incubated with human nuclear extract) - [(fraction of correct repair by NR9161 direct transformation) × (1 - fraction of total repair efficiency)]}.
Incorrect repair efficiency = Total repair efficiency - Correct repair efficiency.
Statistical comparisons of G:T and G:A results were conducted by a non-parametric equivalent to the Student's t-test for differences between proportions. Statistical significance was indicated for those comparisons with a P < 0.05 assuming a 2-tailed distribution [47].
Authors' contributions
ME designed and carried out the MMR assay studies and helped draft the manuscript. HH designed and carried out all other experiments. AS performed all densitometry measurements and helped draft the manuscript. MF and SB helped carry out the MMR assays. KW conceived of the study, participated in its design and coordination and wrote the final manuscript.
Acknowledgements
We thank Dr. Neelima Konuru for her expert technical assistance, and Dr. Dennis G. Fisher for his assistance with the statistical analyses. This work was supported by National Institutes of Health grant to K.J.W. (CA 84412).
Figures and Tables
Figure 1 Specific interaction of hMSH6 and hMSH2 with site-specific mismatched DNA at H-ras codon 12. Protein-DNA binding reactions and gel shifts were performed using nuclear extracts from HCT 116 + Ch. 3 and equal cpm of [32P]-G:T-oligo (69-mer) in the presence of 100X molar excess of cold (unlabeled) homoduplex (G:C). BSA, goat-, or rabbit-nonspecific IgG (lanes 1, 2, 4 respectively), goat anti-hMSH6 (lane 3), and rabbit anti-hMSH2 (lane 5) were also included in the binding reactions as indicated. The lower gel shift band in each lane is due to biotin end-labeling of the probe.
Figure 2 Comparison of binding avidity of G:T-containing oligomer by MMR proficient (HCT 116 + Ch. 3) and MMR deficient (HCT 116; hMLH1-/-) nuclear extracts. Equal aliquots of nuclear extracts from HCT 116 + Ch. 3 (lanes 1–3) or HCT 116 (lanes 4–5) were incubated with equal cpm of [32P]-G:T-oligo (69-mer) and 50X molar excess cold (unlabeled) oligo as indicated.
Figure 3 Binding avidity of hMutSα to G:T versus G:A at H-ras codon 12 (a) Equal cpm of [32P]-G:T-oligo (69 mer) were incubated with equal concentrations of nuclear extract (HCT116 + Ch. 3) in the presence of increasing concentrations of unlabeled (cold) G:T- or G:A-containing oligo, and 100X cold G:C oligo. (b) Equal aliquots of nuclear extract were incubated for 2.5 hours (versus 30 minutes) with equal cpm of each [32P]-G:T- or [32P]-G:A-oligo alone (lanes 1 and 3), or also with respective 100X molar excess cold G:T or G:A oligo (lanes 2 and 4). Bar graphs are densitometry results of corresponding radioactive band intensities.
Figure 4 Repair model of hMutSα MMR. Model of hMutSα initial recognition (equal) of G:T and G:A, followed by repair (differential) of DNA containing a G:T or a G:A mismatch at the H-ras codon 12 location. hMutSα recognizes and forms an initial recognition complex (IRC) with both mismatches equally, and then binds more strongly to G:T, perhaps by undergoing an additional conformational step to the ultimate recognition complex (URC), which does not occur with G:A [40]. This results in more accurate repair of G:T, but more frequent total repair of G:A. See text for further discussion.
Table 1 Mismatch repair at an oncogenic site within MMR proficient and MMR deficient human nuclear extracts.
HCT 116 + Ch. 3; H-ras codon 12 middle G:C HCT 116; H-ras codon 12 middle G:C
G:T G:A G:T G:A
*Total repaira (correct & incorrect) 18.2 % 30 % ∅b ∅
**Correct repaira (% of total) (96%) (60%) ∅ ∅
**Incorrect repaira (% of total) (4%) (40%) ∅ ∅
Total # analyzed 99 100 78 91
a Repair efficiency above background for each mismatch at H-ras codon 12 by each human nuclear extract was determined by the following equations [39].
Total repair efficiency= 100 × (1 - [fraction of nonrepaired mixture incubated with human nuclear extract results / fraction of nonrepaired results of direct transformation of NR9161]).
Correct repair efficiency= 100 × {(fraction of correctly repaired incubated with human nuclear extract) - [(fraction of correct repair by NR9161 direct transformation) × (1 - fraction of total repair efficiency)]}.
Incorrect repair efficiency= Total repair efficiency - Correct repair efficiency.
b No repair detected above background.
* Designates statistical difference between HCT116 + Ch.3 G:T and G:A repair at P < 0.05.
**Designates statistical difference between HCT116 + Ch.3 G:T and G:A repair at P < 0.005.
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| 15766387 | PMC555755 | CC BY | 2021-01-04 16:22:25 | no | BMC Mol Biol. 2005 Mar 14; 6:6 | utf-8 | BMC Mol Biol | 2,005 | 10.1186/1471-2199-6-6 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-5-141577401310.1186/1471-244X-5-14Research ArticleGluten-free diet may alleviate depressive and behavioural symptoms in adolescents with coeliac disease: a prospective follow-up case-series study Pynnönen Päivi A [email protected]ä Erkki T [email protected] Matti A [email protected]ähkönen Seppo A [email protected]ä Ilkka [email protected] Erkki [email protected] Veikko A [email protected] Hospital for Children and Adolescents, Helsinki University Central Hospital, Helsinki, Finland2 Department of Mental Health and Alcohol Research, National Public Health Institute, Helsinki, Finland3 BioMag Laboratory, Engineering Center, Helsinki University Central Hospital, Cognitive Brain Research Unit, University of Helsinki, Helsinki, Finland2005 17 3 2005 5 14 14 15 11 2004 17 3 2005 Copyright © 2005 Pynnönen et al; licensee BioMed Central Ltd.2005Pynnönen 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
Coeliac disease in adolescents has been associated with an increased prevalence of depressive and disruptive behavioural disorders, particularly in the phase before diet treatment. We studied the possible effects of a gluten-free diet on psychiatric symptoms, on hormonal status (prolactin, thyroidal function) and on large neutral amino acid serum concentrations in adolescents with coeliac disease commencing a gluten-free diet.
Methods
Nine adolescents with celiac disease, aged 12 to 16 years, were assessed using the semi-structured K-SADS-Present and Lifetime Diagnostic interview and several symptom scales. Seven of them were followed at 1 to 2, 3, and 6 months on a gluten-free diet.
Results
Adolescent coeliac disease patients with depression had significantly lower pre-diet tryptophan/ competing amino-acid (CAA) ratios and free tryptophan concentrations, and significantly higher biopsy morning prolactin levels compared to those without depression. A significant decrease in psychiatric symptoms was found at 3 months on a gluten-free diet compared to patients' baseline condition, coinciding with significantly decreased coeliac disease activity and prolactin levels and with a significant increase in serum concentrations of CAAs.
Conclusion
Although our results of the amino acid analysis and prolactin levels in adolescents are only preliminary, they give support to previous findings on patients with coeliac disease, suggesting that serotonergic dysfunction due to impaired availability of tryptophan may play a role in vulnerability to depressive and behavioural disorders also among adolescents with untreated coeliac disease.
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Background
Coeliac disease is an under-diagnosed autoimmune type of gastrointestinal disorder resulting from gluten ingestion in genetically susceptible individuals. Non-specific symptoms such as fatigue and dyspepsia are common, but the disease may also be clinically silent. Diagnosis is based on small-bowel biopsy, and a permanent gluten-free diet is the essential treatment. Undetected or neglected, coeliac disease is associated with serious complications. [1-3] Depressive symptoms [4,5] and disorders [6] are common among adult patients with coeliac disease, and depressive and disruptive behavioural disorders are highly common also among adolescents, particularly in the phase before diet treatment [7]. Recently 73% of patients with untreated coeliac disease – but only 7% of patients adhering to a gluten-free diet – were reported to have cerebral blood flow abnormalities similar to those among patients with depressive disorders [8].
Improvement in state anxiety [5], in behavioural symptoms [9], and in depressive disorders [6,10] may occur after the start of a standard gluten-free diet, and after a vitamin B-6-supplemented gluten-free diet [11]. In some cases, however, the more serious depressive episodes have appeared following the commencement of a gluten-free diet [6]. Mechanisms involved have remained unclear. Some studies have suggested the possibility of impaired availability of tryptophan and disturbances in central serotonergic function as playing a role [9,12]. In parallel with this, a significant increase in major serotonin and dopamine metabolite concentrations in the brain has been reported after one year on a gluten-free diet [13].
The present work is a preliminary prospective psychiatric follow-up study of adolescents with newly diagnosed coeliac disease measuring psychiatric symptoms, hormonal status (prolactin, thyroidal function), and large neutral amino acid (LNAA) serum concentrations repeatedly after their commencement of a gluten-free diet, testing the hypothesis that the treatment of coeliac disease may increase the availability of tryptophan and alleviate psychiatric symptoms.
Methods
Subjects
The study sample comprised all nine adolescents (5 girls, 4 boys; aged 14.6 ± 0.8) consecutively diagnosed with coeliac disease between January 1999 and December 2000 in the Department of the Gastrointestinal Services of the Hospital for Children and Adolescents, Helsinki University Central Hospital, in Finland. None of the patients had a history of, or current psychiatric treatment. Duration of coeliac disease symptoms and signs (abdominal pain, diarrhoea, anaemia) leading to a biopsy was 2.3 (± 1.5) years. The study was approved by the institutional Ethics Committee. Written informed consent was obtained from each patient and a parent.
Evaluation
Baseline psychiatric evaluation was conducted 1 to 4 weeks after the diagnostic biopsy, during the wait for the diagnosis of coeliac disease to be established by the pathologist. The adolescent and a parent were interviewed separately by an adolescent psychiatrist (PP) using a semi-structured diagnostic interview, the Schedule for Affective Disorders and Schizophrenia for School-Age Children – Present and Lifetime version (K-SADS-PL) [14]. Seven patients attended the follow-up visits with laboratory tests at > 1 to ≤ 2, 3, and 6 months after starting a gluten-free diet (Table 1). Baseline and follow-up behavioural problems were assessed with the Youth Self Report (YSR) [15] and the Child Behavior Checklist (CBCL) [16], completed by a parent (Table 1), and depressive and anxiety symptoms by the 21-item versions of the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), the 17-item version of the Hamilton Depression Rating Scale (HAM-D), and the 14-item Hamilton Anxiety Rating Scale (HAM-A). CGAS (Children's Global Assessment) served as a part of the K-SADS-PL.
Table 1 Psychiatric symptoms and disease activity among adolescent CD patients (n = 7, mean ± SD)
0 (baseline) 1–2 months p-value1) 3 months p-value1) 6 months p-value2)
CGAS 3) 74.4 (± 16.5)4) 86.9 (± 5.6) 0.043 88.3 (± 9.2) 0.006
HAM-D5) 5.7 (± 7.3) 0.3 (± 0.8) 0.043 1.0 (± 1.3) 0.009
HAM-A6) 6.9 (± 6.9) 0.1 (± 0.4) 0.043 0.7 (± 1.3) 0.010
BDI7) 3.4 (± 6.5) 0.0 (± 0.0) 8) n.s.9) 0.1 (± 0.4) 0.041 0.6 (± 1.1) 0.014
BAI10) 3.0 (± 2.9) 1.3 (± 1.5) 8) n.s. 0.7 (± 1.1) 0.042 1.4 (± 2.2) n.s.
CBCL11) Total problems 28.3 (± 12.8) 18.4 (± 6.9) 0.028 14.0 (± 9.5) 0.028 18.4 (± 16.2) 0.007
Anxious/depressed 3.9 (± 3.1) 1.4 (± 1.7) 0.046 1.3 (± 1.5) 0.026 1.3 (± 1.1) 0.033
Aggressive behaviour 7.3 (± 4.2) 5.6 (± 2.2) n.s. 3.6 (± 3.1) 0.039 6.0 (± 6.4) 0.047
YSR12) Total problems 24.4 (± 13.8) 17.0 (± 12.5) n.s. 8.0 (± 7.8) 0.018 9.4 (± 7.4) 0.001
Anxious/depressed 2.7 (± 3.4) 1.6 (± 2.2) n.s. 0.3 (± 0.5) 0.026 0.6 (± 1.0) 0.002
Aggressive behaviour 5.1 (± 2.7) 4.1 (± 2.7) n.s. 2.0 (± 2.2) 0.044 2.4 (± 1.4) 0.021
Somatic complains 4.9 (± 2.4) 3.1 (± 2.3) 0.038 2.1 (± 1.2) 0.026 2.3 (± 2.1) 0.003
S-EndoAbA8) 800(200–1600) 200 (5–400) 0.018 5 (5–1600) 0.027 5 (5–100) 0.001
S-tTGAbA 139 (± 158) 30 (± 35) 0.018 19 (± 26) 0.018 7 (± 8) < 0.001
Prolactin (mU/l)13) 1569 (± 767) 218 (± 60) 0.028 284 (± 170) 0.018 205 (± 92)14) 0.019
S-T4/TSH (nmol/l:mU/l) 34 (± 6) 64 (± 38)14) 0.043 84 (± 53) 0.043 74 (± 32)15) 0.039
1) non-parametric Wilcoxon signed-ranks test; compared with baseline; 2) repeated measures non-parametric Friedman test; 3) Children's Global Assessment; 4) mean ± SD; 5) Hamilton Depression Rating Scale; 6) Hamilton Anxiety Rating Scale ; 7) Beck Depression Inventory; 8) median (min-max); 9) non significant; 10) Beck Anxiety Inventory; 11) Child Behavior Checklist; 12) Youth Self Report; 13) normal: females 50–300, males 50–500; 14) n = 6 ; 15) n = 4
Coeliac disease activity was followed by determining serum tissue transglutaminase (S-tTGAbA) and endomysium (S-EndoAbA) autoantibodies [17]. Pre-diet blood samples for analysis of amino acids, prolactin, thyroid function [thyroxine (S-T4), thyroid-stimulating hormone (TSH)], vitamins B6 and B12, S-tTGAbA, and S-EndoAbA were obtained on the biopsy morning, and subsequent ones as a part of follow-up visits, both after overnight fasting, between 8 and 10 a.m. All nine patients had amino acid concentrations measured at baseline, and five of them during follow-up (1–2 times). A blood sample (2 ml) was drawn from the ulnar vein into a vacuum tube for serum total and free L-tryptophan, and for other large neutral amino acids (LNAA). The tube was cooled immediately and stored refrigerated in ice until centrifuged. After centrifugation, the serum was frozen and stored at -20°C for 4 to 14 months (median 7.5) until its assay for the amino acids by a modified procedure described by Qureshi et al. [18]. All the samples were analysed in a single run, in Kuopio, Finland, and free and total L-tryptophan and other LNAA's were assessed as described by Tiihonen et al. [19].
Statistical methods
Statistical analysis was carried out with parametric and non-parametric tests as appropriate; tests for two-independent groups (T-test, Mann-Whitney U-test), for repeated measures of two-related groups (Wilcoxon signed-ranks test), for three-related groups (Friedman test) and Spearman's rank correlation testing were used. P-values (2-tailed) < 0.05 were regarded as significant.
Results
Baseline evaluation
At baseline, three adolescents (3/9; 33%) had a depressive disorder: two girls had major depressive disorder (MDD), one with a learning disorder not otherwise specified (NOS), and another with comorbid conduct disorder; one girl had the depressive disorder NOS. Further, one boy had a phobic disorder plus attention-deficit hyperactivity disorder, and another conduct disorder NOS. Four adolescents (44%) had no diagnosis.
Pre-diet free L-tryptophan was positively correlated with pre-diet levels of tTGAbA (n = 8; r = 0.78, P= 0.022), and negatively with vitamin B-6 (r = 0.73, P = 0.039) and S-T4 (r = 0.74, P = 0.035). Prolactin levels (Table 1) from the biopsy morning showed a positive correlation with BDI score (self-report depression inventory; r = 0.89, P = 0.001), and a negative correlation with the ratio of L-tryptophan to amino acids competing for the same cerebral uptake mechanism (CAA) (r = 0.68, P = 0.042), but not with free L-tryptophan levels. The sum of branched-chain amino acids (BCAA: valine, leucine, and isoleucine) showed no correlation with L-tryptophan or free L-tryptophan levels.
Depressive patients (n = 3/9) had significantly higher pre-diet prolactin levels (mU/l: mean ± S.D. = 2450 ± 676 vs. 1194 ± 598, Mann-Whitney U-test, P = 0.039) and pre-diet S-T4 levels (nmol/l: mean 102 ± 3.1 vs. 84 ± 16.1, Mann-Whitney U-test, P = 0.024), and significantly lower L-tryptophan/CAA ratios (100 × pmol/μl: pmol/μl: mean 10.0 ± 0.2 vs. 11.5 ± 1.7, Mann-Whitney U-test, P = 0.020) and free L-tryptophan concentrations (pmol/μl: mean 4.7 ± 0.5 vs. 8.4 ± 3.0, two-independent samples T-test, P = 0.029). Pre-diet free L-tryptophan correlated negatively with biopsy morning S-T4 level (r = -0.74, P = 0.035). No significant differences appeared in L-tryptophan (36.3 ± 5.1 vs. 43.3 ± 6.1) or in L-tyrosine concentrations, nor in BCAA and CAA levels.
Follow-up
Two adolescents with conduct disorders, one a girl with concomitant MDD, did not adhere to the gluten-free diet and dropped out of the psychiatric follow-up. Among others (n = 7), a significant decrease in most of the problem and symptom scores of YSR and CBCL, and in BDI, BAI, and Hamilton scales was evident after 3 months on the gluten-free diet, compared to baseline (Table 1).
Celiac disease-associated antibody titres had decreased in all by the first month on a gluten-free diet, and had already normalised (= S-EndoAbA titre < 5 and S-tTGAbA titre < 8) in 4 of 7 patients by 6 months. Boys had lower biopsy morning prolactin levels (mU/l; mean 972, SD 450 vs. girls mean 2126 ± 756; one-way Anova P = 0.032), but higher levels after one month on the diet. In the first month, the S-T4/TSH ratio (nmol/l:mU/l) reflecting thyroid function increased significantly (Table 1).
An initial increase in CAAs, also in tyrosine levels, and in total and free L-tryptophan was reaching significance after one month on a gluten-free diet. By 3 months, the increases in tyrosine alone and in CAAs as a group were significant, and the increase in free L-tryptophan was approaching significance (repeated measures Friedman test n = 4, Chi-Square 6,000, df 2, P = 0.050). (Table 2)
Table 2 Follow-up of the patients (n = 5): psychiatric symptoms, CD activity, and amino acidconcentrations [median (min-max)].
0 (baseline) ≥1<1.5 months1) p-value2) ≥3 months p-value2)
CBCL Total problems 34 (9–39) 21 (8–29) ** 21 (2–22) **
YSR Total problems 20 (9–36) 11 (5–41) n.s. 2 (0–22) **
S-tTGAbA 42 (10–310) 5 (3–51) ** 3 (1–17) **
Prolactin3) 1100 (635–2850) 256 (127–282) n.s * 244 (110–565) **
L-tyrosine4) 33 (26–44) 39 (37–70) n.s * 40 (38–47) **
CAA5) 353 (316–441) 401 (368–657) n.s * 395 (367–568) **
L-tryptophan4) 40 (32–51) 52 (45–66) n.s * 46 (36–59) n.s.
Tryptophan/CAA6) 11.3 (10.1–14.9) 12.0 (10.1–14.1) n.s. 10.1 (9.5–12.5) n.s.
Free L-tryptophan 4) 4.9 (4.5–11.8) 8.4 (5.3–10.9) n.s * 10.6 (5.0–19.0) n.s. * 7)
Free tryptophan/CAA6) 1.4 (1.0–3.5) 1.9 (1.0–3.0) n.s. 2.6 (1.3–3.4) n.s.
1) amino acid concentrations; n = 4; 2) Wilcoxon signed – ranks test: compared with baseline; ** = P < 0.05; * = P ≥ 0.05 < 0.07; 3) mU/l; normal: females 50–300, males 50–500; 4) pmol/μl; 5) L-valine, L-leucine, L-isoleucine, L-phenylalanine, L-tyrosine; pmol/μl; 6) 100 × pmol/μl: pmol/μl; 7) repeated Measures Friedman test, n = 4, P = 0.050
Discussion
We observed that the majority of adolescents with coeliac disease had depressive and behavioural symptoms before their diagnosis, and that coeliac disease patients with depression (all girls) had significantly lower pre-diet tryptophan/CAA ratios and free tryptophan concentrations and significantly higher biopsy morning prolactin levels. Adolescents with coeliac disease showed improvement in psychiatric symptoms after starting a gluten-free diet, and this improvement coincided with a significant decrease in coeliac disease activity and in prolactin levels, and with a significant increase in serum concentrations of L-tyrosine and other CAAs. The increase in free L-tryptophan levels was approaching significance. The findings of this study – improvement in depressive and behavioural symptoms after the start of a gluten-free diet – are supported by the findings of our larger previous retrospective case-control study [7]. Although the results of the amino acid analysis and prolactin levels are only preliminary, they give support to the hypothesis that impaired availability of tryptophan and the possible consequent serotonergic dysfunction may play a role in vulnerability to depressive disorders among adolescents with untreated coeliac disease. A possible role for tyrosine and the brain's catecholamine metabolism (dopamine and noradrenaline) in these disorders cannot, however, be excluded.
The decrease observed in psychiatric symptoms took place regardless of the stress accompanying being diagnosed with a chronic and restrictive illness, and improvement was not explainable in terms of physical symptoms, since both in the present and in our previous study [7], the presence or alleviation of depression showed no association with somatic symptom severity. Our results from adolescents differ from those reported by Addolorato et al. [5]. In their follow-up study on adult patients with coeliac disease, a significant decrease in anxiety symptoms but not in depressive symptoms appeared after one year on a gluten-free diet. Although converging with the findings of Ljungman and Myrdal (20), the few symptoms of our adolescents with coeliac disease adhering to a gluten-free diet in our present and previous [7] studies are thus in contrast to the findings of depressive symptoms [4,5] and disorders [6] as being common among adult patients with coeliac disease, even during diet treatment.
Since the free tryptophan and the tryptophan/CAA ratios in plasma determine the availability of tryptophan to the brain [21], our findings on depressive patients give preliminary support to suggestions of impaired availability of tryptophan as featuring in coeliac-associated depressive and behavioural disorders associated with celiac disease [9,12,13]. As we did not have a control group of healthy adolescents, we cannot say whether L-tryptophan or L-tyrosine levels or both are generally lower among adolescents with coeliac disease, as could be expected based on the findings of Hernanz and Polanco [9], who reported significantly decreased plasma tryptophan and tyrosine concentrations in untreated and treated children with coeliac disease compared to levels in controls.
In the present study, stress-induced biopsy-morning prolactin levels were significantly higher among depressive patients (all girls) and correlated negatively with L-tryptophan/CAA levels. This finding is only preliminary, but it is, however, interesting: Although disturbances in the central serotonergic system have been associated with depressive and impulse-control disorders among adults [see [22]], and children aged 6 to 12 years with a recent suicide attempt have shown lower whole blood tryptophan content [23], serotonergic dysfunction in adolescents with depression is still poorly studied. The prolactin hypersecretion response to the L-5-hydroxytryptophan challenge (L-5HTP) test reported among pre-pubertal girls with major depressive disorder [24] and among healthy children at high risk for major depressive disorder (= high family loading for major depression) [25], may be consistent with dysregulation of the central serotonergic system in childhood major depression [24]. Moreover, alterations in neuroendocrine responses to L-5HTP challenge tests, such as the prolactin hypersecretion and hyposecretion of cortisol found in healthy children, have been suggested to represent a trait marker for depression in children [25]. Thus, the high biopsy morning prolactin levels in depressed adolescents with untreated coeliac disease in the present study could be associated with serotonergic dysfunction. They could also be associated with dopaminergic dysfunction due to impaired availability of tyrosine, since dopamine is known to exert an inhibitory action on prolactin release in the hypothalamus [26]. In the present study, however, pre-diet prolactin levels did not correlate with tyrosine levels. Moreover, the function of the intestinal Catechol-O-Methyl Transferase enzyme (COMT) – known to play an important role in the peripheral O-methylation of catecholamines – remains unstudied in untreated coeliac disease. It is of some theoretical interest that reduced COMT activity in erythrocytes has at least once been associated with conditions such as primary affective disorders in women [27].
On the other hand, in the present study also non-depressed adolescents with coeliac disease had higher than normal biopsy morning prolactin levels. Significantly higher prolactin levels among untreated coeliac children (5–18 years) compared with treated patients has been reported by Reifen et al. [28]. They suggest that prolactin may play a part in the immune modulation of the intestine and could thus serve as a potential marker for coeliac disease activity.
Our preliminary findings on amino acid levels in adolescents with coeliac disease with or without depression are unlikely to be explained by malabsorption, since pre-diet free L-tryptophan and tryptophan ratios were not correlated with the BCAA levels that reflect the level of protein nutrition. It is of theoretical interest that increased production of interferon-γ (IFN-γ), known to be the predominant cytokine produced by gluten-specific T-cells in active coeliac disease [29], can suppress serotonin function both directly and indirectly by enhancing tryptophan and serotonin turnover [30]. Increased IFN-γ [30] and, for instance, such events as a stress-related increase in liver tryptophan pyrrolase enzyme activity [23], may lead to lowered tryptophan levels by the enhanced tryptophan catabolism induced by increased activity of the kynurenine-niacin pathway [30-32], even without malabsorption.
Conclusion
The alleviation of psychiatric symptoms found among adolescents with coeliac disease after commencement of a gluten-free diet coincides with a rapid decrease in antibody titres indicating coeliac disease activity and in their prolactin levels, and with a significant increase in L-tyrosine and other CAA serum concentrations, and with a nearly significant increase in the free fraction of L-tryptophan. Although these findings are only preliminary, and more research is needed, they give support to previous findings on patients with coeliac disease, suggesting that serotonergic dysfunction due to impaired availability of tryptophan may play a role in vulnerability to depressive and behavioural disorders, also among adolescents with untreated celiac disease. And since diet treatment may alleviate psychiatric symptoms, and earlier diagnosis may have beneficial effects on psychological and even on neurobiological vulnerability to depression, the possibility of psychiatric complications of coeliac disease needs to be taken into account in differential diagnosis of depressive and behavioural disorders.
Declaration of competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PAP, ETI, MAV, ES, VAA contributed to the conception and design of the study, and PAP, MAV, ES to acquisition of the data. All authors (PAP, ETI, MAV, SAK, IS, ES, VAA) contributed to the analysis and interpretation of the data, were involved in drafting and revising the article, and read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank Jyrki Liesivuori, MD, PhD, from the University of Kuopio, Finland for amino acid analysis, and Erkki Komulainen MA, PhD, for statistical advice. The study was supported by a grant from the Jalmari and Rauha Ahokas Foundation.
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| 15774013 | PMC555756 | CC BY | 2021-01-04 16:33:03 | no | BMC Psychiatry. 2005 Mar 17; 5:14 | utf-8 | BMC Psychiatry | 2,005 | 10.1186/1471-244X-5-14 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-5-141577401310.1186/1471-244X-5-14Research ArticleGluten-free diet may alleviate depressive and behavioural symptoms in adolescents with coeliac disease: a prospective follow-up case-series study Pynnönen Päivi A [email protected]ä Erkki T [email protected] Matti A [email protected]ähkönen Seppo A [email protected]ä Ilkka [email protected] Erkki [email protected] Veikko A [email protected] Hospital for Children and Adolescents, Helsinki University Central Hospital, Helsinki, Finland2 Department of Mental Health and Alcohol Research, National Public Health Institute, Helsinki, Finland3 BioMag Laboratory, Engineering Center, Helsinki University Central Hospital, Cognitive Brain Research Unit, University of Helsinki, Helsinki, Finland2005 17 3 2005 5 14 14 15 11 2004 17 3 2005 Copyright © 2005 Pynnönen et al; licensee BioMed Central Ltd.2005Pynnönen 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
Coeliac disease in adolescents has been associated with an increased prevalence of depressive and disruptive behavioural disorders, particularly in the phase before diet treatment. We studied the possible effects of a gluten-free diet on psychiatric symptoms, on hormonal status (prolactin, thyroidal function) and on large neutral amino acid serum concentrations in adolescents with coeliac disease commencing a gluten-free diet.
Methods
Nine adolescents with celiac disease, aged 12 to 16 years, were assessed using the semi-structured K-SADS-Present and Lifetime Diagnostic interview and several symptom scales. Seven of them were followed at 1 to 2, 3, and 6 months on a gluten-free diet.
Results
Adolescent coeliac disease patients with depression had significantly lower pre-diet tryptophan/ competing amino-acid (CAA) ratios and free tryptophan concentrations, and significantly higher biopsy morning prolactin levels compared to those without depression. A significant decrease in psychiatric symptoms was found at 3 months on a gluten-free diet compared to patients' baseline condition, coinciding with significantly decreased coeliac disease activity and prolactin levels and with a significant increase in serum concentrations of CAAs.
Conclusion
Although our results of the amino acid analysis and prolactin levels in adolescents are only preliminary, they give support to previous findings on patients with coeliac disease, suggesting that serotonergic dysfunction due to impaired availability of tryptophan may play a role in vulnerability to depressive and behavioural disorders also among adolescents with untreated coeliac disease.
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Background
Coeliac disease is an under-diagnosed autoimmune type of gastrointestinal disorder resulting from gluten ingestion in genetically susceptible individuals. Non-specific symptoms such as fatigue and dyspepsia are common, but the disease may also be clinically silent. Diagnosis is based on small-bowel biopsy, and a permanent gluten-free diet is the essential treatment. Undetected or neglected, coeliac disease is associated with serious complications. [1-3] Depressive symptoms [4,5] and disorders [6] are common among adult patients with coeliac disease, and depressive and disruptive behavioural disorders are highly common also among adolescents, particularly in the phase before diet treatment [7]. Recently 73% of patients with untreated coeliac disease – but only 7% of patients adhering to a gluten-free diet – were reported to have cerebral blood flow abnormalities similar to those among patients with depressive disorders [8].
Improvement in state anxiety [5], in behavioural symptoms [9], and in depressive disorders [6,10] may occur after the start of a standard gluten-free diet, and after a vitamin B-6-supplemented gluten-free diet [11]. In some cases, however, the more serious depressive episodes have appeared following the commencement of a gluten-free diet [6]. Mechanisms involved have remained unclear. Some studies have suggested the possibility of impaired availability of tryptophan and disturbances in central serotonergic function as playing a role [9,12]. In parallel with this, a significant increase in major serotonin and dopamine metabolite concentrations in the brain has been reported after one year on a gluten-free diet [13].
The present work is a preliminary prospective psychiatric follow-up study of adolescents with newly diagnosed coeliac disease measuring psychiatric symptoms, hormonal status (prolactin, thyroidal function), and large neutral amino acid (LNAA) serum concentrations repeatedly after their commencement of a gluten-free diet, testing the hypothesis that the treatment of coeliac disease may increase the availability of tryptophan and alleviate psychiatric symptoms.
Methods
Subjects
The study sample comprised all nine adolescents (5 girls, 4 boys; aged 14.6 ± 0.8) consecutively diagnosed with coeliac disease between January 1999 and December 2000 in the Department of the Gastrointestinal Services of the Hospital for Children and Adolescents, Helsinki University Central Hospital, in Finland. None of the patients had a history of, or current psychiatric treatment. Duration of coeliac disease symptoms and signs (abdominal pain, diarrhoea, anaemia) leading to a biopsy was 2.3 (± 1.5) years. The study was approved by the institutional Ethics Committee. Written informed consent was obtained from each patient and a parent.
Evaluation
Baseline psychiatric evaluation was conducted 1 to 4 weeks after the diagnostic biopsy, during the wait for the diagnosis of coeliac disease to be established by the pathologist. The adolescent and a parent were interviewed separately by an adolescent psychiatrist (PP) using a semi-structured diagnostic interview, the Schedule for Affective Disorders and Schizophrenia for School-Age Children – Present and Lifetime version (K-SADS-PL) [14]. Seven patients attended the follow-up visits with laboratory tests at > 1 to ≤ 2, 3, and 6 months after starting a gluten-free diet (Table 1). Baseline and follow-up behavioural problems were assessed with the Youth Self Report (YSR) [15] and the Child Behavior Checklist (CBCL) [16], completed by a parent (Table 1), and depressive and anxiety symptoms by the 21-item versions of the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), the 17-item version of the Hamilton Depression Rating Scale (HAM-D), and the 14-item Hamilton Anxiety Rating Scale (HAM-A). CGAS (Children's Global Assessment) served as a part of the K-SADS-PL.
Table 1 Psychiatric symptoms and disease activity among adolescent CD patients (n = 7, mean ± SD)
0 (baseline) 1–2 months p-value1) 3 months p-value1) 6 months p-value2)
CGAS 3) 74.4 (± 16.5)4) 86.9 (± 5.6) 0.043 88.3 (± 9.2) 0.006
HAM-D5) 5.7 (± 7.3) 0.3 (± 0.8) 0.043 1.0 (± 1.3) 0.009
HAM-A6) 6.9 (± 6.9) 0.1 (± 0.4) 0.043 0.7 (± 1.3) 0.010
BDI7) 3.4 (± 6.5) 0.0 (± 0.0) 8) n.s.9) 0.1 (± 0.4) 0.041 0.6 (± 1.1) 0.014
BAI10) 3.0 (± 2.9) 1.3 (± 1.5) 8) n.s. 0.7 (± 1.1) 0.042 1.4 (± 2.2) n.s.
CBCL11) Total problems 28.3 (± 12.8) 18.4 (± 6.9) 0.028 14.0 (± 9.5) 0.028 18.4 (± 16.2) 0.007
Anxious/depressed 3.9 (± 3.1) 1.4 (± 1.7) 0.046 1.3 (± 1.5) 0.026 1.3 (± 1.1) 0.033
Aggressive behaviour 7.3 (± 4.2) 5.6 (± 2.2) n.s. 3.6 (± 3.1) 0.039 6.0 (± 6.4) 0.047
YSR12) Total problems 24.4 (± 13.8) 17.0 (± 12.5) n.s. 8.0 (± 7.8) 0.018 9.4 (± 7.4) 0.001
Anxious/depressed 2.7 (± 3.4) 1.6 (± 2.2) n.s. 0.3 (± 0.5) 0.026 0.6 (± 1.0) 0.002
Aggressive behaviour 5.1 (± 2.7) 4.1 (± 2.7) n.s. 2.0 (± 2.2) 0.044 2.4 (± 1.4) 0.021
Somatic complains 4.9 (± 2.4) 3.1 (± 2.3) 0.038 2.1 (± 1.2) 0.026 2.3 (± 2.1) 0.003
S-EndoAbA8) 800(200–1600) 200 (5–400) 0.018 5 (5–1600) 0.027 5 (5–100) 0.001
S-tTGAbA 139 (± 158) 30 (± 35) 0.018 19 (± 26) 0.018 7 (± 8) < 0.001
Prolactin (mU/l)13) 1569 (± 767) 218 (± 60) 0.028 284 (± 170) 0.018 205 (± 92)14) 0.019
S-T4/TSH (nmol/l:mU/l) 34 (± 6) 64 (± 38)14) 0.043 84 (± 53) 0.043 74 (± 32)15) 0.039
1) non-parametric Wilcoxon signed-ranks test; compared with baseline; 2) repeated measures non-parametric Friedman test; 3) Children's Global Assessment; 4) mean ± SD; 5) Hamilton Depression Rating Scale; 6) Hamilton Anxiety Rating Scale ; 7) Beck Depression Inventory; 8) median (min-max); 9) non significant; 10) Beck Anxiety Inventory; 11) Child Behavior Checklist; 12) Youth Self Report; 13) normal: females 50–300, males 50–500; 14) n = 6 ; 15) n = 4
Coeliac disease activity was followed by determining serum tissue transglutaminase (S-tTGAbA) and endomysium (S-EndoAbA) autoantibodies [17]. Pre-diet blood samples for analysis of amino acids, prolactin, thyroid function [thyroxine (S-T4), thyroid-stimulating hormone (TSH)], vitamins B6 and B12, S-tTGAbA, and S-EndoAbA were obtained on the biopsy morning, and subsequent ones as a part of follow-up visits, both after overnight fasting, between 8 and 10 a.m. All nine patients had amino acid concentrations measured at baseline, and five of them during follow-up (1–2 times). A blood sample (2 ml) was drawn from the ulnar vein into a vacuum tube for serum total and free L-tryptophan, and for other large neutral amino acids (LNAA). The tube was cooled immediately and stored refrigerated in ice until centrifuged. After centrifugation, the serum was frozen and stored at -20°C for 4 to 14 months (median 7.5) until its assay for the amino acids by a modified procedure described by Qureshi et al. [18]. All the samples were analysed in a single run, in Kuopio, Finland, and free and total L-tryptophan and other LNAA's were assessed as described by Tiihonen et al. [19].
Statistical methods
Statistical analysis was carried out with parametric and non-parametric tests as appropriate; tests for two-independent groups (T-test, Mann-Whitney U-test), for repeated measures of two-related groups (Wilcoxon signed-ranks test), for three-related groups (Friedman test) and Spearman's rank correlation testing were used. P-values (2-tailed) < 0.05 were regarded as significant.
Results
Baseline evaluation
At baseline, three adolescents (3/9; 33%) had a depressive disorder: two girls had major depressive disorder (MDD), one with a learning disorder not otherwise specified (NOS), and another with comorbid conduct disorder; one girl had the depressive disorder NOS. Further, one boy had a phobic disorder plus attention-deficit hyperactivity disorder, and another conduct disorder NOS. Four adolescents (44%) had no diagnosis.
Pre-diet free L-tryptophan was positively correlated with pre-diet levels of tTGAbA (n = 8; r = 0.78, P= 0.022), and negatively with vitamin B-6 (r = 0.73, P = 0.039) and S-T4 (r = 0.74, P = 0.035). Prolactin levels (Table 1) from the biopsy morning showed a positive correlation with BDI score (self-report depression inventory; r = 0.89, P = 0.001), and a negative correlation with the ratio of L-tryptophan to amino acids competing for the same cerebral uptake mechanism (CAA) (r = 0.68, P = 0.042), but not with free L-tryptophan levels. The sum of branched-chain amino acids (BCAA: valine, leucine, and isoleucine) showed no correlation with L-tryptophan or free L-tryptophan levels.
Depressive patients (n = 3/9) had significantly higher pre-diet prolactin levels (mU/l: mean ± S.D. = 2450 ± 676 vs. 1194 ± 598, Mann-Whitney U-test, P = 0.039) and pre-diet S-T4 levels (nmol/l: mean 102 ± 3.1 vs. 84 ± 16.1, Mann-Whitney U-test, P = 0.024), and significantly lower L-tryptophan/CAA ratios (100 × pmol/μl: pmol/μl: mean 10.0 ± 0.2 vs. 11.5 ± 1.7, Mann-Whitney U-test, P = 0.020) and free L-tryptophan concentrations (pmol/μl: mean 4.7 ± 0.5 vs. 8.4 ± 3.0, two-independent samples T-test, P = 0.029). Pre-diet free L-tryptophan correlated negatively with biopsy morning S-T4 level (r = -0.74, P = 0.035). No significant differences appeared in L-tryptophan (36.3 ± 5.1 vs. 43.3 ± 6.1) or in L-tyrosine concentrations, nor in BCAA and CAA levels.
Follow-up
Two adolescents with conduct disorders, one a girl with concomitant MDD, did not adhere to the gluten-free diet and dropped out of the psychiatric follow-up. Among others (n = 7), a significant decrease in most of the problem and symptom scores of YSR and CBCL, and in BDI, BAI, and Hamilton scales was evident after 3 months on the gluten-free diet, compared to baseline (Table 1).
Celiac disease-associated antibody titres had decreased in all by the first month on a gluten-free diet, and had already normalised (= S-EndoAbA titre < 5 and S-tTGAbA titre < 8) in 4 of 7 patients by 6 months. Boys had lower biopsy morning prolactin levels (mU/l; mean 972, SD 450 vs. girls mean 2126 ± 756; one-way Anova P = 0.032), but higher levels after one month on the diet. In the first month, the S-T4/TSH ratio (nmol/l:mU/l) reflecting thyroid function increased significantly (Table 1).
An initial increase in CAAs, also in tyrosine levels, and in total and free L-tryptophan was reaching significance after one month on a gluten-free diet. By 3 months, the increases in tyrosine alone and in CAAs as a group were significant, and the increase in free L-tryptophan was approaching significance (repeated measures Friedman test n = 4, Chi-Square 6,000, df 2, P = 0.050). (Table 2)
Table 2 Follow-up of the patients (n = 5): psychiatric symptoms, CD activity, and amino acidconcentrations [median (min-max)].
0 (baseline) ≥1<1.5 months1) p-value2) ≥3 months p-value2)
CBCL Total problems 34 (9–39) 21 (8–29) ** 21 (2–22) **
YSR Total problems 20 (9–36) 11 (5–41) n.s. 2 (0–22) **
S-tTGAbA 42 (10–310) 5 (3–51) ** 3 (1–17) **
Prolactin3) 1100 (635–2850) 256 (127–282) n.s * 244 (110–565) **
L-tyrosine4) 33 (26–44) 39 (37–70) n.s * 40 (38–47) **
CAA5) 353 (316–441) 401 (368–657) n.s * 395 (367–568) **
L-tryptophan4) 40 (32–51) 52 (45–66) n.s * 46 (36–59) n.s.
Tryptophan/CAA6) 11.3 (10.1–14.9) 12.0 (10.1–14.1) n.s. 10.1 (9.5–12.5) n.s.
Free L-tryptophan 4) 4.9 (4.5–11.8) 8.4 (5.3–10.9) n.s * 10.6 (5.0–19.0) n.s. * 7)
Free tryptophan/CAA6) 1.4 (1.0–3.5) 1.9 (1.0–3.0) n.s. 2.6 (1.3–3.4) n.s.
1) amino acid concentrations; n = 4; 2) Wilcoxon signed – ranks test: compared with baseline; ** = P < 0.05; * = P ≥ 0.05 < 0.07; 3) mU/l; normal: females 50–300, males 50–500; 4) pmol/μl; 5) L-valine, L-leucine, L-isoleucine, L-phenylalanine, L-tyrosine; pmol/μl; 6) 100 × pmol/μl: pmol/μl; 7) repeated Measures Friedman test, n = 4, P = 0.050
Discussion
We observed that the majority of adolescents with coeliac disease had depressive and behavioural symptoms before their diagnosis, and that coeliac disease patients with depression (all girls) had significantly lower pre-diet tryptophan/CAA ratios and free tryptophan concentrations and significantly higher biopsy morning prolactin levels. Adolescents with coeliac disease showed improvement in psychiatric symptoms after starting a gluten-free diet, and this improvement coincided with a significant decrease in coeliac disease activity and in prolactin levels, and with a significant increase in serum concentrations of L-tyrosine and other CAAs. The increase in free L-tryptophan levels was approaching significance. The findings of this study – improvement in depressive and behavioural symptoms after the start of a gluten-free diet – are supported by the findings of our larger previous retrospective case-control study [7]. Although the results of the amino acid analysis and prolactin levels are only preliminary, they give support to the hypothesis that impaired availability of tryptophan and the possible consequent serotonergic dysfunction may play a role in vulnerability to depressive disorders among adolescents with untreated coeliac disease. A possible role for tyrosine and the brain's catecholamine metabolism (dopamine and noradrenaline) in these disorders cannot, however, be excluded.
The decrease observed in psychiatric symptoms took place regardless of the stress accompanying being diagnosed with a chronic and restrictive illness, and improvement was not explainable in terms of physical symptoms, since both in the present and in our previous study [7], the presence or alleviation of depression showed no association with somatic symptom severity. Our results from adolescents differ from those reported by Addolorato et al. [5]. In their follow-up study on adult patients with coeliac disease, a significant decrease in anxiety symptoms but not in depressive symptoms appeared after one year on a gluten-free diet. Although converging with the findings of Ljungman and Myrdal (20), the few symptoms of our adolescents with coeliac disease adhering to a gluten-free diet in our present and previous [7] studies are thus in contrast to the findings of depressive symptoms [4,5] and disorders [6] as being common among adult patients with coeliac disease, even during diet treatment.
Since the free tryptophan and the tryptophan/CAA ratios in plasma determine the availability of tryptophan to the brain [21], our findings on depressive patients give preliminary support to suggestions of impaired availability of tryptophan as featuring in coeliac-associated depressive and behavioural disorders associated with celiac disease [9,12,13]. As we did not have a control group of healthy adolescents, we cannot say whether L-tryptophan or L-tyrosine levels or both are generally lower among adolescents with coeliac disease, as could be expected based on the findings of Hernanz and Polanco [9], who reported significantly decreased plasma tryptophan and tyrosine concentrations in untreated and treated children with coeliac disease compared to levels in controls.
In the present study, stress-induced biopsy-morning prolactin levels were significantly higher among depressive patients (all girls) and correlated negatively with L-tryptophan/CAA levels. This finding is only preliminary, but it is, however, interesting: Although disturbances in the central serotonergic system have been associated with depressive and impulse-control disorders among adults [see [22]], and children aged 6 to 12 years with a recent suicide attempt have shown lower whole blood tryptophan content [23], serotonergic dysfunction in adolescents with depression is still poorly studied. The prolactin hypersecretion response to the L-5-hydroxytryptophan challenge (L-5HTP) test reported among pre-pubertal girls with major depressive disorder [24] and among healthy children at high risk for major depressive disorder (= high family loading for major depression) [25], may be consistent with dysregulation of the central serotonergic system in childhood major depression [24]. Moreover, alterations in neuroendocrine responses to L-5HTP challenge tests, such as the prolactin hypersecretion and hyposecretion of cortisol found in healthy children, have been suggested to represent a trait marker for depression in children [25]. Thus, the high biopsy morning prolactin levels in depressed adolescents with untreated coeliac disease in the present study could be associated with serotonergic dysfunction. They could also be associated with dopaminergic dysfunction due to impaired availability of tyrosine, since dopamine is known to exert an inhibitory action on prolactin release in the hypothalamus [26]. In the present study, however, pre-diet prolactin levels did not correlate with tyrosine levels. Moreover, the function of the intestinal Catechol-O-Methyl Transferase enzyme (COMT) – known to play an important role in the peripheral O-methylation of catecholamines – remains unstudied in untreated coeliac disease. It is of some theoretical interest that reduced COMT activity in erythrocytes has at least once been associated with conditions such as primary affective disorders in women [27].
On the other hand, in the present study also non-depressed adolescents with coeliac disease had higher than normal biopsy morning prolactin levels. Significantly higher prolactin levels among untreated coeliac children (5–18 years) compared with treated patients has been reported by Reifen et al. [28]. They suggest that prolactin may play a part in the immune modulation of the intestine and could thus serve as a potential marker for coeliac disease activity.
Our preliminary findings on amino acid levels in adolescents with coeliac disease with or without depression are unlikely to be explained by malabsorption, since pre-diet free L-tryptophan and tryptophan ratios were not correlated with the BCAA levels that reflect the level of protein nutrition. It is of theoretical interest that increased production of interferon-γ (IFN-γ), known to be the predominant cytokine produced by gluten-specific T-cells in active coeliac disease [29], can suppress serotonin function both directly and indirectly by enhancing tryptophan and serotonin turnover [30]. Increased IFN-γ [30] and, for instance, such events as a stress-related increase in liver tryptophan pyrrolase enzyme activity [23], may lead to lowered tryptophan levels by the enhanced tryptophan catabolism induced by increased activity of the kynurenine-niacin pathway [30-32], even without malabsorption.
Conclusion
The alleviation of psychiatric symptoms found among adolescents with coeliac disease after commencement of a gluten-free diet coincides with a rapid decrease in antibody titres indicating coeliac disease activity and in their prolactin levels, and with a significant increase in L-tyrosine and other CAA serum concentrations, and with a nearly significant increase in the free fraction of L-tryptophan. Although these findings are only preliminary, and more research is needed, they give support to previous findings on patients with coeliac disease, suggesting that serotonergic dysfunction due to impaired availability of tryptophan may play a role in vulnerability to depressive and behavioural disorders, also among adolescents with untreated celiac disease. And since diet treatment may alleviate psychiatric symptoms, and earlier diagnosis may have beneficial effects on psychological and even on neurobiological vulnerability to depression, the possibility of psychiatric complications of coeliac disease needs to be taken into account in differential diagnosis of depressive and behavioural disorders.
Declaration of competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PAP, ETI, MAV, ES, VAA contributed to the conception and design of the study, and PAP, MAV, ES to acquisition of the data. All authors (PAP, ETI, MAV, SAK, IS, ES, VAA) contributed to the analysis and interpretation of the data, were involved in drafting and revising the article, and read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank Jyrki Liesivuori, MD, PhD, from the University of Kuopio, Finland for amino acid analysis, and Erkki Komulainen MA, PhD, for statistical advice. The study was supported by a grant from the Jalmari and Rauha Ahokas Foundation.
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| 15774004 | PMC555757 | CC BY | 2021-01-04 16:28:04 | no | BMC Surg. 2005 Mar 17; 5:5 | latin-1 | BMC Surg | 2,005 | 10.1186/1471-2482-5-5 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-471575750810.1186/1471-2105-6-47SoftwareJAtlasView: a Java atlas-viewer for browsing biomedical 3D images and atlases Feng Guangjie [email protected] Nick [email protected] Bill [email protected] Duncan [email protected] Janet [email protected] Mark [email protected] Susan [email protected] Richard [email protected] MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK2 The Institute of Human Genetics, University of Newcastle, International Centre for Life, Central Parkway, Newcastle-upon-Tyne, NE1 3BZ, UK2005 9 3 2005 6 47 47 8 9 2004 9 3 2005 Copyright © 2005 Feng 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
Many three-dimensional (3D) images are routinely collected in biomedical research and a number of digital atlases with associated anatomical and other information have been published. A number of tools are available for viewing this data ranging from commercial visualization packages to freely available, typically system architecture dependent, solutions. Here we discuss an atlas viewer implemented to run on any workstation using the architecture neutral Java programming language.
Results
We report the development of a freely available Java based viewer for 3D image data, descibe the structure and functionality of the viewer and how automated tools can be developed to manage the Java Native Interface code. The viewer allows arbitrary re-sectioning of the data and interactive browsing through the volume. With appropriately formatted data, for example as provided for the Electronic Atlas of the Developing Human Brain, a 3D surface view and anatomical browsing is available. The interface is developed in Java with Java3D providing the 3D rendering. For efficiency the image data is manipulated using the Woolz image-processing library provided as a dynamically linked module for each machine architecture.
Conclusion
We conclude that Java provides an appropriate environment for efficient development of these tools and techniques exist to allow computationally efficient image-processing libraries to be integrated relatively easily.
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Background
Three-dimensional (3D) images are now commonplace in biomedical research. Techniques for direct capture of 3D data are widespread and new techniques are becoming available, [1,2] to complement existing sectioning methods [3], confocal and micro-CT/MRI [4]. In addition such data is being stored in databases that can be accessed freely (EADHB[5], EMAP[6], BIOIMAGE[7], and MRIMA[8]) and many more such atlases and bioinformatics resources will become available. There are a number of tools available for browsing such data, but they are either commercial with a significant cost for the user (e.g. AVS/Express, VolRen, Amira, Analyse) or free but tied to a specific architecture. Systems based purely on an architecture neutral language such as Java (e.g. ImageJ[9]) can be slow when processing large 3D volume images and have not been developed with the 3D atlas browsing application in mind. The purpose of this work is to combine the machine-architecture independence of Java, with a highly portable, freely available fast and efficient C-coded image processing library tuned to the requirements of the atlas browsing and data analysis task. The Java Atlas-Viewer (JAtlasView) interface has been developed as a series of modules that can be readily re-used within other applications to build more complex interfaces. The Java interface elements and the image processing library can be downloaded from the EADHB and EMAP web-sites. 3D images are regularly captured as part of biomedical research. In many fields the most useful and regularly used visualisation of the grey-level or colour voxel image is to view sections. These are 2D images generated by digitally cutting throught the volume and mimic the traditional mechanism of physical microtome sectioning for revealing detailed structure. The benefit of digital models is that the sectioning plane, orientation and position, can be selected arbitrarily to suit the required usage and the volume can be scanned interactively.
For the expert viewer, digital re-sectioning is sufficient for data-analysis but for others, panning through the volume at non-standard angles leads to disorientation. In addition if used in conjunction with atlas information in which the histology images are segmented in terms of the recognisable tissues, the building of a 3D view of the tissue/anatomical components is very difficult, particularly when learning the anatomy. This orientation and structural visualisation problem is solved by using 3D visualisation of the underlying tissue coupled with interactive feedback of the section location within the volume.
The basic structure of the JAtlasView is therefore a combination of a 3D feedback window with a number of section views. Each section view is independent and feedback is provided by displaying the position of the section within the 3D volume either as a simple polygon indicating the plane of section or as a full grey-level image, displayed appropriately in 3D. In addition each section will display the intersection with all other sections currently being viewed.
In this short note we describe the structure of the software and the functionality of the interface. This application is directed to the use of the EADHB and EMAP atlases and for browsing 3D grey-level data. In the first instance the data is formatted as a Woolz image structure [10], tools are available for data conversion and future versions will include this as standard.
Implementation
The software design has been developed to meet a number of code requirements:
• portability to all major architectures – Unix/X11, Microsoft Windows and Macintosh,
• fast and efficient image processing, compatible with existing formats and interfaces,
• freely available code and modular design so display elements and functionality can be easily included in other applications and
• the user-interface should be mappable to the "look and feel" of the specific machine window system.
The portability and user-interface behaviour requirements are satisfied by using Java as the language and environment for the user interface level. For image processing we have adopted the ANSI standard C image processing library Woolz. This already includes the required functionality for calculating and manipulating section views through 3D voxel images and is open-source software.
A potential problem with Java is that it can be very inefficient for heavy numerical work (such as image processing) and the effort required to port existing libraries (for example Woolz is 185 K lines of code) to Java is too high. To solve this we use the tools within Java for accessing "native" code so that the computational work is undertaken in C. The management and coding of the interface is potentially time-consuming and prone to error with any small change in the C code requiring complementary effort to modify the native interface code. We have addressed this problem by implementing an automated method which will build the interface directly from the C-library header files. By adopting a standard convention for function prototyping it is possible to use a parser generator, javacc[11] to build a java program that can analyse the C-headers and automatically generate the Java class files and matching C-library files required for the Java native interface (JNI). This has made it possible to relegate generating the interface to an automatic process hidden from the primary code develoment, in fact without this development the system would be very difficult to manage.
Two other key choices have been made in the design of the code structure. The first is that the 3D visualisation and feedback should be developed at a level independent of the underlying hardware within an environment that allows a high level of abstraction of the 3D view. The java 3D extension to the core Java environment provides such a model and we have adopted this as standard. Java 3D is available for all Java 2 platforms.
The second key choice is that the software will be delivered using Java Webstart[12]. This is a freely available application that will download code across the internet and check system, version and supplementary module requirements. In addition it will start the application and maintain a local cached version. The local cache will be used for fast start if it is the same version as currently available at source or if the machine is off-line. Source code is maintained with CVS[13] for version management and tracking and GNU gmake for compilation. The interfaces are developed using Borland JBuilder[14] or a standard editor (vi) and documented using Javadoc/Doxygen[15].
Help is provided in two ways, the first is a simple popup "balloon" help on mouse-over and as a series of help files arranged using JavaHelp[16] which provides an indexing, search and context help facility. The help html files are generated using DreamWeaver [17] and maintained in a CVS repository.
Java is now widely used and the first choice for new applications that require portability across machine architectures. It is a strict object-oriented language and interfaces adhere to the model-view-controller (MVC) design pattern [18]. Java also defines a standard under the name java-bean, that components should meet to guarantee the MVC behaviour and enable easy re-use in other applications using CASE tools. We have adopted this standard for the JAtlasViewer application so that individual interface elements, e.g. the section panel or even our extended view of the slider, can be used simply and conveniently in other code.
Results
The user interface, shown in figure 1, has a primary window for the 3D view and top-level menu options, and a number of section-views for visualising the virtual sections cut through the data. The basic functionality of the viewer is to allow interactive digital resectioning of a 3D grey-level or voxel image. The special feature of this viewer is that any number of section views, each with an independent and arbitrary orientation and position can be displayed. To aid navigation through the volume a 3D feedback window is provided. This displays the bounding box of the 3D volume and a transparent surface, of e.g. the embryo model. In addition feedback of the current section position is provided in a number of selectable options: an intersecting polygon of the plane with the bounding box, display of the plane filled with a solid colour and display of the image of the section mapped onto the plane in the 3D view.
With appropriate data, the JAtlasViewer will import a mapped "anatomy". This is in two parts, a hierarchy of terms and a set of "domains" linked to specific terms in the hierarchy. The domains are 3D binary images which identify the region of space or set of voxels within the grey-level image associated with that term. The anatomy will then be used to provide feedback within the section views. These anatomy options, the controls for the section views and the main window options are discussed below.
Main dialog
When the application is invoked a top-level dialog is presented to the user. Before anything can be displayed the user must select a grey-level 3D image. Currently this must be formatted as a Woolz image, but converters for many 3D formats are available from the EMAP web site. Once read in, the bounding box of the 3D voxel image will be displayed in the main window along with a surface representation of the data if available. This 3D view can be manipulated interactively using the cursor to provide views from arbitrary orientations and positions.
The menu options of the primary window are:
File
Commands to open image data, save views, save and restore settings, recent file-list and quit.
View Section
Select a section view through the voxel model. A new window will display one of the pre-set sections which are transverse, frontal and sagittal planes if the image model is appropriately aligned. Each of these can be set to display views at arbitrary orientations and locations within the 3D volume image.
Anatomy
If the voxel model data is configured with a set of anatomical regions, these can be selected from the menu and displayed in the section views. For the EADHB and EMAP atlases the menu hierarchy corresponds to the HUMAT and EMAP anatomy ontologies.
3D View
Options to control the 3D visualisation in the main window, toggle the visibility of 3D surface, bounding box and intersection lines, display the focus section and selected anatomy.
Orientation
Preset 3D orientations to provide standard viewing directions.
Help
On-line help menu.
The 3D view window displays the bounding box of the opened volume and a transparent view of the embryo surface. This surface is pre-determined and stored in the visualization toolkit (VTK[19]) format. The 3D rendering is programmed in Java 3D, the objects (surface and bounding box) inside the 3D view window can be freely interactively manipulated with controls (using button drag) for rotation, translations and zoom (translation towards the viewer).
If an anatomy hierarchy and associated data files are provided then an additional window will allow browsing through the ontology and selection of components for display both in the section views and the 3D view window. As for the embryo surface, the surface models are pre-calculated and stored in VTK format. The data layout recognized by the JAtlasViewer is described in detail on the EMAP web-site.
Section views
Each Section View is displayed in its own Section Viewer, either inside the main window (Microsoft Windows style) or in an independent external window. Section Viewers are Java components that can be easily imported into other applications. The primary viewing control is to move the view plane-parallel through the image volume as a form of "digital microtome" with section thickness determined by the underlying resolution of the 3D image, i.e. moving the microtome by a single step will move to the next voxel in the stack. The assumption is that once the section orientation has been determined the typical use will be to explore the volume in this fashion. The section position is determined by the "distance" parameter which is the voxel distance from the fixed point (by default in the centre of the bounding-box). Section orientation is selected by setting a number of view-angles. These control the view-direction which is perpendicular to the view-plane. We use the standard viewing angles defined by [20] which are related to the Euler angles of rotation [21]. Two of the angles determine the view-direction and the third is rotation around that direction. These angles can be understood in nautical/aeronautical terms as pitch, yaw and roll respectively. These viewing controls are hidden by default.
In addition to the primary view-direction controls there are options to assist navigation. These are View-mode: options for automatic roll determination in terms of the pitch and yaw values.
Fixed-point: select the fixed point used as the centre of rotation. The effect of setting this is to keep that voxel in view for all view-directions provided the "distance" is zero.
Fixed-line: set a second fixed-point and constrain the view so that both fixed points remain in the section. The effect of this is to reduce the degrees of freedom to a single parameter of rotation around the line between the two points.
The remaining controls for each section view are to set the feedback options including between section views, between the section view and the 3D view and to allow saving of the view and its settings (view parameters). The within view and between views feedback options are provided by the "Show" menu. This provides toggle controls to enable:
• cursor position in the reference image coordinate space and image grey-value to be displayed,
• line of intersection with section views. If two views intersect then the line of intersection is displayed in the appropriate colour,
• anatomy feedback – shows the domain and name of the anatomical component under the current cursor position,
• visible fixed point,
• visible fixed line.
The 3D view can provide feedback of the viewed sections in terms of the 3D volume. For most users these are important aids to understanding the position and direction of the viewed section. Most publications adhere to a convention for displaying section images, with this interface it is possible to view section data at any orientation and direction, i.e. depending on the view-direction the section may appear "reversed", so positional and directional feedback is critical. The positional feedback is provided in a number of forms but all indicate the intersection of the viewed plane with the bounding-box of the reference image. The most informative choice is to use texture mapping to render the grey-level image of the section into the 3D view. This is computationally expensive and so two other options are provided. These use the intersection polygon between the section plane and bounding box, either as-is, or filled with solid colour. The directional feedback is optional and provided by an arrow displayed in the 3D view.
Anatomy manager
The primary purpose of the JAtlasViewer is to provide an integrated viewer for 3D atlases. These comprise a grey-level (or potentially colour) reference image and a set of domains or regions which are associated with terms in a text hierarchy. For a geographic atlas these would correspond to the physical geography and the areas associated with individual countries. The hierarchy would then list the country names, perhaps under continents and split into counties. For EADHB and EMAP the reference image is the voxel reconstruction of the embryo and the domains are delineated anatomical components. The hierarchy of terms are the corresponding anatomy ontologies [22,23]. The user can select anatomical terms from the ontology for display in the section and 3D views. Once selected the component is handled by the Anatomy Manager (see fig 2) which controls the display properties visibility and colour. The anatomy-manager displays the full component name, visibility control toggles, colour chooser and a delete button. This style has been adopted because the number of possible component selections is large (15–500 depending on stage) and thus the user requires detailed control. In addition, although only selected terms in the anatomical hiearchy have corresponding domains defined, combinations of domains are generated "on-the-fly" so that larger scale structures can also be visualized.
The colour chooser button allows the user to change the colour of an anatomy component using a standard color chooser dialogue. The change is reflected immediately in all open Section Views and and the 3D feedback window..
The text field displays the full name of the anatomy component. Anatomy components fall into 2 broad hierarchies starting either at embryo or extra-embryonic component. The intervening higher level structures are separated with "/" (slash) and the final part of the name is capitalised. An asterisk following a name indicates that this is an atomic component, referred to in the anatomy menu as a (domain). Anatomical components are selected from the anatomy menu using a left mouse click. Higher level components or structures may also be selected from the anatomy menu using a right mouse click or a combination of the shift key and (left) mouse click. The anatomy name may be scrolled by dragging the mouse left or right inside the text box.
The visibility toggles select whether a component is displayed in the section views and in the 3D feedback window. This fine control helps the process of analysis and allows the user to build up a visualization showing all or parts of the anatomy. The delete is a toggle control which removes an anatomy component from the table.
Conclusion
3D images are in widespread use in medical and biological research and there are a large number of options to view this data, but many of these are commercial and expensive, and are architecture and operating system dependent. More recently atlases and spatially mapped databases in biomedicine have been developed and whilst these packages can provide solutions for browsing this data we believe a simple, free-to-use, open-source and architecture-neutral solution provides a useful tool for biological research and teaching. The JAtlasViewer is intended to fill this requirement. The viewer provides the browsing functionality to locate and display arbitrary sections through the data with simultaneous 3D display. The JAtlasVIewer can also read and display a full anatomy atlas.
The JAtlasViewer is programmed in Java. The 3D programming technology is Java3D, which is a wrapper to the OpenGL or DirectX libraries. The Java and Java3D runtime environment are freely available from the Sun Microsystems web site and in most systems Java is pre-installed. These techniques minimize the coding work and developing time. The file size of the JAtlasViewer is less than 1.5 MB. Java WebStart manages the deployment, installation, upgrade and launch via a simple click on a html page link or an icon in the WebStart application. It is portable to any operating system to which Java has been ported and is currently available for Windows, Linux, Solaris and Mac OS.
The JAtlasViewer design is of reusable and extensible components. Based on the viewer a 3D tie-point collector for capturing 3D to 3D correspondences, and an atlas viewer that can also import gene expression data, have been developed.
Availability and requirements
• Project name: The Mouse Atlas Project
• Project home page:
• Application download:
• Operating system(s): Solaris, Linux, Mac OSX, MS Windows.
• Programming language: Java, ANSI C.
• Other requirements: Java 1.4, JavaDoc, Java 3D.
• License: GNU GPL
• Any restrictions to use by non-academics: None
Authors' contributions
Authors GF and NB undertook the main Java development and implementation, BH and RB develop and maintain the Woolz image processing library and BH implemented the automatic generation of the JNI. DD, JK, MS and SL all contributed to the design and testing of the interface and the preparation of the Atlas data for use with the tool.
Acknowledgements
This work was supported by the National Institute of Health, USA under the Human Brain Project, (NIMH and NICHD), grant #HD39928-02. The embryo atlas data was derived from material provided by the Joint MRC-Wellcome Human Developmental Biology Resource at IHG, Newcastle upon Tyne.
Figures and Tables
Figure 1 JAtlasViewer interface. Screen capture of the JAtlasViewer interface. Top-left: main window with 3D visualisation and anatomy tree; RHS: three section views through the data with some high-lighted anatomy; Bottom-left: current list of imported anatomy.
Figure 2 Anatomy manager interface. Anatomy key interface to control displayed colour, anatomy component name and visibilility in the 2D and 3D views.
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| 15757508 | PMC555758 | CC BY | 2021-01-04 16:02:52 | no | BMC Bioinformatics. 2005 Mar 9; 6:47 | utf-8 | BMC Bioinformatics | 2,005 | 10.1186/1471-2105-6-47 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-351576046910.1186/1471-2164-6-35Methodology ArticleSilhouette scores for assessment of SNP genotype clusters Lovmar Lovisa [email protected] Annika [email protected] Mats [email protected]änen Ann-Christine [email protected] Molecular Medicine, Department of Medical Sciences, Uppsala University, Uppsala, Sweden2005 10 3 2005 6 35 35 16 11 2004 10 3 2005 Copyright © 2005 Lovmar et al; licensee BioMed Central Ltd.2005Lovmar 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
High-throughput genotyping of single nucleotide polymorphisms (SNPs) generates large amounts of data. In many SNP genotyping assays, the genotype assignment is based on scatter plots of signals corresponding to the two SNP alleles. In a robust assay the three clusters that define the genotypes are well separated and the distances between the data points within a cluster are short. "Silhouettes" is a graphical aid for interpretation and validation of data clusters that provides a measure of how well a data point was classified when it was assigned to a cluster. Thus "Silhouettes" can potentially be used as a quality measure for SNP genotyping results and for objective comparison of the performance of SNP assays at different circumstances.
Results
We created a program (ClusterA) for calculating "Silhouette scores", and applied it to assess the quality of SNP genotype clusters obtained by single nucleotide primer extension ("minisequencing") in the Tag-microarray format. A Silhouette score condenses the quality of the genotype assignment for each SNP assay into a single numeric value, which ranges from 1.0, when the genotype assignment is unequivocal, down to -1.0, when the genotype assignment has been arbitrary. In the present study we applied Silhouette scores to compare the performance of four DNA polymerases in our minisequencing system by analyzing 26 SNPs in both DNA polarities in 16 DNA samples. We found Silhouettes to provide a relevant measure for the quality of SNP assays at different reaction conditions, illustrated by the four DNA polymerases here. According to our result, the genotypes can be unequivocally assigned without manual inspection when the Silhouette score for a SNP assay is > 0.65. All four DNA polymerases performed satisfactorily in our Tag-array minisequencing system.
Conclusion
"Silhouette scores" for assessing the quality of SNP genotyping clusters is convenient for evaluating the quality of SNP genotype assignment, and provides an objective, numeric measure for comparing the performance of SNP assays. The program we created for calculating Silhouette scores is freely available, and can be used for quality assessment of the results from all genotyping systems, where the genotypes are assigned by cluster analysis using scatter plots.
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Background
High-throughput single nucleotide polymorphism (SNP) genotyping assays generate large amounts of data, which usually is presented as scatter plots of signals corresponding to the two SNP alleles. A robust SNP genotyping assay is characterized by large distances between the three clusters that define the genotypes and small distances between the data points within each cluster. Numeric quality measures for the scatter plots would allow objective and automatic assessment of the success of a SNP assay.
"Silhouettes" were introduced in 1987 as a general graphical aid for interpretation and validation of cluster analysis [1]. In a Silhouettes calculation, the distance from each data point in a cluster to all other data points within the same cluster and to all data points in the closest cluster are determined. Thus Silhouettes provides a measure of how well a data point was classified when it was assigned to a cluster by according to both the tightness of the clusters and the separation between them. This feature renders Silhouettes potentially well suited for assessing cluster quality in SNP genotyping methods. In high-throughput SNP genotyping, Silhouettes could be used for assessing the quality of automatic genotype assignment by alerting the operator if the quality of the genotype clusters fall below a certain limit. During assay development and optimization, Silhouettes could be used to compare the performance of a genotyping assay at different reaction conditions. It could also be applied for comparing the robustness of different SNP genotyping technologies.
In this study we created a program (ClusterA) to calculate numeric Silhouettes for assessing the quality of genotype clusters obtained in SNP genotyping assays. We show the utility of Silhouettes and the program by applying it to our "in-house" developed four-color fluorescence minisequencing system for SNP genotyping in a microarray format [2]. Single nucleotide primer extension ("minisequencing") is the reaction principle underlying several of the commonly used systems for genotyping single nucleotide polymorphisms (SNPs) [3-8]. In minisequencing a DNA polymerase is employed to specifically extend a detection primer designed to anneal directly adjacent to the SNP position in the complementary DNA strand with a single labelled nucleotide analogue. The DNA polymerase is the most important factor that determines the efficiency and specificity of the primer extension reaction, irrespectively of the assay format. We used Silhouettes to compare the performance of three new commercially available DNA polymerases to the ThermoSequenase DNA polymerase, which is routinely used in minisequencing assays in many laboratories, including our own. We found Silhouettes to provide a relevant measure, in addition to signal-to-noise ratios and genotyping success, for selecting the most favourable enzyme for our assay.
Results and Discussion
We created a program, denoted ClusterA, for calculating numeric "Silhouettes" for clustered data, such as for example the three clusters of signal ratios commonly obtained in SNP genotyping assays. Figure 1 illustrates the Silhouette calculation for one data point in a typical scatter plot obtained in a SNP genotyping assays. A Silhouette close to 1.0 is obtained when the average distance from a data point to the other data points within its own cluster is smaller than the average distances to all data points in the closest cluster. A Silhouette close to zero indicates that the data-point could equally well have been assigned to the neighbouring cluster. A negative Silhouette is obtained when the cluster assignment has been arbitrary, and the data point is actually closer to the neighbouring cluster than to the other data points within its own cluster [1]. The mean value from the Silhouette calculations for all data points in each cluster yield an "average Silhouette width" for the cluster.
Figure 1 Principle for Silhouette scores. Principle for quality assessment of genotyping clusters using Silhouette scores, illustrated for one data point (i). The SNP genotypes have been assigned based on cluster formation in scatter plots with the signal intensity fraction on the x-axis and the logarithm of the signals from both alleles on the y-axis. For each data point (i) in the scatter plot, the Silhouette s(i) is calculated by the formula in the figure, where a(i) is the average distance from i to all data points in the same genotype cluster (green lines), and b(i) is the average distances from i to all data points in the cluster closest to the data point, either b1(i) (blue lines) or b2(i) (red lines) [1]. Max and min in the formula denote the largest or smallest of the measures in the brackets. The "average silhouette width" is calculated by calculating the mean of all s(i) for each genotype cluster and the "Silhouette score" for the whole scatter plot (SNP assay) is obtained by taking the mean of the average silhouette width for all clusters.
Here, we applied ClusterA to calculate "Silhouettes" for comparing the quality of the genotype clusters obtained in our "in-house" Tag-array minisequencing system. For each scatter plot, the mean of the average silhouette widths for the three genotype clusters were used to define a "Silhouette score" for each SNP assay. Thus the Silhouette score condenses the cluster quality for each SNP assay into a single measure that ranges from 1.0 to -1.0. When calculating the Silhouette score, the distance between data points can be measured either in one dimension, for example on the x-axis, or in two dimension using vectors, as illustrated in Figure 1. In our Tag-array minisequencing system we used distances measured only in one dimension, along the x-axis, where the signal fraction (SignalAllele2/ (SignalAllele1+SignalAllele2) is plotted, since this is the major determinant for genotype assignment in our system. The logarithm of the sum of the signals from both alleles (SignalAllele1+SignalAllele2) plotted on the y-axis is only used to set the cut-off values for failed genotype calls. Figure 2 shows nine examples of SNP genotype clusters that yielded different Silhouette scores. Negative controls and assays with signals below signal cut-off level are not shown in Figure 2 since they are not included in the Silhouette score calculations.
Figure 2 Examples of Silhouette scores. Examples of genotype clusters from nine SNP assays, each with the results from 16 samples genotyped in duplicate using Tag-array minisequencing with the calculated Silhouette scores shown in the right hand upper corner of each panel. The blue circles represent homozygotes for allele 2, the red triangles are heterozygotes and the green squares are homozygotes for allele 1. The SNPs are denoted by their dbSNP identification number, and the DNA polarities analyzed are indicated by "cod" or "nc".
The examples in panels E, F and G of Figure 2 illustrate how different clustering patterns can yield similar Silhouette scores. Based on the results from the scatter plots used to assign genotypes in this study, our recommendation is to accept the results from SNP assays with Silhouette scores >0.65 and to fail the whole assays if the Silhouette scores is <0.25. Individual genotype calls for assays where the Silhouette score falls between 0.25–0.65 may be accepted or failed after visual inspection. Excluding some of the outliers will then increase the Silhouette score. Our recommendations is in line with Liu et al., who have included silhouette calculations in the complex algorithm used to interpret the data from the Affymetrix 10K HuSNP hybridization microarray [9].
Here we exemplify the use of Silhouette scores by comparing the performance of the TERMIPol, Therminator, KlenThermase and ThermoSequenase DNA polymerases in the Tag-array minisequencing system [2]. Twenty-six SNPs were analyzed in both polarities in 16 DNA samples in two independent experiments. As our Tag-array genotyping system utilizes an "array of arrays" format [10] with 80 subarrays on each microscope slide, we were able to test all four enzymes in all samples on the same slide at exactly the same conditions, to facilitate a fair comparison between the enzymes.
Figure 3 shows the distributions of Silhouette scores in these SNP assays. For all enzymes, 75% of the scatter plots (indicated by light blue rectangles in Figure 3) yielded silhouette scores above or close to our recommended limit of 0.65. Results from a total of 79 scatter plots/SNP assays are included in Figure 3 and Table 1. If a SNP assay failed for all samples with one enzyme, the results from this assay were excluded from the whole enzyme comparison. It should also be noted that a non-stringent genotype calling strategy was applied to reveal possible differences between the enzymes both in clustering properties and genotyping results. This is the reason for the very low Silhouette scores for some SNP assays, which normally would be considered as failed. Using 0.65 as cut-off, 70–76% of the SNP assays would have been successful in this study.
Figure 3 Distribution of Silhouette scores from minisequencing assays using four DNA polymerases. The Silhouette score is given on the y-axis. Each black diamond represents the Silhouette score for one SNP assay. The light blue rectangular boxes indicate those 75% of the scatter plots that yielded the highest silhouette scores for each enzyme. Quartiles are indicated by the black horizontal lines.
Table 1 Silhouette scores, signal to noise ratios and genotyping performance for four DNA polymerases in Tag-array minisequencing1
Silhouette score 2 S/N 3 Genotype calls 4
Average Median Highest Average Highest Correct Errors
n % n % n % n %
TERMIPol 0.72 0.78 20 25.3 4.3 11 13.9 2337 98.9 18 0.8
Therminator 0.69 0.79 15 19.0 3.6 7 8.9 2323 98.3 32 1.4
KlenThermase 0.74 0.79 22 27.8 8.0 21 26.6 2346 99.3 10 0.4
ThermoSequenase 0.71 0.82 22 27.8 8.9 40 50.6 2324 98.3 34 1.4
1 Duplicate experiments, each with duplicate SNP assays in both DNA polarities, were performed and the results are composite values from both experiments.
2 The Silhouette scores were calculated as described in Figure 1. The average and the median score for all SNPs are given for each enzyme together with the number of SNP assays (n) and frequency (%) where an enzyme yielded the highest Silhouette score.
3 Signal to noise ratios (S/N) were calculated from each spot by dividing the fluorescence intensity values from the fluorescently labelled ddNTP/ddNTPs corresponding to a true genotype (signal) by the fluorescent intensity value from the other ddNTPs (noise). The average S/N ratios are given together with the number of SNP assays (n) and frequency (%) where an enzyme yielded the highest S/N.
4 Number of genotype calls (n) and call rate (%). The genotype obtained from the majority of the assays was considered to be the correct one. The percentages of the samples not accounted for in the table failed to give genotypes.
In the comparison between the enzymes, KlenThermase displayed the highest average Silhouette score, ThermoSequenase had the highest median Silhouette score and also obtained the highest Silhouette score most frequently (Table 1). In addition to the Silhouettes scores, that represent a measure of the robustness of a SNP assay, the signal to noise ratios (S/N) and the genotyping success was assessed (Table 1). All four enzymes performed satisfactorily in our minisequencing assay taking into account the non-stringent genotyping criteria used. However, performance varied between the evaluated features with high error rates for Therminator and ThermoSequenase. KlenThermase showed the best results over all and, also taking into account the cost, would be the enzyme of choice based on the results from this study.
Conclusion
We conclude that "Silhouette scores" for assessing the cluster quality is well suited for comparing the performance of SNP assays. Here we used a one-dimensional calculation of the Silhouette scores, by measuring the distances between the data-points along the x-axis only. A two-dimensional Silhouette calculation using vectors should be applied when genotypes are assigned by scatter plots with the fluorescence signals corresponding to the two alleles on the y- and x-axis. Both options are available in the ClusterA program that also calculates mean, variance and F-statistic for the input data set. The program is freely available through our website . We believe that the ClusterA program for calculating Silhouette scores created in the present study is a useful and general tool for any genotyping system, where the genotypes are called by cluster analysis with the aid of scatter plots.
Methods
DNA samples
Genomic DNA was extracted from blood samples from 16 volunteer blood donors using the Wizard genomic DNA purification kit (Promega, Madison, WI).
Genotyping procedure
Twenty-six SNPs, selected to be located in unique PCR amplicons, were included in the test panel. For information on the single nucleotide polymorphisms and oligonucleotides used, see the Additional file 1: SNPinformation.pdf. PCR primers were designed and combined in multiplex PCR reactions. Minisequencing primers with 20 bp 5'-Tag sequences were designed for both DNA polarities. The experimental details of the genotyping procedure have been described in detail previously [11]. In short it included the following steps: The regions containing the sequence variations were amplified in six optimized multiplex PCRs. For each sample the PCR products were pooled and divided into four aliquots, one for each enzyme. The remaining dNTPs and primers from the PCR reaction mixture were removed by treatment with Exonuclease I and shrimp alkaline phosphatase. The cyclic minisequencing reactions were performed in solution as described below, and the extended minisequencing primers were hybridized to microarrays carrying immobilized covalently coupled oligonucleotides (cTags) complementary to the Tag-sequences of the minisequencing primers. The cTags had been immobilized to CodeLinkTM Activated Slides (Amersham Biosciences, Uppsala, Sweden) via their 3'-end NH2-groups to form 80 subarrays per slide, each with 60 cTags as duplicate spots. Finally the microarray slides were scanned, and the fluorescent signals were measured.
Minisequencing reaction
Cyclic minisequencing reactions were performed in solution with 10 nM of each of the 52 tagged minisequencing primers using 0.1 μM ddATP-Texas Red, ddCTP-Tamra and ddGTP-R110 and 0.15 μM ddUTP-Cy5 (Perkin-Elmer Life Sciences, Boston, MA), and 0.064 U/μl of one of the four DNA polymerases in 15μl of 0.02% Triton-X, 4.1 mM MgCl2 and 33.6 mM Tris-HCl pH 9.5. The cyclic extension reactions were performed on a Thermal Cycler PTC-225 (MJ Research, Watertown, MA) with an initial 96°C for 3 min followed by 55 cycles of 95°C and 55°C for 20 s each. The DNA polymerases were; TERMIPol (Solis BioDyne, Tartu, Estonia), Therminator (New England BioLabs Inc., Beverly, MA, USA), KlenThermase (Gene Craft, Lüdinghausen, Germany), or ThermoSequenase (Amersham Biosciences, Uppsala, Sweden). A custom made reaction rack holding the arrayed slides with a silicon grid to give 80 separate reaction chambers was used during capture of the minisequencing reaction products on the Tag-arrays.
Data analysis and genotype assignment
The fluorescence signals were measured from the microarray slides using a ScanArray Express® instrument (Perkin-Elmer Life Sciences, Boston, MA). The excitation lasers were: Blue Argon 488 nm for R110; Green HeNe 543.8 nm for Tamra; Yellow HeNe 594 nm for Texas Red and Red HeNe 632.8 nm for Cy5. The fluorescence signal intensities were determined using the QuantArray®analysis 3.1 software (Perkin-Elmer Life Sciences, Boston, MA). The QuantArray file was exported to the SNPSnapper v4.0 software ) for genotype assignment. Raw data as fluorescence signals and signal ratios are provided as supplementary material, see Additional file 2: Rawdata.txt. Genotypes were assigned based on scatter plots with the logarithm of the sum of both fluorescence signals (SignalAllele1+SignalAllele2) plotted on the y-axis, and the fluorescence signal fraction, obtained by dividing the fluorescence signals from one allele by the sum of the fluorescence signal from both SNP alleles (SignalAllele2/ (SignalAllele1+SignalAllele2), on the x-axis [11]. The result file with the assigned genotypes and the corresponding signal ratios were exported as a text file and used to calculate Silhouettes scores using the ClusterA program. ClusterA is implemented in Microsoft Visual Basic 6.0, and can be run on PCs with the Microsoft Windows operating system. The ClusterA program also provides the mean, variance and F-statistic for the input data.
Authors' contributions
LL planned the experiments, guided the laboratory work and performed the analysis of results, interpreted the data and drafted the manuscript. AA carried out the laboratory work and part of the data analysis and provided input to the manuscript. MJ programmed the ClusterA program and took part in the interpretation of Silhouettes. ACS initiated the study, supervised it, and coordinated the manuscript writing process. All authors have read and approved the final manuscript.
Supplementary Material
Additional File 1
Lists the dbSNP identification numbers and the sequences of the PCR and minisequencing primers.
Click here for file
Additional File 2
Includes the raw fluorescence signals and the fluorescence signal intensity ratios for the two experiments as a tab delimited text file.
Click here for file
Acknowledgements
The study was supported by grants from the Swedish Research Council (VR-NT). The array spotter instrument was purchased by funding from the Wallenberg Consortium North (WCN). We thank Raul Figueroa for producing the Tag-arrays
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| 15760469 | PMC555759 | CC BY | 2021-01-04 16:39:32 | no | BMC Genomics. 2005 Mar 10; 6:35 | utf-8 | BMC Genomics | 2,005 | 10.1186/1471-2164-6-35 | oa_comm |
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BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-6-171577401210.1186/1471-2474-6-17Research ArticleReliability of videotaped observational gait analysis in patients with orthopedic impairments Brunnekreef Jaap J [email protected] Uden Caro JT [email protected] Moorsel Steven [email protected] Jan GM [email protected] Department of Physical Therapy, Radboud University Nijmegen, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands2 Department of Integrated Care, Research Institute Caphri, University Hospital Maastricht, P.O. Box 5800, 6202 AZ Maastricht, the Netherlands3 Department of General Practice, Research Institute Caphri, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands4 Department of Anatomy and Embryology, Radboud University Nijmegen, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands2005 17 3 2005 6 17 17 12 11 2004 17 3 2005 Copyright © 2005 Brunnekreef et al; licensee BioMed Central Ltd.2005Brunnekreef 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 clinical practice, visual gait observation is often used to determine gait disorders and to evaluate treatment. Several reliability studies on observational gait analysis have been described in the literature and generally showed moderate reliability. However, patients with orthopedic disorders have received little attention. The objective of this study is to determine the reliability levels of visual observation of gait in patients with orthopedic disorders.
Methods
The gait of thirty patients referred to a physical therapist for gait treatment was videotaped. Ten raters, 4 experienced, 4 inexperienced and 2 experts, individually evaluated these videotaped gait patterns of the patients twice, by using a structured gait analysis form. Reliability levels were established by calculating the Intraclass Correlation Coefficient (ICC), using a two-way random design and based on absolute agreement.
Results
The inter-rater reliability among experienced raters (ICC = 0.42; 95%CI: 0.38–0.46) was comparable to that of the inexperienced raters (ICC = 0.40; 95%CI: 0.36–0.44). The expert raters reached a higher inter-rater reliability level (ICC = 0.54; 95%CI: 0.48–0.60). The average intra-rater reliability of the experienced raters was 0.63 (ICCs ranging from 0.57 to 0.70). The inexperienced raters reached an average intra-rater reliability of 0.57 (ICCs ranging from 0.52 to 0.62). The two expert raters attained ICC values of 0.70 and 0.74 respectively.
Conclusion
Structured visual gait observation by use of a gait analysis form as described in this study was found to be moderately reliable. Clinical experience appears to increase the reliability of visual gait analysis.
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Background
Patients exhibiting gait deviations caused by orthopedic impairments are often referred to a physical therapist for treatment. In order to determine treatment goals or to evaluate the effect of a therapeutic intervention, physical therapists visually observe the patient's gait [1-3]. This type of gait assessment is cost efficient, quick, and easy to use in comparison to computer-assisted gait analysis [1,3,4].
Several reliability studies on observational gait analysis have been described in the literature. These studies included patients with hemiplegia [5-7], amputation [8], neurological diseases [9], cerebral palsy [10], rheumatoid arthritis [11] and spinal cord injuries [12]. The outcomes of these studies are diverse. The inter-rater reliability score for 'live' observational gait analysis (OGA), varies from reasonable [9] to moderate – good [12]. The inter-rater reliability scores for videotaped observational gait analysis (VOGA) varies from moderate [11,13] to moderate – good [12], while others show that the intra-rater reliability of VOGA is poor [10], moderate [13] or good [12]. The results of the validity of 'live' and videotaped observation varies from reasonably good [5], to not valid [8] as well as valid and accurate [9,12]. Two other studies used VOGA, in which raters had the opportunity to look at a video in slow motion or freeze-frame. One of these studies, by Eastlack et al. [11], found only slight to moderate inter-rater reliability levels. The other study, by Hughes et al. [7], showed that only some parts of a hemiplegic gait analysis form show sufficient inter- and intra-rater reliability levels. All the above mentioned differences stem from a large variety in design, (amount and type of patients and raters, types of gait analysis forms, rating scales and types of statistical methods). Despite the numerous studies on observational gait analysis, patients with orthopedic impairments have received little attention.
In the Netherlands a gait analysis form has been developed which focuses mainly on orthopedic disorders [14]. Visual gait analysis with use of this gait analysis form is used by many physical therapists who practice gait training in patients with lower extremity orthopedic disorders. In addition, the use of this form is recommended by the Royal Dutch College of Physical Therapy for patients with chronic ankle sprain [14]. It is questionable, however, whether results from above described research concerning the reliability and validity of visual gait analysis in patients with neurological or other conditions can be extrapolated to patients with orthopedic problems. For example, gait deviations in patients with orthopedic impairments may result in less obvious gait deviations compared to patients with neurological disorders and may therefore be harder to identify visually.
The purpose of this present study is to determine the inter- and intra-rater reliability of videotaped observational gait analysis with use of an orthopedic gait analysis form when applied to a cohort of patients suffering from orthopedic impairments. In addition, this study determines how well the raters perform observational gait analysis by comparing their assessments with a criterion, based on the experts' opinion. In order to gain insight into how the results may give guidance to physical therapy treatment, this study also investigates which items on the gait analysis form, that have been considered to be disturbed by visual observation, receive high priority in the physical therapy treatment program according to the physical therapist who performs the visual gait analysis.
Methods
Patients
Thirty videotapes of patients' gait were selected from the archives of the department of Physical Therapy of the University Medical Center Nijmegen, the Netherlands. These videotapes involved patients who had been referred to a physical therapist for gait treatment. It is common practice at this department that prior to gait training therapy the gait of each patient is videotaped according to a standardized protocol.
The criteria for inclusion of the videotapes were: (1) the presence of mild to severe gait deviations due to an orthopedic impairment; (2) patient was wearing shorts or underwear to allow for a more accurate observation of the joint movement; (3) ability of a patient to walk 15 meters at least four times, twice in a semi-circle and twice in a straight line on a gymnasium floor; (4) and patient's written informed consent. The first thirty patients who complied with these criteria were included.
The group consisted of 15 male and 15 female patients with a mean age of 37.8 years (range: 15 to 62 years). The type of orthopedic impairments varied from status post hip, knee, ankle surgery (n = 8), status post hip or knee prosthesis (n = 6), status post femur, tibia or ankle fracture (n = 3) and traumatic or non-traumatic non-specific hip, knee or ankle pain (n = 13) (see Table 1).
Table 1 Patient characteristics (N = 30)
Affected side
Subject Age (years) Gender Type of orthopedic impairment Left side Right side
1 25 F Status post hip surgery Hip
2 56 F Status post hip prosthesis Hip
3 48 M Status post knee surgery Knee
4 19 F Trauma induced non-specific knee pain Knee
5 41 M Status post hip surgery and prosthesis Hip
6 62 F Status post hip prosthesis Hip
7 37 M Status post femur fracture Hip/ Knee
8 49 M Status post hip surgery and prosthesis Hip
9 15 F Non-specific knee pain Knee
10 33 M Status post hip and knee prosthesis Hip Hip/ Knee
11 21 M Status post ankle fracture Ankle Ankle
12 37 M Status post ankle surgery Ankle
13 34 F Non-specific knee pain Knee
14 32 F Status post knee surgery Knee
15 19 F Non-specific ankle pain Ankle Ankle
16 46 M Non-specific knee pain Knee
17 51 F Non-specific knee pain Knee
18 62 F Status post hip surgery Hip
19 44 M Status post hip surgery Hip
20 21 M Status post tibia fracture Knee
21 33 F Non-specific knee pain Knee
22 28 F Status post hip surgery Hip
23 31 M Status post ankle surgery Ankle
24 60 F Trauma induced non-specific knee pain Knee
25 52 M Trauma induced non-specific knee pain Knee
26 50 F Status post hip surgery and prosthesis Hip
27 21 F Trauma induced non-specific ankle pain Ankle
28 41 M Non- specific knee pain Knee Knee
29 49 M Non- specific knee pain Knee
30 16 M Trauma induced non-specific knee pain Knee
Raters
Ten raters participated in this study, 4 inexperienced, 4 experienced and 2 experts. The inexperienced raters were two physical therapy students and two human movement science students. These inexperienced raters had no clinical experience in the analysis of gait deviations in orthopedic patients and never analyzed gait deviations by means of an observational gait analysis form.
The group of experienced raters consisted of four senior physical therapists who had all taken part and successfully completed a gait training course. All experienced raters had worked more than ten years as a physical therapist and had at least five years of experience in treating and analyzing gait deviations by means of an observational gait analysis form.
The two expert raters were two senior physical therapists, who were selected based on their exceptional skills and knowledge in the observation of gait deviations due to orthopedic impairments. They have considerable experience with treating patients with orthopedic gait disorders. In addition, these two physical therapists cooperatively developed the orthopedic gait analysis form used in this study and are instructors in a course in which participants are taught to treat and observe orthopedic gait deviations with a functional approach. All four experienced raters had taken part in this course.
Design of the gait analysis form
The 12 items contained in the gait analysis form used in this study describe the trunk, arm, pelvis, hip, knee and ankle during the gait cycle (Table 2). In daily practice, the results of the visual gait analysis are used as a guide for treatment or to evaluate the effect of a therapeutic intervention.
Table 2 Orthopedic gait analysis form
STANCE PHASE SWING PHASE
Item Question Early Mid Late Early Late
General 1 Is a shortened stance phase present? Left Yes / No NA
Right Yes / No NA
Trunk 2 Is the trunk anterior to the hips? Yes / No
3 Is the trunk posterior to the hips? Yes / No
4 Is lateral flexion present? Left Yes / No NA
Right Yes / No NA
5 Is arm-swing reduced? Left Yes / No
Right Yes / No
Pelvis 6 Is the posterior rotation excessive? Left NA Yes / No NA
Right NA Yes / No NA
Hip 7 Is the extension reduced? Left NA Yes / No NA
Right NA Yes / No NA
Knee 8 Is the extension reduced? Left NA NA Yes / No
Right NA NA Yes / No
9 Is the flexion movement absent ? Left Yes / No NA NA
Right Yes / No NA NA
10 Is the flexion reduced? Left Yes / No NA NA
Right Yes / No NA NA
11 Is the extension absent? Left NA Yes / No NA NA
Right NA Yes / No NA
Ankle 12 Is the plantar flexion reduced? Left NA Yes / No NA
Right NA Yes / No NA
NA = not applicable
Visual gait analysis
The gait pattern was analyzed from a lateral (both sides), anterior and posterior view at each of the three sub-phases of stance and the two sub-phases of swing. Early stance was defined as the combined phases of initial contact and loading response. In this phase, the ankle moves from heel contact to foot contact, while the knee is flexed to absorb the shock of limb loading. Mid-stance was defined as the phase of foot contact to heel rise, during this phase the trunk progresses over a single stable limb. Late stance was defined as the combined phases of terminal stance and pre-swing, in which heel-rise and toe-off occurs. Early swing was defined as toe-off until to the swing leg reaches the stationary leg. Late swing was defined as the combined phases of mid swing and terminal swing. In this phase, the moving leg passes the stationary leg and the knee extends as the limb prepares to take the load at initial contact.
Videotape recording
All patients were recorded from a lateral view (both sides) while walking 15 meters in a semi-circle (radius approximately 10 m) at a comfortable self-selected walking speed. We used a semi-circle in order to be able to observe the patient's gait in the sagittal plane from one position. The anterior and posterior views were videotaped while the patient walked five meters toward and away from the camera.
The collected videos were edited with use of the computer program adobe premiere 6.0 (Adobe systems®). Manufactured videos were reduced into a one-minute film-clip in which the patient's gait could be viewed in the lateral and frontal plane. Subsequently, these videos were converted to analog format again, so that they could be played by a regular video player. Sampling frequency was 24 Hz.
Rater instructions
To ensure visual assessment of gait based on comparable criteria, all raters received standardized information about normal gait kinematics prior to the rating sessions (Table 3). Raters were required to use this information during the rating sessions. Before each session, raters viewed a videotaped gait sequence of a non-participating patient and a healthy subject. All raters started rating after they felt completely comfortable with rating the videos.
Table 3 Normal joint-angles during stance and swing phase. Range of motion summary in the sagittal plane measured in degrees.
Phase of the gait cycle STANCE PHASE SWING PHASE
Early 0 – 10% GC. Mid 10 – 30% GC. Late 30 – 60% GC. Early 60 – 70% GC. Late 70 – 100% GC.
Trunk Positioned above the hip Positioned above the hip
Pelvis 5° forward rotation 0° 5° backward rotation 5° backward rotation 5° forward rotation
Hip 25° flexion 0° 30–50% GC: 10° extension 50–60% GC: 0° 15° flexion 25° flexion
Knee 20° flexion 0° 40° flexion 60° flexion 0°
Ankle 10° PF 10° DF 20° PF 10° PF 0°
GC = Gait Cycle, PF = Plantar Flexion, DF = Dorsal Flexion [1]
Rating procedure
The rating session took place in an isolated room in which each rater individually assessed the videotaped gait-patterns of 30 patients twice, with a minimum interval between the two rating sessions of 3 weeks, in order to reduce the effect of recognition. Raters had to rate each item of the form as present or absent. Both legs were assessed and were dealt with in the statistical analysis as independent ratings. Each rater was permitted to view the videotape in slow motion or freeze-frame, allowing the raters to more closely inspect the patient's gait. Each rater was able to rate the patient's gait as many times as necessary until they were satisfied with their rating. The rating of the 30 patients was spread out over two days and a single session lasted for a maximum of two hours. All videos were put in a randomized order to prevent the raters from recognizing the patient and recalling their scores from the last session. The randomization was done through the use of dice and was concealed from all raters.
Raters were also asked to assign priority levels (high or low priority) to the items they scored as disturbed, with respect to a physical therapy treatment program. In other words, which items would receive important attention in the physical therapy intervention if the rater was going to treat this patient for his or her gait disorder.
Level of performance
In order to determine the level of performance of observational gait analysis of all experienced and inexperienced raters, we compared their ratings with a criterion. This gives us an indication about how well the raters were capable in performing visual gait analysis. The criterion was attained during a consensus session of the two expert raters: After individually assessing the 30 patients for the second time, the two expert raters jointly observed the videotaped gait of all 30 patients for the third time.
Data analysis
Inter- and intra-rater reliability levels were assessed by using Intraclass Correlation Coefficients (ICCs), validated for use with multiple raters and calculated in a two-way random model based on absolute agreement. We used ICCs because it has been shown that with data that are rated as a dichotomy, the ICC is equivalent to measures of nominal agreement, simplifying computation in cases where more than two raters are involved [15]. In addition, the ICC computation also provides us with an estimate of accuracy (95% CI) of the reliability levels. The level of performance (quality of assessment) was obtained by comparing the joint assessment of the expert raters to each individual, also using reliability analyses with use of ICCs. Agreement strengths for ICC values have been classified as follows: <0 = poor; 0 – 0.20 = slight, 0.21 – 0.40 = fair; 0.41 – 0.60 = moderate; 0.61 – 0.80 = substantial and 0.81 – 1.00 = almost perfect [16]. All analyses were performed with use of SPSS 11.0.1.
Results
Inter-rater reliability
The inter-rater reliability among experienced raters was 0.42 (95%CI: 0.38–0.46). This level of reliability is comparable to the inter-rater reliability of in-experienced raters, which reached an ICC value of 0.40 (95%CI: 0.36–0.44). The expert raters reached the highest inter-rater reliability (ICC: 0.54 (95%CI: 0.48–0.60)).
There were no differences in inter-rater reliability between the first and second rating session of all three groups separately, based on the overlap of 95% confidence intervals.
Intra-rater reliability
The average intra-rater reliability of the experienced raters was 0.63 (ranging from 0.57 to 0.70). The inexperienced raters reached an average intra-rater reliability of 0.57(ranging from 0.52 to 0.62). The two expert raters attained ICC values of 0.70 and 0.74 respectively.
Level of performance
The agreement between the outcome of the joint assessment of the expert raters (criterion) and those of the individual experienced raters ranged from 0.43 to 0.55 with an average ICC value of 0.48. The inexperienced raterrs attained agreement levels ranging from 0.41 to 0.55, with an average of 0.49. There is no difference in the level of performance of visual gait assessments of experienced or inexperienced raters, when compared to the experts' opinion.
Reliability levels for each item separately
The inter-rater reliability per item on the gait analysis form between the two experts is generally moderate to substantial (see Table 4). However, two items in particular, showed low agreement levels. These are flexion of the knee during early stance (item 9) and posture of the trunk during walking (item 2) (for both: ICC = 0.33). With respect to the experienced and inexperienced raters, the visual observation of the lateral flexion of the trunk (item 4), the arm swing (item 5) and the knee extension in the late swing phase (item 8) showed the highest inter-rater reliability levels (all ICC-values > 0.50).
Table 4 Reliability of the gait analysis list per item
Inter-rater reliability1 Intra-rater reliability2
Item Expert (n = 2) Experienced (n = 4) Inexperienced (n = 4) Expert (n = 2) Experienced (n = 4) Inexperienced (n = 4)
ICC (95% CI) ICC (95% CI) ICC (95% CI) Mean ICC (range) Mean ICC (range) Mean ICC (range)
General 1 0.62 (0.43 – 0.76) 0.25 (0.12 – 0.39) 0.26 (0.14 – 0.40) 0.86 (0.82 – 0.89) 0.54 (0.32 – 0.83) 0.50 (0.36 – 0.65)
Trunk 2 0.33 (0.01 – 0.61) 0.25 (0.12 – 0.39) 0.41 (0.22 – 0.61) 0.87 (0.74 – 1.00) 0.81 (0.64 – 1.00) 0.53 (0.37 – 0.64)
3 - - - - - -
4 0.66 (0.50 – 0.78) 0.58 (0.46 – 0.70) 0.52 (0.39 – 0.65) 0.82 (0.69 – 0.95) 0.68 (0.49 – 0.86) 0.74 (0.66 – 0.84)
5 0.48 (0.17 – 0.68) 0.53 (0.40 – 0.66) 0.55 (0.40 – 0.69) 0.75 (0.70 – 0.79) 0.75 (0.61 – 0.82) 0.81 (0.74 – 0.88)
Pelvis 6 0.58 (0.38 – 0.73) 0.19 (0.08 – 0.34) 0.33 (0.20 – 0.47) 0.65 (0.53 – 0.76) 0.13 (-0.07 – 0.61) 0.45 (0.27 – 0.61)
Hip 7 0.52 (0.23 – 0.71) 0.43 (0.28 – 0.57) 0.24 (0.11 – 0.39) 0.63 (0.59 – 0.67) 0.59 (0.35 – 0.72) 0.47 (0.14 – 0.67)
Knee 8 0.58 (0.34 – 0.73) 0.58 (0.45 – 0.70) 0.60 (0.48 – 0.71) 0.66 (0.62 – 0.69) 0.65 (0.49 – 0.82) 0.63 (0.48 – 0.76)
9 0.33 (0.07 – 0.54) 0.45 (0.32 – 0.59) 0.16 (0.05 – 0.30) 0.82 (0.76 – 0.88) 0.58 (0.44 – 0.72) 0.36 (0.10 – 0.55)
10 0.51 (0.30 – 0.68) 0.23 (0.10 – 0.38) 0.41 (0.28 – 0.55) 0.82 (0.70 – 0.94) 0.42 (0.02 – 0.65) 0.54 (0.50 – 0.64)
11 0.40 (0.16 – 0.59) 0.29 (0.15 – 0.44) 0.36 (0.23 – 0.50) 0.52 (0.42 – 0.61) 0.58 (0.47 – 0.63) 0.22 (0.00 – 0.54)
Ankle 12 0.52 (0.27 – 0.70) 0.30 (0.17 – 0.45) 0.20 (0.09 – 0.35) 0.66 (0.62 – 0.70) 0.30 (0.16 – 0.46) 0.37 (0.17 – 0.67)
The intra-rater reliability levels with respect to the visual gait assessments by expert raters were generally higher compared to the experienced and inexperienced raters. With regard to five items intra-rater reliability was good (>0.80). Only one item, extension movement of the knee during mid stance, had an ICC value for intra-rater reliability of less than 0.6. The experienced raters were able to attain good intra-rater reliability for item 2, posture of the trunk during walking (ICC = 0.81). Three items reached substantial intra-rater reliability (item 4, 5, and 8). Two items of the gait analysis form, pelvis rotation and ankle movement during late stance, were not intra-rater reliable (ICC < 0.40). The inexperienced raters reached the highest intra-rater reliability for the assessment of arm swing during walking (ICC = 0.81). Three items had inadequate intra-rater reliability levels; flexion of the knee in early stance (ICC = 0.36), extension of the knee in mid stance (ICC = 0.22), and ankle movement during the late stance phase (ICC = 0.37).
No reliability score was obtained from item 3, which describes a trunk position behind the hips, because this item was observed only once.
Priority level with respect to physical therapy treatment
On average, with respect to all items, in about a quarter of the cases items were judged to be disturbed by the expert and experienced raters (see Table 5). Except for item three which was considered disturbed only once in the group of experienced raters. Both expert and experienced raters would give hip, knee and ankle movements, which were judged as being disturbed, generally high priority if they were to treat the patient. Expert raters also gave a shortened stance phase of either one of the legs, and an excessive lateral flexion of the trunk high priority, in contrast to the experienced raters for whom these items received generally a low priority. The other items such as movement of the pelvis, arm swing, and position of the trunk (flexed or extended) received generally low priorities in a potential physical therapy intervention.
Table 5 Treatment priority per item when scored as disturbed.
Expert raters Experienced raters
Treatment priorityb Treatment priorityb
Item Times scored as disturbeda High Low Times scored as disturbeda High Low
General 1 15,0% 72,2% 27,8% 13,8% 21,2% 78,8%
Trunk 2 16,7% 20,0% 80,0% 15,8% 47,4% 52,6%
3 0,0% - - 0,8% 100,0% 0,0%
4 26,7% 71,9% 28,1% 26,3% 54,0% 46,0%
5 50,8% 55,7% 44,3% 40,4% 24,7% 75,3%
Pelvis 6 22,5% 44,4% 55,6% 7,9% 26,3% 73,7%
Hip 7 42,5% 82,4% 17,6% 26,7% 82,8% 17,2%
Knee 8 26,7% 59,4% 40,6% 25,4% 75,4% 24,6%
9 20,0% 95,8% 4,2% 41,7% 98,0% 2,0%
10 24,2% 100,0% 0,0% 52,9% 99,2% 0,8%
11 45,8% 67,3% 32,7% 26,3% 92,1% 7,9%
Ankle 12 54,2% 83,1% 16,9% 35,8% 96,5% 3,5%
a This number indicates how many times raters scored this item as being disturbed.
b When raters scored an item as being disturbed they were asked to indicate whether this item would receive high or low priority in their physical therapy treatment program with respect to the patients gait disorder.
Discussion
The results of this study indicate a moderate reliability of observational gait analysis in patients with orthopedic gait disorders while using a structured gait analysis form. In addition, the observation of only three items of the gait analysis form reached substantial levels of inter-rater reliability. These were related to lateral movements of the trunk, arm swing, and the movement of the knee just before heel strike.
This study shows comparable results with similar studies on observational gait analysis in different patient categories. Studies on visual gait analysis that show high reliability levels, generally focused on patients exhibiting severe neurological pathology. Severe neurological pathology causes grossly larger gait deviations, which makes potential gait deviations easier to recognize. Furthermore, most of the gait analysis forms being used contain easy observable items. With respect to the present study, the highest agreement levels are reached on items that are considered easy observable: the lateral flexion of the trunk, the arm swing and the knee extension in the late swing phase. Items that are considered more difficult to observe, like the pelvis rotation and the plantar flexion of the ankle in the late stance phase, scored lower agreement levels. Minute gait deviations displayed by the patients in this study lead to difficult observable items, explaining the moderate reliability level found in this present study.
Another explanation for the moderate results may be that some of the patients in this study displayed an inconsistent gait pattern. This means that, despite the accuracy with which the videos were collected in this study, still some participants performed a slight variability in their gait pattern. This results in small gait deviations present during a few steps and absent a couple of steps later, so when raters do not observe the same gait cycles, differences occur. This might explain relatively low inter- and intra-rater reliability levels, even when raters were 'right' in their assessment. To correct for this disturbance we believe that a gait deviation should only be defined as abnormal when the patient repeats the deviation in a series of gait cycles. This will increase reliability levels of the videotaped observational gait analysis. On the other hand, inconsistent gait patterns are of minor importance during 'live' observation or videotaped gait observation without the opportunity for freeze-frame or slow-motion. In that case more gait cycles are observed, leading to a situation in which an average of the inconsistencies is scored. This consideration is supported by the fact the reliability of gait analysis without the opportunity for freeze-frame or slow-motion is not always found to be worse [12,13].
A weakness of this study is that we have not included an objective standard to assess the validity of raters' visual observations. Nevertheless, we tried to gain insight in raters' performance by using a criterion, which was accomplished during a joint rating session by the two expert physical therapists.
According to this study, experience in gait observation does not improve the reliability of this observation. Inexperienced raters achieve a comparable reliability level to experienced raters. However, expert raters accomplish significant better reliability levels of visual gait observation compared to experienced and inexperienced raters. In other words, some experience does not improve observation skills, but a lot more does.
We have shown that not all movements of body segments during gait can be observed with similar reliability levels. The visual observation of only three items proved to be substantially reliable. This indicates that one should bear in mind when using this 12-item gait analysis form that nine of these items are at the best moderately reliable. However, the results of this study indicate that for at least four items the intra-rater reliability levels are substantial to good (items 2, 4, 5 and 8). Expert raters showed the least variability between the first and second session; five items showed to have a mean intra-rater reliability level that is considered good (ICC > 0.80).
The results of this study suggest that a brief introduction in normal gait kinematics in inexperienced raters gives comparable reliability levels of observational gait analysis in patients with orthopedic impairments compared to experienced physical therapists, who have worked for several years with patients with gait disorders. However, expert raters – those that work significantly more intensive with patients with gait disorders – accomplish higher reliability levels.
As mentioned in the methods section, the gait analysis form used in this study is also used in daily practice to guide the treatment of the patient's gait disorder. In the physical therapist's treatment program, some items on the form will obviously receive higher priority than others. The results of this study show that physical therapists mainly focus their intervention on movement disorders of the lower extremity. However, the expert raters also report to give priority to asymmetry of the stance phase and excessive lateral flexion of the trunk during gait. Of the three items in this study that achieved the highest reliability levels, only the movement of the knee received generally a high priority in the treatment program of experienced raters. This implies that experienced raters will mainly focus their treatment on items that have generally a low inter- and intrarater reliability.
Conclusion
Structured visual observation of a patient's gait by use of a gait analysis form as described in this study is found to be only moderately reliable, but may be a useful guide to the physical therapist in setting up a gait training or exercise therapy program. Intra-rater levels have shown that visual gait analysis will supply the observer with a fair indication of changes in a person's gait. However, to evaluate the effect of an intervention on a patient's gait we recommend more objective instrumentation which has been proven reliable and valid.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JB carried out the data analysis, participated in the design of the study and drafted the manuscript. CvU participated in the design and coordination of the study, assisted with statistical analysis, and helped to draft the manuscript. SvM participated in the design of the study and supplied the videotapes. JK participated in the design and coordination of the study. All authors 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 all raters and patients for their participation in this study.
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| 15774012 | PMC555760 | CC BY | 2021-01-04 16:32:05 | no | BMC Musculoskelet Disord. 2005 Mar 17; 6:17 | utf-8 | BMC Musculoskelet Disord | 2,005 | 10.1186/1471-2474-6-17 | oa_comm |
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BMC NeurolBMC Neurology1471-2377BioMed Central London 1471-2377-5-51577400610.1186/1471-2377-5-5Research ArticleValidity of Simpson-Angus Scale (SAS) in a naturalistic schizophrenia population Janno Sven [email protected] Matti M [email protected] Katinka [email protected] Kristian [email protected] Department of Psychiatry, University of Tartu, Raja 31, 50417, Tartu, Estonia2 Department of Psychiatry, Helsinki University, Helsinki, Finland3 Finnish Institute of Occupational Health, Helsinki, Finland4 STAKES National Research and Development Centre for Welfare and Health, Helsinki, Finland5 Vaasa Central Hospital, Vaasa, Finland2005 17 3 2005 5 5 5 1 11 2004 17 3 2005 Copyright © 2005 Janno et al; licensee BioMed Central Ltd.2005Janno 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
Simpson-Angus Scale (SAS) is an established instrument for neuroleptic-induced parkinsonism (NIP), but its statistical properties have been studied insufficiently. Some shortcomings concerning its content have been suggested as well. According to a recent report, the widely used SAS mean score cut-off value 0.3 of for NIP detection may be too low. Our aim was to evaluate SAS against DSM-IV diagnostic criteria for NIP and objective motor assessment (actometry).
Methods
Ninety-nine chronic institutionalised schizophrenia patients were evaluated during the same interview by standardised actometric recording and SAS. The diagnosis of NIP was based on DSM-IV criteria. Internal consistency measured by Cronbach's α, convergence to actometry and the capacity for NIP case detection were assessed.
Results
Cronbach's α for the scale was 0.79. SAS discriminated between DSM-IV NIP and non-NIP patients. The actometric findings did not correlate with SAS. ROC-analysis yielded a good case detection power for SAS mean score. The optimal threshold value of SAS mean score was between 0.65 and 0.95, i.e. clearly higher than previously suggested threshold value.
Conclusion
We conclude that SAS seems a reliable and valid instrument. The previously commonly used cut-off mean score of 0.3 has been too low resulting in low specificity, and we suggest a new cut-off value of 0.65, whereby specificity could be doubled without loosing sensitivity.
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Background
Reported prevalences for neuroleptic-induced parkinsonism (NIP) in schizophrenia patients are usually in the range 19% to 36% [1-5]. As NIP can severely impair activities of daily life, and it can be treated or at least alleviated, its diagnosis and assessment are an important focus in clinical practice. A reliable diagnosis of NIP is a demanding task [6]. NIP may be missed due to overlap with negative and depressive symptoms in treated schizophrenia patients [7]. Diagnostic and Statistical Manual, fourth edition, (DSM-IV) [8] criteria for NIP consist of parkinsonian tremor, muscular rigidity or akinesia, developing within a few weeks of starting or raising the dose of a neuroleptic medication (or after reducing a medication used to treat extrapyramidal symptoms). Like other motor adverse effects of antipsychotic drugs, the NIP is usually assessed by clinical observation or by rating scales, which are based on clinician's judgement. Movement disorders such as NIP, however, can be measured objectively by recording motor activity [9-12].
Simpson-Angus Scale (SAS) is a 10-item rating scale that has been used widely for assessment of NIP in both clinical practice and research settings [13]. It consists of one item measuring gait (hypokinesia), six items measuring rigidity and three items measuring glabella tap, tremor and salivation, respectively. It is an established rating scale, but some shortcomings have been suggested: the rigidity items may be given too much emphasis, the statistical properties have been studied insufficiently, and the instructions as well as the definitions are somewhat unclear [14]. Several items of the scale have failed to show appropriate interrater reliability or insufficient variability across elderly patients [15], and a modified version has been used to determine the prevalence of spontaneous parkinsonism and the incidence of NIP in this population [16].
According to our recent study [17] there was a discrepancy between SAS and DSM-IV based NIP prevalence estimates. We suggested that the commonly used cut-off point of 0.3 mean SAS score was too low in a naturalistic clinical population [17].
Accelerometric methods have been developed to identify and monitor motor NIP symptoms, such as tremor [18,19] and hypokinesia [20]. A standardized actometric method has been developed for the assessment of neuroleptic-induced akathisia (NIA) [21]. This method discriminated pure NIA patients from healthy controls and from themselves in remission phase with no overlap [21]. In the current clinical population (including patients with NIP and tardive dyskinesia in addition to NIA), however, the method evidenced less diagnostic power [22]. NIP symptoms may have confounded these actometric findings.
The discrepancy between SAS and DSM-IV based NIP prevalence estimates as well as other above mentioned shortcomings suggest that SAS needs an evaluation as a method to assess NIP severity and to find reliably NIP cases.
Our aims were to check the internal consistency of SAS, improve the convergence between DSM IV and SAS based NIP case finding, and to evaluate how well the scale measures objective motor symptoms verified by actometry.
Methods
We recruited 99 chronic schizophrenic institutionalized adult patients from a state nursing home in central Estonia [17]. Inclusion criteria were DSM-IV diagnosis of schizophrenia or schizoaffective disorder, stable antipsychotic medication (for at least one month), and age of 18–65 years. Diagnosis was made using a semi-structured interview according to DSM-IV criteria for schizophrenia by a psychiatrist (SJ) and medical records. Patients with severe somatic illness or neurological illness were excluded. Written informed consent was obtained from the subjects and the study was approved by the Ethics Review Committee on Human Research of the University of Tartu. Data were collected from 29.10.2001 to 27.03.2002.
An experienced clinician (SJ) assessed all the subjects to identify NIP cases in accordance with DSM-IV. The DSM-IV diagnostic criteria for other neuroleptic-induced movement disorders (NIMD) were also checked because of frequent comorbidity and common aetiology. Clinical NIP symptoms were assessed by SAS and the motor activity during rest was measured by actometry. Each item of the 10-item SAS is rated on a 5-point scale (0–4), and the mean score is obtained by adding the items and dividing by 10 [13]. Neuroleptic-induced akathisia and tardive dyskinesia were rated by Barnes Akathisia Rating Scale (BARS) [23] and Abnormal Involuntary Movement Scale (AIMS) [24].
The actometric recording was performed during sitting in a standardized clinical interview for 30 minutes, a method described previously as measuring "controlled rest activity" [19,21]. Controlled rest activity is a parameter of motor activity in a situation where sitting still is adequate and expected, but not instructed or required. The actometers (PAM3, Individual Monitoring Systems, Baltimore, USA) were attached to the ankles of the subjects to measure lower limb motor activity. Actometers are wireless, computerized movement detectors of match-box-size, which do not influence normal moving of the patient.
Cronbach's α was assessed to evaluate the internal consistency of the scale. The correlations between the lower limb activity (the mean of right and left ankle movement indices) and individual item scores and mean SAS scores were analysed. Differences between the NIP and non-NIP, as well as the NIMD and the non-NIMD groups in the SAS mean score and lower limb activity were analysed. The performance of SAS mean score and individual item scores in case identification was evaluated by receiver operating characteristics (ROC) analyses against DSM-IV NIP diagnosis. Validity coefficients (specificity, sensitivity, positive and negative predictive value [PPV and NPV, respectively]) for different mean SAS score thresholds were calculated. To explore the discriminatory power of each single SAS item we performed ROC analyses for each item separately. We also explored the effect on the validity coefficients of merging the six rigidity items of SAS into one single item, to de-emphasise the influence of rigidity on the mean SAS score. The Spearman test was used to correlation analysis and the Mann-Whitney 2-tailed U-test for the comparison between two groups because of the non-normal distribution of the data. The software used in analyses was SPSS 11.0. [25].
Results
Of the 99 participants, 45 (45.5%) were male and 54 (54.5%) female. The mean age was 49.7 (SD 9.5) years. The mean continuous treatment in hospital or in nursing home was 13.6 (SD 9.0) years. Seventy-nine (79.8%) patients used conventional antipsychotics (70 on low-dose, and 9 on high-dose neuroleptics) and 20 (20.2%) used clozapine (one was receiving clozapine combined with sulpiride). Low-dose antipsychotics in this study were haloperidol, cyclopentixol, perphenazine and fluphenazine; high dose antipsychotics were chlorpromazine, thioridazine, levomepromazine, chlorprotixen and sulpiride. Sixteen (16.2%) patients were receiving combinations of typical antipsychotics (either predominantly low-dose [N = 10] or predominantly high-dose [N = 6] neuroleptic regimens), and 63 (63.6%) were receiving monotherapy (haloperidol: N = 29; zuclopenthixol: N = 28; perphenazine, chlorpromazine, or thioridazine: N = 6). No new atypical antipsychotics were used. The mean daily chlorpromazine equivalent conditions. The prevalence of any NIMD according to DSM-IV was 61.6% in the whole sample. Cronbach's α for SAS was 0.79. dose [26] was 328 (SD 221) mg. The prevalence of NIP according to DSM-IV criteria was 23.2%. Fourteen patients, all from non-NIP subgroup, used an anticholinergic drug (trihexyphenidyl). Only 10 of the 23 patients with NIP presented as pure NIP without comorbidity of other motor disorders. Among patients with NIP, 10 had comorbid akathisia and 6 tardive dyskinesia; three of them had all three The SAS mean score correlated significantly with age in our population (r = 0.203, p = 0.044).
Convergence of SAS and actometry to DSM-IV NIP diagnosis
The SAS mean score for DSM-IV NIP patients (1.24, SD = 0.44) was significantly higher from that (0.56, SD = 0.33) of non-NIP patients (U = -6.90, p = 0.000). The mean scores of each single SAS item are presented in Table 1. The mean scores of "glabella tap" and "salivation" items for NIP patients were not significantly higher from that of non-NIP patients. The SAS mean score for NIMD patients was significantly higher from that of non-NIMD patients (U=-5.77, p = 0.000).
Table 1 Mean scores of Simpson-Angus Scale (SAS) items in Neuroleptic-Induced Parkinsonism (NIP) group and non-NIP group.
SAS item NIP-group Non-NIP group
Gait 1.04 0.38
Arm dropping 1.43 0.59
Shoulder shaking 1.09 0.33
Elbow rigidity 1.83 0.47
Wrist rigidity 0.91 0.16
Leg pendulousness 0.91 0.28
Head dropping 1.48 0.66
Glabella tap 1.09 0.86
Tremor 1.78 1.14
Salivation 0.83 0.71
Mean of SAS items 1.24 0.56
Actometric data was missing for one male patient due to non-co-operation. The median lower limb activity for NIP patients was not significantly higher than that of non-NIP patients (U = -0.46, p = 0.643). The median lower limb activity for NIMD patients was significantly higher from that of non-NIMD patients (U=-2.66, p = 0.008).
Convergence of SAS to actometry
The SAS mean score did not correlate significantly with actometric lower limb activity either in the whole population (r = 0.04, p = 0.717), in the NIP group (r = -0.29, p = 0.192), or in the pure NIP subgroup (r = -0.21, p = 0.587). Even after a post-hoc analysis of co-variance in the whole population, where the effect of akathisia (BARS global score) and tardive dyskinesia (AIMS severity score) were controlled for, no significant correlation between SAS mean score and the lower limb activity could be found (r = 0.07, p = 0.494).
The tremor item of the SAS correlated significantly with the lower limb activity in the whole population (r = 0.25, p = 0.013) but not in the NIP population (r = 0.26, p = 0.248) or in the pure NIP subgroup (r = 0.51, p = 0.160). No correlation was evidenced between the hypokinesia item of the SAS and lower limb activity in the whole population (r = -0.07, p = 0.513) either in NIP population (r = -0.24, p = 0.290) or in pure NIP subgroup (r = -0.37, p = 0.797).
No correlation was evidenced between the mean of rigidity items of the SAS and lower limb activity in the whole population (r = -0.12, p = 0.256) either in NIP population (r = -0.37, p = 0.090) or in pure NIP subgroup (r = -0.30, p = 0.426).
NIP case finding by SAS
ROC-curve for screening performance of SAS mean score is presented in Fig 1. Area under the ROC-curve (AUC) for SAS mean score was 0.92 (CI = 0.87–0.97). AUC of the ROC curve for SAS elbow rigidity item was 0.93 (CI = 0.86 – 1.0). AUC for the other items was less than 0.82. AUC in ROC analyses may range from of 0.5 (no case finding power) to 1.0 (optimal case finding performance). The validity coefficients of the SAS mean score are presented in Table 2.
Figure 1 Receiver Operating Characteristic (ROC) curve for SAS mean score against DSM-IV defined Neuroleptic-Induced Parkinsonism (NIP).
Table 2 Validity coefficients of the Simpson-Angus Scale (SAS) mean score at different cutoff values. The optimal cut-off point range is presented in bold text.
SAS mean cut-off 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 1.05 1.15
Sensitivity 1.0 1.0 1.0 1.0 1.0 0.96 0.87 0.78 0.70 0.52
Specificity 0.17 0.36 0.45 0.49 0.62 0.74 0.86 0.86 0.89 0.93
Positive Predictive Value 0.27 0.32 0.35 0.37 0.44 0.96 0.65 0.64 0.67 0.71
Negative Predictive Value 1.0 1.0 1.0 1.0 1.0 0.98 0.96 0.93 0.91 0.87
ROC-curve for screening performance of SAS mean with single averaged rigidity item was clearly inferior to the original SAS mean curve with AUC of 0.80 (CI = 0.70–0.89).
The screening performances of the individual SAS items for NIP case finding are shown at Table 3.
Table 3 Area under the ROC curve of Simpson-Angus Scale (SAS) parameters against DSM-IV diagnosis of Neuroleptic-Induced Parkinsonism.
SAS item Area under the ROC curve
Gait 0.71
Arm dropping 0.79
Shoulder shaking 0.81
Elbow rigidity 0.93
Wrist rigidity 0.75
Leg pendulousness 0.73
Head dropping 0.75
Glabella tap 0.57
Tremor 0.66
Salivation 0.53
Mean of rigidity items 0.92
Mean of mean rigidity items and other SAS items 0.80
Mean of SAS items 0.92
As SAS elbow rigidity item had case finding power similar to SAS mean score, we calculated optimal cut-off for this item. Cut-off threshold of 1.5, with sensitivity of 0.826 and specificity of 0.974, was superior to cut-off threshold of 0.5 with sensitivity of 0.957 and specificity 0.553.
Discussion
Our study aimed to evaluate some of the characteristics of the SAS and its utility for identifying and measuring NIP in a naturalistic schizophrenia sample. The internal consistency of SAS was satisfactory, which suggests sufficient reliability for the scale. We compared the SAS with the DSM-IV to assess its discriminant validity and evaluate it in detecting NIP cases. The comparison with objective movement assessment aimed to estimate the concurrent validity of SAS in NIP severity measurement.
As expected, the SAS had discriminant validity for a clinical diagnosis of NIMD. SAS mean score discriminated NIMD patients well from those without NIMD, and more specifically, also NIP patients from other patients. Actometry discriminated NIMD patients from non-NIMD patients, but did not identify DSM-IV NIP patients.
According to ROC analysis the SAS had good case finding properties converging with the DSM IV NIP diagnosis. In our population the commonly used threshold 0.3 was inappropriate: according to our results the optimal cut-off point should be between 0.65 – 0.95 depending on the emphasis in the trade-off between sensitivity and specificity. We suggest that the new cut-off value for screening NIP could be 0.65, whereby specificity could be doubled without loosing any sensitivity. To be useful for diagnostic purposes a combination of high specificity and high positive predictive value (PPV) is reached at cut-off – 0.75 [27]. To answer to criticism about the overrepresentation of rigidity items, we averaged the six items into one item. This procedure worsened the NIP case detection capacity of the SAS.
Using the single elbow rigidity item for case detection had the same (or slightly better) case detection capacity as the SAS mean score. This finding supports the use of elbow rigidity testing when assessing parkinsonism in clinical settings, as cut-off value 0.5 has good sensitivity and specificity for DSM-IV NIP.
We found that SAS mean score did not correlate with actometric lower limb activity, and hypokinesia observed during gait item of SAS did not correlate with actometric motor activity during the 30-minute recording. There are a few explanations for that:
First, actometry measures only the productive motor dimension of the parkinsonian symptoms while SAS takes into account also rigidity, gait, salivation and glabella tap, with a clear emphasis on rigidity. Lack of correlation with actometric findings in NIP subgroup indicates that tremor may not be the core feature of NIP. This is also supported by the small AUC for the tremor item of SAS.
Secondly, we used lower-limb actometry while the clinical assessment by SAS and DSM-IV considered predominantly upper limbs. Parkinsonism may be more symptomatic in upper limbs, and the upper limb disturbances may have influenced our SAS and DSM IV assessments more than lower limb disturbances. Our findings indicate that lower limb actometry is not suitable for diagnosing NIP.
Thirdly, diurnal naturalistic actometry may have more power in detecting hypokinesia.
Limitations
This study was limited to a few aspects of utility/validity of the SAS: internal consistency, convergence to DSM-IV NIP diagnosis and convergence to objectively measured motor activity. Many aspects of the scale's reliability (e.g. test-retest and inter-rater reliability) and validity (e.g. construct) were not evaluated.
DSM-IV was used as a standard in this study, but there is not much data available on the validity of NIP criteria of the DSM-IV. A better golden standard in this study would probably have been an expert-consensus diagnosis. Furthermore, as there was only one rater for the scales, a cross-scale contamination issue might have occurred.
It is known that with age the prevalence of spontaneous NIMD rises. Our material did not allow a thorough examination of the issue, but age correlated with SAS mean score in our sample.
The measurement of motor activity here was purely quantitative; we did not assess the patterns of the disordered movements.
Conclusion
As a conclusion, SAS seems be a reliable and a valid instrument. It performs well and similarly to DSM-IV in NIP case detection. The optimal SAS mean score cut-off value in a naturalistic population of neuroleptic-treated schizophrenia patients is higher than the commonly used 0.3. We suggest that the new cut-off value for screening NIP could be 0.65, whereby specificity could be doubled without loosing sensitivity. Combining SAS rigidity items does not seem to improve the performance of the scale.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
SJ contributed to the study design, collected the data, contributed to the analyses and data interpretation, made the literature search and was responsible for manuscript preparation with MMH.
MMH contributed to study design, made most of the analyses and data interpretation, and was equally responsible for manuscript preparation with SJ.
KT contributed to study design, statistical analysis, data interpretation and manuscript preparation.
KW supervised the study design and contributed to statistical analysis, data interpretation and preparation of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The study was supported by a grant from Finska Läkaresällskapet (Finnish Medical Society) (KW) and the Eli Lilly (Suisse) S.A. Estonian Affiliate (SJ).
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| 15774006 | PMC555761 | CC BY | 2021-01-04 16:28:53 | no | BMC Neurol. 2005 Mar 17; 5:5 | utf-8 | BMC Neurol | 2,005 | 10.1186/1471-2377-5-5 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-4-151573332010.1186/1475-2875-4-15OpinionDo malaria ookinete surface proteins P25 and P28 mediate parasite entry into mosquito midgut epithelial cells? Baton Luke A [email protected] Lisa C [email protected] Division of Infection and Immunity, Institute of Biomedical and Life Sciences, Joseph Black Building, University of Glasgow, Glasgow, G12 8QQ, UK2005 25 2 2005 4 15 15 1 1 2005 25 2 2005 Copyright © 2005 Baton and Ranford-Cartwright; licensee BioMed Central Ltd.2005Baton and Ranford-Cartwright; 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
P25 and P28 are related ookinete surface proteins highly conserved throughout the Plasmodium genus that are under consideration as candidates for inclusion in transmission-blocking vaccines. Previous research using transgenic rodent malaria parasites lacking P25 and P28 has demonstrated that these proteins have multiple partially redundant functions during parasite infection of the mosquito vector, including an undefined role in ookinete traversal of the mosquito midgut epithelium, and it has been suggested that, unlike wild-type parasites, Dko P25/P28 parasites migrate across the midgut epithelium via an intercellular, rather than intracellular, route.
Presentation of the hypothesis
This paper presents an alternative interpretation for the previous observations of Dko P25/P28 parasites, based upon a recently published model of the route of ookinete invasion across the midgut epithelium. This model claims ookinete invasion is intracellular, with entry occurring through the lateral apical plasma membrane of midgut epithelial cells, and is associated with significant invagination of the midgut epithelium localised at the site of parasite penetration. Following this model, it is hypothesized that: (1) a sub-population of Dko P25/P28 ookinetes invaginate, but do not penetrate, the apical surface of the midgut epithelium and thus remain within the midgut lumen; and (2) another sub-population of Dko P25/P28 parasites successfully enters and migrates across the midgut epithelium via an intracellular route similar to wild-type parasites and subsequently develops into oocysts.
Testing the hypothesis
These hypotheses are tested by showing how they can account for previously published observations and incorporate them into a coherent and consistent explanatory framework. Based upon these hypotheses, several quantitative predictions are made, which can be experimentally tested, about the relationship between the densities of invading Dko P25/P28 ookinetes in different regions of the midgut epithelium and the number of oocyst stage parasites to which these mutant ookinetes give rise.
Implications of the hypothesis
The recently published model of ookinete invasion implies that Dko P25/P28 parasites are greatly, although not completely, impaired in their ability to enter the midgut epithelium. Therefore, P25 and/or P28 have a novel, previously unrecognized, function in mediating ookinete entry into midgut epithelial cells, suggesting that one mode of action of transmission-blocking antibodies to these ookinete surface proteins is to inhibit this function.
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Background
P25 and P28 are related major ookinete surface proteins under consideration as candidates for inclusion in transmission-blocking vaccines [1-4]. Consequently, the expression [5-18], localisation [8,12,17-24] and function [21,25-29] of these molecules, together with the effect on parasite development of specific antibodies against them [6,8,21,22,24,30-35], have been extensively studied in a range of malaria parasite species.
P25 and P28 are structurally similar proteins, highly conserved throughout the Plasmodium genus [11,12,31,35-43], which contain four epidermal growth factor-like domains [36], putatively involved in cell-cell and/or cell-matrix interactions [21,25,26,28,29], that are expressed throughout the early life-cycle stages of the malaria parasite within the mosquito vector – from the macrogamete through to the oocyst stage [8,12,17-24]. P25 and P28 are located on the parasite surface, from which they are shed during ookinete gliding motility and traversal of the mosquito midgut epithelium [19-21,44,45]. The conservation of sequence, expression and location suggests that P25 and P28 have functionally equivalent roles in diverse malaria parasite species.
Previous research using transgenic Plasmodium berghei rodent malaria parasites lacking P25 and P28 demonstrated that these proteins have multiple and partially redundant functions during parasite infection of the mosquito vector [26,27]. Although Dko P25/P28 P. berghei parasites exhibit greatly reduced levels of oocyst infection compared to wild-type or Sko P25/P28 parasites, ookinetes lacking both P25 and P28 are still able to cross the midgut epithelium and establish oocyst infections [27]. Wild-type P. berghei ookinetes migrate intracellularly through the midgut epithelium causing significant damage to invaded midgut epithelial cells [44-48], which subsequently exhibit distinct morphological abnormalities [44-48], including loss of microvilli [44,45], protrusion into the midgut lumen [44,45,48] and up-regulation of molecules implicated in mosquito immune responses such as NOS [44,49] and SRPN10 [45,50]. Furthermore, P28 is found on the apical surface, and within the cytoplasm, of these abnormal midgut epithelial cells suggesting release/secretion from penetrating parasites during their intracellular migration [44,45]. Dko P25/P28 ookinetes have also been found deep within the midgut epithelium [27,45]. Initially, these parasites were suggested to be retarded in their transit through the midgut epithelium and killed by the epithelial cell defence reactions triggered by wild-type parasites [27]. Recently, however, Dko P25/P28 parasites were observed apparently deep within the midgut epithelium between morphologically normal midgut epithelial cells [45]. These midgut epithelial cells did not exhibit the abnormal characteristics typically associated with invasion by wild-type ookinetes, such as protrusion into the midgut lumen and up-regulation of SRPN10 [44,45,48]. Consequently, these Dko P25/P28 parasites were proposed to be migrating through the midgut epithelium via a solely intercellular route [45]. However, a recently published model of ookinete invasion across the mosquito midgut epithelium [51] suggests an alternative interpretation for the previously published observations of Dko P25/P28 parasites.
Presentation of the hypothesis
A unified model of the route of ookinete invasion across the mosquito midgut epithelium
The route of ookinete migration across the midgut epithelium of the mosquito vector has long been controversial [51]. The major argument in the literature has been whether ookinete invasion is either solely intercellular between, or intracellular through, midgut epithelial cells [51]. Recently, an attempt has been made to unify the apparently conflicting literature and integrate it with other recent observations [44,47,52] in order to provide a single general model of the route of ookinete invasion across the midgut epithelium applicable to diverse malaria parasite and mosquito vector species [51]. Subsequent observations of ookinete invasion of the midgut epithelium in vivo support this model [48]. According to the model, ookinete entry into the midgut epithelium is initially intracellular, occurring through the lateral apical plasma membrane of midgut epithelial cells (Figure 1) [47,51,52]. Significantly, ookinete entry into midgut epithelial cells is often associated with substantial local invagination of the midgut epithelium [52], an observation supported by re-interpretation of previously published images (Figure 2 in Ref. [19] and Figure 5 in Ref. [53]). Ookinetes pass intracellularly through one or more midgut epithelial cells, causing significant damage similar to that described for wild-type P. berghei ookinetes [44-48,51,52,54,55]. Subsequently, ookinetes exit invaded epithelial cells into the basolateral extracellular space between adjacent midgut epithelial cells [48,52,56], migrate intercellularly to the basal surface of the midgut epithelium and transform into oocyst stage parasites [51].
Figure 1 A general model of ookinete entry into the mosquito midgut epithelium. (a) Ookinetes (shown in green) enter the apical surface of the midgut epithelium, through the microvillar brush border (MV), where the lateral membranes (LM) of adjacent epithelial cells (EC) converge [47,51,52]. (b-c) Ookinete movement into the midgut epithelium causes substantial localized invagination of the latter (indicated by small blue arrows) [52,57]. (d) Ookinetes subsequently enter midgut epithelial cells through the lateral apical membrane immediately adjacent to the site where the intercellular junctions (IJ) begin [47,51,52]. (e) The ookinete proceeds intracellularly towards the basal membrane (BM) of the invaded midgut epithelial cell which exhibits morphological abnormalities including protrusion (indicated by large black arrow) into the midgut lumen (LUM) [44–48,52,54,55].
Figure 2 Dko P25/P28 P. berghei ookinete invasion of the midgut epithelium. The unified model of the route of ookinete invasion across the mosquito midgut epithelium (Figure 1) [51] implies that there are two sub-populations of Dko P25/28 parasites: (1) a major sub-population of Dko P25/28 ookinetes (shown in green) unable to penetrate midgut epithelial cells, which remain extracellular within the midgut lumen, embedded against the invaginated apical surface of the midgut epithelium (indicated by small blue arrow); and (2) a minor sub-population of Dko P25/28 ookinetes able to penetrate midgut epithelial cells, causing activation of mosquito immune responses and protrusion of invaded midgut cells, in a manner similar to wild-type parasites. Whether the latter parasites migrate through multiple adjacent midgut epithelial cells (as shown) is uncertain.
Significance of the unified model for understanding Dko P25/P28 P. berghei ookinete invasion
Following the model of ookinete invasion of the midgut epithelium outlined above (and Figure 1), two hypotheses about Dko P25/P28 P. berghei parasite infection of the mosquito vector are proposed. First, some Dko P25/P28 ookinetes invaginate, but are unable to penetrate, the apical surface of the midgut epithelium. Second, other Dko P25/P28 parasites are able to successfully enter and migrate across the midgut epithelium via an intracellular route, in a manner similar to wild-type parasites.
Testing the hypothesis
Re-interpretation of previously published observations of Dko P25/P25 P. berghei parasites
If the unified model of ookinete invasion is correct, the Dko P25/P28 P. berghei ookinetes observed deep within the midgut epithelium between morphologically normal midgut epithelial cells are actually extracellular parasites, outside the midgut epithelium and within the midgut lumen, attempting to enter the lateral apical membrane of midgut epithelial cells. The significant invagination of the midgut epithelium that occurs during parasite entry into midgut epithelial cells creates the appearance that these ookinetes are in intercellular locations within the midgut epithelium. This would be similar to the phenotype recently reported for P. berghei ookinetes in which the maop gene has been knocked out [57]. Ookinetes lacking MAOP are unable to rupture the apical plasma membrane of midgut epithelial cells [57]. Consequently, although MAOP-deficient ookinetes invaginate the midgut epithelium, these parasites are unable to enter into midgut epithelial cells and remain extracellular embedded against the apical surface of the midgut epithelium [57].
The actual extracellular location of Dko P25/P28 ookinetes apparently "within" the midgut epithelium is also suggested by the presence of unmelanized parasites in a refractory line of Anopheles gambiae mosquitoes [27]. Unmelanized parasites were observed apparently deep within the midgut epithelium exhibiting an abnormal gelatinous appearance suggested to result from exposure to either epithelial cell defence reactions or an early stage of the melanisation reaction [27]. However, as mentioned above, most Dko P25/P28 parasites do not appear to induce the epithelial cell defence reactions triggered by invading wild-type parasites [45]. Furthermore, the refractory An. gambiae line melanises wild-type parasites after their passage through midgut epithelial cells into the basolateral extracellular space between adjacent midgut epithelial cells [55,58,59]. Consequently, an alternative interpretation is that Dko P25/P28 ookinetes are unmelanized because of their extracellular location against the apical surface of the midgut epithelium, which fails to expose them to either epithelial cell or melanisation immune responses triggered by wild-type parasites. The gelatinous appearance of unmelanized parasites could be explained by prolonged exposure of ookinetes delayed in the process of midgut epithelium entry to the environment of the midgut lumen; for example, prolonged exposure to the mosquito digestive proteases secreted into the midgut lumen. Dko P25/P28 parasites have been shown to be significantly more susceptible to protease digestion in vitro than wild-type parasites [27].
However, there is also evidence that some Dko P25/P28 ookinetes do enter the midgut epithelium. A minority of Dko P25/P28 ookinetes are found within midgut epithelial cells, which exhibit the re-distribution and up-regulation of SRPN10 associated with invasion by wild-type parasites [45]. Some Dko P25/P28 parasites are also melanized in the refractory An. gambiae line [27] implying entry into and passage through midgut epithelial cells to the basal surface of the midgut epithelium. Further, Dko P25/P28 parasites induce transcriptional up-regulation of mosquito immune response genes, defensin and GNBP, associated with midgut invasion by wild-type parasites [27]. These immune response genes are not induced by transgenic ctrp-disrupted P. berghei parasites that are unable to invade midgut epithelial cells [27,60]. Again, this implies that at least some Dko P25/P28 parasites successfully invade the midgut epithelium and trigger mosquito immune responses.
Experimentally testable predictions of our interpretation
There are several experimentally testable predictions that follow from the alternative interpretation for the previous observations of Dko P25/P28 P. berghei ookinete invasion of the midgut epithelium outlined above.
First, all melanized Dko P25/P28 parasites in the refractory An. gambiae line should be associated with morphologically abnormal midgut epithelial cells – cells through which these parasites have migrated intracellularly – exhibiting protrusion into the midgut lumen, and up-regulation of NOS and SRPN10. In contrast, unmelanized parasites should not be associated with any morphologically abnormal midgut epithelial cells, as these parasites have failed to enter the midgut epithelium and invade midgut epithelial cells. Unmelanized parasites are, however, expected to reside deep "within" the midgut epithelium in apparently intercellular locations between morphologically normal midgut epithelial cells (assuming that ookinetes on the apical surface of the midgut epithelium cannot be melanized). If Dko P25/P28 ookinetes do migrate across the midgut epithelium via a solely intercellular route there is no known reason why these parasites should not also be melanized in the basal region of the midgut epithelium. Consequently, if solely intercellular migration does occur melanized parasites should be found that are not associated with any morphologically abnormal midgut epithelial cells.
Second, there should be a quantitative relationship between the density of Dko P25/P28 ookinetes associated with morphologically abnormal midgut epithelial cells and the number of oocysts that subsequently develop on the basal surface of the midgut epithelium. Specifically, the number of oocyst stage parasites should be equal to or less than the number of Dko P25/P28 ookinetes associated with morphologically abnormal midgut epithelial cells, as only ookinetes migrating intracellularly are predicted to become oocysts. The Dko P25/P28 ookinetes located between morphologically normal midgut epithelial cells are not expected to transform into oocysts, as these parasites do not enter, and hence cross, the midgut epithelium. The number of ookinetes apparently migrating via a solely intercellular route greatly exceeds the number of intracellular ookinetes [45]. Consequently, if Dko P25/P28 ookinetes do migrate across the midgut epithelium via a solely intercellular route, the number of oocysts should greatly exceed the number of ookinetes migrating via an intracellular route (i.e. those associated with morphologically abnormal midgut epithelial cells).
Implications of the hypothesis
The re-interpretation presented here of previously published work on Dko P25/P28 P. berghei parasites implies that there are two sub-populations of Dko P25/P28 ookinetes, neither of which migrate across the midgut epithelium via a solely intercellular route (Figure 2). A major sub-population of Dko P25/28 ookinetes is unable to penetrate into midgut epithelial cells and remains extracellular within the midgut lumen, outside but embedded against the invaginated apical surface of the midgut epithelium. Consequently, these parasites appear to be in intercellular locations deep within the midgut epithelium, between the lateral membranes of adjacent midgut epithelial cells. It is proposed that these parasites fail to elicit mosquito immune responses triggered by intracellularly invading parasites, are not melanized in refractory An. gambiae and do not give rise to oocyst parasite stages. These parasites remain surrounded by morphologically normal midgut epithelial cells, which do not exhibit the morphological abnormalities associated with parasites invading intracellularly [45]. A second minor sub-population of Dko P25/28 ookinetes is able to penetrate into midgut epithelial cells, in a manner similar to wild-type parasites. These parasites are proposed to elicit mosquito immune responses, including up-regulation of defensin [27], GNBP [27], NOS and SRPN10 [45], undergo melanization in refractory An. gambiae [27], and form the few oocysts observed in Dko P25/P28 infections [27]. Accordingly, the latter parasite sub-population should be associated with midgut epithelial cells exhibiting morphological abnormalities associated with intracellular invasion by wild-type parasites [45]. However, if loss of P25 and/or P28 prevents entry into midgut epithelial cells, intracellular movement between multiple adjacent epithelial cells may also be inhibited in Dko P25/P28 parasites.
The reason for the existence of the two distinct sub-populations of Dko P25/P28 P. berghei ookinetes is unknown. One explanation is that loss of P25 and/or P28 impedes, but does not entirely prevent, penetration of the apical plasma membrane of midgut epithelial cells. Consequently, the entry of Dko P25/P28 ookinetes into the midgut epithelium may be protracted, prolonging the period of exposure to the hostile environment of the midgut lumen, which results in the death of most parasites before completion of midgut epithelial cell penetration. This interpretation is consistent with the observation of lysed Dko P25/P28 parasites on the luminal side of the midgut epithelium [45].
In summary, the unified model of the route of ookinete invasion across the mosquito midgut epithelium suggests a novel, previously unrecognized, function for P25 and/or P28 in mediating ookinete entry into the midgut epithelium. Specifically, the interpretation presented implies that the loss of P25 and/or P28 greatly impairs, but does not entirely abolish, ookinete entry into midgut epithelial cells and probably has relatively little effect on the ability of ookinetes to traverse through the cytoplasm of midgut epithelial cells. A role for P28 in parasite entry into the midgut epithelium is suggested by the deposition of this molecule at the site of ookinete penetration into midgut epithelial cells [44,45]. This interpretation contrasts with the original studies of Dko P25/P28 parasites, which concluded that P25 and P28 do not play a critical role in recognition, attachment or penetration of the luminal surface of the mosquito midgut epithelium [26,27] and suggests that one mode of action of transmission-blocking antibodies to these ookinete surface proteins is to inhibit parasite entry into midgut epithelial cells, as previously hypothesized [8].
List of Abbreviations
CTRP = circumsporozoite and thrombospondin-related anonymous protein-related protein; Dko P25/P28 = double knockout of P25 and P28; GNBP = gram-negative binding protein; MAOP = membrane-attack ookinete protein; NOS = nitric oxide synthase; Sko P25/P28 = single knockout of either P25 or P28; SRPN10 = serine protease inhibitor 10.
Authors' contributions
LAB wrote the manuscript and prepared the figures. LRC revised the manuscript. Both authors read and approved the final version of the manuscript.
Acknowledgements
We acknowledge our debt to the many researchers whose work has contributed to our understanding of the P25 and P28 ookinete surface molecules, especially that of Tomas et al [27] and Danielli et al [45], without whom the hypotheses presented within this paper could not have been formulated.
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| 15733320 | PMC555762 | CC BY | 2021-01-04 16:37:30 | no | Malar J. 2005 Feb 25; 4:15 | utf-8 | Malar J | 2,005 | 10.1186/1475-2875-4-15 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central London 1465-9921-6-241574828110.1186/1465-9921-6-24ResearchInhibition of breathing after surfactant depletion is achieved at a higher arterial PCO2 during ventilation with liquid than with gas Rieger-Fackeldey Esther [email protected] Richard [email protected] Anders [email protected] Andreas [email protected] Gunnar [email protected] Department of Women's and Children's Health, Section for Pediatrics, Uppsala University, Uppsala, Sweden2 Department of Neuroscience, Division of Physiology, Uppsala University, Uppsala, Sweden3 Department of Obstetrics and Gynecology, Division of Neonatology, Klinikum Grosshadern, Ludwig Maximilian University, Munich, Germany2005 4 3 2005 6 1 24 24 23 12 2004 4 3 2005 Copyright © 2005 Rieger-Fackeldey et al; licensee BioMed Central Ltd.2005Rieger-Fackeldey 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
Inhibition of phrenic nerve activity (PNA) can be achieved when alveolar ventilation is adequate and when stretching of lung tissue stimulates mechanoreceptors to inhibit inspiratory activity. During mechanical ventilation under different lung conditions, inhibition of PNA can provide a physiological setting at which ventilatory parameters can be compared and related to arterial blood gases and pH.
Objective
To study lung mechanics and gas exchange at inhibition of PNA during controlled gas ventilation (GV) and during partial liquid ventilation (PLV) before and after lung lavage.
Methods
Nine anaesthetised, mechanically ventilated young cats (age 3.8 ± 0.5 months, weight 2.3 ± 0.1 kg) (mean ± SD) were studied with stepwise increases in peak inspiratory pressure (PIP) until total inhibition of PNA was attained before lavage (with GV) and after lavage (GV and PLV). Tidal volume (Vt), PIP, oesophageal pressure and arterial blood gases were measured at inhibition of PNA. One way repeated measures analysis of variance and Student Newman Keuls-tests were used for statistical analysis.
Results
During GV, inhibition of PNA occurred at lower PIP, transpulmonary pressure (Ptp) and Vt before than after lung lavage. After lavage, inhibition of inspiratory activity was achieved at the same PIP, Ptp and Vt during GV and PLV, but occurred at a higher PaCO2 during PLV. After lavage compliance at inhibition was almost the same during GV and PLV and resistance was lower during GV than during PLV.
Conclusion
Inhibition of inspiratory activity occurs at a higher PaCO2 during PLV than during GV in cats with surfactant-depleted lungs. This could indicate that PLV induces better recruitment of mechanoreceptors than GV.
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Background
Partial liquid ventilation (PLV) combines liquid ventilation and gas ventilation (GV). Perfluorocarbon is administered to the trachea in a volume equivalent to the pulmonary functional residual capacity, and ventilation is maintained with conventional gas ventilation of the liquid-filled lung [1]. The improvement of gas exchange during PLV is mainly due to recruitment of collapsed alveoli [2], decreased physiological shunting and increased compliance [3].
During breathing of gas the rate and depth of breathing is influenced by mechanoreceptors in the lung [4-6], and by peripheral and central chemoreceptors, which modulate the phrenic motoneurone output representing central inspiratory activity [7]. An increase in tidal volume and flow rate during mechanical ventilation with gas results in a decrease in magnitude or duration of the phrenic nerve signal [8,9], with absence of that response after vagotomy [8]. It has been shown that inhibition of inspiratory activity can be achieved with air with high frequency positive pressure ventilation (HFPPV) [10] at ventilatory frequencies of 60–100/min and with different positive end-expiratory pressures (PEEP) and insufflation periods in animals [11] and in humans [12] at normo-ventilation. To achieve inhibition of phrenic nerve activity (PNA) during ventilation with air at lower ventilatory frequencies than 60, a lower arterial PCO2 and a higher pH will be needed [11].
No studies have been presented concerning PNA during PLV, but it has been shown in studies of animals that spontaneous breathing can take place during PLV with beneficial physiological effects [13,14]. Inhibition of PNA can thus provide a physiological setting at which ventilatory pressures, volumes and arterial blood gases can be compared during GV and during PLV in surfactant-depleted animals.
This study was therefore undertaken to determine whether inhibition of PNA can be achieved at the same airway or transpulmonary pressures during GV and PLV and to find out at what levels of arterial blood gases and pH inhibition occurs with these modes of ventilation in cats with healthy and surfactant-depleted lungs.
Methods
Animal Preparation
Juvenile cats (n = 9; mean ± SD; age 3.8 ± 0.5 months, weight 2.3 ± 0.1 kg) were anaesthetised with chloroform, placed in a supine position and endotracheally intubated (tube 4.0 mm inner diameter). The tube was then connected to an infant ventilator (Stephanie®, F. Stephan Biomedical Inc., Gackenbach, Germany) and the animal was placed on assist control (A/C) ventilation during the surgical procedures with the following settings: peak inspiratory pressure (PIP) 1.0 kPa, positive end-expiratory pressure (PEEP) 0.3 kPa, inspiratory time (Ti) 1 sec, respiratory rate (RR) 30/min.
The right femoral vein and artery were dissected and catheters were inserted with the tip of each catheter placed in the thorax close to the heart. Anaesthesia was continued with 0.72% α-chloralose (Sigma-Aldrich Chemie GmbH, Steinheim, Germany) (50 mg/kg) and maintained at regular intervals via the venous line. A continuous infusion of 10% glucose (2/3) and 5% 0.6 M sodium bicarbonate (1/3) was given at a rate of 6.4 mL/kg/h (7.15 mg/kg/min of glucose) through the venous catheter throughout the experiment. The arterial line was used for continuous monitoring of blood pressure and intermittent determination of blood gases (Acid-Base Laboratory ABL 505®, Radiometer Corp., Copenhagen, Denmark). The cat's core body temperature, measured as deep rectal temperature, was maintained at 38°C by a heating blanket and an overhead warmer.
A pretracheal midline incision was performed for preparation of the trachea, the oesophagus and both phrenic nerves. A tight ligature was tied around the trachea in order to prevent air leakage around the tube. An 8 French catheter with an oesophageal balloon (40 × 7.5 mm; flat frequency response up to 5 Hz) was inserted into the distal part of the oesophagus and a ligature was softly tied around the oesophagus to avoid air entrance into the stomach [15]. Both phrenic nerves were exposed and the connective sheath was removed. The intact right phrenic nerve was then placed on two platinum electrodes. For reasons of isolation the phrenic nerves, the electrodes and the dissected area were submerged in mineral oil [16].
Measurements and data collection
Airflow was measured by a sensor placed between the endotracheal tube connector and the Y connector of the tubing circuit of the Stephanie® infant ventilator. This sensor is a pneumotachometer with the dynamic properties of an original Fleisch 00 pneumotachograph, but with less dead space (0.6 ml) and resistance (1.1 kPa/l/s at 5 L/min) [17]. Airflow was calibrated with a precision flowmeter (Timeter RT 200 ®, Timeter Instrument Corporation, Lancaster, PA, USA). Airway pressure (Paw) was measured at the connector of the endotracheal tube. Oesophageal pressure (Poes) was recorded from the oesophageal balloon catheter by a pressure transducer (Druck Ltd. Transducer, Leicestershire, UK) and, like Paw, was calibrated with a water manometer. Arterial blood pressure and heart rate were measured using the same type of transducer (Druck Ltd. Transducer, Leicestershire, UK) connected to the arterial catheter with the tip of the catheter at the same level as the transducer. Continuous recordings of arterial blood pressure and heart rate were made with a polygraph recorder (Recorder 330P, Hellige AG, Freiburg, Germany).
PNA was amplified, filtered and rectified with a Neurolog system® (Digitimer Research Instrumentation Inc., Welwyn Garden City, Hertforshire, UK; preamplifier NL 103, AC-amplifier NL 105, filters NL 115). The rectified nerve signal was fed to a spike trigger to produce spikes of uniform amplitude (Digitimer 130® and Spike Trigger NL 200, Digitimer Research Instrumentation Inc., Welwyn Garden City, Hertforshire, UK) and subsequently integrated by a resistance-capacitance low-pass filter with a leak (time constant 250 ms), providing a moving time average of PNA. Monitoring of the signals was achieved by means of an oscilloscope (Tektronix Inc., Portland, Oregon, USA).
Signals of airflow and Paw were obtained directly from the analogue outlets of the ventilator. Together with signals of Poes and the PNA they were transferred to an analogue-digital converter and recorded on disk at a sampling rate of 10 kHz per channel by a data acquisition system (Windaq Pro+®, Dataq Instruments Inc., Akron, OH, USA). Compliance and resistance values were given by the ventilator.
Protocol
The cats were kept ventilated with air using A/C ventilation and the ventilation was adjusted so that normal arterial blood gases were achieved. The cats were then treated with endotracheal continuous positive airway pressure with 0.3 kPa PEEP in order to monitor and record the spontaneous breathing activity of each cat. Pressure-controlled mechanical ventilation with sinusoidal inspiratory waveform was then initiated with the following settings: RR 60/min; Ti 0.33 sec; PIP 0.8 to 1.0 kPa using a PEEP of 0.5 kPa. PIP was adjusted so that blood gas values were in a normal range. The fraction of inspired oxygen was kept at 0.21. PIP was then gradually increased until rhythmic PNA disappeared. Three breaths after inhibition of PNA, data from 20 consecutive breaths were recorded and arterial blood gases were analysed.
Thereafter lung lavage was performed by filling the lungs with warmed saline solution (37.5°C, 30 mL/kg) through a funnel connected to the endotracheal tube. Very gentle chest compressions were performed to allow the saline to be well distributed, before it was removed by suctioning. This procedure was repeated 7 to 8 times and mechanical ventilation was provided in between the lavage procedures. After a 30-minute period of stabilisation on mechanical ventilation (PIP/PEEP 3.0/0.5 kPa, RR 60/min, Ti 0.33 sec, FiO2 1.0), ventilation was increased until PNA was inhibited. Airway pressures were then recorded and arterial blood gases and pH were measured again.
In the next step a bolus of 30 ml/kg prewarmed (38°C) perfluorocarbon (PFC) (Perfluorodecaline®, F2 chemicals Ltd, Preston, Lancashire, UK) was instilled into the endotracheal tube via an adapter with a side port. Instillation of PFC into the lung was performed within 10 minutes during pressure-controlled ventilation (PIP/PEEP 3.2/0.5 kPa, RR 60/min, Ti 0.33, FiO2 1.0). Sufficient filling was ascertained by disconnecting the endotracheal tube from the ventilator circuit at the end of the filling procedure and observing to see that a meniscus was present in the endotracheal tube at end-expiration. If no meniscus could be observed prior to recording, additional PFC was instilled. After a stabilisation period of 10 minutes, the cats were studied with the same protocol during PLV as during GV, but with an FiO2 of 1.0 and a PIP adjusted to blood gases in the normal range.
Data on PNA could be recorded and the whole protocol could be completed in all nine cats. Lavage and instillation of PFC were well tolerated, with no coughing or gasping. No bradycardia or arterial hypotension occurred during the procedure.
The experiments were performed at the Biomedical Centre of Uppsala University and were approved by the Uppsala University Animal Research Ethics Board (No. C224 / 0).
Data Analysis and Statistics
Windaq Playback® Software (Dataq Instruments, Inc., Akron, OH, USA) was used to review the recorded signals. Analysis was done by means of Windaq Playback® and Excel® (Office 2000, Microsoft Corporation, USA). For statistical evaluation, Sigmastat® (SPSS Inc, IL, USA) was used.
The amplitude of the integrated PNA was monitored and inhibition of spontaneous breathing activity was defined as total disappearance of PNA, i.e. total inhibition of inspiratory activity.
Gas flow, Paw and Poes were measured at peak inspiratory pressures. The airflow signal was integrated to obtain tidal volumes (Vt) at different PIPs. Transpulmonary pressure (Ptp) was calculated as Paw – Pes. Lung compliance (CL) is given as the ratio of Vt over Ptp. In three cats an endotracheal tube leak of > 20% of the tidal volume was found, and in those cats no volume values were therefore calculated and consequently no compliance values can be given.
After inhibition of PNA, the 20 breaths were evaluated at the three settings studied, i.e. during GV with a normal lung, and during GV and PLV after surfactant depletion. Data are presented as mean ± SD or median and 25th and 75th percentiles. One way repeated measures analysis of variance (ANOVA) or RM ANOVA on ranks was performed to test for differences between the three groups. Student-Newman-Keuls tests were applied for comparisons between two groups when a difference was detected by ANOVA. The level of significance was set at p < 0.05 in all tests. An a posteriori power analysis revealed that the study had a power of 99% to detect a difference in PIP between healthy and surfactant-depleted lungs during GV, and of 100% to detect such a difference between healthy and liquid-filled lungs (n = 9). The power values for detecting differences in tidal volume between the same groups were 98% and 61% respectively (n = 6).
Results
Inhibition of PNA could be achieved in all cats during GV and PLV both before and after lavage at the applied ventilatory frequency of 60/min, insufflation time 0.33% of the period time and PEEP of 0.5 kPa. Figure 1 shows examples of recordings before and at inhibition of spontaneous breathing after lavage during GV (A and B) and during PLV (C and D) in one representative cat.
Figure 1 Recording before and after inhibition of breathing. Recording of airway pressure (Paw), oesophageal pressure (Poes) and phrenic nerve activity (PNA) before inhibition of spontaneous breathing in a representative cat after lung lavage during gas ventilation (GV) (A) and during partial liquid ventilation (PLV) (C), and at inhibition during GV after lung lavage (B) and during PLV (D).
Ventilatory parameters and lung mechanics
Inhibition of PNA occurred at a lower PIP (Table 1), a lower Ptp and lower tidal volumes (Table 1 and Fig. 2) before lavage than after lavage. Compliance at inhibition of inspiratory activity was higher before than after lavage (Table 1 and Fig. 2). Resistance was lower before than after lavage during GV.
Table 1 Ventilatory parameters, lung mechanics and arterial blood gases at inhibition of spontaneous breathing
GV PLV
before lavage after lavage after lavage p
PIP (kPa) 1.3 ± 0.2 2.8 ± 0.6* 2.9 ± 0.6* *<0.001
Ptp (kPa) 0.98 ± 0.2 2.36 ± 0.7 * 2.46 ± 0.6* *<0.001
Vt (ml/kg) 10 ± 1.2 17 ± 2.6* 19 ± 5.6* *<0.02
CL (ml/kPa) 41.5 [34;47] 18 [16;25]* 17 [14;20]* *<0.05
Resistance (kPa/L/s) 2.58 ± 0.59 4.94 ± 0.54* 5.49 ± 0.59 *‡ *<0.001
‡ = 0.038
pH 7.42 ± 0.05 7.38 ± 0.07 7.33 ± 0.8* * = 0.008
PaCO2, kPa 5.5 ± 0.9 5.2 ± 0.6 6.3 ± 1.7‡ ‡ = 0.027
PaO2, kPa 14.1 ± 1.8 11.0 ± 6.0 29.2 ± 17.1*‡ * = 0.01
‡ = 0.01
BE 1.71 ± 1.47 -2.08 ± 2.97* -1.89 ± 3.95* *<0.001
* different from GV before lavage ‡ different from GV after lavage
Mean ± SD; RM ANOVA and Student-Newman-Keuls Tests or RM ANOVA on ranks with 25 and 75 percentiles (for compliance values only)
PIP = peak inspiratory pressure; Ptp = transpulmonary pressure; Vt = tidal volume; CL = lung compliance; BE = base excess
Figure 2 Lung mechanics and blood gases. Multipanel figure showing (A) transpulmonary pressure; (B) tidal volume; (C) lung compliance; (D) resistance; (E) arterial pH; (F) arterial pCO2 during gas ventilation (GV) before lavage and during GV and partial liquid ventilation after lavage in each cat (unbroken lines) and as mean (broken line).
After lavage, PIP and Ptp were similar at inhibition during GV and during PLV. After lavage, compliance at inhibition remained the same during GV and PLV and resistance was lower during GV than during PLV (Table 1 and Fig. 2).
Figure 3 shows the pressure-volume loops at inhibition during GV before lavage and during GV and PLV after lavage in a representative cat. The loop obtained before lavage shows the highest compliance, whereas the loop obtained during PLV after lavage shows the highest resistance.
Figure 3 Pressure-volume curves. Pressure-volume curve during (A) gas ventilation (GV) before lavage and (B) during GV and partial liquid ventilation (C) after lavage in a representative cat.
Arterial blood gases
Before lavage, inhibition of PNA during GV occurred at an arterial pH of 7.42, which did not differ significantly from the post lavage arterial pH at inhibition of PNA. There was no statistically significant difference in arterial pH at PNA inhibition between GV and PLV. At inhibition of PNA the arterial PCO2 was lower during GV before lavage than after lavage, but was higher during PLV than during GV after lavage (Table 1 and Fig. 2). Arterial PO2 was at a level which provided sufficient oxygenation at all settings (Table 1).
Discussion
This study shows that in cats ventilated with gas, inspiratory activity is inhibited at higher peak airway pressures and tidal volumes after lung lavage than before. In cats with surfactant-depleted lungs, inhibition of inspiratory activity occurs at about the same airway pressures and tidal volumes during GV and during PLV, but at higher arterial PCO2 during PLV than during GV.
PLV with perfluorocarbon is a method of ventilatory support introduced by Fuhrman in 1991, wherein gas is ventilated into a partially liquid (perfluorocarbon) filled lung (1). PLV has been shown to decrease the alveolar surface tension mainly in dependent parts of the lung, resulting in alveolar recruitment and reduced ventilation-perfusion mismatch, thereby improving gas exchange and lung mechanics [18]. These beneficial effects of PLV have been demonstrated not only in animal models of respiratory distress and meconium aspiration syndrome [19,20], but also in adults and newborn infants with severe respiratory distress syndrome [21,22].
In the present study a ventilatory strategy of a moderate PEEP (0.5 kPa) and positive pressure ventilation at 60/min was chosen in a model of surfactant depletion to simulate a relevant clinical situation in which lung recruitment and possibly low tidal ventilation could be promoted. The point of inhibition of PNA represents the time point at which central inspiratory activity ceases and at which all spontaneous breathing activity has disappeared completely. It has been used as a point of comparison between different ventilatory patterns [11].
Lung compliance did not differ between PLV and GV in surfactant-depleted lungs, but resistance was higher during PLV, as reported elsewhere [2]. This might not represent a real increase in resistance of the airways, but is more likely due to higher inertia of the liquid than of the gas.
In this study all experiments were performed in the same order and time sequence, i.e. first GV in the healthy lung, and then GV and PLV in that order in the surfactant-depleted lung. We avoided randomisation of the order of GV and PLV in the surfactant-depleted lung, as that approach would have meant a much longer period of mechanical ventilation in the group randomised to PLV as the first part of the protocol, to allow evaporation of the perfluorocarbon.
In cats with normal lungs the pulmonary stretch receptor (PSR) activity is similar during GV and PLV, indicating that there is no extensive stretching of the lung during PLV [15]. On the other hand, the impulse frequencies of PSRs are higher at the start of inspiration with PLV than with GV at the highest insufflation pressures used [15]. This might also be the case when the lung has been lavaged and surfactant-depleted.
In animals with surfactant-depleted lungs, which may be partially atelectatic, mechanoreceptors in some well-ventilated areas may be active, whereas other receptors in atelectatic areas may not give any impulses. In the present study all receptors which were active during GV were also active during PLV. The study showed that during GV inhibition of PNA occurred at much higher airway pressures after than before lung lavage, but at similar arterial blood gases, findings which might be due to an altered stretch receptor input from, for example, areas that are surfactant-depleted and/or partially atelectatic. As instillation of perfluorocarbon might exert an effect similar to that of surfactant on lavaged lungs, increased mechanoreceptor discharge during PLV due to increased stretch receptor activity might explain why PNA inhibition occurs at a higher arterial PCO2 during PLV than during GV. This possibility is supported by the finding that administration of surfactant increases the activity of mechanoreceptors in surfactant-depleted animals [23]. It is unlikely that a high arterial PO2 during PLV influences the respiratory drive.
Conclusion
Higher airway pressures are needed to achieve inhibition of inspiratory activity during GV in animals with surfactant-depleted lungs than in animals with normal lungs. After surfactant depletion, inhibition of inspiratory activity during PLV occurs at about the same peak inspiratory and end-expiratory pressures and tidal volume as during GV. Inhibition of inspiratory activity occurs at a lower arterial pH and a higher arterial PCO2 during PLV than during GV in animals with surfactant-depleted lungs, which might be explained by recruitment of pulmonary stretch receptors during PLV. This may be a reason why inhibition of spontaneous breathing is more easily achieved during PLV than during GV in animals with surfactant-depleted lungs.
List Of Abbreviations
PNA, phrenic nerve activity
GV, gas ventilation
PLV, partial liquid ventilation
PIP, peak inspiratory pressure
Vt, tidal volume
HFPPV, high frequency pressure ventilation
PEEP, positive end-expiratory pressure
A/C ventilation, assist/control ventilation
Ti, inspiratory time
RR, respiratory rate
Paw, airway pressure
Poes, oesophageal pressure
Ptp, transpulmonary pressure
CL, lung compliance
RM-ANOVA, one way repeated measures analysis of variance
Competing Interests
The authors declare that they have no competing interests.
Authors' Contributions
ERF participated in designing the study, was involved in the preparation and care of the animals, was responsible for the acquisition and analysis of the data and drafted the manuscript. RS participated in the design of the study, was responsible for the preparation of the animals, was involved in the acquisition and analysis of the data, and revised the manuscript. AJ participated in the design of the study, was responsible for the preparation of the animals and for the neurophysiological recordings, and revised the manuscript. AS made substantial contributions to the data collection and their interpretation, and revised the manuscript. GS conceived of the study and its design, performed the lavage and PFC instillation procedures, helped to interpret the data, and revised the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors are indebted to Barbro Kjällström for skilled laboratory assistance.
This study was supported financially by the Swedish Research Council K2003-73VX-14729-01A, K2002-72X-04998-26B, HRH the Crown Princess Lovisa's Fund for Scientific Research, the Åke Wiberg Foundation, and a grant awarded by the University of Munich (Esther Rieger-Fackeldey: Habilitationsstipendium, Hochschul- und Wissenschaftsprogramm des Bundes und der Länder).
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| 15748281 | PMC555763 | CC BY | 2021-01-04 16:36:26 | no | Respir Res. 2005 Mar 4; 6(1):24 | utf-8 | Respir Res | 2,005 | 10.1186/1465-9921-6-24 | oa_comm |
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Immun AgeingImmunity & ageing : I & A1742-4933BioMed Central London 1742-4933-2-61574352710.1186/1742-4933-2-6ResearchThe role of the MAPK pathway alterations in GM-CSF modulated human neutrophil apoptosis with aging Larbi Anis [email protected] Nadine [email protected] Carl [email protected] Annie [email protected] Gilles [email protected] Tamas [email protected] Laboratory of Immunology, Research Center on Aging, University of Sherbrooke, Qc, Canada2 Immunology Graduate Program, Clinical Research Center, Faculty of Medicine, University of Sherbrooke, Qc, Canada3 Signal Transduction Laboratory, Department of Biochemistry, University of Sherbrooke, Qc, Canada4 Department of Medicine, Geriatrics Division, Faculty of medicine, University of Sherbrooke, Qc, Canada2005 2 3 2005 2 6 6 15 2 2005 2 3 2005 Copyright © 2005 Larbi et al; licensee BioMed Central Ltd.2005Larbi 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
Neutrophils represent the first line of defence against aggressions. The programmed death of neutrophils is delayed by pro-inflammatory stimuli to ensure a proper resolution of the inflammation in time and place. The pro-inflammatory stimuli include granulocyte-macrophage colony-stimulating factor (GM-CSF). Recently, we have demonstrated that although neutrophils have an identical spontaneous apoptosis in elderly subjects compared to that in young subjects, the GM-CSF-induced delayed apoptosis is markedly diminished. The present study investigates whether an alteration of the GM-CSF stimulation of MAPKs play a role in the diminished rescue from apoptosis of PMN of elderly subjects.
Methods
Neutrophils were separated from healthy young and elderly donors satisfying the SENIEUR protocol. Neutrophils were stimulated with GM-CSF and inhibitors of the MAPKinase pathway. Apoptosis commitment, phosphorylation of signaling molecules, caspase-3 activities as well as expression of pro- and anti-apoptotic molecules were performed in this study. Data were analyzed using Student's two-tailed t-test for independent means. Significance was set for p ≤ 0.05 unless stated otherwise.
Results
In this paper we present evidence that an alteration in the p42/p44 MAPK activation occurs in PMN of elderly subjects under GM-CSF stimulation and this plays a role in the decreased delay of apoptosis of PMN in elderly. We also show that p38 MAPK does not play a role in GM-CSF delayed apoptosis in PMN of any age-groups, while it participates to the spontaneous apoptosis. Our results also show that the alteration of the p42/p44 MAPK activation contributes to the inability of GM-CSF to decrease the caspase-3 activation in PMN of elderly subjects. Moreover, GM-CSF converts the pro-apoptotic phenotype to an anti-apoptotic phenotype by modulating the bcl-2 family members Bax and Bcl-xL in PMN of young subjects, while this does not occur in PMN of elderly. However, this modulation seems MAPK independent.
Conclusion
Our results show that the alteration of p42/p44 MAPK activation contributes to the GM-CSF induced decreased PMN rescue from apoptosis in elderly subjects. The modulation of MAPK activation in PMN of elderly subjects might help to restore the functionality of PMN with aging.
NeutrophilsapoptosisagingGM-CSFMAPK pathway
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Introduction
Neutrophils represent the first line of defence against aggressions [1]. They are the first cells to arrive at the site of the aggression. Neutrophils can directly eliminate the invading organisms, but most of the time they set the stage, with other cells of the innate immune system including macrophages and dendritic cells, to the development of the adaptive immune response [2]. The interaction between the innate and adaptive immune response confer to the organism an efficacious defence against infections, cancers and other aggressions.
Once the neutrophils finished their cleaning and modulating role, they should disappear in an ordered manner without releasing toxic products from their granules that eventually harmed the surrounding tissues. If they do not disappear they would induce a chronic inflammatory process. Their elimination for the sake of the organism is occurring through apoptosis [3-5]. Thus, neutrophils are programmed to die spontaneously in the absence of pro-inflammatory stimuli [6,7]. This death assures a proper resolution of the inflammation in time and place.
However, in the presence of pro-inflammatory stimuli including lipopolysaccharide (LPS), granulocyte-colony-stimulating factor (G-CSF), granulocyte-macrophage colony-stimulating factor (GM-CSF) neutrophils are able to postpone their spontaneous propensity for dying and thus, remain active during more than 72 hours [8]. This delayed apoptosis confers to neutrophils a highly efficacious manner to maintain their activity be able to eliminate properly the aggressors.
It is well accepted that aging is linked to an increase in the susceptibility to various infections [9]. This is mostly related to the dysregulation of the immune response [10-12]. The most studied part is the T-cell induced cellular immune response, which is considered to be the most affected by the aging process. However, nowadays it is also accepted that neutrophils functions are also altered with aging, even in healthy elderly satisfying the SENIEUR protocol requirements [13-15]. The most affected functions are the chemotaxis, the free radical productions and killing. Recently, we have demonstrated that although neutrophils have an identical spontaneous apoptosis in elderly subjects compared to that in young subjects, the GM-CSF induced delayed apoptosis is markedly diminished [6]. This observation was confirmed by other groups [17,18]. This decreased GM-CSF induced delay in PMN apoptosis could have far reaching consequences for PMN functions and the body defence against infections.
The mechanism of the GM-CSF induced delay of PMN apoptosis is under intense investigation. It is well known that GM-CSF induces three distinct signalling pathways in neutrophils: the Jak/STAT, the MAPK and the PI3K pathways [19-21]. Recently, it became evident that the MAPK and PI3K pathways are participating in the GM-CSF induced delayed apoptosis of PMN in young subjects [22]. Recently, we provided evidence that the Jak/STAT pathway is also contributing to the apoptosis delaying effect of GM-CSF (manuscript submitted). Furthermore, these signalling pathways modulate the expression of Bcl-2 family members as well as that of caspases which play a determinant role in the fate of cells towards survival or apoptotic death [23-25].
MAPKinases have been shown to have important roles in intracellular mechanisms responsible for neutrophils activation induced by various stimuli, as well as the modulation of their apoptosis [23,26-30]. There exist three different MAPKs: the extracellular regulated kinase (ERK1/2 or p42/44), the p38 and the c-Jun terminal kinase (JNK). Activation of the p42/44 MAPK occurs through phosphorylation of threonine and tyrosine residues by an upstream MAPKK (MEK1 and 2) [31]. Both kinases are known to be weakly auto-phosphorylated on tyrosine. The activation of p38 is occurring in the same manner with the MEK3/6 [32]. The p42/p44 MAPK is definitively involved in the PMN apoptosis rescuing activity of various agents including LPS, GM-CSF [26-30] while the role of p38 MAPK remains controversial and seems to depend on the stimuli used [33-36].
The present study investigates whether an alteration of the GM-CSF MAPK stimulation play a role in the diminished rescue from apoptosis of PMN of elderly subjects. In the present paper we present evidence that an alteration in the ERK1/2 activation occurs in PMN of elderly subjects under GM-CSF stimulation and this plays a role in the decreased delay of apoptosis of PMN in elderly. We also show that p38 MAPK does not play a role in GM-CSF delayed apoptosis in PMN of any age-groups. Our data also show that the alteration of the p42/p44 MAPK activation results in the inability of GM-CSF to convert the pro-apoptotic phenotype of PMN of elderly subjects to an anti-apoptotic phenotype by modulating the Bcl-2 family members Bax and Bcl-xL as well as the caspase-3.
Materials and methods
Reagents and antibodies
Human recombinant GM-CSF was purchased from Calbiochem-Novabiochem (La Jolla, CA). Ethylene glycol-bis (β-aminoethyl ether)-N, N, N', N'-tetraacetic acid (EGTA), aprotinin, sodium orthovanadate (Na3VO4), phenylmethylsulfonyl fluoride (PMSF) and ethylenediaminetetraacetic acid (EDTA) were obtained from Sigma-Aldrich (St Louis, MO). Leupeptin, chymostatin and pepstatin were from Boehringer Mannheim (Mannheim, Germany). Iscove's medium was purchased from Life Technologies (Grand Island, NY). The anti-caspase-3 antibody recognizing the p17 fragment of cleaved caspase-3 was a generous gift of Dr. D. Nicholson (Merck Frosst Co., Montreal, QC). Anti-phosphotyrosine mAb (4G10), anti-Bax and anti-Bcl-xL were purchased from Upstate Biotechnology Inc. (Lake Placid, NY). Polyclonal anti phospho-p42/p44 MAPK (Thr202/Tyr204), anti-p42/p44 MAPK, anti phospho-p38 MAPK (Thr180/Tyr182) and anti-p38 MAPK antibodies were from Santa Cruz (Santa Cruz, CA). The MEK inhibitor PD98059 and p38 inhibitor SB203580 were from Calbiochem. Cell permeable inhibitors of caspase-3 (Z-DEVD-FMK) and caspase-8 (Z-IETD-FMK) were purchased from Bio-Rad Laboratories (Mississauga, ON). Fluorometric caspase-3 substrate (Ac-DEVD-AMC), caspase-3 inhibitor (DEVD-CHO), caspase-8 substrate (Ac-IETD-AMC) and caspase-8 inhibitor (IETD-CHO) were from Biosource International. Propidium iodide was from R&D Systems (Minneapolis, MN). Other reagents were obtained from Sigma-Aldrich unless stated otherwise.
PMN isolation
Venous blood was collected from 20 young (20–25 years) and 20 elderly (65–85 years) individuals satisfying the SENIEUR protocol criteria for immunogerontological studies, as described [37]. All subjects gave their informed consent and the protocol was approved by the Hospital Ethical Committee. Neutrophils were separated by sequential sedimentation on 2% Dextran T-500 (Amersham Biosciences) in 0.9% sodium chloride, centrifugation on a Ficoll-Hypaque cushion (specific gravity 1.077, Amersham Health, Baie d'Urfé, QC) and hypotonic lysis of erythrocytes, as described [16]. Light microscopy showed that more than 97% of the cell population was composed of neutrophils. Cell viability was greater than 95% as assessed by Trypan blue exclusion.
PMN cultures
All experiments were performed using media, serum and reagents that were free of endotoxins to avoid non-specific activation of PMN. Purified PMN were suspended (5 × 106 cells/ml) in Iscove's modified Dulbecco's medium supplemented with 10% autologous serum, 50 U/ml penicillin and 25 μg/ml streptomycin and incubated in the presence or absence (control) of GM-CSF, at a concentration of 20 ng/ml which had been previously determined to induce maximal PMN response. Other agents used included: H2O2 (250 μM) and FMLP (10-8 M). These concentrations were previously determined to induce maximal activation (16). The cells were incubated for various periods of time (6 and 18 hours) in polypropylene tubes (Becton Dickinson Labware, Lincoln Park, NJ) at 37°C in a humidified 5% CO2-95% air incubator. For some experiments the MEK1/2 inhibitor, PD98059, and the p38 inhibitor, SB203580, were used at 30 μM and 10 μM, respectively for 1 hour prior to GM-CSF stimulation, as this concentration (from 7,5 to 100 μM) and time point (from 30 to 240 min) had been shown to be the most effective to block these MAPK activities. Stock solution of GM-CSF, H2O2, FMLP, and PD98059 as well as SB203580 were prepared in DMSO (final concentration < 0.01% v/v). Preliminary experiments showed that these concentrations of DMSO did not increase cell necrosis or the rate of PMN apoptosis.
Assessment of PMN apoptosis
Apoptosis of PMN was measured by flow cytometry using FITC-conjugated Annexin V to label externalized phosphatidylserine, whereas propidium iodide staining was used to differentiate apoptosis from necrosis [38]. The cells were washed in cold PBS and then gently resuspended in 0.5 ml of binding buffer (10 mM HEPES, 140 mM NaCl, 2.5 mM CaCl2, pH 7.4) in 12 × 75 mm polystyrene tubes. FITC-Annexin V at a saturating concentration was added to the cell suspension and incubations were performed in the dark at room temperature for 15 min. Non-specific staining was determined using a Ca2+-free buffer (10 mM HEPES, 140 mM NaCl, 10 mM EDTA, pH 7.4). Fluorescence was filtered through a 530/30 nm band pass to record FITC-Annexin V emission and a 582/42 nm band pass to detect PI emission. Fluorescence intensity was measured on a FACScan flow cytometer (Becton Dickinson, Mountain View, CA) equipped with a 15 mm air cooled 488 nm argon-ion laser. Gating on physical parameters was used to exclude cell debris and clumps. A minimum of 10,000 events was analyzed in each experiment.
Western blot analysis of p42/p44 MAPK, p38 MAPK, Bax, Bcl-xL and caspase-3
PMN (107 cells) were cultured in the absence or presence of GM-CSF (20 ng/ml) and lysed in a buffer containing 20 mM Tris-HCl, pH 7.4, 137 mM NaCl, 10% glycerol, 1% Nonidet P-40, 2 mM sodium vanadate and 100 mM sodium fluoride for a 30 min on ice. Cell lysates were centrifuged at 16,000 × g for 15 min and protein concentration of the supernatants was determined by using the Bradford protein assay reagent (Bio-Rad). 20 μg of cell lysates were resolved on a 8% SDS-PAGE, transferred to Hybond nitrocellulose membranes (Amersham Biosciences) and antigens revealed by probing the membrane with an anti-phosphotyrosine antibody (4G10) or the relevant antibodies p42/p44MAPK and p38MAPK (phosphorylated or not), Bax, Bcl-xL, caspase-3. Membranes were then washed six times with TBS and incubated with horseradish peroxidase-conjugated secondary antibody for 1 h at room temperature. The protein bands were revealed by densitometry analysis was performed using the Chemigenius2 Bio Imaging System (Syngene, Frederick, MD) [37].
Determination of caspase 3-activity
PMN (5 × 106 cells) were lysed in 100 μl of a 10 mM potassium phosphate, 1 mM EDTA buffer (pH:7.4) containing 0.5% Triton X-100 supplemented with 2 mM PMSF, 10 μg/ml leupeptin, 10 μg/ml pepstatin, and 10 mM dithiotreitol for 15 min on ice and spun at 14,000 rpm for 20 min. The lysate (100 μg) was diluted to 1 ml with ICE buffer (50 mM HEPES, 10% sucrose, 0.1% CHAPS, pH 7.5) containing 50 μM of the caspase-3 substrate Ac-DEVD-AMC (aspartate-glutamate-valine-aspartate-AMC) [23] and 10 mM freshly prepared dithiotreitol. Five hundred μl of reaction mixture were diluted with 1.5 ml of ICE buffer and fluorescence (excitation wavelength 400 nm, emission wavelength 505 nm) was read at 1 h time point. The release of fluorochrome was linear with time and with the protein concentration used. Standards containing 0–1500 nM AMC were used to determine the amount of spontaneous release of fluorochrome. Specificity for caspase-3 activity was demonstrated by using the caspase-3 inhibitor DEVD-CHO (24). A very low level of non-specific activity was present, and the effects of the inhibitor were concentration-dependent. The inhibitor of caspase-8 IETD-CHO [25] activity assessed by the fluorochrome substrate Ac-IETD-AMC was also included to insure the specificity of caspase-3 activation.
Statistical analysis
Results are representative of experiments performed with 20 individual donors of each age group. Results are presented as pooled data from the entire series of experiments (mean ± SD). Data were analyzed using Student's two-tailed t-test for independent means. Significance was set for p ≤ 0.05 unless stated otherwise.
Results
1. Spontaneous and GM-CSF-induced apoptosis of PMN: effect of aging
1.1 Spontaneous apoptosis
In control neutrophils obtained from young subjects, the mean percentage of apoptotic cells increased steadily from 5 ± 2.7% at 0 hour to 30 ± 6.5% at 18 hours (Fig 1A and 1B, white columns, NS). Similar observations were found for PMN of elderly subjects where we found that 8 ± 3.0%, 14 ± 3.2% and 44 ± 7.0% were apoptotic, measured by the expression of Annexin-V at 0 h and 18 h, respectively (Figure 1A, filled columns, NS). Even if a tendency towards a higher susceptibility to apoptosis is observed with aging, this is not significant. It is of note that, the differences in PMN apoptosis between the basal state and after 18 h of culture are statistically significant in case of PMN of young (p < 0.01), as well as of elderly (p < 0.01) subjects.
Figure 1 Measurement of human neutrophils apoptosis with aging by flow cytometry. A) GM-CSF-treated (20 ng/ml) or untreated neutrophils were cultured during various time periods (from t = 0 to t = 18 h) and stained with Annexin-V conjugated to FITC. FACS analyses were performed for fluorescence intensity and data were reported in a time-dependent manner for the different experimental conditions and compared to control neutrophils from young (white columns) and elderly donors (black columns). Identical experiments were done with Z-DEVD-FMK, inhibitor of caspase-3 activity (iC3) in the presence or absence of GM-CSF. Data represent percentage of apoptotic cells from the neutrophil pool. Data were statistically analyzed for 20 different donors of each group indicated by *p < 0.01. B) The same experiments were done using H2O2 (250 μM), Dexamethasone (10-8 M) and FMLP (10-8 M). Data are from 10 independent experiments with *p < 0.01.
1.2 Effects of various stimulants on apoptosis of neutrophils cultured in vitro
We evaluated, by the expression of Annexin-V using flow cytometry measurements, the effects of GM-CSF, H2O2, Dexamethasone (Dex), FMLP and Z-DEVD-FMK, an inhibitor of caspase-3 (iC3) on the modulation of the PMN apoptosis for various periods of time. It is well known that GM-CSF is able to rescue PMN from their spontaneous apoptosis [6]. As shown in Figure 1A the percentage of apoptotic PMN of young subjects treated by GM-CSF decreased significantly (p < 0.01) after 18 hours of culture compared to control cultures, starting already at 6 hours (data not shown). In contrast, in PMN of elderly subjects after GM-CSF treatment a decrease in apoptosis starting at 6 hours of culture could be also observed, however this did not become statistically significant at any culture time (at 18 hours) compared to control cultures (Figure 1A, filled columns). It is of note that no age-related differences could be demonstrated for the other treatments (Figure 1B), except for dexamethasone. In contrast to GM-CSF, dexamethasone did not decrease significantly the PMN apoptosis in young subjects, but slightly increased that of elderly subjects, which resulted in a significant difference with age (Figure 1B). Finally, we studied the effect of an inhibitor of caspase-3 on PMN spontaneous apoptosis. We found that the iC3 significantly decreased the spontaneous apoptosis of PMN in both age-groups (Figure 1A). The caspase-3 inhibitor significantly increased the anti-apoptotic effect of GM-CSF as compared to GM-CSF alone in PMN of young subjects (5 % vs 14 %, respectively; p < 0.05). These results indicate an efficient synergism at 18 h between the effect of GM-CSF and of the caspase-3 inhibitor when compared to spontaneous apoptosis in PMN of young subjects (5 % vs 30 %, respectively; p < 0.05). The synergism seen in the case of young donors is missing in the case of elderly donors since GM-CSF is inefficient. Furthermore, these results indicate that caspase-3 is implicated in the spontaneous and GM-CSF modulated apoptosis of PMN of young and elderly subjects. Altogether, these results suggested that GM-CSF sustained survival is defective with aging. Thus, the question rose, whether an alteration in the signal transduction of GM-CSF receptor could contribute to the altered rescue of PMN of elderly subjects from apoptosis.
2. Study of the involvement of the MAPK signalling pathways in the GM-CSF induced rescue from apoptosis of PMN of young and elderly subjects
PMN of young and elderly subjects were stimulated with GM-CSF during various periods of time (1, 5 and 10 minutes, see figure 2 lanes 2, 3, 4), cytosolic proteins were sized by SDS-PAGE and their pattern of tyrosine phosphorylation was assessed by Western blotting with an anti-phosphotyrosine mAb (4G10). At the basal level, the protein-tyrosine phosphorylation was significantly already enhanced in PMN of elderly subjects compared to that of young subjects (Figure 2A lane 1, p < 0.01). There is a kinetic increase of the protein-tyrosine phosphorylation of almost each bands (e.g. p42/44, p110) in PMN of young subjects after GM-CSF stimulation at 1 min and 5 min (lane 2 and lane 3 respectively, left panel, p < 0.01 compared to basal state) starting to decrease at 10 min (lane 4 left panel). In contrast, no significant kinetics in the protein-tyrosine phosphorylation could be observed in PMN from elderly subjects stimulated with GM-CSF, probably because of the intense protein-tyrosine phosphorylation level at the basal state (Figure 2A lane 1 right panel). Figure 2B is representing the densitometric scanning of the gels as described in the Materials and Methods section.
Figure 2 Protein tyrosine phosphorylation of whole PMN cell lysates after short stimulation with GM-CSF Freshly prepared neutrophils were stimulated with GM-CSF for 1 to 10 min and immediately lysed. Lysates were sized on SDS-PAGE followed by western-blotting using anti-phosphotyrosine mAbs to reveal protein phosphorylation. The gel shown here is representative of 10 independent experiments. Image analyzer was used to measure the amount of phosphorylation and represented above the gel for each experimental condition in the case of young (white columns) and elderly donors (black columns). Similar phosphorylation patterns were obtained from others experiments. A significant increase in total phosphorylation in indicated by *p < 0.01, **p < 0.05. A significant difference in basal phosphorylation status of PMN from young and elderly donors was found and indicated by ***p < 0.05.
We then focused on p42/p44 MAPK proteins which were shown to be activated in PMN following GM-CSF receptor stimulation, as well as by other stimulants [26-30]. It was already demonstrated that p42/p44 MAPK activation contributed to the decrease of spontaneous apoptosis by GM-CSF [22]. Therefore, we studied the GM-CSF induced ERK1/2 activation in PMN in both age-groups. It is of note that the ERK1/2 tyrosine phosphorylation already detectable at basal level did not change during the non-stimulated culture times (1 h, 6 h, 18 h) when compared to the protein level expression. However, the pre-incubation of the PMN of young subjects with an inhibitor of MEK1/2, PD98059 (i-ERK), lead to the complete abrogation of p42/p44MAPK phosphorylation after 18 hours (Figure 3, young subjects, left panel). In PMN of young subjects the protein-tyrosine phosphorylation of p42/p44MAPK increased by two times after 5 minutes of GM-CSF stimulation and remained thereafter for 18 hours with a slight decrease at 6 hours (Figure 2, young subjects, right panel). This increase was mainly due to the p44 components of the ERK1/2. Once more the PD98059 abrogated the GM-CSF induced ERK1/2 activation measured by tyrosine phosphorylation. In contrast, in PMN of elderly we observed a very strong basal tyrosine phosphorylation (non-stimulated) compared to that of young subjects (p < 0.01), which decreased thereafter during the non-stimulated incubation (Figure 3, elderly subjects, left panel). We could not demonstrate any changes in tyrosine phosphorylation of p42/p44MAPK during the 6 first hours activation by GM-CSF in PMN of elderly donors (Figure 3, elderly subjects, right panel), which even decreased thereafter during the 18 hours of stimulation. The i-ERK could only partially inhibit the GM-CSF induced ERK1/2 tyrosine phosphorylation. Altogether these data indicate that GM-CSF activates the p42/p44 MAPK by phosphorylation on tyrosine in a sustained manner during the 18 hours of culture in PMN of young, while this activation is completely absent in PMN of elderly subjects.
Figure 3 Activation of p42/p44 MAPK following short and long-time incubation with GM-CSF, effect of aging. Neutrophils of young (A) and elderly subjects (B) were stimulated either for 5, 30, 60 min, 6 and 18 hours with GM-CSF alone or pre-treated with PD98059 (i-ERK) for 1 hours prior to GM-CSF stimulation for 18 hours. Neutrophils were then lysed and analyzed by western-blotting experiments for p42/p44 MAPK phosphorylation revealing by phospho-anti-p42/p44 MAPK polyclonal Abs. Control loading are shown under each blot for p44. Experiments were performed for 15 different donors of each group.
Next, we assessed the protein tyrosine phosphorylation of p38 MAPK (Figure 4). The role of p38 in PMN apoptosis is still controversial [33-36]. There is no evidence that GM-CSF modulate its activation. However, p38 MAPK activation has been implicated in the spontaneous apoptosis of PMN [33,34]. We present here data that p38 MAPK is tyrosine phosphorylated already at the basal level, in a significantly (p < 0.01) higher level in PMN of elderly subjects compared to young subjects (Figure 4A and 4B). We also show an increase in p38 MAPK phosphorylation after 5 min of stimulation by GM-CSF in PMN of young subjects sustained for 6 hours (Figure 3A, p < 0.01) and returning to the basal level after 18 hours when comparing with control protein loading. It is of note than when the PMN were left untreated for 18 hours the phosphorylation of p38 on tyrosine was significantly increased either compared to the basal non-stimulated status (p < 0.05) or to the 18 hours GM-CSF stimulation (p < 0.05). There was no tyrosine phosphorylation modulation of p38 in PMN of elderly subjects in response to GM-CSF stimulation (Figure 3B), which was already very high at basal level compared to PMN of young subjects (p < 0.01). These data also suggest that p38 MAPK participate to the spontaneous apoptosis of PMN in both age-groups, but did not participate to the GM-CSF delay of apoptosis. These results further indicate that we assist to an alteration of the signal transduction in PMN of elderly subjects in regard to the MAPK pathways.
Figure 4 Time-dependant activation of p38 MAPK following incubation with GM-CSF, effect of aging. Neutrophils were stimulated with GM-CSF from 0 to 18 hours and immediately lysed. Western-blotting experiments of neutrophils of young (A) and elderly subjects (B) lysates were conducted by revealing with anti-phospho-p38 MAPK polyclonal Abs. Control loading for p38 are shown under each blot. Blots are representative of 15 independent experiments.
Then, we tried to link the alteration described previously in the MAPK pathways in PMN of elderly subjects to the inability of GM-CSF to rescue them from apoptosis using the pharmacological inhibitors of p42/p44 and p38 MAPKs. We assessed the spontaneous and GM-CSF delayed apoptosis of PMN after 18 hours treatment with PD98059 and SB203580 (Table 1). We found by Annexin-V/PI staining that apoptosis commitment was not significantly affected by any of these inhibitors in PMN of young and elderly subjects (Table 1). When the PD98059 was applied as pre-treatment to GM-CSF stimulation, it completely abolished the GM-CSF anti-apoptotic effect in PMN of young subjects, while it had no effect on GM-CSF of elderly subjects (Table 1). When the SB203580 was applied as a pre-treatment prior to GM-CSF stimulation it could not abrogated the GM-CSF apoptosis rescuing effect, indicating that p38 is not concerned by the GM-CSF induced rescue. In PMN of elderly, the SB203580 pre-incubation had a similar effect than in PMN of young individuals. Altogether, these data revealed two main observations, first, the role of the p42/p44 MAPK pathway in PMN survival in contrast to that of p38 MAPK under GM-CSF stimulations and second, the altered stimulating effect of GM-CSF via the decreased p42/p44 MAPK phosphorylation due to an increased basal over-activation contributes to the decreased survival of PMN of elderly subjects.
Table 1 Neutrophil apoptosis modulation by MAPKinase inhibitors
Treatment Culture time (hours) Annexin-V positive cells (%)
Young Elderly
0 11 ± 2 15 ± 3
None 6 11 ± 3 20 ± 3
18 34 ± 6 48 ± 10
GM-CSF 6 11 ± 4 10 ± 5
18 18 ± 3 41 ± 9*
PD98059 18 33 ± 7 45 ± 10
GM-CSF + PD98059 18 40 ± 10 50 ± 15
SB203580 18 43 ± 2 53 ± 5*
GM-CSF + SB203580 18 24 ± 3 33 ± 5*
The cells were cultured for the indicated time in the presence or absence of GM-CSF (20 ng/ml) and the presence or absence of PD98059 (30 μM) or SB203580 (10 μM). The percentage of apoptotic was determined by labeling with FITC-conjugated Annexin-V and cell integrity, by Propidium iodide (PI) staining. The cells were analyzed by cytofluorimetry. Data are shown as the mean ± SD of five 5 independent experiments. Significant differences between young and elderly subjects are indicated by an asterisk (*, p < 0.05 and **, p <0.01).
3. Role of the Bcl-2 family members Bax and Bcl-xL in GM-CSF induced rescue of PMN from apoptosis
The Bcl-2 family members are actively participating in the apoptotic process by being either pro-apoptotic such as Bax or anti-apoptotic such as Bcl-xL [6]. Their role in the PMN spontaneous and GM-CSF induced apoptosis was recently studied [9]. It was shown that the ratio of the pro- and anti-apoptotic molecules was essential. However, the presence of Bcl-xL in PMN is still debated [40]. Here, first we studied how the GM-CSF affected Bax and Bcl-xL expression after 18 hours of culture in PMN of young and elderly subjects. We found that in PMN of young subjects concomitantly to its anti-apoptotic effect the GM-CSF decrease significantly the expression of Bax (p < 0.01) and increase the expression of Bcl-xL (Figure 5A and 5C, Figure 6A). In PMN of elderly subjects the GM-CSF was unable to modulate the expression of Bax, however slightly increased that of Bcl-xL (Figure 5B and 5D, Figure 6A). Nevertheless, when the ratio is calculated there is a significant shift towards survival (Bcl-xL) in PMN of young subjects (Figure 6B), while this is the contrary in PMN of elderly (increase of Bax) (Figure 6B). When the inhibitors were used they did not modulate the expression neither of Bax nor of Bcl-xL in any age-groups. These data altogether indicate that the GM-CSF stimulation create an anti-apoptotic milieu in the ratio of the Bcl-2 pro-and anti-apoptotic members, while this is the contrary in PMN of elderly subjects. Moreover, the MAPK pathways do not seem to intervene in the modulation of the expression of the Bcl-2 family members in PMN.
Figure 5 Expression of bcl-2 family members Bax and Bcl-xL in PMN with aging upon treatment with GM-CSF and PD98059 and SB203580 for 18 h. Neutrophils were cultured in presence of GM-CSF alone or in combination with either the p42/p44 MAPK inhibitor, PD98059, or the p38 MAPK inhibitor, SB203580, for 18 h. Then, neutrophils were lysed and western-blotting experiments were executed to investigate the amount of Bax in young (A) and elderly donors (B). The same experiments were conducted for studying Bcl-xl expression in PMN of young (C) and elderly subjects (D). GM-CSF significantly decreased the expression of Bax in PMN of young subjects (A, p < 0.01), without any effect in PMN of elderly subjects (B). GM-CSF significantly increased the expression of Bcl-xL (p < 0.05) in both age groups (C and D), Blots are representative of 15 independent experiments.
Figure 6 Densitometric analyses of the expression of bcl-2 family member Bax in PMN with aging upon treatment with GM-CSF and PD98059 and SB203580 for 18 h as well as the ratio of the expression of Bax/Bcl-xL. Densitometric analysis were performed as described in the Materials and Methods section for the expression of Bax in neutrophils of young (white columns) and elderly subjects (black columns) under GM-CSF stimulation (A, *p < 0.01) and after the application of inhibitors, PD98059 (i-ERK) and SB203580 (i-p38) (B, p < 0.01). The ratio of Bax to Bcl-xL in PMN of young (white columns) and elderly subjects (black columns) under GM-CSF stimulation and modulation by inhibitors is represented (C).
4. Role of caspase-3 in the apoptosis of PMN and its modulation by GM-CSF through the MAPK pathway
We have shown that caspase-3 is implicated in the spontaneous and GM-CSF modulated apoptosis of PMN of young and elderly subjects. Caspase-3 is in an uncleaved form when not activated (procaspase-3). Here we analyzed by western-blotting experiments the amount of activated caspase-3 after different experimental conditions including GM-CSF and inhibitors treatment. We found that after 18 hours, GM-CSF-treated PMN of young subjects reduced significantly the expression of activated caspase-3 as compared to untreated cells (Figure 7A, young, lane 2 and 3, p < 0.01). In PMN of elderly subjects the expression of activated caspase-3 did no change after 18 hours of GM-CSF stimulation, compared to the non-stimulated status (Figure 7A, lane 2 and 3, elderly). When we applied the inhibitor PD98059, we found a reestablishment of the activated caspase-3 (Figure 7A lane 4, upper panel) compared to the GM-CSF-treated PMN. The p38 inhibitor, SB203580, did not influence significantly the activated caspase-3 expression modulation by GM-CSF. No modulation of the activated caspase-3 expression was found at any experimental conditions in PMN of elderly (Figure 7A lane 4, lower panel). These last data indicate a link between p42/p44MAPK and caspase-3 activation, however other transduction pathways could also play a role.
Figure 7 Expression of activated caspase-3 in PMN with aging upon treatment with GM-CSF and PD98059 and SB203580 for 18 h A) Neutrophils of young (upper panel) and elderly subjects (lower panel) were cultured in presence of GM-CSF alone or in combination with either the inhibitor PD98059 (i-ERK) or SB203580 (i-p38), for 18 h. Then, neutrophils were lysed and western-blotting experiments were performed to investigate the amount of active caspase-3. With aging no changes were found by any agents used (lower panel) while neutrophils from young donors (upper panel) showed significant modulation by GM-CSF and PD98059. B, Densitometric analyses were performed as described in the Materials and Methods section for the expression of activated caspase-3 in neutrophils of young (white columns) and elderly donors (black columns) with *p < 0.01.
To confirm these results, we measured the caspase-3 activity by a specific fluorescent substrate (Table 2). In PMN of young subjects the activity of caspase-3 increased progressively after 6 hours of culture for reaching a peak value at 18 hours. For all the incubation times, the caspase-3 activity was significantly higher (p < 0.05) in the case of elderly donors (Table 2), except at 6 hours. Under GM-CSF stimulation, caspase-3 activity was significantly decreased at 6 hours (p < 0.05), 18 hours (p < 0.01) in PMN of young subjects. It is of note that GM-CSF could not restore the caspase-3 activity to the level of freshly prepared PMN. In the aged group, in PMN under GM-CSF treatment no significant decrease could be detected in caspase-3 activity compared to the spontaneous activities of caspase-3. These data support the fact that GM-CSF is involved in the modulation of caspase-3 activity which decreases in PMN of young subjects concomitantly to their rescue from apoptosis, while PMN of elderly subjects are unable to decrease caspase-3 activity when GM-CSF is provided in the milieu.
Table 2 Modulation of caspase-3 activity in PMN by MAPkinase inhibitors
Treatment Culture time (hours) Caspase-3 activity (arbitrary fluorescence units)
Young Elderly
0 92 ± 20 126 ± 29
None 6 218 ± 38 188 ± 53
18 908 ± 118 1396 ± 207*
GM-CSF 6 113 ± 31 149 ± 42
18 447 ± 87 979 ± 96*
PD98059 18 781 ± 216 1024 ± 413
GM-CSF + PD98059 18 1126 ± 376 1145 ± 387
SB203580 18 1055 ± 267 792 ± 291
GM-CSF + SB203580 18 459 ± 119 900 ± 147 *
PMN isolated from young and elderly donors were maintained in culture for the indicated periods of time. The cells were left untreated, or exposed to GM-CSF (20 ng/ml) or a combination of GM-CSF (20 ng/ml) and either in the presence or absence of PD98059 (30 μM) or SB203580 (10 μM). Caspase-3 activity was measured using a fluorescent substrate. Data are representative of 10 independent experiments. The asterisk (*) indicate significant differences (p < 0.05) between the two groups of donors.
To further link the MAPK pathways to survival/apoptosis of PMN we assessed the caspase-3 activity under GM-CSF stimulation after PD98059 and SB203580 pre-treatment. We found that any of these inhibitors alone did not modulate significantly the caspase-3 activity during the spontaneous apoptosis of PMN obtained either from young or elderly subjects (Table 2). The diminution of caspase-3 activity by GM-CSF in PMN of young subjects (from 908 ± 118 to 447 ± 87) could be completely reversed by PD98059 (from 447 ± 87 to 1126 ± 376), while the SB203580 could not modulate the effect of GM-CSF on caspase-3 activity. These inhibitors had no significant effect on PMN of elderly subjects. These data further confirm the essential role of the p42/p44 MAPK pathway in PMN survival by modulating the caspase-3 activity, while p38 MAPK is not concerned.
Discussion
Many clinical data indicate that elderly subjects are more susceptible to infections, particularly to that of the higher respiratory tracts, more likely caused by atypical organisms as well as to sepsis by gram negative bacteria [9,14]. PMN are the first cells to arrive at the site of invasion and respond very quickly by the destruction of the aggressor. Recently, besides the alterations of the adaptive immune response it was demonstrated that some functions of the PMN including chemotaxis, killing and production of bactericidal substances are decreasing with aging [13] while others remain unchanged such as phagocytosis [41]. One particular aspect of neutrophils homeostasis is their propensity to die spontaneously i.e in the absence of pro-inflammatory stimuli for preserving the organism from undue destruction or chronic inflammation. In contrast, when an inflammatory process increases the level of pro-inflammatory mediators such as GM-CSF, G-CSF and LPS, PMN remain functional for 72 hours and die thereafter by apoptosis.
We have found that in PMN of elderly subjects the GM-CSF was not able to rescue them from apoptosis as efficiently as in PMN of young subjects [16]. Here we present data confirming and extending these results. Except GM-CSF any other agents used for modulating PMN apoptosis was found to demonstrate significant age-related differences. GM-CSF acts as a ligand for its specific receptor for exercising its anti-apoptotic effect. This means that if in PMN of elderly the GM-CSF is not able to exert its anti-apoptotic activity, an alteration in the signalling of GM-CSF may exist. In fact, in this study we demonstrate an alteration in the p42/p44 MAPK activation in PMN of elderly subjects participating in the altered rescue of PMN from apoptosis. Moreover, we show that p38 MAPK does not participate in the rescue from apoptosis either in young or elderly subjects. We present also data that molecules modulating the apoptotic fate of PMN were differentially related to the MAPK pathways. We demonstrate that the p42/p44 MAPK is not linked to the modulation of the expression of the Bcl-2 family members, while caspase-3 is partially modulated by this MAPK.
GM-CSF is activating three signalling pathways, Jak/STAT, MAPK and PI3K [19]. It is now well accepted that in eosinophils all three pathways are implicated in the GM-CSF induced rescue from apoptosis [42]. In PMN it is now also well established that p42/p44 MAPK and PI3K are implicated in the GM-CSF induced rescue from apoptosis [22]. We have recently shown that the Jak/STAT signalling pathway was also involved. We were the first to demonstrate that GM-CSF is unable to rescue PMN of elderly subjects from apoptosis [16]. This was since confirmed by other groups. This fact could have far reaching consequences on the susceptibility of elderly to infections. Thus it is very important to understand why this phenomenon is occurring in PMN of elderly.
As the p42/p44 MAPK was implicated in PMN of young subjects in the rescue from apoptosis, we studied whether this could contribute to the failure of rescue with aging. In fact, the activation of this MAPK by GM-CSF, as well as by other substances is strongly related to its anti-apoptotic effect, showed by the use of specific MEK1/2 inhibitors [26-30]. Our present data show that p42/p44 MAPK could not be activated by GM-CSF in the PMN of elderly. This inability exists either for the short stimulation times or for longer periods. It is clear from the experiments on PMN of young subjects that the activation of p42/p44 MAPK is sustained at least for 18 hours induced by GM-CSF. To confirm that the p42/p44 MAPK is participating to the rescue of PMN from apoptosis we used the inhibitor PD98059. We found, as others [26-30] that in PMN of young subjects the use of PD98059 reversed the GM-CSF inhibitor of apoptosis clearly demonstrating that the ERK/1/2 is effectively involved in the rescue of apoptosis. In the case of elderly subjects the lack of p42/p44 MAPK activation contributes to the altered rescue of PM from apoptosis and in fact, the PD98059 could not modulate the PMN apoptosis.
We also studied whether another member of the MAPK family namely the p38 MAPK participates to the GM-CSF induced rescue from apoptosis. The role of the p38 MAPK in PMN apoptosis is controversial [33-36]. Nevertheless, the consensus seems to exist that its activation participates in the PMN spontaneous apoptosis. Our present results confirm this contention in both age-groups. There is a controversy whether the activation of p38 MAPK participates in the rescue from apoptosis under various stimulations. In this regard the results of the literature seem to suggest that p38 MAPK does not participate in the PMN apoptosis delaying effect of GM-CSF [33-36]. Our present results indicate that p38 does not contribute to the rescue from apoptosis neither in PMN of young or elderly subjects.
Finally to understand how the p42/p44 MAPK can be implicated in the GM-CSF induced apoptosis of PMN we studied molecules intervening in the modulation of PMN apoptosis. These molecules enclosed those of the Bcl-2 family members and the caspases, mainly caspase-3. It was shown that the Bcl-2 family members are either pro-apoptotic or anti-apoptotic. In PMN the most important pro-apoptotic molecule is Bax and the most important anti-apoptotic ones are the Mcl-1, A1 and Bcl-xL [6]. The presence of Bcl-xL is still controversial. The ratio between these molecules determines the fate of PMN. We wanted to see whether the p42/p44 MAPK activation by GM-CSF is linked to the expression of the Bax and Bcl-xL molecules and their ratio. It is known that the p42/p44 MAPK is linked to the phosphorylation of Bad and freeing in this way the Bcl-xL, leading to an anti-apoptotic milieu [43]. We found that GM-CSF decreases the expression of Bax after 18 hours of stimulation, while it increased the expression of Bcl-XL which gives a ratio in favour of Bcl-xL, representing the survival. The contrary is occurring in PMN of elderly where the ratio is in favour of Bax and so pro-apoptotic. Weinmann et al. [44] showed that GM-CSF did not modulate the expression of Bcl-xL. Discrepancies may come from the fact that the concentration of GM-CSF used was 300 U/ml in their case and only 200 U/ml in our study. When we treated PMN with higher concentration we also found a drop in the effect of GM-CSF on Bcl-xL expression. The use of PD98059 could not reverted the effect of GM-CSF on the ratio of Bax/Bcl-xL indicating that the p42/p44 MAPK is not acting directly on these molecules of the Bcl-2 family. This indicates also that the p42/p44 MAPK is not the only signalling pathway participating in the GM-CSF induced rescue from apoptosis. As indicated the Jak/STAT and PI3K pathways are also participating. In PMN of elderly as the p42/p44 MAPK could not be activated by GM-CSF at any time points, it was evident that no modulation by ERK1/2 inhibitor was found. The p38 MAPK is not involved in the modulation of these molecules in PMN at any age-groups.
Furthermore, we were also interested to study the effects of p42/p44 MAPK on the expression and activity of another very important executioner protein, the caspase-3. Caspase-3 was implicated in the spontaneous and GM-CSF induced rescue of PMN from apoptosis. In fact we confirmed that GM-CSF could decrease the activated caspase-3 expression and activity in PMN of young subjects, while has no effect in PMN of elderly. It was also known that the p42/p44 MAPK is inhibiting the activation of caspase-8 [23]. The use of PD98059 indicated in PMN of young subjects that the p42/p44 MAPK is implicated in the GM-CSF induced inhibition of caspase-3, however this inhibition was not complete. This also indicates that the GM-CSF has other pathways to modulate the proteins participating in the executioner phase of apoptosis. In case of PMN of elderly subjects once again no modulation of caspase-3 could be demonstrated in any experimental conditions. For the participation of p38 MAPK in the modulation of caspase-3 by GM-CSF more work is needed as the results were not so clear cut.
Altogether, our data demonstrate to our best knowledge for the first time that the lack of activation of p42/p44 MAPK contribute to the decreased rescue of PMN from apoptosis in elderly individuals. This decreased activation results in a pro-apoptotic Bcl-2 family members over-expression and the activation of caspase-3 in contrast to PMN of young subjects. We also present data that the activation of p42/p44 MAPK should be sustained for at least 18 hours to ensure an efficient rescue of PMN from apoptosis by GM-CSF. Moreover, our results confirm that the p38 MAPK participates to the spontaneous apoptosis of PMN in both age-groups while it did not participate in the rescue of PMN apoptosis by GM-CSF. Thus, the perspective of modulation of the p42/p44 MAPK activation in PMN of elderly subjects may be useful to restore the effectiveness of GM-CSF and contribute to more effective PMN functionality in elderly. We explore actually what means can be used to achieve such an increase in p42/p44 MAPK activation, including the modification of the PMN membrane composition.
Acknowledgements
This work was partially supported by a grant-in-aid from the Research Center on Aging, the Canadian Institute of Health Research (No. 63149), the ImAginE Consortium (No EU contract QLK6-CT-1999-02031) and ZINCAGE project (EU contract FOOD-CT-2003-506850).
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| 15743527 | PMC555764 | CC BY | 2021-01-04 16:36:32 | no | Immun Ageing. 2005 Mar 2; 2:6 | utf-8 | Immun Ageing | 2,005 | 10.1186/1742-4933-2-6 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-241572369710.1186/1471-2164-6-24DatabaseComparative promoter region analysis powered by CORG Dieterich Christoph [email protected] Steffen [email protected] Andrea [email protected]öpcke Stefan [email protected] Peter F [email protected] Peter F [email protected] Martin [email protected] Computational Molecular Biology Department, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany2 Institute for Theoretical Chemistry and Structural Biology, University of Vienna, Währingerstrasse 17, A-1090 Wien, Austria3 Bioinformatics Group, Department of Computer Science, University of Leipzig, Kreuzstraße 7b, D-04103 Leipzig, Germany2005 21 2 2005 6 24 24 26 10 2004 21 2 2005 Copyright © 2005 Dieterich et al; licensee BioMed Central Ltd.2005Dieterich 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
Promoters are key players in gene regulation. They receive signals from various sources (e.g. cell surface receptors) and control the level of transcription initiation, which largely determines gene expression. In vertebrates, transcription start sites and surrounding regulatory elements are often poorly defined. To support promoter analysis, we present CORG , a framework for studying upstream regions including untranslated exons (5' UTR).
Description
The automated annotation of promoter regions integrates information of two kinds. First, statistically significant cross-species conservation within upstream regions of orthologous genes is detected. Pairwise as well as multiple sequence comparisons are computed. Second, binding site descriptions (position-weight matrices) are employed to predict conserved regulatory elements with a novel approach. Assembled EST sequences and verified transcription start sites are incorporated to distinguish exonic from other sequences.
As of now, we have included 5 species in our analysis pipeline (man, mouse, rat, fugu and zebrafish). We characterized promoter regions of 16,127 groups of orthologous genes. All data are presented in an intuitive way via our web site. Users are free to export data for single genes or access larger data sets via our DAS server . The benefits of our framework are exemplarily shown in the context of phylogenetic profiling of transcription factor binding sites and detection of microRNAs close to transcription start sites of our gene set.
Conclusion
The CORG platform is a versatile tool to support analyses of gene regulation in vertebrate promoter regions. Applications for CORG cover a broad range from studying evolution of DNA binding sites and promoter constitution to the discovery of new regulatory sequence elements (e.g. microRNAs and binding sites).
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Background
Comparative sequence analysis has been a powerful tool in bioinformatics for addressing a variety of issues. Applications range from grouping of sequences (e.g. protein sequences into families) to de novo pattern discovery of functional signatures.
Speaking of gene regulation, it has been known for a long time that there is considerable sequence conservation between species in non-coding regions of the genome. A comprehensive explanation of this observation is still elusive. However, sequence conservation within promoter regions of genes often stems from transcription factor binding sites that are under selective pressure (see [1] for a review and [2] for a systematic assessment of binding site conservation in man and mouse comparisons).
Conserved sequence elements of other types have recently caught much attention. Not all non-coding conserved DNA in the vicinity of a gene's transcription start site necessarily functions at the level of transcriptional regulation. For example, most known methylation-guide snoRNAs are intronencoded and processed from transcripts of housekeeping genes [3]. A few microRNAs are apparently linked to protein coding genes, most notably mir-10 and mir-196 which are located in the (short) intergenic regions in the Hox gene clusters of vertebrates [4-7].
A second class of conserved sequence elements exert their function as regulatory motifs in the untranslated region (UTR) of the primary transcript or the mature mRNA. The UTRsite database [8], for example, lists about 30 distinct functional motifs including the Histone 3'UTR stem-loop structure (HSL3) [9], the Iron Responsive Element (IRE) [10], the Selenocysteine Insertion Sequences (SECIS) [11], and the Internal Ribosome Entry Sites (IRES) [12]. Most of these elements are contained in CORG since short intergenic regions or introns upstream of the translation start site are entirely covered by our definition of an upstream region.
Phylogenetic footprinting
The CORG framework aims at detecting and describing regulatory elements that are proximal to the transcription start site. In this context, the comparison of upstream regions of orthologous genes is particularly valuable. This concept is called "phylogenetic footprinting" and an overview of this approach can be found in [13].
Phylogenetic footprinting in a strict sense is carried out on orthologous promoter regions. Local sequence similarities can then be directly interpreted as related regions harboring conserved functional elements. We denote these similarities as Conserved Non-coding Blocks (CNBs).
Multi-species sequence conservation
Comparative approaches gain power from the inclusion of sequences from more than two species [14]. Multi-species comparisons help to increase specificity at the expense of intra-species sensitivity since supporting evidence (conservation) stems from many observations. To give an example, Man-mouse-rat comparisons enhance the detection of transcription factor binding sites since the rat genome is more divergent from the mouse genome than anticipated [15]. A nice property of vertebrate microRNAs is the high degree of sequence conservation which is found in alignments of man, mouse and fish microRNAs [16]. Both types of comparisons are available in CORG. In CORG, we consider cross-species conservation between promoter regions from 5 vertebrate genomes, namely Homo sapiens, Mus musculus, Rattus norvegicus, Danio rerio and Fugu rubripes. Multiple alignments are built from pairwise CNBs as described in the subsequent section.
Construction and content
Groups of orthologous genes
In this work, we take a gene-centered view of phylogeny. Homology among proteins and thus genes is often concluded on the basis of sequence similarity. The EnsEMBL database [17] allows to distinguish orthologous from merely homologous genes by taking information on conserved synteny into account. We employed single linkage clustering on the graph of EnsEMBL orthologous gene pairs to define the CORG gene groups.
Genomic mapping of validated promoter regions
Various recent experimental efforts supply information about the position of transcriptional start sites in the human and mouse genome. Table 1 gives an overview on the resources that were employed in CORG.
Table 1 Resources for validated transcription start sites
Database name Features
Eukaryotic promoter database (EPD) [44] The Eukaryotic promoter database is the smallest in size, but largely consists of manually curated entries.
DataBase of Transcriptional Start Sites (DBTSS) [45] The DBTSS contains reliable information on the transcriptional start sites for man and mouse promoters. They exploit the oligo-capping technique to enrich their pool of clones for full-length 5'-to-3' cDNAs
H-Invitational Database (H-InvDB) [46] H-InvDB is an international effort to integrate annotation of 41,118 full-length human cDNA clones that are currently available from six high throughput cDNA sequencing projects.
FANTOM 2 (RIKEN) [47] The RIKEN consortium presented the FANTOM collection of RIKEN full-length cDNA clones. FANTOM stands for Functional Annotation of Mouse cDNA clones.
The Reference Sequence project (RefSeq) [48] The Reference Sequence project aims to provide a comprehensive, integrated, non-redundant set of sequences, including full-length transcripts (mRNA)
Some repositories offer genomic coordinates for their start site entries. Existing genomic mapping information was incorporated unless the underlying genome assembly build differed. The remaining data were projected onto the genome with SSAHA (Sequence Search and Alignment by Hashing Algorithm), a rapid near-exact alignment algorithm [18].
Sequence retrieval
The notion of "promoter region" deserves some further explanation in the context of our approach. Typically, though not exclusively, we expect conserved regulatory regions to appear in the vicinity of the transcription start site of a gene. Since we do not know the precise location of the start of transcription for each and every gene, we chose to compare the sequence regions upstream of the start of translation from orthologous genes. If verified transcription start sites are known, we define a sequence window that is large enough to hold both, translation and transcription start sites, plus 5 kB upstream sequence. In case we lack this information, our observations on known transcription start sites indicate that most promoter regions should be captured in a sequence window of 10 kb size (Additional File 1). The size of a promoter region may be bounded by the size of the corresponding intergenic region. If an annotated gene happens to lie within the primary sequence window, the promoter region is shortened to exclude exonic sequence.
Figure 1 Genomic context of human SRF. This image is displayed after the user selected a gene identifier on the search page. It provides the user with the genomic context of the selected gene. Known and predicted transcription start sites are shown as labelled red dots. Local similarities to homologous regions from other species are shown as connected purple boxes. Blue bars depict all upstream regions as contained in CORG. The structure of the corresponding EnsEMBL transcripts as well as the extent of RefSeq transcripts is show in the bottom track.
Detection of pairwise local sequence similarities
Significant local sequence similarities (phylogenetic footprints) in two sequences are computed with an implementation of the Waterman-Eggert algorithm. We have already given an account of the algorithm and statistics in [19,20]. The underlying alignment scoring scheme is the general reversible model [21]:
where Q is the transition rate matrix. We left out the elements on the diagonal, which are constrained by the requirement that the sum of all elements in a row equals zero.
The πi are the stationary nucleotide frequencies, their sum is constrained to be one. Although the two genomes under consideration are in general not in their stationary state with respect to the substitutional process we take the mean of the two observed nucleotide frequencies, , to be the best estimate of the stationary base composition.
From other studies we have further knowledge about the relative rates between transversions, the transition A:T→G:C, and the transition G:C→A:T, which occur in roughly in the ratio 1:3:5 along vertebrate lineages [22]. These ratios of rates would generate sequences with 40% GC in their stationary state. To accommodate the observed nucleotide frequencies πi we have to allow for deviation from those ratios. We do this by choosing for example α ∝ (R(A → T)/πT + R(T → A)/πA)/2, where R(i → j) is either 1, 3, or 5 depending on the process under consideration. At the end we scale the matrix Q, such that the PAM distance [23] of the substitution model equals the observed degree of divergence between the two species under comparison.
Since we were mainly interested in highly conserved regulatory elements, we demanded an average similarity level at least as high as the average exon conservation between the species under comparison.
The score for aligning two nucleotides i and j is then s(i, j) = log(P(i, j)/(πiπj)) where P(i, j) is the probability of finding the pairing of i and j under the above substitution model [21].
Joining pairwise into multiple alignments
All CNBs from pairwise sequence alignments are split up into groups as defined by gene homology. For each group a graph O = (V, E) with vertices V and edges E is constructed, which represents the species-internal overlap of CNBs on the genomic coordinate level. Each vertex a ∈ V represents a footprint, which is a pairwise local alignment between two species. An undirected edge is placed between two vertices if the corresponding CNBs have only one species in common and show an overlap of at least 10 bp on the sequence level.
In our graph O, cliques of minimal size three are detected with an implementation of the Bron-Kerbosh algorithm [24]. Only those cliques are selected whose species count is equal to their size. This move prohibits the emergence of multiple alignments by similarity of multiple short CNBs to a single long CNB. Multiple alignments are then computed based on all cliques that meet the outlined criteria. We chose to employ the multiple alignment method of [25] who applies partial order graphs (POG) to the multiple alignment problem.
Partial order graphs belong to the class of directed acyclic graphs (DAGs). A DAG is a graph consisting of a set of nodes N and edges E, which are one-way edges and form no cycles.
The multiple alignment problem is then reduced to to subsequent alignment steps of individual sequences to a growing multiple alignment graph. If the sequences to be aligned share substantial sequence similarity, the number of bifurcation points within the POG stays low and allows rapid computation of the multiple alignment.
Alignment results are subsequently trimmed to encompass the leftmost and rightmost ungapped block of at least 6 nucleotides.
Annotation of promoter regions
Exon detection with assembled EST clusters
Promoter regions in CORG always extend upstream from the most downstream coding start (ATG). As a consequence, promoter regions may contain exons that are not translated. Our way of detecting such exons is a similarity search of man-mouse footprints versus GENENEST [26], a database of assembled EST clusters. Database searches are carried out for human and mouse footprints with the BLASTN program [27]. An E-value cut-off of 10-4 is applied.
Annotation with predicted binding sites
The TRANSFAC database [28] is a repository of experimentally verified binding site sequences and representations thereof. These representations are used for querying the collection of man-mouse CNBs for known binding site patterns.
Potential binding sites are detected with TRANSFAC weight matrices by the method of [29]. Here, the intuition is that there are two random models for a given sequence S: one is given by the signal profile F and the other one by the background model B. Under both models the distribution of weight matrix scores can conveniently be calculated by convolution, since the score is a sum of independent random variables. Probability mass distributions of PF (Score(S)) as well as PB(Score(S)) can be computed by dynamic programming if column scores are reasonably discretized. This allows a fine tuning of the proportions of false positives and negatives for each TRANSFAC weight matrix. Both error levels were set to be equal. All details are given in [29].
Utility and discussion
We now present an overview of the web interface of the database and several example applications.
Interface
The CORG database is accessible via its home page and offers a redesigned web interface. From the search page one can quickly jump to gene loci via EnsEMBL or other standard identifers (e.g. HUGO symbol, LocusLink identifier, ...). The search query is processed according to the chosen reference source and a list of all matching database entries is returned to the user. This list serves as a springboard to a summary page where the genomic context of the selected gene and its similarities to other upstream regions is visualized as in Figure 1.
Pairwise as well as multiple comparisons are displayed on demand at this stage with a JAVA applet that complies with the JDK 1.1 standard. Alternatively, upstream region sequence and corresponding annotation can be exported in EMBL format (sequence data also in FASTA format). The JAVA applet should run on all JAVA-compatible web browsers. Detailed information about the conserved non-coding block structure are simultaneously shown for multiple upstream regions of different species. If available, annotation information on putative binding sites of transcription factors and EST matches are displayed for the query sequence. The applet facilitates zooming into sequence and annotation. In addition, web links are assigned to sequence features that relate external data sources to the corresponding annotation.
CORG data may be also embedded into other viewers or programs via the distributed annotation system (DAS, [30]). DAS facilitates the display of distributed data sources in a common framework with respect to a reference sequence. Our DAS server constitutes such an external data source. Position information on all conserved non-coding blocks and mapped promoters is accessible from this DAS server. Each DAS sequence feature provides a link to the corresponding CORG database entry. New DAS sources can be easily added to the ENSEMBL display. A small tutorial on installing external DAS data sources is available on our web page .
Additionally, tools for on-site batch retrieval of CORG data will be added to the web portal in the near future.
Phylogenetic profiling of binding sites
One potential application of CORG is phylogenetic profiling of promoter regions. We define phylogenetic profiling in the context of gene regulation as comparative analysis of presence/absence patterns of binding sites in promoter regions. Here, we consider conserved predicted binding sites and contrast them with validated ones.
Serum Response Factor (SRF) promoter
SRF, a MADS-box transcription factor, regulates the expression of immediate-early genes, genes encoding several components of the actin cytoskeleton, and cell-type specific genes, e.g. smooth, cardiac and skeletal muscle or neuronal-specific genes [31,32]. Mouse embryos lacking SRF die before gastrulation and do not form any detectable mesoderm [33,34]. SRF mediates transcriptional activation by binding to CArG box sequences (Consensus pattern: CC(AT)6GG) in target gene promoters and by recruiting different co-factors. SRF regulates transcription downstream of MAPK signaling in association with ternary complex factors (TCFs) (for a review see [35]). TCFs bind to ets binding sites present adjacent to CArG boxes in many SRF target gene promoters.
Figure 1 gives an overview of the genomic context of human SRF. As expected, the upstream region of SRF shows substantial conservation to its rodent orthologs. Additionally, significant alignments were found in comparisons with fish homologs (one from zebrafish and two from fugu). The same data is presented in the multiple alignment view of the JAVA applet in Figure 2. This view gives a better idea on the location of alignments in the corresponding source sequences. Note, that the spacing between translation start and alignment is greater in fish than in mammals, which hints at different extension of the promoter region in the two subgroups.
Figure 2 Graphical multiple alignment view (JAVA applet). Multiple alignment view of 6 homologous sequences from 5 species. All consistent local similarities in the upstream region of SRF homologs are placed relative to the species-specific translation start sites. The distance of the aligned segment to the translation start site is almost equal for all mammals and larger for the fish. The extent of each upstream region is shown as orange bar. Regions covered by flanking genes would be shown in red.
We get a better idea on the cause of sequence conservation by browsing the multiple alignment. Textual information can be obtained by clicking on the alignment boxes. Then, the alignment appears in a pop-up window and may be copied to another destination. In Figure 3, we used CLUSTAL X ([36]) to render the conservation structure on to the nucleotide level. Here, a striking observation is the conservation of the regulatory feedback loop of SRF to its own promoter in all species under consideration. So far, this feedback loop was experimentally verified in the mouse system [37] but could exist in all other species under comparison.
Figure 3 Textual multiple alignment view. Multiple alignment as rendered by CLUSTAL X. The largest multiple alignment was retrieved from the JAVA applet by a cut and paste operation and rendered in CLUSTAL X [36]. Conserved binding sites are highlighted by red or blue boxes. Known sites as given in TRANSFAC are marked with a dollar sign [42]. Note that the validated Egr-1 site is only conserved in mammals. This site is bound by the serum-inducible Krox-24 zinc finger protein.
Non-coding RNAs
Non-coding RNA can be classified as transcribed regulatory elements. Non-coding RNAs are also accessible to the user via the CORG database. Since we were primarily interested in non-coding RNAs rather than small mRNA motifs we restricted our search here to long CNBs. A blast search of our multiple alignments with length L ≥ 50 against the Rfam database [38] and the microRNA Registry [39] identifies 21 alignments as 7 distinct microRNAs and a single snoRNA, Table 2.
Table 2 Rfam non-coding RNAs in CORG A + sign indicates that a sequence fragment from the corresponding species (hsa Homo sapiens, mmu Mus musculus, rno Rattus norvegicus, dre Danio rerio, tru Takifugu rubripes) is contained in the CORG CNB; ∅ indicates that a blast search for an orhologous sequence in the Ensemble database was unsuccessful; n.d. mean no descriptive Ensemble gene annotation. The CNBs containing mir-196a-2 are shifted compared to the known microRNA sequences, preventing the detection of the correct stem-loop structure. The B columns marks whether a candidate was identified by a blast search against the Rfam or microRNA Registry, the A column shows whether a hairpin structure was identified by RNAalifold.pRNAz is the p-value for being an evolutionary conserved RNA secondary structure element returned by RNAz.
CNB B A pRNAz ncRNA hsa mmu rno dre tru gene
119596 + + 0.995 mir-34c + + + + ∅ n.d. (BCT-4)
119607 + + 0.938 mir-34b in hsa
119658 + + 0.985
159914 + + 0.998 mir-138-2 + + + + ∅ SLC12A3, n.d. in teleosts
159932 + + 0.999
159939 + + 0.998
194777 + + 0.998 mir-196b + - + + + HOXA9, dre: HOXA9a and HOXA9b
194820 + + 0.999
194839 + + 0.999
194941 + + 0.999
226470 + + 0.999 mir-10a + + + + + HOXB4, dre: HOXB4a and HOXB4b
226514 + + 0.999
226555 + + 0.999
226677 + - 0.004
238163 + + 0.992 mir-10b + + + + + HOXD4, dre: HOXD4a, n.d in tru
238188 + + 0.984
238265 + + 0.994
391314 + - 0.125 mir-196a-2 + + - + + HOXC9, dre: HOXC9a
391315 + - 0.999
391318 + - 0.511
470004 + - 0.218 U93 + + + 0 + n.d.
110374 - + 0.995 IRES ? + + + + + DGCR8
146100 - + 0.891 + + + + 0 Ptf1a
393794 - + 0.999 IRE + + + + + SLCA1
The snoRNA U93 is an unusual mammalian pseudouridinylation guide RNA which accumulates in Cajal (coiled) bodies and it is predicted to function in pseudouridylation of the U2 spliceosomal snRNA [40]. It appears to be specific for mammals. The genomic copy of the human U93 RNA is located in an intron of a series of reported spliced expressed sequence tags (ESTs); furthermore, it has been verified experimentally that U93 is indeed spliced from an intron [40]. It was detectable in the CORG footprint dataset because of its location upstream of a conserved putative gene C14orf87 with unknown function.
The known microRNAs belong to four different groups. The mir10 and the mir196 precursors are located at specific positions in the Hox gene clusters [4-7]. The mir-196 family regulates Hox8 and Hox7 genes, the function of mir10 is unknown.
Substitution pattern of non-coding RNAs
For a microRNA we expect a subsequence of about 20 nt that is almost absolutely conserved among vertebrates (the mature miRNA) and a well-conserved complementary sequence forming the other side of the stem from which the mature microRNA is excised. In contrast, the substitution rate should be much larger in the loop region of the hairpin [41]. mir10 is a good example of this typical substitution pattern, which gives rise to a hairpin structure. The pairwise correlation structure of nucleotides is depicted on top of the multiple alignment in Figure 4. A different pattern is observed for the Iron Responsive element in the 5'UTR of SLCA1, a member of the sodium transporter family. This time the substitution pattern does not meet the minimal length of the microRNA definition above. Nevertheless, it is conserved across all vertebrate species as shown in Figure 5.
Figure 4 Alignment and predicted RNA structure of mir-10b. The mir-10b CNB shows the typical pattern of substitutions in a microRNA precursor hairpin: There are two well-conserved arms, of which the mature microRNA is almost absolutely conserved, and a much more variable loop region. [43].
Figure 5 Alignment and predicted RNA structure of the Iron Response Element. The Iron Responsive Element (UTRdb [8] identifier: BB277285) shows a substitution pattern that is different from the hairpin structure in Figure 4. Additional orthologous sequences from the frog Xenopus tropicalis (xtr), the chicken Gallus gallus (gga) and the pufferfish Tetraodon nigroviridis are included.
Conclusion
We have improved and extended our framework of comparative analysis and annotation of vertebrate promoter regions over previous releases (see [20]). The following features have been added to the CORG framework:
• Mapping of validated promoter regions and proper adjustment of the extent of upstream regions.
• Multiple alignments from significant local pair wise alignments.
• Novel approach to predict transcription factor binding sites.
• Web site offers now a genomic context view (as in Figure 1) and an option to export sequence and annotation data.
The CORG database is accessible via our web site. The user is guided step-by-step through the process of selecting and analyzing her promoter region of choice. CORG features an interactive viewer based on JAVA technology, which is tailored to detailed promoter analysis. Large-scale studies make direct use of our DAS service or the MySQL implementation of CORG in conjunction with an application interface (contact authors for details).
We presented selected application examples from the realm of vertebrate gene regulation. Conserved regulatory elements of different kinds (binding sites, microRNAs and UTR elements) are readily accessible to CORG users. New genomes and annotation will be continuously added to CORG.
Availability and requirements
The database is freely accessible through the website . Programs, scripts and MySQL database dumps are available from the authors upon request.
Authors' contributions
Christoph Dieterich built the entire pipeline and some parts of the web interface. Steffen Grossmann annotated transcription factor binding sites and provided parts of the web interface. Andrea Tanzer analyzed known and novel RNA elements in the multiple alignments of the CORG database. Stefan Röpcke set up our database of binding site descriptions. Peter F. Arndt worked on an appropriate alignment scoring scheme. Peter F. Stadler and Martin Vingron initiated this work and provided all necessary infrastructure.
Supplementary Material
Additional File 1
Distribution of distance between start of transcription and translation. Histogram of observed genomic distances between start sites of transcription and translation in man for 1,700 entries from the EPD. The red and blue line indicates the 90% and 95% quantiles, respectively. Distances greater than 106 bp were exluded from the analysis as they mostly occur due to mismappings in the ENSEMBL database.
Click here for file
Acknowledgements
We greatly acknowledge funding by the EU (BioSapiens Network of Excellence). Steffen Grossmann is supported by the Deutsche Forschungsgemeinschaft (DFG) as a member of the Sonderforschungsbereich (SFB) 618 – Theoretical Biology: Robustness, Modularity and Evolutionary Design of Living Systems.
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| 15723697 | PMC555765 | CC BY | 2021-01-04 16:39:32 | no | BMC Genomics. 2005 Feb 21; 6:24 | utf-8 | BMC Genomics | 2,005 | 10.1186/1471-2164-6-24 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-261573332710.1186/1471-2164-6-26Research ArticleGC-compositional strand bias around transcription start sites in plants and fungi Fujimori Shigeo [email protected] Takanori [email protected] Masaru [email protected] Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0035, Japan2 Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa 252-8520, Japan3 Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan4 Department of Environmental Information, Keio University, Fujisawa, Kanagawa 252-8520, Japan2005 28 2 2005 6 26 26 27 8 2004 28 2 2005 Copyright © 2005 Fujimori et al; licensee BioMed Central Ltd.2005Fujimori 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 GC-compositional strand bias or GC-skew (=(C-G)/(C+G)), where C and G denote the numbers of cytosine and guanine residues, was recently reported near the transcription start sites (TSS) of Arabidopsis genes. However, it is unclear whether other eukaryotic species have equally prominent GC-skews, and the biological meaning of this trait remains unknown.
Results
Our study confirmed a significant GC-skew (C > G) in the TSS of Oryza sativa (rice) genes. The full-length cDNAs and genomic sequences from Arabidopsis and rice were compared using statistical analyses. Despite marked differences in the G+C content around the TSS in the two plants, the degrees of bias were almost identical. Although slight GC-skew peaks, including opposite skews (C < G), were detected around the TSS of genes in human and Drosophila, they were qualitatively and quantitatively different from those identified in plants. However, plant-like GC-skew in regions upstream of the translation initiation sites (TIS) in some fungi was identified following analyses of the expressed sequence tags and/or genomic sequences from other species. On the basis of our dataset, we estimated that >70 and 68% of Arabidopsis and rice genes, respectively, had a strong GC-skew (>0.33) in a 100-bp window (that is, the number of C residues was more than double the number of G residues in a +/-100-bp window around the TSS). The mean GC-skew value in the TSS of highly-expressed genes in Arabidopsis was significantly greater than that of genes with low expression levels. Many of the GC-skew peaks were preferentially located near the TSS, so we examined the potential value of GC-skew as an index for TSS identification. Our results confirm that the GC-skew can be used to assist the TSS prediction in plant genomes.
Conclusion
The GC-skew (C > G) around the TSS is strictly conserved between monocot and eudicot plants (ie. angiosperms in general), and a similar skew has been observed in some fungi. Highly-expressed Arabidopsis genes had overall a more marked GC-skew in the TSS compared to genes with low expression levels. We therefore propose that the GC-skew around the TSS in some plants and fungi is related to transcription. It might be caused by mutations during transcription initiation or the frequent use of transcription factor-biding sites having a strand preference. In addition, GC-skew is a good candidate index for TSS prediction in plant genomes, where there is a lack of correlation among CpG islands and genes.
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Background
A prominent GC-compositional strand bias or GC-skew (=(C-G)/(C+G)), where C and G denote the numbers of cytosine and guanine residues, was reported recently around the transcription start sites (TSS) of Arabidopsis genes [1]. It is well known that GC-skews occur bi-directionally in circular bacterial genomes along the direction of replication, and that GC-skew is an effective index for predicting the replication origin in some bacteria [2,3]. In this case the numbers of G and T (thymine) residues in the leading strand of these genomes exceed those of C and A (adenine). Several models have been proposed to explain this bias [4]. A similar strand bias related to the direction of replication has also been observed in human mitochondrial genomes [5]. Also, mammalian and enterobacterial genomes have been reported to show a strand bias associated with transcribed regions [6-8]. An excess of G+T over A+C was observed in mammals within the sense strand of genes. A transcription-coupled DNA-repair system might be involved in this bias [9]. However, existing models cannot explain the excess of C over G in the sense strand around the TSS in Arabidopsis. Although slight GC-skews (regardless of the direction) were reported recently around the TSS in metazoans [10,11], it remains unclear whether the strong GC-skew of Arabidopsis is similar to that observed in metazoans.
Although large amounts of genomic and full-length cDNA sequence data from plants are now publicly available, knowledge of the promoters and TSS in plants is still limited compared to mammals, such as human and mouse. It has been reported that the CpG island [12] is the most effective index for predicting the promoter regions or TSS in mammals[13,14]. However, the CpG islands are not specifically located in the promoter regions in Arabidopsis, so they cannot be used for the prediction of TSS or promoters [15]. Identifying another, more suitable, index for the prediction of plant-specific TSS has therefore become a priority.
Answers are required for two key issues. First, which eukaryotic species, phyla or kingdoms have Arabidopsis-like GC-skews around the TSS? Second, what is the biological significance of these regions? In the present study, we used sequences from various animal, fungus, protist and plant species to conduct comparative analyses. We explored the potential value of GC-skew as an index for TSS prediction in plants. Finally, we considered the biological meaning of the GC-skew around the TSS of plant genes.
Results
GC-skew around the TSS of plant genes
The shift in GC-skew values around TSS was assessed by calculating the GC-skew for regions between 1.0-kb upstream and 0.5-kb downstream of the TSS in Arabidopsis (a dicotyledonous plant), rice (a monocotyledonous plant), human and Drosophila. Full-length cDNAs and genomic sequences were exploited from the species with available TSS data. The sliding-window method was used, and a value for the GC-skew was computed at the central position for each 100 bp. The average of the GC-skew values at each position was calculated for all of the genes. The results confirmed that the mean GC-skew spectra of both Arabidopsis and rice peaked at the same position as the TSS (Fig. 1). The mean GC-skew values were approximately zero in regions upstream (<-0.2 kb) from the TSS, as the numbers of C and G residues were equal in both plant species. The values increased from the proximal region (-0.2 to -0.1 kb) and peaked at the TSS. Downstream from the TSS, the mean GC-skew values were inversely low. Although a small GC-skew was detected around the TSS in Drosophila and an opposite skew (C < G) was observed in human, these were less prominent when compared to those in plants. Our findings for Drosophila and human were consistent with previous data [10,11], indicating that this is a plant- or angiosperm-specific phenomenon. Intriguingly, the mean GC-skew values at the TSS were identical in Arabidopsis and rice genes, which were 0.20 (standard deviation = 0.30) and 0.20 (standard deviation = 0.29), respectively. However, it should also be noted that the mean values for the G+C content in the TSS region (-50 to +50 bp) in Arabidopsis and rice were significantly different: 37 and 53%, respectively. In contrast, the G:C ratios at the TSS in Arabidopsis and rice were identical, despite the considerable difference in G+C content.
Figure 1 GC-skew in up- and downstream regions of the TSS. The up- and downstream regions of the TSS, which were 1.0 and 0.5-kb long, were analyzed using data from four species: 7,708 loci for Arabidopsis, 14,868 loci for rice, 14,053 loci for human and 8,344 loci for Drosophila. The graph shows the mean GC-skew values calculated for sequences of the four species using the sliding-window technique (window size = 100 bp; shift size = 1 bp).
We also determined the frequencies of the four nucleotides in the regions up- and downstream of the TSS in plants (see Fig. S1 in Additional file 1). High C-residue frequencies were observed in both Arabidopsis and rice (approximately 50 to 100 bp from the TSS). In contrast, G-residue frequencies decreased slightly around the TSS (+/-10 bp). These findings suggest that the peaks of GC-skew values observed near the plant TSS were caused by an increased frequency of C residues and a slight reduction in the G-residue frequency. No significant biases were observed in the frequencies of A and T residues, indicating a lack of AT-skew at the TSS (See Fig. S2 in Additional file 1).
The number of plant genes with a strong GC-skew in proximal regions of the TSS, was calculated by assessing the distributions of GC-skew values in regions +/-100 bp of the TSS. In order to take into account the wobble of the TSS, and to avoid experimental or mapping artifacts, only the maximum GC-skew values from these regions were used. The distributions of the maximum GC-skew values in the proximal regions of the TSS were similar in both plant species (Fig. 2). Over 70% of Arabidopsis genes and over 68% of rice genes showed a strong GC-skew (>0.33) near the TSS (that is, the number of C residues being more than double that of the G residues).
Figure 2 Distribution of maximum GC-skew values around the TSS in Arabidopsis and rice genes. The graph illustrates the distribution of maximum GC-skew values within 100-bp up- and downstream of the TSS in Arabidopsis and rice. The GC-skew values were computed using the sliding-window technique (window size = 100 bp; shift size = 1 bp). The numbers of sequences analyzed were 7,708 for Arabidopsis and 14,868 for rice.
Our results suggest that many plant (Arabidopsis and rice) genes have strong GC-skews around the TSS. Furthermore, this characteristic is common among both monocot and eudicot plants. In contrast, the GC-skews (including the C < G skew) that were observed in human and Drosophila were qualitatively and quantitatively different from those identified in plants.
GC-skew in eukaryotes
The existence of GC-skew peaks around the TSS in various eukaryotes was examined by calculating GC-skew values 100-bp downstream of the 5' -ends of virtually assembled transcripts [16,17]. Although these did not represent the actual TSS, we were able to approximate the GC-skew values in the downstream regions. Table 1 shows the mean GC-skew values in the downstream regions of the TSS for several species. A prominent GC-skew (an excess of C residues) was confirmed in the 5' -ends of the transcripts in seven out of the nine plant species examined, and in five out of seven species of fungi. Although opposite skews (C < G) were observed in several protist and animal species, no significant excess of C residues was detected in any of the 10 animal species or the 11 protist species analyzed.
Table 1 GC-skew in various eukaryotes
Group Species GC-skew Mean Std. Dev. No. of sequences
Plant Sorghum bicolor ++ 0.126 0.278 4,482
Oryza sativa ++ 0.118 0.304 18,676
Triticum aestivum + 0.095 0.255 13,133
Arabidopsis thaliana + 0.094 0.303 18,714
Gossypium + 0.092 0.304 1,561
Zea mays + 0.075 0.247 7,904
Glycine max + 0.050 0.311 6,162
Chlamydomonas reinhardtii 0.032 0.184 3,343
Pinus luchuensis -0.045 0.227 3,896
Fungus Filobasidiella neoformans ++ 0.222 0.341 243
Neurospora crassa ++ 0.184 0.276 2,763
Coccidioides immitis ++ 0.174 0.317 52
Aspergillus nidulans ++ 0.139 0.238 254
Magnaporthe grisea ++ 0.126 0.224 2,799
Saccharomyes cerevisiae -0.012 0.188 2,642
Schizosaccharomyces pombe -0.032 0.189 1,489
Protist Eimeria tenella 0.046 0.195 300
Tetrahymena thermophila 0.040 0.249 171
Trichomonas vaginalis 0.037 0.167 47
Dictyostelium discoideum 0.015 0.328 2,032
Neospora caninum -0.004 0.169 636
Toxoplasma gondii -0.019 0.184 1,328
Sarcocystis neurona -0.035 0.183 91
Trypanosoma brucei - -0.080 0.230 231
Plasmodium berghei -- -0.102 0.308 86
Cryptosporidium parvum -- -0.124 0.259 70
Plasmodium falciparum -- -0.191 0.354 1,905
Animal Caenorhabditis elegans 0.008 0.194 8,848
Drosophila melanogaster -0.011 0.170 14,310
Amblyomma variegatum -0.015 0.152 77
Ictalurus punctatus -0.017 0.216 382
Rattus norvegicus -0.023 0.193 12,594
Danio rerio -0.029 0.198 9,350
Homo sapiens -0.045 0.213 53,459
Mus musculus -0.045 0.217 50,029
Xenopus laevis - -0.064 0.212 13,444
Schistosoma mansoni - -0.088 0.230 195
The mean values and standard deviations (Std. Dev.) of the GC-skew values 100-bp downstream of the 5' -end were calculated in virtually assembled transcripts of nine plant species, seven species of fungus, 11 protist species and 10 animal species, which were downloaded from [16, 17]. The symbols + and ++ denote the predominance of C: ++ (≥0.10) and + (≥0.05). The symbols - and -- denote the predominance of G: -- (≤-0.10) and - (≤-0.05).
We determined whether GC-skew peaks were actually present in the TSS of fungal genes, by investigating the regions around the translation initiation sites (TIS) of fungal genomic sequences. Genomic sequence data and information on open reading frames (ORFs; including predicted ones) are publicly available for some fungi, although there is insufficient information about the TSS. Nevertheless, it was possible to estimate the tendency towards GC-skew around the TSS by analyzing sequences both up- and downstream of the TIS in the available genomic sequences. Figure 3 shows the mean GC-skew values in regions between 1.0-kb upstream and 0.5-kb downstream of the TIS in fungal species: Aspergillus nidulans, Fusarium graminearum, Magnaporthe grisea, Neurospora crassa, Saccharomyces cerevisiae and Schizosaccharomyces pombe. GC-skew peaks similar to those of plants were observed in 50- to 100-bp upstream regions of the TIS in all of these species, with the exception of S. cerevisiae.
Figure 3 GC-skew around TIS in fungal genes. The mean GC-skew values in regions 1.0-kb upstream and 0.5-kb downstream of TIS in the genomes of six species of fungi were calculated using the sliding-window technique (window size = 100 bp; shift size = 1 bp) for 9,432, 11,407, 10,054, 9,872, 5,825 and 4,305 loci in A. nidulans, F. graminearum, M. grisea, N. crassa, S. cerevisiae and S. pombe, respectively.
Correlation between the two nucleotide frequencies in plants
A possible mechanism causing the GC-skew around the TSS is nucleotide substitution, raising the question of what kind of substitution could be responsible. Correlations between the two nucleotide frequencies in regions both up- and downstream of the TSS were expected to indicate the answer (see Fig. 4 for Arabidopsis and rice, and Fig. S3 in Additional file 1 for human and Drosophila). If the substitution rate between two specific nucleotides in one region is higher than that in other regions, a larger negative correlation would be expected between the two nucleotide frequencies, compared to other regions. In both plant species, the correlation coefficient (r) of A-T and G-C decreased dramatically around the TSS: the values were -0.4 to -0.7, and 0.0 to -0.5, in Arabidopsis (Fig. 4(a)), respectively; and 0.18 to -0.2, and 0.2 to -0.4, in rice (Fig. 4(b)), respectively. In contrast, the r values of A-G and T-C increased significantly around the TSS: values were -0.4 to 0.1, and -0.4 to 0.2, in Arabidopsis (Fig. 4(a)), and -0.5 to 0.0, and -0.6 to -0.3, in rice (Fig. 4(b)).
Figure 4 Correlation between the two nucleotide frequencies in plants. The figure illustrates the correlation coefficients (r) between the two nucleotide frequencies (A-T, A-G, A-C, G-C, T-C and T-G) at each position around the TSS in (a) Arabidopsis and (b) rice genes. Each nucleotide frequency at a particular position was defined as the frequency of the nucleotide in a 100-bp window at that position. Correlation coefficients were calculated using these frequencies (see Methods section). The numbers of sequences analyzed were 7,708 for Arabidopsis and 14,868 for rice.
GC-skew and gene-expression level
In order to examine the relationship between GC-skew at the TSS and gene expression in plants, we conducted a statistical test using serial analysis of gene expression (SAGE) data for Arabidopsis (10-day-old seedlings) [18]. The mean GC-skew value in the TSS of highly-expressed genes (see Methods section for details) was significantly higher (P = 0.0003, paired t-test) than in genes with low expression in Arabidopsis: the mean values were 0.25 (standard deviation = 0.31) and 0.19 (standard deviation = 0.31).
Potential value of GC-skew as an index for TSS prediction
Many of the GC-skew peaks were preferentially located near the TSS, therefore we assessed the potential value of GC-skew as a predictive index for TSS in plants. Sequences 1-kb upstream of the predicted ORF start positions in plant genomic sequences were used in the following analysis.
First, the GC-skew values were computed using the sliding-window technique, in which one window and the shift size were set to 100 and 1 bp, respectively. GC-skew peaks satisfying a particular cut-off value were identified as primitive TSS candidates from the noisy GC-skew spectrum using the Savitzky-Golay (S-G) filter [19], which simultaneously smoothes and differentiates. Next, TSS candidates that were located within 50 bp of another candidate were considered to be identical and were merged into the position with the highest GC-skew. TSS prediction was validated by counting as true positives (TP), the candidates that were located within 100-bp up- or downstream of the actual TSS. If more than two candidates coexisted in the appropriate region, they were regarded as one TP.
Using this method and the criteria described above, we validated the predictive performance of GC-skew under cut-off values ranging from -0.9 to 0.9 (Table 2). The specificity (SP = TP/(TP+FP)) ranged from 14 to 87%, and the sensitivity (SN = TP/(TP+FN)) ranged from 1 to 95% in Arabidopsis. The SP in rice ranged from 12 to 56%, and the SN ranged from 1 to 99%. The false-positive rate (FPR = FP/(TN+FP)) varied with the cut-off values in a similar manner to SN. The difference between our results and random cases was clarified using a receiver-operating characteristic (ROC) curve (Fig. 5). This is a plot of FPR versus SN, with each cut-off value corresponding to a point on the curve. Good ROC curves lie closer to the top left-hand corner, whereas the random cases are represented as a diagonal line (defined by FPR = SN). Predictions made using the GC-skew appeared to differ from the random cases, and lay closer to the top left-hand corner in both Arabidopsis and rice. In addition, the correlation coefficient (φ; see Methods section for details) corresponding to each GC-skew cut-off value was calculated in order to compare their predictive performances. The GC-skew value that maximized φ was 0.4 in both Arabidopsis and rice (SP = 45%, SN = 41% and FPR = 8%, and SP = 47%, SN = 38% and FPR = 10%, respectively).
Table 2 TSS prediction results for a stepwise increase of cut-off values
Arabidopsis Rice
GC-skew cutoff SN (%) SP (%) FPR (%) φ SN (%) SP (%) FPR (%) φ
-0.9 95.3 13.6 24.4 0.149 98.7 11.6 6.4 0.068
-0.8 95.3 13.6 24.5 0.149 98.7 11.6 6.4 0.068
-0.7 95.3 13.6 24.5 0.150 98.6 11.7 6.5 0.068
-0.6 95.2 13.7 24.7 0.150 98.6 11.7 6.7 0.069
-0.5 95.2 13.7 25.2 0.153 98.4 11.7 7.2 0.072
-0.4 94.9 13.9 26.5 0.157 98.2 11.8 8.4 0.078
-0.3 94.2 14.3 29.4 0.168 97.8 12.1 11.1 0.092
-0.2 93.0 15.1 34.6 0.187 96.6 12.6 16.4 0.115
-0.1 90.7 16.4 42.3 0.213 94.4 13.6 25.3 0.147
0.0 86.7 18.6 52.6 0.247 90.4 15.4 37.9 0.186
0.1 80.0 21.8 64.2 0.282 83.5 18.2 53.2 0.230
0.2 70.2 26.4 75.6 0.315 74.1 22.7 68.5 0.279
0.3 58.7 32.9 85.0 0.345 62.0 29.4 81.4 0.322
0.4 45.0 40.9 91.9 0.354* 47.3 37.7 90.2 0.340*
0.5 31.6 50.5 96.1 0.343 32.3 46.7 95.4 0.327
0.6 20.6 60.6 98.3 0.312 19.1 55.3 98.1 0.281
0.7 11.1 70.2 99.4 0.251 9.1 60.2 99.2 0.204
0.8 4.6 77.4 99.8 0.171 3.2 59.4 99.7 0.119
0.9 1.1 86.9 100.0 0.089 0.8 56.3 99.9 0.057
Only the TSS of genes in which a TIS was defined within the 1.0-kb downstream region are included. Sequence data from 1.0-kb upstream of the TIS (6,850 sequences for Arabidopsis and 13,111 sequences for rice) were used for the TSS prediction. The asterisks denote maximum φ.
Figure 5 ROC curves for predictions by GC-skew peak. ROC curves for (a) Arabidopsis and (b) rice. The areas under the curves (AUCs) for ROC curves were 0.80 for Arabidopsis and 0.78 for rice. The diagonal lines in the two graphs correspond to the ROC curve produced by random prediction.
Discussion
This study confirmed the presence of significant GC-skews (C > G) around the TSS (or upstream regions of the TIS) of genes in some species of plants and fungi. In contrast, our analysis revealed no significant excess of C residues in either animals or protists. However, an opposite GC-skew (C < G) has been reported previously in several animal species (Mus musculus, Rattus norvegicus, Fugu rubripes, Danio rerio and human) [10]. Although small skews were detected for these species in our present analysis (Table 1; Fig. 1), they were not significant compared to those observed in plants. Aerts et al. [10] reported a GC-skew (C > G) close to the TSS in two nematode species (Caenorhabditis elegans and Caenorhabditis briggsae) however, no significant GC-skew was detected in C. elegans in our analysis. This inconsistency was probably due to the fact that our analysis targeted only regions downstream of the TSS in C. elegans (thus, regions upstream were not examined); alternatively, the skew might have been too small to be detected using our method. In either case, it is difficult to compare GC-skews between nematodes and plants, since trans-splicing at the 5' -ends of genes has been reported in nematodes [20,21], as pointed out by Aerts et al [10]. As noted above, the GC-skews near the TSS in plants and fungi differed from those of other species (or kingdoms) in both quality and quantity. To our knowledge, this is the first report to describe the prominent GC-skew (C > G) around the TSS specific to plants and fungi.
We propose two possible explanations for the GC-skew peaks found close to the TSS. First, regulatory elements, such as transcription factor-biding sites (TFBS), which are present in regions both up- and downstream of the TSS, might contribute to (or even cause) this phenomenon. Moreover, some TFBS have a strand preference (see for example [22]). Therefore, if these types of TFBS are preferentially located around the TSS of plant and fungal genes, they might influence the local GC-compositional strand bias. Second, the GC-skew might be involved in transcription-coupled events, such as transcription-associated mutational asymmetry [6-9]. Tatarinova and colleagues [1] mentioned such transcription-associated mutational asymmetry and suggested that the GC-skew around the TSS of genes might be caused by the substitution of C residues with T residues, due to C deamination in the template strand. However, this hypothesis cannot fully explain all of the findings of our present study. If the C-to-T transition occurred preferentially around the TSS in the template strand, an AT-skew would also be expected in these regions. However, no significant AT-skew (see Fig. S2 in Additional file 1) was observed at the TSS of either Arabidopsis or rice genes, indicating that the C-to-T transition was not the main cause of the GC-skew. Furthermore, in our analyses, significant changes in the correlation coefficient (decreased A-T/G-C and increased A-G/T-C) were observed in both Arabidopsis and rice (Fig. 4). Assuming that the GC skew is caused by mutations during transcription initiation, changes in the correlation coefficient might be interpreted as an increase in the transversion ratio around the TSS. The increased negative correlation between C and G, coupled with the high GC-skew value in the same region, led us to speculate that G-to-C transversion occurred at relatively high rates in this region in plants and fungi, but not in animals and protists. If mutations that yield a GC-skew occur mainly around the TSS of single-stranded DNA, highly-expressed genes would be expected to have a high GC-skew around the TSS. In fact, the mean GC-skew value in the TSS of highly-expressed genes was significantly higher than in genes with low expression in Arabidopsis. This indicates that the GC-skew is associated with the level of gene expression, at least in Arabidopsis. Strand-specific mutational rates are believed to be a by-product of transcription-coupled DNA repair in mammals [8], therefore the GC-skew observed around the TSS of plant genes might result from the plant-specific DNA lesion and repair system. Alternatively, if the GC-skew was caused by the higher frequency of strand-specific TFBS, the higher mean GC-skew value in highly-expressed genes might be interpreted as a greater effect of strand-specific TFBS for the transcription efficiency. In either strand-specific TFBS or mutation, highly-expressed genes appear to have a high GC-skew around the TSS. As a GC-skew was also detected in the upstream regions of the TIS in fungal genes, this feature might be generated by a common mechanism in both groups. However, additional sequence data, and investigations into the patterns of nucleotide substitutions and their rates around the TSS, both between and within groups, will be necessary to elucidate the origin and mechanism of GC-skew around the TSS of plant and fungal genes.
TSS prediction by the GC-skew peak was validated through a stepwise increase of cut-off values which demonstrated that the GC-skew could contribute to TSS prediction. Although the optimal GC-skew cut-off value depends on the specific situation, our results will be helpful in determining the optimal cut-off. Figure 6 shows representative cases in which the GC-skew peaks are located near the TSS in Arabidopsis and rice genes. The single index presented in this paper might not be sufficient to achieve accurate TSS prediction. However, our results indicate that GC-skew is a good candidate index for the TSS, promoter or first exon in plants. Thus, the combined use of GC-skew and other indices, or the incorporation of this index into pre-existing programs appears to be a realistic and effective approach for TSS prediction.
Figure 6 Representative cases of GC-skew peaks located in the TSS of plant genes. These figures show typical examples of GC-skew peaks located near the TSS in Arabidopsis (a) and rice (b). Red solid triangles represent the positions of the GC-skew peak. The corresponding GenBank entry IDs are: AF361813, AF380632, AK105340 and AK102437.
Conclusion
Significant GC-skew (C > G) around the TSS is strictly conserved among monocot and eudicot plants (that is, angiosperms), and a similar skew is also seen in some fungi. The mean GC-skew at the TSS in the highly expressed genes was greater than that in the group with low expression. We therefore propose that the GC-skew around the TSS in some species of plants and fungi is associated with transcriptional activity. This is probably a result of DNA mutations during transcription initiation or the frequent use of strand-specific TFBS. Our findings also confirm that GC-skew has the potential to assist TSS prediction in plant genomes, where there is a lack of correlation among CpG islands and genes.
Methods
Data sources
Data for 13,095 full-length cDNAs from Arabidopsis thaliana were downloaded from The Institute for Genomic Research (TIGR) [23] (as of March 2, 2001) and the RIKEN Arabidopsis Genome Encyclopedia [24] (as of May 9, 2002). Genomic sequences for Arabidopsis were downloaded from The National Center for Biotechnology Information (NCBI) [25] (as of January 31, 2003). ORF information for Arabidopsis genes was obtained from the NCBI Entrez database [26], based on each of the original sequence IDs. For rice (O. sativa ssp. japonica c.v. Nipponbare), 28,469 full-length cDNA sequences [27] were retrieved from the Knowledge-Based Oryza Molecular Biological Encyclopedia (KOME) [28] with ORF information, and the genomic sequences were obtained from the Rice Genome Research Program [29] (as of October 16, 2002) and Syngenta Biotechnology Inc. (SBI) [30]. For analysis of human (Homo sapiens) DNA, 21,245 full-length cDNAs and genomic sequences were downloaded from the DNA Data Bank of Japan (DDBJ) [31] and Ensembl (Ver. 9.30) [32], respectively. Data for 9,872 full-length cDNA sequences (as of July 17, 2002) and genomic sequences of Drosophila melanogaster were obtained from the Berkeley Drosophila Genome Project [33,34]. Fungal genomic sequence data, including ORF information, were downloaded from the NCBI [35,36] for S. cerevisiae and S. pombe, and from the Fungal Genome Initiative (FGI) [37] for A. nidulans, F. graminearum, M. grisea, and N. crassa. Virtually assembled transcripts from 10 animal species, nine plant species, six species of fungi and 11 protist species were retrieved from the TIGR Gene Indices (TGI) [16,17].
Mapping of cDNA to the genomic sequences
Redundant cDNA sequences showing at least 95% similarity in at least 95% of the regions, compared with other sequences were excluded from the full-length cDNA dataset for four species – Arabidopsis, rice, human and Drosophila. They were identified using BLASTN homology searching and CAP3 [38]. Also, any poly(A) tracts at the 3' -end of the cDNA sequences were eliminated. Finally, by mapping the cDNA sequences for corresponding genomic sequences using the BLASTN program and SIM4 [39], sequences both up- and downstream of the TSS were determined.
Gene-expression data
SAGE data for Arabidopsis (10-day-old seedlings) [18] were used to examine the relationship between GC-skew in the TSS and gene-expression levels. In assigning the SAGE tags to genes, only data that showed one-to-one correspondence between the genes and tags were included. Genes with <100 counts per million were defined as the low-expression group (504 genes) and those with >100 per million the high-expression group (689 genes). The mean GC-skew values in a 100-bp window at the TSS were calculated for both groups. Genes with a G+C content at the TSS of <0.3 were eliminated from the dataset in advance.
GC-skew peak detection
As an initial step towards detecting the GC-skew peaks, GC-skew values at each position in the sequences were calculated using the sliding-window technique (window size = 100 bp; shift size = 1 bp). Next, we attempted to smooth the spectrum, to reduce the noise and to simultaneously determine the peaks, using the S-G filter [19]. The first-order derivative of the smoothed GC-skew value gi at position i is given by the following equation:
Here, fi+n is the original GC-skew value in the position i + n. nl and nr represent the lengths of the filter window to the left and right of position i, and were set to 20 in this analysis. Cn denotes the set of weight coefficients and corresponds to the first-order derivatives of the quartic polynomial. The position of the GC-skew peak i was defined as the zero-crossing point, which is the point satisfying the following condition:
gi·gi + 1 ≤ 0 ∩ gi + 1 < 0
The peak was only counted as a TSS candidate if a GC-skew value at that peak satisfied the particular cut-off value, and the G+C content was ≥ 0.3 in the window. When more than one peak occurred within 50 bp, they were regarded as identical and the peak with the highest GC-skew value was accepted. Using this procedure, TSS prediction was conducted with stepwise changes in the cut-off values of the GC-skew (-0.9 to 0.9) at the peak.
The predictive accuracy was verified by counting TSS candidates located within 100-bp up- and downstream of the actual TSS as true positives (TP). Where more than two TSS candidates coexisted within 100-bp either up- or downstream of the TSS, they were counted as one TSS candidate. The rest of the candidates, which were located in inappropriate regions, were counted as false positives (FP). To compare results for different cut-offs, the correlation coefficient (φ), which is one of the measures used to compare predictive performance [40], was calculated as follows:
Here, TN and FN denote the number of true and false negatives, respectively. In this analysis, the total number of negatives (N) was defined as the maximum number of possible TSS candidates in the non-TSS regions. Thus, TN was calculated as N-FP.
Correlation coefficient between the two nucleotide frequencies
The correlation coefficient rij(p) between nucleotide i and j at a position p up- and downstream of the TSS was defined as follows:
Here, ik(p) and jk(p) are the frequencies of i and j at a position p in the sequence k, respectively. and are the mean frequencies of i and j at p. The nucleotide frequency in each position was the number of each nucleotide in a 100-bp window.
Authors' contributions
SF carried out the computational and statistical analysis, and drafted the manuscript. TW and MT participated in the design and coordination of the study. All of the authors have read and approved the final manuscript.
Supplementary Material
Additional File 1
This file contains three figures: S1, showing the nucleotide frequency around the TSS; S2, showing the AT/GC-skew in both up- and downstream regions of the TSS in plants; and S3, showing the correlation between the two nucleotide frequencies around the TSS in human and Drosophila.
Click here for file
Acknowledgements
We would like to thank Keitaro Senda, Sayaka Kitamura, Haruo Suzuki, Hitomi Ito and members of the Institute for Advanced Biosciences for helpful discussions during the course of this work. We also thank the Rice Full-Length cDNA Consortium (the National Institute of Agrobiological Sciences Rice Full-Length cDNA Project Team, the Foundation of Advancement of International Science Genome Sequencing & Analysis Group and the RIKEN Institute) for providing the full-length cDNA data on rice. This work was supported by the Ministry of Agriculture, Forestry and Fisheries of Japan (Rice Genome Project SY-1104). This work was also supported by the Ministry of Education, Culture, Sports, Science and Technology of Japan, through "the 21st Century COE Program" and "Special Coordination Funds Promoting Science and Technology".
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| 15733327 | PMC555766 | CC BY | 2021-01-04 16:39:33 | no | BMC Genomics. 2005 Feb 28; 6:26 | utf-8 | BMC Genomics | 2,005 | 10.1186/1471-2164-6-26 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-5-51576046610.1186/1471-2210-5-5Research ArticlePharmacokinetic-pharmacodynamic modelling of the cardiovascular effects of drugs – method development and application to magnesium in sheep Upton Richard N [email protected] Guy L [email protected] Anaesthesia and Intensive Care, Royal Adelaide Hospital, North Terrace, Adelaide, SA 5000, Australia2 Anaesthesia and Intensive Care, University of Adelaide, North Terrace, Adelaide, SA 5005, Australia2005 10 3 2005 5 5 5 28 10 2004 10 3 2005 Copyright © 2005 Upton and Ludbrook; licensee BioMed Central Ltd.2005Upton and Ludbrook; 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 have been few reports of pharmacokinetic models that have been linked to models of the cardiovascular system. Such models could predict the cardiovascular effects of a drug under a variety of circumstances. Limiting factors may be the lack of a suitably simple cardiovascular model, the difficulty in managing extensive cardiovascular data sets, and the lack of physiologically based pharmacokinetic models that can account for blood flow changes that may be caused by a drug. An approach for addressing these limitations is proposed, and illustrated using data on the cardiovascular effects of magnesium given intravenously to sheep.
The cardiovascular model was based on compartments for venous and arterial blood. Blood flowed from arterial to venous compartments via a passive flow through a systemic vascular resistance. Blood flowed from venous to arterial via a pump (the heart-lung system), the pumping rate was governed by the venous pressure (Frank-Starling mechanism). Heart rate was controlled via the difference between arterial blood pressure and a set point (Baroreceptor control). Constraints were made to pressure-volume relationships, pressure-stroke volume relationships, and physical limits were imposed to produce plausible cardiac function curves and baseline cardiovascular variables. "Cardiovascular radar plots" were developed for concisely displaying the cardiovascular status. A recirculatory kinetic model of magnesium was developed that could account for the large changes in cardiac output caused by this drug. Arterial concentrations predicted by the kinetic model were linked to the systemic vascular resistance and venous compliance terms of the cardiovascular model. The kinetic-dynamic model based on a training data set (30 mmol over 2 min) was used to predict the results for a separate validation data set (30 mmol over 5 min).
Results
The kinetic-dynamic model was able to describe the training data set. A recirculatory kinetic model was a good description of the acute kinetics of magnesium in sheep. The volume of distribution of magnesium in the lungs was 0.89 L, and in the body was 4.02 L. A permeability term (0.59 L min-1) described the distribution of magnesium into a deeper (probably intracellular) compartment. The final kinetic-dynamic model was able to predict the validation data set. The mean prediction error for the arterial magnesium concentrations, cardiac output and mean arterial blood pressure for the validation data set were 0.02, 3.0 and 6.1%, respectively.
Conclusion
The combination of a recirculatory model and a simple two-compartment cardiovascular model was able to describe and predict the kinetics and cardiovascular effects of magnesium in sheep.
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Background
The effective use of some drugs can be limited by their adverse effects on the cardiovascular system, particularly when they are used intravenously in relatively high doses. There have been many studies documenting the cardio-vascular effects of drugs. Similarly, many mathematical models of the cardiovascular system, of varying complexity, have been presented in the literature [1,2]. In pioneering work, models of the cardiovascular system have been linked to pharmacokinetic models of volatile anaesthetic disposition [3-5]. These kinetic-dynamic models have since been developed into mannequin based anaesthesia simulators, which now have a pivotal role in the training of anaesthetists. This approach has been facilitated by the fact that models of volatile anaesthetic disposition have traditionally been physiologically based (e.g. using representations of tissue:blood partition coefficients and blood flows for individual organs or groups of organs). It is therefore possible to equate blood flow in the cardiovascular model to blood flow in the pharmacokinetic model. Nevertheless, a limiting factor in the implementation of this approach is the availability of experimental data on concentration-effect relationships [5].
In contrast, for traditional ("non-volatile") drugs, there have been very few instances in which kinetic models of a drug have been linked to cardiovascular pharmacodynamic models. The work of Francheteau et al. is an important exception [6], but even this early work was restricted to analysis of only one aspect of the cardiovascular system (i.e. accounting for heart rate mediated control of blood pressure but not Frank-Starling control of cardiac output). However, it is clear this approach has the potential to provide a more rational basis for designing dose regimens of cardio-active drugs, and could provide insight into strategies for controlling their cardio-vascular effects. It maybe possible to predict a priori the cardiovascular consequences of, for example, a change in dose regimen of a drug.
There are a number of difficulties in implementing this approach for traditional drugs. One problem is that most drugs do not cause changes in one single cardiovascular variable (such as blood pressure) that can be described in the usual manner using a simple semi-empirical dynamic model (e.g. an Emax model). Rather, a number of cardiovascular variables may be altered simultaneously in a manner that is complex and interrelated. Thus, any dynamic model used must account for these intrinsic relationships between cardiovascular variables. Another problem is that changes in the cardiovascular system (in particular blood flow distribution) invariably alter the kinetics of the drug under study. Therefore, the kinetic model of the drug must be able to account for the effects of blood flow changes on the disposition of the drug. This requires the kinetic model to have a physiological basis, and importantly excludes the widely used mamillary compartmental pharmacokinetic model.
The general aims of this study were threefold. First, to develop a simple dynamic model of the cardiovascular system that was of sufficient complexity to account for the major mechanisms by which drugs can alter cardiovascular variables. Second, to examine whether recirculatory kinetic models [7] have sufficient physiological basis to account for drug related blood flow changes. Third, to examine approaches for identifying the important concentrations (and their sites in the body) that can be used to link the kinetic and dynamic models.
The specific aim was to use previously published data collected using a chronically instrumented sheep preparation [8,9] to develop a kinetic-dynamic model for the cardiovascular effects of magnesium. Magnesium is given intravenously to treat a number of diseases, including pre-eclampsia. It relaxes smooth muscles in blood vessels thereby lowering systemic vascular resistance, with a consequent decrease of mean arterial blood pressure and increase in cardiac output. It provides a useful drug for initial analysis as its kinetics and dynamics are relatively simple and well understood.
The overall hypothesis of this work is that it is possible to construct a faithful model of the cardiovascular effects of drugs such as magnesium. While doing so requires more assumptions and estimates of parameter values than normally associated with semi-empirical pharmacokinetic-pharmacodynamic modelling, a physiological approach greatly increases the utility of the resulting models. It is proposed that the general methods presented here could be applied to the development of similar models for other drugs with acute cardiovascular effects.
Methods
General rationale
With respect to devising a pharmacodynamic model of the cardiovascular system, the important steps are:
1. Identifying which cardiovascular variables (e.g. heart rate, blood pressure) are important. This depends on the drug and the intended use of the model, but it is proposed that there is a minimum set of variables that is needed for a basic description of cardiovascular status.
2. Devising a way of conveniently presenting the output of the dynamic model for a range of cardiovascular variables for comparison with data.
3. Identifying a cardiovascular model of the appropriate complexity. Ideally the model must be of the minimum complexity that includes the cardiovascular variables identified above, and the major sites of action of the drugs.
4. Identifying which parameters of the cardiovascular model can be estimated by curve-fitting, and which require prior estimates or measurements of physiological values. Most cardiovascular models are stiff numerical systems with many parameters, and only a small number can be estimated by curve-fitting the data in the traditional way.
With respect to the pharmacokinetic model, there remains one crucial step:
5. Constructing a kinetic model with a physiological basis that is sufficiently realistic to describe and predict the concentration of the drug in the key target organs controlling the cardiovascular system. On first principles, these could be expected to include:
a. the myocardial concentrations when the drug has a direct myocardial effect (e.g. causes myocardial depression);
b. the CNS concentrations when the drug affects the cardio-respiratory control centre of the brain;
c. the arterial blood concentration when the drug affects baro-receptors or smooth muscle in the walls of the arterial vascular system;
d. the venous blood concentration when the drug affects smooth muscle in the wall of the capacitance vessels of the venous vascular system.
It is known that these concentrations can follow different time-courses, particularly after bolus administration or a change of infusion rate [10,11]. However, it may not be necessary to know the time-course of these concentrations for every drug, depending on its mechanism of action.
Data sets and software
The data used to construct the model were collected in the same laboratory using a conscious chronically instrumented sheep preparation and have been published previously [8,9]. This facilitated the model building process, as the effect of differences in species and measurement methods could be discounted.
Data set 1 [9] (for model development) was a detailed set of cardiovascular measurements made after the administration of 30 mmol of magnesium over 2 min to 5 sheep. Measurements included arterial and coronary sinus (effluent from the heart) magnesium concentration, cardiac output, mean arterial blood pressure, heart rate, an index of myocardial contractility (Maximum positive rate of change of left ventricular Pressure, dp/dt) and an index of filling pressure (Left ventricular end diastolic pressure) and myocardial blood flow. These were made until 25 min after the start of administration.
Data set 2 [8] (for model validation) was a less comprehensive set of cardiovascular measurements made after the administration of 30 mmol of magnesium over 5 min to 5 sheep (not the same sheep as Data set 1). Measurements included arterial magnesium concentration, cardiac output, and mean arterial blood pressure, and were made until 25 min after the start of administration. The blood pressure data for one animal in this data set was excluded, as it was idiosyncratically low.
The time-course of the data averaged across sheep were used for all modelling – the resultant model therefore represents the response of the average sheep. Inter- and intra-animal variability were not considered, although it is noted that the final model may provide insight into sources of kinetic and dynamic variability for later study.
The software used was the Scientist for Windows program (Version 2.01, Micromath, Salt Lake City, Utah, USA), predominantly for curve-fitting. The R language, Version 1.9.0, [12] was used for graphical data analysis, data handling and simulations. Coding the same model in the two different programs provided a useful check for errors.
For the least squares curve-fitting, the best fit was determined as that with the highest Model Selection Criteria (MSC) and without non-identifiable parameters. The MSC is essentially an inverse Akaike Information Criterion scaled to compensate for data sets of different magnitudes (Scientist for Windows manual, Micromath, Salt Lake City, Utah, USA), and is calculated as follows where wi is a weighting term, p is the number of parameters and n is the number of data points:
All data points were weighted equally. A parameter was arbitrarily defined as non-identifiable if the standard deviation of the parameter returned by the fitting program was greater than the parameter estimate (i.e. the coefficient of variation was greater than 100%). A model with non-identifiable parameters means that the data do not contain sufficient information to estimate the parameter with precision.
The symbols used throughout have been based on standards for the pharmacokinetic literature. Unfortunately, the use of C for both concentration (in pharmacokinetics) and compliance (in cardiovascular physiology) creates of conflict for this paper. To avoid confusion, CPL will be used for compliance here.
Pharmacodynamic model of the cardiovascular system
Identification of important cardiovascular variables
The choice of the cardiovascular variables used in the model is clearly dependent on the drug under study and the intended purpose of the model. However, we propose that a minimum of 7 fundamental cardiovascular variables is sufficient for most pharmacological purposes. These variables are shown with their default unit of measurement in the model: Central venous pressure (CVP, mmHg), Myocardial contractility (CNT, L mmHg-1), Stroke volume (SV, L), Heart rate (HR, min-1), Cardiac output (CO, L min-1), Systemic vascular resistance (SVR, Resistance units, RU) and mean arterial blood pressure (MAP, mmHg).
This choice of variables requires several assumptions:
Assumption 1
All variables are time averaged in that beat to beat variation is ignored (e.g. mean arterial blood pressure is used rather than systolic and diastolic blood pressures).
Assumption 2
That the function of the left and right side of the heart is the same, and there are no abnormalities in the pulmonary vasculature so that the heart-lung system can be treated as one pump.
Assumption 3
Long time-scale events such as fluid shifts and renal mechanisms controlling blood pressure are ignored.
Furthermore, this choice of variables is dictated by several fundamental relationships between the variables. Firstly, that myocardial contractility is a proportionality constant between CVP pressure and stroke volume (the volume of blood pumped with each beat of the heart).
CVP * CNT ≈ SV ...(2)
Mathematically, CNT must therefore have the units of volume / pressure. However, contractility is difficult to measure in vivo, and that there are a number of surrogate measures including dp/dt. These can also be used with appropriate scaling factors.
Assumption 4
That there are no factors affecting the relationship between myocardial fibre length (the true determinant of stroke volume) and central venous pressure (e.g. changes in myocardial compliance). CVP is therefore used as an easily measured index for myocardial fibre length – the assumption is that the two are related using a scaling factor. Left ventricular end diastolic pressure is also as an alternative index when data are presented as percent change from baseline.
The second relationship is that between stroke volume and heart rate to give the cardiac output (the volume of blood pumped by the heart per unit time):
SV * HR = CO ...(3)
The last relationship is that between cardiac output and systemic vascular resistance to approximate the mean arterial blood pressure.
CO/SVR ≈ MAP ...(4)
This is because MAP usually greatly exceeds the CVP:
CO/SVR = MAP-CVP ...(5)
SVR therefore has the units of pressure over flow. In this paper, the resistance units (RU) are therefore mmHg L-1 min.
Presentation of relationships between cardiovascular variables
The effect of a drug on one or two variables can usually be summarised on a plot of the variable (drug effect) against time. However, it is more difficult to summarise the dynamic effect of a drug on the cardiovascular system for the following reasons: First, the large number of variables required in the summary, where the seven described above could be considered a minimum. Second, the fixed inter-relationships (e.g. Eqns. 2–4) between these variables that should be revealed by the summary (e.g. if SV increased by 25% and all else remains the same, then CO should also increase by 25% (Eqn. 3)). Third, usually an analysis requires comparing one cardiovascular state (e.g. pre-drug) with another (e.g. post-drug), or examining the time-course of drug effects.
It is proposed that a "cardiovascular radar plot" with a modified logarithmic, normalised scale is an efficient means of limiting these problems. An example radar plot is shown and described in Fig. 1. Radar plots are particularly useful for visually testing whether a model of the cardiovascular system behaves appropriately for all 7 key cardiovascular variables when challenged with a particular drug or physiological circumstance. It is particularly useful to see if the pattern of changes is internally consistent. For example, in Fig. 1 it is clear that magnesium dropped SVR, but the drop in MAP was not great as expected because there was a baroreceptor mediated increase in heart rate.
Figure 1 An example of a cardiovascular radar plot. A cardiovascular radar plot of the effect of magnesium (n = 5 sheep) on the cardiovascular system under baseline conditions and for a number of time-points until 25 min after the intravenous administration of magnesium. A scale for each of the 7 key cardiovascular variables radiates from the centre of the plot. LVEDP (left ventricular end diastolic pressure) is a surrogate for CVP; both should change proportionally. The data are transformed and the scale constructed so that 3 is the baseline (pre-drug) value. Thus, the blue line for the baseline data is a ring passing through 3 for each variable. Baseline conditions therefore have a characteristic equilateral 7 sided shape. The full scale is structured as follows: 1 one quarter baseline 2 half baseline 3 baseline 4 twice baseline 5 4 times baseline This scale has the property that for an equivalent increase or decrease in a cardiovascular variable compared to baseline, the line will move an equal distance in or out from the baseline value. It can be seen that following magnesium there was a drop in SVR with a baroreflex increase in HR to compensate for the drop in blood pressure. LVEDP also dropped, but with minimal change in contractility. Drugs that affect the cardiovascular system via different mechanisms produce plots with characteristic shapes, which can be recognised with experience. Note: The order of the variables on the radar plot has been chosen to account for key relationships between the variables, with CVP (as given by LVEDP) as the most fundamental variable at the top: In an anti-clockwise direction the following relationships or approximations hold: CVP * CNT ≈ SV SV * HR = CO CO/SVR ≈ MAP
Cardiovascular model – Structure and parameter estimation
There are many published models of the cardiovascular system of various levels of complexity and intended for various tasks [1]. However, in this paper, the cardiovascular model was constructed progressively from first principles, with adaptations and increases in complexity as dictated by the requirements of the modelling process and the data. This ensured the model was the minimum that was needed for the task at hand.
In vivo, the cardiovascular system has two major control systems; control of cardiac output via the Frank-Starling mechanism, and control of blood pressure via baroreceptor control of heart rate. These were added progressively to the model.
A simple Frank-Starling model
A simple model of the Frank-Starling mechanism was developed (Fig. 2) assuming the blood is predominately in two pools – the arterial and venous vasculature. The two pools are connected by a pump (the heart) moving blood from the venous to arterial side for which the rate of pumping is proportional to the venous pressure. Blood flows from the arterial to venous side through a passive resistance (the SVR). The pressure in each pool is a function of the compliance in the pool. Compliance (CPL) governs the relationship between volume and pressure:
Figure 2 A simple Frank-Starling model of the cardiovascular system. A simple two compartment model of the circulation, with control of the cardiac output via the Frank-Starling mechanism. When the heart is not pumping, the pressures on the venous (Pv) and arterial (Pa) sides of the circulation are equal (the mean circulatory pressure (MCP = Pv = Pa) is approximately 7 mmHg). The unstressed volumes of the venous (Vv0) and arterial sides (Va0) are governed by the relative compliance of the venous and arterial pools (CPLv and CPLa, respectively). If the pumping action of the heart is initiated, a fraction of the blood (dV) moves from the venous to the arterial side thereby increasing arterial pressure and decreasing venous pressure. The pressure gradient causes blood to flow from the arterial side to the venous side (at a rate given by the venous return, COR). This depends on the pressure gradient (Pa-Pv) and the systemic vascular resistance (SVR).
Pressure = Volume/Compliance ...(6)
The solution to the simple Frank-Starling model can be found algebraically, but for consistency is shown in Additional File 1 as differential equations.
Central to the Frank-Starling model is the concept of cardiac function curves – usually given as the pressure in each pool as cardiac function (contractility) is increased from zero to a normal value. These curves are useful for finding appropriate initial estimates for blood volume, arterial and venous compliance, and systemic vascular resistance. To achieve the physiologically plausible cardiac function curve shown in Fig. 3, blood volume was set at 3.5 L [13]. Given that in a normal (50 kg) sheep the baseline cardiac output is approximately 6 L/min and mean arterial blood pressure is 100 mmHg (Table 1), baseline systemic vascular resistance is therefore 100/6 ≈ 17 RU. The remaining unknowns of this system (kc, CPLa, CPLv) were chosen to duplicate the following behaviour (Fig. 3) which is consistent with measurements in this species: When CNT is zero (i.e. the heart is not pumping) then dV is zero and the mean circulatory pressure (MCP) is approximately 7 mm Hg. When CNT is such that the cardiac output is approximately 6 L min-1, then MAP and CVP are approximately 100 and 2 mmHg, respectively (Table 1). In practice, it was found easier to express the arterial compliance (CPLa) as the ratio of arterial compliance to venous compliance (Cratio).
Figure 3 Cardiac function curves for the Frank-Starling model. A summary of the behaviour of the simple Frank-Starling model. The relationship between cardiac function (contractility) and the arterial and venous pressures matches well that reported in many textbooks. The venous compliance CPLv was 0.45, and the ratio of CPLv/CPLa was 15.
Table 1 Baseline (pre-drug) cardiovascular variables. A set of target values that was representative of the sheep studied in our laboratory was compiled from previous measurements and literature values as indicated. A set of parameter values for the final (Constrained-Frank-Starling-Baroreceptor) was derived (Table 2) that produced an internally consistent model that closely replicated these target values (also shown for comparison).
Variable Name Target Value Target value origin Model derived Value Units
Vblood Blood volume 3.5 literature [13] 3.5 L
CVP Central venous pressure 2.00 unpublished previous measurements and literature [20] 2.00 mmHg
CPLv Venous compliance 0.45 inferred from Vblood & CVP 0.46 L mmHg-1
MAP Mean arterial pressure 100 previous measurements [21] 100.9 mmHg
SVR Systemic vascular resistance 17.00 calculated from CO & MAP 17.0 RU
CO Cardiac output 6 previous measurements [21] 5.8 L min-1
HR Heart rate 100 previous measurements [21] 98.3 beats min-1
SV Stroke volume 0.06 calculated from CO & HR 0.059 L
CNT Contractility 3000 previous measurements [9, 21] 3000 mmHg sec-1
S1 Sympathetic tone – chronotropy 1 scaling factor only 1 dimensionless
S2 Sympathetic tone – Contractility 1 scaling factor only 1 dimensionless
Frank-Starling and Baroreceptor model
The control of arterial blood pressure via baroreceptor control of heart rate was added to the simple Frank-Starling model, as shown in Fig. 4. The arterial pressure set point (MAPset) was used to calculate the difference between the actual and set pressure (MAPdelta). This pressure difference was used to change heart rate with a gain given by "HRgain". When HRgain is zero, the model reduces to the simple Frank-Starling model. As HRgain is increased, the more heart rate is adjusted to defend changes in arterial pressure. A value of 3 was initially used for HRgain. The resultant cardiac function curve for this model is shown in Fig. 5, and the equations for the model are shown in Additional File 2.
Figure 4 A Frank-Starling-Baroreceptor model of the cardiovascular system. The Frank-Starling model of the circulation from Fig. 2 combined with baroreceptor control of arterial blood pressure (Pa) via changes in heart rate (HR). MAPset is the set point of the control system, and HRgain is the gain of the control system that operates on the difference between the actual and set arterial blood pressures (MAPdelta; Eqn. 7). The right side cardiac output term is expanded to include the role of myocardial contractility (CNT), stroke volume (SV) and heart rate (HR). "kc" is a conversion factor to adjust for the index used to measure myocardial contractility (Eqn. 8). Strictly, myocardial contractility is the proportionality factor between Pv and stroke volume (SV = COR/HR). However, it is often quantified using indirect indices, such as maximum positive change of ventricular pressure (dP/dtmax). The value of kc will depend of what index of contractility is used (see Eqn. 8).
Figure 5 Cardiac function curves for the Frank-Starling-Baroreceptor model. A summary of the behaviour of the Frank-Starling-Baroreceptor model. The relationship between cardiac function (contractility) and the arterial and venous pressures matches well that reported in many textbooks. However, the vascular volumes show the majority of the blood in the arterial compartment, which is at odds with the fact that the majority of the blood under baseline conditions is in the venous vessels. Furthermore, no constraints have been placed on the model so that unrealistic values (e.g. large negative pressures) can be achieved in some circumstances.
Constraining the model to increase physiological plausibility
The final version of the model introduced a number of constraints to increase its physiological plausibility. These were: 1) Assuming that under baseline conditions that approximately 1/3 of the total blood pool is in the arterial system. 2) That the intercepts of the pressure-volume "curves" for the venous and arterial compartments were linear such that both curves gave the mean circulatory pressure (MCP) at the unstressed volumes (Vv0 and Va0, see Fig. 6). 3) That the venous pressure could not be less than zero, and that the arterial pressure could not be less than the MCP. 4) That heart rate was constrained to be between 0 and 220. 5) That the venous pressure – stroke volume relationship was non-linear and reached a plateau consistent with the finite pumping capacity of the heart (Fig. 6). 6) For convenience, two additional parameters were introduced (S1 and S2) representing the state of the sympathetic nervous system. These gave the capacity to adjust the proportionality term between blood pressure and heart rate (HRgain) and between CVP and stroke volume (kc). This allowed these scaling constants to be separated into a constant term that is solely used to convert measurement units (HRgain or kc) and another term (S1 or S2) that represents changes in underlying physiology for use when fitting data. Their normal values were 1 in each case (giving no effect for baseline conditions) and their function is summarised in the following equations:
Figure 6 Effect of introducing constraints on the model. Top: Venous pressure – volume curves. Venous volume starts at Vv0 when the heart is not pumping, at which point the venous pressure is the mean circulatory pressure (MCP). With increased pumping, the venous volume and venous pressure is reduced. In the simple (Frank-Starling-Baroreceptor) model, the CVP – volume relationship was linear, with an intercept of zero. In the constrained model, a lower intercept was used which was necessary to produce realistic venous volumes under baseline conditions. The multiple curves in the plot show the effect of changing venous compliance (CPLv). Middle: Arterial pressure – volume curves. Arterial volume starts at Va0 when the heart is not pumping, at which point the arterial pressure is the mean circulatory pressure (MCP). With increased pumping, the arterial volume and arterial pressure are increased. In the simple model, the MAP – volume relationship was linear, with an intercept of zero. In the constrained model, a lower intercept was the used, which was necessary to produce realistic arterial volumes under baseline conditions. The multiple curves in the plot show the effect of changing compliance (CPLa). Bottom: "Cardiac output curves" In this case cardiac output is given by stroke volume, which is plotted against central venous pressure (CVP). In the simple model, this relationship was linear. In the constrained model the relationship was given by a logistic equation which rose to a limit. The left-hand side of the curves are pseudo-linear, and the slope of the lines increase with increasing contractility. This behaviour mimics the "Cardiac output curves" found in many cardiovascular textbooks (e.g. Guyton [19]).
HR = MAPset + MAPdelta*(HRgain*S1) ...(7)
The resultant cardiac function curves for this model are shown in Fig. 7, and the equations for the model are shown in Additional File 3.
Figure 7 Cardiac function curves for the Constained-Frank-Starling-Baroreceptor model. A summary of the behaviour of the final cardiovascular dynamic model.
Baseline values for the cardiovascular model
For convenience, the target cardiovascular variables of the final constrained model discussed above are summarised in Table 1 with references to their origins. The parameter set that produced cardiovascular variables similar to the target values is summarised in Table 2. This was derived semi-empirically by inspection of cardiovascular function curves (Fig. 7) and pressure-volume relationships (Fig. 6) with incremental adjustment of parameter values. Note that some variables are also listed as parameters – this is purely for convenience. The distinction between variables (time-dependent) and parameters (time-independent) is semantic and depends on the proposed use of the model.
Table 2 Baseline model parameters The parameters chosen as those producing representative baseline (pre-drug) cardiovascular variables (Table 1). The co-efficient of variation (CV (%)) of these parameter values as determined by the Monte-Carlo sensitivity analysis is also shown.
Parameter Name Value Units CV (%)
CPLv Venous compliance 0.45 L mmHg-1 12.7
CPLratio Ratio of venous over arterial compliance 20 dimensionless 17.7
Vblood Blood volume 3.5 L 17.9
SVR Systemic vascular resistance 17 RU 5.5
MAPset Mean arterial pressure set point 100 mmHg 4.2
HRgain Gain for heart rate control 1.8 bpma mmHg-1 23.6
CNT Contractility 3000 mmHg sec-1 4.9
abpm = beats per minute
The sensitivity of the baseline cardio-vascular model to changes in parameter values was determined via Monte-Carlo simulation [14]. Multi-variate normally distributed noise was added to the parameter values for a series of 10,000 simulations of the resulting cardiovascular variables. Those parameter sets that produced a set of cardiovascular variables within 10% of the target set were selected and analysed for with respect to parameter variability and correlation.
Fitting the cardiovascular model to the magnesium data
Changes in cardiovascular variables with the administration of magnesium were analysed as percentage change from baseline. This removed the contribution of inter-animal variability in baseline cardiovascular variables (which was nevertheless minor [8,9]) to variability in the cardiovascular effects of magnesium. The analysis involved fitting cardiovascular radar plots to the measured magnesium data (Data set 1) for key time-points (1, 2, 4, 10 and 25 min) during and after magnesium administration. The cardiovascular model was parameterised in terms of primary cardiovascular variables that could be directly influenced by magnesium. These were SVR, CPLv, CPLratio, CNT, S1 and S2. Vblood could also be considered a primary variable, but it was considered unlikely that magnesium could change the blood volume. The remaining cardiovascular variables were considered secondary in that they would change in response to changes in the primary variables as given by Eqns 2 to 4.
Initially, the only primary parameter fitted to the data for each time point was SVR while the other parameters were held constant. This was based on the prior knowledge that this was the primary mechanism of action of magnesium. If the MSC was low and the cardiovascular radar plot showed a poor fit between model predictions and the data, an additional parameter was fitted one at a time from the remaining parameters listed above. A parameter was removed from the fit if it produced an undefined estimate. The parameter was kept in the fit if it improved the MSC and the fidelity of observed vs. predicted plots on the cardiovascular radar. By this process, the values of the primary cardiovascular parameter at each key time point required to describe the observed data were determined.
Recirculatory pharmacokinetic model of magnesium disposition
Conventional mamillary pharmacokinetic models are essentially empirical and do not include parameters (other than clearance) that represent defined physiological processes. This is problematic when drugs affect the cardiovascular system, or it is necessary to predict the kinetics of the drug when the underlying physiology has changed. This was the case for magnesium, which affected cardiac output significantly (Figs. 1 &10). Full physiological pharmacokinetic (PBPK) models are an alternative, but often require extensive data sets for their parameterisation. Recirculatory models have been used [7,15] as an alternative that retain the key physiological descriptions of important organs, but have lumped descriptions of the less important organs. Often, they can be parameterised by fitting blood concentrations alone.
Figure 10 Best fits for the recirculatory pharmacokinetic model for magnesium. Top: The observed changes in cardiac output for Data set 1 (symbols). Also shown is the line of best fit for the empirical forcing function used for development of the kinetic model. The large and consistent increase in cardiac output illustrates why it was necessary to use a kinetic model that could account for the significant flow changes caused by magnesium. Middle: The observed arterial concentrations of magnesium for Data set 1 (symbols). Also shown is the line of best fit for the final kinetic model (not linked to the cardiovascular model) based on the parameter values given in Table 4. Bottom: A sensitivity analysis of the final kinetic model with respect to cardiac output when used to simulate the dose regimen used for Data set 1. Cardiac output was given values of 2, 4, 6, 8 or 10 L min-1 while the other parameters were fixed at the values given in Table 4. This illustrates how the cardiac output changes caused by magnesium can influence its own kinetics. This feedback process was inherent in the structure of the final kinetic-dynamic model.
The magnesium concentration data from Data set 1 were used to develop a recirculatory model of magnesium kinetics that could account for the observed cardiac output changes. The processed used was similar to that described by the authors for other drugs [15]. The final form of the model is shown in Fig. 9.
Figure 9 Final recirculatory pharmacokinetic model for magnesium. A pictorial representation of the model. Parameter names are given in Table 4.
Key points during the model development process were: 1) The representation of the lungs as a single compartment. 2) The representation of the cardiac output change as an empirical forcing function (see Fig. 10, this would later be replaced by the predictions of the final cardiovascular model). 3) The representation of the body as extracellular and intracellular spaces connected by a permeability term, in keeping with the known slow cellular uptake of magnesium. 4) The clearance of magnesium is renal, but it can be reabsorbed or excreted in the tubules, as dictated by homeostatic requirements [16]. Thus, renal clearance may be variable.
To confirm that the kinetics of magnesium were cardiac output dependent, the final kinetic model was subjected to a sensitivity analysis for this parameter. Cardiac output was assigned values of 2, 4, 6, 8 or 10 L min-1 while the other parameters were fixed at their best fit value. The time-course of the arterial magnesium concentration was recorded in each case.
Linking the pharmacokinetic and pharmacodynamic models
The relationship between the key cardiovascular parameters (effects) and the concentrations of magnesium in arterial and coronary sinus blood were examined using hysteresis plots (effect vs. concentration). A concentration-effect relationship was considered plausible if produced a predictable relationship with minimal hysteresis that was consistent with the known mechanisms of action of the drug.
By these criteria, it was found that the arterial concentrations were the better predictor of the fitted cardiovascular parameters shown in Table 3. The concentration – effect relationships are summarised in Fig. 11. The major effect of magnesium was to drop systemic vascular resistance (SVR). SVR was related to the arterial magnesium concentration by a link model based on a linear relationship with a threshold (Fig. 11A):
Table 3 The fitted primary cardiovascular parameters for Magnesium data set 1 Units are as for Table 2. The parameter estimates are given with the standard deviation returned by the curve-fitting program. S1 could not be reliably fitted to the data.
0 min 1 min 2 min 4 min 10 min 25 min
Fitted parameter (baseline) estimate (sd) estimate (sd) estimate (sd) estimate (sd) estimate (sd)
MSC n/a 3.88 2.96 4.75 1.67 4.74
CPLv 0.45 0.490 (0.0028) 0.495 (0.0075) 0.497 (0.0019) 0.505 (0.0049) 0.492 (0.0007)
SVR 17 11.99 (0.12) 9.84 (0.26) 11.84 (0.084) 14.64 (0.27) 16.74 (0.077)
CNT 3000 2988 (41) 3031 (104) 3388 (32) 3108 (80) 3180 (23)
S2 1 0.978 (0.023) 0.972 (0.055) 0.877 (0.014) 1.22 (0.06) 1.25 (0.017)
Figure 11 Link models for concentration-effect relationships. A: The systemic vascular resistance (SVR) parameter (symbols, obtained by the fitting process that gave the radar plots shown in Fig. 8 and summarised in Table 3) plotted against the concurrent exogenous arterial magnesium concentrations. The final link model (Eqn. 9; line) based on a linear relationship with a threshold is also shown. B: The venous compliance (CPLv) parameter (symbols, via Fig. 8) plotted against the concurrent exogenous arterial magnesium concentrations. The final link model (Eqn 10; line) based on a simple threshold that switches between two states of venous compliance is also shown. This is plausible if it is considered that magnesium, even at relatively low concentrations, causes maximal dilation of the venous capacitance vessels. C: The contractility (CNT) parameter (symbols, via Fig. 8) plotted against the concurrent exogenous arterial magnesium concentrations. The final link model (line) was based on the assumption that contractility was unaffected by magnesium (i.e parameter value was fixed). D: The sympathetic tone coefficient for contractility (S2) parameter (symbols, via Fig. 8) plotted against the concurrent exogenous arterial magnesium concentrations. The final link model (line) was based on the assumption that S2 was unaffected by magnesium (i.e parameter value was fixed).
if Cart < 2.66 then
SVR = 17
else
SVR = -1.759*Cart + 21.68 ...(9)
Magnesium also raised venous compliance (CPLv). This was related to the arterial concentration using a simple threshold (Fig. 11B):
if Cart < 2 then
CPLv = 0.45
else
CPLv = 0.50 ...(10)
Magnesium had little effect on myocardial contractility (Fig. 11C), and the linking function assumed that CNT remained at baseline values. Magnesium appeared to increase the sympathetic tone coefficient for contractility (S2) by approximately 25% at between concentrations of 2 and 4 mmol L-1 (Fig. 11D). However, this rise in S2 only occurred late in the study (Table 3). It indicates subtle changes in the relationship between the filling pressure index (LVEDP) and the contractility index (dp/dt). This may reflect measurement error in these variables, non-stationarity in the experimental preparation or subtle delayed changes in myocardial compliance caused by magnesium. However, it was found that a link function assuming S2 remained at baseline values (Fig. 11D) was an adequate account of the data and did not compromise the predictive power of the model in the validation stage.
The final kinetic-dynamic model therefore consisted of the kinetic model shown in Fig. 9 linked to the Constrained-Baroreceptor-Frank-Starling cardiovascular dynamic model (Figs. 4 &7) via the link Equations 9 and 10. This is summarised in Fig. 12. The equations for the model are shown in Additional file 4.
Figure 12 Overview of the kinetic-dynamic model linking process. A schematic representation of how the final model was derived from Data set 1. The pharmacokinetic (PK) component of the model was developed by fitting the observed arterial magnesium concentrations (Fig. 10, middle). As cardiac output was a parameter of the recirculatory model, the magnesium induced changes in cardiac output were represented as a forcing function during fitting (Fig. 10, top). In the final model, this forcing function was replaced by the cardiac output predicted by the cardiovascular (CV) model. For the CV model, target baseline cardiovascular variables were derived from previous measurements and the literature (Table 1). A unique parameter set for the CV model was found that reproduced these values (Table 2). To account for the changes in cardiovascular variables from baseline following magnesium, four parameters (SVR, CPLv, CNT and S2) were fitted to the observed magnesium CV data (expressed as change from baseline) at selected time-points (Fig. 8; Table 3). Of these, two parameters (SVR, CPLv) showed concentration dependent changes that could be related via link functions to the time-course of magnesium concentrations (Fig. 11). The other parameters of the CV model were fixed at their baseline values. The final model was able to predict the concentrations and CV effects of magnesium for a different dose regimen (Data set 2, Fig. 13).
Validation of the final model
The final kinetic-dynamic model developed using Data set 1 was used to predict the arterial magnesium concentrations, cardiac output and mean arterial blood pressure for Data set 2. Data set 2 differed from Data set 1 in that the dose of magnesium was given over 5 min instead of 2 min. Consequently, although the dose was the same, the cardiovascular effects were less pronounced. For example, the lowest blood mean arterial pressure for Data set 1 was 76% of baseline, while for Data set 2 this was 86% of baseline. The only change made to the parameters of the final model was to alter the duration of infusion of the magnesium.
Results
Parameter sensitivity of cardiovascular model (baseline conditions)
The baseline cardiovascular variables and the parameters that produced them are summarised in Tables 1 and 2, respectively. Of the 10,000 random parameter sets examined in the Monte-Carlo sensitivity analysis, only 37 produced a set of cardiovascular variables that was within 10% of the target cardiovascular variables. The variability of these successful parameter values was low (Table 2), and the spread of each parameter showed a unimodal, approximately normal distribution. This suggests that there was a unique set of parameter values for the model that was consistent with normal baseline physiology. Visual inspection showed no obvious correlation between parameter values, except for CPLv and CPLratio (correlation coefficient = 0.83). This suggests that specifying the value for one of these parameters significantly constrains the value that can be taken for the other, as would be expected on physiological grounds. It can be concluded that each parameter had an important role to play in the model, and that each could only take a limited range of values to be consistent with the required baseline physiology. By extension, the assumptions regarding the values of these parameters are likely to be appropriate. Furthermore, the changes in these parameters observed following magnesium administration therefore reflect the effects of this drug rather than uncertainty in the parameter space of the model.
Parameter estimates – cardiovascular data
The method of estimating cardiovascular model parameters from cardiovascular data for individual time points was effective. Thus, it was possible to find a parameter set at each time point (Table 3) that produced a fitted cardiovascular radar plot that closely matched the observed plot (Fig. 8). In general, the parameter estimates were precise. The most obvious effect of magnesium was a drop in systemic vascular resistance and a rapid and sustained increase in venous compliance. The changes in the other cardiovascular variables (e.g. HR and MAP) simply reflected reflex changes in response to these primary drug effects.
Figure 8 Best fit cardiovascular radar plots for each key time point. Cardiovascular radar plots of the observed data (blue) and the best fit of the final cardiovascular model (red). Note that the shape of the radar plot changes with time, indicating the evolving effects of magnesium on the circulation. For each time-point, the fit was an adequate account of the data (Table 3).
Parameter estimates – pharmacokinetic data
The recirculatory pharmacokinetic model was able to fit the observed concentrations with adequate fidelity (Fig. 10, middle) and produce precise parameter estimates (Table 4). As the clearance of magnesium was low, it would be expected that the permeability term into the deep compartment governed the rate of decline of the magnesium concentration rather than its clearance from the body.
Table 4 The fitted pharmacokinetic parameters for the Magnesium data set 1 The parameter estimates are given with the standard deviation returned by the curve-fitting program.
Fitted variable Value Units
MSC 3.13
Vlung 0.887 (0.221) L
CL 0.0021 (0.1286) L min-1
Vbody 4.023 (0.486) L
PS 0.589 (0.227) L min-1
Vdeep 8.63 (5.39) L
A feature of recirculatory pharmacokinetic models is that their initial kinetics are governed by first-pass passage of drug through the lungs, and the dilution of the injected drug with the cardiac output [7]. The cardiac output sensitivity analysis for the model confirmed this behaviour for magnesium (Fig. 10, bottom). This reinforces the need for a common cardiac output term for the cardiovascular and recirculatory kinetic model (Fig. 12). The resultant final model therefore accounts for the fact that by altering cardiac output, magnesium alters its own kinetics.
Link functions
Relating the estimated cardiovascular parameters in Table 3 to the concurrent arterial concentrations produced the concentration-effect curves shown in Fig. 11. Link functions were established for SVR and CPLv, but not CNT or S2. The overall role of the link functions is summarised in Fig. 12.
Model validation – pharmacokinetic component
The recirculatory model of magnesium disposition was able to accurately predict the time-course of the arterial magnesium concentrations observed for the validation Data set 2, despite the large change in cardiac output produced by magnesium (Fig. 13). The mean prediction error was 0.02%
Figure 13 Observed and predicted results for Data set 2. The final kinetic-dynamic model developed using Data set 1 was used to predict the exogenous arterial magnesium concentrations (Cart,x), cardiac output (CO) and mean arterial pressure (MAP) for Data Set 2. The only change in the model between the two data sets was to increase the duration of the infusion from 2 to 5 min. The observed data are shown as symbols, together with the upper and lower 95% confidence intervals of the data (dotted lines). The predictions of the model are shown by the solid lines.
Model validation – pharmacodynamic component
The final pharmacodynamic model was able to accurately predict the time-course of the cardiac output changes observed for the validation Data set 2 (Fig. 13). The mean prediction error was 3.0%.
The dynamic model captured the general trend of the mean arterial blood pressure for the validation data (Fig. 13), but some systematic deviations were evident. The model was accurate until the end of the infusion, but thereafter slightly over-estimated the rate of recovery of blood pressure. However, the model did predict that the drop in blood pressure would be considerably less for a 5 min versus 2 min infusion, and the overall magnitude of the changes in blood pressure for the 5 min infusion were small (less than 10% change). The mean prediction error was 6.1%.
Discussion
Concentration-effect relationships and recirculatory models
In this paper, all cardiovascular effects were related to the arterial concentration of magnesium. As covered in the introduction, there may be other sites in the body that have a theoretical claim to being the most appropriate link concentration for certain cardiovascular dynamic effects. For example, the reductions in myocardial contractility caused by thiopental have been shown to have a better temporal relationship to the thiopental concentrations in the myocardium itself rather than in arterial blood [17]. This consistent with a direct thiopental effect on the myocardium.
In recirculatory models, it is possible to add a "target organ" to represent organs such as the heart [18]. The fact that this was not necessary for magnesium may be the exception rather than the rule. As magnesium has small volumes of distribution, there is little difference in the time-course of the arterial and regional venous concentrations. Furthermore, the predominant effects of magnesium were directly on blood vessels (arterioles for SVR and large veins for capacitance) in direct equilibrium with blood rather than organs such as the heart or brain. Thus, a "systemic" recirculatory model was sufficient for magnesium. As other drugs are studied using this method, data on target organ kinetics and their incorporation into the kinetic model may be necessary.
Limitations
There are a number of limitations of this modelling approach, many of which are inherent in the assumptions made in the construction of the model. Other limitations may become apparent if the model is used outside of the range of the data used to develop the model. For example, the CL term in the kinetic model was very low (Table 4). This may reflect extensive tubular re-absorption, but may also reflect the fact that the concentrations were followed for only 25 min in the original paper (the time by which most cardiovascular variables had returned to baseline). Studies of a longer duration would help to define this clearance term better.
The cardiovascular model also assumes an instantaneous baroreceptor response. While it is relatively easy (in modelling terms) to add a delay to this response, this was not supported by the data. However, if the model is extended to situations with very rapid blood pressure changes (e.g. orthostatic hypotension) this deficiency may become significant.
Constructing physiologically based models, even of the simplicity presented here, requires crossing many decisions points where a choice must be made from multiple options – sometimes the choices are data driven, sometimes theory driven, sometimes the subjective experience of the model maker must be called upon. While a "wrong" model is evident because it does no match the data, there is clearly no "right" model of the cardiovascular system. It is anticipated that more limitations of the cardiovascular dynamic model will become apparent when model is rigorously compared to data for other drugs, and for other cardiovascular scenarios. It is should be expected that the model will continue to evolve as these data are collected and analysed.
Conclusion
The combination of the recirculatory kinetic model and the simple cardiovascular dynamic model was able to describe and predict the concentrations and cardiovascular effects of magnesium in sheep. It is proposed that the general methods used here could be applied to other drugs with cardiovascular effects. The authors are currently applying the method to intravenous anaesthetics.
Abbreviations
Cardiovascular term Description
Frank-Starling model
Vblood Blood volume
CVP = Pv Central venous pressure
MAP = Pa Mean arterial pressure
MCP Mean circulatory pressure
Va Volume of blood in arterial compartment
Vv Volume of blood in venous compartment
Va0 Volume of blood in arterial compartment at MCP
Vv0 Volume of blood in venous compartment at MCP
CPLa Arterial compliance
CPLv Venous compliance
CPLratio Ratio of venous over arterial compliance
SVR Systemic vascular resistance
CO Cardiac output
COL Cardiac output (left side)
COR Cardiac output (right side)
HR Heart rate
SV Stroke volume
kc unit conversion factor – contractility
CNT Contractility
additional for Frank-Starling-Baroreceptor model
MAPset Mean arterial pressure set point
HRgain Gain for heart rate control
additional for Constrained-Frank-Starling-Baroreceptor model
PaS Pressure in arterial compartment when stressed
PvS Pressure in venous compartment when stressed
VaS Volume in arterial compartment when stressed
VvS Volume in venous compartment when stressed
slopeMAP slope for arterial pressure-volume relationship
intMAP intercept for arterial pressure-volume relationship
slopeCVP slope for venous pressure-volume relationship
intCVP intercept for venous pressure-volume relationship
S1 Sympathetic tone coefficient – Chronotropy
S2 Sympathetic tone coefficient – Contractility
SVmax maximum for stroke volume-CVP relationship
SV50 half-volume for stroke volume-CVP relationship
nSV "Hill factor" for stroke volume-CVP relationship
Pharmacokinetic term Description Description
Recirculatory model
R0 doserate of zero order infusion
tau duration of zero order infusion
Cart Arterial magnesium concentration (total)
Cven Venous magnesium concentration (total)
Cart,e Arterial magnesium concentration (endogenous)
Cven,e Venous magnesium concentration (endogenous)
Cart,x Arterial magnesium concentration (exogenous)
Cven,x Venous magnesium concentration (exogenous)
Vlung Apparent distribution volume of the lung
CL Clearance
Vbody Apparent distribution volume of the body compartment
PS Permeability-surface area product of deep compartment
Vdeep Apparent distribution volume of the deep compartment
Authors' contributions
RNU participated in the original magnesium studies and performed the modelling. GLL participated in the original magnesium studies and acted as a resource for cardiovascular theory. Both authors contributed to the manuscript.
Supplementary Material
Additional File 1
The simple Frank-Starling model. The simple Frank-Starling model written in pseudo-code to generate cardiac function curves. The code is intended to run in the "Scientist" differential equation solving program.
Click here for file
Additional File 2
The Frank-Starling-Baroreceptor model. The equations for the Frank-Starling-Baroreceptor model with baroreceptor control written in pseudo-code to generate cardiac function curves.
Click here for file
Additional File 3
The Constrained Frank-Starling-Baroreceptor model. The equations used for the final cardiovascular model written in pseudo-code to generate cardiac function curves.
Click here for file
Additional File 4
The final pharmacokinetic-pharmacodynamic model. The equations used for the final model written in pseudo-code.
Click here for file
Acknowledgements
Supported by the National Health and Medical Research Council of Australia (Project Grant 157952)
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| 15760466 | PMC555767 | CC BY | 2021-01-04 16:33:00 | no | BMC Pharmacol. 2005 Mar 10; 5:5 | utf-8 | BMC Pharmacol | 2,005 | 10.1186/1471-2210-5-5 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central London 1465-9921-6-211572768710.1186/1465-9921-6-21ResearchMonitoring the initial pulmonary absorption of two different beclomethasone dipropionate aerosols employing a human lung reperfusion model Freiwald Matthias [email protected] Anagnostis [email protected] Andreas [email protected] Monika [email protected]ürdter Thomas [email protected] Peter [email protected] Godehard [email protected] Michael [email protected]ögger Petra [email protected] Institut für Pharmazie und Lebensmittelchemie, Bayerische Julius-Maximilians-Universität, Würzburg, Germany2 Klinik Schillerhöhe der LVA Württemberg, Gerlingen, Germany3 Dr. Margarete Fischer-Bosch-Institut für Klinische Pharmakologie, Stuttgart, Germany4 Pathologisches Institut am Robert Bosch Krankenhaus, Stuttgart, Germany5 Internistische Onkologie der Thoraxtumoren, Thoraxklinik GmbH am Universitätsklinikum Heidelberg, Heidelberg, Germany2005 24 2 2005 6 1 21 21 25 11 2004 24 2 2005 Copyright © 2005 Freiwald et al; licensee BioMed Central Ltd.2005Freiwald 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 pulmonary residence time of inhaled glucocorticoids as well as their rate and extend of absorption into systemic circulation are important facets of their efficacy-safety profile. We evaluated a novel approach to elucidate the pulmonary absorption of an inhaled glucocorticoid. Our objective was to monitor and compare the combined process of drug particle dissolution, pro-drug activation and time course of initial distribution from human lung tissue into plasma for two different glucocorticoid formulations.
Methods
We chose beclomethasone dipropionate (BDP) delivered by two different commercially available HFA-propelled metered dose inhalers (Sanasthmax®/Becloforte™ and Ventolair®/Qvar™). Initially we developed a simple dialysis model to assess the transfer of BDP and its active metabolite from human lung homogenate into human plasma. In a novel experimental setting we then administered the aerosols into the bronchus of an extracorporally ventilated and reperfused human lung lobe and monitored the concentrations of BDP and its metabolites in the reperfusion fluid.
Results
Unexpectedly, we observed differences between the two aerosol formulations Sanasthmax®/Becloforte™ and Ventolair®/Qvar™ in both the dialysis as well as in the human reperfusion model. The HFA-BDP formulated as Ventolair®/Qvar™ displayed a more rapid release from lung tissue compared to Sanasthmax®/Becloforte™. We succeeded to explain and illustrate the observed differences between the two aerosols with their unique particle topology and divergent dissolution behaviour in human bronchial fluid.
Conclusion
We conclude that though the ultrafine particles of Ventolair®/Qvar™ are beneficial for high lung deposition, they also yield a less desired more rapid systemic drug delivery. While the differences between Sanasthmax®/Becloforte™ and Ventolair®/Qvar™ were obvious in both the dialysis and lung perfusion experiments, the latter allowed to record time courses of pro-drug activation and distribution that were more consistent with results of comparable clinical trials. Thus, the extracorporally reperfused and ventilated human lung is a highly valuable physiological model to explore the lung pharmacokinetics of inhaled drugs.
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Introduction
Current asthma management guidelines recommend inhaled glucocorticoids as preferred therapy for control of mild persistent, moderate and severe asthma [1,2]. Glucocorticoids are the most effective anti-inflammatory agents and inhalation is an efficient way to deposit the compound in the therapeutic target tissue. The dose and the percentage of lung deposition as well as the specific receptor binding affinity determine the therapeutic efficacy of the corticosteroid [3,4]. Prolonged residence of an inhaled glucocorticoid in the lung tissue is associated with an extended duration of action. In contrast, the rate and extend of absorption of the glucocorticoid into systemic circulation might result in systemic adverse effects such as adrenal suppression or decreased bone mineral density [5]. Though the tissue residence time and the time course of distribution into systemic circulation significantly contribute to the risk-benefit value of inhaled corticosteroids [4,6] the precise determination of the pulmonary absorption is a challenge.
The time course of inhaled drug absorption has been frequently studied in life animals [7]. For this purpose, the drugs are usually administered intratracheally as solution or via intratracheal nebulization [8]. This, however, is not directly comparable to the administration of therapeutically used glucocorticoids in humans which are usually formulated as aerosols or dry powder inhalers containing micronized drug crystals.
The in vivo distribution of inhaled glucocorticoids between human lung tissue and plasma has been determined in patients undergoing thoracotomy. During surgery tissue and plasma samples were obtained and analyzed for drug concentrations which were found to be significantly higher in lung tissue compared to plasma [9-11]. The strength of this type of evaluation is that tissue concentrations of the drug can be measured up to ten or more hours after inhalation. However, one patient provides one data point only and thus many patients are needed to sufficiently describe a time course of tissue – plasma distribution.
Plasma concentrations of glucocorticoids after inhalation of therapeutic doses are low and thus highly sensitive analytical methods are required [12]. Blood samples from an antecubital vein are collected at defined time intervals after inhalation and analyzed. Since an unknown percentage of the corticosteroid in this blood sample might have already undergone metabolization when passing the liver, it cannot be excluded that the measured concentration underestimates the amount of active drug delivered from the lung tissue.
The purpose of this study was to monitor and compare the combined process of drug particle dissolution, pro-drug activation and time course of distribution from human lung tissue into plasma in the absence of hepatic metabolism. We chose beclomethasone dipropionate (BDP) as a model compound because different formulations are commercially available and pharmacokinetic data from clinical studies is accessible for comparison. BDP is activated in human lung tissue to yield its active metabolite beclomethasone-17-monopropionate (17-BMP) [13]. We initially developed a dialysis model to monitor the drug transfer of BDP and metabolites from human lung tissue homogenate into human plasma. To compare the results of these experiments with a more physiological model we studied drug diffusion kinetics employing resected intact human lung lobes. This human lung reperfusion model was previously developed by Linder et al. [14] and successfully used to study the uptake kinetics of anticancer agents from the perfusion fluid into normal and tumour lung tissue [15,16]. To our knowledge, human lung reperfusion settings have not been employed so far for evaluation of distribution kinetics of inhaled drugs. Thus, we used the human reperfusion model in a novel experimental context. The ventilated lung lobe offered the unique potential to administer the BDP formulation from a commercially available aerosol directly into the bronchus. The concentration of BDP and its metabolites could be then monitored by analyzing samples from the main venous vessel.
Materials and Methods
Chemicals, reagents and drug preparations
Beclomethasone dipropionate (BDP) pressurized metered dose inhalers (MDI) with hydrofluorocarbon (HFA) propellant (Sanasthmax®/Becloforte™ 250 μg/dose [Asche Chiesi GmbH, Hamburg, Germany] and Ventolair®/Qvar™ 100 μg/dose [3 M Medica, Neuss, Germany]) or chlorofluorocarbon (CFC) propellant (Sanasthmax®/Becloforte™) were obtained from a local pharmacy. BDP, beclomethasone-17-propionate (17-BMP), beclomethasone-21-propionate (21-BMP), beclomethasone (B) and fluticasone propionate (FP) were a generous gift from GlaxoSmithKline (Greenford, England). Diethylether (HPLC grade) was purchased from Fluka (Buchs, Switzerland) and acetonitrile (ACN, HPLC gradient grade) from Fisher Scientific (Schwerte, Germany). Water was obtained from a Millipore™ water purification unit. Bovine serum albumin (BSA), dextrane 70000, and N-(2-hydroxyethyl)piperazine-N'-2-ethanesulfonic acid (HEPES) were purchased from GERBU (Heidelberg, Germany), glucose monohydrate from Gruessing GmbH (Filsum, Germany), and stock solution containing 10000 IU/mL penicilline and 10000 μg/mL streptomycine in 0.9% NaCl from Biochrom AG (Berlin, Germany). All other chemicals were obtained from E. Merck (Darmstadt, Germany).
Source and handling of human specimen for dialysis and scanning electron microscopy experiments
Human lung tissue specimen were obtained from patients with bronchial carcinomas who gave informed consent. Only cancer-free tissue was used for the experiments. None of the patients was treated with glucocorticoids for the last 4 weeks prior to surgery. Tissue samples were shock frozen in liquid nitrogen after resection and stored at -70°C until usage. To collect sufficient material for the experiments, tissue samples of three or more patients were pooled. Tissue was thawed and cut into small pieces. One part of the tissue pieces was homogenized in two parts of Krebs-Ringer-HEPES buffer (118 mM NaCl, 4.84 mM KCl, 1.2 mM KH2PO4, 2.43 mM MgSO4 × 6 H2O, 2.44 mM CaCl2 × 2H2O and 10 mM HEPES; pH = 7.4). Homogenization was performed under continuous cooling using an Ultraturrax (Janke & Kunkel, Staufen, Germany). Before starting the series of dialysis experiments the required amount of human lung homogenate was estimated and subsequently a sufficient amount was prepared and divided into aliquots. Since all aliquots descended from this preparation protein content and enzymatic activity of the homogenate was identical for each experiment.
Plasma samples were obtained from healthy volunteers who gave informed consent. Samples were either used immediately or were shock frozen in liquid nitrogen and stored at -70°C until usage.
Bronchial fluid was collected from patients undergoing bronchoscopy for diagnostic purposes after having obtained informed consent. Bronchial fluid was obtained through a sterile plastic catheter inserted into the biopsy channel of the bronchoscope (Olympus BF 1 T 30; München, Germany), wedged into a subsegment bronchus. Small bronchial fluid aliquots of four patients were collected and pooled. The specimen was frozen and stored at -70°C until usage.
Patients and lung preparations for perfusion experiments
Six patients with a bronchial tumour undergoing standard thoracotomy, were included in the study. None of the patients was treated with glucocorticoids for the last 4 weeks prior to surgery. Only patients with tumours that were located peripherally within the lung lobe were included. Each patient signed a written informed consent before surgery, and a local Ethics Committee approved the use of resected human lungs for perfusion. Immediately after perfusion, the lung preparations were examined as usually by a pathologist.
Dialysis experiments
Dialysis was performed with a dialysis unit (designed by our working group) consisting of two individual tightly fitting Teflon chambers separated by a dialysis membrane (Figure 1). One chamber (inner diameter: 45 mm, internal depth: 3 mm) was prepared for lung tissue homogenate, the other chamber (inner diameter: 45 mm, internal depth: 6 mm) was supposed to be filled with human plasma. The chamber for plasma had two apertures for obtaining dialysis samples and addition of fresh plasma.
Figure 1 Schematic illustration of the dialysis experiments. [1] 500 μg BDP ex-valve is applied to human lung homogenate. [2] Dialysis membrane is placed on homogenate, the dialysis unit is closed by attaching the second chamber, and the second chamber is filled with human plasma. [3] Sampling aperture is closed and the dialysis unit is incubated by 37°C. [4] Sample of 500 μl is drawn and the volume is replaced with fresh plasma.
In initial experiments we evaluated the functionality and reliability of the experimental setting with respect to the convection, appropriate sample recovery and absence of trapped air. Therefore, the upper chamber was replaced by a chamber with a top made of acrylic glass instead of Teflon to monitor the processes within the unit. After filling the lower chamber with buffer the dialysis membrane was placed on this chamber and the dialysis unit was closed by attaching the second chamber. The complete and air bubble free filling of the chamber by smooth negative pressure was visually controlled. To check whether there was sufficient convection for a homogenous distribution of the analyzed compounds in the plasma and whether replacement of the sample volume was readily achieved, buffer in the reservoir was stained with a green dye. Then sampling was performed as described above. The appropriate moment for closing the valve was defined as the point of time where no visible flow of stained buffer into the chamber could be observed. It was also determined if any air bubbles gained access through the sampling aperture during sampling or after removing the pipette. Control experiments with the dye verified that the sample was not adulterated during the process of sampling and synchronous replacement of the sample volume with fresh solution. The degree of convection was controlled by following the distribution of the green dye into the clear buffer after sampling. A visually homogenous solution was obtained within a few minutes under experimental conditions. Accurate and reproducible sample volume was assured by weighing ten replicates of samples drawn under experimental conditions. Parameters for accuracy and reproducibility were within the specifications of the manufacture of the used pipettor.
For the dialysis experiments a 5 g aliquot of pre-warmed human lung homogenate (37°C) was filled into one chamber. 500 μg BDP ex valve was applied to the homogenate employing a dosing device (designed by our working group, Figure 1) that was composed of a fitting for the MDI, the tested MDI, and a glass tube to assure reproducible dosing conditions. Time in which the particle cloud was allowed to sediment was kept constant. The lung homogenate was stirred briefly for a fixed period of time. Subsequently, a dialysis membrane (Spectra/Por™ 6, MWCO 2000, Spectrum Laboratories, Rancho Dominguez, USA) was placed on the homogenate, the dialysis unit was closed by attaching the second chamber. The second chamber was filled with plasma of 37°C. Therefore, the valve connecting the dialysis unit to the plasma reservoir was opened and the dialysis unit was filled with plasma by producing a mild negative pressure using a pipette bulb. Afterwards the valve was closed again. The whole appliance was free of trapped air. The dialysis unit was incubated at 37°C for 6 hours (Incubator, Memmert, Schwabach, Germany). For sampling the aperture on the top of the unit was opened and samples were drawn using an Eppendorf™ pipette, pipette tips of which were tightly fitting to the sampling aperture. Samples of 0.5 mL were drawn. Therefore, the valve connecting the dialysis unit to the reservoir was opened synchronously to sample drawing. Replacement of the sample volume with fresh pre-warmed plasma occurred due to negative pressure produced by the pipette. The appropriate moment for closing the valve was determined in control experiments. Samples were stored at -20°C until further analysis. To determine the BDP dose that was actually applied to the homogenate by the respective aerosol one dialysis chamber was filled with 5 ml of Krebs-Ringer-HEPES buffer (pH = 7.4) containing 150 μg FP as internal standard instead of homogenate. Dosing was performed analogous to the dialysis experiments and the BDP concentration in buffer was analyzed.
Analysis of drug concentrations in dialysis samples
Samples of the dialysis experiments were mixed with 50 μL internal standard solution (3 μg/mL FP in methanol) and extracted twice with 2 ml diethylether for 20 min using a roller mixer, followed by centrifugation at 3000 rpm (Labofuge II, Heraeus-Christ GmbH, Osterode am Harz, Germany) for 5 min. The combined organic phases were evaporated to dryness under a gentle stream of nitrogen at 25°C. The resulting residue was reconstituted in 0.2 mL methanol and analyzed by liquid chromatography using a Waters HPLC (Milford, USA) consisting of a 1525 binary pump, a 717plus autosampler and 2487 dual wavelength absorbance detector. Data collection and integration were accomplished using Breeze™ software version 3.30. Analysis was performed on a Symmetry C18 column (150 × 4.6 mm I.D., 5 μm particle size, Waters, USA). Typically, 20 μL of sample were injected, a flow rate of 1 mL/min was used, and detection wavelength was set to 254 nm. Mobile phase consisted of water containing 0.2 % (v/v) acetic acid (A) and acetonitrile (B). The gradient elution started at 62 % eluent A, decreasing nonlinearly to 53 % A by 22 min and finally decreasing linearly to 28 % eluent A by 18 min. The lower limit of quantification of the assay was 20 ng/mL for all glucocorticoids.
Calculation of the fraction of applied dose determined in plasma
The amount of the parent compound BDP and its metabolites 17-BMP (active metabolite), 21-BMP and beclomethasone that were distributed from the lung tissue into plasma were determined and calculated as percentage of the applied dose. Amount Ai of drug related to the parent compound found in whole plasma at i-th sample was calculated by the following equations:
(1) VP = r2• π • h
ci Concentration of the compound at the i-th sample [m/V]
h Inner height of the plasma containing chamber
MW Molecular weight of the compound
r Inner radius of the plasma containing chamber
VP Calculated volume of plasma in the dialysis unit
On basis of the calculated amount Ai the release of the drug into plasma Di at the i-th sample can be expressed as percentage of applied dose using equation (3) or (4):
Aj-1 Amount of drug related to the parent compound found in whole plasma at the (j-1)-th sample
Dapplied Actually applied dose to the human lung homogenate
Human lung perfusion experiments
The lobe preparations were perfused extracorporally for about 60 min as described previously [14,15]. Immediately after lung resection, the pulmonary arteries were cannulated and the bronchus was connected to a bronchial tube. After the lung was rinsed through the arteries with 0.5 L of perfusion buffer (85 mM NaCl, 3.5 mM KCl, 2.5 mM CaCl2 × 2 H2O, 1.18 mM MgCl2 × 6 H2O, 2.5 mM KH2PO4, 20 mM NaHCO3, 5.5 mM glucose, 5 % bovine serum albumin and 2 % dextran; 100 μl of a stock solution containing 10,000 IU penicilline and 10,000 μg/mL streptomycine in 0.9% NaCl were added to 1 L perfusion buffer and pH was adjusted to 7.4 by addition of 10% NaHCO3), it was placed within the perfusion apparatus in a tempered water bath (37°C) and ventilated using a respirator (Engström Erica 2; Engström Elektromedizin GmbH, München, Germany) with air (Figure 2). The perfusion buffer was pumped from a reservoir through a heat exchanger, an oxigenator, and a bubble trap and was delivered through a valve into one to three segmental arteries. After leaving the opened vein, perfusate flowed back to the reservoir, which was held at 37°C.
Figure 2 Schematic illustration of the experimental human lung reperfusion setting.
During lung perfusion, pH, pO2, pCO2, K+, and Na+ in the perfusate were monitored continuously with an Eschweiler System 2000-D03 (L. Eschweiler & Co., Kiel, Germany) and registered via a computer system. By addition of CO2 using a conventional oxigenator, perfusate pH was maintained within the physiological range of about 7.4. Lung preparations were weighed before and after perfusion to check for oedema formation during perfusion.
After ventilation and perfusion were established the system was equilibrated for 5 min, and a 5 mL sample (blank sample) was drawn before administration of the glucocorticoid. Dosing was performed using a glass spacer (constructed by our group) that was placed between the respirator and the ventilated lung tissue. During application the tidal volume was increased 1.5-fold in comparison to the volume during perfusion. The dose applied exvalve was 500 μg BDP for each MDI. Samples of 5 mL were drawn from the main venous output and the sample volume was replaced with fresh perfusion buffer. Samples were immediately frozen and transported on dry ice, and stored at -20°C until further analysis. In order to calculate the fraction of dose reaching the human lung lobe the spacer and all fittings were rinsed quantitatively and analyzed for BDP. The fraction of BDP that was determined in the spacer and connecting fittings was subtracted from the dose of 500 μg applied ex valve. The amount of the parent compound BDP and its metabolites 17-BMP (active metabolite), 21-BMP and beclomethasone that were distributed from the lung tissue into perfusion fluid were determined and calculated as percentage of the applied dose analogous to the dialysis experiments.
Analysis of drug concentrations in perfusion samples
Perfusion samples of 1 mL were mixed with 25 μl internal standard solution (300 ng/mL FP in methanol). Glucocorticoids were extracted twice with 3 mL diethylether for 20 min using a roller mixer, followed by centrifugation at 3000 rpm (Labofuge II, Heraeus-Christ GmbH, Osterode am Harz, Germany) for 5 min. The organic layers were evaporated to dryness under a gentle stream of nitrogen at 25°C. The residue was reconstituted in 50 μl methanol and analyzed by HPLC – MS/MS using an Agilent 1100 HPLC system (Agilent Technologies, Waldbronn, Germany) consisting of a binary pump, a vacuum degasser, a temperature-controlled autosampler and a variable wavelength absorbance detector coupled with an Agilent LC/MSD Trap SL mass sensitive detector. An ESI interface was used in the positive ionization mode. Data collection and integration were accomplished using ChemStation-for-LC-3D™ and LC/MSD-Trap™ version 4.2 software. Analysis was performed on a Symmetry C18 column (150 × 4.6 mm I.D., 5 μm particle size, Waters, USA). The mobile phase was a mixture of water containing 0.1 % (v/v) formic acid (A) and acetonitrile (B), the flow rate was 0.6 mL/min. A gradient elution started at 50 % eluent B, increasing linearly to 65 % B by 8 min and then increasing linearly to 80 % B by 8 min. A total sample volume of 25 μL was injected. The mass spectrometer was operated in selective reaction monitoring observing the transitions from 465 m/z to 355 m/z, 501 m/z to 313 m/z, and 521 m/z to 411 m/z for 17-BMP, FP (internal standard) and BDP, respectively. The lower limit of quantification of the assay was 400 pg/mL for BDP and 17-BMP.
Particle topology and dissolution of drug crystals in human bronchial fluid visualized by scanning electron microscopy (SEM)
Beclomethasone dipropionate (BDP) from different devices was either directly applied on regular glass microscopy slides or on microscopy slides with incubation slots of 15 × 2 mm (Karl Roth GmbH, Karlsruhe, Germany) containing human bronchial fluid. For application of BDP the glass microscopy slide was placed in a plastic spacer of about 900 cm3. The aerosol device was actuated and the glucocorticoid particles were allowed to alight on the glass slide. Number and distribution of BDP particles was visually controlled by light microscopy (ECLIPSE TS100, Nikon, Düsseldorf, Germany). After BDP application onto human bronchial fluid the glass microscopy slide was sealed with silicone paste and a cover slide and incubated at 37°C for one hour. Thereafter the cover slide was removed and the fluid was evaporated under a gentle stream of nitrogen. Subsequently the slide was carefully washed twice with 50 μL purified water to remove bronchial fluid proteins. The water was evaporated under a stream of nitrogen.
A Zeiss DSM 962 scanning electron microscope (Carl Zeiss, Oberkochen, Germany) was used to obtain the SEM photographs. The samples mounted on the glass microscopy slides were coated with palladium/coal for 3 min using a Baltec SCD 005 sputter-coater in an argon atmosphere (45 Pa and 50 mA).
Results
Lung tissue-plasma distribution of HFA-BDP determined in a dialysis model
The time course of distribution of two different HFA-BDP formulations from human lung tissue into human plasma was initially determined using a simple dialysis model. Therefore, equal doses of 500 μg BDP (2 × 250 μg Sanasthmax®/Becloforte™ and 5 × 100 μg Ventolair®/Qvar™) ex valve were applied to each 4.8 mL human lung tissue homogenate (Figure 1). The mean total dose that was actually deposited in this dialysis chamber was analyzed by HPLC and calculated to be 152.8 ± 15.4 μg for Sanasthmax®/Becloforte™ and 60.6 ± 1.5 μg for Ventolair®/Qvar™. Subsequently the distribution of BDP and its metabolites from lung tissue into the second chamber filled with 9.5 mL human plasma was monitored over 6 hours.
The time course of HFA-BDP distribution in to the plasma chamber was different for the two formulations (Figure 3). After application of Ventolair®/Qvar™ about 14 % of the applied dose was delivered into plasma after one hour. After 6 hours about 65 % of BDP and metabolites were determined in the plasma compartment. In contrast, after one hour only about 8 % of the total dose of Sanasthmax®/Becloforte™ was distributed into plasma. After 6 hours 30 % of Sanasthmax®/Becloforte™ were found in plasma (Figure 3 A).
Figure 3 Time course of distribution of Sanasthmax®/Becloforte™ and Ventolair®/Qvar™ from human lung tissue into human plasma at 37°C as determined in dialysis experiments. Concentrations of [3 A] sum of beclomethasone dipropionate (BDP) and its metabolites, [3 B] BDP and [3 C] beclomethasone-17-monopropionate (17-BMP) were analyzed in plasma and expressed as percentage of the total dose applied to the lung tissue. Each data point represents the mean and mean deviation of the mean of three independent experiments.
When the time course of delivery into plasma was regarded separately for BDP and 17-BMP, respectively, the clear differences between the two formulations were confirmed. After 30 min of incubation the plasma concentration of BDP delivered from Ventolair®/Qvar™ was twice as high as that of BDP delivered from Sanasthmax®/Becloforte™ (Figure 3 B). This difference became even more pronounced after 6 hours of incubation when the BDP concentration from Sanasthmax®/Becloforte™ decreased while it remained constant after application of BDP from Ventolair®/Qvar™. Up to 10 % of the applied dose of Ventolair®/Qvar™ were released unmetabolized as BDP into plasma while only about up to 6 % of the total dose Sanasthmax®/Becloforte™ were released unmetabolized.
The release of the active metabolite 17-BMP into plasma steadily increased during incubation (Figure 3 C). After 6 hours about 50 % of the total applied dose of Ventolair®/Qvar™, but only about 25 % of Sanasthmax®/Becloforte™ was distributed into plasma.
Pulmonary absorption of HFA-BDP in a human lung perfusion model
A human lung perfusion model was employed to determine whether the differences of the distribution kinetics between the two BDP formulations were also present in a more physiological model. Lung lobes of cancer patients were extracorporally ventilated and reperfused in a closed system at 37°C directly after resection. BDP aerosols were applied via a glass spacer after increasing the respiration volume to the 1.5 fold of the basal volume. Again equal ex valve doses of 500 μg HFA-BDP were administered, 2 × 250 μg of Sanasthmax®/Becloforte™ and 5 × 100 μg of Ventolair®/Qvar™. After rinsing the glass spacer and all connecting tubes the fraction of BDP that adhered to those materials was analyzed after each experiment. By subtracting this amount from the nominal administered dose we calculated that mean doses of 343 ± 13.3 μg of Sanasthmax®/Becloforte™ and 392 ± 40.1 μg of Ventolair®/Qvar™ were deposited in the lung lobes.
Samples of the perfusion buffer were obtained directly from the main venous vessel of the lobe. Again the time course of HFA-BDP distribution into the perfusion fluid was different for the two formulations (Figure 4). The mean percentage of the applied dose that was delivered into the perfusion fluid after application of Ventolair®/Qvar™ was at all time points about twice a high as the distributed dose of Sanasthmax®/Becloforte™ (Figure 4 A). After application of Ventolair®/Qvar™ about 0.8 % of the BDP was immediately detectable in the perfusion fluid (Figure 4 B). This equals a mean concentration of 1.7 ng/mL in the perfusion fluid after only 3 min. Die BDP concentration then rapidly decreased over 30 min and then remained stable at a very low level. Though a very low percentage of BDP was also detectable in the perfusion fluid after application of Sanasthmax®/Becloforte™ it did not change over the incubation period. Only about 0.18 % of the total dose of BDP was continuously distributed into the perfusion buffer.
Figure 4 Time course of distribution of Sanasthmax®/Becloforte™ and Ventolair®/Qvar™ from a ventilated human lung preparation into perfusion fluid at 37°C. Samples were obtained form the main venous vessel of the lung lobe and the drug concentration was expressed as percentage of the total dose applied to the lung lobe. [4 A] Sum of beclomethasone dipropionate (BDP) and its metabolites, [4 B] BDP and [4 C] beclomethasone-17-monopropionate (17-BMP). Each data point represents the mean and mean deviation of the mean of three independent experiments.
Analysis of 17-BMP concentrations after aerosol administration confirmed the differences in distribution observed between the two formulations. Over the whole incubation period about twice as high 17-BMP concentrations were observed after application of Ventolair®/Qvar™ compared with Sanasthmax®/Becloforte™ (Figure 4 C). While between 1.5 and 2 % of 17-BMP of the total applied dose from Ventolair®/Qvar™ were found, only between 0.5 and 1 % of 17-BMP were detected in the perfusion fluid after application of Sanasthmax®/Becloforte™. Interestingly, the active metabolite 17-BMP was already detectable in the perfusion fluid 2–3 min after application of both aerosols. As expected, the concentration of 17-BMP in the perfusion fluid gradually increased over the incubation time. This is consistent with a slow dissolution process of drug crystals.
Particle topology and dissolution of drug crystals in human bronchial fluid visualized by scanning electron microscopy (SEM)
Beclomethasone dipropionate (BDP) particles delivered by pressurized metered dose inhalers (MDI) with hydrofluorocarbon (HFA) propellant (Sanasthmax®/Becloforte™ and Ventolair®/Qvar™) were analyzed by SEM. To visualize the particle topology the devices were actuated and the particles were allowed to alight directly on regular glass microscopy slides. Representative HFA-BDP particles delivered by Sanasthmax®/Becloforte™ were about 2 μm (Figure 5, I a). BDP delivered by Sanasthmax®/Becloforte™ is solved in the HFA propellant. The particle size visualized by SEM is consistent with the published mass mean median aerodynamic diameter of about 2.6 μm [17]. These BDP particles showed a unique structure, they appeared round and highly porous. Obviously, a big surface area of the sponge-like form was generated by a rapid evaporation of the propellant.
Figure 5 Scanning electron microscopy image of BDP particles either as delivered directly from the device on a glass slide [I a, II a] or in the stage of partial dissolution after incubation for one hour at 37°C with human bronchial fluid [I b, II b]. [I a] BDP particle as delivered from the Sanasthmax®/Becloforte™ formulation propelled by hydrofluoroalkane (HFA), [I b] HFA-BDP particles delivered from the Sanasthmax®/Becloforte™ formulation after incubation with human bronchial fluid at 37°C for one hour, [II a] HFA-BDP particles as delivered from the Ventolair®/Qvar™ formulation, [II b] HFA-BDP particles delivered from the Ventolair®/Qvar™ formulation after incubation with human bronchial fluid at 37°C for one hour.
To get an impression of the process of dissolution of these BDP particles in human bronchial fluid the Sanasthmax®/Becloforte™ device was actuated and the glucocorticoid particles were allowed to alight on microscopy slides with incubation slots. Each incubation slot accommodated about 40–50 individual BDP particles as controlled under a light microscope. The incubation slot was filled with 200 μL human bronchial fluid, sealed and incubated for one hour at 37°C. After this incubation time the SEM picture revealed that the form of the particles changed significantly. While the particles were still around 2 μm they now resembled solid cubic crystals (Figure 5, I b). Obviously, BDP particles re-crystallized after they came in contact with the bronchial fluid and formed crystals with a thermodynamically preferred smaller surface area.
BDP delivered by Ventolair®/Qvar™ is solved in the HFA propellant. BDP particles delivered by Ventolair®/Qvar™ are smaller than those delivered by the Sanasthmax®/Becloforte™ device. Representative particles shown (Figure 5, II a) are about 1 μm which is in agreement with the published mass mean median aerodynamic diameter of about 1.1 μm [18,19]. Again, these particles showed a typical structure, they appeared round and not porous, but droplet-like.
This BDP formulation was also incubated with human bronchial fluid for one hour at 37°C. The form of the particles changed to solid cubic crystals as seen with the other HFA-BDP formulation. However, most of the particles were now clearly smaller than 1 μm and the edges of the cubes appeared rounded. Obviously, the crystals were in a more advanced state of dissolution in bronchial fluid (Figure 5, II b).
For comparison, we show a SEM image of BDP formulated as Sanasthmax®/Becloforte™ delivered from a MDI with CFC propellant and applied directly on a glass microscope slide (Figure 6). The particles were typically crystal-like, clearly bigger and less homogeneous than the particles delivered from any of the HFA driven aerosols. The size of these representative crystals is again consistent with the published mass mean median aerodynamic diameter of about 3.5 to 4.0 μm [18,19].
Figure 6 Scanning electron microscopy image of BDP particles as delivered from the Sanasthmax®/Becloforte™ formulation propelled by chlorofluorocarbon (CFC).
Discussion
In the present study we successfully demonstrated that the intial distribution of beclomethasone dipropionate (BDP) from lung tissue into extrapulmonary circulation can be assessed employing a human lung perfusion model. Therefore, we applied BDP delivered by two different commercially available HFA-propelled aerosols (Sanasthmax®/Becloforte™ and Ventolair®/Qvar™) into a reperfused and ventilated lung lobe and measured the rate and extend of pulmonary absorption. We succeeded to explain kinetic differences we observed between the two aerosols with unique particle topology and divergent dissolution behaviour in human bronchial fluid.
Prolonged retention times in the therapeutic target tissue lung are a highly favourable characteristic of inhaled glucocorticoids and contribute to an improved therapeutic index. This principle was obviously taken into consideration for newly developed glucocorticoids for pulmonary application. All inhaled glucocorticoids of the latest generation reveal high lipophilicity and high tissue binding affinity. As additional mechanisms of tissue retention the formation of intracellular conjugates was reported for budesonide and ciclesonide's active metabolite [20,21].
Compound properties such as dissolution of drug crystals [22,23] and the glucocorticoids' tissue binding affinity [24] might be separately elucidated in vitro. The general differences determined between various glucocorticoids in vitro roughly agree with relation between tissue and plasma concentrations of these compounds at a certain time after inhalation in patients [9-11]. To elucidate the kinetics of pulmonary absorption plasma samples of patients or volunteers might be collected over a certain time interval after inhalation [12]. However, with this approach it cannot be excluded that the concentration of active drug being absorbed from the lungs is slightly underestimated due to partial hepatic metabolism. If glucocorticoids with a significant oral bioavailability are investigated gut absorption after swallowing has to be discerned from pulmonary absorption.
We evaluated novel approaches to elucidate pulmonary absorption of an inhaled glucocorticoid in vitro. Therefore, we compared BDP formulations delivered from two different commercially available pressurized metered dose inhaler devices. With both experimental settings, the dialysis experiments and the human lung perfusion model, we had precise information about the dose applied to the lung tissue. With both models were able to study the combined processes of drug dissolution, activation to the active metabolite beclomethasone-17-monopropionate (17-BMP) and the distribution of BDP and 17-BMP from lung tissue into plasma or perfusion fluid, respectively.
We used the newly developed dialysis model to monitor the kinetics of drug diffusion over six hours. During this time we observed constant drug transfer into plasma and continuous generation of the active BDP metabolite. The latter suggests that the non-specific esterases responsible for pro-drug activation were functional within the chosen time interval. We ventilated and reperfused the resected human lung lobe over about one hour to be as close as possible to physiological conditions and avoid problems such as oedema formation that might be associated with prolonged perfusion times. The time interval of one hour is consistent with the time to reach maximal plasma levels (tmax) of BDP and its ester metabolites after inhalation of HFA-BDP in various clinical studies [25-28]. Since these peak plasma levels were observed between 0.34 [28] and 0.9 hours [25] the reperfusion time of one hour appeared acceptable for our experiments. In our human lung reperfusion model we observed a continuous generation of 17-BDP and rapid transfer of BDP and 17-BMP into the perfusion fluid. Though the absorption rates of BDP and 17-BMP were quantitatively higher in the dialysis model, they were qualitatively consistent in both models.
Unexpectedly, we observed differences between the two HFA aerosol formulations Sanasthmax®/Becloforte™ and Ventolair®/Qvar™ in both the dialysis and the lung reperfusion setting. The mean release of BDP and 17-BMP from lung tissue into plasma or perfusion fluid was about twice as high after application of Ventolair®/Qvar™ compared with Sanasthmax®/Becloforte™.
In contrast to the unanticipated results of our absorption experiments with two HFA formulations, differences in pulmonary absorption after administration of CFC-BDP compared to HFA-BDP would have been expected based on the results of clinical studies. After inhalation of HFA-propelled BDP higher lung deposition rates were achieved and higher plasma concentrations of 17-BMP were observed in adults [25,26] and children [28]. The rationale of a higher lung deposition and thus the need for lower doses of HFA-BDP in clinical settings is the difference in fine particle mass compared to the CFC-BDP [29]. The smaller particles delivered by HFA formulations result in a higher respirable fraction and improved deposition of particles especially in smaller airways [30]. The higher lung deposition of smaller drug particles correlates with a shorter the time to maximum plasma concentration (tmax) of 17-BMP or beclomethasone esters, respectively, after inhalation of HFA-BDP compared to CFC-BDP [25,26]. The median tmax of 17-BMP after inhalation of CFC-BDP was not significantly altered when activated charcoal was co-administered and thus gut absorption was prevented [17,31]. A plausible explanation for the delayed tmax of 17-BMP after inhalation of CFC-BDP is the particle size as it should be expected that bigger drug crystals dissolve more slowly. HFA-BDP with its smaller particles exhibited a faster and more efficient systemic drug delivery [26].
Based on the comprehension of the interrelation of aerosol particle size and rate of pulmonary absorption we suspected that the different time courses of distribution into extrapulmonary compartments we observed were due to different particle sizes of HFA-BDP formulated as Sanasthmax®/Becloforte™ or Ventolair®/Qvar™, respectively. Scanning electron microscopic experiments illustratively confirmed this postulate. Particles of BDP delivered by Ventolair®/Qvar™ were significantly smaller and displayed faster dissolution in human bronchial fluid compared to particles delivered by Sanasthmax®/Becloforte™. Additionally, for the first time we provide evidence that BDP particles delivered by HFA-propelled aerosols are subjected to a re-crystallization process once they come in contact with the bronchial fluid. Obviously, the particles as delivered from the device dissolve rapidly due to their large surface area. Subsequently, locally oversaturated solutions tend to crystallize and crystals with a thermodynamically preferred smaller surface area are formed. It seems very likely that this might also occur in the life human lung since the volume of bronchial fluid is very limited. This re-crystallization of particles with high surface area to solid cubic particles with smaller surface area would be a highly desired effect as it slows down the further dissolution and therefore prolongs the pulmonary residence time.
The notion that BDP delivered by HFA-propelled inhalers initially rapidly dissolves and then locally re-crystallizes would also explain the detection of BDP in reperfusion fluid of the human lung preparation after only 2–3 min. It seems plausible that a certain amount of dissolved drug is instantly distributed into extrapulmonary compartments since the human lung has a high inner surface area and is the organ with the highest blood flow of about 15 L/min/kg [32]. The detection of BDP in the perfusion fluid is consistent with earlier studies reporting unchanged BDP in plasma samples of volunteers after inhalation of BDP [17,31]. Interestingly, an enhanced pulmonary absorption rate after inhalation of HFA-BDP compared to CFC-BDP was reported to correspond with an increased absorption of unchanged BDP [17,33]. In our human lung perfusion model we also detected higher levels of BDP in perfusion fluid after application of Ventolair®/Qvar™ with its smaller BDP particles. The fact that we also detected the active metabolite 17-BMP in the perfusion fluid of the extracorporally perfused and ventilated lung lobe after only 2–3 min allows the conclusion that the hydrolysis of BDP is an extremely rapid process. This is in excellent agreement with the results of earlier in vitro assays with human lung tissue [34]. Hence, the activation of the pro-drug BDP to 17-BMP is no rate-limiting step while the dissolution of drug crystals determines the rate of pulmonary absorption.
To summarize, we employed two different models to monitor the distribution kinetics of two HFA-propelled BDP aerosols from human lung tissue into extra-pulmonary compartments. With both the dialysis setting as well with the human lung perfusion model we detected significant differences between the two BDP formulations. The HFA-BDP formulated as Ventolair®/Qvar™ displayed a rapid release from lung tissue while Sanasthmax®/Becloforte™ exhibited a prolonged residence time in tissue. We succeeded to explain the observed differences in lung tissue residence time on the basis of the particle size and dissolution behaviour in human bronchial fluid. We conclude that though the ultrafine particles of Ventolair®/Qvar™ are beneficial for high lung deposition [30,35], they also yield an undesired more rapid systemic drug delivery. While the differences between Sanasthmax®/Becloforte™ and Ventolair®/Qvar™ were obvious in both the dialysis and lung perfusion experiments, the latter allowed to record time courses of pro-drug activation and distribution that were more consistent with results of comparable clinical trials. Though it is not clear whether the kinetic differences observed between Sanasthmax®/Becloforte™ and Ventolair®/Qvar™ might be of clinical relevance the detection of divergent absorption kinetics confirm the functionality and sensitivity of our experimental setting. Thus, the extracorporally perfused and ventilated human lung is a highly valuable physiological model to explore the comparative lung pharmacokinetics of inhaled drugs.
Acknowledgements
We would like to thank Prof. Georg Krohne for his highly valuable help with the scanning electron microscopy. We greatly appreciate the expert technical assistance of Roswitha Skrabala. Parts of this work were supported by a grant of the Fonds der Chemischen Industrie (FCI).
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| 15727687 | PMC555845 | CC BY | 2021-01-04 16:36:26 | no | Respir Res. 2005 Feb 24; 6(1):21 | utf-8 | Respir Res | 2,005 | 10.1186/1465-9921-6-21 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-4-121576298810.1186/1476-4598-4-12ResearchHeat shock protein 72 expression allows permissive replication of oncolytic adenovirus dl1520 (ONYX-015) in rat glioblastoma cells Madara Jonathan [email protected] James A [email protected] Maulik [email protected] Saint Louis University Cancer Center, Saint Louis University, USA2 School of Medicine, Saint Louis University, USA3 Center for Anatomic Studies, Saint Louis University, USA4 Department of Pediatrics, Division of Medical Genetics, Saint Louis University, USA2005 11 3 2005 4 12 12 5 11 2004 11 3 2005 Copyright © 2005 Madara et al; licensee BioMed Central Ltd.2005Madara 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.
In this study we have made novel observations with regards to potentiation of the tumoricidal activity of the oncolytic adenovirus, dl1520 (ONYX-015) in rat glioblastoma cell lines expressing heat shock protein 72 (HSP72) due to permissive virus replication. ONYX-015 is a conditionally replicating adenovirus that is deleted for the E1B 55 kDA gene product whose normal function is to interact with cell-cycle regulatory proteins to permit virus replication. However, many murine and rodent cell lines are not permissive for adenovirus replication. Previously, it has been reported that the heat shock response is necessary for adenovirus replication and that induction of heat shock proteins is mediated by E1 region gene products. Therefore, we hypothesized that HSP72 expression may allow for permissive replication of ONYX-015 in previously non-permissive cells. Rat glioma cell lines 9L and RT2 were transfected with a plasmids expressing HSP72 or GFP. After infection with ONYX-015, no tumoricidal activity is observed in GFP expressing cell lines despite adequate transduction. In contrast, HSP72 transfected cells show cytopathic effects by 72 hours and greater than 75% loss of viability by 96 hours. Burst assays show active virus replication in the HSP72 expressing cell lines. Therefore, 9L-HSP72 and RT2-HSP72 are ideal models to evaluate the efficacy of ONYX-015 in an immunocompetent rat model. Our study has implications for creating rodent tumor models for pre-clinical studies with E1 region deleted conditionally replicating adenovirus.
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Background
Adenovirus vectors are commonly utilized in cancer gene therapy experiments. They readily infect numerous tumor cell types and are easily manipulated allowing for transgene expression[1]. In an effort to improve selectivity of these vectors for malignant cells, replication selective or conditionally replicating adenoviruses were created[2]. With greater understanding in the molecular aspects of adenovirus replication, these viruses were designed such that replication was predicated on alterations in cell cycle regulation, thus rendering only malignant cells susceptible. These adenovirus systems rely on virus replication as a means of exerting tumoricidal effect. One of the first of these conditionally replicating adenoviruses was dl1520 or ONYX-015[3]. It has been used extensively in cancer gene therapy clinical trials[4,5]. This virus is deleted for the E1B-55 kDA gene[6].
The E1 region genes of the common serotypes of adenovirus optimize their own replication in target cells and interact with many cell cycle associated gene products. The E1a region genes promote transition of cells into S phase through their interaction with the retinoblastoma protein (pRB)[7]. pRB in conjunction with p53 can inhibit transition of cells into the S-phase of the cell cycle. Binding of E1a to pRB prevents association of pRB to the E2F transcription factor resulting in activation of E2F[8]. E2F then functions to transition cells into the S-phase of the cell cycle which is conducive for optimal adenovirus replication. In wild type adenoviruses, the role of the E1B-55 kDa gene product is to neutralize p53[9,10]. p53 induction is thought to promote cell cycle arrest resulting in termination of the virus replicative lifecycle in that cell[11]. Thus it was hypothesized that deletion of the E1B-55 kDa gene product would result in p53 induced termination of virus replication in normal cells while being replication permissive in malignant cells with abnormal p53 expression or regulation[9]. Early reports with ONYX-015 indicated replication selectivity for tumor cells with abnormal p53 functioning[12]. However, the exact mechanism of replication selectivity has been brought to question with subsequent reports of ONYX-015 replication independent of p53[13] and researchers have looked at other factors associated with adenovirus replication.
Various heat shock proteins have been shown to be necessary for efficient adenovirus replication. The avian adenovirus CELO requires the induction of HSP70 and HSP40 for production of viral proteins[14]. CELO mutants lacking the E1 region genes were replication incompetent in A549 cells. However, heat shock protein induction or expression allowed for permissive replication of these mutants. Similarly in a number of human cell lines, both heat shock and HSP72 expression enhanced the oncolytic effect of E1 containing adenovirus but not for E1 deleted adenovirus[15].
Adenovirus E1 region genes from serotype 5 and 12 have previously been shown to induce HSP72 expression [16]. HSP72 is a molecular chaperone protein involved in the repairing denatured proteins. Through this role, it can inhibit apoptosis downstream of caspase activation but prior to loss of mitochondrial membrane potential[17]. Stably transfected or E1 transformed cell lines show constitutive expression of the inducible HSP72 [18]. HSP72 levels are correlated with cell-cycle with increases in HSP72 during S-phase with a maximum level in the post-S-phase period [19]. In 293 cells which are transformed with the adenovirus E1 region gene, E1A mRNA accumulated just prior to HSP72 expression suggesting that E1A is responsible for the cell cycle regulation of HSP72 expression [18]. Additionally, HSP72 co-localizes with E1A gene products in infected cells [20]. HSP72 is predominately cytoplasmic but translocates to the nucleus following adenovirus infection. Double labelling experiments with E1A and HSP72 in infected cells showed co-localization of these molecules within the nucleoli and exhibited similar reticular and punctuate nuclear staining patterns depending on cell cycle. HSP72 transport to the nucleus was E1A and virus infection dependent. In 293 (E1 region transformed) cells, HSP72 and E1A co-localization was not observed until after infection with virus [20]. These data suggest a physical complex is necessary between adenovirus E1A, HSP72 and other adenovirus gene products
One of the difficulties with studying E1 adenovirus deletion mutants is the lack of appropriate animal tumor models which allow for permissive growth of these viruses. Common animal tumor models for glioblastoma are the cell lines 9L and RT2. Previously, it has been shown that replication competent E1 deletion mutants can transduce 9L cells and result in transgene expression but there is a block against virus replication[21]. In this study we addressed if HSP72 expression could allow for permissive replication of the ONYX-015 adenovirus in these rat glioblastoma cells.
Results
Transduction Efficiency
Prior to studying the efficacy of ONYX-015 to result in tumor cell lysis, we determined the optimal dose necessary for tissue transduction. Adenovirus infection efficiency is predicated on the expression of adenovirus surface receptors[22]. Using a recombinant adenovirus expressing green fluorescent protein (GFP), we transduced various glioma cell lines and analyzed them by fluorescence microscopy. Results are summarized in Table 1. Both the rat cell lines, 9L and RT2 required a significantly high dose of adenovirus to result in effective transduction. In contrast, the human glioma cell lines, DBTRG and NB4, require much less virus for efficient transduction.
Table 1 Transduction efficiency of various CNS tumor cell lines by AD-GFP Numbers represent percentage of cells transduced with a recombinant adenovirus vector expressing green fluorescent protein (AD-GFP) at the stated multiplicity of infection (MOI). Transduction was determined by fluorescence microscopy of cells on a hemacytometer and is represented as % of total cells.
Multiplicity of Infection
Cell Line 0 1 10 100 300 500 1000
9L 0 0 1 27 54 63 74
RT2 0 1 8 41 68 81 93
DBTRG 0 69 100 100 - - -
NB4 0 74 100 100 - - -
Cytopathic Effect
The dramatic difference in tumoricidal effect between HSP72 transfected and control tumor cells is best observed by cytopathic effect. In Figure 1 we show the affects of virus infection on RT2-GFP and RT2-HSP72 cells 96 hours after infection with ONYX-015. Non-infected RT2-GFP and RT2-HSP72 cells showed normal glioma histological features and growth characteristics (Figure 1, panel A and B). The cells were infected when 50% confluent. At 96 hours, RT2-GFP cells infected at MOI of 100 or 300 showed little cytopathic effect and very little difference was ascertained in comparison to non-infected cells (Figure 1, panel C and E). In contrast, the majority of RT2-HSP72 cells were dead or dying (Figure 1, panel D and F). The few remaining cells were larger in size with oval or round morphology. These cells showed morphological characteristics consistent with adenovirus replication as observed in 293 cells. Similar results were obtained when assessing 9L-HSP72 cells. In table 2 we show the time to full CPE in the rat glioblastoma cell lines and a variety of human controls. Given the differences in transduction efficiency, the human cell lines were infected at a lower multiplicity of infection. No CPE is detected in the 9L-GFP and RT2-GFP cell lines. In contrast, 9L-HSP72 and RT2-HSP72 show CPE in as little as 4 days comparable to human glioblastoma cells when infected at an MOI of 10 and show CPE more rapidly than the human cell lines despite a lesser transduction efficiency.
Table 2 Time to Cytopathic Effect Cells grown in 96 well plates were infected with ONYX-015 at the stated multiplicity of infection per row of cells. Values represent days until 50% of wells in a row showed cytopathic effect.
Multiplicity of Infection
Cell Line 0 1 10 100 300
9L-GFP 0 0 0 0 0
9L-HSP72 0 0 8 5 4
RT2-GFP 0 0 0 0 0
RT2-HSP72 0 0 0 4 4
DBTRG 0 6 4 - -
NB4 0 5 4 - -
293 0 2 1 - -
Figure 1 Cytopathic effect of ONYX-015 on GFP and HSP72 transfected glioma cells. Photographs (100× magnification) of RT2-GFP and RT2-HSP72 cells 96 hours after ONYX-015 infection at various dosages are represented. A – RT2-GFP cells mock infected with virus. B – RT2-HSP72 cells mock infected with virus. C – RT2-GFP cells infected at MOI of 100. D – RT2-HSP72 cells infected at MOI of 100. E – RT2-GFP cells infected at MOI of 300. F – RT2-HSP72 cells infected at MOI of 300.
HSP72 Expression Augments ONYX-015 Toxicity
When HSP72-transfected cells were infected with a low dose (MOI 100) of ONYX-015, there is no loss of cell viability. However, comparison of cell numbers to GFP-transfected cells or AD-GFP infected cells showed a dramatic cytostatic effect (Figure 2). 9L-GFP and 9L-HSP72 cells were transduced with either AD-GFP or ONYX-015. There is no effect on cell proliferation over time of the 9L-GFP cells infected with either AD-GFP or ONYX-015 (data not presented). In contrast, the 9L-HSP72 cells show normal proliferation when infected with AD-GFP infected cells and their cell numbers increase over time. However, ONYX-015 infected 9L-HSP72 cells showed loss of proliferative potential with very little increase in cell number over time. After 72 hours, the AD-GFP infected cells showed a decrease in proliferation but this is likely due to overcrowding of cells in the tissue culture vessel and/or the rapid utilization of consumable energy in the media.
Figure 2 Cytostatic effect of ONYX-015 infection on HSP72 transfected 9L cells. 9L-HSP72 cells were transduced with either AD-GFP (□) or ONYX-015 (■) at a multiplicity of infection of 100. Cell numbers were determined over time (X-axis) by direct counting using a Coulter Counter. Values (Y-axis) represent log of cell number as a fraction of the initial cell number plated.
When assessing for cell viability, 9L-GFP (figure 3) and RT2-GFP (figure 4) cells exhibited minimal toxicity when infected with ONYX-015. In contrast, both 9L-HSP72 and RT2-HSP72 cell lines were more susceptible to ONYX-015 cell lysis. In both HSP72 transfected cell lines, loss of cell viability began as early as 24 hours after infection. Both 9L-HSP72 and RT2-HSP72 show greater than 90% loss of viability by 96 hours.
Figure 3 9L Cell viability after ONYX-015 infection. Cell viability (as % viable cells; y-axis) of 9L-GFP (■) and 9L-HSP72 (●) cell lines after infection with ONYX-015 at a MOI of 300 over time (x-axis). There is statistical significance (p < 0.05) when comparing GFP and HSP72 transfected cell numbers by analysis of variance at the 72 and 96 hour time points.
Figure 4 RT2 Cell viability after ONYX-015 infection. Cell viability (as % viable cells; y-axis) of RT2-GFP (■) and RT2-HSP72 (●) cell lines after infection with ONYX-015 at a MOI of 300 over time (x-axis). There is statistical significance (p < 0.05) when comparing GFP and HSP72 transfected cell numbers by analysis of variance at the 72 and 96 hour time points.
Burst Assays
The mechanism of cell death associated with replication competent adenoviruses has been attributed to release of virus from cells following virus replication. Having observed cytopathic effects (figure 1) in association with ONYX-015 infection of 9L-HSP72 and RT2-HSP72 cells, we presumed the same mechanism applied. To confirm this hypothesis we evaluated adenovirus replication in GFP and HSP72 transfected tumor cells by burst assay. In a normal burst assay, an increase in virus titer after 72 hours is indicative of virus replication. Tumor cells were also infected with the replication incompetent virus (AD-GFP) as a negative control and no significant virus replication was measured (data not presented). Since HSP72 transfected cells were susceptible to ONYX-015 mediated cell death we expected an increase in virus titer if the cell death was a result of permissive adenovirus replication. After infection of cells with ONYX-015, the 9L-HSP72 and RT2-HSP72 cells showed an increase in virus titer confirming virus replication (figure 5) in contrast to 9L-GFP and RT2-HSP72 transfected cells. There was a direct correlation between virus titer and cell death. Virus titer in both GFP transfected and HSP72 transfected cells increased in proportion to cell death.
Figure 5 Burst assays. Burst ratio is given as the virus titer at 72 hours compared to 4 hours of various cell lines (x-axis) infected while 50% confluent with ONYX-015 at MOI of 100. Results are representative of a typical experiment of at least three performed. (*) represents statistical significance (p < 0.05) by Student's t-test between 9L-HSP72 and 9L-GFP. (**) represents statistical significance (p < 0.05) by Student's t-test between RT2-HSP72 and RT2-GFP.
Discussion
ONYX-015 is a promising new cancer gene therapy vector which exerts its tumoricidal effect by selective replication in cancer cells[6]. Replication selectivity previously was attributed to interactions of E1 gene products with p53[3]. Subsequent reports have shown that ONYX-015 replicates in cells with wild-type p53 as efficiently as in cells with mutant-p53[13]. Although the mechanism of cancer cell selectivity is still under investigation, the various hypotheses consistently involve cell-cycle regulatory proteins and their interactions within apoptosis pathways. HSP72 is a potent regulator of apoptosis [23]. Also, there is evidence that activation of the heat shock response by adenovirus early region genes is necessary for virus replication[14]. Previously it has been shown that heat shock induced HSP72 expression resulted in increased production of adenovirus proteins[24]. Additionally, the E1A adenovirus gene products necessary for virus genome replication have previously been shown to co-localize with HSP72[20].
To study the role of HSP72 in augmenting ONYX-015 replication, we chose two glioma cell lines with different derivations. 9L is a chemically induced gliosarcoma while RT2 is a virally transformed glioblastoma. Compared to many human tumor cell lines, these cell lines show relative resistance to the cytotoxic affects of ONYX-015. Resistance in part was secondary to low adenovirus transduction efficiency. At the multiplicities of infection utilized in this study, we never achieved 100% cell transduction (table 1). In most cases, when less than 30% of permissive HSP72-transfected cells were transduced, tumor cell growth was rapid and outpaced ONYX-015 virus replication resulting in no discernable cytotoxic or cytostatic effect after 1 week in culture (data not presented). Therefore, replicative adenovirus cytotoxicity is likely a balance between virus transduction efficiency, replication efficiency and cell growth characteristics. HSP72 transfection had no bearing on cell doubling time of either 9L or RT2 cells (data not presented) RT2 cell-doubling time is approximately 8 hours while 9L cell-doubling time is approximately 14 hours. Both cell lines after HSP72 transfection showed equal susceptibility to ONYX-015. Further evidence for the balance between transduction efficiency, replication efficiency and cell doubling time is evident in that ONYX-015 infection resulted in a predominantly cytostatic affect resulting in decreased cell division before evidence of cytotoxicity.
The most dramatic affect of the role HSP72 in promotion of virus replication is evident in figure 1. At 96 hours, the GFP transduced cells showed very little cytotoxic effect of ONYX-015 infection. In contrast, there is a distinct cytopathic effect at the same doses of ONYX-015 infection in the HSP72 transduced cells. These cells were rendered susceptible to ONYX-015 toxicity. The mechanism of cell death is related to increased virus replication. Significantly more ONYX-015 was isolated from HSP72 transduced cells than the GFP-transfected controls.
The E1a gene products of adenovirus are responsible for activation of the HSP72 promoter and may affect HSP72 levels during the cell cycle[18]. Conversely, there is no evidence that HSP72 interacts with adenovirus promoters to stimulate viral transcription. There is evidence of HSP72 interactions with adenovirus structural proteins such as hexon[25] and fiber[26]. Adenovirus assembly is known to be inefficient with only a small percentage of total structural proteins eventually utilized in the production of infectious virus [26].
In conclusion, our studies demonstrated that HSP72 expression in rodent glioma tumor cells potentiated ONYX-015 replication and oncolysis of tumor cells. Further studies on the role of HSP72 in tumor types with wild-type and mutant p53 tumor suppressor genes is warranted to understand the molecular interactions of this chaperone protein in promoting virus replication. Additionally, addressing the role of HSP72 as an inhibitor of apoptosis in virus replication would further help characterize the interaction of cell-cycle regulatory pathways with virus replication. Furthermore, we intend to evaluate the role of HSP72 associated permissive replication in other animal cell lines to establish animal models for the study of ONYX-015 and similar replication competent adenoviruses.
Conclusion
For clinical applicability, these findings have a number of implications. Currently, clinical trials are ongoing with ONYX-015. Due to the previous association of ONYX-015 replication with p53 status, the current tumor types chosen for clinical trials are those exhibiting abnormal p53 expression in a majority of cells. Our study indicates, that tumor types with high HSP72 may also be viable candidates to evaluate ONYX-015 efficacy, as they are likely to demonstrate enhanced susceptibility to this replication competent virus. As HSP72 expression in tumors is associated with a more aggressive tumor phenotype, ONYX-015 may be ideal as adjunctive therapy for advanced disease. Additionally, one of the greatest limitations to adenovirus gene therapy is patient safety at high doses of adenovirus administration. Since, HSP72 expression resulted in oncolysis at much lower MOIs, a lower dose of virus administered to a HSP72 positive tumor may have the same benefit as a higher dose, allowing one to use lower doses to achieve the same effect. In addition, for some tumor types such as breast cancer, hyperthermia has been shown to significantly improve response rates[27]. HSP72 is readily induced with hyperthermia. Therefore, HSP72 induction by hyperthermia may also serve as an effective strategy to augment ONYX-015 oncolytic activity. We are currently in the process of studying this hypothesis. Lastly, the role of HSP72 in cell cycle regulation suggests alternate pathways for the mechanism of E1b deleted adenovirus replication in tumorigenic cells. HSP72 overexpression in non-transformed fibroblasts did not result in ONYX-015 oncolysis (data not presented). Understanding the molecular interactions of adenovirus proteins with molecular chaperones would help determine cell selectivity to replication competent adenoviruses influencing the design of more potent viruses.
Methods
Cell Lines
9L and RT2 cells are rat glioma cell lines with different derivations. 9L cells are chemically induced gliosarcoma cells[28] while RT2 are virally transformed and have glioblastoma histology[29]. These cell lines were obtained from Dr. Martin Graf (Medical College of Virginia, Richmond, VA). Cells were kept at 37°C, 5% CO2 and 95% humidity in Dulbecco's modified eagle medium (Cellgro, Herndon, VA) supplemented with 10% (v/v) heat inactivated fetal bovine serum (BioWhittaker, Walkersville, MD), 2 mM L-glutamine and 100 units/ml Penicillin and 1000 ug/ml Streptomycin (Invitrogen). 9L and RT2 cells were transfected with the plasmids pEGFP-N2 (Clontech) or pHSP72 using Lipofectamine (Invitrogen) according to the manufacturers protocol followed by selection in 0.2 mg/ml G418 (Invitrogen). 293 cells were obtained from American Type Culture Collection (ATCC, Manassas, VA; CRL-1573) were used for virus propagation and purification.
Plasmid Creation
pHSP72 was created by ligation of the DNA Polymerase blunt ended 2.3 KB Bam H1-Hind III fragment from plasmid pH2.3 (ATCC) into the blunt ended Xho1-XbaIA vector pEGFP-N2.
Adenovirus
Creation of ONYX-015 has previously been described[6]. The virus was propagated on 293 cells and purified by cesium chloride gradient followed by dialysis. Virus was stored at -80°C until use. Titer was determined by tissue culture infectious dose-50 method (TCID-50). Briefly, 293 cells were plated at 104 cells/well in 96 well flat-bottomed tissue culture plates. Virus titer from combined supernatant and freeze-thawed cell lysate was determined by serial 10-fold dilution into different rows of the titer plate. After 10 days, wells were scored for cytopathic effect. The titer was calculated using the formula T = 101+d(S-0.5) where d is the Log 10 of the dilution and S is the sum of the ratios of positive wells in each row. A more lengthy description of this method is provided in the adenovirus application manual at .
Adenovirus Infection
Cells were infected when 50% confluent with the dose of adenovirus given in the appropriate table. The infection was performed in serum free DMEM media for 90 minutes at 37°C and 5% CO2. Cells were subsequently washed with phosphate buffered saline and then cultured in complete medium.
Cell Viability Determination
Cell viability was determined by the standard Trypan-blue exclusion test. Cells were washed with PBS and then lifted from tissue culture plates with Trypsin-EDTA solution (Invitrogen). Cells were stained with 0.2% Trypan blue solution for 5 minutes and then counted on a hemacytometer.
Authors' contributions
JM performed all of the experiments outlined in this study. JAK independently replicated results for validity. JAK and MRS contributed equally to the molecular cloning of the plasmids and adenovirus propagation and purification. All authors participated in maintenance of cells in culture. The hypothesis, study design and statistical analysis were all performed by MRS. MRS drafted the entire manuscript with editing and approval by all of the authors.
Acknowledgements
Funding for J.M. was kindly provided by the Summer Research Program sponsored by the Saint Louis University School of Medicine. This work was supported by funding from the Saint Louis University Cancer Center and the Fleur-de Lis Foundation
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| 15762988 | PMC555846 | CC BY | 2021-01-04 16:36:35 | no | Mol Cancer. 2005 Mar 11; 4:12 | utf-8 | Mol Cancer | 2,005 | 10.1186/1476-4598-4-12 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-151577177810.1186/1477-7525-3-15ResearchFactor structure of the Hospital Anxiety and Depression Scale (HADS) in German coronary heart disease patients Barth Jürgen [email protected] Colin R [email protected] University of Freiburg – Institute of Psychology, Department of Rehabilitation Psychology, 79085 Freiburg, Germany2 The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Esther Lee Building, Chung Chi College, Shatin, New Territories, Hong Kong, China2005 16 3 2005 3 15 15 26 1 2005 16 3 2005 Copyright © 2005 Barth and Martin; licensee BioMed Central Ltd.2005Barth and Martin; 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
Depression and anxiety in patients with coronary heart disease (CHD) are associated with a poorer prognosis. Therefore the screening for psychological distress is strongly recommended in cardiac rehabilitation. The Hospital Anxiety and Depression Scale (HADS) is a widely used screening tool that has demonstrated good sensitivity and specificity for mental disorders.
Methods
We assessed mental distress in in-patient cardiac rehabilitation in Germany. The factor structure of the German language version of the HADS was investigated in 1320 patients with CHD. Exploratory factor analysis and confirmatory factor analysis were used to determine the underlying factor structure of the instrument.
Results
Three-factor models were found to offer a superior fit to the data compared to two-factor (anxiety and depression) models. The German language HADS performs similarly to the English language version of the instrument in CHD patients. The German language HADS fundamentally comprises a tri-dimensional underlying factor structure (labelled by Friedman et al. as psychomotor agitation, psychic anxiety and depression).
Conclusion
Despite of clinical usefulness in screening for mental disturbances the construct validity of the HADS is not clear. The resulting scores of the tri-dimensional model can be interpreted as psychomotor agitation, psychic anxiety, and depression in individual patient data or clinical investigations.
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Background
Coronary heart disease (CHD) is of profound interest to health and clinical psychology due to the high levels of anxiety and depression observed in patients following the occurrence of a coronary event [1-5]. CHD was the most leading diagnosis for treatment in hospitals in Germany for men (320,000 patients per year), and, after childbirth and breast cancer, the third reason for in-patient treatment for women (150,000 patients per year) [6]. Most in-patient rehabilitation hospitalizations in Germany for men (about 60,000 per year) were caused by CHD [7,8]. Recent research showed, that at least one in five patients in cardiac rehabilitation suffer from a psychological disorder [9]. Accurate identification of significant anxiety and depression as soon as possible following a cardiac event is essential in order to facilitate delivery of an effective and comprehensive treatment package which takes into account psychological as well as coronary disease symptoms [10]. This is particularly relevant since anxiety and especially depression have been demonstrated to be predictors of mortality in this clinical group [11]. The availability of easy to administer, reliable and valid screening tools would logically be a critical component of a clinical protocol seeking to identify CHD patients with psychological disturbance. A suitable measure would readily identify those patients for whom additional referral to a clinical psychologist or to a liaison psychiatry service would be more appropriate.
A candidate screening tool that has been widely and increasingly used with CHD patients is the Hospital Anxiety and Depression Scale (HADS: [12]), an easily administered 14-item self-report measure comprising 7 anxiety items and 7 depression items from which separate anxiety and depression sub-scale scores are calculated [13]. The HADS was designed to exclude symptoms that might arise from the somatic aspects of illness such as insomnia, anergia, and fatigue, therefore the instrument has been designed for use within the clinical context of general medicine. The HADS has been used for screening purposes in a diverse and broad range of clinical groups [14-24]. A number of investigations have suggested that the HADS is a suitable instrument to accurately assess anxiety and depression in CHD patients [10,17,24-27]. A fundamental assumption underpinning the clinical usefulness of the HADS across a broad range of clinical groups, including CHD, is that the instrument reliably assesses anxiety and depression as two distinct and separable dimensions [28].
On the other hand, recent psychometric evaluations of the HADS in a range of clinical populations have suggested that the proposed factor structure of the instrument may indeed be compromised by the physiological aspects of the disease or by changes in health status [23,29,30]. Conversely, there is accumulating evidence that the fundamental factor structure of the HADS comprises three factors instead of two [24,26,31-33]. The finding that the three-factor structure offers a superior fit to clinical data than the two-factor (anxiety and depression) model formulated as part of the original instrument development by Zigmond and Snaith [12] has implications in terms of the use, scoring and future development of this assessment tool.
Dunbar et al. [32] found a three-factor structure of the HADS in a non-clinical population (for an overview see table 1) and interpreted their findings in relation to the conceptually rich 'tripartite' model proposed by Clark and Watson [34]. Extending these observations to a clinical population, Friedman et al. [33] found a three-factor structure to the HADS in a patient group being treated for major depression, which incidentally, was similar to that observed by Dunbar et al. [32]. Martin and Newell [35] found that Friedman et al.'s [33] three-factor model offered the best-fit to their data examining individuals with significant facial disfigurement compared to competing two-factor models. Martin and Thompson [22] observed a three-factor structure to the HADS in myocardial infarction patients and, in a later study, Martin et al. [26] extended further the findings of both Dunbar et al. [32] and Friedman et al. [33] to myocardial infarction patients finding additional support for the three-factor structure suggested by these researchers to underlie the HADS. A recent study [24] of the psychometric properties of the HADS in Chinese acute coronary syndrome (ACS) patients has established further support for the three-factor structure of the HADS furnishing evidence that the three-dimensional structure of the instrument appears to be consistent across diverse cultures. Caci et al. [31] suggested a three-factor underlying structure to the HADS that represents a modification to the three-factor model identified by Friedman et al. [33] and replicated by Martin et al. [26]. However, Caci et al.'s [31] model was based on a student population and it should be remembered that the presence of significant pathology or physiological change states does impact on the underlying factor structure of this instrument [23,30,36]. The most consistent contemporary observation in terms of the underlying factor structure of this instrument in cardiac patients is strongly indicative that the HADS comprises three underlying dimensions. However, these cardiac studies have used either clinical populations from the United Kingdom [22,26] or from the Far East [24].
Table 1 Characteristics of each factor model tested in earlier studies.
Model, author, year Number of factors Clinical population n Factor extraction method # Allocation of items to scales
Zigmond & Snaith (1983) 2 Medical 100 No Factor analysis conducted anxiety: 1, 3, 5, 7, 9, 11, 13
depression: 2, 4, 6, 8, 10, 12, 14
Moorey et al. (1991) 2 Cancer 568 PCA anxiety: 1, 3, 5, 9, 11, 13
depression: 2, 4, 6, 7, 8, 10, 12, 14
Dunbar et al. (2000) 3 Non-clinical 2,547 CFA autonomic anxiety: 3, 9, 13
negative affectivity: 1, 5, 7, 11
anhedonic depression: 2, 4, 6, 7,
Friedman et al. (2001)* 3 Depressed 2,669 PCA psychomotor agitation: 1, 7, 11
psychic anxiety: 3, 5, 9, 13
depression: 2, 4, 6, 8, 10, 12
Razavi et al. (1990) 1 Cancer 210 PCA All items included
Caci et al. (2003) 3 Non-clinical 195 CFA anxiety: 1, 3, 5, 9, 13
restlessness: 7, 11, 14
depression: 2, 4, 6, 8, 10, 12
*The three-factors are correlated in this model.
#PCA: Principal Components Analysis; CFA: Confirmatory Factor Analysis.
There has been little systematic investigation of the factor structure of the translated HADS in German cardiac patients, though interestingly a German language version of the instrument has been developed in Germany using cardiac patients within the context of the original assumed two-dimensional (anxiety and depression) structure [37]. To date, the HADS has been found to comprise a two-factor structure consistent with the anxiety and depression sub-scales proposed by Zigmond and Snaith [12] in cardiac [38] and non-clinical [39] populations in Germany. However, these large studies did not investigate the possibility that alternative factor models may provide a better explanation of the data.
Identification of a coherent three-factor underlying structure of the HADS has a number of significant implications in terms of the validity of the tool as a screening instrument. Firstly, referral to mental health services could be undermined based on a two-dimensional (anxiety and depression) interpretation of HADS scores in cardiac patients. Secondly, further replication of a three-factor structure of the HADS in a German cardiac population would be valuable in determining if the HADS should be more effectively used as a screening instrument when comprised of three sub-scales in this group. Thirdly, replication of a consistent three-factor structure in the German-translated version of the HADS would provide strong evidence that the three-dimensional structure is implicit to the instrument and not a language-based artifact. Finally, the widespread international use of the HADS provides a compelling rationale to establish the psychometric properties of the instrument not only in broad diagnostic categories, but also across culturally-diverse groups.
The purpose of the present study was to determine whether the three-factor structure of the HADS identified by Martin and colleagues [26] in myocardial infarction patients in the UK and Martin et al. [24] in Chinese ACS patients has the same psychometric properties as that of the German-translated version of the HADS in a cohort of German patients presenting with CHD. The present study addresses two research questions:
1) Do exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) techniques concord to the proposed bi-dimensional structure of the HADS in German CHD patients?
2) Does a three-dimensional factor structure provide a superior fit compared to competing bi-dimensional factor structures?
Methods
Design
The study used a cross-sectional design with all measures taken at one observation. The dependent variables were sum scores obtained on the HADS (all items), and the anxiety (HADS-A) and depression (HADS-D) sub-scales. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) methods were used to address the research questions using a pooled HADS data set from all patients. Ethical approval for the study was given by the local ethical committee of the University Hospital of Freiburg. Written informed consent was obtained from all participants prior to the commencement of the study.
Procedure
The study was conducted in three German cardiac rehabilitation hospitals. The patients stay in these hospitals for three to four weeks for a comprehensive cardiac rehabilitation program consisting of medical advice, exercise, patient education, relaxation and psychosocial interventions. All cardiac patients who agreed to take part in the PROTeCD-study (Psychotherapeutic Resource-Orientated Treatment for Cardiac Patients with Depression) were screened for mental distress with the German version of the HADS [37] at admission to hospital. Sociodemographic data were collected by self report and somatic data were reported by physicians at study entry.
Statistical analysis
Exploratory factor analysis
Exploratory factor analysis was performed on the full 14-item HADS using SPSS 12.0 statistical software. The criterion chosen to determine that an extracted factor accounted for a reasonably large proportion of the total variance was based on an eigenvalue greater than 1. A maximum likelihood factor extraction procedure was chosen since this method of factor condensation is consistent with our previous research [22] and is particularly useful for extracting psychologically meaningful factors [40]. An oblimin non-orthogonal factor rotation procedure was chosen [40] due to the possibility that extracted factors are likely to be correlated. Determination of a significant item-factor loading was set at a coefficient level of 0.30 or greater, this level based on a rationale of generating a more complete psychological interpretation of the data set, this being a level consistent with investigators who have used EFA [22,30,36,41].
Confirmatory factor analysis
Confirmatory factor analysis was conducted using the Analysis of Moment Structures (AMOS) version 4 [42] statistical software package. Eight models were tested. These were Zigmond and Snaith's [12] original two-factor model, Moorey et al.'s [43] two-factor model, Razavi et al.'s [44] single-factor model, two versions of Clark and Watson's [34] three-factor model as evaluated by Dunbar and colleagues [32], Friedman et al.'s [33] three-factor model and two versions of Caci et al.'s [31] three-factor model. The characteristics and the allocation of the items to the factors in each tested modelare shown in Table 1. For all models, independence of error terms was specified. Factors were allowed to be correlated where this was consistent with the particular factor model being tested. Multiple goodness of fit tests [45] were used to evaluate the eight models, these being the comparative fit index (CFI; [46]), the Akaike information criterion (AIC; [47]), the consistent Akaike information criterion (CAIC; [48]), the normed fit index (NFI; [45]), the goodness of fit index (GFI; [49]) and the root mean squared error of approximation (RMSEA). A CFI greater than 0.90 indicates a good fit to the data [50]. A NFI and GFI greater than 0.90 indicates a good fit to the data [51]. A RMSEA with values of less than 0.08 indicates a good fit to the data [52], while values greater than 0.10 suggest strongly that the model fit is unsatisfactory. The AIC and CAIC are useful fit indices for allowing comparison between models [32]. The Chi-square goodness of fit test was also used to allow models to be compared and to determine the acceptability of model fit. A statistically significant χ2 indicates a significant proportion of variance remains unexplained by the model [45].
Results
Participants
1320 patients (1035 male) enrolled in an in-patient cardiac rehabilitation programme in three hospitals in Germany provided complete HADS data sets for analysis. Inclusion criteria for participation in the study was a confirmed diagnosis of CHD; for details see [53]. The patient group comprised patients with a diagnosis of myocardial infarction (N = 666), coronary artery bypass graft (N = 382), percutaneous transluminal coronary angioplasty (N = 303) and unstable angina pectoris (N = 40). It is noted that diagnostic N exceeds total cohort N because many patients will have multiple CHD diagnoses. Patients were required to have had a diagnosis of CHD and had a recent cardiac event (MI, CABG, PTCA) in the past weeks. Female patients (mean age = 62.88; SD = 12.14) were significantly older, (t(401.14) = 4.14, p < 0.001) than male patients (mean age = 59.58; SD = 10.59).
Descriptive findings
The mean HADS-A sub-scale score was 6.14 (SD = 4.15, range 0–20) and the mean HADS-D sub-scale score was 5.41 (SD = 4.00, range 0–20). Using Snaith and Zigmond's [28] cut-off criteria of HADS-A and HADS-D scores of eight or over, 467 participants (35%) demonstrated possible clinically relevant levels of anxiety and 373 participants (28%) possible clinically relevant levels of depression. Adopting Snaith and Zigmond's [28] higher threshold for sensitivity of HADS-A and HADS-D scores of eleven or over, 204 participants (15%) demonstrated probable clinically relevant levels of anxiety and 161 participants (12%) probable clinically relevant levels of depression.
Exploratory factor analysis
The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and the Bartlett Test of Sphericity (BTS) were conducted on the data prior to factor extraction to ensure that the characteristics of the data set were suitable for the EFA to be conducted. KMO analysis yielded an index of 0.94, and BTS (χ2 = 7758.34, df = 91, p < 0.001) was highly significant indicating the data satisfied the psychometric criteria for the factor analysis to be performed based on data distribution characteristics. Examination of individual item skew and kurtosis characteristics confirmed the suitability of the maximum likelihood factor extraction procedure [54]. Following extraction and oblimin rotation, two factors with eigenvalues greater than 1 emerged from analysis of the complete HADS and accumulatively accounted for 53% of the total variance. Factor loadings of individual HADS items in relation to the two-factor solution are shown in Table 2. Factor scores were calculated for each participant using regression and revealed the two extracted factors to be highly statistically and positively correlated, r = 0.82, p < 0.001, explaining 67% of the common variance between factors.
Table 2 Factor loadings of HADS items following maximum likelihood factor extraction with oblimin rotation
HAD Scale item Factor 1 Factor 2
Anxiety sub-scale
(1) I feel tense or wound up (AGI) 0.23 0.45
(3) I get a sort of frightened feeling as if something awful is about to happen (ANX) -0.02 0.75
(5) Worrying thoughts go through my mind (ANX) 0.09 0.69
(7) I can sit at ease and feel relaxed (AGI) 0.35 0.33
(9) I get a sort of frightened feeling like 'butterflies' in the stomach (ANX) -0.02 0.72
(11) I feel restless as if I have to be on the move (AGI) -0.01 0.43
(13) I get sudden feelings of panic (ANX) -0.02 0.75
Depression sub-scale
(2) I still enjoy the things I used to enjoy 0.95 -0.14
(4) I can laugh and see the funny side of things 0.87 -0.06
(6) I feel cheerful 0.72 0.01
(8) I feel as if I am slowed down 0.38 0.21
(10) I have lost interest in my appearance 0.40 0.08
(12) I look forward with enjoyment to things 0.65 0.09
(14) I can enjoy a good book or TV programme 0.42 0.19
Note: Bold indicates that item loading on a factor is 0.30 or above.
Factors in final model in figure 1; AGI: psychomotor agitation; ANX: psychic anxiety; DEP: remains the same
Confirmatory factor analysis
The factor models tested and accompanying fit indices are shown in Table 3. χ2 goodness of fit analyses for all models were highly statistically significant (p < 0.001) indicating that a proportion of the total variance was unexplained by each model. Examination of the fit indices revealed that the best fit to the data was offered by Friedman et al.'s [33] three-factor model (see figure 1). This model provided consistently the best fit across all but one model fit assessment criteria. It was also found that both models of Dunbar et al.'s [32] three-factor model evaluation of Clark and Watson's [34] 'tripartite' model provided a 'best fit' to the data on a number of the fit indices tested (CFI, NFI and GFI) as did model 1 (CFI, NFI, and GFI) and model 2 (CFI, AIC, NFI and GFI) of Caci et al.'s [31] three-factor model. The two-factor models of Zigmond and Snaith [12] and Moorey et al. [43] offered poorer fits to the data compared to all three-factor models evaluated, however against accepted model fit convention, these two-factor models still offered an acceptable fit to the data. The single-factor model of Razavi et al. [44] was observed to offer the poorest fit to the data across all model fit estimates.
Table 3 Factor structure of the HADS determined by testing the fit of models derived from factor analysis. All χ2 analyses were statistically significant at p < 0.01 (χ2 degrees of freedom in parentheses).
Model χ2 RMSEA CFI CAIC AIC NFI GFI
Zigmond and Snaith (1983) 481.47(76) 0.06 0.95 718.84 539.47 0.94 0.95
Moorey et al. (1991) 480.77(76) 0.06 0.95 718.15 538.77 0.94 0.95
Caci et al. (2003) model 1 391.55(74) 0.06 0.96 645.30 453.55 0.95 0.96
Caci et al. (2003) model 2 * 352.02(62) 0.06 0.96 589.40 410.02 0.95 0.96
Dunbar et al. (2000) model 1 396.56(73) 0.06 0.96 658.49 460.56 0.95 0.96
Dunbar et al. (2000) model 2 # 399.52(73) 0.06 0.96 661.45 463.52 0.95 0.96
Friedman et al. (2001) 361.41(74) 0.05 0.96 584.16 423.41 0.95 0.96
Razavi et al. (1990) 986.48(77) 0.09 0.88 1215.67 1042.48 0.87 0.88
Note: Bold indicates best model fit as a function of model fit index criterion.
* Three-factor model excludes item 10.
# Hierachical arrangement of factors.
Figure 1 Standardised factor loadings and between-factor correlations of the Friedman et al. [33] model. Boxes represents HADS items labelled as shown as in table 2. Circles represents factors. One-way and two-way arrows indicate factor loadings and between-factor correlations, respectively.
Discussion
The findings of the current study offer a further important contribution to the evidence base regarding the underlying factor structure of the widely used HADS. It is worthy of note that high levels of HADS assessed anxiety and depression were observed in the study. This finding is consistent with investigators using this instrument in cardiac populations in other parts of the world [22,24,26] and verification of the need to screen for psychological disturbance in patients presenting with CHD.
The findings from the factor analyses conducted on the HADS data are of pertinent methodological as well as clinical interest. EFA of the HADS revealed two factors, the loadings of individual items being consistent with the anxiety and depression sub-scale domains. However, it was also observed that the HADS-A item-7 'I can sit at ease and feel relaxed' was jointly loading on both anxiety and depression factors, this split-loading slightly in favour of the depression factor. A recent EFA of the HADS conducted with patients with significant facial disfigurement [35] also revealed item-7 to be split-loaded between anxiety and depression latent domains. Martin and Newell [35] suggest that in circumstances of split-loading such as those observed in the current study, a two-factor solution may offer the most parsimonious solution in EFA, but may not provide the best identification of factors in terms of model fit. Martin and Newell's [35] rationale for this is that EFA is not a model evaluation technique, therefore identification of factors based on arbitrary cut-points such as eigenvalues and scree plots is likely to produce a psychometrically reductionist account of sophisticated relationships between observed and latent variables. Martin and Newell [35] proposed that the lack of apriori model specification in EFA provides a convincing psychometrically plausible explanation of inconsistencies between EFA and CFA in extracted factors and interpretation of data. Indeed, Martin and Newell [35] found a similar finding to that of the current investigation, EFA support for a two-factor model and CFA support for the superiority of three-factor compared to two-factor models. Interestingly, Dagnan et al. [55] and Mykletun et al. [56] identified three-factor initial solutions within the HADS but chose to dismise the third-factor, without a sound psychometric rationale. It is likely that an expectation of a presumed two-factor model makes it difficult to reconcile an unexpected three-factor model emerging from the data, therefore it is explainable why these researchers might choose to dismiss a third factor.
The findings from the CFA revealed the best model fit to be provided by Friedman et al.'s [33] three-factor mode (see figure 1). The 'next best' fit to the data is offered by Caci et al.'s [31] three-factor model 2. It was also observed that the remaining three-factor models tested [31,32] not only offered a good fit to the data but also provided a superior fit to the data compared to the two-factor models evaluated on a number of estimates of model fit. The two-factor models of Zigmond and Snaith [12] and Moorey et al. [43] did however offer an acceptable fit to the data. The uni-dimensional model of Razavi et al. [44] was found to offer a poor fit to the data, a finding consistent with previous research on the HADS across a variety of clinical groups [19,24,26,30,35]. There remains little doubt from the CFA analysis that the best fit to the data is offered by three-factor models irrespective of the clinical population from which the three-factor model was derived. The findings from the CFA have furnished compelling support of the HADS as a tri-dimensional instrument, consistent with contemporary research with this instrument across diverse clinical presentations [19,26,30,31,33,35].
Conclusion
In conclusion, the current study found the German language version of the HADS to have an underlying three-dimensional factor structure following CFA in CHD patients, an observation consistent with UK [26] and Chinese [24] CHD populations. The traditional interpretation of the HADS as a two-factor (anxiety and depression) structure was also found to offer an acceptable fit to data, though inferior to that of the three-factor models. It can be concluded that the HADS may serve as useful screening purpose by being scored as two sub-scales of anxiety and depression. The clinical utilisation of the HADS continues to be invaluable in screening for mental disorders. Our results suggest that the assessment of the efficacy of interventions in evaluation studies by the HADS may be biased by problems in construct validity. Two decades have passed since the HADS was introduced to the clinical screening battery. The findings of this study and those of others, suggests that despite the clinical usefulness in screening the individual results of the HADS could be interpreted more precise in clinical routine. The differentiation of the anxiety scale in "psychomotor agitation" and "psychic anxiety" in the best fitting model may be helpful in the interpretation of individual results of patients. These results may improve our understanding of the process of adaptation in patients with somatic illness. A separate analysis of subscales in clinical trials may reduce bias caused by somatic medical conditions of the patients. Agitation might be more likely biased by the medical status of the patients.
Authors' contributions
JB designed the study, carried out the data collection and clinical assessment. JB drafted part of the manuscript and was involved in the interpretation of the findings. CRM developed the statistical framework, carried out the statistical analysis and drafted part of the manuscript. Both authors have no competing financial or other interest in relation to this manuscript.
Funding
The study was funded by the Federal Ministry of Education and Research, Germany; Regional Pension Insurance Institute, Baden-Wuerttemberg, Germany, LVA 02 804 and is part of the Rehabilitation Research Network South-West (Germany).
Acknowledgements
We would like to thank the cooperation clinics (Klinikum für Akut- und Rehabilitationsmedizin Bad Krozingen, Germany, Prof. Bönner, Prof. Holubarsch; Theresienklinik Bad Krozingen, Germany, Prof. Jost) and all participating patients. Our thanks goes also to all members of the PROTeCD working group (Prof. Dr. Dr. Bengel, Dipl. Psych. Nicole Englert, Prof. Dr. Dr. Martin Härter, Dipl. Psych. Juliane Paul) for their work in study design and patient recruitment. We thank also the reviewers of HQLO for helpful comments on an earlier version of this manuscript.
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| 15771778 | PMC555847 | CC BY | 2021-01-04 16:38:14 | no | Health Qual Life Outcomes. 2005 Mar 16; 3:15 | utf-8 | Health Qual Life Outcomes | 2,005 | 10.1186/1477-7525-3-15 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-171578014510.1186/1477-7525-3-17ResearchHealth-state valuations for pertussis: methods for valuing short-term health states Lee Grace M [email protected] Joshua A [email protected] Charles W [email protected] Tracy A [email protected] Center for Child Health Care Studies, Department of Ambulatory Care and Prevention, Harvard Pilgrim Health Care and Harvard Medical School, 133 Brookline Ave, 6th floor, Boston, MA 02215, USA2 Division of Infectious Diseases, Children's Hospital Boston, MA, USA3 Department of Population and International Health, Center for Population and Development Studies, Harvard School of Public Health, Boston, MA, USA4 National Immunization Program, Centers for Disease Control and Prevention, Atlanta, GA, USA5 Division of General Pediatrics, Children's Hospital Boston, MA, USA2005 21 3 2005 3 17 17 1 12 2004 21 3 2005 Copyright © 2005 Lee et al; licensee BioMed Central Ltd.2005Lee 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 incidence of reported adolescent and adult pertussis continues to rise in the United States. Acellular pertussis vaccines for adolescents and adults have been developed and may be available soon for use in the U.S. Our objectives were: (1) to describe patient valuations of pertussis disease and vaccination; and (2) to compare valuations for short-term and long-term health states associated with pertussis.
Methods
We conducted telephone surveys with 515 adult patients and parents of adolescent patients with pertussis in Massachusetts to determine valuations of pertussis-related health states for disease and vaccination using time trade-off (TTO) and contingent valuation (CV) techniques. Respondents were randomized to complete either a short-term or long-term TTO exercise. Discrimination between health states for each valuation technique was assessed using Tukey's method, and valuations for short-term vs. long-term health states were compared using the Wilcoxon rank-sum test.
Results
Three hundred three (59%) and 309 (60%) respondents completed and understood the TTO and CV exercises, respectively. Overall, respondents gave lower valuations (lower TTO and higher CV values) to avoid a given state for adolescent/adult disease compared to vaccine adverse events. Infant complications due to pertussis were considered worse than adolescent/adult disease, regardless of the method of valuation. The short-term TTO resulted in lower mean valuations and larger mean differences between health states than the long-term TTO exercise.
Conclusion
Pertussis was considered worse than adverse events due to vaccination. Short-term health-state valuation is better able to discriminate among health states, which is useful for cost-utility analysis.
pertussistime trade-offwillingness-to-payshort-term health-state
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Background
The incidence of reported pertussis continues to rise in the United States despite high levels of childhood vaccination [1,2]. Waning immunity is thought to contribute to the particularly steep rise seen among adolescents and adults over the past two decades [3,4]. Acellular pertussis booster vaccines have been developed already and recommended for use in several other countries including Canada, France, Germany, and Australia [5-7]. A combined booster (TdaP) also may become available soon for use in the U.S.
Recently completed clinical trials suggest that the booster may prevent cough illness related to pertussis among adolescents and adults [8,9]. Though such illness does not result in mortality in this age group, it can be prolonged and associated with significant complications such as pneumonia or urinary incontinence [10,11]. However, implementation of a vaccination program for adolescents and/or adults would carry a significant cost. Policymakers will need to decide whether or not to recommend use of a vaccine where the health benefits to adolescents and adults are reductions in short-term morbidity, rather than mortality, and the health risks include adverse events from vaccination. Thus, further information regarding the relative valuations by patients of different potential consequences should be considered. Quantifying patient preferences is relevant to decisions about allocation of limited resources and is needed to assess the cost-effectiveness of vaccination in comparison to other well-accepted health interventions [12].
Methods commonly used for measuring health-state valuations include contingent valuation (CV) and time trade-off (TTO) [13]. Contingent valuation is an economic approach to valuing different outcomes using monetary value (e.g. willingness-to-pay, WTP) as a common metric; for example, the relative amounts that individuals would pay to avoid one health state or another may be interpreted as measures of their strength of preferences for time spent in these different states. An advantage to this approach is that respondents may find it relatively easy to value short-term health states in monetary terms since they are accustomed to assessing the dollar value of goods and services in everyday transactions. However, some outcomes are difficult to quantify using contingent valuation, e.g. how much a person is willing to pay to avoid death. Additionally, CV may be subject to anchoring effects and income effects [14].
Another common approach to measuring the benefits and harms of health interventions relies on health-state utilities. Utilities measure a person's preferences for specific outcomes on a scale of 0 to 1, on which 0 typically represents a state equivalent to death while 1 represents the best imaginable health. The time trade-off method is one of several approaches used to assess health utilities. Using the TTO method, respondents are asked how much longevity they would be willing to give up, if any, to avoid living with a particular health outcome. Traditionally, TTO questions have been framed as giving up time to avoid a long-term or chronic health state [15]. However, for many common health problems, including those caused by infections, the duration of the relevant health states is limited, not permanent. A more realistic approach would be to frame these conditions as short-term health states.
We conducted a survey using TTO and CV methods to determine the health-state valuations of adult patients and parents of adolescent patients diagnosed with pertussis. We compared two alternative approaches to framing TTO questions, based on either short-term or long-term health states. We hypothesized that framing questions as short-term rather than as long-term health outcomes would allow for better discrimination between states.
Methods
Study participants
Structured telephone interviews were conducted with adult patients (≥ 18 years) and parents of adolescent patients (11–17 years) diagnosed with confirmed pertussis in Massachusetts from December 1, 2001 to January 31, 2003 [11]. There were 800 cases of confirmed pertussis among adolescents and adults during this time period, and 517 (65%) respondents completed the telephone interviews, although two were excluded because the wrong health-state valuation survey was administered. Interviews included questions about medical and non-medical costs of illness and questions regarding health-state valuations for pertussis disease and vaccination. There were no significant differences in age, gender or race/ethnicity between respondents and all confirmed cases during the enrollment period. The study was reviewed and approved by the Institutional Review Boards of Children's Hospital Boston, Harvard Pilgrim Health Care, Massachusetts Department of Public Health, and Centers for Disease Control and Prevention.
Survey protocol
Descriptions of the health states were derived with input from 3 pertussis experts (Table 1). Adults were asked questions about themselves while parents were asked to respond in reference to outcomes in their adolescents. We also asked both adults and parents of adolescents to value the prevention of infant health states (respiratory complications, neurologic complications) due to pertussis. All surveys included open-ended TTO and CV questions; in other words, respondents were asked once about the maximum amount of longevity they would give up, or the maximum amount of money they would be willing to pay, to avoid the health outcome in question. We chose the open-ended format [16-19] due to the large number of items evaluated and for ease of administration by telephone. Additionally, prior methodologic work on open-ended CV techniques has demonstrated similar results to the commonly used but more intensive alternative involving dichotomous-choice questions.[18]
Table 1 Health-state descriptions for outcomes associated with disease and vaccination.
Health states Description
Local reaction A sore upper arm that is slightly red, swollen, and tender after receiving a vaccination
Systemic Reaction Low-grade fevers, headache, body ache, and decreased energy after receiving a vaccination
Mild cough Coughing attacks that last for 1–2 minutes at a time and occur up to 8–10×/day. These coughing attacks wake you up at night several times a week, but you otherwise feel well between coughing attacks.
Severe cough A cough that is so frequent and severe that it causes vomiting at least several times a week, difficulty eating or drinking, and difficulty sleeping every night.
Pneumonia A severe cough with high fevers, chills, fatigue, and shortness of breath
Respiratory complications (apnea and cyanosis) A 1-month-old baby that has coughing episodes so hard that he/she stops breathing and turns blue for 10–15 seconds. These episodes happen 8 to 10 times a day and the baby needs to be hospitalized, but is completely healthy afterwards.
Neurologic complications (seizures and encephalopathy) A 1-month-old baby with seizures or convulsions. The seizures cause brief periods of being unconscious and the baby's arms and legs shake. They can last for up to 5 minutes at a time and happen several times a day. The baby needs to be hospitalized, but is completely healthy afterwards.
For TTO questions, respondents were asked the maximum amount of time they would be willing to trade from the end of their lives to avoid a particular health outcome now. Adults were asked how much time they would give up from the end of their lives to avoid living in a particular health state themselves, while parents were asked how much time they would give up from the end of their own lives to avert the health state in the adolescent [17,20]. This approach was adopted after pre-testing the survey instrument with parents, who were more willing to answer questions about trading time from their own lives than from their children's lives. For infant health states, both sets of respondents were asked to give up time from the ends of their own lives to avoid a long-term or short-term health state in an infant.
Although TTO questions traditionally have been framed using permanent health states for valuations of chronic disease [15], the health states associated with pertussis and vaccination are limited, lasting anywhere from days to weeks. Thus, asking respondents to imagine that they had to live for the rest of their lives with an infection or vaccine adverse event is not realistic. In order to address this concern and to test the hypothesis that framing the question as a short-term health state would significantly alter the TTO response, we created 2 versions of the survey – one with short-term and one with long-term (permanent) health states. Long-term health states were described as lasting for the lifetime of the infant, adolescent, or adult. Short-term health states were described as lasting for a duration of 8 weeks for the infant, adolescent or adult. We chose a constant duration for the short-term health states to ensure consistent responses regarding rank order and comparability of health states. In order to determine which version of the survey the parent or adult would receive, we used a random number generator to assign interviews to respondents once they consented to participate in the study.
CV questions elicited the amount of money that a respondent would be willing to pay to avoid living in a particular health state for 8 weeks. We chose to frame the CV questions as short-term health states for both versions of the survey based on prior work [21,22]. Respondents were instructed not to consider any money lost from missed work or any co-payments that would be required.
Telephone interviews were conducted in English or Spanish using standardized forms. Some respondents either were unable to complete or refused to answer the entire set of TTO questions or CV questions. If any answers were missing within a set of questions, respondents were excluded from analyses (of that set) in order to assess population means. If the respondent completed either set of questions, trained interviewers judged how well the respondent understood the TTO or CV questions separately based on a 3-point scale (good understanding, some understanding, limited understanding). Respondents were excluded from further analyses if they were thought to have either some or limited understanding of the tasks presented [23].
Calculation of utilities – long-term states
We calculated utilities based on the TTO exercise, under alternative assumptions about discounting of future health outcomes. For long-term health states, the utility was based on the proportion of time that the respondent would be willing to give up to avoid a lifetime health state for themselves, for their adolescent child, or for a hypothetical infant (Figure 1). Life expectancy (LE) was calculated using age- and sex-specific cohort life tables [24]. In the cases of adolescents and infants, since the time given up would come from the adult respondent's lifespan, while the healthy time gained would accrue to the adolescent or infant, the computations required LE estimates for both the respondent and the beneficiary in the trade-off.
Figure 1 Conceptual model for calculating utilities for long-term and short-term health states for adults and adolescents or infants. In each of the four panels, the top bar indicates the amount of longevity that is given up from the end of the respondent's life in each of the trade-offs, and the bottom bar indicates the averted duration of time in a given health state. Abbreviations: TTO, time trade-off; LE, life expectancy.
It is most straightforward computationally to start with the disutility, rather than the utility, of a particular health state, computed as the ratio of the duration of life that would be given up (to avoid the lifetime health state) to the expected duration of time lived in the health state. In the absence of discounting, the disutility was calculated simply by dividing the amount of time traded from the end of the respondent's life by the LE of the beneficiary. The utility was then calculated by subtracting this result from one. With discounting, we assumed that individuals compared the present values of the two different streams of life in the trade-off, in a way that reflects declining relative weight for future consequences, and we computed utilities based on a discount rate (r) of 3% per year [12]. As empirical studies on time preference have reported a range of discount rates [25-27], we also examined the sensitivity of our findings to alternative assumptions about discounting, including discount rates of 5% and 10% (as well as the 0% rate implied by the no-discounting case).
Using the formula for discounting a continuous stream of life [28], we obtained the present value of future time traded from the end of life (the numerator in the disutility calculation) by taking the difference between the discounted stream of normal life expectancy for the respondent and the discounted stream of shortened life expectancy:
(1/r) * (1 - e-r (LE of respondent)) - (1/r) * (1 - e-r (LE of respondent - years of life traded))
The present value of the current life expectancy for the beneficiary (the denominator) was
(1/r) * (1 - e-r (LE of beneficiary))
The disutility for a given health state was computed as the ratio of the two quantities, as in the undiscounted case, and the utility was computed by subtracting the ratio from one. For adult valuations, the respondent and beneficiary were the same. For parents of adolescents or for respondents considering a hypothetical infant, the numerator was based on years traded from the respondent's life, while the denominator was based on the life expectancy of the adolescent or infant.
Calculation of utilities – short-term states
Utilities were calculated for the short-term health states in an analogous fashion, except that time from the end of the life of the respondent was traded to avoid 8 weeks of illness in the present time for the respondent, for the adolescent child, or for a hypothetical infant (Figure 1). The numerator was calculated in the same way as for the long-term states, assuming a 3% discount rate in the baseline analysis (and alternatives in sensitivity analysis):
(1/r) * (1 - e-r (LE of respondent)) - (1/r) * (1 - e-r (LE of respondent - years of life traded))
For the denominator, discounting would have minimal impact because the duration considered is only 8 weeks and begins at the present, but we nevertheless converted this duration to its present value for consistency:
(1/r) * (1 - e-r (8/52))
Again, the disutility was the ratio of these two quantities, and the utility was computed by subtracting the ratio from one.
Statistical analysis
Utilities and WTP values are presented as means (with standard deviations) and medians (with interquartile ranges). We assumed that the maximum amount of discounted time traded from the end of the respondent's life could not exceed the duration of the present health state; thus, any utilities that would be negative based on the computations described above were instead set to 0. For parent respondents who were asked questions about how much time they would trade to avoid long-term health states in their adolescents, we used interval regression with left censoring to calculate mean utilities [29]. In interval regression, when parents were willing to trade off their full life expectancy to avoid a lifetime health outcome in their child, we treated this observation as providing only partial information about the amount that parents would give up, since they were limited by their life span – which was always shorter than the lifespan of the beneficiary adolescent. These observations were assumed to indicate a range of time spanning between the longevity of the parent and that of the adolescent. Interval regression was used to limit bias as a result of this constraint. When parents traded-off less than their full life expectancy, interval regression was equivalent to ordinary least squares regression. For infant health states, an analogous approach based on interval regression was applied.
To compare demographic characteristics of respondents for the short-term vs. long-term TTO surveys, we used the chi-squared test for categorical variables and the t-test for continuous variables. To determine if mean health state utilities and WTP values were significantly different from one another, we used Tukey's method, which is a non-parametric test that allows for multiple pairwise comparisons assuming all sample sizes are equal [30] Comparison of utilities for short-term vs. long-term health states was performed using the Wilcoxon rank sum test based on 2 independent samples [30]. Spearman rank correlation was used to determine associations between demographic characteristics and TTO or CV responses [30]. A p-value of <0.05 was considered statistically significant.
Results
Respondent characteristics
Five hundred fifteen adult pertussis patients and parents of adolescent pertussis patients were eligible and participated in the survey (Figure 2). Characteristics of the respondents are described in Table 2. There were no significant differences between respondents who received either form of the survey. Overall, 303 (59%) respondents completed and understood the TTO portion of the survey and 309 (60%) respondents completed and understood the CV portion of the survey. When response rates of parents and adults were compared, we found no significant differences in response rates to the short-term TTO questions (p = 0.28); however, adults were significantly more likely to respond than parents of adolescents to the long-term TTO questions (p = 0.006). Other respondents were not included for analysis because: (1) the TTO (27%) or CV (22%) survey was not completed by respondents; or (2) one or more answers within a set of TTO (9%) or CV (12%) questions were not completed; or (3) respondents completed but were thought not to understand the TTO (6%) or CV (5%) exercise.
Figure 2 Study enrollment. Percentages indicate proportion of respondents who were given the survey that completed the entire set of questions and understood the TTO and WTP exercises. Abbreviations: TTO, time trade-off; WTP, willingness to pay.
Table 2 Characteristics of respondents interviewed using short-term TTO (N = 267) vs. long-term TTO (N = 248).*
Characteristics Short-term TTO Long-term TTO P-value
Mean age of respondent [range]** 42.4 [18–87] 41.7 [18–81] 0.49
Gender of respondent
Female 207 (78%) 205 (83%) 0.35
Male 56 (21%) 40 (16%)
Not available 4 (2%) 3 (1%)
Race/ethnicity of respondent:
White 238 (89%) 219 (88%) 0.67
Black 7 (3%) 4 (2%)
Hispanic 11 (4%) 15 (6%)
Other or unknown 11 (4%) 10 (4%)
Educational level of respondent:
Up to high school 67 (25%) 52 (21%) 0.48
Up to college or technical school 136 (51%) 143 (58%)
>College 60 (22%) 49 (20%)
Refused to answer 4 (2%) 4 (2%)
Annual household income:
<$20,000 28 (10%) 26 (10%) 0.79
$20,000–49,999 55 (21%) 53 (21%)
$50,000–79,999 52 (19%) 58 (23%)
≥ $80,000 98 (37%) 80 (32%)
Refused to answer 34 (13%) 31 (13%)
*Numbers may not add up to 100% due to rounding
**Missing ages for 8 parents of adolescents
We compared demographic characteristics of respondents who completed and understood the survey with those who did not. Parents of adolescents with higher household incomes (p = 0.022) and higher educational levels (p= 0.017) were more likely to complete and understand the CV survey. Also, parents who were white (p = 0.011) with higher educational levels (p = 0.010) were more likely to complete and understand the TTO survey. Adult respondents who completed and understood the CV and TTO survey were significantly younger (p = 0.006 for CV; p = 0.025 for TTO) and had higher educational levels (p = 0.005 for CV; p = 0.012 for TTO) than those who did not.
Adolescent health states
CV and TTO responses for short-term and long-term health states for adolescents are described in Table 3. Based on mean utilities, parents of adolescents ranked the following long-term health states from best to worst: local reaction, systemic reaction, mild cough, severe cough, and pneumonia. Short-term health state rankings were similar, except mean utilities for severe cough and pneumonia were equivalent. For both short-term and long-term health states (Figure 3), we found significant differences in mean utilities (zero not included in the confidence interval) for most pairwise comparisons (Tukey's method, p<0.05). However, the mean utilities for health states that ranked close to each other were not significantly different, such as local reaction vs. systemic reaction, systemic reaction vs. mild cough, and severe cough vs. pneumonia. CV responses reflected rankings similar to TTO responses for parent respondents (Table 3). However, there were no significant differences between the amounts individuals were willing to pay to avoid adolescent health states.
Table 3 Adolescent Pertussis – days or years traded, utilities, and willingness-to-pay to avoid health states
Vaccination health states Disease health states
Local reaction Systemic reaction Mild cough Severe cough Pneumonia
Short-term TTO (N = 94)
Days traded
Mean (SD) 17 (46) 29 (61) 55 (117) 90 (162) 79 (114)
Median [25%–75%] 2 [0–14] 7 [2–28] 25 [7–56] 45 [14–56] 41 [14–70]
Utilities*
Mean (SD) 0.92 (0.19) 0.86 (0.23) 0.78 (0.27) 0.67 (0.33) 0.67 (0.33)
Median [25%–75%] 0.99 [0.93–1.0] 0.96 [0.85–0.99] 0.87 [0.72–0.96] 0.78 [0.61–0.92] 0.78 [0.61–0.91]
Long-term TTO (N = 81)
Years traded
Mean (SD) 2.6 (4.1) 5.5 (5.9) 8.0 (6.7) 11.6 (9.0) 12.0 (9.5)
Median [25%–75%] 1 [0.1–5] 5 [1–5] 5 [5–10] 10 [5–20] 10 [5–20]
Utilities*
Mean (SD) 0.97 (0.07) 0.93 (0.10) 0.89 (0.12) 0.83 (0.17) 0.82 (0.17)
Median [25%–75%] 0.99 [0.96–1.0] 0.95 [0.92–0.99] 0.93 [0.87–0.95] 0.88 [0.78–0.94] 0.88 [0.76–0.94]
Willingness-to-pay (N = 183)
Mean (SD) $18 (58) $61 (174) $3,003 (15,889) $3,981 (16,797) $4,265 (16,860)
Median [25%–75%] $3 [1–13] $13 [6–38] $300 [150–1,500] $750 [225–1,500] $750 [263–1,500]
*Utilities were calculated assuming the maximum amount of time traded could not exceed the duration of the health state in the adolescent and assuming a discount rate of 3%.
Figure 3 Mean difference between TTO utilities and 95% confidence intervals for short-term (squares) and long-term (circles) health states for adolescents.
Adult health states
Short-term and long-term TTO responses for adult respondents are described in Table 4. Based on mean utilities, adults ranked short-term health states in the following order: local reaction, systemic reaction, mild cough, pneumonia, and severe cough. Mean rankings for long-term health states were similar, except pneumonia and severe cough were equivalent. For short-term health states (Figure 4), mean differences in utilities were significantly different for 5 out of 10 pairwise comparisons (Tukey's method, p < 0.05). However, the only significant differences in utilities for long-term health states were: local reaction vs. severe cough, local reaction vs. pneumonia, systemic reaction vs. severe cough, and systemic reaction vs. pneumonia (Tukey's method, p < 0.05). CV responses again reflected a rank order similar to the TTO exercise (Table 4). We were unable to detect significant pairwise differences in the WTP amounts to avoid adult health states.
Table 4 Adult pertussis – days or years traded, utilities and willingness-to-pay to avoid health states
Vaccination health states Disease health states
Local reaction Systemic reaction Mild cough Severe cough Pneumonia
Short-term TTO (N = 72)
Days traded
Mean (SD) 24 (135) 26 (130) 80 (366) 99 (446) 101 (448)
Median [25%–75%] 0 [0–2] 2.5 [0–7] 8.5 [0.5–28] 14 [2–56] 14 [2–49]
Utilities*
Mean (SD) 0.95 (0.18) 0.93 (0.18) 0.85 (0.26) 0.81 (0.30) 0.82 (0.30)
Median [25%–75%] 1.0 [0.99–1.0] 0.99 [0.95–1.0] 0.96 [0.88–1.0] 0.95 [0.81–0.99] 0.96 [0.83–0.99]
Long-term TTO (N = 56)
Years traded
Mean (SD) 0.4 (0.8) 1.4 (2.2) 2.7 (3.4) 4.7 (6.3) 4.7 (6.2)
Median [25%–75%] 0.03 [0–0.9] 0.8 [0.04–1.5] 1 [0.6–4.5] 2 [1–5] 2 [0.8–5]
Utilities*
Mean (SD) 0.995 (0.01) 0.98 (0.03) 0.96 (0.06) 0.92 (0.14) 0.92 (0.16)
Median [25%–75%] 1.0 [0.99–1.0] 0.99 [0.98–1.0] 0.99 [0.96–1.0] 0.97 [0.92–0.99] 0.97 [0.91–0.99]
Willingness-to-pay (N = 126)
Mean (SD) $8 (17) $41 (78) $3,249 (14,062) $4,141 (15,409) $8,748 (66,907)
Median [25%–75%] $3 [0–9] $13 [6–38] $450 [150–1,200] $750 [300–1,500] $750 [300–1,500]
*Utilities were calculated assuming the maximum amount of time traded could not exceed the duration of the health state in the adult and assuming a discount rate of 3%.
Figure 4 Mean difference between TTO utilities and 95% confidence intervals for short-term (squares) and long-term (circles) health states for adults.
Infant health states
TTO and CV responses for infant health states are described in Table 5. We asked all respondents to imagine they had a 1-month-old infant who developed pertussis that could result in either short-term (8 weeks followed by perfect health) or long-term health states. Mean utilities for short-term infant health states, such as respiratory or neurologic complications due to pertussis, were lower than mean utilities for vaccine adverse events or adolescent/adult disease. However, mean utilities for long-term infant health states were not significantly different from adolescent/adult disease utilities. All respondents were willing to pay significantly more to avoid infant disease compared to vaccine adverse events or adolescent/adult disease. Neurologic disease was considered significantly worse than infant respiratory disease regardless of the method of valuation.
Table 5 Infant pertussis – days or years traded, utilities, and willingness-to-pay to avoid health states*
Infant health states
Infant respiratory complications Infant neurologic complications
Short-term TTO (N = 166)
Days traded
Mean (SD) 174 (360) 226 (431)
Median [25%–75%] 56 [28–168] 56 [28–183]
Utilities
Mean (SD) 0.58 (0.37) 0.51 (0.38)
Median [25%–75%] 0.72 [0.10–0.88] 0.64 [0.0–0.85]
Long-term TTO (N = 137)
Years traded
Mean (SD) 12.3 (10.9) 15.2 (12.3)
Median [25%–75%] 10 [5–20] 10 [5–20]
Utilities**
Mean (SD) 0.82 (0.21) 0.77 (0.25)
Median [25%–75%] 0.89 [0.75–0.96] 0.87 [0.69–0.95]
Willingness-to-pay (N = 309)
Mean (SD) $13,016 (52,443) $19,426 (61,074)
Median [25%–75%] $1,500 [750–7,500] $3,000 [750–10,000]
*Responses from adults and parents of adolescents were pooled.
**Utilities were calculated assuming the maximum amount of time traded could not exceed the duration of the health state assuming a discount rate of 3%.
Comparison of utilities for short-term vs. long-term health states
Overall, mean utilities were higher for long-term health states than for short-term health states. These differences were significant for adolescents with mild cough (p = 0.045), severe cough (p = 0.001), and pneumonia (p = 0.001). No significant differences were found between short-term and long-term health states for adults. For infants, we also found significant differences with higher mean utilities reported for long-term health states compared to short-term health states (p < 0.001 for respiratory complications; p < 0.001 for neurologic complications).
Association between demographic variables and TTO or WTP estimates
We evaluated associations between demographic characteristics such as age, race/ethnicity, education, and household income and estimates provided by respondents for the TTO or CV exercise. Older age was associated with lower utilities for short-term health states for adolescent/adult disease (i.e. mild cough, severe cough, pneumonia) and infant disease (i.e. respiratory and neurologic complications) (p < 0.05). Older age was also associated with lower utilities for long-term health states such as systemic reaction, adolescent/adult disease, and infant disease (p < 0.05). Higher income was associated with lower utilities for long-term mild cough and short-term respiratory complications, although older respondents were more likely to report higher household incomes (p < 0.001).
Higher income was significantly associated with higher WTP values for the following health states: systemic reaction, adolescent/adult disease, and infant disease (p < 0.05). We also found an association between higher respondent education and higher WTP estimates for pneumonia, although we note that education and income were themselves positively correlated (ρ = 0.358, p < 0.0001).
Alternative assumptions about the discount rate
Tables 6A and 6B describe estimated mean utilities for short-term and long-term health states associated with disease and vaccination in adolescents, adults, and infants. At higher discount rates, the mean differences between utilities for different health states became smaller, regardless of age group or method of valuation.
Table 6 Utilities based on alternative discount rates of 0%, 5%, and 10%, for (A) adolescents, adults, and (B) infants. Utilities were calculated assuming the maximum amount of time traded could not exceed the duration of the health state
A. Adolescents and adults
Vaccination health states Disease health states
Local reaction Systemic reaction Mild cough Severe cough Pneumonia
Mean (SD) adolescent utilities for short-term TTO (N = 94)
0% 0.80 (0.32) 0.68 (0.36) 0.51 (0.39) 0.35 (0.38) 0.35 (0.37)
5% 0.95 (0.14) 0.92 (0.16) 0.87 (0.22) 0.80 (0.28) 0.80 (0.26)
10% 0.99 (0.03) 0.99 (0.04) 0.97 (0.08) 0.96 (0.11) 0.96 (0.08)
Mean (SD) adolescent utilities for long-term TTO (N = 81)
0% 0.96 (0.06) 0.92 (0.09) 0.88 (0.10) 0.82 (0.13) 0.82 (0.14)
5% 0.97 (0.07) 0.94 (0.11) 0.91 (0.12) 0.85 (0.18) 0.85 (0.17)
10% 0.99 (0.05) 0.97 (0.09) 0.95 (0.11) 0.91 (0.17) 0.91 (0.15)
Mean (SD) adult utilities for short-term TTO (N = 72)
0% 0.91 (0.24) 0.83 (0.29) 0.67 (0.38) 0.58 (0.42) 0.62 (0.40)
5% 0.97 (0.13) 0.96 (0.14) 0.90 (0.22) 0.88 (0.23) 0.88 (0.25)
10% 0.99 (0.04) 0.99 (0.04) 0.97 (0.07) 0.97 (0.08) 0.97 (0.08)
Mean (SD) adult utilities for long-term TTO (N = 56)
0% 0.99 (0.02) 0.97 (0.06) 0.93 (0.09) 0.88 (0.17) 0.88 (0.18)
5% 1.0 (0.01) 0.99 (0.03) 0.97 (0.05) 0.94 (0.13) 0.94 (0.15)
10% 1.0 (0.00) 1.0 (0.01) 0.99 (0.02) 0.97 (0.09) 0.96 (0.12)
B. Infants
Infant health states
Infant respiratory complications Infant neurologic complications
Short-term TTO (N = 166)
0% 0.27 (0.36) 0.21 (0.33)
5% 0.71 (0.35) 0.66 (0.36)
10% 0.92 (0.17) 0.90 (0.19)
Long-term TTO (N = 147)
0% 0.36 (0.18) 0.33 (0.19)
5% 0.84 (0.21) 0.78 (0.26)
10% 0.89 (0.20) 0.84 (0.27)
Discussion
Vaccination programs in the US have traditionally been life-saving and cost-saving [31,32]. However, the focus of newer vaccines being developed has shifted from preventing mortality to preventing morbidity. In this situation, the risks of vaccine adverse events need to be weighed carefully against their benefits. Health-state valuation studies are useful to assess the relative risks and benefits of potential future vaccination programs under consideration in the US. Our study examined valuations associated with adolescent/adult pertussis disease and vaccination. Overall, respondents rated adolescent and adult pertussis as worse than vaccine adverse events. Also, infant complications due to pertussis were ranked as worse than adolescent/adult disease.
We explored differences in utilities for short-term and long-term health states using open-ended TTO questions. Other methods for short-term health state valuation described in the literature include chained TTO, sleep tradeoff (STO), and waiting tradeoff (WTO). The chained TTO has been shown to have good consistency and reliability [13,33,34]. However, the chained procedure involves an extra step and may result in a significant cognitive burden due to the complexity of the task. The sleep tradeoff asks people how much time they would be willing to sleep in a non-refreshing/non-dream state to avoid living with a short-term health problem [35,36]. Unfortunately, this method may not be appropriate for valuing health states associated with pertussis since sleep disturbance occurs in a majority of infected individuals, which may confound responses regarding sleep [11]. The waiting tradeoff proposed by Swan et al. is an alternative approach for assessing process utility [37]. While this approach is clearly useful for situations that involve diagnostic procedures, its applicability to other short-term health states such as infections is limited. We felt the open-ended format was the most appropriate method for our study population given the limitations of alternatives and due to improved ease of administration by telephone.
We found that the rankings of health states based on mean utilities were essentially the same using either short-term or long-term health states. However, the short-term TTO resulted in lower mean utility estimates and larger mean differences between health states than the long-term TTO exercise, thus allowing for better discrimination of health states in cost-utility analyses. These results suggest that responses may not fulfill the constant proportional trade-off assumption, which requires that the TTO utility be independent of the duration of the specified health state. While previous studies using TTO and other valuation methods have also found that the constant proportional trade-off assumption does not always hold, the direction of the discrepancy has been mixed [38-40].
The short-term health state approach may have violated the constant proportional trade-off assumption because respondents were less averse to giving up small amounts of time from the ends of their lives (days or weeks) compared to large amounts of time (months or years) in the long-term approach. In other words, asking respondents to give up a few days or weeks from the ends of their lives may not be considered a significant loss, even to avoid a short-duration health state lasting only 8 weeks. However, giving up months or years of life is considerably more difficult for individuals, even to avoid an intermediate- or long-term health state. It may be that a threshold exists whereby individuals are more willing to give up a very small portion of their lives for perfect health, but as the duration of health states increase, they are less willing to give up time per health unit gained, resulting in failure to behave according to the constant proportional trade-off assumption. This aversion to giving up larger amounts of time may play an important role in measuring utilities, particularly since the short duration of these health states over the lifetime of individuals would otherwise lead to nearly imperceptible, but arguably important differences in terms of quality-adjusted life years.
The impact of discounting is important to consider since assuming no discounting can lead to a downward bias, while a high discount rate can lead to an upward bias, which may strongly devalue the benefits of any preventive intervention [12,41-43]. We assumed a 3% discount rate in our baseline analysis, though there is no clear standard regarding the optimal discount rate for societal decisions. We also examined the implications of varying the discount rate between 0 and 10%. If we assumed no discounting of health preferences over time, the mean utilities for all health states were lower and spread over a wider range. At a discount rate of 10%, the mean values for all health states approached 1.0 and mean differences between health states were much smaller. Further empirical investigation of societal discount rates for prevention programs is needed.
The CV exercise resulted in similar mean health state rankings to our TTO exercise, and these estimates were positively correlated with income, which is not surprising since individuals were asked to respond in consideration of their actual household income [44]. For the TTO, we found an inverse association between age and utility estimates. This finding is consistent with previous studies that have shown older individuals provide lower utility estimates for health states [45,46]. It may be that increased awareness of the reality of living in poor health is better understood by older respondents [46].
As always, there are limitations to our study. First, our respondents were either adult pertussis patients or parents of adolescents with pertussis. We elicited patient and caregiver valuations as part of a larger study to determine societal costs of pertussis in adolescents and adults. While the U.S. Panel on Cost-effectiveness in Health and Medicine suggests that community preferences be used where possible[12], there is ongoing debate over whose preferences should be included in cost-effectiveness analyses.[47] There are certain practical advantages to surveying patients and caregivers. Because these individuals had recent first-hand experience with the disease, reasonably short descriptions could be used to sufficiently characterize a series of health states associated with pertussis, making administration by telephone feasible. While there is no perfect measure of health, patient preferences can help to inform societal values and should be given further consideration. If community valuations are collected in subsequent studies, it will be useful to compare them to the patient and caregiver valuations collected here.
Second, selection bias might arise from our survey completion rate of around 60%, although this is comparable to other published valuation studies [20,23]. In our study, a significant proportion of respondents did not complete the survey (22–27%), refused to answer the entire set of questions (9–12%), or did not understand the exercise (5–6%). We believe this may have been due in part to respondent burden, because preference questions were asked at the end of a lengthy cost interview. Also, we asked both WTP and TTO questions for 7 separate health states. In a separate analysis that included all answers to the set of questions regardless of the level of understanding, we found that the rank order of health states remained consistent, which is reassuring.
Because of the complexity of the task required, it is not surprising that respondents with higher educational levels were more likely to complete and understand the preferences exercise. In addition, most respondents were white, well educated, and had relatively high household incomes. Household income was associated with utilities as well as willingness-to-pay to avoid pertussis. To address this limitation, economic analyses of pertussis should vary willingness-to-pay and utilities over wide ranges that would reflect the preferences of a general population. Further research in more socioeconomically diverse populations should also be considered.
Another issue that should be explored more thoroughly is the impact of parents as surrogate respondents for children and the method of preference elicitation. We asked parents of adolescents to serve as proxy respondents for their child. Interestingly, parents of adolescents were less likely to provide answers to the long-term TTO exercise than adult respondents, which may suggest that parents had difficulty answering the TTO question for long-term illnesses in their children. Also, trading time from the parent's life to avoid illness in a child may result in preferences that incorporate other aspects of their relationship such as altruism. Previous work in the CV literature has suggested that altruism may significantly affect valuations. For example, Liu et al. found that a mother's WTP to prevent a cold is approximately twice as large for the child as for the mother[21]. While parents are often considered to be the health care decision makers for their child, further work on eliciting health state valuations directly from children would provide useful information. In addition, we asked parents how much time they would be willing to give up from the end of their lives to avoid illness in their child because we found that some parents refused to trade time from their child's life whereas they were willing to trade from their own life. Though this did not affect our calculation of short-term utilities (since a common denominator of 8 weeks was used), it did affect our calculation of long-term utilities where the denominator was based on the life expectancy of the child.
Conclusion
In this study, we estimated health-state valuations regarding pertussis disease and vaccination among adult patients and parents of adolescent patients. Patient preferences in conjunction with health outcomes will be key factors in deciding whether or not to implement a universal vaccination policy for adolescents or adults. The results from our study suggest that short-term health-state valuation may provide a reasonable approach to assessing preferences given its superior ability to discriminate between states, which may be particularly useful for cost-utility analyses for future vaccination programs.
Authors' contributions
Dr. Lee had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Lee, Salomon, Lieu
Acquisition of data: Lee, Lieu
Analysis and interpretation of data: Lee, Salomon, LeBaron, Lieu
Drafting of the manuscript: Lee
Critical revision of the manuscript for important intellectual content: Lee, Salomon, LeBaron, Lieu
Statistical analysis: Lee, Salomon
Obtained funding: Lieu
Administrative, technical, or material support: Lieu
Study supervision: Salomon, Lieu
Financial support
This study, part of the Joint Initiative on Vaccine Economics Project, was supported by the National Immunization Program, Centers for Disease Control and Prevention, via cooperative agreement with the Association of Teachers of Preventive Medicine, Task order #TS-0675. Dr. Lee's work was also supported in part by the grants T32 HS00063 and K08 HS013908-01A1 from the Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services. Dr. Salomon was supported by the National Institute on Aging (Grant P01 AG17625).
Acknowledgements
We gratefully acknowledge the following individuals who provided their expertise regarding pertussis health states: Kathy Edwards, Scott Halperin, and Colin Marchant. We also wish to thank our colleagues for their assistance and input at the Massachusetts Department of Public Health (Susan Lett, Stephanie Schauer, Kirsten Buckley, Nancy Harrington, Elissa Laitin, Marija Popstefanija, James Ransom, Kurt Seetoo, Jill Sheets, and Kristin Sullivan), Centers for Disease Control (Mark Messonier and Trudy Murphy), and DACP (Lisa Prosser). Finally, we would like to acknowledge the contribution to this work by our study coordinator at DACP, Donna Rusinak, and administrative assistance provided by Charlene Gay.
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| 15780145 | PMC555848 | CC BY | 2021-01-04 16:38:14 | no | Health Qual Life Outcomes. 2005 Mar 21; 3:17 | utf-8 | Health Qual Life Outcomes | 2,005 | 10.1186/1477-7525-3-17 | oa_comm |
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Thromb JThrombosis Journal1477-9560BioMed Central London 1477-9560-3-21574829610.1186/1477-9560-3-2Original Clinical InvestigationClotting state after cardioversion of atrial fibrillation: a haemostasis index could detect the relationship with the arrhythmia duration Hatzinikolaou-Kotsakou Eleni [email protected] Zafirios [email protected] Dimitrios [email protected] Dimitrios [email protected] Athanasios [email protected] Georgios [email protected] Georgios [email protected] Dimitrios I [email protected] Academic Cardiology Department, Academic Hospital Dragana Alexandroupolis, Demokritus University of Thrace, Greece2 Academic Hematology Department, Academic Hospital Dragana Alexandroupolis, Demokritus University of Thrace-Greece2005 6 3 2005 3 2 2 19 10 2004 6 3 2005 Copyright © 2005 Hatzinikolaou-Kotsakou et al; licensee BioMed Central Ltd.2005Hatzinikolaou-Kotsakou 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
Fibrin D-dimer levels have been advocated as an useful clinical marker of thrombogenesis.
Hypothesis
We hypothesized that i) there is a hyperclotting state after the return of atrial fibrillation to sinus rhythm, ii) the measurement of plasma D-Dimer levels might be a good screening tool of this clotting status, and iii) the duration of arrhythmia influences the haemostasis measured by plasma D-Dimer levels.
Methods
Forty-two patients with atrial fibrillation undergoing cardioversion were divided into two groups: in Group A (n = 24,14 male, 56 ± 11 years) the duration of atrial fibrillation was 72 hours or more (142.7 ± 103.8 hours), in Group B (n = 18, 10 male, 61 ± 13 years) the duration of atrial fibrillation was less than 72 hours (25 ± 16 hours). Plasma fibrin D-dimer levels were measured by enzyme immunoassay before, and 36 hours after, cardioversion. The change of plasma D-dimer levels 36 hours after cardioversion was calculated as delta-D-dimer.
Results
There were no significant differences in demographic, clinical, and echocardiographic data, and the success of cardioversion between the two groups. Compared to the control, the baseline D-dimer levels were significantly higher in both groups. The delta D-dimer levels were significantly higher in Group A than in Group B (p < 0.005). Furthermore, plasma D-dimer levels 36 hours after cardioversion (r = 0.52, p = 0.0016) and delta-D-dimer levels (r = 0.73, p < 0.0001) showed significant correlations with the duration of atrial fibrillation.
Conclusion
The longer duration of the atrial fibrillation episode could lead to a more prominent cardiovascular hyperclotting state after cardioversion, and the mean changes of plasma D-Dimer levels could be used as an useful clinical marker of the clotting state after atrial systole return.
Atrial fibrillationhaemostatic markers
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Introduction
Atrial fibrillation is the most common sustained arrhythmia in clinical practice. It is associated with an increased risk of thrombus formation, resulting in substantial morbidity, with the augmented risk of stroke being the most serious. This could be explained by haemostasis conditions favouring thrombosis: previous studies have demonstrated that in most patients, AF is a high risk factor for hypercoagulability, irrespective of underlying structural heart diseases or aetiology [1-8].
In addition, it is well known that the direct current cardioversion of atrial fibrillation, especially if persisting >48 h, carries a great risk of thromboembolism, which extends to 5% of cases not receiving anticoagulant therapy. The mechanism and pathogenesis of thromboembolic episodes after restoration of sinus rhythm in these patients is not completely understood.
There is some evidence that the prothrombotic state associated with atrial fibrillation might contribute towards the risk of thromboembolism following cardioversion, but reports are not clear. In this context, having a marker of coagulation activation would be useful in identifying patients at highest thromboembolic risk. Indicators of hypercoagulability, such as D-dimers, which are indicative of a prothrombotic state, might also be indicative of thromboembolic risk [9]. D-dimers, which originate from the formation and lysis of cross-linked fibrin, are therefore specific markers of coagulation activation. In AF elevated D-dimer levels have been reported to be associated with left atrial appendage dysfunction [10], and the potential presence of atrial thrombi. Anticoagulation does reduce D-dimer levels, but there were no significant correlations of D-dimer levels with either warfarin dose or the INR [11,12]. D-dimer levels may also increase as a result of comorbidity conditions causing intravascular (i.e. in thrombosis) or extravascular cross-linked fibrin turnover, such as in renal failure, liver impairment, acute or chronic infection, neoplastic disease, hypertension, acute cardiovascular syndromes, bleeding, haematoma and surgery. The interpretation of D-dimer levels can, therefore, be considered as reflecting the prothrombogenic state of patients without these acute clinical conditions, and without overt thrombosis. [9] However, the age of the patient must be considered: D-dimer levels are reported to increase with age [13-15], which makes the interpretation of D-dimer measurement very difficult and hazardous in older people. Thus, before an evaluation of the predictive role of D-dimer levels for thromboembolic events in AF patients can be made, two conditions must be fulfilled: D-dimer levels should be characteristic of each patient with AF, and the presence of co-morbidity should be excluded.
We therefore hypothesized that i) there is still a hyperclotting state after return of sinus rhythm, ii) the measurement of plasma D-dimer levels pre- and post-cardioversion might be a good screening tool of this clotting status, and iii) the duration of arrhythmia could be a good predictor of thromboembolic events after cardioversion, due to the influence on haemostasis measured by plasma D-dimer levels.
Methods
Over a period of 18 months, we studied 42 consecutive patients, aged between 39–68 years old, with non-valvular atrial fibrillation who underwent successful electrical cardioversion and who remained in sinus rhythm at the one-month visit. Exclusion criteria were other acute causes of atrial fibrillation (for example, thyrotoxicosis, pneumonia or other infections), acute cardiovascular or cerebro-vascular events (myocardial infarction, congestive heart failure, stroke, etc) occurring within five months, valvular heart disease, malignancy, connective tissue disease, infectious or inflammatory conditions and chronic renal/hepatic disease. The patients were divided into two groups. In Group A (24 pts, 14 male, 56 ± 11 years), the duration of atrial fibrillation was 72 hours or more (142.7 ± 103.8 hours). In Group B (18 pts, 10 male, 61 ± 13 years) the arrhythmia had a duration of less than 72 hours (25 ± 16 hours). We included only patients treated with anticoagulant-coumadin for chronic prophylaxis. There was not a statistically significant difference between the mean duration of anticoagulation treatment between the two groups (14 ± 4 months for group A and 12 ± 6 months for Group B). Prothrombin time to an INR (international normalized ratio) of 2.5–3 was considered as a necessary inclusion criterion for all patients. The total population had a history of drug refractory atrial fibrillation, with a serious number of arrhythmia episodes. We only analyzed the documented arrhythmia episodes. The total number of documented episodes of paroxysmal atrial fibrillation in Group A was 33, and in Group B the patients had 36 confirmed arrhythmia episodes. There were no significant differences in age, sex, hematocrit, hemoglobin, plasma fibrinogen level, underlying heart disease, success ratio of electrical cardioversion, echocardiographic data, presence of diabetes mellitus or hypertension between the two groups. The clinical characteristics of the patients are shown in Table 1. AF was seen at the 12 lead surface electrocardiogram. We compared the D-dimer levels of these two groups at baseline, before cardioversion with a matched control group (n = 19) without atrial fibrillation. Plasma Fibrin D-dimer levels were measured before, and 36 hours after, cardioversion. Anticoagulation reduces D-dimer levels, but as we analyzed the D-dimer levels of the same patients pre- and post-cardioversion, we did not need to use a cut-off value, and all patιents were under coumadin treatment with an INR (international normalized ratio) of 2.5 to 3 at least 3 weeks before cardioversion, and 36 hours post-cardioversion. The study protocol was approved by the local ethics committee (Academic Hospital of Alexandroupolis decision 09/11/2002). All patients received oral and written information concerning the background of the study, and signed informed consent.
Table 1 Clinical Characteristics for Total Study Group
Group A (n = 24) Group B (n = 18) Control Group (n = 19)
Age (years) 56 ± 11 61 ± 13 59 ± 12
Male gender 14(58%) 10(56%) 10(57%)
Smokers 15(62.5%) 11(62%) 11(62.5%)
Systolic Blood Pressure (mmHg) 145 ± 20 143 ± 22 139 ± 25
Diastolic Blood Pressure (mmHg) 80 ± 10 82 ± 11 80 ± 11
Known hypertension (160/90 mmHg) 8 (33.4%) 6 (34%) 7(35%)
Lone AF 11(45.8%) 8 (44.2%) -
Coronary artery Disease 5(20.8%) 4(22%) 5(22.8%)
Diabetes mellitus 6(25%) 4(22%) 4(22.3%)
Hct(%) 45.8 ± 3.9 44.9 ± 4.2 45 ± 3.6
Hb(g/dl) 15.3 ± 1.34 14.8 ± 2.9 15.2 ± 3.1
Fbg (mg/dl) 229.9 ± 27.0 237 ± 37.6 227 ± 26
P value: non significant (NS)
Echocardiographic data for Both Groups pre cardioversion
Group A Group B
Left atrial diameter(cm) 4.2 ± 0.6 4.0 ± 0.8
Left ventricular diastolic dimension (cm) 5.2 ± 1.0 5.0 ± 0.9
Left ventricular systolic dimension (cm) 4.1 ± 0.5 4.0 ± 0.8
P value: non significant (NS)
Laboratory
An intravenous line was placed and blood samples for D-dimer measurement were taken from the patients immediately before cardioversion, and 36 hours after recovery of sinus rhythm. Citrated plasma was obtained from venous blood by centrifugation at 2,500 rpm for 15 min at 4°C. Aliquots were stored at -70°C to allow batch analysis. The plasma D-dimer levels were measured by the enzyme-linked immunosorbent assay method. The measurements were obtained with the use of a quantitative sandwich immunochromatographic technique (Cardiac D-Dimer; Roche Diagnostics, Mannheim, Germany). For every blood sample, measurements were done twice. The investigators and attending physicians were blinded to the D-dimer test results. Inrta-assay coefficients of variation for assays were <5%, inter-assay variances were 10%. The changes of plasma D-dimer levels 36 hours after cardioversion were calculated as delta- D-dimer.
Echocardiography
Echocardiographic examinations were performed in all patients, immediately prior to the procedure and 36 hours after successful cardioversion. Transthoracic echocardiographic two-dimensional imaging and guided pulsed wave Doppler recordings were obtained. Transmitral Doppler inflow velocities were recorded from the apical four-chamber view.
Peak velocities of early fillings (E) wave and atrial filling (A) wave were determined. The inter- and intra-observer variability was <5% for these measurements. We did not detect the presence of thrombi in the left atrium. However, the absence of thrombus on transthoracic echocardiography does not preclude the real absence of thrombus.
Statistical Analysis
Clinical variables are expressed as the mean value ± SD. The effects of duration from the onset of atrial fibrillation on the measured indexes were analyzed by two-way repeated measures analysis of variance (ANOVA). Sequential data pre- and post-cardioversion were analyzed by Friedman's repeated measures analysis of variance. Correlations were performed by Spearman's rank correlation method. Stepwise multiple regression analyses were performed to determine independent predictors for plasma D-dimer levels, using age, sex, left-atrial size, left ventricular dimensions (diastole, systole) presence of underlying medical disease, smoking status, and the presence of atrial fibrillation. A p value < 0.05 was considered statistically significant. Analyses were performed with SAS for Windows 8.02 (SAS Institute Inc., Cary, North Carolina) and GraphPad Prism, version 3.00 (GraphPadSoftware, San Diego, California) statistical software packages.
Results
The baseline D-dimer levels of each group are shown in Table 2. The D-dimer levels in the control group were significantly lower than both groups (p < 0.05) (Table 2).
Table 2 Mean D-Dimer levels at baseline before cardioversion in both groups and the relationship with he control group
Group A Group B p value
Mean D-Dimer 117 ± 74.7 ng/ml 102 ± 53.8 NS
Group A Control Group p value
Mean D-Dimer 117 ± 74.7 ng/ml 39.7 ± 28.6 0.05
Group B Control Group p value
Mean D-Dimer 102 ± 53.8 39.7 ± 28.6 0.05
The patients' sinus rhythm was restored by applying electrical cardioversion in all study patients. Delta D-dimer levels were significantly higher in Group A than in Group B. (p < 0.005).
There were no significant differences in plasma D-dimer levels before, and 36 hours after, cardioversion between the two Groups (Table 3). Furthermore, plasma D-dimer levels 36 hours after cardioversion, and delta D-dimer levels showed significant correlations with the duration of atrial fibrillaton: D-dimer 36 hours after CV; r = 0.52, p = 0.0016, delta D-dimer; r = 0.73, p < 0.0001.
Table 3 D-dimer levels (ng/ml) in both Groups of Patients
D-dimer before CV D-dimer after CV delta D-dimer
Group A 117.9 ± 74.7 ng/ml 104.1 ± 59.7 ng/ml 34.2 ± 63.6 ng/ml°
Group B 102 ± 53.8 ng/ml 84.7 ± 78. 2 ng/ml -17.3 ± 37.8 ng/ml
Values are expressed as mean ± SD, °p = 0.005 vs Group B.
The echocardiographic data 36 hours after CV have changed. There was a return of atrial contractility using Doppler echocardiography, as shown by the progressive increase in A-wave velocity (Friedman's repeated measures ANOVA, p < 0.0001).
Two embolic stroke events were recorded during the six hours after cardioversion in Group A (8,33%) and none in Group B. The embolic events occurred in two women with an INR of 2.6 and 2.9 respectively. At the onset of the event one patient had D-dimer levels of 112.2 ng/ml while the second patient had D-dimer levels of 109.8 ng/ml. Fortunately, these were transient events. The computerized tomography detected non-extentend thromboembolic areas.
Discussion
In the present study we analyzed D-dimer levels as a screening index of a hyperclotting state after cardioversion of atrial fibrillation. It would be more useful if we used the term abnormal haemostasis, because the fibrin D-dimer is a cross-linked degradation product, resulting from the balance between thrombogenesis and the fibrinolysis process.
Fibrin D-dimer levels have been established as a useful clinical marker of thrombogenesis. [16] The use of D-Dimer levels in the investigation and management pathway of venous thromboembolism is well established. [17] This marker has a high sensitivity and specificity in excluding thromboembolism, when a well-defined assay is used in the appropriate clinical setting. [18]
In "normal" patients, elevated D-dimer levels have been associated with a higher risk of developing cardiovascular disease. [19] Also, such cross-linked fibrin degradation products have been shown to be strong and independent predictors of the severity of occlusive peripheral artery disease [20].
Patients with long-lasting episodes of atrial fibrillation or chronic atrial fibrillation are characterized by increased levels of plasma D-dimers [21,22], platelet activation, and endothelial damage and/or dysfunction, which is consistent with the increased predisposition to thrombus formation in this group of patients [23]. Some investigators have shown that the cardioversion of atrial fibrillation to sinus rhythm results in a decrease of D-dimer levels [24]. Our data showed elevated D-dimer levels in individuals who received appropriate anticoagulation therapy at the time of cardioversion. This may suggest the presence of some remaining clot. But we could not estimate this hypothesis because we did not have the profile of D-dimer levels of all patients during a long period before the time of enrolment.
In the present study, the plasma D-dimer levels decreased in both groups after successful cardioversion, but the mean change in D-dimer levels pre- and post- cardioversion was significantly lower in Group A than in Group B, and D-dimer levels continued to be high, even 36 hours after the procedure in Group A. There were reported embolic events in 8.33% of Group A patients, with the higher delta D-dimer.
These data confirm the existence of an abnormal clotting state after the cardioversion of atrial fibrillation, which could be the cause of the thromboembolic events observed after the return to sinus rhythm, even under anticoagulation.
The mechanism of thromboembolism is debatable. The results we found could suggest an embolism might be caused by detachment of a formed thrombus during the phase of passage from AF to sinus rhythm, due to being heavily compromised by stunning the mechanical function of the left atrium and left atrium appendage after sinus rhythm restoration. The stunning of the left atrium and left atrium appendage might attenuate the thrombogenetic activation which our result showed.
In the present study we also provided evidence that this hypercoaguable state after successful cardioversion of atrial fibrillation related directly to the duration of the arrhythmia episode. It has been accepted that chronic atrial fibrillation is characterized by increased levels of plasmatic D-dimers, with a wide inter-individual variability, depending on the patients and therapeutic characteristics. However, it has not been established if this level could also be characteristic of paroxysmal or persistent atrial fibrillation in patients, and whether it could be a predictive factor for the risk of thromboembolic events after cardioversion to sinus rhythm.
Our results show that longer duration of atrial fibrillation could lead to a more prominent cardiovascular hyperclotting state after cardioversion and that the duration of the arrhythmia episode might be a risk factor for the high incidence of post-cardioversion thromboembolic events. Importantly, this hypercoaguable state does not appear to be subject to any other clinical variables, nor is it related to whether or not the patient had a lone atrial fibrillation or not, suggesting that the duration of the arrhythmia episode confers a constant prothrombotic state per se, after cardioversion to sinus rhythm, which was independent of the etiology but dependent on the duration of the arrhythmia. Interestingly enough, this hyperclotting state after cardioversion exists even under appropriate anticoagulative treatment. The relation of the markers of accelerated coagulation to clinical or echocardiographic risk factors for thromboembolism is controversial.
Our results clearly demonstrate a positive correlation of the based on the arrhythmia duration clinically predicted embolic risk to the mean change of plasma D-dimer levels 36 hours after successful cardioversion. We could not detect any relation to the echocardiographic risk factors, probably due to our inability to perform a transesophageal echocardiography. The present study suggests that even in the absence of clinical conditions causing increased embolic risk, patients with signs of accelerated coagulation are at risk of thrombus formation in the future.
In conclusion, a longer duration of the atrial fibrillation episode could lead to a more prominent cardiovascular hyperclotting state after cardioversion, and the mean changes of plasma D-Dimer levels could be used as an useful clinical marker of the clotting state after atrial systole return.
Clinical implications
Our study might have practical implications for the management of patients with an episode of atrial fibrillation, regardless of the anticoagulative treatment they are receiving: the episode must be terminated as soon as possible, because the pathogenesis of thrombus formation in atrial fibrillation is very complex and not yet completely defined. Further investigations with a large population are needed to define all the pathophysiologic mechanisms of thrombus formation.
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| 15748296 | PMC555849 | CC BY | 2021-01-04 16:36:24 | no | Thromb J. 2005 Mar 6; 3:2 | utf-8 | Thromb J | 2,005 | 10.1186/1477-9560-3-2 | oa_comm |
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J Autoimmune DisJournal of Autoimmune Diseases1740-2557BioMed Central London 1740-2557-2-11576298010.1186/1740-2557-2-1ReviewImmunogenetics of Hashimoto's thyroiditis Chistiakov Dimitry A [email protected] Laboratory of Aquatic Ecology, Katholieke Universiteit Leuven, Ch. De Beriotstraat 32, B-3000 Leuven, Belgium2005 11 3 2005 2 1 1 23 7 2004 11 3 2005 Copyright © 2005 Chistiakov; licensee BioMed Central Ltd.2005Chistiakov; 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.
Hashimoto's thyroiditis (HT) is an organ-specific T-cell mediated disease. It is a complex disease, with a strong genetic component. To date, significant progress has been made towards the identification and functional characterization of HT susceptibility genes. In this review, we will summarize the recent advances in our understanding of the genetic input to the pathogenesis of HT.
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Introduction
Hashimoto's thyroiditis (HT) is one of the most common human autoimmune diseases responsible for considerable morbidity in women [1]. It is an organ-specific T-cell mediated disease that affects the thyroid, and genetics play a contributory role in its complexity. To date, significant progress has been made in identifying and characterizing those genes involved in the disease. In this review, we will summarize recent advances in our understanding of the genetic contribution to the pathogenesis of HT.
Epidemiology and clinical features of Hashimoto's thyroiditis
Goitrous autoimmune thyroiditis, or Hashimoto's thyroiditis is a common form of chronic autoimmune thyroid disease (AITD). The disorder affects up to 2% of the general population [2] and is more common in older women and ten times more frequent in women than in men [3]. In the NHALES III study, performed in the USA, the prevalence of subclinical and clinical hypothyroidism was 4.6% and 0.3% respectively [4]. Another US epidemiological study, the Whickham survey, showed the prevalence of spontaneous hypothyroidism to be 1.5% in females and less than 0.1% in males [5]. These prevalence rates are similar to those reported in Japan [6] and Finland [7]. A significant proportion of patients have asymptomatic chronic autoimmune thyroiditis and 8% of woman (10% of woman over 55 years of age) and 3% of men have subclinical hypothyroidism [8]. According the data of the 20-year follow-up to the Whickham survey cohort, the risk of developing overt hypothyroidism is four times higher in women aged between 60 and 70 years than for women between 40 and 50 years of age [1].
Subclinical hypothyroidism is characterized by an increase in serum thyrotropin (TSH) whilst serum levels of thyroxine (T4) and triiodothyronine (T3) remain normal. The overt disease is defined by the dramatic loss of thyroid follicular cells (thyrocytes), hypothyroidism, goitre, circulating autoantibodies to two primary thyroid-specific antigens, thyroglobulin (Tg), thyroid peroxidase (TPO), and lowered concentrations of serum TSH and T4 [9]. Histological and cytological features of HT include a dense thyroidal accumulation of lymphocytes, plasma cells and occasional multinuclear giant cells. The epithelial cells are enlarged, with a distinctive eosinophilic cytoplasm, owing to increased number of mitochondria [10].
HT has been shown to often coexist with other autoimmune diseases such as type 1 diabetes (T1D), celiac disease, rheumatoid arthritis, multiple sclerosis, vitiligo, etc [11-14]. HT can also be expressed as part of an autoimmune polyendocrine syndrome type 2 (APS-2), which is usually defined by the occurrence of two or more of the following: Addison's disease (always present), AITD and/or type 1 diabetes [15], in the same patient.
In common with probably all autoimmune disorders, the harmful interaction between internal (genetic) and external (environmental and endogenous) factors is required to initiate Hashimoto's disease (Fig. 1). Environmental triggers of HT include iodine intake [16,17], bacterial and viral infections [18,19], cytokine therapy [20] and probably pregnancy [21,22]. The role of dietary iodine is well defined in epidemiological studies [23,24] and in animal models [25-27] and seems to be the most significant environmental factor to induce thyroiditis.
Figure 1 Possible pathogenic mechanism of Hashimoto's thyroiditis. Genetically predisposed individuals could be influenced by an environmental trigger (i.e., dietary iodine, infection, pregnancy, cytokine therapy) that induces an autoimmune response against thyroid-specific antigens by infiltrating immune cells. The autoimmune process results in preferential T helper type 1 (TH1)-mediated immune response and induction of apoptosis of thyroid cells that leads to hypothyroidism.
Pathogenesis of Hashimoto's thyroiditis
Autoimmunity in Hashimoto's thyroiditis
The development of the autoimmune failure of the thyroid is a multistep process, requiring several genetic and environmental abnormalities to converge before full-blown disease develops (Fig. 2). At the onset of disease, major histocompatibility complex (MHC) class II-positive antigen-presenting cells (APC), particularly dendritic cells, and different subclasses of macrophages, accumulate in the thyroid [28,29]. APC present thyroid-specific autoantigens to the naïve T cells, leading to activation and clonal expansion of the latter. Thus, the initial stage of the disease is followed by a clonal expansion phase and maturation of autoreactive T and B lymphocytes in the draining lymph nodes.
Figure 2 A scheme of autoimmune events in Hashimoto's thyroiditis. In an initial stage, antigen-presenting cells (APC), mostly dendritic cell and macrophage (Mφ) derived, infiltrate the thyroid gland. The infiltration can be induced by an envinromental triggering factor (dietary iodine, toxins, virus infection, etc.) which causes insult of thyrocytes and releasing of thyroid-specific proteins. These proteins serve as a source of self-antigenic peptides that are presented on the cell surface of APC after processing. Taking up relevant autoantigens, APC travel from the thyroid to the draining lymph node. A central phase occurs in the draining lymph node in which interactions between APC, autoreactive (AR) T cells (that survive as result of dysregulation or breakage of immune tolerance) and B cells result in inducing production of thyroid autoantibodies. In the next step, antigen-producing B lymphocytes, cytotoxic T cells and macrophages infiltrate and accumulate in the thyroid through expansion of lymphocyte clones and propagation of lymphoid tissue within the thyroid gland. This process is preferentially mediated by T helper type 1 (TH1) cells which secrete regulatory cytokines (interleukin-12, interferon-γ and tumor necrosis factor-α). In a final stage, the generated autoreactive T cells, B cells and antibodies cause massive depletion of thyrocytes via antibody-dependent, cytokine-mediated and apoptotic mechanisms of cytotoxity that leads to hypothyroidism and Hashimoto's disease.
In autoimmune thyroditis animal models, genetically determined immune defects have been suggestively linked to the breakdown of immunological self-tolerance that results in the presentation of host autoantigens and expansion of autoreactive lymphocyte clones. These immune defects are associated with the presence of particular MHC class II haplotypes, but other immune and immune regulatory genes (i.e., CTLA-4 and others) are also involved [30-32].
Breakdown of the immune tolerance might occur in several ways including interrupting central tolerance (e.g. deletion of autoreactive T cells in the thymus), defects in maintaining peripheral tolerance (e.g. activation-induced T-cell death and suppressing activity of regulatory T lymphocytes) and anergy (e.g. the expression of MHC class II molecules on non-professional APC). Animal models genetically predisposed to develop an autoimmune disease, and patients with AITD, showed a lack of, or a deficiency in, a subpopulation of regulatory T cells with suppressive function [33-35].
The mechanisms, whereby autoreactive T cells escape deletion and anergy, and become activated, remain uncertain. There is evidence that the thyroid cell itself, by "aberrantly" expressing MHC molecules, can play the role of "non-professional " APS and present disease-initiating antigen directly to the T cells [36,37]. The concept of aberrant MHC class II expression was supported by studies in mice. They developed a type of Graves' Disease (GD) after being injected with fibroblasts coexpressing MHC class II and the TSH receptor (TSHR). TPO antibody production was induced after injection with fibroblasts coexpressing class II molecules and TPO [38,39].
Iodine is a necessary component of normal thyroid hormonogenesis. Incorporation of iodine into thyrosine residues of Tg leads to the formation of mono-iodotyrosine and di-idothyrosine derivates that subsequently undergo an oxidative coupling event resulting in the producing of T3 and T4. Iodine can promote antithyroid immunity in a number of ways. Several studies suggest that iodination of Tg is crucial for recognition by Tg-reactive T cells [40,41]. Iodine excess can affect the Tg molecule directly, creating new epitopes or exposing "cryptic" epitopes. It has been demonstrated that a highly iodinated thyroglobulin molecule is a better immunogen than Tg of low iodine content [41,42]. Therefore, highly iodinated Tg may facilitate antigen uptake and processing by APC. Additionally, high doses of iodine were shown to directly affect macrophages, dendritic cells, B and T lymphocytes, resulting in stimulation of macrophage myeloperoxidase activity, acceleration of the maturation of dendritic cells, increasing the number of circulating T cells and stimulating B cell immunoglobulin production [25]. Excessive amounts of iodide ion are rapidly oxidized by TPO, thereby generating excessive amounts of reactive intermediates such as hypoiodous acid and oxygen radicals. These oxidative species damage thyrocyte cell membrane by oxidation of membrane lipids and proteins causing thyrocyte necrosis [43]. The state of severe iodine deficiency itself namely leads to a lowering of thyroid autoimmunity and an immunodeficient state in autoimmune-prone BB-DP rats. This hampers the autoreactcive T-cell generation and autoantibody production [25]. A lower degree of Tg iodination also makes this molecule less antigenic [42].
An influx of dendritic cells and macrophages to the thyroid may occur as a consequence of inflammatory events in the gland. Early non-specific necrosis of thyrocytes due to toxins (i.e. iodine, etc.), and perhaps viral or bacterial infection, can attract these cells to the thyroid. Moreover, these immune cells are normal constituents of the thyroid that are able to regulate the growth and function of thyrocytes via interleukin-1 (IL-1) and IL-6-mediated pathways [44].
A central phase of HT is characterized by the recognition of presented autoantigens by the lymphocytes, followed by an apparent uncontrolled production of autoreactive CD4+ T cells, CD8+ cytotoxic T cells and immunoglobulin G (IgG) autoantibodies. Initially, the production of self-reactive cells and autoantibodies occurs in the draining lymph nodes (Fig. 2). Later, the lymphoid tissue often develops directly in the thyroid gland itself. This tissue is generally very well organized, with cords of anti-Tg-antibody-producing plasma cells in the periphery. It is usually non-destructive and shows a peaceful co-existence with adjacent thyrocytes.
Thyroglobulin, the main protein synthesized in the thyroid, serves both in the synthesis and in the storage of thyroid hormones. Human Tg molecules contain at least four thyroid hormone synthesis sites from the iodinated tyrosine residues at positions 5, 2553, 2567 and 2746 [45]. The hormone synthesis sites and the iodine content of Tg play an important role in its autoantigenicity [40]. Tg is one of the major autoantigens in thyroid autoimmunity and serologic studies have shown that there are at least 40 antigenic epitopes on human Tg [16,46]. Tg-antibodies are detected in almost all patients with AITD [47]. Anti-thyroglobulin antibodies were also reported in up to 27% of normal individuals [48]. However, numerous studies have clearly shown that the epitope recognition pattern of the natural anti-Tg antibodies is differented from that of AITD-associated anti-Tg antibodies. Most studies have demonstrarted a restricted epitope recognition pattern of AITD subjects by anti-Tg antibodies, in contrast to polyclonal reactivity observed with anti-Tg antibodies from healthy individuals [49,50]. Human or mouse Tg immunization induces experimental autoimmune thyroiditis (EAT) in mice [51]. The EAT induction is HLA-dependent implying an interaction between the Tg molecule and the MHC glycoproteins [52]. In addition, alterations to Tg could explain interactions between genetic and environmental factors in the aetiology of HT.
Thyroid peroxidase is another significant autoantigen in the thyroid of patients affected with HT and AITD. This enzyme catalyses the oxidation of iodine to an iodinating species that forms iodotyrosines in a Tg molecule and subsequently iodotyronines [53]. TPO antibodies are heterogeneous. To date, around 180 human TPO anribodies have been cloned and sequenced. This allows for the possible identification of major features of the TPO-directed antibodies repertoire during AITD. In Graves' disease patients, heavy chain VH domains of anti-TPO antibodies preferentially use D proximal IGHV1 genes. IGHV3 genes, mainly located in the middle of the immunoglobulin heavy chain gene (IGH) cluster on chromosome 15q11, characterize HT patients more frequently. A large proportion of the anti-TPO heavy chain VH domain comes about following a VDJ recombination process that uses inverted D genes [54,55].
Autoantibodies against other thyroid-specific antigens such as thyrotropin receptor and sodium iodide symporter were also found in serum of HT patients. However, these antibodies occur at low frequency and do not appear to contribute any diagnostic power for HT [56,57].
In a final, destructive step of Hashimoto's thyroiditis, the autoreactive T cells diffusely accumulate in large numbers and infiltrate thyroid parenchyma (Fig. 2). In the BB-DP rat model, T-helper type 1 (TH1)-mediated mechanisms involving production of IL-12, tumor necrosis factor-α (TNF-α) and interferon-γ play a major role in the destruction of thyrocytes, rather than TH2 type mechanisms directed by IL-4 and IL-10 [58]. The infiltration of activated scavenger macrophages into the thyroid follicles, thus destroying the thyroid cells, is compatible with TH1-mediated mechanisms [59]. Fas and Fas ligand (FasL) expression was higher in rats with lympholytic thyroiditis indicating a role of these apoptotic molecules in thyrocyte death [60].
Apoptosis in Hashimoto's thyroiditis
Autoimmune responses against specific antigens are primary determinants in thyroid autoimmunity. Other molecular mechanisms including cell apoptosis may play a role in determining the opposite phenotypic outcomes of AITD such as thyroid destruction in HT and thyroid hyperplasia in GD. T-helper lymphocytes produce cytokines that influence both immune and target cells at several levels. The predominance of TH1 or TH2 cytokines might regulate thyrocyte survival through the induction of pro-apoptotic and anti-apoptotic proteins. TH1-mediated mechanisms lead to thyrocyte depletion in Hashimoto's thyroiditis through the involvement of death receptors and cytokine-regulated apoptotic pathways [61,62].
The normal thyroid gland has been shown to act as an immune privileged site having carefully regulated mechanisms of cell death and self-protection against attack by infiltrating activated T-cells induced by apoptosis [63,64]. Cell apoptosis occurs in the normal thyroid at a low level. As new thyrocytes are produced, old cells are destroyed in order to maintain normal thyroid volume and function. Deregulation of apoptosis, which is weakly determined by genetic susceptibility, can lead to destructive processes. Initiation of an out-of-control apoptotic mechanism in thyroid cells may be caused by various non-genetic injuries that affect expression of apoptosis inhibitor molecule Bcl-2 or membrane ligand FasL [65]. Thyrocytes from HT thyroid glands are able to hyperproduce Fas and FasL on their surfaces thus inducing fratricide apoptosis [66]. IL-1β, abundantly produced in HT glands, induces Fas expression in normal thyrocytes, the cross-linking of Fas resulting in massive thyrocyte apoptosis. This can play a role in the progression of Hashimoto's thyroiditis [67].
Immune-mediated apoptosis of thyrocytes is directed by CD8+ cells. Receptors on the target cell are triggered by lymphocyte ligands and/or released soluble factors are delivered to the target cell [68]. Receptors involved in immune-mediated apoptosis include the TNF R1 receptor, the Fas receptor and death receptors DR3 and DR4, whereas soluble mediators include substances such as perforines and TNF [68-70].
The common apoptotic pathway consists of subsequent activation of specific intracellular proteases known as caspases. These caspases are themselves activated by specific proteolytic cleavage or may be activated by cleavage performed by other caspases. The caspase cascade ultimately induces enzymes that progressively destroy the cell and its genetic material, finally lead to cell death. The apoptosis, or programmed cell death, can be initiated by binding death ligands, such as TNF, TNF-related apoptosis-induced ligand (TRAIL) and FasL, to the cell surface. This in turn starts intracellular signal cascading of caspases [71].
Several apoptosis signalling pathways, initiated by molecules such as FasL and TRAIL, have been shown to be active in thyrocytes and may be involved in destructive thyroiditis [72]. Fas-mediated apoptosis seems to be a general mechanism of cell destruction in AITD. In GD patients, reduced levels of Fas/FasL and increased levels of antiapoptotic molecule Bcl-2 favour thyroid cell survival and apoptosis of infiltrating lymphocytes. In contrast, the regulation of Fas/FasL/Bcl-2 expression in HT can promote thyrocyte apoptosis through homophylic Fas-FasL interactions and a gradual reduction in thyrocyte numbers leading to hypothyroidism [61].
Thus, the rate of thyrocyte apoptosis dictates the clinical outcome of thyroid autoimmunity. Though rare in normal thyroid, it markedly increases during HT, but not in GD. Therefore, regulation of thyrocyte survival is a crucial pathogenic determinant.
Genetics of Hashimoto's thyroiditis
Evidence for genetic susceptibility to Hashimoto's thyroiditis
Abundant epidemiologic data (population-based and family-based studies, twin studies) suggest a strong genetic contribution to the development of HT. The disease clusters in families [22,73]. Thyroid abnormalities with clinical outcomes were observed in 33% of offspring of patients with HT or GD [73]. The sibling risk ratio (λS), that is the ratio of the prevalence of disease in siblings to the prevalence in the general population, can be used as a quantitative measure of the genetic contribution to the disease. Usually, a λS of more than five indicate a significant genetic contribution to the disease development. Based on historical data, the λS for AITD is estimated to be greater than 10, supporting a strong case of genetic influence on disease development [74]. Using HT prevalence data from the NHAHES III study, an estimated λS value is about 28 for HT [74].
In Danish twin study, the concordance rates for Hashimoto's disease were 38% for monozygotic (MZ) twins and 0 for dizygotic (DZ) twins [75]. For HT, a recent twin study in California confirmed these results, showing concordance rates of 55% and 0% in MZ and DZ twins, respectively [76]. For thyroid antibodies, the concordance rate in the Danish twin study was twice high in MZ twins (80%) than that in DZ twins [75]. In a recent twin study in the UK, the concordance rates for Tg-antibodies were 59% and 23% in in MZ and DZ twins, respectively [77]. In this study, the concordance rates for TPO-autoantibodies were 47% and 29% in MZ and DZ twins, respectively [77]. These data suggest that HT and other AITD outcomes such as antibody production against thyroid-specific antigens have a substantial inherited susceptibility. HT seems to be a polygenic disease with a complex mode of inheritance. Immunomodulatory genes are expected to play an important role in predisposing and modulating the pathogenesis of Hashimoto's thyroiditis.
Animal models of autoimmune thyroiditis
Animal models of AITD still hold immense promise for the discovery of pathways, genes and environmental factors that determine the development of thyroid autoimmunity. Animals affected by experimental autoimmune thyroiditis (EAT) provide a unique opportunity to uncover disease-associated pathways, which are complicated to define in man.
One of the oldest inbred models is the obese strain chicken (OS), which develops goitrous lympholytic thyroiditis with the subsequent atrophic lympholytic thyroiditis followed by a rapid onset of hypothyroidism [78]. The biobreeding diabetes-prone (BB-DP) rat expresses a form of focal lympholytic thyroiditis that under normal conditions does not lead to hypothyroidism [79]. The nonobese diabetes (NOD) mouse strain NOD-H2h4 spontaneously develops iodine-induced autoimmune thyroiditis but not diabetes [26]. In particular, this murine strain has been extensively used to evaluate the role of iodine in the development of autoimmune thyroiditis [16].
EAT can be induced in mice by injecting with murine or human Tg, [80] and in normal syngenic recipients it is induced by the adoptive transfer of in vitro activated T cells from Tg-immunized mice [81]. The induced disease is characterized by the production of murine Tg-specific antibodies and infiltration of the thyroid by lymphocytes and other monocytes, with murine or human Tg-specific CD4+ T cells as the primary effector cells [80,82].
Clinical features of EAT induced in the animal models mentioned above are similar to those of human HT. For example, autoimmune thyroiditis in the NOD-H2h4 mouse is induced by dietary iodine that supports epidemiologic data on human populations. In addition, the iodinified mouse represents high levels of IgG2b that is similar to HT patients expressing the predominance of IgG2 subclass, the human analog of murine IgG2b [83]. IgM class generally restricts Tg-antibodies of normal individuals and mice, while HT individuals and affected mice commonly produce Tg-antibodies of the IgG isotype [17]. However, anti-TPO antibodies generally detectable in HT patients could not be found in NOD-H2h4 mice. Despite some differences between EAT and HT, these animal models have greatly contributed to the knowledge concerning the etiology and the pathogenesis of thyroid autoimmunity, most notably on the events occurring in the very early prodromal phases.
Major Histocompatibility Complex (MHC) molecules are thought to play an important role in the initial stages of the development of HT and AITD. MHC molecules, or Human Leukocyte Antigen (HLA) homologs, play a pivotal role in T-cell repertoire selection in the thymus and in antigen presentation in the periphery. Crystal structures of MHC molecules show a peptide-binding cleft containing the variable region of these molecules. Genetic polymorphism of the MHC molecule determines the specificity and affinity of peptide binding and T-cell recognition. Therefore, polymorphisms within MHC class I and class II loci can play a significant role in predisposition to autoimmune disease [84].
A role of selected HLA class II genes susceptible to HT has been significantly clarified using transgenic NOD (H2Ag7) class II-knockout mice with EAT as a model for HT [85,86]. In mouse genome, the H2 class II locus is homologous to the human HLA class II region [51]. A role for HLA-DRB1 polymorphism as a determining factor in HT-susceptibility, with DR3-directed predisposition and DR2-mediated resistance to the disease, was demonstrated using H2 class II-negative mice injected with HLA-DRA/DRB1*0301 (DR3) and HLA-DRB1*1502 (DR2) transgenes [85]. A role for HLA-DQ polymorphism was shown with human thyroglobulin-induced EAT in HLA-DQ*0301/DQB1*0302 (DQ8), but not HLA-DQ*0103/DQB1*0601 (DQ6), transgenic mice [52]. In summary, DR3 and DQ8 alleles are found to be susceptible, whereas DR2, DR4 and DQ6 alleles are resistant [30,87]. Studies on EAT-developing mice showed the differential effects of class II molecules on EAT induction. Susceptibility can be determined when class II molecules from a single locus, H2A or HLA-DQ, are examined in transgenic mice, but the overall effect may depend upon the presence of both class II molecules H2A and H2E in mice and HLA-DQ and HLA-DR in humans [88]. Polymorphism within DQ alleles can determine predisposition to HT while DRB1 molecules associated with susceptibility to HT may appear to play a permissive role. The combination of susceptibility-inducing HLA-DQ and permissive DR alleles is responsible for the association of the HLA class II region with the disease.
T cells recognize an antigenic peptide via interaction of their membrane T cell receptors (TcR) with antigen-MHC complexes presented on the surface of APC. Biased or restricted TcR gene use has been reported in a variety of human or murine autoimmune diseases [89]. Biased TcR V gene in intrathyroidal T cells was also observed in mice with spontatenous (NOD strain) or human Tg-induced (CBA/J strain) thyroiditis. This confirms the primary role played by T cells in initiating EAT and the phenomenon of oligoclonal expansion of intrathyroidal T lymphocytes in early thyroiditis [90]. Sequencing of amplified TCR V beta cDNA showed that within each NOD thyroid sample at least one of the overexpressed V beta gene families was clonally expanded. For example, in the CBA/J mouse immunized with human Tg, clonally expressed T cells were shown to primarily express the murine TcR Vβ1 and Vβ13 sequences [91].
A new murine model that developed destructive thyroiditis with histological and clinical features comparable with human HT has been recently reported [92]. The transgenic mice express the TcR of the self-reactive T-cell clone derived from a patient with autoimmune thyroiditis. The T-cell clone is specific for the autoantigen thyroid peroxidase (TPO) peptide comprising amino acid residues at positions 535–551 (TPO535–551) of the TPO amino acid sequence. This includes a cryptic epitope (TPO536–547) preferentially displayed after endogenous processing during inflammation [93]. These results underline the pathogenic role of autoreactive human T cells and the potential significance of recognition of cryptic epitopes in target molecules such as TPO for inducing thyroid-specific autoimmune response.
The two-signal theory for T cell activation requires TcR engagement of its cognate antigen-MHC complex and CD28 binding to B7 ligands (B7-1 and B7-2) on APC. Activation of T cells results in increased expression of the cytotoxic T cell antigen-4 (CTLA-4) molecule that shares homology with CD28. Although B7-1 (CD80) and B7-2 (CD86) expressed on APC can bind to both CD28 and CTLA-4 (CD152), because of higher affinity, they preferentially bind to CTLA-4 on activated T cells and attenuate the T cell response [94].
The importance of CTLA-4 in the down-regulation of T cell responses and in the induction of anergy and tolerance to alloantigens, tumors and pathogens, has been clearly demonstrated in experiments with CTLA-4 deficient mice. The mice developed a severe inflammatory disorder due to up-regulated proliferation of T cells [95,96]. CTLA-4 can down-regulate T cell responses involving binding and sequestering B7 molecules from CD28, therefore preventing CD28-mediated co-stimulation. Another possibility is that CTLA-4 through its intracellular domain could actively transmit a negative signal resulting in down-regulation of activated T cells [97]. The crucial role of CTLA-4 in maintaining self-tolerance breakdown of which leads to the initiatition of a primary autoimmune response has been demonstrated in several murine models of autoimmune diabetes [98] and autoimmune thyroiditis [32].
Human Leukocyte Antigen class I and II genes
Genes of the human MHC region are clustered on chromosome 6p21 and encode HLA glycoproteins and a number of additional proteins, which are predominantly related to immune response. The MHC locus itself contains three groups of genes: class I genes encoding HLA antigens A, B and C, class II genes encoding HLA-DR, DP and DQ molecules and class III genes [99].
Previous studies in the early 1980s investigated the HLA locus in relation to the genetics of HT. Associations between HLA and HT have both been analysed by serologic typing of HLA and DNA typing using sequence-specific oligonucleotide probe analysis or restriction fragment length polymorphism. In Asians, HLA class I (A2, B16, B35, B46, B51, B54, C3) and HLA class II (DR2, DR9, DR53, DQ4) genes showed an association with the disease [31,100-105]. In Caucasians, HT is associated with HLA class II genes such as DR3, DR4, DR5, DQA1*0301, DQB1*0201 and DQB1*0301 [106-120] but not with the HLA-DP and HLA class I (HLA-A, HLA-B and HLA-C) genes [113,114,121]. However, some studies could not reveal an association between HLA-DQ and DR genes and Hashimoto thyroiditis [114,122,123]. Reports of disease-associated alleles are not consistent, but associations appear to be strongest with alleles in the HLA-DR and -DQ loci. This has also been suggested by studies in transgenic mouse [30,52,85-87].
Early linkage, non-genome-wide studies of the HLA region have failed to detect linkage between the HLA locus and HT [124-129]. Using dataset of 56 US Caucasian multigenerational families, genome-wide scans has revealed a susceptibility locus AITD-1 located on chromosome 6p [130]. The AITD-1 locus is common for both general forms of thyroid autoimmunity, HT and GD [130]. This locus was replicated in the expanded dataset of 102 US Caucasian families but is distinct from the HLA gene cluster [131]. Whole-genome scans of a large family with members affected with vitiligo and HT mapped a HT susceptibility locus that shared both the MHC region and the non-MHC AITD-1 [132]. However, evidence for linkage between the HLA locus and HT (or autoimmune thyroid disease) has not been confirmed by further whole-genome scans of other affected families [133,134], sibling pairs [135], or within HLA-DR3 positive families [120]. The lack of linkage means, for instance, the DR3 gene did not cause the familial segregation of Hashimoto's disease while a relatively strong and consistent association showed that HLA-DR3 conferred a generalized increased risk of HT in the general population. These data did not support a major role for the HLA region in the susceptibility to HT and may imply that the DR3 gene modulates the effect of other non-HLA susceptibility gene.
However, a linkage between the HLA region and HT was recently shown in the data set of 40 US multiplex families affected with AITD and type 1 diabetes [136]. The linkage to HT was found to be weaker than to diabetes, suggesting that additional, non-HLA loci were contributing to the joint susceptibility to AITD and T1D. Among HLA-DR alleles, HLA-DR3 was detected as the only associated gene for Hashimoto's thyroiditis and diabetes [136]. Indeed, DR3 seems to represent the major HLA allele, which contributes to the shared susceptibility to T1D and AITD. These findings, however, need to be replicated in larger data sets because early family [137,138] and case-control [139,140] studies have not shown the unique role for HLA-DR3 allele in conferring shared susceptibility to T1D and thyroid autoimmunity.
The HLA region has been established to be involved in multiple autoimmune disorders [141]. The mechanisms by which HLA molecules influence the susceptibility to autoimmune disorders become more and more clear. Different HLA alleles could have different affinities to autoantigenic peptides. Therefore, certain alleles can bind the autoantigenic peptide, with the subsequent recognition by T cells that have escaped self-tolerance, whereas others may not [142]. The possibility of certain class II alleles to bind and present thyroid-specific antigens such as TSHR or Tg peptides has been shown in vitro [143] and in mice with EAT [144].
Thyroid autoantigens need to occur in the thyroid or its draining lymph nodes in order for them to be presented by HLA molecules. It has been suggested that an aberrant intrathyroidal expression of MHC class II molecules by thyrocytes is necessary to initiate thyroid autoimmunity [145,146]. This hypothesis is supported by detection of the expression of HLA class II molecules by thyroid epithelial cells in HT and GD patients [147,148] and in studies on animal models with experimentally induced thyroid autoimmunity [85,145,149,150]. The aberrant expression of HLA class II antigen by thyrocytes can initiate autoimmune responses through direct thyroid self-antigen presentation or a secondary event following on from cytokine secretion by infiltrated T lymphocytes [148,151].
Genetic contribution of HLA varies depending on the disease. HLA involvement in T1D, rheumatoid arthritis or multiple sclerosis is large and can constitute more than 50% of the genetic risk [84,152]. Contributions of HLA alleles as genetic risk factors to HT are much weaker [118,153]. HLA class I and II genes appear to contribute to the autoimmunity in general but not to organ specificity. Their role in the predisposition to HT is rather non-specific [62,117]. The HLA class I and II genes appear not to be the primary HT genes, and are likely to be modulating genes that increase the risk for AITD contribution by other genes. HLA class III and other non-HLA genes, located in the HLA region, are also critical to the immune response. It is possible that HLA associations as seen in thyroid autoimmunity are due partially to genetic variation in these closely linked immune regulatory genes and their linkage disequilibrium with class I and II genes [154].
HLA class III genes and non-HLA genes of the HLA region
The HLA class III region lies between class I and II genes and encodes important immunoregulatory proteins such as cytokines [tumour necrosis factor (TNF), lymphotoxin alpha (LT-α) and beta (LT-β)], complement components (C2, C4, properdin factor B) and heat shock proteins (HSP) [155]. Both TNF and LT-α mediate B-cell proliferation and humoral immune responses [154]. TNF has been found to enhance cellular expression of HLA class I and II antigens, and enhances adhesion and complement regulatory molecules in the thyroid gland of HT patients. Alterations to the above could promote the autoimmune process [156]. However, case-control studies showed no association between polymorphisms within the TNF and LT-α genes and HT in Germans [112], UK Caucasians [118] and Koreans [157].
HSP70 gene cluster consists of three genes encoding HSP70-1, HSP70-2 and HSP-Hom proteins. They are expressed in response to heat shock and a variety of other stress stimuli (e.g. oxidative free radicals, toxic metal ions and metabolic stress). HSPs are also important for antigen processing and presentation [158]. Genetic variations within all three HSP70 genes were tested in British patients with HT and no associations were found [118]. Polymorphisms of complement component-encoding genes have not yet been evaluated in relation to HT. Meanwhile, finding a link between frequency disturbances in BI and C4A allotypes and one of the forms of thyroid autoimmunity, postpartum thyroiditis [159], may be an intriguing future study in HT patients.
Other genes crucial to the immune response, including TAP (transporters associated with antigen processing), LMP (large multifunctional protease), DMA and DMB genes are located within the HLA class II region [155]. Protein products of TAP (TAP1 and TAP2) and LMP2 (LMP2 and LMP7) genes participate in the proteolysis of endogenous cytoplasmic proteins into small fragments and subsequent transportation of these self-peptides from the cytoplasm into the endoplasmic reticulum, the site of HLA class I assembly [160]. To date, one investigation has been concerned with the association between TAP1 and TAP2 genes and Hashimoto's thyroiditis. No significant association was observed in the British population [118]. The genetic role of LMP in HT has not yet been examined. An association between the R60 allele of the LMP2 gene and GD was observed [161]. Additionally, quantitative defects in the amount of transcription products of TAP1, TAP2, LMP2 and LMP7 genes were found in lymphocytes of patients with AITD [160]. These findings suggest that defective transcription of HLA class I-processing genes could contribute to the quantitative defect in cell-surface expression in autoimmune lymphocytes in HT. Further evaluation of the role of such class I-processing genes as TAP and LMP is necessary.
DMA and DMB genes are involved in the assembly of HLA class II peptides. These genes encode subunits of a functional heterodimer that is critical for class II antigen presentation [160,162]. Based on nucleotide variation within exon 3, three rare DMB alleles (DMB*01kv1, DMB*01kv2 and DMB*01kv3) have been detected in Korean HT patients while these DMB variants have not been found in healthy subjects [163]. However, these DMB alleles have not yet been functionally characterized. In summary, there is a significant dearth of information on how HLA class III genes and non-HLA genes, located in the HLA region, contribute to the pathogenesis of HT. Further studies are required to clarify the involvement of these genes in HT susceptibility.
CTLA-4 gene
The CTLA-4 gene is the most frequently studied of the immune modulatory genes located outside the HLA region, in relation to the genetics of HT. This gene encodes a costimulatory molecule, which suppresses T-mediated immune response and is crucial in the maintenance of peripheral immunological self-tolerance [164]. An inventory of case-control studies based on the association between three polymorphic markers within the CTLA-4 gene [A49G dimorphism in the leader peptide, C (-318) T substitution in the promoter region and a dinucleotide repeat polymorphism at the 3'-untranslated region (3'-UTR)] and HT is reviewed in [165]. Results of these studies, except for those for the C (-318) T single nucleotide polymorphism, suggest that polymorphisms within the CTLA-4 gene are associated with the development of HT.
Family studies showed linkage between CTLA-4 and GD [166], thyroid antibody production [167] and autoimmune thyroid disease [12,62] but not specifically to HT, probably due to lack of their power [129,130,135]. Classical linkage analysis is suitable for detecting susceptibility loci with major genetic effects. CTLA-4 demonstrates a modest but significant effect in the genetics of HT. To detect a locus with a modest genetic effect, a large number (at least 400) of affected families should be tested [168].
This investigation has recently been performed involving about 600 AITD families and more than 1300 affected patients [169]. The CTLA-4 gene has been found to play a critical role in the pathogenesis of autoimmune diseases such as GD, HT and T1D [62,153,169]. Disease susceptibility was mapped in the 6.1-kb 3' untranslating region of CTLA-4. Allelic variation was correlated to altered mRNA levels of soluble form of CTLA-4 [169]. This alternative splice form of CTLA-4 lacks exon 3 encoding the transmembrane domain but maintains exon 2 encoding the ligand-binding domain [170]. The short form of CTLA-4 can bind CD80/86 and inhibit T-cell proliferation [171]. The soluble CTLA-4 (sCTLA-4) is expressed constitutively by T regulatory cells suppressing the effector T-cell response [172]. Its role in autoimmune disease is not exactly clear, but sCTLA-4 was observed significantly more often in patients with AITD [173] and myasthenia gravis [174] in comparison with non-affected subjects. Patients with AITD and myasthenia gravis had an aberrant expression of the CTLA-4 products, with high levels of sCTLA-4 and low levels of the intracellular form [175]. Soluble CTLA-4 might play an important role in immune regulation by binding with the B7 molecules, thus interfering with the binding of CD28 and/or full-length CTLA-4. Interference of sCTLA-4 with B7/CTLA-4 interactions could block suppressive signals transferred via surface-bound CTLA-4. Therefore, high concentrations of sCTLA-4 in serum might contribute to disease manifestations through interference of sCTLA-4 with B7/CTLA-4 interaction.
It may be that the amino acid change at codon 17 of the signal peptide could alter the function of the signal peptide to direct intracellulat trafficking of CTLA-4. In in vitro expreriments, the Ala17 (G49) allele was found to represent a translation product, which was not glycosylated in one of two N-linked glycosylation sites [176]. This aberrantly glycosylated product was shown to be further translocated from the endoplasmic reticulum back to cytoplasm and, probably, to become a target for proteolytic degradation. In addition, the distribution of Ala17 CTLA-4 variant on the surface of COS1 cells is significantly less density than the Thr17 variant of CTLA-4 [176]. These fundings suggest that the Ala17 allele is linked to the inefficient glycosylation of CTLA-4, which subsequently could affect suppressing effects of the CTLA-4 molecule. This could also explain observations showing that the G49 allele of the CTLA-4 signal peptide is associated with accelerated proliferation of T lymphocytes in human subjects homozygous for this allele, and with suppression of the downregulation of T-cell activation in response to IL-2 [177,178].
The codon 17 single nucleotide polymorphism (SNP) is shown to be in tight linkage disequilibrium with another SNP situated at position (-318) of the CTLA-4 promoter region and with the (AT)n repeat polymorphism at the 3'-UTR of the CTLA-4 gene [176,179-181]. For the C (-318) T SNP, the protective T (-318) allele demonstrated higher promoter activity than the alternative C allele in a luciferase expression assay [182]. Since the (-318) dimorphism occurs in a potential regulatory region, this suggets that this nucleotide substitution may influence the expression of CTLA-4. However, this possibility remains to be explored.
The (AT)n repeat polymorphism at the 3'-UTR of the CTLA-4 gene has been shown to affect the expression of this costimulatory molecule [174]. Adenylate- and uridylate-rich elements (AUREs) presented in the 3'-UTRs can regulate stability of eukaryotic mRNAs, and their presence correlates with rapid RNA turnover and translational and posttranslational control [183,184]. The AT repeats in the 3'-UTR of CTLA-4 might represent a special type of of AUREs. CTLA-4 mRNA with longer (AT)n alleles have shorter half-lives and, hence, are more unstable [174,185]. Indeed, the (AT)n microsatellite in the 3'-UTR influences the mRNA stability. Additionally, the CTLA-4 AT-repeat polymorphism was recently shown to alter the inhibitory function of CTLA-4. The long AT-repeat allele is associated with reduced control of T-cell proliferation and thus contributes to the pathogenesis of GD [186].
The AT-repeat may also affect splicing of one or more of the alternative CTLA-4 transcripts but this should be clarified. Ueda et al. [169] showed that another polymorphism (A6230G, or CT60 SNP) located in the first position of the 3'-UTR correlates with higher expression of a soluble CTLA-4. In this study, the highest power of linkage with GD was found for this SNP and three other SNPs (JO27, JO30 and JO31) within a 6.1-kb segment of the 3'-UTR, but not for the (AT)n repeat polymorphism [169]. However, no T-cell function data were presented. Thus, further investigations are necessary to evaluate functional significance of these SNPs. Due to the linkage disequilibrium, it is currently not possible to determine whether one, or both, are of physiological importance. It can not be excluded that allele combination of several closely linked CTLA-4 polymorphisms might form a functionally significant haplotype that is directly involved in the susceptibility to autoimmune disease [187,188].
It should be noted that the genomic region 2q33 linked to autoimmune disease contains cluster of three genes encoding costimulatory molecules CTLA-4, CD28 and inducible costimulator (ICOS) [189]. However, genetic studies showed that the AITD gene in the 2q33 locus is the CTLA-4 gene and not the CD28 or ICOS genes [167,169,181].
The CTLA4 gene should be recognised as the first major known non-HLA locus of human autoimmunity and that its role in the pathogenesis of HT is rather general and non-specific [74,153]. Association of CTLA-4 with the production of thyroid antibodies [167,190], an event that often represents the subclinical stage of AITD [1], can explain non-specific mechanism of CTLA-4-mediated susceptibility to the development of thyroid autoimmunity. The association of the CTLA-4 gene with several autoimmune diseases such as T1D [153,169], Addison's disease [191,192], multiple sclerosis [193,194], myasthenia gravis [175] and all clinical outcomes of AITD [74], can also explain the general contribution of CTLA-4 to autoimmunity. Interestingly, AITD, Addison's disease and autoimmune diabetes frequently coexist in patients with the autoimmune polyendocrine syndrome type II as mentioned above. The above disorders seem to share a genetic background, and CTLA-4 could represent a common susceptibility focus for them [195]
Other immune regulatory genes
In initial phases of AITD, oligoclonal expansion of T lymphocytes occurs in the thyroid gland. These T cells are restricted by their T cell receptor V gene use [89,90]. Therefore, the TcR may be considered a likely candidate gene for AITD and HT. Early case-control investigations showed a lack of association between HT and the T-cell receptor-α gene in the US white population [111] but not the T-cell receptor-β gene in the Japanese [102]. Linkage analysis using a US Caucasian AITD family dataset [129] and Tunisian affected pedigree [196] has eliminated the T-cell receptor V alpha and V beta gene complexes, located on 14q11 and 7q35, respectively, as candidate genes for susceptibility to thyroid autoimmunity. Therefore, the TcR genes are not major susceptibility genes for HT and AITD.
Another likely candidate among immune-related genes was the IGH gene because HT individuals commonly produce Tg-autoantibodies restricted by IgG class [50]. Early investigations found an association between IgH Gm allotypes and AITD in the Japanese [197,198]. However, these findings have not been confirmed in Caucasians [129,196].
Cytokines are crucial in the regulation of immune and inflammatory responses. Multiple investigations showed the important role of these regulatory molecules in directing autoimmune and apoptotic pathogenic processes, of particular, in central and late stages of the development of HT [72,80,199]. Therefore, cytokine genes might be good candidates for HT. Intrathyroidal inflammatory cells and thyroid follicular cells produce a variety of cytokines, including interleukin-1α (IL-1α), IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-13, IL-14, tumor necrosis factor-α, and interferon-γ [200]. Hunt et al. [201] evaluated 15 polymorphisms within nine cytokine genes for IL-1α, IL-1β, IL-1 receptor antagonist (IL1RN), IL-1 receptor 1, IL-4, IL-4 receptor, IL-6, IL-10, and transforming growth factor-β in British patients with AITD. They only found a significant association for one of those. The T-allele of the IL-4 promoter [T (-590) C] polymorphism was associated with lower risk of GD and AITD but not HT [201]. Blakemore et al. [202] failed to find an association between a polymorphic minisatellite in the IL1RN gene and HT in another group of affected patients from UK. Thus, it may be concluded that these genes are not major susceptibility genes for thyroid autoimmunity but need to be further studied.
The autoimmune regulator (AIRE1) gene is known to contribute to the pathogenesis of autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED), a rare monogenic autoimmune disease with endocrine components including T1D, adrenal failure, and thyroid dysfunction, with major autoantibodies directed against adrenal, pancreas, and thyroid tissue [203]. However, studies in UK patients showed no relation between a 13-bp deletion at nucleotide 964 in exon 8 (964del13) of the AIRE1 gene, a common disease-associated marker for APECED in British population, and HT [204].
The vitamin D-mediated endocrine system plays a role in the regulation of calcium homeostasis, cell proliferation and (auto) immunity. 1,25-Dihydroxi-vitamin D3 (1,25(OH)2D3) is the most active natural vitamin D metabolite that effectively prevents the development of autoimmune thyroiditis in an animal model [205] and inhibits HLA class II expression on endocrine cells [206]. C/T polymorphism located at intron 6 of the vitamin D 1α-hydroxylase (CYP1α) gene failed to show association with HT in Germans [207]. Two polymorphic markers within the vitamin D-binding protein gene encoding another member of the vitamin D metabolic pathway also showed no association with HT in the German population [208]. However, among two polymorphic sites tested at the vitamin D receptor (VDR) gene, the Fok I(+) allele of the FokI/restriction fragment length polymorphism was found to be associated with higher risk HT in Japanese females [209]. Meanwhile, the VDR gene remains to be a likely candidate for the common autoimmune susceptibility gene because it has been found to be associated with autoimmune disorders such as GD [210], Addison's disease [211], multiple sclerosis [212] and T1D [213].
Thus, a wide variety of non-HLA immune regulatory genes located outside the HLA region showed no significant linkage or association with HT and AITD except for the CTLA4 gene. However, we still cannot estimate whether or not these genes significantly contribute to HT susceptibility due to a serious shortfall in information about their role in this disorder. It cannot be excluded that other genes in linkage disequilibrium with these genes are the susceptibility genes at these loci.
Thyroid-specific genes
Antibodies against thyroid peroxidase are one of the most specific features of HT [214]. Therefore, the TPO gene is expected to be a putative candidate responsible not only for susceptibility to HT but also for specific determination between two common outcomes of AITD, such as HT and GD. Genetic transmission of the recognition by antibody of the TPO immunodominant region and the TPO B domain has been described in families affected with HT [215]. This transmission could be explained by genetic variations within the TPO gene. However, case-control studies showed lack of association between the TPO gene polymorphisms and AITD [113,216]. These data suggest that the thyroid peroxidase gene does not play an important role in predisposition to HT. Subsequent studies are necessary to clarify exactly whether this gene is a true susceptibility gene for AITD.
Within the other thyroid-specific gene, the TSHR gene, the T52P amino acid substitute was examined in US white and Thai populations but no association with HT was found [217,218]. Various genome-wide scans have failed to detect linkage between the thyrotropin receptor gene and HT or AITD [130,133-135,219,220]. However, two microsatellites, an (AT)n marker at intron 2 of the TSHR gene and a (CA)n marker that was mapped to approximately 600 kb of the TSHR gene, have been shown to be strongly associated with HT in Japanese patients [221,222]. The TSHR gene, therefore, does not seem to be a major susceptibility gene for HT, although a minor role cannot be excluded.
Tg-specific autoantibodies are common in AITD. The thyroglobulin gene makes a significant contribution to HT and AITD. Whole-genome scans in Japanese-affected sibling pairs have detected a HT susceptibility locus on chromosome 8q24, with a maximum linkage to marker D8S272 [135]. This marker is separated by 4.6 megabases (Mb) from the Tg gene. Subsequent studies of the mixed US and European Caucasian family dataset has confirmed the susceptibility locus to be on chromosome 8q24, with the maximum linkage to markers D8S514 and D8S284 [74,131,223]. These markers border a large region of chromosome 8 spanning about 15 Mb. The thyroglobulin gene is located within this region. Moreover, a new microsatellite marker Tgms2 inside intron 27 of the Tg gene showed strong evidence of linkage and association with AITD [223,224]. Two new microsatellites have recently been described in introns 29 and 30 of the thyroglobulin gene that can be useful for further linkage studies in families with autoimmune thyroid diseases [225]. Using a high-density panel of SNPs within the human and murine Tg genes, Ban et al. [226] identified a unique SNP haplotype, consisting of an exon 10–12 SNP cluster in both genes and, additionally, exon 33 SNP in the human gene, associated with AITD in humans and with EAT in mice. Taken together, these data strongly suggest that the thyroglobulin gene could represent the susceptibility gene for HT and AITD on 8q24 [74,227] and, therefore, be characterized as the first thyroid-specific susceptibility gene for thyroid autoimmunity [228].
The Tg gene spanning over 300 kilobases long is expected to harbour more than one haplotype block associated with AITD since the length of a linkage disequilibrium block of SNPs is shown to be less than 100 kilobases [229]. It seems that this gene is AITD-specific but is not a HT-specific susceptibility gene. The manner in which the Tg gene can be a predisposition to AITD remains unclear. It could be that amino acid variations within the Tg gene can affect the immunogenicity of Tg. The evidence that iodination of thyroglobulin affects its immunogenicity favours this suggestion [230,231]. However, additional studies are required to evaluate that.
Recent investigation in Tunisians showed significant association of two polymorphic microsatellites (D7S496 and D7S2459) close to the PDS gene (7q31) with GD and HT, and one of them, D7S496, was linked to GD only [232]. The PDS gene encodes a transmembrane protein known as pendrin. Pendrin is a chloride/iodide transporting protein identified in the apical membrane of the thyroid gland [233]. Data of Kacem et al. [232] suggest that the PDS gene might be considered a new susceptibility gene to autoimmune thyroid diseases, having a different involvement with different diseases. However, studies in other populations are necessary to support a role for the PDS gene in thyroid autoimmunity and HT.
Finally, a role for other genes specifically expressed in the thyroid gland, has yet to be defined. These genes include those encoding thyrotropin-β, thyroid-specific factor-1, sodium iodide (Na+/I) symporter and paired box transcription factor-8, among others. They also need to be evaluated for any putative impact on HT.
Apoptotosis-related genes
Two polymorphic sites within the FasL gene were recently tested in HT Caucasian patients from Italy and Germany. No association between these polymorphisms and the disorder was shown [234]. Assuming a lack of association of the naturally occurring FasL gene polymorphisms with multiple other autoimmune diseases tested, we conclude that genetic variation within this gene does not contribute to autoimmunity. Inactivating mutations within the Fas and FasL genes are associated with carcinogenesis [235,236]. This situation is common among apoptotic-related genes encoding caspases, death receptors, decoy receptors and death ligands as well as for genes that encode other types of signalling molecules [237]. However, since apoptotic mechanisms play a critical role in pathogenesis and progression of HT, genes associated with programming cell death should be evaluated whether or not they confer susceptibility to HT.
Other genes
Due to the prevalence of thyroid autoimmunity in females, gender-related genes could also be considered as putative candidates for HT susceptibility. Some of these genes, such as the CYP19 gene encoding aromatase that participates in estrogen synthesis, and genes for estrogen receptor-α (ESR1) and -β (ESR2), were examined but showed no linkage with HT [238]. The ESR1 and ESR2 genes demonstrated no association with AITD in the Japanese [239,240]. It seems that the CYP19 and both estrogen receptor genes do not predispose to HT and AITD. Other gender-specific genes could contribute to AITD. A possible involvement of such genes has been shown for GD with the discovery of a susceptible locus on chromosome X [238].
The SEL1L gene, encoding a novel transcription factor, was recently described [241]. The gene is located on chromosome 14q24.3-q31 close to the GD-1 susceptibility locus [128,130,219] and considered a likely candidate for thyroid autoimmunity. However, a case-control study in the Japanese population detected no association of a dinucleotide (CA)n repeat polymorphism in the intron 20 of the SEL1L gene with AITD [242]. This gene may be a potentially predisposing gene to T1D because it is specifically expressed in adult pancreas and islets of Langerhans [241]. It lies in the vicinity to IDDM11, a susceptibility locus to this autoimmune disease, on chromosome 14q24.3-q31 [243].
A new zink-finger gene designated ZFAT (a novel zink-finger gene in AITD susceptibility region) has been recently found on chromosome 8q24 [244]. The T allele of the Ex9b-SNP10 dimorphism representing an adenine-to-thymidine substitution within intron 9 of this gene was shown to be associated with high risk for AITD in Japanese patients. Functional studies showed that the Ex9b-SNP10 significantly affects the expression of the small antisense transcipt of ZFAT (SAS-ZFAT) in vitro and this expression results in the decreasing expression of the truncated form of ZFAT (TR-ZFAT) [244]. This SNP is located in the 3'-UTR of TR-ZFAT and in the promoter region of SAS-ZFAT. Full-length ZFAT and TR-ZFAT encode a protein with unknown function, which has eighteen and eleven repeats of zink-finger domains, respectively. Both molecular variants of ZFAT are expressed in different tissues including peripheral blood lymphocytes, while SAS-ZFAT is exclusively expressed in peripheral blood B cells and represents a non-coding RNA with putative regulatory function [245]. The disease-associated polymorphism can play a significant role in B cell function by enfluencing the expression level of TR-ZFAT through regulation of transcription of SAS-ZFAT. Interestingly, Shirasawa et al. found no association of the thyroglobulin gene with AITD when studying different ethnic groups [244]. These results suggest that the ZFAT gene could implicate the susceptibilty to AITD on chromosome 8q24 but that it needs to be strongly replicated in other populations. Additionally, the ZFAT gene should be functionally studied to clarify whether the ZFAT or thyroglobulin gene are true contributors of genetic susceptibility to AITD and HT on 8q24.
Non-defined susceptibility loci for Hashimoto's thyroiditis and autoimmune thyroid disease
At present, the CTLA-4 (chromosome 2q33), thyroglobulin (or ZFAT) (8q24) and likely HLA genes (6p21.3) are the only susceptibility loci for HT and thyroid autoimmunity to be mapped. Two HT-specific susceptibility loci that have been detected in mixed Caucasian families from USA and Europe, HT-1 (13q) near marker D13S173 and HT-2 on chromosome 12q in the vicinity of marker D12S351, are still not defined [130]. HT-2 locus has been subsequently replicated in the extended dataset, with a peak linkage close to marker D12S346, which HT-1 does not have [223]. Possible candidate genes for susceptibility to HT positioned within the HT-2 locus may include the BTG1 and CRADD genes. The BTG1 gene encodes B-cell translocation protein-1, which play an immune regulatory role as a negative regulator of the proliferation of B cells [246]. The GRADD gene encodes CASP-2 and RIPK-domain-containing adaptor with death domain, that represents apoptotic function, inducing cell apoptosis via recruiting caspase 2/ICH1, TNF receptor 1, RIPK-RIP kinase and other proteins [247].
AITD-1 locus located on chromosome 6p is very close yet distinct from the HLA region [120,130]. It has been shown that the AITD-1 is positioned in the same location as susceptibility loci for T1D (locus IDDM15) [248] and systemic lupus erythematosus [249]. This may imply that a general autoimmunity susceptibility gene is located in this region. The AITD-1 locus contains an interesting positional candidate gene such the SOX-4 gene, encoding a transcription factor that modulates differentiation of lymphocytes [250].
A whole-genome scan in Japanese showed evidence for linkage with AITD on chromosome 5q31-q33 [135,251]. The 5q31 locus was replicated by recent genome-wide scan in Caucasian population, the Old Order Amish of Lancaster County, from Pennsylvania [220]. This locus harbours a cluster of cytokine genes and, therefore, several positional candidate genes occur in this region and need to be evaluated.
In the Chinese, a whole-genome screening for AITD susceptibility found two chromosome regions (9q13 and 11q12) linked to AITD [134]. Susceptibility genes have yet to be defined within these regions. However, the 9q13 locus harbours a putative candidate gene such as the ANXA1 gene, whose product annexin A1 prevents the production of inflammatory mediators [252]. The 11q12 locus contains several interesting candidate genes encoding immune modulators (CD5 and CD6) and possible components of antigenic peptide processing (PSMC3) and transport (PTH2).
In a large Tunisian family affected with AITD, a susceptibility locus was mapped on 2p24 [133] This locus harbors two possible candidate genes such as the FKBP1B gene, product of which demonstrates immune modulating activity [253], and the TP53I3 gene encoding p53-inducible protein 3 that is involved in p53-mediated apoptotic pathway [254].
These data suggest that both HT and AITD show genetic heterogeneity in different populations. Susceptibility loci differ in their chromosome location depending on the population being tested. The contributory value of these genes to the disease pathology varies significantly depending on the ethnic background. A gene, that has a major effect on the susceptibility to HT in one population, may contribute weakly in other population. To date, several regions of linkage to HT and AITD have been defined. Further studies are required to find a true susceptibility gene in these genomic regions to reveal the functional significance of disease-associated polymorphisms within these genes.
Conclusion
AITD can be initiated in individuals genetically predisposed to AITD by non-genetic (environmental) triggers such as dietary iodine, infection, pregnancy, cytokine therapy (Fig. 1). This interaction leads to different clinical phenotypes of thyroid autoimmunity such as Graves' disease, Hashimoto's thyroiditis or production of thyroid antibodies. HT and GD are two distinct but related clinical outcomes of AITD. It seems that both thyroid diseases have common pathogenic mechanisms as their initial steps including breakdown of the immune tolerance and accumulation of T lymphocytes in the thyroid gland.
Sequence variants of CTLA-4, associated with increased levels of the soluble form of this immune costimulator and with stability of CTLA-4 mRNA, could play a crucial role in the earliest stages of AITD (i. e. breakdown of self-tolerance and surviving autoreactive T lymphocytes). This role might be sufficient to regulate subsequent steps in the development of autoimmune responses to the production of thyroid autoantobodies [167].
Environmental factors (particularly, iodine intake and infection) could cause insult of the thyrocyte followed by abnormal expression of MHC class I and class II molecules, as well as changes to genes or gene products (such as MHC class III and costimulatory molecules) needed for the thyrocyte to become an APC [255]. In this stage, a modulating role of sequence variants of HLA class II molecules could become pivotal in binding and presenting thyroid antigenic peptides derived from Tg, TPO and TSHR. Genetic variations in Tg, and probably in TSHR and other thyroid-specific genes, might be responsible for generating an autoimmune response.
In later stages, thyroid autoimmunity could be switched towards GD or HT. GD is characterized by TH2-mediated switching of thyroid-infiltrating T cells. These induce the production of stimulating anti-TSHR antibodies by B cells and anti-apoptotic mechanisms that lead to clinical hyperthyroidism. In HT, preferential TH1 response initiates apoptosis of thyroid cells and results in clinical hypothyroidism [22].
It is clear that a number of loci and genes determine genetic predisposition to HT, with varying effects. These loci could be unique to HT or general for both HT and GD. Several whole-genome scans showed results suggesting that there is significant shared susceptibility to HT and GD [130,131,134,135]. This is also supported by the frequent coexistance of both diseases in affected families [74,133]. Preliminary data suggest that shared genetic susceptibility involves both immune regulatory (i. e. CTLA-4 and HLA) and thyroid-specific genes (i.e. Tg). These genes are not responsible for the determination of pathogenic mechanisms of thyroid autoimmunity distinct for HT and GD. It remains unclear which susceptibility genes are specifically involved in the HT pathogenesis.
The CD40 gene, an important immune modulator, appears to act as a GD-specific susceptibility gene. The gene is located within the 20q11 locus and shows significant linkage to GD, but not to HT, in UK Caucasians [74,130,131,256,257]. Subsequent analysis found the CD40 gene to be associated with GD [258]. However, this finding needs to be independently confirmed in other population samples.
A probable susceptibility gene that could direct switching towards GD or HT is thought to be located within the 5q31 locus, which is linked to AITD and contains a cytokine gene cluster. Different sets of cytokines are known to regulate switching to TH1 or TH2 type mechanisms [58]. There may be two susceptibility genes, each of which uniquely contributes to the development of HT- or GD-specific pathogenesis. The IL-4 promoter [T(-590) C] polymorphism also appears to be associated with GD, but not with HT [199]. IL-4 mediates TH2 type mechanism, which can lead to hyperthyroidism [259].
Another HT-specific susceptibility gene(s) may be an apoptotic gene. Apoptosis of thyroid follicular cells is the hallmark of HT and might be the primary cause of death of thyrocytes compared to T cell-mediated cytotoxity [69].
Thus, it is necessary to identify additional susceptibility genes and disease-associated polymorphisms in apoptotic genes in AITD- and HT-linked loci by using a fine mapping approach and high-density panels of SNPs. Further functional analysis and search for correlations between genotype and phenotype will help to evaluate the role of these genes in the development of autoimmune thyroid disease. Susceptibility genes interact with thyroid autoimmunity [62,130], and the level of these interactions could affect disease severity and clinical expressions. The molecular mechanisms of these interactions is unknown. However, significant progress has been made in identifying susceptibility genes to HT and AITD along with intriguing findings regarding the functional characterization of disease-associated polymorphisms. These should stimulate further studies towards the in-depth understanding of the mechanisms by which these genes contribute to thyroid autoimmunity.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
ABFG
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| 15762980 | PMC555850 | CC BY | 2021-01-04 16:38:03 | no | J Autoimmune Dis. 2005 Mar 11; 2:1 | utf-8 | J Autoimmune Dis | 2,005 | 10.1186/1740-2557-2-1 | oa_comm |
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J Circadian RhythmsJournal of Circadian Rhythms1740-3391BioMed Central London 1740-3391-3-21574829410.1186/1740-3391-3-2DebateTheodor Hellbrügge: 85 years of age – Ad multos transannos, sanos, fortunatos et beatos Halberg Franz [email protected]élissen Germaine [email protected] George [email protected] Othild [email protected] Dana [email protected] Halberg Chronobiology Center, University of Minnesota, Minneapolis, MN 55455, USA2005 5 3 2005 3 2 2 17 2 2005 5 3 2005 Copyright © 2005 Halberg et al; licensee BioMed Central Ltd.2005Halberg et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We honor Theo Hellbrügge's acclaimed endeavors in the rehabilitation, or rather the prehabilitation of handicapped children. So far, he has focused on obvious handicaps, and we trust that he will include concern for everybody's silent handicaps in the future by screening for abnormal variability inside the physiological range. Therein, we introduce cis- and trans-years, components of transdisciplinary spectra that are novel for biology and also in part for physics. These components have periods, respectively, shorter and longer than the calendar year, with a counterpart in magnetoperiodism. Transyears characterize indices of geomagnetic activity and the solar wind's speed and proton density. They are detected, alone or together with circannuals, in physiology as well as in pathology, as illustrated for sudden cardiac death and myocardial infarction, a finding calling for similar studies in sudden infant death syndrome (SIDS). As transyears can beat with circannuals, and depend on local factors, their systematic mapping in space and time by transdisciplinary chronomics may serve a better understanding of their putative influence upon the circadian system. Longitudinal monitoring of blood pressure and heart rate detects chronome alterations underlying cardiovascular disease risk, such as that of myocardial infarction and sudden cardiac death. The challenge is to intervene in a timely fashion, preferably at birth, an opportunity for pediatricians in Theo Hellbrügge's footsteps.
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Laudatio
The discovery in biology of far-transyears, 15–20 months in length [1-3], is in keeping with oscillations of the same longer-than calendar-yearly period in the speed and proton density of the solar wind [4,5]. Hence, this wish for healthy, lucky and blessed transyears rather than years. Let us speculate that we are genetically programmed for a certain number of transyears (or years) and that an attempt to synchronize transyears rather than years, also pure speculation, could automatically prolong the remaining lifespan by one or two-thirds in the case of far-transyears or by some weeks in the case of a near-transyear. What is not speculation is that transyears are a transdisciplinary fact of life and that they can beat with a spectral component with a period of the length of the calendar year [1-3], and, what seems critical for this journal, each about-yearly component can influence the circadian system.
Figure 1 presents a tentative scheme for classification of trans-yearly spectral components. The suggestions are tentative; they imply that the cis- and trans-annuals, as defined here, have an amplitude (A) different from zero, established by the non-overlap of zero by the 95% confidence interval (CI) of A, and that the component is anticipated, i.e., confirmed by analyses of an independent separate prior series. In addition to these considerations of statistical significance and prior documentation, there is a most important added consideration of reciprocal mutually supporting cyclicities found in and around us. These are much more numerous in the case of the spectral region around the year than in that of the day. Moreover, about-yearly cycles, notably the non-photic magnetoperiodisms, usually are mere influencers of the biological year, rather than necessarily long-term synchronizers, being often transients themselves, by contrast to cycles with a period corresponding in length to the day. In the case of the year, the far-transyears centering around 1.3 years and around 1.6 years are all different and transient, and, this is new, their influence is also dependent upon local factors. The far-transyears were discovered by physicists in the solar wind with prior hints from geomagnetics and auroral counts [4,5] while the near-transyears in the solar wind, in the antipodal geomagnetic index as well as in biology, were found and validated by us. Because of the wobbliness of the period and the circumstance that the external cycles may not lock-in the biological ones, variability is much greater in the about-yearly spectral region than in the circadian domain. In the case of the about-yearly vs. that of the about-daily variation, about-yearly asynchronization must be considered rather than desynchronization, as in the case of circadians.
Figure 1 Tentative scheme for classification of cis- and trans-yearly periods, based on length and 95% confidence interval (CI), without implication as to mechanisms. Period (τ, dot), with its 95% CI (length of horizontal line), indicated for near and far trans- and cis-yearly components in transdisciplinary, including physical-environmental and biologic spectra, the latter at all levels of organization, from single prokaryote to ecosystems. Circannual (about calendar-yearly) components under usual conditions are defined as components with a τ, the 95% CI of which overlaps the precise yearly τ; trans- and cisannuals are components with a 95% CI of τ not overlapping the precise yearly τ, longer (trans) or shorter (cis) than 1 year, respectively, with distant limits indicated on the scheme. They are subdivided further into near- and far- cis- or transyears, if the 95% CIs are within the limits also shown on this graph.
For discussion by transdisciplinary nomenclature committees, terms in English are emphasized. With advice by Prof. Robert Sonkowsky, proposed Latin equivalents are added for vanishing classicists. Essentially, "ad-transannual" means "a little longer than a year"; "ad-cisannual" means "a little shorter than a year"; "transior-annual" means "much longer than a year"; and "citerior-annual" means "much shorter than a year". Some specific limits that seem reasonable in the light of available physical and biological evidence are given in the scheme. The single syllable 'ad' is preferred to the 2-syllable 'prope', 'juxta', 'propter', 'minus' (paired with 'plus') or the 3- or 4-syllable 'proprior', 'proximus', 'vicinus', or propinquus'. While to a purist among grammarians the coinages adtransannual and adcisannual may seem preposterous (a word constituting itself an illustration of cumulative prefixes) precisely because of the piling on of prefixes, there are also other precedents in Late Latin such as exinventio ("discovery") and perappositus ("very suitable/apposite"). Normal assimilation of 'd' to 't' and 'c', respectively, may then result in the spellings and pronunciations "attransannual" [at-trans-annual] and "accisannual" [ak-sis-annual] acceptable as English pronunciation, notably by speakers with native romance languages, who may face difficulty with the near and far as added prefixes.
Difficulties may stem from the fact that analyses usually provide estimates in frequency (not period) terms, and from the criterion of 95% CIs that may not be available. We need to allow for situations when, because of too-wide (or unavailable) CIs, we can diagnose only a candidate trans- or cis-annual component, when 95% CIs of τ overlap the limit distant from the year. By the same token, we may not be able to specify near or far, e.g., because of the brevity of the series. In other words, we cannot say whether we have a near- or a far- trans- or near- or far- cis-year, when there is an overlap by 95% CIs with the corresponding finer limits, shown on the scheme (Figure 1).
For the case of "circannual", we again go by 95% CIs rather than by the point estimate. In the circannual case, the 95% CI overlaps the 1-year estimate under usual conditions, bearing in mind that under unusual, e.g., constant conditions, circannuals are also amenable to free-running, in which case the 95% CI may no longer cover 1 year but will have to be tested further for non-overlap with the pertinent environmental cycle in the case of a biologic cycle and vice versa for non-overlap of a natural environmental cycle with an anthropogenic cycle. In the trans- or cis-annual case, the 95% CI does not cover the 1-year period under usual conditions, i.e., cis- or trans-annuals can be asynchronized rather than desynchronized. Strictly speaking, circannual cannot be an overall term, but almost certainly, whatever committees may decide, it will be (mis-)used as such. "Far-" and "near-", "cis-" and "trans-" and "citerior-" and "transior-" annual are hyphenated here only to indicate their derivation and need not be written with hyphens. We propose using circannual, transannual or cisannual and their refinements, only operationally as a function of periods and their 95% CIs. Matters of synchronization, desynchronization or asynchronization may then possibly emerge from the context of a given situation and from further testing.
Trans- and cis-years lead to a novel chrono-helio-geobiology, awaiting application of the tools of transdisciplinary chronomics. It has been a challenge to look at circadians for the past half-century, but knowledge concerning them will not be completely useful before we answer another set of questions based on the evidence in Table 1.
Table 1 Geomagnetic/Geographic Differences among Cycles with Periods in the Range of 0.8 – 2.0 years Characterizing the Incidence of Sudden Cardiac Death and Myocardial Infarction
Sudden Cardiac Death (SCD)1*
Site Span T, Δt, N SC (N) Period (y) (95%CI) Amplitude (95%CI) A(% MESOR) P-value2
Transyear (TY) or Candidate Transyear (cTY) Detected
Minnesota 1999–2003 5 y, 1 d, 1826 343 1.392 (TY) (1.173, 1.611) 0.042 (0.00, 0.09) 22.0 0.014
Arkansas 1999–2003 5 y, 1 d, 1826 273 1.095 (0.939, 1.251) 0.032 (0.00, 0.07) 21.1 0.040
1.686 (cTY) (1.293, 2.071) 0.031 (0.00, 0.07) 20.7 0.044
Czech Rep. 1999–2003 5 y, 1 d, 1826 1006 0.974 (0.856, 1.091) 0.078 (0.00, 0.16) 14.2 0.007
1.759 (cTY) (1.408, 2.110) 0.077 (0.00, 0.15) 13.9 0.010
1994–2003 10 y, 1 d, 3652 1792 1.726 (TY) (1.605, 1.848) 0.074 (0.02, 0.13) 15.1 <0.001
1.000 (0.944, 1.056) 0.052 (0.00, 0.10) 10.6 0.010
Candidate Transyear Not Detected
North Carolina 1999–2003 5 y, 1 d, 1826 752 0.929 (0.834, 1.023) 0.069 (0.00, 0.14) 16.9 0.007
Tbilisi, Georgia Nov'99–2003 4.1 y, 1 d, 1505 130 0.988 (0.862, 1.114) 0.035 (0.00, 0.07) 40.7 0.007
Hong Kong 2001–2003 3 y, 1 m, 36 52 0.843 (0.651, 1.036) 0.022 (NS) 44.9 0.077
Myocardial Infarction (MI)
Site Span T, Δt, N MI (N) Period (y) (95%CI) Amplitude (95%CI) A(% MESOR) P-value2
Coexisting Year (Circannual) and Transyear (TY)
Czech Rep. 1999–2003 5 y, 1 d, 1826 52598 1.014 (0.989, 1.038) 2.85 (2.22, 3.48) 9.88 <0.001
1.354 (TY) (1.252, 1.456) 1.35 (0.69, 2.02) 4.68 <0.001
1994–2003 10 y, 1 d, 3652 115520 0.998 (0.988, 1.009) 3.03 (2.47, 3.60) 9.58 <0.001
1.453 (TY) (1.417, 1.489) 1.91 (1.34, 2.49) 6.04 <0.001
1.15 (TY) (1.116, 1.184) 1.23 (0.64, 1.82) 3.88 <0.001
* With focus on transyears with periods longer than 1.0 year.
1International Classification of Disease (ICD10) Code I46.1, excluding MI and sudden death of unknown or unspecified cause (except before 1999). T: Length of data series (y = years); Δt: sampling interval (d = day, m = month); N: number of data (including 0s). Period and 95% confidence interval (CI) estimated by nonlinear least squares. In longer (10-y) series, a neartransyear (cycle with a period between 1.0 and 1.2 y) is detected for MIs in addition to a fartransyear. Brevity of series and lack of ordering statistical significance qualify results from Hong Kong. Note that transyears are found in 3 of 6 locations (P < 0.05 by linear least squares) with a relative amplitude >12 (% of MESOR).
2From linear least squares analysis, not corrected for multiple testing. Amplitude expressed in N/day.[62]
Table 1 demonstrates in the incidence of myocardial infarction (MI) in the Czech Republic and, for sudden cardiac death (SCD), in the strict sense, excluding MI, both a calendar year and a candidate transyear component in Arkansas as well as in the Czech Republic yet only a transyear, no calendar year for SCD in Minnesota. Signatures and thus perhaps a putative influence of magnetic cycles on human SCD constitute a new feature of SCD pathology, which gains in prominence when death from MI and from other unknown or unspecified causes is ruled out, as it is likely to be when ICD10 code I46.1 is used, as is the case in Table 1.
Of interest are great geographic/geomagnetic differences insofar as no transyears, only calendar-yearly components, were detected in 3 locations, while in 3 other locations, transyears were present, in two of these, with a coexisting calendar-yearly component, with nearly equal prominence, while in Minnesota, only a transyear was thus far detected. A clarification of the roles played by local as well as global influences could also be based on transyear vs. calendar-yearly amplitude ratios when both components are present, which, however, is not the case in 4 of 6 locations. There is the challenge of developing eventual countermeasures.
But first, we seek a clue as to why, for SCD in Minnesota, the prominence of the transyear exceeds by far any seasonal, thus far undetected influence of the harsh environmental temperature change in its mid-continental climate in the summary of 5 consecutive years, and why, in Arkansas and the Czech Republic, the transyear's prominence is about the same as that of the seasons, and why it seems to be absent in 3 other locations and furthermore why in MI the prominence (gauged by the amplitude) of the calendar year is so far greater than that of the transyear (by contrast to the case of SCD). Systematically collected data from different areas of the world will open a new chapter in transdisciplinary science, with particular pertinence at the extremes of extrauterine life, in natality as well as in mortality.
Optimization of the about-yearly spectral region may also be considered, along with Hufeland's consideration of the daily routine in studies aimed at prolonging high-quality life [6]. Notably in the baby, but also in the elderly, the far-transyear's amplitude can exceed that of a spectral component with the length of a calendar year, and hence transyears are especially important to pediatricians and geriatricians alike and, perhaps, for scholars in the field of circadian rhythms.
Beyond 85 years of age, Theodor Hellbrügge, chronopediatrician par excellence and professor emeritus of social pediatrics at the University of Munich, continues actively as a mentor of the specialty he founded [7-9]. Our earlier laudatios [7,10-14] include a symposium dedicated to Theo [14], which competes with his 2 honorary professorships, 17 honorary doctorates, and many more institutes built for handicapped children after his model center in Munich. Theo started as a solid contributor of chronobiological data, he continued in the field via a school of medical students who wrote their doctoral theses and participated broadly in this field, most of them in Minnesota [15-58], many of them concerned with prehabilitation in terms of vascular disease prevention [24-34,38-47,49,52,53,55]. Methodological papers were critical [15-19] to a time-microscopic inferential statistical assessment of both drug-induced phase shifts and circadian phase-response maps, given in each case with the uncertainties involved (Figures 2, 3, and 4) [15].
Figure 2 Importance of timing treatment: Phase shift (ΔΦ) of peak expiratory flow (PEF) rhythm as a function of timing of prolonged corticosteroid therapy in children with severe asthma. Drastic differences in direction and extent of drug-induced shift of a circadian acrophase as a function of medication timing. The reference phase (0°) is the phase of PEF of a group of untreated children with asthma in remission. Vertical 95% confidence intervals indicate detection of statistically significant circadian rhythm (by cosinor) [15].
Figure 3 Importance of timing treatment: Phase shift (ΔΦ) of circadian rhythm in urinary potassium excretion as a function of timing of prolonged corticosteroid therapy in children with severe asthma. Drastic differences in direction and extent of drug-induced shift of a circadian acrophase as a function of medication timing. The reference phase (0°) is the phase of urinary potassium excretion of a group of children with moderate asthma not treated by corticosteroid. Vertical 95% confidence intervals indicate detection of statistically significant circadian rhythm (by cosinor) [15].
Figure 4 Importance of timing treatment: Phase shift (ΔΦ) of circadian rhythm in urinary chloride excretion as a function of timing of prolonged corticosteroid therapy in children with severe asthma. Drastic differences in direction and extent of drug-induced shift of a circadian acrophase as a function of medication timing. The reference phase (0°) is the phase of urinary chloride excretion of a group of children with moderate asthma not treated by corticosteroid. Vertical 95% confidence intervals indicate detection of statistically significant circadian rhythm (by cosinor) [15].
Theo himself turned in the interim to the care of children with obvious disabilities. He continues with concerns about them to detect early alterations for timely remedies, a preventive task par excellence, which could benefit from chronomics, the resolution of time-structural (chronome) alterations in the physiological range. Accordingly, chronobiologists honored Theo at a meeting on "Time structures – chronomes – in child development", leading to a proceedings volume of 256 pages [14]. On the basic side, this conference documented that the human newborn may recapitulate the development of life on earth by a chronome different from that of an adult. The amplitude of about 7-day vs. about-24-hour variation in the human circulation has been shown in gliding spectra in this journal earlier [59]. The amplitudes of spectral components' longer-than-yearly periods can be more prominent than about-yearly changes [14]. About 21-yearly cyclicities (Figure 5) pose interesting problems of geographical differences [14]. These about 21-year cycles correspond in period length to the sunspots' bipolarity cycle [60], but are nearly in antiphase in Minnesota vs. Denmark (Figure 6), raising the question of how different aspects of the earth's surface may bring about antiphasic responses to putative non-photic solar effects, with contributions that are hardly negligible (Figure 7). Possible geomagnetic or other environmental effects on the period and thus indirectly on the phase are implied in Table 1 with respect to sudden cardiac death in a strict sense, excluding death from MI [62]. In conjunction with chaos and trends – in chronomes – these complex cycles provide insight into many developmental biological processes and behavioral patterns in infancy and childhood [14] and also at the other end of life [62] (Table 1).
Figure 5 "Secular" trends in birth statistics from Minnesota uncovered as putative testable cosmic signatures. Shown are the residuals from second-order polynomial fit. Period (τ), double amplitude (2A) and MESOR (chronome-adjusted mean value) assessed by nonlinear least squares, listed with 95% confidence limits. Birth weight in Minnesota undergoes changes that could be signatures during evolution and/or contemporaneously of the cycle in sunspot bipolarity (N of babies: 2,136,745 = 1,097,283 boys and 1,039,462 girls).
Figure 6 Geographic/geomagnetic differences? Near-antiphase of circadidecadal changes in neonatal body weight (BW) in Minnesota (MN) (N = 2,136,745 babies) or neonatal body weight and length in Denmark (N = 1,166,206 babies). Putative signatures of the Hale bipolarity cycle of sunspots are in antiphase. Did K.F. Gauss anticipate geographic/geomagnetic differences due to the little but close magnet Earth itself, reversing the phase of a putative effect upon the period of the large yet far magnet Sun, when Gauss, like A. von Humboldt, each started mapping geomagnetics at different latitudes?
Figure 7 What we do not see can be more important than the visible: Relative contribution of mainly non-photic (shaded) versus mainly photic (white) spectral components in human neonates. The extent of change (double amplitude) of the non-photic, probably circadidecadal Hale cyclicity, a signature of sunspot bipolarity, can exceed that of the usually solely considered yearly component to the population pattern of human neonatal body length. Amplitude ratios were assessed by the variance of each selected component given as percentage of their sum (top) and as amplitude ratios (bottom). Linearly determined is the relative prominence of biological counterparts of about 21-year (Hale) and about 10.5-year (Schwabe) solar activity cycles, with a 5.25-year harmonic assessed to account for any non-sinusoidality; 0.5-year component is counterpart of geomagnetic disturbance cycle. Meta-analysis of Danish National Birth Registry for all children (N = 1,166,206) born from 1973 to 1994 (The Lancet 1998, 352 (26): 1990).
In his own recent words [63], Theo also "had an interest in the work in Prague of pediatricians and psychologists like Matajcek, Dolanski and Donovski, who were interested in systematically analyzing a neonatal deprivation syndrome. From their lessons, [Theo] formulated the concept of developmental rehabilitation in Munich, with new programs for early diagnosis, early therapy and early incorporation into society." In seeking a niche for his endeavor, he called his program "rehabilitation" rather than "prehabilitation" [64,65]. Thus, for his endeavors, he was able to tap into a source of funds already officially earmarked for rehabilitation.
To continue in his words [63], in practice, Theo "used the plasticity of the central nervous system in early childhood to develop a targeted treatment of children who have innate or early-acquired disturbances or actual damage in order to save them from the fate of a lifelong handicap. In so doing, he is proud that he helped completely deaf children, via their mothers, to learn normal speech when they were offered speech treatment in the first weeks and months of life. This concept was extended worldwide and led to the publication of books for parents on 'The First 365 Days of a Child's Life' [8]." Theo believes that "this is the most important discovery of the newest pediatric research, in which Czech and Slovak researchers like Janos Papousek participated and discovered that the newborn is already a very competent 'learning system'." Indeed, the evaluation of hearing loss in infants and young children requires early identification and assessment of hearing impairment, an endeavor of critical importance to cite John Jacobson and Kara Jacobson [66]: "New technology and techniques have helped make the process more efficient and accurate for pediatricians."
By 1960 at Cold Spring Harbor [67] and again thereafter at the New York Academy of Science [68], Theo had reported that the human child exhibits its ubiquitous and important about 24-hour rhythms with a delay after birth. His data have gained from chronomics from the analysis of time structures, a development comparable to the mapping of genes – genomics – both chronomics and genomics spawned by genetics [14]. Chronomics is a time-structurally qualified physiological genomics, based on time series analyzed for rhythms (as well as, whenever the data density will permit, for chaos, and, whenever time series length will permit, for trends). To Theo's lasting credit, he systematically distanced himself from single sample spotchecks.
Theo Hellbrügge's contributions illustrate a solidly founded now widely distributed conceptual structure resting on a productive life's work available again in his own words [9]. A few graphs and a few numbers (e.g., for rhythms with their periods and other characteristics) can meaningfully in time summarize thousands or millions of data [10,14].
With one of his colleagues [7], we can summarize how Hellbrügge's original evidence has borne many fruits in preventive health care:
• some in ethology as a method to account for the development of children,
• mother-infant-interactions as a decisive requisite of social development, the topic of the last symposium he sponsored in October 2004
• preverbal communication, as a condition for early speech promotion, especially for infants with impaired hearing,
• the plasticity of the infant's brain as a neurobiological basis for early health promotion,
• enriching integration of infant and child as part of a socially intact community,
• preventive medical-check ups aiming at an early diagnosis of abnormality,
• earliest diagnosis of risks as a condition of PREhabilitation – which he called rehabilitation, to gain a financial niche for his actions in existing laws.
Hellbrügge's conference on chronomes [14] showed advanced chronobiologic and chronomic follow-ups on what he had discovered many decades earlier [67,68]. His contributions encouraged further investigations. Furthermore a cosmic view, visualized already by Bernhard de Rudder [69], another chronobiologically active predecessor of Theo in pediatrics in Munich, is being added to child development in health and disease [14]. Preventive pediatrics can gain in Theo's footsteps a thoroughly grounded, scientific, biological yet also transdisciplinary basis. Theo's social pediatrics focuses upon the obviously handicapped child. A follow-up could focus on risks that are not obvious but may be detected chronomically as alterations of blood pressure and heart rate series. These alterations represent greater dangers than hypertension itself [65,70-72]. It is the pediatrician's opportunity to nip them in the bud in Theo's footsteps.
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Arbogast H Sothern R Halberg F Macroscopic differentiation by plasma LH of Stein-Leventhal syndrome (S) from clinical health (H) quantified by cosinor Chronobiologia 1985 12 71
Beyzavi K März W Sothern RB Halberg F Circadiseptan prominence in systolic (S) & circaseptan in diastolic (D) blood pressure (BP) & heart rate (HR) of a 20-year-old woman Chronobiologia 1985 12 235
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Halberg F Halberg E Cornélissen G März W Carandente F Automatic chronobiologic blood pressure self-monitoring in hospital, home and workplace Ric Sci Ed Perm Suppl 1985 49 9 12
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März W Scarpelli PT Livi R Romano S Cagnoni M Cornélissen G Halberg F Chronobiologic reference norms for time-specified measurements and circadian characteristics of systolic and diastolic blood pressure in 9-year-olds Abstract, 2nd Eur Mtg on Hypertension, June 9-12, 1985 Ric Sci Ed Perm Suppl 1985 49 #340
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Baranowska B Lazicka-Frelek M Migdalska B Zgliczynski S Zumoff B Rosenfeld RS Cornélissen G Arbogast B Eckert E Halberg F Halberg F, Reale L, Tarquini B Circadian timing of serum cortisol in patients with anorexia nervosa Proc 2nd Int Conf Medico-Social Aspects of Chronobiology, Florence 1986 Rome: Istituto Italiano di Medicina Sociale 535 555 Oct 2, 1984
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Halberg F Cornélissen G Schwartzkopff O Hardeland R Ulmer W Messung und chronobiologische Auswertung der Variabilitäten von Blutdruck und Herzfrequenz zur Prophylaxe schwerwiegender Krankheiten Proc Leibniz Soz 2003 54 127 156
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| 15748294 | PMC555851 | CC BY | 2021-01-04 16:39:13 | no | J Circadian Rhythms. 2005 Mar 5; 3:2 | utf-8 | J Circadian Rhythms | 2,005 | 10.1186/1740-3391-3-2 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-2-181575751210.1186/1742-4690-2-18ReviewBioAfrica's HIV-1 Proteomics Resource: Combining protein data with bioinformatics tools Doherty Ryan S [email protected] Oliveira Tulio [email protected] Chris [email protected] Sivapragashini [email protected] Michelle [email protected] Sharon [email protected] Molecular Virology and Bioinformatics Unit, Africa Centre for Health and Population Studies, Doris Duke Medical Research Institute, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa2 Biomedical Informatics Research Division, South African Medical Research Council, Cape Town, South Africa3 Department of Medical Virology, University of Pretoria, Pretoria, South Africa2005 9 3 2005 2 18 18 30 9 2004 9 3 2005 Copyright © 2005 Doherty et al; licensee BioMed Central Ltd.2005Doherty 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.
Most Internet online resources for investigating HIV biology contain either bioinformatics tools, protein information or sequence data. The objective of this study was to develop a comprehensive online proteomics resource that integrates bioinformatics with the latest information on HIV-1 protein structure, gene expression, post-transcriptional/post-translational modification, functional activity, and protein-macromolecule interactions. The BioAfrica HIV-1 Proteomics Resource is a website that contains detailed information about the HIV-1 proteome and protease cleavage sites, as well as data-mining tools that can be used to manipulate and query protein sequence data, a BLAST tool for initiating structural analyses of HIV-1 proteins, and a proteomics tools directory. The Proteome section contains extensive data on each of 19 HIV-1 proteins, including their functional properties, a sample analysis of HIV-1HXB2, structural models and links to other online resources. The HIV-1 Protease Cleavage Sites section provides information on the position, subtype variation and genetic evolution of Gag, Gag-Pol and Nef cleavage sites. The HIV-1 Protein Data-mining Tool includes a set of 27 group M (subtypes A through K) reference sequences that can be used to assess the influence of genetic variation on immunological and functional domains of the protein. The BLAST Structure Tool identifies proteins with similar, experimentally determined topologies, and the Tools Directory provides a categorized list of websites and relevant software programs. This combined database and software repository is designed to facilitate the capture, retrieval and analysis of HIV-1 protein data, and to convert it into clinically useful information relating to the pathogenesis, transmission and therapeutic response of different HIV-1 variants. The HIV-1 Proteomics Resource is readily accessible through the BioAfrica website at:
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Background
Although the HIV-1 genome contains only 9 genes, it is capable of generating more than 19 gene products. These products can be divided into three major categories: structural and enzymatic (Gag, Pol, Env); immediate-early regulatory (Tat, Rev and Nef), and late regulatory (Vif, Vpu, Vpr) proteins. Tat, Rev and Nef are synthesized from small multiply-spliced mRNAs; Env, Vif, Vpu and Vpr are generated from singly-spliced mRNAs, the Gag and Gag-Pol precursor polyproteins are synthesized from full-length mRNA. The matrix (p17), capsid (p24) and nucleocapsid (p7) proteins are produced by protease cleavage of Gag and Gag-Pol, a fusion protein derived by ribosomal frame-shifting. Cleavage of Nef generates two different protein isoforms; one myristylated, the other non-myristylated. The viral enzymes (protease, reverse transcriptase, RNase H and integrase) are formed by protease cleavage of Gag-Pol. Alternative splicing, together with co-translational and post-translational modification, leads to additional protein variability [1].
Phylogenetic analysis, on its own, provides little information about the conformational, immunological and functional properties of HIV-1 proteins, but instead, focuses on the evolution and historical significance of sequence variants. To understand the clinical significance of genetic variation, sequence analysis needs to be combined with methods that assess change in the structural and biological properties of HIV-1 proteins. At present, information and tools for the systematic analysis of HIV-1 proteins are limited, and are scattered across a wide-range of online resources [2,3]. To facilitate studies of the biological consequences of genetic variation, we have developed an integrated, user-friendly proteomics resource that integrates common approaches to HIV-1 protein analysis (Figure 1). We are currently using this resource to better understand the structure-function relationships underlying the emergence of antiretroviral drug resistance, and to examine the process of immune escape from cytotoxic T-lymphocytes (CTLs).
Figure 1 Site map of BioAfrica's HIV-1 Proteomics Resource, showing the separation of Beginner's and the Advanced area of the website, along with all major subject headings.
We have categorized the Proteomics Resource into the following main subject headings (Figure 2 &3):
Figure 2 Schematic representation of BioAfrica's HIV-1 Proteomics Resource, showing its five major components: the HIV-1 Proteome (General Overview, Domains/Folds/Motifs, Genomic Location, Protein-Macromolecule Interactions, Primary and Secondary Database Entries, and References and Recommended Readings), the HIV-1 Protease Cleavage Sites section, the HIV-1 Protein Data-mining Tool, the HIV-1 BLAST Structure Tool, and the Proteomics Tools Directory (for Beginners and Advanced investigators).
Figure 3 The central webpage of BioAfrica's HIV Proteomics Resource
1. HIV Proteome – Information about structure and sequence, as well as references and tutorials, for each of the HIV-1 proteins (Figure 4);
Figure 4 The central webpage of the HIV-1 Proteome section of the BioAfrica website .
2. HIV-1 Cleavage Sites – Information about the position and sequence of HIV-1 Gag, Pol and Nef cleavage sites (Figure 5);
Figure 5 The HIV-1 Protease Cleavage Sites section of the BioAfrica website .
3. HIV Protein Data Mining Tool – Application for detecting the characteristics of HIV-1 M group isolate (subtype A to K) proteins using information available in public databases and tools (Figure 6);
Figure 6 The central webpage of the HIV-1 Protein Data Mining Tool section of the BioAfrica website, where a specific HIV-1 genomic region is selected to be analyzed .
4. HIV Structure BLAST – Similarity search for analyzing HIV protein sequences with corresponding structural data (Figure 7);
Figure 7 The BLAST HIV-1 protein structure similarity search is an online tool that searches for all protein structure data within the PDB that have an amino acid sequence similar to the query sequence .
5. Proteomics Online Tools – Directory of data resources and tools available for both protein sequence and protein structure analyses of HIV (Figure 8 &9).
Figure 8 The introductory listing of proteomics resources for HIV research chosen to give a general overview of online tools and databases relevant for the analysis of HIV protein data .
Figure 9 The advanced listing of online tools and databases relevant for the analysis of HIV protein data .
The proteome link
In the HIV-1 Proteome section, each of the 19 HIV-1 proteins has a webpage that is divided into six parts: "general overview", "genomic location", "domains/folds/motifs", "protein-macromolecule interactions", "primary and secondary database entries", and "references and recommended readings" (Figure 4). The overview includes a description of the protein, a list of known isoforms, a representative tertiary structure animated image (GIF format) of the protein and its co-ordinates (PDB format), a link to chime tutorials, if available, and information about cleavage sites, localization, and functional activity. The genomic location section provides information on the location of the sequence relative to the reference sequence, HIV-1HXB2 [4], sequence data (fasta format), and information about the length, molecular weight and theoretical isoelectric point (pI) of the protein. The domains/folds/motifs section contains information about functional domains and predicted motifs (glycosylation, myristoylation, amidation, phosphorylation and cell attachment sites) of HIV-1HXB2 [4], and provides structural predictions (secondary structure, transmembrane regions, low complexity regions, and coiled-coil regions). The section on protein-macromolecule interactions includes information on protein complexes, protein-protein/DNA/RNA interactions, signal-transduction pathways, and potential interactions with other pathogens. The section on primary and secondary databases contains a list of database entries that are needed to retrieve information on protein structure, nucleotide/amino acid sequence data, protein sequence annotation, proteins with similar sequence and structure (such as Los Alamos National Laboratories HIV Sequence Database and the RCSB Protein Data Bank), as well as information on post-translational modification and protein-protein interactions. A list of key reviews and publications, used in the development of the BioAfrica HIV-1 Proteomics Resource, is provided in the references and recommended readings section. As an example, the proteome webpage for Tat, describes how this protein up-regulates HIV-1 gene expression by interacting with the long-terminal repeat (LTR) of HIV-1, promoting the elongation phase of viral transcription, allowing full-length HIV-1 mRNA transcripts to be produced [5,6] (Figure 10). The webpage also gives information on the structural organization of tat gene. The mRNA is derived from spliced exons encoded in two different open reading frames. In HIV-1HXB2, these reading frames are separated by a distance of 2334 nucleotides. Some HIV-1 isolates, including HIV-1HXB2, contain an artifact of laboratory strains consisting of a premature stop codon at position 8424 of exon 2. The presence of this stop codon leads to the synthesis of a truncated form of Tat that is 86, rather than 101 amino acids in length. The protein has two different isoforms – one translated from early-stage multiply spliced mRNA (p14); the other from singly-spliced mRNA (p16) [7]. Important functional domains include the acidic, amphipathic region (1-MEPVDPRLEPWKHPGSQPKTA-21; the hydrophobic residues are highlighted in bold, and polar residues are italicized) at the N-terminus of the protein; the cysteine-rich disulphide bond region (22-CTNCYCKKCCFHCQVC-37); the core, basic and glutamine-rich region (49-RKKRRQRRRAHQNSQTHQASLSKQ-72) that is important for nuclear localization and TAR-binding activity, and the RGD cell-attachment site that binds to cellular integrins. In addition to being expressed in HIV-1-infected cells, Tat is also released into the extracellular fluid where it acts as a growth factor for the development of Kaposi's Sarcoma. Additional information about Tat and its protein-protein interactions can be found on the proteome page of the BioAfrica website located at .
Figure 10 A general overview of the HIV-1 Proteome section of the BioAfrica website, as exemplified by the Tat web page .
Protease cleavage sites link
Post-translational cleavage of the Gag, Gag-Pol and Nef precursor proteins occurs at the cell membrane during virion packaging, and is essential to the production of infectious viral particles. Drugs that inhibit this process, the protease inhibitors (PIs), are the most potent antiretroviral agents currently available. Thus it is important to collect information, not only on the sequence of protease enzymes from different HIV-1 subtypes, but also on the natural polymorphisms and resistance mutations that may effect their catalytic activities, drug responsiveness, substrate specificities, and cleavage site characteristics. Studies have shown that resistance mutations in the protease of subtype B are associated with impaired proteolytic processing and decreased enzymatic activity, and that compensatory mutations at Gag and Gag-Pol cleavage sites can partially overcome these defects [8]. These findings suggest that variation at protease cleavage sites may play an important role, not only in regulation of the viral life cycle, but also in disease progression and response to therapy.
The cleavage site section of the BioAfrica webpage is the direct extension of a recent publication in the Journal of Virology describing the location and variability of protease cleavage sites [9] (Figure 5). Together, these two resources provide information on the structure, amino acid composition, genetic variation and evolutionary history of protease cleavage sites, and on the natural selection pressures exerted on these sites. The section also serves as a baseline for understanding the impact of natural polymorphisms and resistance mutations on the catalytic efficiency of the protease enzyme, and on its ability to recognize and cleave individual Gag, Gag-Pol and Nef substrates. Such studies are important for understanding the mechanisms underlying the emergence of PI-induced drug resistance, and for designing alternative, optimized therapies.
Protein data-mining tools link
The HIV-1 Protein Data-Mining Tool contains twelve sequence analysis techniques for assessing protein variability among different strains of HIV-1 (Figure 6). These tools allow the user to manipulate, analyze and compare published [9-12] and newly-acquired data in a user-friendly, hands-on manner. The analysis is initiated by selecting a particular subset of HIV-1 proteins, either from the user's database, or from the representative dataset of group M viruses (subtypes A through K). Using this dataset, the investigator can then perform a variety of protein-specific analyses. With a single click of the mouse, users can download the amino acid sequence in fasta format; obtain sequence annotations from SwissProt [13] or GenBank [14]; identify functional motifs using BLOCKS [15], PROSITE [16] or ProDom [17]; perform similarity searches using the BLAST program available at Genbank [18], conduct structural comparisons using the BioAfrica BLAST Structure program; determine amino acid composition, predict hydrophobicity and tertiary structure using the Swiss-Model homology modelling server [19], and obtain a list of potential protein-macromolecule interactions from the Database of Interacting Proteins (DIP) [20]. A representative analysis of HIV-1 Tat is shown in Additional file 1. The selected dataset, consisting of eight reference strains – four subtype B (HXB2-1983-France, RF-1983-US, JRFL-1986-US, WEAU160-1990-US) and four subtype C (92BR025-1992-Brazil, 96BW0502-1996-Botswana, TV002c12-1998-SouthAfrica, TV001c8.5-1998-SouthAfrica) isolates – were analyzed using PROSITE [16]. As shown in Additional file 1, all eight isolates had identical amidation, cysteine-rich and myristylation motifs at amino acid codons 47–50, 22–37 and 44–49, respectively. Three (75%) of the B isolates contained a second myristylation site at codons 42–47, as did three (75%) subtype C viruses. One (25%) of the C viruses carried an extra GNptGS myristylation motif at position 79–84. In addition, all four (100%) C isolates contained a novel myristylation motif, GSeeSK, at amino acid position 83–88, that was not present in four B viruses selected for study. However, the most striking difference between the two subtypes was the increased number of phosphorylation motifs in subtype C relative to B viruses. This increase, which occurs in cAMP/cGMP-dependent kinase, protein kinase C (PKC) and casein kinase II (CKII) phosphorylation sites, has been reported previously [21], but the significance of these findings remain to be established. The analysis also highlighted the atypical nature of the HIV-1HXB2 isolate, which, in addition to a premature stop codon, contained no cAMP/cGMP, PKC or CKII phosphorylation sites.
The blast structure tool link
The HIV-1 BLAST Structure Tool facilitates the analysis of HIV-1 protein structure by allowing for rapid retrieval of archived structural data stored in the public databases (Figure 7). Users may input any HIV-1 amino acid sequence and obtain a list of similar HIV protein sequences for which structural data have been experimentally determined and deposited into the Protein Data Bank (PDB) [22]. After downloading the data from the PDB, subsequent structural analyses can be performed using the software programs and web-servers listed in the Proteomics Tools Directory. For example, a query using an amino acid sequence of HIV-1 Integrase protein from NCBI (gi|15553624|gb|AAL01959.1) results in a list of 54 structural models (ie. PDB_ID|1K6Y) within the PDB. Each of these structural models can be retrieved from the PDB, and the most appropriate structural model could be used for generating a homology model using the query protein sequence.
The proteomics tools directory link
The HIV-1 Proteomics Tools Directory is divided into two web pages. The initial webpage is a concise compilation of some of the most commonly used protein-specific Internet resources (Figure 8). This "beginners" page displays a short list of websites for each of the following twelve categories: "protein databases", "specialized viral-protein databases", "motif and transcription factor databases", "protein sequence similarity searches", "protein sequence alignment", "protein sequence prediction tools", "protein sequence analysis", "protein sequence manipulation", "protein structure analysis", "molecular modelling tools", "tutorials", and "downloads". In addition, the Proteomics Tools Directory has an advanced web page for users who are looking for alternative, or more specialized, protein analysis tools (Figure 9). The advanced webpage displays a list of more than 200 links to different websites and web-servers. These data sources contain a variety of information ranging from specialized protein sequence databases to software programs capable of performing rigid body protein-protein molecular docking simulations.
Conclusion
The impending rollout of antiretroviral therapy to millions of HIV-1-infected people in sub-Saharan Africa provides a unique opportunity to monitor the efficacy of non-B treatment programs from their very inception, and to obtain critical new information for the optimization of treatment strategies that are safe, affordable and appropriate for the developing world. An integral part of this massive humanitarian effort will be the collection of large amounts of clinical and laboratory data, including genetic information on viral subtype and resistance mutations, as well as routine CD4+ T-cell counts and viral load measurements. The mere collection of this data, however, does not ensure that it will be used to its maximum potential. To achieve full benefit from this explosive source of new information, the data will need to be appropriately collated, stored, analyzed and interpreted.
The rapidly emerging field of Bioinformatics has the capacity to greatly enhance treatment (and vaccine) efforts by serving as a bridge between Medical Informatics and Experimental Science. By correlating genetic variation and potential changes in protein structure with clinical risk factors, disease presentation, and differential response to treatment and vaccine candidates, it may be possible to obtain valuable new insights that can be used to support and guide rationale decision-making, both at the clinical and public health levels. The HIV-1 Proteomics Resource, described in this report, is an initial first step in the development of improved methods for extracting and analyzing genomics data, converting it into biologically useful information related to the structure, function and physiology of HIV-1 proteins, and for assessing the role these proteins play in disease progression and response to therapy. The Resource, developed at the Molecular Virology and Bioinformatics Unit of the Africa Centre of Health and Population Studies, is a centralized user-friendly database that is easily accessed through the BioAfrica website at [23].
List of abbreviations used
AA – Amino Acid
BLAST – Basic Local Alignment Search Tool
CKII – casein kinase II
CTLs – cytotoxic T-lymphocytes
DIP – Database of Interacting Proteins
DNA – deoxyribonucleic acid
Env – envelope glycoprotein
Gag – group-specific antigen polyprotein
GIF – Graphics Interchange Format
HIV – Human Immunodeficiency Virus
HIV-1 – Human Immunodeficiency Virus Type-1
HTTP – Hypertext Transfer Protocol
LTR – long-terminal repeat
mRNA – messenger RNA
NCBI – National Center for Biotechnology Information
Nef – negative factor
PDB – Protein Data Bank
pI – isoelectric point
PIs – protease inhibitors
PKC – protein kinase C
Pol – polymerase polyprotein
Rev – ART/TRS anti-repression transactivator protein
RNA – ribonucleic acid
RNase H – ribonuclease H
Tat – transactivating regulatory protein
Vif – virion infectivity factor
Vpr – viral protein R
Vpu – viral protein U
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
RSD created and maintains BioAfrica's HIV proteomics resource, HIV proteome section, proteomics tools directory, HIV-1 protein data-mining tool and HIV structure BLAST tool; performed protein sequence and structural model analyses; and wrote the manuscript.
TDO conceived and maintains the BioAfrica website, and continues to oversee its rapid expansion; created the cleavage sites section; and participated in the design and implementation of the HIV proteomics resource.
CS participated in the design of the HIV proteomics resource, with an emphasis on the proteomics tools directory.
SD participated in the design and creation of the HIV proteome section, with an emphasis on the HIV-1 Tat protein.
MG participated in the design of the HIV proteomics resource, with an emphasis on the HIV proteome section.
SC supervised the project, and participated in the design and implementation of the HIV proteomics resource.
All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
A table containing a comparative summary of potential functional motifs (cysteine-rich region, myristoylated Asparagine, amidation, cAMP- and cGMP- dependent kinase phosphorylation, Protein Kinase C phosphorylation, and Casein Kinase II phosphorylation) in the HIV-1 Tat proteins of subtypes B and C, as identified using PROSITE.
Click here for file
Acknowledgements
Development of the Bioafrica HIV-1 Proteomics Resource was supported by a program grant from the Wellcome Trust U.K. (#061238). The website is hosted by the South African Medical Research Council (MRC).
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| 15757512 | PMC555852 | CC BY | 2021-01-04 16:36:40 | no | Retrovirology. 2005 Mar 9; 2:18 | utf-8 | Retrovirology | 2,005 | 10.1186/1742-4690-2-18 | oa_comm |
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Front ZoolFrontiers in Zoology1742-9994BioMed Central London 1742-9994-2-51577178310.1186/1742-9994-2-5ResearchComparative performance of the 16S rRNA gene in DNA barcoding of amphibians Vences Miguel [email protected] Meike [email protected] der Meijden Arie [email protected] Ylenia [email protected] David R [email protected] Institute for Biodiversity and Ecosystem Dynamics, Zoological Museum, University of Amsterdam, Mauritskade 61, 1092 AD Amsterdam, The Netherlands2 Institute for Genetics, Evolutionary Genetics, University of Cologne, Weyertal 121, 50931 Köln, Germany3 Department of Biology (Evolutionary Biology), University of Konstanz, 78457 Konstanz, Germany4 Department of Integrative Biology, Museum of Vertebrate Zoology, 3101 Valley Life Sciences Bldg., University of California, Berkeley, CA 94720-3160, USA2005 16 3 2005 2 5 5 25 10 2004 16 3 2005 Copyright © 2005 Vences et al; licensee BioMed Central Ltd.2005Vences 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
Identifying species of organisms by short sequences of DNA has been in the center of ongoing discussions under the terms DNA barcoding or DNA taxonomy. A C-terminal fragment of the mitochondrial gene for cytochrome oxidase subunit I (COI) has been proposed as universal marker for this purpose among animals.
Results
Herein we present experimental evidence that the mitochondrial 16S rRNA gene fulfills the requirements for a universal DNA barcoding marker in amphibians. In terms of universality of priming sites and identification of major vertebrate clades the studied 16S fragment is superior to COI. Amplification success was 100% for 16S in a subset of fresh and well-preserved samples of Madagascan frogs, while various combination of COI primers had lower success rates.COI priming sites showed high variability among amphibians both at the level of groups and closely related species, whereas 16S priming sites were highly conserved among vertebrates. Interspecific pairwise 16S divergences in a test group of Madagascan frogs were at a level suitable for assignment of larval stages to species (1–17%), with low degrees of pairwise haplotype divergence within populations (0–1%).
Conclusion
We strongly advocate the use of 16S rRNA as standard DNA barcoding marker for vertebrates to complement COI, especially if samples a priori could belong to various phylogenetically distant taxa and false negatives would constitute a major problem.
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Background
The use of short DNA sequences for the standardized identification of organisms has recently gained attention under the terms DNA barcoding or DNA taxonomy [1-3]. Among the promising applications of this method are the assignments of unknown life-history stages to adult organisms [4,5], the large-scale identification of organisms in ecological or genomic studies [1,6] and, most controversially, explorative studies to discover potentially undescribed "candidate" species [4,7,8]. Although it is not a fundamentally new technique [9], DNA barcoding is promising because technical progress has made its large-scale, automated application feasible [3,6] which may accelerate taxonomic progress [10].
Although not necessarily under the specific concepts of DNA barcoding and DNA taxonomy, the diagnosis and even definition of taxa by their DNA sequences are realities in many fields and organism groups, such as prokaryotes, fungi, and soil invertebrates [1,6]. To use this approach on a large and formalized scale, consensus of the scientific community is essential with respect to the most suitable genes that allow robust and repeatable amplification and sequencing, and that provide unequivocal resolution to identify a broad spectrum of organisms. While D. Tautz and co-workers [3] proposed the nuclear ribosomal RNA genes for this purpose, P. D. N. Hebert and colleagues have strongly argued in favor of a 5' fragment of the mitochondrial gene for cytochrome oxidase, subunit I (COI or COXI) [2,11]. This gene fragment has been shown to provide a sufficient resolution and robustness in some groups of organisms, such as arthropods and, more recently, birds [2,4,7,11].
A genetic marker suitable for DNA barcoding needs to meet a number of criteria [2]. First, in the study group, it needs to be sufficiently variable to discriminate among most species, but sufficiently conserved to be less variable within than between species. Second, priming sites need to be sufficiently conserved to permit a reliable amplification without the risk of false negatives when the goal is the analysis of pooled samples, e.g. when the total of invertebrates from a soil sample is to be studied without separating individuals, or of environmental DNA such as subfossil DNA remains from the soil [12,13]. Third, the gene should convey sufficient phylogenetic information to assign species to major taxa using simple phenetic approaches. Fourth, its amplification and sequencing should be as robust as possible, also under variable lab conditions and protocols. Fifth, sequence alignment should be possible also among distantly related taxa.
Here we explore the performance of a fragment of the 16S ribosomal RNA gene (16S) in DNA barcoding of amphibians. As a contribution to the discussion about suitable standard markers we provide experimental data on comparative amplification success of 16S and COI in amphibians, empirical data on conservedness of priming sites, and an example from the 16S-based identification of amphibian larval stages.
Results
Amplification experiments
We performed independent amplification experiments with one set of 16S primers and three published sets of COI primers [2,7] focusing on representatives of different frog, salamander and caecilian genera. The experiments were concordant in yielding more reliable and universal amplifications for 16S than COI. In a set of fresh and well-preserved samples from relatively closely related mantellid frogs from Madagascar (Table 1, Additional file 1), the 16S amplification success was complete, whereas the three sets of COI primers yielded success rates of only 50–70%. Considering all three primer combinations, there were two species of frogs (10%) that did not amplify for COI at all (Boophis septentrionalis and B. tephraeomystax).
Priming sites
The variability of priming sites was surveyed using nine complete amphibian mitochondrial sequences from Genbank (Fig. 1), and 59 mt genomes of fishes, reptiles, birds and mammals (Fig. 2). A high variability was encountered for COI. The sequences of some species were largely consistent with the primers: Xenopus had two mutations only at each of the priming regions. However, other sequences were strongly different, with up to seven mutations, all at third codon positions. No particular pattern was recognizable for any major group that would facilitate designing COI primers specific for frogs, salamanders or caecilians. Interestingly the variability among the amphibian sequences available was as large as or larger than among the complete set of vertebrates at many nucleotide positions of COI priming sites (Fig. 2), indicating a possible higher than average variability of this gene in amphibians.
Figure 1 Variability of priming sites in amphibians. Variability of priming sites for 16S rRNA and COI in amphibians.
Figure 2 Variation of priming sites vertebrates. Variation in priming sites of 16S rRNA (a, F-primer; b, R-primer) and COI (c, Bird-F1, LCO1490; d, HCO2198; e, Bird-R1, Bird-R2) fragments studied herein. Values are nucleotide variability as calculated using the DNAsp program. Grey bars show the values for nine amphibians, black bars the values for a set of 59 other vertebrates (see Materials and Methods, and Figs. 3-4).
In contrast, the 16S priming sites were remarkably constant both among amphibians and among other vertebrates (Fig. 1, 2). A wider survey of priming sites, i.e., the alternative reverse priming sites used in arthropod and bird studies [2,7], confirmed the high variability of COI in amphibians, and in vertebrates in general (Fig. 2). A screening of the first 800 bp of the C-terminal part of the gene in nine amphibians of which complete mitochondrial genes were available did not reveal a single fragment of 20 bp where all nine species would agree in 80% or more of their nucleotides.
Recovery of major groups
The phenetic neighbor-joining analysis using the 16S fragment produced a tree that contained eight major groupings that conform to or are congruent with the current classification and phylogeny (Fig. 3): cartilaginous fishes, salamanders, frogs, turtles, eutherian mammals, mammals, squamates, birds. Of these, the COI tree (Fig. 4) recovered only the lineages of cartilaginuous fishes and birds. The COI analysis did not recover any additional major lineage.
Figure 3 16S Neighbor-joining tree of selected vertebrate taxa. Neighbor-joining tree of selected vertebrate taxa based on the fragment of the 16SrRNA gene amplified by primers 16SaL and 16SbH. Numbers in black circles indicate major clades that were recovered by this analysis: (1) cartilaginous fishes, (2) salamanders, (3) frogs, (4) turtles, (5) eutherian mammals, (6) mammals, (7) squamates, (8) birds.
Figure 4 COI Neighbor-joining tree of selected vertebrate taxa. Neighbor-joining tree of selected vertebrate taxa based on the fragment of the COI gene amplified by primers LCO1490 and HCO2198. Numbers in black circles indicate major clades that were recovered by this analysis, corresponding to the numbering in Supp. material D. Only two of the clades recovered by the 16S analysis are also monophyletic here: (1) cartilaginous fishes, (8) birds.
16S rDNA barcoding of tadpoles
From an ongoing project involving the large-scale identification of tadpoles of Madagascan frogs [5] we here provide data from larval and adult frog species from two sites of high anuran diversity in eastern Madagascar, Andasibe and Ranomafana. These two localities are separated by a geographical distance of ca. 250 km. The results will be presented in more detail elsewhere.
We selected target species for which morphological and bioacoustic uniformity suggests that populations from Ranomafana and Andasibe are conspecific. All these species belong to the family Mantellidae. We then analysed haplotypes within and between these populations. In addition we assessed divergences among sibling species of mantellid frogs (Tables 2-4, Additional file 1). These were defined as morphologically similar species that are phylogenetically sister to each other, or are in well-defined but phylogenetically poorly resolved clades of 3–5 species. Results revealed a low intrapopulational variation of 0–3% uncorrected pairwise distances in the 16S gene, a surprisingly large differentiation among conspecific populations of 0–5.1%, and a wide range of differentiation among species, ranging from 1–16.5% with a mode at 7–9% (Fig. 5). The few species separated by low genetic distances were allopatrically distributed. The interspecific divergence was higher in those species pairs in which syntopic occurrence has been recorded or is likely (2.7–16.5% divergence, mean 8.5%) as compared to those that so far only have been found in allopatry (1.0–12.9%, mean 6.9%).
Figure 5 16S inter- and intraspecific genetic variation in Malagasy frogs. Variation in the fragment of the 16S rRNA gene (ca. 550 bp) studied herein, (a) within populations, (b) among conspecific populations and (c) among sibling species of frogs in the family Mantellidae from Madagascar. The values are uncorrected p-distances from pairwise comparisons in the respective category. Only one (mean) value per species was used in (a) and (b), even when multiple individuals were compared. Grey bars in (a) and (b) show the mean values from all possible individual comparisons within a species, black bars are the maximum divergences encountered between two individual sequences.
Phylogenetic and phenetic analyses (Bayesian and Neighbor-joining) of these and many additional sequences (to be published elsewhere) mostly grouped sequences of those specimens from Ranomafana and Andasibe that a priori had been considered to be conspecific (exceptions were Mantidactylus boulengeri, not considered in the intraspecific comparisons here, and M. blommersae). This indicates that cases in which haplotypes of a species are more similar to those of another species than to those of other conspecific individuals or populations, are rare in these frogs. Sharing of identical haplotypes among individuals belonging to different species, in our dataset, was limited to three closely related species pairs of low genetic divergences: Boophis doulioti and B. tephraeomystax, B. goudoti and B. cf.periegetes, Mantella aurantiaca and M. crocea. Depending on the taxonomic scheme employed, our complete data set contains 200–300 species of Madagascan frogs. Hence, haplotype sharing was demonstrated in 2–3% of the total number of species only.
To explore the reliability of tadpole identification using the 16S gene we used local BLAST searches against a database containing about 1000 sequences of adult frogs from a wide sampling all over Madagascar. 138 tadpoles from the Andasibe region and 84 tadpoles from the Ranomafana region were compared with adult sequences in the database. In 77% of the cases the highest scores were those from comparisons to adults from the same site as the tadpoles. In most of the unsuccessful comparisons, adult sequences of the corresponding species were not available from the tadpole site (21%). In only 5 cases (2%) conspecific adults collected from a different site than the tadpoles yielded higher BLAST scores although adult sequences from the same site were in the database.
Conclusion
DNA barcoding in amphibians
DNA barcoding has great appeal as a universally applicable tool for identification of species and variants of organisms, possibly even in automated handheld devices [14]. However, doubtless severe restrictions exist to its universal applicability [9]. Some taxa, e.g. cichlid fishes of Lake Victoria, have radiated so rapidly that the speciation events have not left any traces in their mitochondrial genomes [15]; identifying these species genetically will only be possible through the examination of multiple nuclear markers, as it has been done to assess their phylogeny [16]. Some snails are characterized by a high intraspecific haplotype diversity, which could disable attempts to identify and distinguish among species using such markers [17].
Haplotype sharing due to incomplete lineage sorting or introgression is also known in amphibians [18] although it was not common in mantellid frogs in our data set. However, a number of species showed haplotype sharing with other species, or non-monophyletic haplotypes, warranting a more extensive discussion. In Mantidactylus boulengeri, specimens from Andasibe and Ranomafana have similar advertisement calls and (at least superficially) similar morphologies, but their 16S haplotypes were not a monophyletic group (unpublished data). This species belongs to a group of direct-developing frogs that, like the Neotropical Eleutherodactylus [19] may be characterized by a high rate of cryptic speciation. Further data are necessary to decide whether the populations from Ranomafana and Andasibe are indeed conspecific. In contrast, there is little doubt that the populations of Mantidactylus blommersae from these two sites are conspecific, yet the Ranomafana haplotypes are closer to those of the clearly distinct species M. domerguei. The species pairs where haplotype sharing has been observed (see Results) all appear to be allopatrically to parapatrically distributed and show no or only low differences in advertisement calls, indicating that occasional hybridization along contact zones may be possible [e.g., [20]]. Haplotypes of each of these species pairs always formed highly supported clusters or clades, and had divergences below 3%, indicating that haplotype sharing in mantellids may only constitute a problem when individuals are to be assigned to such closely related sister species.
Although our data show that DNA barcoding in mantellids is a largely valid approach when both reference and test sequences come from the same site, the occurrence of non-monophyletic and highly divergent haplotypes within species characterizes these and other amphibians as a challenging group for this technique. Certainly, DNA barcoding is unable to provide a fully reliable species identification in amphibians, especially if reference sequences do not cover the entire genetic variability and geographic distribution of a species. However, the same is true for any other morphological or bioacoustic identification method. Case studies are needed to estimate more precisely the margin of error of molecular identification of amphibian species. For many approaches, such as the molecular survey of the trade in frog legs for human consumption [21], the error margins might be acceptable. In contrast, the broad overlap of intraspecific and interspecific divergences (Fig. 5) cautions against simplistic diagnoses of presumably new amphibian species by DNA divergences alone. A large proportion of biological and evolutionary species will be missed by inventories that characterize candidate species by DNA divergences above a previously defined threshold.
Comparative performance of DNA barcoding markers in amphibians
Phenomena of haplotype sharing or non-monophyletic conspecific haplotypes will affect any DNA barcoding approach that uses mitochondrial genes, and are also to be expected with nuclear genes [e.g., [22]]. Nevertheless, some genes certainly outperform others in terms of discriminatory power and universal applicability, and these characteristics may also vary among organism groups. The mitochondria of plants are characterized by very different evolutionary patterns than those of animals, including frequent translocation of genetic material into and from the nucleus [23], which limits their use for DNA barcoding purposes. Nuclear ribosomal DNA (18S and 28S), proposed as standard marker [3], has a high potential in invertebrate DNA barcoding but its high-throughput amplification encounters difficulties in vertebrates.
As a consequence, despite the need of consensus on markers for universal applicability of DNA barcoding, the use of different genes in different groups of organisms seems reasonable. It has been hypothesized that universal COI primers may enable amplification of a 5' terminal fragment from representatives of most animal phyla due to their robustness [2]. The success in DNA barcoding of lepidopterans and birds suggests that this gene fragment can indeed be used as a standard for many higher animal taxa [2,4,7].
In our experiments we compared 16S primers commonly used in amphibians to COI primers that had been developed for other vertebrates [7] or invertebrates [2]. It may well be possible, with some effort, to design primers that are more successful and consistent in amplifying COI from amphibians. However, our results from mantellid frogs (Table 1, Additonal file 1) indicate a very good amplification success of the primers for some species, but failure for other, related species. This and our results on variability of priming sites predict enormous difficulties in designing one pair of primers that will reliably amplify this gene fragment in all vertebrates, all amphibians, or even all representatives of any amphibian order. A set of one forward and three reverse COI primers have been successfully used to amplify and sequence a large number of bird species [7], but birds are a much younger clade than amphibians with a probably lower mitochondrial variability.
A further optimization of COI amplification may also be achieved regarding the PCR protocol. Herein we used standard protocols that optimized annealing temperature only, whereas more complex touchdown protocols have been used for birds and butterflies [4,7]. However, one major requirement for a DNA barcoding marker is its robustness to variable lab conditions. If DNA barcoding is to be applied as a standard in many different labs, primers and genes need to be chosen that amplify reliably under very different conditions and under standard protocols. This clearly applies to 16S, which we have amplified with very different annealing temperatures and PCR conditions in previous exploratory studies (results not shown).
Alignment of 16S sequences is complicated by the prevalence of insertions and deletions, and this gene is less variable than COI [2]. Nevertheless, our results indicate that even using an uncritical automated alignment this gene has a higher potential than COI to assign vertebrate sequences to the level of classes and orders.
The 16S gene is a highly conserved mitochondrial marker but mutations are common in some variable regions, corresponding to loops in the ribosomal RNA structure. In amphibians, where many species are relatively old entities [24], this ensures a sufficient amount of mutations among species. Our results for amphibians, and previous experience with fishes, reptiles and mammals, indicates that 16S is sufficiently variable to unambiguously identify most species.
A further mitochondrial gene that has been widely used in amphibian phylogenetic and phylogeographic studies is cytochrome b. This gene can easily be amplified in salamanders and archaeobatrachian frogs using primers that anneal with adjacent tRNA genes. However, neobatrachian frogs (the wide majority of amphibian species) are characterized by rearrangements of the mitochondrial genome [25,26], and cytochrome b in these species borders directly to the control region. Although cytochrome b primers are available that work in many neobatrachians [27,28], they are not fully reliable. According to our own observations in mantellid frogs, these primers may amplify this gene in one species but fail in other closely related species, presumably because of mutations at the priming sites and similar to the COI primers tested here.
In contrast, the 16S primer pair used here can be considered as truly universal not only for amphibians but even for vertebrates. This is also reflected by the high number of amphibian 16S sequences in Genbank (2620 hits for 16S vs. 483 hits for COI, as of September 2004). Moreover, the 16S and 12S rRNA genes have been selected as standard markers for phylogeny reconstruction in amphibians [29], which will lead to a near-complete global dataset of amphibian 16S sequences in the near future. If the development of handheld devices [14] is envisaged as a realistic goal, then the universality and robustness of primers should be among the most relevant characteristics of a gene for DNA barcoding. When pooled samples containing representatives of various higher vertebrate taxa are to be analysed, the risk of false negatives strongly increases with decreasing universality of primers. As a consequence we recommend the use of 16S as additional standard DNA barcoding marker for vertebrates, especially for but not limited to applications that involve pooled samples.
Methods
To test for universality of primers and cycling conditions, we performed parallel experiments in three different laboratories (Berkeley, Cologne, Konstanz) using the same primers but different biochemical products and thermocyclers, and slightly different protocols.
The selected primers for 16S [30] amplify a fragment of ca. 550 bp (in amphibians) that has been used in many phylogenetic and phylogeographic studies in this and other vertebrate classes: 16SA-L, 5' - CGC CTG TTT ATC AAA AAC AT - 3'; 16SB-H, 5' - CCG GTC TGA ACT CAG ATC ACG T - 3'.
For COI we tested (1) three primers designed for birds [7] that amplify a 749 bp region near the 5'-terminus of this gene: BirdF1, 5' - TTC TCC AAC CAC AAA GAC ATT GGC AC - 3', BirdR1, 5' - ACG TGG GAG ATA ATT CCA AAT CCT G - 3', and BirdR2, 5' - ACT ACA TGT GAG ATG ATT CCG AAT CCA G - 3'; and (2) one pair of primers designed for arthropods [2] that amplify a 658 bp fragment in the same region: LCO1490, 5' - GGT CAA CAA ATC ATA AAG ATA TTG G - 3', and HCO2198, 5'-TAA ACT TCA GGG TGA CCA AAA AAT CA-3'. Sequences of additional primers for COI that had performed well in mammals and fishes were kindly made available by P. D. N. Hebert (personal communication in 2004) and these primers yielded similar results (not shown).
The optimal annealing temperatures for the COI primers were determined using a gradient thermocycler and were found to be 49–50°C; the 16S annealing temperature was 55°C. Successfully amplified fragments were sequenced using various automated sequencers and deposited in Genbank. Accession numbers for the complete data set of adult mantellid sequences used for the assessment of intra- and interspecific divergences (e.g. in Fig. 5) are AY847959–AY848683. Accession numbers of the obtained COI sequences are AY883978–AY883995.
Nucleotide variability was scored using the software DNAsp [31] at COI and 16S priming sites of the following complete mitochondrial genomes of nine amphibians and 59 other vertebrates: Cephalochordata: AF098298, Branchiostoma. Myxiniformes: AJ404477, Myxine. Petromyzontiformes: U11880, Petromyzon. Chondrichthyes: AJ310140, Chimaera; AF106038, Raja; Y16067, Scyliorhinus; Y18134, Squalus. Actinopterygii: AY442347, Amia; AB038556, Anguilla; AB034824, Coregonus; M91245, Crossostoma; AP002944, Gasterosteus; AB047553, Plecoglossus; U62532, Polypterus; U12143, Salmo. Dipnoi: L42813, Protopterus. Coelacanthiformes: U82228, Latimeria. Amphibia, Gymnophiona: AF154051, Typhlonectes. Amphibia, Urodela: AJ584639, Ambystoma; AJ492192, Andrias; AF154053, Mertensiella; AJ419960, Ranodon. Amphibia, Anura: AB127977, Buergeria; NC_005794, Bufo; AY158705; Fejervarya; AB043889, Rana; M10217, Xenopus. Testudines: AF069423, NC_000886, Chelonia; Chrysemys; AF366350, Dogania; AY687385, Pelodiscus; AF039066, Pelomedusa. Squamata: NC_005958, Abronia; AB079613, Cordylus; AB008539, Dinodon; AJ278511, Iguana; AB079597, Leptotyphlops; AB079242, Sceloporus; AB080274, Shinisaurus. Crocodilia: AJ404872, Caiman. Aves: AF363031, Anser; AY074885, Arenaria; AF090337, Aythya; AF380305, Buteo; AB026818, Ciconia; AF362763, Eudyptula; AF090338, Falco; AY235571, Gallus; AY074886, Haematopus; AF090339, Rhea; Y12025, Struthio. Mammalia: X83427, Ornithorhynchus; Y10524, Macropus; AJ304826, Vombatus; AF061340, Artibeus; U96639, Canis; AJ222767, Cavia ; AY075116, Dugong; AB099484, Echinops; Y19184, Lama; AJ224821, Loxodonta; AB042432, Mus; AJ001562, Myoxus; AJ001588, Oryctolagus; AF321050, Pteropus; AB061527, Sorex; AF348159, Tarsius; AF217811, Tupaia; AF303111, Ursus (for species names, see Genbank under the respective accession numbers).
16S sequences of a large sample of Madagascan frogs were used to build a database in Bioedit [32]. Tadpole sequences were compared with this database using local BLAST searches [33] as implemented in Bioedit.
The performance of COI and 16S in assigning taxa to inclusive major clades was tested based on gene fragments homologous to those amplified by the primers used herein (see above), extracted from the complete mitochondrial sequences of 68 vertebrate taxa. Sequences were aligned in Sequence Navigator (Applied Biosystems) by a Clustal algorithm with a gap penalty of 50, a gap extend penalty of 10 and a setting of the ktup parameter at 2. PAUP* [34] was used with the neighbor-joining algorithm and LogDet distances and excluding pairwise comparisons for gapped sites. We chose these simple phenetic methods instead of maximum likelihood or maximum parsimony approaches because they are computationally more demanding and because the aim of DNA barcoding is a robust and fast identification of taxa rather than an accurate determination of their phylogenetic relationships.
Authors' contributions
MV designed the study and drafted the manuscript. MT performed parts of the PCR experiments and carried out the molecular identifications of tadpoles. AVDM and DRV performed part of the PCR experiments. YC provided results on 16S differentiation among Madagascan frogs. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Summary of results of amplification experiments, and detailed data of inter- and intraspecific divergences in mantellid frogs.
Click here for file
Acknowledgements
For comments, technical help and/or discussions we are grateful to Paul D. N. Hebert, Axel Meyer, Dirk Steinke, Diethard Tautz and David B. Wake. We are further indebted to Simone Hoegg, Pablo Orozco and Mario Vargas who provided help in the lab, and to the Madagascan authorities for research permits. The DNA barcoding project on Madagascan tadpoles was supported by a grant of the Volkswagen foundation to MV and to Frank Glaw. DRV was supported by the AmphibiaTree project (NSF grant EF-O334939).
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| 15771783 | PMC555853 | CC BY | 2021-01-04 16:38:34 | no | Front Zool. 2005 Mar 16; 2:5 | utf-8 | Front Zool | 2,005 | 10.1186/1742-9994-2-5 | oa_comm |
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Aust New Zealand Health PolicyAustralia and New Zealand Health Policy1743-8462BioMed Central London 1743-8462-2-51577177310.1186/1743-8462-2-5CommentaryCompetition in health research: the experience of the John Curtin School of Medical Research Whitworth Judith A [email protected] John Curtin School of Medical Research, Australian National University, Canberra ACT 0200, Australia2005 16 3 2005 2 5 5 27 8 2004 16 3 2005 Copyright © 2005 Whitworth; licensee BioMed Central Ltd.2005Whitworth; 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 2002 the Australian National Competitive Grants System was opened to the Institute of Advanced Studies at the Australian National University as part of a commitment to transparency, competitiveness, and collaboration in national research funding.
Results
The block grant to the John Curtin School of Medical Research had progressively eroded over many years. Access to the National Competitive Grants Schemes and associated infrastructure (through an agreed 'buy-in' price of 20% of block funding) has succeeded in its aims and in reversing this progressive effective decrease in funding.
Conclusion
Access to the National Competitive Grant Scheme has allowed the John Curtin School of Medical Research to contribute more broadly to Australia's health and medical research effort through increased collaboration, in a transparent and competitive funding environment.
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Discussion
Australia has developed a unique blend of medical research institutions, from universities, teaching hospitals, independent medical research institutes to a range of smaller centres. Universities with medical schools have been the primary forces in medical research and many of the university teaching hospitals fostered the development of research institutes within their grounds [1]. The spectacular development of medical research in Australia over the last 50 years was reviewed recently in recognition of Australia's Centenary of Federation celebrations [2].
The Australian National University (ANU) was established as a research university [1] with four founding schools. The John Curtin School of Medical Research (JCSMR) was funded for its first 50 years through a one-line Commonwealth Grant to the ANU. Although the School had always sought external funds (its first grant was £1500 to Frank Fenner from the Rural Credits Development Fund in 1952), it was ineligible for National Health and Medical Research Council (NHMRC), Australian Research Council (ARC) and other national competitive grants. The School unquestionably benefited from generous (by local standards) funding in its early years and its science profited accordingly. Fundamental discoveries have included Nobel Prize winning work on the elucidation of mechanisms of transmission of signals in the nervous system (Eccles) and the discovery of the role of the major histocompatibility complex (Doherty and Zinkernagel). Today, the School has a wide range of research in such areas as infection, neurosciences, genomics and molecular bioscience, and has spawned a major national research facility, the Australian Phenomics Facility. Some key discoveries in recent years are summarised in Table 1.
Table 1 Recent Research Achievements, John Curtin School of Medical Research
Year Achievement Researchers(s) Comment Reference
1999 Heparanase cloned Hulett, Freeman and colleagues Inhibiting the enzyme is the basis of cancer treatments based on sulfated polysaccharides Nature Medicine 5:803-809, 1999
2001 First diabetes susceptibility gene identified Slattery and colleagues Serendipitous discovery providing a potential therapeutic target in type 1 diabetes Proc Nat Acad Sci, USA 98:11533-11538, 2001
2002 Amiloride derivatives block ion channel activity and enhancement of virus-like particle budding caused by an HIV-1 protein Gage and colleagues Research on compounds that block viral ion channels raises the possibility of inhibiting viruses that utilize ion channels Eur. Biophys. J. 31:26-35, 2002
2002 Antibody 'tail sequence' identified that has significant implications for immunological memory Martin and Goodnow Finding has wide implications for vaccination, allergy and autoimmunity Nature Immunology 3:182-188, 2002
2002 Phase II clinical trials of PI 88 anti-cancer drug Parish and colleagues Promising results in treating advanced melanoma Eur. J Cancer 38(S7):74, 2002
2003 New approach to vaccination against cancer Parish Approach is potentially less susceptible to immune evasion Immunology and Cell Biology 81, 106-113, 2003.
It can be argued that JCSMR's position as one of the original block-funded research schools of the Institute of Advances Studies (IAS) has given it the opportunity to pursue long-term, independent medical research. Many staff and alumni of JCSMR, including its present Director, argue that the Nobel Prizes, the Albert Einstein World Award, the Japan Prize, the Copley Medal, and a host of other international awards attest to the success of this funding strategy over the past 50 years, and that the scientific achievements that those prizes signify could only have been made by research that could be conducted over long (10 to 20 year) time frames.
But the funding climate has changed dramatically over the last few decades. Twenty five years ago many tenured Australian university or teaching hospital staff were able to undertake research even without external funding. Infrastructure was relatively generous, there was still funding for university technical research staff, hospitals provided drugs and consumables and even beds at no cost to the researcher, and research activities merged relatively seamlessly into those of teaching and patient care. Today research is increasingly separated from teaching and care, and the days of in-kind support are long gone. Research is an investment for the nation and the future, but in contemporary funding climates within universities and hospitals it is increasingly becoming an optional extra, undertaken only when researchers are able to attract sufficient external resources.
The change in environment and the disappearance of cross subsidisation from patient care and/or teaching has both advantages and disadvantages. Today's research (and research funding) is more transparent: what is spent on research as opposed to care and teaching, at least at the micro level, is much better defined. And, in parallel, the loss of capacity to undertake research that is not peer reviewed has almost certainly raised research standards. The negatives are equally obvious. The University of Melbourne submission to the Wills review stated 'The danger is that if the importance of the nexus between research and learning is not visible to tertiary students because University research is allowed to run down through poor infrastructure, equipment or lack of opportunity of research scientists then research institutes and teaching hospitals will inevitably suffer from a lack of quality of graduates available to them'[3].
The JCSMR has not been exempted from these pressures, and over the last 20 years the value of the block grant has been progressively eroded. The JCSMR grant was a one-line grant to the Director, which provided flexibility, but inhibited collaboration because of restrictive rules around the National Competitive Grants Scheme. Accordingly, a desire for transparency, competitiveness and collaboration led to a decision in 2000 by the Commonwealth Government that the IAS of the ANU could enter the National Competitive Grants Scheme. The negotiations that led to partial entry of JCSMR into the NHMRC schemes in 2001 (for 2002) were long and complex. However the negotiations between JCSMR and NHMRC were paralleled by those between the ARC and ANU and led to a jointly agreed 'buy in' price of 20% of 2000 funding for the IAS to gain access to the various national funding, research training and related schemes. This was $1.7 million per annum for JCSMR access to the National Competitive Grant Schemes and a similar amount for access to the training and infrastructure schemes. It was determined initially that entry would be phased, but after one year the ARC determined that the phase-in was unnecessary and gave the IAS full entry from 2002 (for 2003), and the NHMRC followed suit.
The JCSMR felt that, at last, it was able to redress the competitive disadvantage the School faced because of its essentially fixed funding over the last 10 to 15 years, at a time when government funding for the NHMRC system had doubled and then redoubled, from around $65 million in the late eighties to $176 million in 1999 to $381 million in 2004. The rest of the system waited, not without apprehension, for the outcome, but there had long been a wish within the research community that JCSMR be subject to the same forms of peer review as the broader medical research community.
Removing barriers to collaboration, the outcome of these changes, is important for 21st century approaches to improving health. The World Health Organisation has stated that the likely trends in global health in the 21st century include widespread absolute and relative poverty, demographic changes, ageing, growth of cities, epidemiological changes, continuing high influence of infectious diseases, increasing incidence of non-communicable diseases, injuries and violence, global environmental threats to human survival, new technologies, information and telemedicine services, advances in biotechnology, evolving partnerships for health that include private and public sectors and civil society, and globalisation of trade, travel and the spread of values and ideas. Research to deal with global health problems will therefore necessarily be multidisciplinary, involving biomedical, clinical, public health and health services research, and include the social sciences, information sciences and engineering, physics, chemistry, ecology and environmental sciences and economics. As part of the IAS, the JCSMR is accordingly strongly positioned. Not that this need for a multidisciplinary approach is really a new concept – in 1902 Osler stated that the remit of medical research was 'to wrest from nature the secrets which have perplexed philosophers in all ages, to trace to their sources the causes of disease, to correlate the vast stores of knowledge, that they may be quickly available for the prevention and the cure of disease – these are our ambitions' [4].
In the first three years that JCSMR has been eligible to apply for research funding, their researchers have been awarded a total of $18 million (88% NHMRC, 12% ARC) in competitive funding for periods of up to five years. All three NHMRC program grants held by researchers at the JCSMR are collaborative with other Australian institutions and School researchers hold four ARC linkage grants. This collaboration bodes well for Australian health and for biotechnology growth. In a highly competitive world, Australian researchers need every opportunity to succeed.
Acknowledgements
Thanks to Professor Warwick Anderson who provided helpful advice and Mrs Amanda Jacobsen for preparation of the manuscript.
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Fenner F Curtis D The John Curtin School of Medical Research: the first fifty years, 1948–1998 2001 Brolga Press, Gundaroo
Chalmers J Whitworth JA A half century of Australian health and medical research Medical Journal of Australia 2001 174 29 32 11219788
Health and Medical Strategic Review The Virtuous Cycle, working together for health and medical research 1999
William Bennett Bean Sir William Osler: Aphorisms from His Bedside Teachings and Writings Collected by Robert Bennett Bean 1968 Charles C Thomas, Springfield Illinois (third printing)
| 15771773 | PMC555854 | CC BY | 2021-01-04 16:38:28 | no | Aust New Zealand Health Policy. 2005 Mar 16; 2:5 | utf-8 | Aust New Zealand Health Policy | 2,005 | 10.1186/1743-8462-2-5 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-391574061410.1186/1471-2105-6-39Methodology ArticleThe Use of Edge-Betweenness Clustering to Investigate Biological Function in Protein Interaction Networks Dunn Ruth [email protected] Frank [email protected] Christopher M [email protected] The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK2 MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK3 MRC Rosalind Franklin Centre for Genomics Research, Hinxton, Cambridge CB10 1SB, UK2005 1 3 2005 6 39 39 13 9 2004 1 3 2005 Copyright © 2005 Dunn 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 paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters.
Results
Protein interaction graphs were separated into subgraphs of interconnected proteins, using the JUNG implementation of Girvan and Newman's Edge-Betweenness algorithm. Functions were sought for these subgraphs by detecting significant correlations with the distribution of Gene Ontology terms which had been used to annotate the proteins within each cluster. The method was implemented using freely available software (JUNG and the R statistical package). Protein clusters with significant correlations to functional annotations could be identified and included groups of proteins know to cooperate in cell metabolism. The method appears to be resilient against the presence of false positive interactions.
Conclusion
This method provides a useful tool for rapid screening of small to medium size protein interaction datasets.
==== Body
Background
Protein interaction datasets are typically presented as graphs (or networks), in which the nodes are proteins and the edges represent the interactions between the proteins. These graphs can be used to investigate the functions of unannotated proteins through their interactions with neighbouring annotated proteins. Protein interaction datasets frequently contain many false positives and false negatives, (Bader et al [1], von Mering et al [2]) but studies have shown that true positives are frequently associated with areas where there are many interactions between neighbours (clusters). For example Giot et al [3] used independent datasets to remove false positives from a large-scale protein interaction dataset and as a result were able to demonstrate that true positives had a strong positive correlation with the clusters. Spirin and Mirney [4] found that clusters of highly interconnected proteins are significant features of protein interaction networks. These could not have occurred by chance and are therefore likely to represent groups of proteins that have co-evolved to serve a common biological function. Identification of clusters is therefore likely to capture the biologically meaningful interactions in large scale datasets.
Edge-Betweenness clustering [5], the method used here, has been exploited in the social and ecological sciences to study communities [6] and in the study of biochemical pathways [7]. It has proved to be a useful and adaptable method. As discussed by Holme et al [7] edge-betweenness uses properties calculated from the whole graph, allowing information from non-local features to be used in the clustering. Many other clustering methods, which have proved useful for clustering protein interaction graphs, are based on calculation of local quantities such as node degree (number of attached edges) [8,9]. These 'local' methods will exclude nodes with a low degree e.g. the many prey nodes attached to their bait by a single edge, which are common in yeast two-hybrid (Y2H) datasets. Methods using whole graph properties will automatically include these poorly connected nodes in clusters [5], whilst a 'local' method would need to restore such nodes in a post-processing step [9]. Clusters created using edge-betweenness clustering are therefore useful when the information associated with these nodes is required. Other methods based on whole graph properties will also have this advantage, for example Markov Clustering [10]. A discussion of different clustering methods can be found in [11]
We applied the edge-betweenness method to a set of human protein interactions from our laboratory [12,13]. In these experiments interactions were identified using the Y2H method. For comparison, two datasets of yeast protein interactions [14,15] were also analysed. One yeast dataset also used the Y2H method [14] whereas the other was prepared using affinity purification [15]. The functions identified for clusters by the automatic method were compared with the expert biologists' interpretations presented in these papers.
Results
Allocation of GO terms
Differences in clustering between the datasets
The three datasets used differ in content, purpose, size, structure and species. A more detailed description of each dataset is given in the 'Methods' section and in Table 1, but briefly, the Gavin and Uetz datasets were large scale screens of the yeast proteome, not focused on particular metabolic pathways, whereas the Lehner dataset is focused on a few metabolic areas/complexes related to the human MHC class III region. While Lehner and Uetz both used the Y2H method to detect protein-protein interactions, Gavin used a combination of affinity purification and mass-spectroscopy. The two yeast datasets (Gavin and Uetz) have approximately 5× more nodes than the Lehner dataset. Whilst the Gavin and Uetz datasets have roughly the same number of nodes, the Gavin (affinity purification) dataset has twice as many edges (3145 vs 1498) as the Uetz (Y2H) dataset. The affinity purification method (Gavin) retrieves fairly stable complexes of proteins whereas the Y2H method detects direct protein-protein interactions which may be weak or transient.
From Tables 2 and 3 it can be seen that the affinity purification dataset gives much bigger clusters with the removal of a similar proportion of edges, when compared to the Y2H datasets. When 15% of edges were removed from the Gavin dataset, the clusters (with more than one member) had an average of 23 nodes whilst for Uetz the average was just over 7 nodes. The Lehner dataset fell between these values. Diagrams showing the Lehner dataset before and after clustering are presented in Additional files 11 and 12.
The choice of the number of edges removed needs to be guided by the dataset and problem under consideration. A number of criterion could be used. (i) Range of cluster sizes: To decide what a sensible distribution of cluster sizes would be, the range of sizes of clusters found by affinity purification was used as a guide. Gavin [15] reported the distribution of cluster sizes as follows:-51% had 1–5 nodes, 18% 6–10 nodes, 15% 11–20 nodes, 6% 21–30 nodes, 4% 31–40 nodes, and 6% > 40 nodes. In order to emulate this type of distribution with the automatic clustering (see Table 2) it is necessary to remove more than 13% of edges from the Uetz and Lehner datasets and more than 25% from the Gavin dataset. Therefore it is necessary to remove a much higher proportion of edges from the affinity purification dataset.
Other results from Tables 2, 3 and 4 that could also be used to try and determine the appropriate number of edges to remove are (ii) increasing the significant number of GO terms per protein (iii) aiming for an average size of cluster of 5–20 proteins (iv) reducing the size of the biggest cluster to < 20% of the dataset, a useful metric to indicate reasonable decomposition of the dataset (but which could be varied according to the total number of nodes in the dataset) (v) reducing the number of nodes not associated with any other nodes to < 30%. The proportion of edges that need to be removed in order to attain each of these criteria would be:-
(i)distribution cluster size Gavin 25% Uetz 13% Lehner 14% edges
(ii) significant GO terms For all datasets, the more edges that are removed the more terms become significant down to the smallest cluster sizes investigated
(iii)average cluster 5–20 Gavin 25% Uetz 2–13% Lehner 7–25% edges
(iv)biggest cluster < 20% Gavin 25% Uetz 13% Lehner 14% edges
(v)single nodes < 10 % Gavin 25% Uetz 27% Lehner 25% edges
The data above shows that most of these criteria give similar results and suggest that the method used to produce the data (Y2H or affinity purification) will be a major determinant of the proportion of edges to remove. To summarise, for Y2H, useful results are obtained by removing 10%–15% of edges whereas for affinity purification, removing 25% edges gives better results. Newman and Girvan [16] have developed methods for assessing the 'modularity' of the clusters produced by edge-betweenness clustering. It would also be possible to use methods of this type, as a more objective way of deciding how many edges to remove in different datasets.
Size of cluster is important, because the quantity of significant annotation information i.e. the average number of significant GO terms per protein, (Table 4) increased, for all datasets, as cluster size decreased. However the detail of the information, measured as average depth of GO per node, did not change with cluster size. It is noticeable that human proteins in the Lehner dataset [12,13] had been annotated to a greater level of detail (average depth of nearly 6 in the GO hierarchy) than the yeast proteins (average dept of approx 4.7, see Table 4) and whereas virtually all of the clusters in the Lehner dataset had a correlation with at least one GO term there were many clusters in the yeast dataset which had no significant GO terms (the majority in the case of the Uetz dataset). This could be a peculiarity of the metabolic areas chosen for the Lehner study.
Scaling
The utility of this approach is currently restricted by the size of the dataset being analysed, especially when a large number of edges are being removed. For the Gavin dataset, when 57 edges were removed the total time to cluster was 1 h 25 min but when removing 1500 edges it took 10 h 10 min. According to the software documentation [17] and as discussed by Newman [6] the running time for sparse graphs (such as these) is proportional to both the number of edges removed and the total number of nodes. The Ito dataset (see below) took >>24 h when > 500 edges were removed. This method is therefore of greater utility for small to medium datasets, having less than 2000 nodes or edges.
Significance of GO terms
After performing the Chi Squared tests and checking them against a random reallocation of GO terms across the network, all the significant GO cluster correlations remained significant. In no case were more than 5% of the lowest p values of the randomly reallocated GO terms lower than the lowest p value in the original dataset.
In almost every case the significant annotations were informative about a potential function for the clusters (see Table 5), providing distinctive groupings of annotations which distinguished different functions for the different clusters (the aim of the method). It was often a very small proportion of the proteins which provided the annotations which were used to characterise the cluster, (Table 5 and Additional Files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, which provide complete sets of clustering results and details of the proteins which contributed the significant annotation).
Correlation with biological function
One test of this method was to determine whether the clusters generated and the associated GO terms corresponded to clusters previously identified by expert biologists.
With respect to the Lehner et al dataset [12], the authors identified groups of interacting proteins which appeared to be involved in distinct biological processes including transcription regulation, protein-ubiquination, cell cycle regulation and mRNA processing
When edge-betweenness clustering was used to remove 57 edges, 21 clusters (with size greater than one) were created (Table 3). From Table 5 (and from the more detailed information in Additional file 7), it can be seen that these clusters differ in the significant GO terms associated with them i.e. the method does separate groups of proteins with different metabolic functions. Significantly, clusters were generated with functions corresponding to all of the metabolic areas identified by informed biological interpretation. These were transcription (cluster 9), ubiquination (cluster 15), cell cycle reg (cluster 14) and mRNA processing (cluster 21, cluster 3, cluster 6) Only one cluster, cluster 10, had a description ("biological process") which was too general to give useful information about function. However when this cluster was broken down further (in the test with 100 edges removed) more informative terms ("response to abiotic stimulus", "eukaryotic translation elongation factor 1 complex") were associated with the new, smaller parts of the cluster. Interestingly cluster 10 contained very few proteins with GO terms assigned to them and therefore may represent an under-investigated module in the human proteome. This highlights the dependence of this method on the quality (depth) and quantity of the GO annotations available. This was good for the H.sapiens proteins but less good for the yeast proteins.
One important question is whether the functions identified for these protein clusters are confirmed by biological experimentation. The Lsm complex is mentioned by the authors of all 3 papers [13-15], It has been extensively studied in both yeast and human [13]. The Lsm complex has been shown to have a number of functions related to RNA processing, including the splicing of nuclear pre-mRNA and the decapping of cytoplasmic mRNA prior to degradation.
Clusters in the Lehner dataset
In the Lehner dataset two GO terms, GO:6371 "mRNA splicing" and GO:8380 "RNA splicing", were always associated with only one cluster in the dataset. This was a good candidate for the Lsm complex.
Of the 8 Lsm proteins examined in [13], all eight were found in the cluster associated with these two GO terms for the tests when 10, 30 and 57 edges were removed. A diagram showing the cluster containing these proteins, in the dataset with 57 edges removed, can be seen in Additional file 13. When 100 edges were removed, the cluster labeled as RNA splicing contained 5/8 of the Lsm proteins. The three clusters containing the other 3 proteins had the following significant descriptions (the number in parenthesis shows; the number of proteins with this annotation / the total number of proteins in the cluster).
GO:15980 energy derivation by oxidation of organic compounds (2/19)
GO:5837 26S proteosome (2/16)
GO:6350 transcription (2/3)
For the Lehner data, when 15, 30 and 57 edges were removed, the clusters labeled as being associated with RNA splicing are large containing 190, 143 and 60 proteins respectively (see below). The cluster with 5/8 Lsm proteins (100 edges removed) had only 17 proteins. In addition to the Lsm proteins the large clusters contained other proteins known (i.e. having GO labels) to be involved in RNA splicing. The proportions are shown in Table 13.
This data clearly shows that as the cluster size gets smaller, the cluster is more focused round the RNA splicing function. Larger clusters must have sub-clusters related to other functions. The last column in the table above shows that many of the RNA splicing proteins grouped in these clusters were the prey of the Lsm proteins in the original experiments [13], which is what we hoped this method would achieve.
Therefore for the Lehner data, the cluster identified by Edge-Betweenness clustering as the "RNA splicing" cluster, did contain the proteins expected to be associated with this process. However this is a small dataset focused around a specific biological process. A more stringent test of this method is provided by the yeast proteome datasets where screening was not functionally focused.
Clusters in the yeast datasets
Gavin et al [15] and Uetz et al [14] both describe the Lsm complex. One complication in both of these datasets, is that the yeast proteins are not annotated to the same level of detail as the human proteins. For example there is no annotation for "RNA splicing" but only the higher level GO term GO:16070 "RNA metabolism", which covers a much broader range of cellular processes.
In Gavin et al [15], the Lsm proteins are found in the complex described as TAP-C128. This contained 36 proteins. The distribution of the TAP-C128 proteins between the clusters are shown in Table 6. It can be seen that a minimum of 6/7 Lsm proteins and proteins associated with RNA metabolism are clustered together, at all numbers of edges removed.
Therefore in a dataset not focused round RNA metabolism, the edge-betweenness algorithm successfully clustered the Lsm proteins with a number of other proteins that were co-purified in the TAP-C128 complex and a cluster produced using the graph topology was shown to correspond to a cluster of known function.
In Uetz et al 2000 [14], the Lsm complex is described as a set of 16 interacting proteins. The one cluster containing all of these proteins does not correlate with the GO term for "RNA metabolism" in the datasets with 30 or 57 edges removed. This correlation only emerged once 100 edges had been removed. With 400 edges removed 11/16 are still in the same "RNA metabolism" cluster (the other 5 are spread between 5 different clusters).
Therefore in the Uetz dataset although all the Lsm proteins clustered together, it was only once more than 10% of edges had been removed that it was possible to get a significant association with the relevant GO term. Finding the correct number of edges to remove is obviously essential to extracting the required information.
Overall it can be seen that the method is capable of finding clusters of proteins with known biological function and of correctly assigning a relevant annotation to a particular group.
Stable and transient clusters
In Gavin et al [15] the authors discuss two clusters which are described as "stable and "transient". TAP-C162 is an example of a "stable" complex which was always isolated with the same members. It is part of the poly-adenylation machinery. In contrast, TAP-C151, the "transient" complex was frequently isolated with different components. It is a signaling complex formed around protein phosphatase 2a.
The distribution of these two complexes between the clusters generated by edge-betweenness clustering, was compared at different levels of clustering, (see Tables 7 and 8). While TAP-C162 remains mainly associated with one cluster at all numbers of edges removed, TAP-C151 becomes distributed much more evenly between a greater number of clusters. Therefore it seems likely that the method described here favours the detection of more stable clusters, as the number of edges removed increases.
False positive interactions
Clustering the Lehner dataset with added false positive edges (see "Methods" section and Table 9) gave no obvious difference in cluster size (Tables 10 and 11) or quality or quantity of GO annotation (Table 12). The dataset with false positives is slightly larger than the original dataset, but this did not change the number of clusters. The slight increase in average cluster size led to a commensurately small fall in annotation quality (GO per node), but there were no dramatic differences in cluster size distribution or any of the other measurements.
Fourteen out of twenty-one of the clusters in the original dataset remained completely intact, and even when this was not the case a minimum of 70% of the original proteins in the other clusters could still be found together in one of the new clusters. Therefore adding the false positives did not render any of the original clusters unrecognisable.
When the dataset with the false positive edges removed was compared to the dataset with the same number of edges removed at random, the differences were more marked. The dataset where edges were removed at random had smaller clusters (Tables 10 and 11) and more single nodes (Table 11 last column). The identity of the clusters was perturbed to a greater extent. Further analysis showed that when the false positives were removed 12/21 clusters still remained completely intact. With removal of random edges only 4/21 clusters were completely intact. However even in this dataset 14/21 clusters had 80% of proteins from the original clusters co-occurring i.e. 3/4 of clusters were still recognisable. Randomly removed edges can be considered to be false negatives and so the method is also showing good tolerance to false negatives, and can still preserve a good level of cluster identity.
Overall, even though the false negatives reduce the average sizes of the clusters and splits off many single nodes (as would be expected because nodes with single edges are much more abundant than nodes with multiple edges, in Y2H datasets) the same clusters are still being found 75% of the time. In other words the presence of false positives and false negatives in the dataset does not seem to distort the composition of the clusters created by the Edge-Betweenness method in a way that obliterates cluster identity. But false negatives do appear to have a slightly more detrimental effect than false positives.
Looking at the edges which were removed during clustering, when 57 edges were removed (from the dataset containing false positive edges) 3/57 (5%) had false positive nodes at one or both ends. When clustering was done by removing 100 edges 15/100(15%) were attached to false positive nodes. This compares with 68/465(14.6%) edges attached to false positive nodes in the whole dataset. There is no obvious bias in the presence of false positive edges between or within clusters.
Overall it appears that the clustering is fairly robust to the presence of false positives and also to the random removal of edges i.e. false negatives.
With the Ito et al [18] dataset it was hard to say whether there was much effect from the removal of false positives or addition of false negatives, as the proportion of nodes and edges affected was so small, but again there were no obvious differences.
Discussion
Edge-Betweenness clustering can be used to separate protein interaction networks into clusters which have correlations with annotated gene functions. This can be done in an automated fashion and thus can provide a means of rapidly screening the results of protein interaction experiments. Clusters produced by this method contain groups of proteins which are known to cooperate to perform common functions, described by the correlating annotations. Therefore the clusters detected by this method correspond to active protein complexes found in the cell. Moreover the method worked for different types of dataset (Y2H and affinity purification) different organisms (yeast and human) and for datasets with a 5× difference in the number of edges.
The smaller the clusters generated by this method, the higher the average number of significant annotations. The preliminary results presented here suggest that, in general, useful information was obtained once approximately 10% of edges were removed from Y2H datasets and a slightly higher proportion (25%) from affinity purification data. This method is particularly good at detecting "stable" clusters. The method is also flexible and can be adjusted according to the nature of the dataset and to the function being studied. Currently scaling to very large datasets when large numbers of edges need to be removed is problematic, but this may soon be alleviated by new developments of the algorithm [6]. The level of detail and amount of available annotation will have a significant effect on the utility of this method although it is possible to tune the amount of annotation found by the method, by altering the number of edges removed. The amount of available annotation will increase as proteome annotation progresses.
Spirin and Mirny [4] have demonstrated the robustness to false positives and negatives of various clustering methods (not including the Edge-Betweenness method used here). They found that 80% of clusters could still be detected if up to 20% of links were added or removed. Our results suggest that Edge-Betweenness clustering is similarly robust. This robustness is undoubtedly for the reason identified in [4] which is "the use of multiple interactions to identify a cluster", in other words the interconnectedness of a pair of proteins is reconfirmed by the interconnectedness of their neighbours. The biological significance of these interconnected sets of proteins was shown by the high correlation between true positive interactions and clusters in Drosophila protein interaction networks, found by Giot et al [3].
Giot et al [3] also found that prey (but not bait) with a large number of neighbours had a significant negative correlation with the reliability of the interactions. These highly connected prey correspond to the promiscuous prey which we identified as false positives and which although highly connected do not have neighbours which are themselves highly interconnected. As this method appears robust to the presence of such proteins it is not necessary to "clean up" the datasets before using them.
The hierarchical nature of the Gene Ontology made this a very useful system of annotation to exploit in this method. It allows proteins to be grouped according to the most detailed shared level of annotation but also enables higher level (less informative) annotation to be used when this is all that is available. The very high level terms which apply to almost all proteins are usually ignored as they are not concentrated in a particular cluster, although these terms occasionally appear as significant, in clusters with higher than average levels of annotation.
Conclusion
Edge-Betweenness clustering provides a quick way of picking out functionally interesting areas of protein interaction datasets. It also appears to be robust against false positives and negatives. As such this approach can be applied to any quality of data. It also deals effectively with poorly connected nodes, such as the many prey with single connections found in Y2H graphs. Because the Edge-Betweenness algorithm does not scale well to larger graphs, this method is currently most appropriate for studies focused on specific areas of the proteome. However, modifications of the algorithm are being developed and these should allow it to be applied to larger datasets in the future [6]. The implementation described here is particularly effective where good quality GO annotation is available, which is especially true for many human proteins. It will be a useful method for detecting functions for unannotated proteins based on the knowledge of the functions of their neighbours and for exploring functional modules within the proteome.
Methods
Datasets
The datasets used for analysis are described in Table 1. Briefly the Lehner dataset comes from our work on the function of the MHC class III region [12,13] and is a small, highly focused dataset of H. sapiens protein interactions, detected using the Y2H method [12]. The other datasets, Gavin [15] and Uetz [14], are larger datasets resulting from mass screens of the yeast proteome, using either Y2H (Uetz) or affinity purification (Gavin). The method presented here was developed for the Lehner dataset. In order to test the method, it was applied to the larger, less selective yeast datasets.
The Ito dataset [18], an even larger yeast dataset, was included in order to test the effect of false positive proteins. This dataset contained 16 proteins identified by Gavin et al [15] as false positives. However it was not used for other aspects of the investigation as clustering takes a long time when large number of edges are removed. Thus the Ito dataset represents the upper limit of the size of datasets suitable for use with the method described here.
Protein function
The Gene Ontology (GO) [19] was used as the source of functional annotations. It was chosen because it provides hierarchically structured, controlled vocabularies. Genes or gene products may be labeled with terms from any level in any of the three hierarchies (ontologies). By searching up through the hierarchy, it was possible to find terms shared by proteins which had been initially labeled with different descriptions. The search through the hierarchy is easy to automate, which makes it possible to group together proteins participating in the same general functions, even when they were originally annotated for different, more specific functions.
Steps of the analysis
The steps of our method to cluster the graph and assign functions to the clusters, were as follows:-
1. Transform the protein interaction data to GraphML (an XML format for graphs [20]), removing any parallel edges, to make the data ready for import into JUNG.
2. Use the JUNG graph analysis framework [21] to cluster the data using the "Edge-Betweenness" [5] algorithm.
3. Find GO terms and the parents of those GO terms for each GO annotated protein in every cluster.
4. Test the association between each GO term and each cluster, from a 2 by 2 contingency table.
5. Correct the association tests for multiple comparisons, using a permutation test with random re-allocation of GO terms to proteins.
6. Generate reports on cluster size and significant GO terms.
Perl scripts were used to perform most of these steps, the other software used is described below. Details of the steps listed above are as follows:
Clustering
JUNG version 1.3 [21] was used to cluster the graph by the Edge-Betweenness clustering method [5]. This algorithm removed those edges which lay on routes between interconnected clusters. "Betweenness" is calculated by finding the shortest path(s) between a pair of vertexes and scoring each of the edges on this/these path(s) with the inverse value of the number of shortest paths. (So if there was only one path of the shortest length, each edge on it would score 1 and if there were 10 paths of that length, each edge would score 1/10.) This is done for every pair of vertexes. In this way each edge accumulates a "betweenness" score for the whole network. The network is separated into clusters by removing the edge with the highest "betweenness", then recalculating betweenness and repeating until the desired number of edges have been removed. The method is fully described in [5].
The number of edges to remove was supplied as a parameter. Removing a larger number of edges reduced the size of the clusters produced. The number of edges removed was varied to see whether (a), clusters of certain sizes gave better correlations with GO terms and (b), whether datasets of different types cluster in different ways (likely, as the affinity purification dataset has approximately 3× as many edges as the Y2H dataset with a similar number of nodes).
Source of GO annotations
GO terms available for each of the proteins in the graph were retrieved. In the case of the Lehner dataset these were taken from the RefSeq records [22], for the Uetz and Gavin data these were provided by BIND [23].
Processing GO annotations
The Gene Ontology "termdb" release from December 2003 was used as the source of the parent GO terms [24]. Tables to hold these GO data were set up using the PostgreSQL relational database management system [25] (version 7.3.4-RH). The parents of each GO term were found by using an adaptation of the sample query provided on the GO web site [26]. This query was called from either perl scripts or Java programs, which allocated the terms to the clusters.
Detecting GO terms with significant associations to clusters
The 'R' statistical package [27] (version R 1.8.1 (2003-11-21)) was used to perform the statistical analysis on the data retrieved. The association between each cluster and each GO term was tested using a 2 by 2 contingency table by Fisher's exact test.
Re-testing significant GO associations
The GO terms (significant and non-significant) were redistributed across the clustered network at random. The p value was recalculated for each GO/cluster combination. This randomisation was repeated 1000 times. The overall significance was calculated as the proportion of randomisations in which the smallest p value for a GO-cluster association was less than or equal to the smallest p value in the original data. We considered the GO numbers to be significantly associated with the clusters if the overall significance was less than 5% (i.e. fewer than 50 of the 1000 randomisations' lowest p values were smaller than the smallest p value from the observed data).
Reports on significant GO/cluster associations
In order to compare the informativeness of the GO/cluster associations, the following ratios were calculated (a), the average number of GO terms per node in the clusters and (b), the average depth of the GO terms per node per cluster. These provided an indication of the 'quantity' and 'quality' of the GO information. A GO at a greater depth in the GO hierarchy provides more detailed information than one higher in the GO hierarchy.
False positives
In our original experiments [12,13] there were a number of prey that interacted with many different bait. Prey found by more than three different bait were defined as false positives (of the 'promiscuous' type). There were 14 of these (approximately 4% of the dataset nodes). 10 of these 14 had been excluded from the original data. To investigate their effect on clustering, these nodes and all associated edges were added back to the data. This contributed 59 new edges to the dataset (13% of dataset edges). This dataset was clustered and the clusters compared to those found in the original experiment.
If these nodes were disconnected this removed 68 edges, so nine of the edges connected to false positives were part of the original data. In a control experiment 68 edges were removed at random (from the dataset with false positives added), this dataset was clustered. This was repeated 100 times and the results were compared to the clusters obtained from the dataset which had false positive edges removed.
Gavin 2002 Supplementary Information Table S2 [15] provided a list of false positive proteins, which were excluded from their yeast dataset. They were excluded because they either appeared in more than 20 of the purifications or were isolated in mock transformations. The data describing the edges created by these proteins was not provided, therefore it was not possible to add them back to the Gavin data. The Uetz data contained only 2 of the false positive proteins, however the Ito dataset contained 16(0.5% of dataset). The Ito dataset is large and 16 out of 3271 nodes is a very small proportion, so any effect will not be large. Disconnecting these nodes removed 26 edges from the dataset (0.6% of edges). A control dataset had 26 edges removed at random before clustering
All false positive datasets (see Table 9) and controls were clustered by removing 57 edges (a number chosen originally because it gave a tractable number of clusters of a reasonable size in the Lehner dataset).
Authors' contributions
RD: analysis, interpretation and writing. FD: statistical analysis. CS: data acquisition and supervision.
Supplementary Material
Additional File 11
Additional file 11 shows the Lehner dataset before it was clustered. The Lsm proteins are highlighted.
Click here for file
Additional File 12
Additional file 12 shows all the clusters produced when the Lehner dataset was clustered by removing 57 edges. The whole cluster containing the Lsm proteins is highlighted.
Click here for file
Additional File 1
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 2
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 3
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 4
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 5
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 6
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 7
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 8
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 9
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 10
These files provide information on all the clusters (of size > 1) formed when the Gavin, Uetz and Lehner datasets were clustered. Each file name begins with the name of the dataset used in clustering, followed by the number of edges removed to perform the clustering. The files with a name ending 'clusters.txt' list all the members of each cluster and all the GO terms which were found to be significant for that cluster. Files ending with 'proteins-per-GO.txt', give further detail, showing the transcripts in the cluster which had the significant GO annotations. The examples provided show each dataset with the 'best' number of edges removed (see Results section: Difference in clustering between the datasets). For the smallest (Lehner) dataset, examples with greater and fewer numbers of edges removed are also provided.
Click here for file
Additional File 13
Additional file 13 shows more detail for this cluster, including the transcript ID for each node. The images were produced using the BioLayout [32] graph visualisation tool
Click here for file
Acknowledgements
The work was funded by MRC Link Grant G0100814, in association with Genetix. FD is supported by European Commission grant 503485. We would like to thank Anton Enright for helpful comments during the revision of this manuscript.
Figures and Tables
Table 1 Datasets used for analysis Numbers of nodes and edges in each of the datasets used and a brief description of the methods used to generate the datasets.
Name Nodes Edges Description and Reference
Gavin 1343 3145 Mass screen of yeast protein complexes using affinity purifica tion [15] (Note 1)
Ito 3271 4469 Mass screen of yeast protein interactions using Y2H [18] (Note1)
Lehner 329 406 Y2H interactions between H. sapiens proteins A dataset focused on RNA degradation and other MHCIII functions. [12, 13] (Note 2)
Uetz 1358 1498 Mass screen of yeast protein interactions using Y2H [14] (Note 1).
Notes: 1. The Gavin, Ito and Uetz graphs were all generated from BIND [28] derived datasets, which had GO annotations added and were supplied with v0.9.1 of the 'Osprey' graph visualisation tool [29,30]
2. The Lehner dataset is a combined set of the data from the two cited papers. These data are available in both IntAct [31] (experiment references EBI-348647, EBI-368082 and EBI-368083) and BIND [28] (refs 130691–130793 and 153087–153089)
Table 2 range of cluster sizes The distribution of cluster sizes in 3 datasets, after clustering with different numbers of edges removed.
Dataset Number Edges Removed Edges Re-moved % Nodes per Cluster
1 2–5 6–20 21–50 51–200 201+
Number of Clusters in Size Range
Uetz 30 2% 13 128 9 3 0 1
Uetz 57 4% 13 128 9 3 1 1
Uetz 100 7% 13 128 11 5 4 1
Uetz 200 13% 13 130 32 19 1 0
Uetz 400 27% 21 256 71 0 0 0
Gavin 57 1.5% 0 33 8 2 0 1
Gavin 400 15% 0 33 16 4 3 2
Gavin 800 25% 4 58 57 15 2 0
Gavin 1500 50% 263 154 67 1 0 0
Lehner 15 4% 1 6 5 2 1 0
Lehner 30 7% 1 6 7 3 1 0
Lehner 57 14% 1 6 10 4 1 0
Lehner 100 25% 4 15 23 0 0 0
Table 3 cluster characteristics The average cluster size, number of clusters and other properties of the dataset, after clustering with different numbers of edges removed.
Dataset Number of Edges Removed Edges Removed % Number of clusters size > 1 Average Cluster Size Biggest cluster(%) Single Nodes(%)
Uetz 30 2% 141 9.5 849(61%) 13(1 %)
Uetz 57 4% 142 9.5 715(53%) 13(1 %)
Uetz 100 7% 149 9.0 459(38%) 13(1 %)
Uetz 200 13% 182 7.4 53(4 %) 13(1 %)
Uetz 400 27% 327 4.1 13(1 %) 21(1.5%)
Gavin 57 1.5% 44 30.5 1106(82%) 0(0 %)
Gavin 400 15% 58 23.1 360(27%) 0(0 %)
Gavin 800 25% 132 10.1 56(4 %) 4(0.3%)
Gavin 1500 50% 222 4.9 23(2 %) 263(19 %)
Lehner 15 4% 14 23.4 190(58%) 1(0.3%)
Lehner 30 7% 17 19.3 143(43%) 1(0.3%)
Lehner 57 14% 21 15.6 60(18%) 1(0.3%)
Lehner 100 25% 38 8.6 19(6 %) 2(0.6%)
Table 4 cluster quality Association between the size of the clusters and the quality and quantity of significant GO terms with different numbers of edges removed.
Dataset Number of Edges Removed Edges Removed % GO per Cluster GO per Node Depth of GO per Node Number of Clusters with no significant annotation
Uetz 30 2% 0.7 0.1 4.9 120
Uetz 57 4% 0.8 0.1 4.9 120
Uetz 100 7% 0.9 0.1 4.9 121
Uetz 200 13% 1.1 0.2 4.8 137
Uetz 400 27% 3.5 0.7 4.5 261
Gavin 57 1.5% 2.1 0.1 4.6 23
Gavin 400 15% 3.4 0.2 4.7 24
Gavin 800 25% 2.8 0.3 4.6 59
Gavin 1500 50% 2.2 0.5 4.6 336
Lehner 15 4% 22.9 1.0 5.8 1
Lehner 30 7% 21.3 1.1 5.8 1
Lehner 57 14% 19.2 1.2 5.8 1
Lehner 100 25% 15.5 1.8 5.8 2
Table 5 significant GO terms for the Lehner dataset A selection of GO terms with significant correlations to the 20 clusters in the Lehner dataset, clustered by removing 57 edges. (The numbers after the descriptions show the proportion of proteins in the cluster which were annotated with that GO term). The complete set of GO terms for each of these clusters can be seen in Additional file 7 and the identity of the transcripts associated with the significant GO terms can be found in Additional file 8.
Cluster Number Size of Cluster Significant GO descriptions
15 20 ubiquitination 4/20
4 49 protein biosynthesis 7/49, RNA catabolism 4/49, translation 3/49
19 3 ubiquitin 1/3, cell defence 1/3
8 24 electron transport 2/24
11 10 transcription regulation3/10
16 22 transport 6/22, glucose catabolism 2/22
18 7 DNA repair 2/7
3 60 RNA splicing 14/60, spliceosome 5/60
7 10 ribosome assembly 2/10, cytoplasmic exosome 1/10
12 8 protein metabolism 3/8, phosphorylation 3/8
22 1 morphogenesis 1/2, membrane 1/2
2 19 signal transduction 4/19, ER 3/19
9 4 transcription reg 1/4
21 4 mRNA catabolism 1/4
6 12 mRNA export 1/12, DNA binding 4/12
1 18 cytoskeleton 3/18
20 2 ATP biosynthesis 2/2
14 14 DNA replication 2/14, cell cycle 3/14
10 23 biological process 8/23 oncogenesis 2/23
Table 6 the distribution of proteins associated with RNA metabolism from TAP-C128 The number of proteins from TAP-C128 [15] which cluster together when different numbers of edges are removed and also the proportions which are annotated for RNA metabolism.
Edges removed Number of clusters associated with the GO term 'RNA metabolism' Largest group of TAP-C128 found together Proportion of proteins * Number of Lsm proteins in this cluster
57 3 clusters 36/36 128/1106 7/7
400 7 clusters 27/36 57/142 7/7
800 8 clusters 13/36 43/56 7/7
1500 11 clusters 7/36 5/7 6/7
*Proportion of proteins in the cluster containing most TAP-C128 proteins which were associated with RNA metabolism
Table 7 the distribution of affinity purified proteins from TAP C-162 TAP C-162 [15] is an mRNA polyadenylation complex of 36 proteins, thought to be a stable complex
Edges Removed Number of Clusters Containing TAP C-162 proteins Numbers of the TAP C-162 proteins in each of the Clusters
57 1 (36)
400 5 (25,7, 4 × 1)
800 9 (23,4, 9 × 1)
1500 16 (16,22, 16 × 1)
Table 8 the distribution of affinity purified proteins from TAP C-151 TAP C-151 [15] is a signaling protein complex of 45 proteins, thought to be more labile than TAP C-162
Edges Removed Number of Clusters Containing TAP C-151 proteins Numbers of the TAP C-151 proteins in each of the Clusters
57 1 (45)
400 3 (43,1,1)
800 11 (14,12,7,3,2,2, 5 × 1)
1500 21 (9,7,6,4,3,2, 14 × 1)
Table 9 datasets used to investigate false positives These datasets were used to investigate the effect of false positive edges on the clustering of the datasets
Name Nodes Edges
Lehner original 329 406
Lehner plus False Positive proteins and edges 353 465
Lehner with false positive proteins' edges disconnected 353 397
Ito [18] 3271 4469
Table 10 cluster size distribution with and without false positives
Dataset Number Edges Removed Edges Removed % Nodes per Cluster
1 2–5 6–20 21–50 51–200 201+
Number of Clusters in Size Range
Lehner 57 14% 1 6 10 4 1 0
Lehner plus False Positive Edges(FPE) 57 12.3% 1 6 11 2 2 0
Lehner minus 68 FPE 57+68* 26.9% 32 6 9 5 1 0
Lehner random edges removed** 57+68* 26.9% 39.7 ± 3.2 6.5 ± 1.0 13.4 ± 2.1 4.1 ± 1.1 0.05 ± 0.2 0 ± 0
Ito minus 26 FPE 57+26* 1.9% 25 183 4 0 0 1
Ito minus 26 random edges 57+26* 1.9% 11 189 5 0 0 1
* edges removed for clustering + false positive or random edges removed
**Lehner plus FPE with 68 edges removed at random (for 100 replicates mean ± standard deviation)
Table 11 cluster characteristics with and without false positives
Dataset Number of Edges Removed Edges Removed % Number of clusters size > 1 Average Cluster Size Biggest cluster(%) Single Nodes(%)
Lehner 57 14% 21 15.6 60(18 %) 1(0.3 %)
Lehner plus False Positive Edges (FPE) 57 12.3% 21 16.8 67(19.0%) 1(0.3 %)
Lehner (FPE edges removed) 57+68* 26.9% 21 15.3 56(15.9%) 32(9.0 %)
Lehner random edges removed** 57+68* 26.9% 24.0 ± 1.5 13.1 ± 0.8 39.2 ± 6.1(11.1%) 39.2 ± 3.2(11.2%)
Ito minus FPE 57+26* 1.9% 188 17.3 2798(85.5%) 25(0.8 %)
Ito minus 26 random edges 57+26* 1.9% 195 16.7 2787(85.2%) 11(3.4 %)
* edges removed for clustering + false positive or random edges removed
**Lehner plus FPE with 68 edges removed at random (for 100 replicates mean ± standard deviation)
Table 12 cluster quality with and without false positives
Dataset Number of Edges Removed Edges Removed % GO per Cluster GO per Node Depth of GO per Node Number of Clusters with no significant annotation
Lehner 57 14% 19.2 1.2 5.8 1
Lehner plus False Positive Edges (FPE) 57 12.2% 18.2 1.1 5.7 1
Lehner (FPE edges removed) 57+68* 26.9% 19.33 1.3 5.7 10
Lehner random edges removed** 57+68* 26.9% 17.5 ± 4.6 1.3 ± 0.4 5.7 ± 0.08 4.2 ± 5.2
Ito minus FPE 57+26* 1.9% 1.3 0.1 4.7 149
Ito minus 26 random edges 57+26* 1.9% 1.3 0.1 4.7 146
* edges removed for clustering + false positive or random edges removed
**Lehner plus FPE with 68 edges removed at random (for 100 replicates mean ± standard deviation)
Table 13 Clustering of RNA splicing proteins in the Lehner dataset with different numbers of edges removed.
edges removed size of 'RNA splicing' cluster proportion of proteins annotated for 'RNA splicing' proportion of proteins which were prey of Lsm proteins in [13]
15 190 18/190 51/190
30 143 17/143 49/143
57 60 14/60 49/60
100 17 10/17 14/17
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| 15740614 | PMC555937 | CC BY | 2021-01-04 16:02:51 | no | BMC Bioinformatics. 2005 Mar 1; 6:39 | utf-8 | BMC Bioinformatics | 2,005 | 10.1186/1471-2105-6-39 | oa_comm |
==== Front
BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-441574829810.1186/1471-2105-6-44Research ArticleQuantitative inference of dynamic regulatory pathways via microarray data Chang Wen-Chieh [email protected] Chang-Wei [email protected] Bor-Sen [email protected] Lab. of System Biology, National Tsing Hua University, 101, Sec 2, Kuang Fu Road, Hsinchu, 300, Taiwan2 Department of Electrical Engineering, National Tsing Hua University, 101, Sec 2, Kuang Fu Road, Hsinchu, 300, Taiwan2005 7 3 2005 6 44 44 17 8 2004 7 3 2005 Copyright © 2005 Chang 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 signaling pathway (network) is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach.
Results
In this study, a second-order dynamic model is developed to describe the regulatory mechanism of a target gene from the upstream causality point of view. From the expression profile and dynamic model of a target gene, we can estimate its upstream regulatory function. According to this upstream regulatory function, we would deduce the upstream regulatory genes with their regulatory abilities and activation delays, and then link up a regulatory pathway. Iteratively, these regulatory genes are considered as target genes to trace back their upstream regulatory genes. Then we could construct the regulatory pathway (or network) to the genome wide. In short, we can infer the genetic regulatory pathways from gene-expression profiles quantitatively, which can confirm some doubted paths or seek some unknown paths in a regulatory pathway (network). Finally, the proposed approach is validated by randomly reshuffling the time order of microarray data.
Conclusion
We focus our algorithm on the inference of regulatory abilities of the identified causal genes, and how much delay before they regulate the downstream genes. With this information, a regulatory pathway would be built up using microarray data. In the present study, two signaling pathways, i.e. circadian regulatory pathway in Arabidopsis thaliana and metabolic shift pathway from fermentation to respiration in yeast Saccharomyces cerevisiae, are reconstructed using microarray data to evaluate the performance of our proposed method. In the circadian regulatory pathway, we identified mainly the interactions between the biological clock and the photoperiodic genes consistent with the known regulatory mechanisms. We also discovered the now less-known regulations between crytochrome and phytochrome. In the metabolic shift pathway, the casual relationship of enzymatic genes could be detected properly.
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Background
Biological phenomena at different organismic levels have revealed some sophisticated systematic architectures of cellular and physiological activities implicitly. These architectures were built upon the biochemical processes before the emergence of proteome and transcriptome [1-3]. Under the molecular machinery, the biochemical processes are mostly interpreted as frameworks of connectivity between biochemical compounds and proteins, which are synthesized from genes to function as transcription factors binding to regulatory sites of other genes, as enzymes catalyzing metabolic reactions, or as components of signal transduction pathways [4-6]. This implies that, in order to understand the molecular mechanism of genes in the control of intracellular or intercellular processes, the scope should be broadened from DNA sequences coding for proteins to the systems of genetic regulatory pathways determining which genes are expressed, when and where in the organism and to which extent [7]. In the experience of engineering field, the systematic architecture and dynamic model could investigate the characteristics of signaling regulatory pathways [8]. Therefore, how to construct the dynamic model of a signaling pathway from the system structure point of view might be the first key to the door of system biology. Most biological phenomena directly or indirectly influenced by genes such as metabolism, stress response, and cell cycle are well studied on the molecular basis. Thus, identification of a signal transduction pathway could be traced back to the genetic regulatory level. The rapid advances of genome sequencing and DNA microarray technology make possible the quantitative analysis of signaling pathway besides the qualitative analysis. More particularly, the embedded time-course feature of microarray data would promote the system analysis of signal regulatory pathways as well, which is very mature in the field of engineering.
In addition to northern blots and reverse transcription-polymerase chain reaction (RT-PCR), which study a small number of genes in a single assay, the transcriptome analysis has, via DNA microarray technology [9], managed to achieve high-throughput monitoring of the almost genome-wide mRNA expression levels in living cells or tissues. Two types of available microarrays, the spotted cDNA and in situ synthesized oligonucletide [10] chips, which permit the spatiotemporal expression levels of genes to be rapidly measured in a massively parallel way, are used in different experimental requirements and stocked in the databases on net, such as Stanford Microarray Database (SMD) [11], Gene Expression Omnibus(GEO) [12] in NCBI, and ArrayExpress [13] in EBI. Microarray experiments are now routinely used to collect large-scale time series data that facilitate quantitative genetic regulatory analysis while qualitative discussion is the traditional thinking [14-17].
Several analytic methods have been proposed to infer genetic interrelations from gene expression data. In the coarse-scale approach of clustering, the underlying conjecture is that co-expression is indicative of the co-regulation, thus clustering may identify genes that have similar functions or are involved in the related biological processes. The most widely used method is the unsupervised hierarchical clustering [18]. This approach has an increasing number of nested classes by similarity measurement and resembles a phylogenetic classification. If we know the number of clusters in advance, the k-means clustering [19] could assign gene elements into a fixed number k of clusters in a way to minimize the overall Pearson or Euclidean distances of each member internally in the same cluster. Other algorithms such as the neural-network-based self-organizing maps (SOM) [20], singular value decomposition (SVD) or principal component analysis (PCA) [21], and fuzzy clustering methods [19] also have their own advantages and limitations. Alternative supervised clustering algorithm of support vector machine [22], which uses prior biological information of cluster for training, would enhance the accuracy of clustering. However, the nature of clustering algorithms apparently cannot uncover the causal interactions between genes just by grouping. Regarding the causality of pathways, the clustering analysis needs to cooperate with sequence motif detection [23]. It is also important to note that models using clustering analysis are static and thus can not describe the dynamic evolution of gene expression, even in the type of time-course microarray data.
A statistical model of Bayesian network [24] was proposed to model genetic regulatory networks. Basically, the technique uses a probabilistic score to evaluate the networks with respect to the expression data and searches for the network with the optimal score. The dynamic Bayesian network [25] was proposed to learn the network structure and parameters by maximizing the posterior probability via Bayes rule of prior probability and marginal likelihood. Another algorithm of Boolean networks [26] can also be employed to model the dynamic evolution of gene expression, where the state of a gene can be simplified to being either active (on, 1) or inactive (off, 0). The probabilistic nature of Bayesian networks is capable of handling noise inherent in both the biological processes and the microarray experiments. This makes Bayesian networks superior to Boolean networks, which are deterministic in nature. The validity of dynamic Bayesian networks is evaluated by the sensitivity-specificity score ratios [25], which depend on the training size, the degree of accuracy of prior assumption. A genetic regulatory network based on the first order differential equation with given decay rates was discussed in [27].
In this study, the dynamic system approach could be employed to model how a target gene's expression profile is regulated by its upstream regulatory genes from the system causality point of view. Then, with the causal dynamic model, the upstream regulatory function can be extracted from the expression profile of the target gene by the optimal estimation method, i.e. maximum likelihood estimation. Since merely the second-order differential equation is employed to model the dynamic evolution of the target gene, only a few parameters need to be estimated. Furthermore, the derived regulatory function is closely related to the causal upstream information of the pathway and will create a basis for inferring the regulatory pathway from the system biology point of view.
In either eukaryote or prokaryote, signaling regulatory pathways are considered as responses to the physiological activities or the deviation from homeostasis, which would affect the normal states of an organism. Among these signaling regulatory pathways, cell cycle [17] is one of the most conspicuous features of life which plays an important role in growth and cellular differentiation in all organisms. In plants, the stress-induced pathways [28] are very important to survivability under the abiotic environmental treatment such as drought, salinity and cold[29]. If these critical pathways can be identified from quantitative analysis in silico, the defect of biological processes would be predicted and corrected before hand. Our aim is to construct signaling regulatory pathways quantitatively by the system inference approach with a dynamic model and microarray data.
In this study, a second-order differential equation, which has been widely used to model many physical dynamic systems with good characteristics, is proposed to model the time-profile evolutional behavior of a target gene. The regulatory function is taken as the driving input of the dynamic equation of the target gene. Using the dynamic equation and microarray data, we first extract the regulatory function for each target gene. According to the extracted regulatory function, we deduce their upstream regulators to trace back upstream signaling pathways. Then, upstream regulatory genes are taken as target genes to trace back their upstream regulatory genes. Iteratively, we can construct the whole regulatory pathway to the genome wide using the dynamic regulatory model and microarray data from the system biology point of view. Finally, we give some independent validation of our approach by repeating the analysis with randomly reshuffling the time order of microarray data and see if the proposed pathways are destroyed.
We have applied our dynamic system approach to two genetic regulatory pathways with microarray data sets publicly available on net [15,30]. One is the circadian regulatory pathway in Arabidopsis thaliana [31,32], and the other is the metabolic shift pathway from fermentation to respiration in yeast Saccharomyces cerevisiae [33]. The circadian system is an essential signaling pathway that allows organisms to adjust cellular and physiological processes in anticipation of periodic changes of light in the environment [34-38]. According to the synchronously dynamic evolution of microarray data, we have successively identified the core signaling transduction from light receptors to the endogenous biological clock [39,40], which is coupled to control the correlatively physiological activity with paces on a daily basis. On the other hand, the diauxic shift [41] from the exhausted fermentable sugar of anaerobic metabolism to aerobic growth is correlated with widespread changes in the expression of genes involved in fundamental cellular processes such as carbon metabolism, protein synthesis, and carbohydrate storage. [28,31,32,42-47] The architecture of the signaling pathway correlative to glycolysis or gluconeogenesis during the diauxic shift is properly built up. With the dynamic system approach, not only the regulatory abilities between causal genes could be derived, but also the delays of regulatory activity are specified. These quantitative characteristics will help determine the intrinsic frameworks of connectivity in the above interesting pathways from the system biology point of view.
Results
The proposed methods in this study would be divided into four steps. In the first step, a dynamic model using the second-order differential equation is developed to describe the expression profile data as output and the regulatory function as input to denote the implicit characteristics of each gene with some parameters. With the help of the second-order dynamic model, we would then extract the upstream regulatory function from the expression profile of the target gene using the optimal estimation method. In the third step, the regulatory function estimated will help seek the correlative regulatory signals from the upstream paths. Iteratively, we can reconstruct the whole signaling regulatory pathway by linking up the upstream regulatory paths. Finally, some biological filters using available biological knowledge are employed to prune the constructed signaling regulatory pathway to improve the accuracy of the proposed method.
I. Dynamic system description of signaling regulatory model
The second-order differential equation is well used in the description of dynamic system evolved from the causality of gene regulatory function. Let Xi (t) denote the expression profile of the i-th gene at time point t. The following second-order differential equation is proposed to model the expression level of the i-th gene,
where Gi(t) is the upstream regulatory function to influence the expression profile Xi(t) of the i-th gene while ai, and biare the parameters that characterize the dynamic inherent property of the gene like degradation and oscillation, and εi(t) is the noise of current microarray data or the residue of the model. In general, the second-order differential equation has been widely used to model dynamic systems to characterize efficiently the dynamic properties of damping and resonance of systems in physics and engineering.
Obviously, the clue of upstream regulatory pathways is in Gi(t). Thus, the first step is to detect the upstream regulatory function Gi(t) from both dynamic equation in (1.1) and microarray data. However, to detect the input regulatory function Gi(t) from both equation (1.1) and microarray data directly is not easy. In this situation, a Fourier decomposition technique is employed to decompose Gi(t) as a synthesis of some harmonic sinusoid functions so that the signal detection problem of Gi(t) is reduced to a simple parameter estimation problem.
Accordingly, we can decompose Gi(t) by the following Fourier series,
Then the detection of Gi(t) becomes how to estimate the Fourier coefficients of αn and βn, which are the magnitudes of different harmonics of cos(nωt) and sin(nωt), for n = 0,..., N in equation (1.2), respectively. In science and engineering, the Fourier series has been widely employed to synthesize any continuous functions with finite energy. The estimation of αn, βn and the detection of Gi(t) in equation (1.2) are given in Methods in the sequel.
As a result of parameter estimation in Methods, the detection of regulatory function Gi(t) could be derived as follows,
Since the input regulatory function Gi(t) of a target gene is usually due to the transcriptional binding or some physical interactions from the upstream regulatory genes, in the following, we would trace back to the corresponding regulatory genes from input regulatory function of the target gene.
II. Inference of the regulatory pathway via
Apparently the input regulatory function Gi(t) in equation (1.1) contains the driving information for the target gene's expression from the upstream regulatory genes. The identified regulatory function from equation (1.3) could be interpreted as the regulatory connectivity through transcriptional binding or protein-protein interaction imposed on the i-th target gene. Nevertheless, the expression data of protein type which should be considered directly in practice are by now unavailable and unreliable to trace back upstream regulatory genes. Instead, the expression data on mRNA level which is now widely available from microarray assays would make tracing back the upstream regulatory pathway possible under proper assumptions. All along the paper we assume that the expression levels of mRNA transcripts are proportional to the actual number of corresponding proteins in the cell. This assumption is indeed a strong approximation since post-transcription is known to play a very important role in down regulating the number of the transcription factor in the cell.
Before the inference of upstream regulatory genes, it is reasonable to confine the effect of the regulatory genes on the regulated target gene. The saturated activity of expression level reveals that the regulatory ability cannot extend unlimitedly. The sigmoid function is often chosen to express the nonlinear saturation with proper parameters. Here, we apply the sigmoid transformation to represent the 'on' and 'off activities of the regulatory genes on binding or not to motifs of the target gene. So the regulatory signal shown below with the parameter set of θj = {γ, Mj, τj} is the sigmoid transformation of Xj(t), the expression profile of the j-th regulatory gene.
where γ is the transition rate, Mj is the mean expression of the j-th regulatory gene's profile, and τj is the corresponding signal transduction delay.
The delay activity should be considered in order to describe the signal transduction delay τj from the j-th regulatory gene to the target gene. The delay τj would be computed by statistical correlation between the regulatory signal transformed from the j-th regulatory gene and the identified regulatory function of the target gene. The delay τj is determined by the following maximum correlation criterion,
where rτ is the correlation between and under variable delay τ. If there are many τ to achieve the maximum correlation in (2.2), then only the smallest one is chosen.
Using the correlation method, we trace back Ri regulatory genes whose regulatory signals are most correlated with the regulatory function of the ith target gene, i.e. choose Ri genes with maximum correlation but with smaller τj in (2.2). The determination of number Ri will be discussed later. Then, we construct the regulatory pathway by tracing back Ri regulatory genes from the identified regulatory function of the target gene as the following kinetic relationship,
where cij is the pathway kinetic parameters from the regulatory gene j to the target gene i, Ri are the searched upstream regulatory genes, the constant ci0 represents the basal level to denote the regulatory function other than upstream regulatory genes, and ei(t) is the residue of the model.
Furthermore, to estimate the pathway kinetic parameters cij, equation (2.3) for m time points should be written in the following regression form,
where , .
We assume that each element in the error vector, ei(tk), k = {1,..., m}, is an independent random variable with a normal distribution with zero mean and variance σ2. By maximum likelihood parameters estimation method (see Methods), the estimates of σ2 and Ωi are given as follows, which is solved as
and
It should be noted that with the combination of biological knowledge about the transcriptional factors, protein phosphorylation, post-transcriptional and specific enzyme regulation of target genes, lots of putative and verified genes correlated with the target genes are pruned by this biological filter for the more efficient and accurate searching of Ri upstream regulatory genes in equation (2.3). For example, suppose the expression profile of gene j has a high correlation with regulation function Gi(t) of target gene i. However, if gene j is not a transcription factor, protein phosphorylation, post-transcription or specific enzyme of target gene i, it will be deleted from the candidates of Ri upstream regulatory genes because it may be only a co-expressed gene with the target gene corregulated by the other gene. On the contrary, a verified regulatory gene should be recruited into the candidates even with small correlation with Gi(t).
Finally, we take the well-known Akaike Information Criterion (AIC) into account for determining the number Ri of regulatory signal [42],
The first term in AIC is the residual variance and the second term Ri is the number of regulatory genes. AIC includes both the estimated residual variance and model complexity in one statistic, which decreases as σ2 decreases and increases as Ri increases. AIC has been widely employed to determine the complexity of system modeling science and engineering [42]. The optimal number Ri of the upstream regulatory genes will be determined by the minimization of the AIC value in equation(2.7).
Now, for the selected target genes in the interesting pathway, we could search for the optimal Ri upstream regulatory genes by AIC in equation (2.7) after the biological filtering and determine their pathway kinetic parameters cij of regulatory signal by equation (2.6). After biological filter pruning, if the number of candidates of regulatory genes is still less than Ri determined by AIC, then some genes, which are highly correlative to Gi(t) but not of transcription factors or signaling proteins of target gene i, should be recruit into candidates to uncover regulatory relationships that were not suspected to be connected. After the combination of equations (2.3) and (1.1), the whole regulatory pathway is obtained as
for i = {l, 2,..., L}, and L is the number of target genes in the pathway. The sub-paths related to the i-th target gene in the interesting pathway could be detected by the inference algorithm. Then, it is natural that the whole regulatory pathway would be constructed by the links of all the sub-paths. We also outline the whole flowchart of our dynamic inferring algorithm as shown in Figure 1 for an overview.
Discussion
Data set of analysis
The two famous modeling organisms, Arabidopsis thaliana and yeast Saccharomyces cerevisiae, have been well studied biologically and their microarray assays are abundant. Thus, we chose different types of pathways, one is the plant behavior under environmental variation and the other is the cellular metabolism in response to exhaustion of external source, as examples in this study. In other words, two signaling pathways, i.e. circadian regulatory pathway in Arabidopsis thaliana and metabolic shift pathway from fermentation to respiration in yeast Saccharomyces cerevisiae, are constructed from microarray data to confirm the accuracy of our proposed method.
For cells grown in the light/dark cycle according to circadian rhythm, Harmer and colleagues [15] used highly reproducible oligonucleotide-based arrays representing about 8200 different genes to determine steady-state mRNA levels in Arabidopsis thaliana that are measured in replicate hybridization of 12 samples harvested every 4 hours over 2 days. With their investigation on the circadian regulatory system, Harmer et al. have provided an abundance of correlated genes for the regulatory pathway inference.
As for the metabolic pathway, an cDNA microarray assay from DeRisi et al. [30], containing approximately 6400 distinct expression sequence tags (ESTs) in yeast Saccharomyces cerevisiae, is harvested at seven successive 2-hour intervals after an initial nine hours of growth under the diauxic shift. Adoption of the diauxic shift data set would make possible the inference of metabolic shift pathways.
Process of raw microarray data
With the second-order equation and the optimal estimation method, the dynamic model should be developed first for the regulatory scheme of target genes in the signaling regulatory pathway. Because the raw microarray data sample of the biological assays that will be analyzed is small with less than 15 data points for an individual gene, the cubic spline method is used to interpolate the observed data to increase the data points of each gene's time-course microarray data. As shown in Figure 2, the expression profiles of Cry1 (CRYTOCHROME 1) and PhyA (PHYTOCHROME A) genes in the circadian regulatory pathway of Arabidopsis thaliana are interpolated by the cubic spline method among raw data points on the left-hand side. Similarly, Pgi1 (PHOSPHOGLUCOSE ISOMERASE 1) and Pgm2 (PHOSPHOGLUCOMUTASE 2) genes in the metabolic shift pathway of yeast Saccharomyces cerevisiae are on the right-hand side. After the expression profiles are smoothed by the cubic spline technique, we can obtain the data of the first derivative and the second derivative more accurately and abundantly.
Extraction of regulatory information
After data expansion by the cubic spline method, we would have enough data to estimate the parameters of the regulatory dynamic model of the target gene from equation (2.4). Following the dynamic model in equation (3.1), the parameters which characterize the dynamic regulatory mechanism are estimated successfully for each target gene in the pathway. By dynamic model fitting, the expression profiles of the mentioned genes in Figure 2 can be reconstructed in Figure 3 with time progression again. Hence, we not only could predict the dynamic evolution of the target gene's expression profile accurately, but also deduce the regulatory function simultaneously as the scheme of Figure 4. The regulatory information between target genes and their upstream genes can be extracted properly with this method.
Inference of the regulatory pathway
For illustrations, the inferring strategy is applied to the selected core genes (X1~X13 and Y1~Y11) in two pathways of the circadian regulatory system in Arabidopsis thaliana and the metabolic shift pathway in yeast Saccharomyces cerevisiae to recognize their upstream regulatory genes, respectively. Their regulatory abilities with signal transduction delays are shown in the form of dynamic equation in Table 1 and Table 2, respectively. These regulatory abilities implying different degrees of influence are converted into a red-colored line as positive regulation (activation) and a blue-colored line as negative regulation (inhibition) for each target gene. Then, according to the dynamic regulatory equations in Table 1 and Table 2, the pathways of the circadian regulatory system and the metabolic shift pathway are described in Figure 5 and Figure 6, respectively.
a. Pathway of circadian regulatory system
The circadian rhythm controls processes ranging from cyanobacteria cell division to human wake-sleep cycles. In plant, especially for Arabidopsis thaliana, the growth and development have adapted to the diurnal cycling of light and dark [28,31,32,42,44,46-49]. The ability of plants to respond to light is achieved through some photoreceptors. Two classes of photoreceptors are well known to form the photo-transduction pathway under the circadian regulatory system in Arabidopsis thaliana [50]. One is the crytochrome of blue-light photoreceptors, containing Cry1 and Cry2. The other is the phytochrome of mainly red-light photoreceptors, including PhyA, PhyB, PhyD and PhyE.
In the photo-transduction related genes (Table 1 and Figure 5), containing both crytochrome (Cry1 and Cry2) and phytochrome (PhyA, PhyB, PhyD and PhyE), Cry1 [X6] and Cry2 [X10] are commonly regulated by Lhy [X3] (LATE ELONGATED HYPOCOTYL) in reciprocal ways with significant values (0.7569 in Eq.(6) and -1.8773, Eq.(10) of Table 1, respectively), implying the essentially regulatory role of Lhy on crytochrome genes. In addition, from Eq.(10) in Table 1, we further observe that Ccal [X4] (CIRCADIAN CLOCK ASSOCIATED 1) has the greatest positive regulation (2.3465) on Cry2, meaning that Cry2 is jointly regulated by Lhy and Ccal. Because the binding sites of Lhy and Ccal found in the promoter regions of Cry2 [51] are consistent with our inference, the transcriptional binding might be the mechanism of Cry2 affected by both Lhy and Ccal. In addition, the mutual activations of phosphorylation between Cry1 [X6] and PhyA [X7] in Eq.(6) and Eq.(7) of Table 1 are specifically identified consistent with the previous work [52]. At present, little is known about the nature of interactions between these two classes of photoreceptors. From Eq.(10) in Table 1, Cry2 [X10] is also positively regulated by PhyA [X7] with 0.5-hr activation delay similar to that in Cry1 (Eq.(6) in Table 1). Therefore, PhyA is considered as a post-transcriptional regulator of phosphorylation to crytochrome within 1.0-hr after transcription. On the other hand, PhyB [X11] down-regulates Cry2 with a significant effect (-0.7141) while Cry2 [X10] up-regulates PhyB (0.0511) weakly by feedback (see Eqs.(10), (11) in Table 1.). The mutual interactions between Cry2 and PhyB in nuclear speckles that are formed in a light-dependent fashion are also confirmed by Mas et al. [48]. Because Cry1 and Cry2 are both negatively co-regulated by PhyD [X8] and PhyE [X12] significantly (see Eqs.(6), (10) in Table 1), PhyA has apparently different behavior from PhyB, PhyD, and PhyE in activating crytochrome. This might suggest the mechanism that PhyA mediates the blue light by up-regulating Cry1 and Cry2, whilst PhyB, PhyD, and PhyE would mediate the red light by inhibiting blue photoreceptors [53,54].
In the mainly red-light photoreceptors of phytochrome (PhyA, PhyB, PhyD and PhyE) in Figure 5, undoubtedly Lhy [X3] and Ccal [X4], well-known biological clock genes in the circadian system [40,46], are core regulators involved in the transcriptions of both phytochrome (see Eqs.(7), (8), (11), and (12) in Table 1) and crytochrome (see Eqs.(6), (10) in Table 1) via feedback transcriptional binding. Similarly, Gi [X15] (GIGANTEA) in Figure 5 has been identified as a manifested regulator to all the phytochromes (also see Eqs.(7), (8), (11), and (12) in Table 1), although Gi sequence lacks any motifs suggesting that it is a transcription factor of phytochromes [55]. Hence, Gi might be a post-transcriptional regulatory factor. However, there is another gene Elf3 [X16] (EARLY FLOWERING 3) opposite to Gi on phytochrome, especially for PhyA, PhyB and PhyE (Eqs.(7), (11) and (12) in Table 1). Because of lower regulatory ability than transcription factor Lhy or Ccal, Elf3 might play the same role as Lhy and Ccal. Just as expected, Elf3 contains glutamine-rich motif suggesting that it is a transcription factor [56].
Before entrance of the biological oscillator of the circadian system formed by Toc1, Lhy, and Ccal, a crucial gene of Pif3 [X9] (Figure 5) is mediated significantly by PhyA [X7] (-0.7631) and PhyB [X11] (0.1223) (see Eq.(9) in Table 1). This is consistent with the post-transcriptional interactions of Pif3-PhyA and Pif3-PhyB complexes. As a core gene in the biological oscillator, Toc1 [X13] is transcriptionally regulated by Lhy [X3] (0.7009) and Ccal [X4] (-1.4704) whilst Pif3 [X16] (-0.1698) is presumably considered as the bridge between Toc1 and phytochrome (Eq.(13), Table 1). From Lhy and Ccal point of view, they are both positively affected simultaneously by Pif3 implying the regulation on the transcriptional level [57]. In addition, Toc1 inhibits both Lhy and Ccal to form the structure of mutual transcriptional regulation (please compare Eqs.(3), (4) with Eq.(13) in Table 1). So we conclude that Lhy and Ccal function as principal transcription factors.
We also infer some downstream pathways of Chs [X5] (CHALCONE SYNTHASE), Pap1 [X1], and Co [X2] (CONSTANS) in Figure 5. Chs is known as correlated with UV-B protection. It seems that Ccal and Lhy have greater effect (2.7078, -0.7631, respectively) on Chs than Pap1 (-0.0455) as a transcription factor (Eq.(5) in Table 1). This might mean that Chs is regulated by Pap1 in a small scale with amplifying effect on the cis-regulatory level. Co is recognized as a pivotal gene of photoperiodic regulation of flowering. Indeed, strong regulations from Ccal and Lhy are identified to show that Co is regulated with a large-scale attenuation effect on the cis-regulatory level (Eq.(2) in Table 1).
In the overview of the circadian system in Figure 5, most red lines of activating regulation are found in the photo-transduction pathway between phytochrome (light blue ovals) and crytochrome (light yellow ovals) implying the chain interactions after the external light input. By the feedback regulations of Lhy and Ccal (orange ovals), represented by black lines with more linking to upstream genes, the photo-transduction pathways are stabilized to provide oscillation. On the other hand, more blue lines of inhibitive interactions are revealed in the biological-clock regulatory pathways relevant to Co, Pap1, and Chs (light green ovals) underlying the anti-phase functional regulation between these output pathways and the oscillator. In addition, the essential signal transduction factors of Fkf1, Gi, Elf3, and Pif3 (gray ovals) make some critical links between the functional blocks mentioned above in the circadian system[58]. Finally, in order to validate the proposed approach, an independent validation is also given by randomly reshuffling the time order of microarray experiment [see Additional file 2] but with the same choices of target gene and regulatory genes, as shown in Figure 7. It is seen that the proposed circadian regulatory pathway in Figure 5 is destroyed by reshuffling the experimental data.
b. Metabolic shift pathway
Sugars, such as glucose and sucrose, are excellent carbon sources for yeasts and almost all of the energy requirements of the cell can be satisfied by glycolysis [6,45,59-61,63-66]. Saccharomyces cerevisiae can switch from fermentatioon at high levels of glucose to respiration at low levels of glucose with major changes in metabolic activity (diauxic shift). In their experiment on the diauxic shift [30], DeRisi et al. inoculated cells from an exponentially growing culture into fresh medium and grew them at 30 for 21 hrs. This offers a resource to infer the possible allosteric regulation of enzymatic activities, protein modification and transcriptional regulation as shown in Table 2. In addition, the scheme of the corresponding inferred pathway is shown in Figure 6. In the overview of the inferring relationships in Table 2, the gluconeogenesis from Pyk1 to pgm2 and the partial fermentation from Pyk1 (PYRUVATE KINASE 1) to Adh1 and Adh2 (ETHANOL DEHYDROGENASE ISOZYME 1, 2) are unraveled as a result of the diauxic shift, so two sub-pathways in opposite directions are concluded.
In the fermentation direction, Pykl [Y4] encoding an enzyme, which catalyzes PEP (Phosphoenolpyruvate) to pyruvate, is negatively regulated (-5.8763) by Pck1 [Y26] (Eq.(4) in Table 2). Pck1 could be intepreted here as an indirect upstream transcription factor or regulatory gene for Pyk1 due to its function of decarboxylation and phosphorylation of oxalacetat in the presence of a nucleoside triphosphate and a divalent metal ion to yield PEP. Another Gcrl [Y15] gene is also identified as the strongest positive regulation (5.9829) to Pyk1 (also see Eq.(4) in Table 2), which is putatively considered as a transcription factor. This candidate transcription factor Gcrl of Pdc1 [Y6] (PYRUVATE DECARBOXYLASE ISOZYME 1) plays a more essential role (-2.5615, Eq.(6) in Table 2) than Rap1 [Y17] (0.1164), and Pyk1 [Y4] is an upstream regulatory factor coding an enzyme with the most positive effect (3.1295) on Pdc1 according to the production of acetaldehyde from pyruvate. In the last kernel of the fermentation, Adh1 [Y7] and Adh2 [Y3] are involved in the ethanol metabolism of carbohydrate storage. Adh2 is implicated to up-regulate Adh1 (0.5145, Eq.(7) in Table 2) under the catabolism from ethanol to acetaldehyde and is significantly up-regulated by Adh1 (1.0746, Eq.(3) in Table 2) to produce ethanol reversely. The mutual regulations of these two isozymes are within a tiny activation delay of 0.5-hr implying their close relationship. In addition, Gcr2 [Y16] and Sfp1 [Y30] with consistently dominant negative influences on Adh2 and Adh1 respectively would be at the transcriptional level presumably (see Eqs.(3), (7) in Table 2).
In the sub-pathway of glyconeogenesis, Eno2 [Y2] (ENOLASE ISOZYME 2) is regulated by Pck1 [Y26] (-0.7195) in the same way as Pyk1 while the main transcription factor is Stp2 [Y31] with significantly positive regulation (0.2147, see Eq.(2) in Table 2). As seen in Table 2, a causal cascade of Eno2, Gpm1 [Y8] (PHOSPHOGLYCERATE MUTASE l), Tpi1 [Y10] (TRIOSE-P ISOMERASE 1), Fba1 [Y11] (ALDOLASE 1), and Pgi1 [Y9] (PHOSPHOGLUCOSE ISOMERASE 1) indicates the construction of a trunk of the glyconeogenesis (see Eqs.(8), (9), (10), and (11) in Table 2). Among them, Rap1 [Y17] and Gcrl [Y15] are the common regulators of Gpm1, Pgi1, and Tpi1. This means that Rap1 and Gcrl might be the most important regulators in the glyconeogenesis pathway by the transcriptional binding. Finally, Pgm2 [Y5] (PHOSPHOGLUCOMUTASE 2) co-regulated by Glk1 [Y27] (GLUCOKINASE 1), Hxk1 [Y28], and Hxk2 [Y29] (HEXOKINASE 1, 2) significantly confers another pathway leading to the synthesis of UDP-GLU from Glucose-6-P (see Eq.(5) in Table 2).
In the overview of the metabolic shift pathway in Figure 6, extremely significant regulations (vivid red lines or blue lines) from most transcription factors (gray ovals) means that transcriptional regulations are feasibly identified. However, in the fermentation sub-pathway (light blue ovals), the mutual regulations between Adh1 and Adh2 are apparent when compared with the obscure relationships in glyconeogenesis (light yellow ovals). Interestingly, three transcription factors Gcr1, Gcr2 and Rap1 (black-line signals) appear to have very significant effects on the metabolic shift pathway. Finally, in order to validate the proposed method, an independent validation is also given by randomly reshuffling the time order of microarray experiment but with same choices of target gene and regulatory gene, as shown in Figure 8. Obviously, the proposed metabolic shift in Figure 6 is destroyed by reshuffling the experimental data.
Conclusion
Microarray expression analysis by the dynamic system approach offers an opportunity to generate functional regulation interpretation on the genome-wide scale. The crucial ontology behind using dynamic system techniques is that the causality between gene expression profiles could be identified according to the differential equation underlying a dynamic system. Therefore, because the microarray data were harvested with time progression, the simultaneously varied gene expressions implicated in a genetic regulatory system would be detected to infer the regulatory pathways in spite of the versatile interactions such as transcriptional control, protein phosphorylation, or specific enzyme regulation.
The clustering method answers the problem of what is the functional catalogue of a specific gene by the identification of resembling patterns of gene expressions. Similarly, the co-regulations of upstream genes in our method also imply their concurrent functions. In contrast to the clustering algorithm, the causality of time-course data has been smoothly drawn by our dynamic method. The Bayesian networks were used merely for forward probabilistic estimation with time transition lacking in the feedback linkages. This unidirectional problem would not happen in our algorithm. Owing to the quantitative regulatory abilities of our model, we have a greater diversity of regulatory influence than the Boolean networks, which are deterministic with merely two states.
In our dynamic system approach, we not only can link qualitatively the upstream genes to the downstream ones iteratively, but also indicate quantitatively their regulatory relationships, including the regulatory abilities and the activation delays. In terms of the regulatory abilities, the comparison between the upstream regulatory genes of a target gene can inspire us to ask which one is significant biologically and whether it is a positive or negative influence on the investigated gene. Moreover, the speculation of activation delays benefits the empirical reference by providing us when the upstream regulatory genes might interact with their target genes. Since any gene can be considered as a target gene to trace back its upstream regulators, these regulators are then considered as target genes to trace back their upstream regulators. Iteratively, the genetic regulatory pathway (or network) can be constructed to the genome-wide. According to the qualitative and quantitative features imbedded, two regulatory pathway examples are characterized as in Figure 5 and Figure 6 for the identification of the proposed method. In addition, using the Akaike Information Criterion (AIC), a proper number of regulatory genes would be affirmed. As a result, many links overlap with well-known regulatory and signaling pathways in the previous literature and several putative ones are also found. Furthermore, the activation or repression relationships inferred via the microarray data would distinctly uncover the overall effect of regulatory interactions among casual genes in pathways on the transcriptional level.
In the two pathways under investigation, we have a more detailed understanding about the regulatory interactions among phytochrome, crytochrome and biological clock in the circadian regulatory system. On the other hand, the sophisticated knowledge of the metabolic pathway after the diauxic shift can be unfolded properly in our analysis. Furthermore, the independent validation of our approach is also given by randomly reshuffling the time order of microarray experiment. We found that the proposed pathways in Figures 5 and 6 are all destroyed as shown in Figures 7 and 8, respectively. The successful analysis of these two pathways implies the development of a valid and high-throughput method. All of the programs have been released [see Additional file 1]
There are some shortcomings in our study. First, although the time-course microarray data are available, its lower samplings will distort the real changes of gene expressions, especially for quick dynamic evolution. A more sampling experiment with respect to the intrinsic turnover rate is expected to have more precise analysis. Secondly, a regulatory gene with larger activation delay would not be recognized because the less activation delay criterion is used, but this might be overcome by properly relaxing the criterion. Thirdly, activation profiles under the proteome should be highly correlated with the transcriptional profiles to elevate the interpretation of our system model. In general, the synchronous time-course microarray assay is more suitable to underlie the transcriptional binding among causal genes, but an inference of physical interactions in the post-transcriptional level also has sufficient feasibility in our study.
In the near future, the most pressing task is to investigate our presumed paths in the laboratory. As the pathway construction algorithms are further developed, we expect this system approach to have immense impact in elucidating the underlying molecular mechanisms of pathways in a variety of organisms, especially after the maturation of the protein chips. Ultimately, we envision that biologists will perform routine pathway inference to seek some novel regulations and to identify the evolutionarily conserved links.
Methods
I. Detection of regulatory function Gi(t) in equation (1.2)
After the decomposition of Gi(t) in equation (1.2), we substitute equation (1.2) into equation (1.1) to obtain the following dynamic equation for the expression profile of the i-th gene,
In the above dynamic equation, parameters ai, bi, αn, and βn should be estimated by the time profile of microarray data of the i-th gene, i.e. these parameters should be specified so that the simulating output Xi(t) of the dynamic model in equation (3.1) should meet the empirical expression profile of the i-th gene. The least-squares estimation method is employed to solve this parameter estimation problem.
To make the dynamic model effective, the dynamic equation in (3.1) should meet the expression profile at all time points t = t1,…, tm and is then arranged in a vector differential form. Consequently, the vector differential form underlined in this equation is applied to m time points in order.
where , , and m denotes the number of time points.
In the next step, formula (3.2) can be translated into a differential matrix equation as follows,
Yi = AiΦi + Ei (3.3)
where , Φi = [ai bi α0 β0 … αN βN]T, and are in vector forms, while is a matrix.
To estimate the relevant unknown parameters in Φi, the least-squares method below is used to derive the optimal parameters estimation of ,
Actually, the modeling error could be concluded into Ei as the noise of the gene-expression profile or of the microarray chips. So the consideration of modeling error makes equation (3.3) approach more the reality. By the way, in order to get accurate data of and from the expression profile of the target gene, the cubic spline should be employed to interpolate the time profile of the target gene. Furthermore, the choice of N is based on the tradeoff between the accuracy of approximation in (1.2) and the complexity of parameter estimation in (3.4). In this study N = 6 is chosen because these harmonics are enough to approximate regulation functions.
II. Maximum likelihood Estimate of kinetic parameters Ωi in equation (2.4)
Maximum likelihood method for Ωi in equation (2.4) is given as follows:
The log-likelihood function for given m data points is then described by
The necessary condition for the maximum likelihood estimation of variance σ2 is , by which equation (2.5) is obtained.
Substituting equation (2.5) into equation (4.2) yields,
meaning that we can find the maximum likelihood estimation of Ωi by minimizing the value of σ2 in equation(2.5). Then, the maximum likelihood estimate in equation (2.6) is obtained by in equation(2.5).
Authors' contributions
W.C. Chang and Chang-Wei Li carried out the computational studies and analysis. B.S. Chen gave the topic and suggestions. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
The raw data has been transform to .mat file, which is one of the Matlab files. This file has been divided into three parts: Name, Time, and Profile. [see Additional file 1] Name denotes the names of microarray data. Time denotes the time points of microarray data. Profile denotes the gene profiles of microarray data.
Click here for file
Additional File 2
The 'Shuffled_data' is the Shuffled raw data. [see Additional file 2]
Click here for file
Acknowledgements
We thank the National Science Council, Taiwan for grants NSC 93-3112-B-007-003.
Figures and Tables
Figure 1 Illustration of the overall flowchart of the pathway inference algorithm.
Figure 2 The expression profiles derived from the cubic spline interpolations. The expression profiles after performing the cubic spline interpolations for Cry1, PhyA (pathway of circadian regulatory system of Arabidopsis thaliana) on the left-hand side, and PGI1, PGM2 (metabolic shift pathway of yeast Saccharomyces cerevisiae) on the right-hand side. The red open triangles are the raw microarray data, and the blue dotted points are the interpolation data.
Figure 3 The second-order dynamic model fitting of pathway genes. The expression profile with corresponding second-order dynamic model fitting for Cry1, PhyA (pathway of circadian regulatory system of Arabidopsis thaliana) on the left-hand side, and PGI1, PGM2 (metabolic shift pathway of yeast Saccharomyces cerevisiae) on the right-hand side. The blue dotted points are the cubic spline interpolations of microarray data, and the red dashed lines are the estimated dynamic evolution of expression data.
Figure 4 The extracted upstream regulatory functions of pathway genes. The upstream regulatory function extracted from expression profiles of corresponding target genes Cry1, PhyA (pathway of circadian regulatory system of Arabidopsis thaliana) on the left-hand side, and PGI1, PGM2 (metabolic shift pathway of yeast Saccharomyces cerevisiae) on the right-hand side.
Figure 5 The pathway of circadian regulatory system of Arabidopsis thaliana according to the dynamic regulatory modeling in Table 1. The related genes are represented as ovals with different colors of light yellow (crytochrome), light blue (phytochrome), orange (biological clock genes), light green (some physiologically light-dependent downstream genes), and gray (other relevant genes not as target genes). There are three types of lines with colors of red (activation), blue (repression), and black. In addition to the black line representing the signal pipes from genes, the red lines are shown depending on the degree of activation whilst the blue lines are shown depending on the degree of repression. The black square symbol attached on the lines is the bifurcate node from the same pipe of signal. The circle symbol attached on the lines is the collecting nodes from different signal sources. The colored degree bar between activation and repression is also shown in the bottom of the figure.
Figure 6 The metabolic shift pathway of yeast Saccharomyces cerevisiae according to the dynamic regulatory modeling in Table 2. The related genes are represented as ovals with different colors of light blue (fermentation), light yellow (gluconeogenesis), and gray (other relevant genes not as target genes). There are three types of lines with colors of red (activation), blue (repression), and black. In addition to the black line representing the signal pipes from genes, the red lines are shown depending on the degree of activation whilst the blue lines are shown depending on the degree of repression. The black square symbol attached on the lines is the bifurcate node from the same signal source. And the circle symbol attached on the lines is the collecting nodes from different signal sources. The colored degree bar between activation and repression is also shown in the bottom of the figure.
Figure 7 The pathway of circadian regulatory system of Arabidopsis thaliana in Fig. 5 is repeated as independent validation by randomly reshuffling the time order of microarray experiment but with the same choices of target and regulatory genes. Obviously, the proposed pathway in Fig. 5 is destroyed by the reshuffling of experimental data. The related genes are represented as ovals with different colors of light yellow (crytochrome), light blue (phytochrome), orange (biological clock genes), light green (some physiologically light-dependent downstream genes), and gray (other relevant genes not as target genes). There are three types of lines with colors of red (activation), blue (repression), and black. In addition to the black line representing the signal pipes from genes, the red lines are shown depending on the degree of activation whilst the blue lines are shown depending on the degree of repression. The black square symbol attached on the lines is the bifurcate node from the same pipe of signal. The circle symbol attached on the lines is the collecting nodes from different signal sources. The colored degree bar between activation and repression is also shown in the bottom of the figure.
Figure 8 The pathway of metabolic shift regulatory system of yeast Saccharomyces cerevisiae in Fig. 6 is repeated as independent validation by randomly reshuffling the time order of microarray experiment but with the same choices of target and regulatory genes. Obviously, the proposed pathway in Fig. 6 is destroyed by the reshuffling of experimental data. The related genes are represented as ovals with different colors of light blue (fermentation), light yellow (gluconeogenesis), and gray (other relevant genes not as target genes). There are three types of lines with colors of red (activation), blue (repression), and black. In addition to the black line representing the signal pipes from genes, the red lines are shown depending on the degree of activation whilst the blue lines are shown depending on the degree of repression. The black square symbol attached on the lines is the bifurcate node from the same signal source. And the circle symbol attached on the lines is the collecting nodes from different signal sources. The colored degree bar between activation and repression is also shown in the bottom of the figure.
Table 1 The dynamic equation set of the identified upstream regulators and their regulatory relationships to the specific target genes in the pathway of circadian regulatory system of Arabidopsis thaliana.
Target Genes X1 X2 X3 X4 X5 X6 X7 X8
Papl* Co Lhy* Ccal* Chs Cryl PhyA PhyD
X9 X10 X11 X12 X13
Pif3 Cry2 PhyB PhyE Tocl*
Other Genes X14 X15 X16
Fkfl Gi Elf3
Table 2 The dynamic equation set of the identified upstream regulators and their regulatory relationships to the specific target genes in the metabolic shift pathway of yeast Saccharomyces cerevisiae.
Target Genes y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8
Fbpl Eno2 Adh2 Pykl Pgm2 Pdcl Adhl Gpm1
Y9 Y10 Y11
Pgil Tpil Fbal
Other Genes Y12 Y13 Y14 Y15 Y16 Y17 Y18 Y19
Tdhl Tdh2 Tdh3 Gcrl* Gcr2* Rap1* Abfl* GrflO*
Y20 Y21 Y22 Y23 Y24 Y25 Y26 Y27
Ino4* Pgkl Pfk2 Hsfl* Pdc5 Pdc6 Pckl Glkl
Y28 Y29 Y30 Y31
Hxkl Hxk2 Sfpl* Stp2*
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| 15748298 | PMC555938 | CC BY | 2021-01-04 16:35:47 | no | BMC Bioinformatics. 2005 Mar 7; 6:44 | utf-8 | BMC Bioinformatics | 2,005 | 10.1186/1471-2105-6-44 | oa_comm |
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BMC GenetBMC Genetics1471-2156BioMed Central London 1471-2156-6-121576046710.1186/1471-2156-6-12Methodology ArticleSearching QTL by gene expression: analysis of diabesity Brown Aaron C [email protected] William I [email protected] Charles J [email protected] Marjorie E [email protected] Jürgen K [email protected] Daniel J [email protected] Derry C [email protected] The Jackson Laboratory, 600 Main Street, Bar Harbor, Maine 04609, USA2005 10 3 2005 6 12 12 28 9 2004 10 3 2005 Copyright © 2005 Brown et al; licensee BioMed Central Ltd.2005Brown 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 developments in sequence databases provide the opportunity to relate the expression pattern of genes to their genomic position, thus creating a transcriptome map. Quantitative trait loci (QTL) are phenotypically-defined chromosomal regions that contribute to allelically variant biological traits, and by overlaying QTL on the transcriptome, the search for candidate genes becomes extremely focused.
Results
We used our novel data mining tool, ExQuest, to select genes within known diabesity QTL showing enriched expression in primary diabesity affected tissues. We then quantified transcripts in adipose, pancreas, and liver tissue from Tally Ho mice, a multigenic model for Type II diabetes (T2D), and from diabesity-resistant C57BL/6J controls. Analysis of the resulting quantitative PCR data using the Global Pattern Recognition analytical algorithm identified a number of genes whose expression is altered, and thus are novel candidates for diabesity QTL and/or pathways associated with diabesity.
Conclusion
Transcription-based data mining of genes in QTL-limited intervals followed by efficient quantitative PCR methods is an effective strategy for identifying genes that may contribute to complex pathophysiological processes.
==== Body
Background
Understanding the molecular etiology of disease processes is a pressing goal of 21st century medicine. Completion of the mouse genome holds considerable promise in the discovery of genes responsible for genetically determined complex diseases. Quantitative trait loci (QTL) are allelically variant regions detected by virtue of their contribution to the overall complex disease phenotype and thus are "experiments in nature", which mark chromosomal intervals carrying genes with a proven disease involvement. Since gene expression is a key link between the genome and the plethora of phenotypic traits exhibited, tools that permit the analysis of the tissue expression pattern of genes in their chromosomal context provides a bridge between QTL and the genes responsible.
Global microarray gene expression technologies offer a promising, unbiased approach toward this goal in that they reveal gene expression changes, which can be correlated with the disease phenotype. However, such global methods of analysis are not routine analytical tools and can suffer from incomplete gene coverage, as well as lack of sensitivity. Because only a small fraction of the transcriptome is typically involved in any given etiopathological process, bioinformatic data mining tools that allow for the intelligent prioritization of genes would make it possible to employ more routine and sensitive expression technologies, such as quantitative polymerase chain reaction (QPCR). ExQuest [1,2] organizes pre-existing information-rich expression databases in a way that quantitative tissue and/or developmental gene expression patterns can be extracted and displayed within the context of whole chromosomes. By overlaying ExQuest "chromosomal expression maps" with QTL coordinates, one can search within defined genomic intervals for candidate genes with enhanced expression in tissues consistent with disease pathology. Screening candidates by QPCR will determine whether or not they exhibit expression changes in genetically resistant versus susceptible mice.
Type II diabetes (T2D), also referred to as non-insulin dependent diabetes mellitus, is characterized by insulin resistance of target cells combined with insufficient insulin production and/or abnormal secretion by pancreatic β-cells, which eventually results in chronically elevated glucose levels in the circulation. Obesity is a major risk factor for T2D, and the term "diabesity" has been coined to collectively describe these overlapping conditions [3]. This paper describes an ExQuest compilation of novel candidate genes from mouse diabesity QTL intervals mapped to chromosomes 1, 4, 10, 17, 19 and X. This is followed by QPCR analysis of the ExQuest candidates, in addition to known diabetes and metabolism genes selected from the literature, using tissues derived from the newly defined T2D model, Tally Ho (TH) [4]. By selecting genes from QTL intervals derived from multiple diabesity models and using the TH mouse as a susceptible strain to test for differential expression, we have identified a number of genes whose altered expression may be involved in the development of diabesity.
Results
Diabesity-relevant candidate gene selection
To identify candidate genes that contribute to diabesity, we first established the local boundaries of 29 QTL from the LocusLink database that contribute to body weight, adiposity or T2D, and map to mouse chromosomes 1, 4, 10,17, 19 and X (Table 1). Using ExQuest, we were able to cluster publicly available ESTs to genomic fragments, extract and normalize tissue information from EST datasheet records, and display quantitative expression information linearly along the six chromosomes for over 70 tissues. We then narrowed the chromosomal search by overlaying the in silico expression data with QTL intervals. We were particularly interested in genes with known or potential metabolic function whose expression patterns were biased towards high expression in three diabesity relevant-tissues, pancreas, liver and adipose tissue, for which there was good EST library representation. (Skeletal muscle was not included due to the lack of available mouse EST libraries.) An example is illustrated in Figure 1A, in which a region of genes with expression strongly biased toward pancreatic tissue is found centered over the 1-LOD 95% confidence interval of the TH QTL Tafat [4]. By using ExQuest's zoom-in capability, resolution down to the expression of individual exons revealed that the expression bias was explained by a single gene, Elastase 2 (Ela2), with a pattern of expression that includes pancreas, stomach and tongue (Fig. 1B). A second example is illustrated in Figure 1C, in which a cluster of pancreatic expressing genes is localized to two chromosome 19 T2D QTL, T2dm2 [5] and Tanidd1 [4]. The expanded chromosomal view revealed that this cluster contained pancreatic lipase (Pnlip) and two paralogs, Pnliprp1 and Pnliprp2. From all QTL intervals, we chose the 71 "best expression" candidates (Table 1) out of a possible 4,013 genes as predicted by Ensembl's gene annotation. From PubMed searches, we selected an additional 24 genes considered to be highly relevant to diabesity and/or metabolism, as well as the standard normalizer 18S RNA, bringing our total to 96, a convenient number for QPCR profiling (for gene list and oligonucleotide primer sequences, see Additional file 1).
Table 1 Genes selected for expression analysis by ExQuest.
Chr QTL1 Genes picked via ExQuest Total Genes in Region Interval Range (Mb)
1 Obq2 Gsta3 73 13.7
Obq7, Wt6q1, Insq2, Insq6 Aox1, Fn1, Pecr, Igfbp2, Plcd4, Scg2, IRS-1, Inpp5d 401 44.1
Nidd6 Qscn6 85 9.4
Obq9 Fmo1, Fmo3, Apoa2 117 11.7
Wt6q2 Hsd11b1 63 7.3
4 Bwq1 Decr1 64 12.1
Triglq1, Bglq4 Ttpa, Bhmt, Baat, Aldo2 40 9.8
Nidd1, Dob1 Lepr, Dio1, Scp2, Faah, Usp1 350 32.4
Tafat Ela3b, Ela2, Dvl1 319 18.0
10 Insq9, Igfbp3q2 Ass1, Ftcd, Col18a1, Itgb2, Agpat3, Ndufs7, Oaz1, Pah, Igf-1, Nr1h4, Kitl 605 55.1
Bgeq8 Rdh7, Hsd17b9 257 19.8
17 Obq4, Wta4 Plg, Acat2, Actl, Hagh, Igfals, Decr2, Clps, Tff1, Tff2, Tff3, Apom 554 27.2
Hdl4 Gnmt, Lrg, Sepr 161 10.7
Insq5 Abcg5 69 10.0
19 Iba4 Aldh1a1, Aldh1a7, Vldlr, Plce1 190 23.3
Afw8 Pi4k2a, Cpn1, Elovl3 159 8.8
Nobq2, Bglq13 Ins1, Gpam, Facl5 39 8.4
Tanidd1, T2dm2 Gfra1, Pnlip, Pnliprp1, Pnliprp2 40 3.6
X Bw1 Rgn 149 51.5
Bw3 Rab9 278 30.0
1QTL name, abbreviation: Obesity, Obq; Body weight, 6 weeks, Wt6q; Insulin, Insq; Non-insulin-dependent diabetes mellitus, Nidd; Body weight, Bwq; Triglyceride, Triglq; Body growth late, Blgq; Dietary obesity, Dob; Tally ho associated mesenteric fat pad weight, Tafat; Insulin-like growth factor binding protein 3, Igfbp3q; Body growth early, Bgeq; Weight adult, Wta; High density lipoprotein level, Hdl; Induction of brown adipocytes, Iba; Abdominal fat weight, Afw; New Zealand obese, Nobq; Tally ho associated non-insulin dependent diabetes mellitus, Tannid; Type 2 diabetes mellitus, T2dm; Body weight, Bw.
Figure 1 Mirrored tissue-specific expression (red) and absolute EST representation (green) for whole ExQuest chromosomes. Maximum scales set for specific or absolute expression is within the circle at the centromere. (a) Pancreas expression for mouse chromosome 10 showing peak position of the LOD for the QTL Tafat over a pancreas expression peak that contains the gene Ela2. (b) Quantitative tissue specific expression profile for the gene Ela2. Results are derived from the number of Ela2-aligned EST hits normalized for library density. (c) Pancreas expression for mouse chromosome 19. Genes in the Tinidd1 and T2dm2 region are pancreas specific (red) and highly expressed (green). Arrow shows a 20X zoom of 3 individual genes Pnlip (1810018F18Rik), Pnliprp1 and Pnliprp2, which contribute to the large pancreatic histogram peak.
Real-time QPCR profiling
The newly-described TH T2D mouse model develops obesity, hyperinsulinemia, hyperlipidemia, and male-limited hyperglycemia [4]. To test the validity of our candidate gene selection and determine a quantitative molecular signature for this mouse model, we extracted adipose, liver and pancreas RNA from three early diabetic (as verified by glycemic index) 7-week-old male TH mice, as well as three age and sex matched B6 controls. Real-time QPCR was then independently performed for all 96 genes on the three biological replicates, and the results were processed using GPR software (for raw Ct values and GPR Reports, see Additional files 2, 3 and 4). Twenty-one of 96 genes exhibited significant expression changes between TH and control B6 mouse tissues (Table 2). Fifteen were among the 71 candidates chosen by ExQuest, while six were from the 24-gene PubMed pool (Table 3).
Table 2 GPR report of B6 vs. TH gene expression comparisons.1
Gene # Total hits2 GPR Score3 Fold change4
Adipose
Pparg 44 0.92 -14.9
Lep 40 0.83 5.3
Alb1 39 0.81 -9.4
Gsta3 38 0.79 -10.6
Glut2 37 0.77 -28.1
Fmo1 35 0.73 -4.8
Scp2 25 0.52 -2.4
Agpat2 24 0.50 -2.9
Agpat3 19 0.40 -2.1
Pancreas
Ela2 39 0.95 -55.8
Pnliprp2 39 0.95 -21.8
Kitl 29 0.71 -4.3
Col18a1 18 0.44 -1.7
Ela3b 16 0.40 -1.8
Liver
Tff3 63 1.00 -593.0
Vldlr 58 0.92 15.6
Hsd17b9 57 0.91 9.5
Pparg 56 0.89 -11.5
Igfbp2 46 0.73 -2.7
Kitl 39 0.62 -2.4
Decr2 28 0.44 -1.3
Lpl 25 0.40 -1.2
Pi4k2a 25 0.40 -1.1
1 Complete GPR report and raw CT data in Supplementary Data.
2Number of qualified normalizer genes against which test gene ΔCT values are statistically significant at p = 0.05.
3 # of total hits / # of qualified normalizers. A GPR score = 0.40, meaning that the test gene ΔCT comparisons differed at p= 0.05 compared with at least 40% of the qualified normalizers, is considered most reliable.
4Relative to 18S RNA.
Table 3 Medline citations to genes showing altered expression in TH mice.
Gene name Diabesity-relevant articles General function
Genes chosen known to be involved in diabetes with expression change
Alb1 5548 Lipid binding and carrier activity
Leptin 3170 Hormone activity
Lpl 775 Lipid catabolism or fatty acid metabolism
Pparg 702 Lipid metabolism and steroid hormone receptor activity
Glut2 142 Glucose transport and carbohydrate metabolism
Agpat2 5 Phospholipid biosynthesis
ExQuest chosen genes with expression change, known or unknown to be involved in diabetes
Col18a11 7 Potent antiangiogenic protein, structural molecule
Vldlr2 7 Lipid transport
Scp2 4 Sterol carrier activity, lipid binding, and steroid biosynthesis
Fmo1 3 Electron transport and oxidoreductase activity
Pnliprp2 1 Lipid catabolism
KitL 0 Signal transduction
Igfbp2 0 Insulin-like growth factor binding
Ela2 0 Proteolysis and peptidolysis
Ela3b 0 Proteolysis and peptidolysis, and cholesterol metabolism
Gsta3 0 Biosynthesis of steroid hormones
Agpat3 0 Phospholipid metabolism
Hsd17b9 0 Steriod biosynthesis and oxidoreductase activity
Decr2 0 Peroxisome organization and biogenesis, and oxidoreductase activity
Tff3 0 Epithelium healing
Pi4k2a 0 Inositol/phosphatidlinositol kinase activity
Functional summary of genes with expression changes3
Lipid related gene activity 8
Hormone related gene activity 6
Inflammation or response to injury 4
Oxidoreductase activity 3
Signal transduction 3
Proteolysis and peptidolysis 2
Carbohydrate related gene activity 1
1Hsd17b9 medline search: there are 88 articles for the homolog "11-beta hydroxysteroid dehydrogenase", and 157 with "hydroxysteroid dehydrogenase".
2 Vldlr MEDLINE search: 4818 diabesity relevant articles found for Vldl.
3 Genes with dual functions are counted twice.
Novel genes potentially implicated in the progression or response to diabetes
To assess the novelty of the differentially expressed ExQuest-selected genes in regard to diabesity, we performed an extensive PubMed search for articles associated with both the gene name (or its alternative nomenclature as defined by LocusLink gene name aliases), and diabetes or obesity. The number of articles retrieved were typically one to two orders of magnitude lower for the differentially expressed ExQuest selected genes in comparison to the genes analyzed with known diabesity or metabolism involvement, with many having no literature citations (Table 3). QTL-based ExQuest expression mining thus revealed a number of novel diabesity candidate genes with little to no prior association with this disease.
Discussion
Our results suggest that in silico data mining focused on gene expression in diabesity-affected tissues and limited to intervals containing diabesity-specific QTL (and thus enriched in genes that contribute to diabesity) is an efficient method to identify genes whose expression is altered in T2D-susceptible mice. This analysis not only demonstrated expression alterations in genes known to be associated with the development of diabesity, but also identified a number of novel genes whose expression changes may contribute to the development of diabesity. Expression changes in genes mapping within TH QTL could be considered as candidates for transcriptional polymorphisms contributing to the associated TH QTL. However, genes selected on the basis of other diabesity QTL that showed transcriptional differences in the TH/B6 comparison are more likely minor QTL in the TH model or symptomatic effects of diabesity rather than a major genetic cause of the TH disease.
As other diabesity studies commonly observe, there were highly significant changes in adipose tissue expression of Pparg and Lep. This included a ~5-fold increase in leptin expression in TH adipose with no accompanying increase in expression of the leptin receptor (Lepr). Since leptin is responsible for the regulation of food intake [6], this increase is most likely in response to incipient weight gain. In contrast, the observed decrease in the expression of peroxisome proliferator activated receptor gamma (Pparg), both in TH adipose tissue and liver, is consistent with a decrease in adipocyte differentiation often observed in obese states [7,8]. In addition, Pparg agonists are generally associated with promoting insulin sensitization in the context of obesity [9].
Other genes previously associated with diabesity and showing differential expression in TH vs. B6 tissues included a 28.1-fold decrease of glucose transporter 2 (Glut2) in adipose, a 2.9-fold decrease in 1-acylglycerol-3-phosphate O-acyltransferase 2 (Agpat2) in adipose, and a 1.2-fold decrease in lipoprotein lipase (Lpl) in liver. Their reduced expression in the TH model might be in response to disease onset.
Serum albumin levels are decreased in T2D patients (reviewed in [10]). The primary source of serum albumin is liver. Albumin synthesis is decreased in both diabetic humans and in rat T2D models[11,12] and in liver cells deprived of insulin [13]. We failed to detect any alteration in the normally high levels of Alb1 expression in liver, but TH mice showed a 9.4-fold decrease in Alb1 expression in adipose tissue. Whether this fat-specific decrease in Alb1 expression is an early manifestation of subsequent hypoalbuminemia remains to be established.
Altered expression of a number of ExQuest-selected genes was also found. TH livers showed a 15.6-fold expression increase compared with B6 for the very low-density lipoprotein receptor (Vldlr). As a deficiency in Vldlr has been reported to reduce adipocyte size and obesity in the ob/ob mouse model [14], this expression alteration may contribute to TH diabesity. The expression of hydroxysteroid (17β) dehydrogenase 9 (Hsd17b9), which localizes to the early body weight QTL Bgeq8 QTL, was elevated 9.5-fold in TH livers. While the function of this gene is not described in the literature, its paralog 11beta-hydroxysteroid dehydrogenase type 1 (Hsd11b1) bidirectionally catalyzes the conversion of cortisol to the inactive metabolite cortisone. Over 150 articles describe an association of Hsd11β1 with diabesity, and an intronic Hsd11b1 polymorphism is associated with obesity and insulin resistance in children [15]. However, whether the elevated expression of the Hsd17b9 paralog in TH liver is a candidate for Bgeq8 remains to be established.
Exocrine secretion of pancreatic lipases are known to hydrolyze triglycerides to free fatty acids in the small intestine. Pancreatic lipase (Pnlip) and two paralogs, Pnliprp1 and Pnliprp2, were expressed preferentially in pancreas and mapped very close to the peak of the LOD values for the overlapping diabesity QTL, T2dm2 and Tannidd1 (Fig. 1b). T2dm2 and Tannidd1 are responsible for increased insulin levels [5] and elevated plasma glucose [4], respectively. Long-term high fat feeding, leading to glucose intolerance, occurs with a simultaneous decrease in mRNA expression of Pnlip [16]. While no expression alteration in the normally high levels of expression of Pnlip or its closely related Pnliprp1 paralog were found, Pnliprp2 expression was decreased 21.8-fold in TH pancreas when mice were fed a 4% fat diet. By facilitating fat storage, the consequence of which is hyperglycemic diabetes, the selective decrease in Pnliprp2 in TH mice may explain Tanidd1, and potentially, the genetically overlapping T2dm2 QTL.
Trefoil 3 (Tff3), which maps to the Obq4 obesity QTL, was the most significantly changed of all genes analyzed. It is quite transcriptionally active in control B6 liver but virtually undetectable in TH liver. Trefoils are small, stable secretory proteins expressed in goblet cells in the gastrointestinal mucosa where they stabilize the mucus layer and promote epithelial healing [17]. Mice deficient in Tff3 are highly susceptible to colon damage [18], which is not commonly associated with T2D. While the colon is the primary tissue of expression, Tff3 has also been reported to be expressed in the bile ducts of normal human liver and is upregulated in diseased livers [19]. However, a potential association of the remarkable reduction of Tff3 expression in TH liver with diabesity remains to be established.
While not detectably expressed in the liver or fat tissue in B6 or TH mice, Ela2 was ranked by GPR as the gene most significantly changed in the pancreas, with a 55-fold reduction in TH vs. B6 mRNA (Table 2). Originally cloned from the pancreas [20], Ela2 is located within 75 kb of the peak LOD score for the TH QTL Tafat. ExQuest expression profiling suggests that Ela2 expression is biased strongly towards digestive tissues (pancreas, stomach and tongue) of normal mice (Fig. 1A). Both whole pancreas and Islet of Langerhans EST libraries show high levels of Ela2 expression, suggesting that the endocrine pancreas actively transcribes it. Ela2 (alias polymorphonuclear neutrophil elastase) encodes a serine protease present in neutrophil endosomal granules, which are known to be important in myelopoiesis (reviewed in [21]). The substantially decreased expression in TH pancreas may result in secretory granules deficient in this serine protease.
Conclusion
We have tested the concept that transcription-based ExQuest data mining of genes in QTL-limited intervals is an effective method to identify genes that contribute to the complex genetic disease, diabesity. By limiting the in silico candidate genes to those with expression biased to tissues normally affected by this disease, we have shown that sensitive, high-throughput QPCR methods reveal expression changes in novel genes in the TH model. The search within additional diabesity QTL using this technique may facilitate the identification of a limited number of genes that comprise a 'complete' diabesity molecular phenotype. Moreover, this general approach may be an efficient method to identify genes that contribute to complex pathophysiological processes.
Methods
QTL analysis
Obesity and T2D QTL on mouse chromosomes 1,4,10,17,19, and X, were identified by a LocusLink search [22]. Information regarding individual QTL boundaries was gathered through the Mouse Genome Informatics site (MGI) [23]. Simple sequence length polymorphism (SSLP) markers extracted from MGI were queried at the Ensembl website [24] to determine the exact chromosomal location.
In silico expression analysis
ExQuest [1,2] is a gene expression program that uses public EST databases to create a comprehensive transcriptome map overlaid with tissue specific expression. In brief, chromosomal sequence was downloaded from Ensembl and stringently masked using the RepeatMasker program. ESTs were clustered to the chromosomes using the MegaBlast algorithm on a 32-node, 64 cpu Beowulf cluster. Tissue or library source information was extracted from EST datasheet records for each 10,000 base pair genomic fragment and normalized based upon library density and EST hit frequency. Whole chromosomal plots display total EST hits for each tissue as well as normalized data, which is a measure of a specific tissue expression level compared to all other tissues available in dbEST. In this way, the expressional bias of a genomic region towards a specific tissue is determined. QTL intervals were overlaid onto the chromosomal plots displaying adipose, liver and pancreas expression and these regions were then scanned for areas that exhibited high tissue specificity. ESTs clustering to genomic regions showing high tissue specificity were linked to Unigene [25], to determine if the particular EST aligned to a known gene cluster from which mRNA sequence could be extracted for primer design.
Tissue procurement, RNA preparation and cDNA synthesis
Three 7 week old, male TH mice (nonfasting blood glucose levels 295 to 433 mg/dl) and their respective T2D-resistant, sex- and age-matched C57BL/6J (B6) controls, were maintained on a 4% fat diet [4]. Following CO2 asphyxiation, pancreas, adipose, and liver tissues were collected. Each biological replicate sample was processed in parallel. Whole pancreas tissue was collected, placed in 3.5 mL lysis/binding solution from the RNAqueous®-4PCR Kit (Ambion # 1914), homogenized and stored in an ethanol/dry ice bath until the RNA could be extracted. Approximately 25 mg of adipose and liver tissue were collected and stored in 1 mL of RNALater (Ambion # 7020) until RNA was extracted.
For pancreatic RNA extraction, we prepared a 5.7 M CsCl solution containing 0.05 M EDTA, pH 7.0. The solution was made RNase free using RNASecure (Ambion # 7005), conforming to manufacturer's recommendations. 5.0 mL SW 55.1 ultracentrifuge tubes were made RNase free by treatment with RNAZap (Ambion # 9780) for five minutes followed by washing with Nuclease-Free water (Ambion # 9932). 2.0 mL of the 5.7 M CsCl solution was added to each tube. 3.0 mL of pancreatic solution was layered carefully on top. The tubes were centrifuged at 36,000RPM for 16–20 hours at 25°C in a SW 55.1 rotor. The supernatant was carefully removed 1.0 mL at a time, using fresh pipette tips. When approximately 1.0 mL remained, the tubes were quickly inverted and dried with filter paper to prevent residual RNases from contact with the RNA pellet at the bottom of the tube. The RNA was resuspended in 2 portions of 150 μL of RNA resuspension solution (Ambion #7010), conforming to manufacturer's recommendations.
Adipose and liver tissues stored in RNAlater were processed using the RNAqueous®-4PCR Kit, conforming to the manufacturer's recommendations.
All RNA was subsequently digested with DNAse in accordance with the aforementioned protocol and analyzed for purity using the Agilent Bioanalyzer 2100. RNA concentrations were determined using a NanoDrop® ND-1000 Spectrophotometer. The collected RNA was converted into cDNA via MessageSensor™ RT Kit (Ambion # 1745), conforming to manufacturer's recommendations. The adipose tissue cDNA reaction synthesis contained 350 ng of RNA while pancreas and liver samples utilized 1000 ng of RNA. All RNA was stored at -80°C when it was not being processed.
Primer design
Sequences for the candidate genes were extracted from GenBank and imported into Primer Express software v2.0 from Applied Biosystems, Inc (ABI). All primers were prepared in accordance with universal thermocycling parameters as described for real-time PCR on the ABI 7900HT. The primer sequences were then blasted [26] to ensure specificity of the primers. Forward and reverse primers (MWG Biotech) were combined in a 96-well master plate at a final concentration of 50 μM. Short amplicons of approximately 75 bp were generated to ensure a high level of sensitivity. Dissociation curves confirmed that only single amplicons were generated. Amplicons were TA cloned (Invitrogen # K204040) and bidirectionally sequenced to confirm sequence identity for instances in which genes exhibited significant expression. For gene names and primer sequences, see Additional file 1.
Real-Time QPCR
Each reaction consisted of 5.0 μl of 2X SYBR Green Master Mix (ABI # 4309155), 3.0 μL of dH2O, 1.5 μL of 0.5 μM forward and reverse primer solution, and 0.5 μL of cDNA. Pancreas and liver cDNA was diluted 1:10 before addition to the master-mix, while adipose cDNA was diluted 1:3. A 384-well plate format was utilized such that 4 samples × 96 genes were amplified per plate. The plate was sealed with Optical Adhesive Covers (ABI # 4311971) and centrifuged. The samples were assayed on the ABI Prism 7900HT Signal Detection System v2.0 using default conditions, and baseline range values were set from 3 to 10 cycles.
The data were then analyzed using Global Pattern Recognition (GPR) analytical software [27,28]. In typical QPCR experiments, the comparative expression of all genes is based on single gene normalizer, whose expression is assumed to be invariant. In contrast, the Global Pattern Recognition (GPR) algorithm employs a global normalization feature in which the expression data from each gene are normalized against that of every other gene, thus eliminating the reliance on single gene normalization. GPR's ranking is based on biological replicate consistency, and is thus not skewed by fold change in the magnitude of expression. For raw CT values and GPR Reports, see Additional files 2, 3 and 4.
Abbreviations used in this paper
QPCR, quantitative polymerase chain reaction; QTL, quantitative trait loci; GPR, global pattern recognition; ExQuest, expressional quantification of ESTs
Authors' contributions
ACB and DCR conceived of the study and were primarily responsible for its coordination and design. ACB and WIO were responsible for maintenance and execution of software algorithms as well as candidate gene selection. WIO and DJS preformed all tissue extraction and QPCR. CJD and MEM provided supercomputer and database management. JKN provided all mice, animal husbandry, and diabesity technical expertise. ACB, WIO and DCR drafted the manuscript and figures.
Supplementary Material
Additional File 1
Gene names and primer sequences used for QPCR.
Click here for file
Additional File 2
Provides adipose tissue raw Ct values and GPR report.
Click here for file
Additional File 3
Provides liver raw Ct values and GPR report.
Click here for file
Additional File 4
Provides pancreas raw Ct values and GPR report.
Click here for file
Acknowledgements
We express our gratitude to Jennifer Torrance for graphical assistance. This work was supported by grants from the National Institutes of Health.
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| 15760467 | PMC555939 | CC BY | 2021-01-04 16:38:18 | no | BMC Genet. 2005 Mar 10; 6:12 | utf-8 | BMC Genet | 2,005 | 10.1186/1471-2156-6-12 | oa_comm |
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BMC BiolBMC Biology1741-7007BioMed Central London 1741-7007-3-81578809810.1186/1741-7007-3-8Research ArticleHeterologous expression of the filarial nematode alt gene products reveals their potential to inhibit immune function Gomez-Escobar Natalia [email protected] Clare [email protected] Lidia [email protected] Toni [email protected] Clare C [email protected] Rick M [email protected] Institute of Immunology and Infection Research, University of Edinburgh, UK2 Institute for Stem Cell Research, University of Edinburgh, UK3 Max-Planck-Institut für Infektionsbiologie, Berlin, Germany2005 23 3 2005 3 8 8 7 3 2005 23 3 2005 Copyright © 2005 Gomez-Escobar 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
Parasites exploit sophisticated strategies to evade host immunity that require both adaptation of existing genes and evolution of new gene families. We have addressed this question by testing the immunological function of novel genes from helminth parasites, in which conventional transgenesis is not yet possible. We investigated two such novel genes from Brugia malayi termed abundant larval transcript (alt), expression of which reaches ~5% of total transcript at the time parasites enter the human host.
Results
To test the hypothesis that ALT proteins modulate host immunity, we adopted an alternative transfection strategy to express these products in the protozoan parasite Leishmania mexicana. We then followed the course of infection in vitro in macrophages and in vivo in mice. Expression of ALT proteins, but not a truncated mutant, conferred greater infectivity of macrophages in vitro, reaching 3-fold higher parasite densities. alt-transfected parasites also caused accelerated disease in vivo, and fewer mice were able to clear infection of organisms expressing ALT. alt-transfected parasites were more resistant to IFN-γ-induced killing by macrophages. Expression profiling of macrophages infected with transgenic L. mexicana revealed consistently higher levels of GATA-3 and SOCS-1 transcripts, both associated with the Th2-type response observed in in vivo filarial infection.
Conclusion
Leishmania transfection is a tractable and informative approach to determining immunological functions of single genes from heterologous organisms. In the case of the filarial ALT proteins, our data suggest that they may participate in the Th2 bias observed in the response to parasite infection by modulating cytokine-induced signalling within immune system cells.
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Background
Pathogens have evolved many ingenious mechanisms to manipulate innate and adaptive host immune responses [1-6]. The nematode parasite Brugia malayi is a causative agent of the disease lymphatic filariasis, which afflicts over 100 million people in tropical countries. Mosquito-borne infective stage larvae gain entry to the human body during a blood meal, and establish long-lived infections characterised by down-regulation of host T cell and macrophage reactivity [7,8]. We have studied the profile of genes expressed in infective larvae and reported that ~5% of the mRNA transcripts from this stage correspond to two closely related genes, which we have named abundant larval transcript (alt) -1 and -2 [9,10]. The two genes encode proteins with 79% amino acid identity, but no similarity to any gene of known function. alt-like genes are present in other filarial nematode species [11,12] and are characterised by a signal peptide, a variable acidic domain, and a conserved, cysteine-rich domain. A distantly-related gene is also present in the genomes of the free-living nematodes Caenorhabditis elegans and C. briggsae but in both cases the acidic domain is absent (Gregory, Maizels and Blaxter, unpublished observation).
ALT proteins are stockpiled in the oesophageal glands of infective larvae [12] and are secreted by the parasites when they encounter mammalian culture conditions. Thus, their function may be to promote parasite survival within the host physiological or immunological environment. For example, ALTs may interfere with the critical first interaction between the innate immune system and the nematode invaders. It has been established that larval stages rapidly elicit a strong Th2 response in mice [13] and induce host macrophages to adopt a counter-inflammatory phenotype [14]. Although ALT antigens are not expressed on the parasite surface, they can induce protective immunity in animal models [9,15,16], indicating that neutralization of ALT function may be sufficient to protect the host from infection.
Transgenesis and targeted gene deletion have yet to be established for parasitic helminths, so it is not possible to investigate the biological role of ALT proteins by conventional reverse genetics. We reasoned, however, that if ALT function is fulfilled within the host rather than within the parasite, we can validly study these proteins by transgenic expression in a more tractable carrier species. We chose to test this approach with the protozoal parasite Leishmania, several species of which are infective to laboratory mice. Leishmania can readily be modified genetically [17-20] to yield lines expressing high levels of exogenous transgenes. We selected L. mexicana as it establishes infections in murine macrophages in vitro, providing experimental access to a key cell type known to be modified in filarial infection [14,21-23]. L. mexicana will also infect mice, and although the immunological factors determining resistance and susceptibility are not as well-defined as in L. major [24-26], it offers the advantage of slower in vivo kinetics and consequently is anticipated to be more sensitive to altering factors. Using this model, we show here that transgenic L. mexicana expressing the ALT proteins are more virulent in macrophages in vitro, and that this property is abolished by deletion of the filarial-specific acidic domain. We also show that mice infected with alt-transgenic L. mexicana harbour higher parasite burdens than controls. By studying the responses of macrophages infected with transgenic parasites, we suggest that the ALT products modulate cytokine-induced signalling and render parasites more resistant to IFN-γ-induced killing.
Results
Expression of B. malayi ALTs in L. mexicana
Bm-alt-1 and Bm-alt-2 genes were subcloned in their entirety, including endogenous signal peptide sequences, into the recombination vector pSSU, which contains flanking sequences homologous to the 18S small subunit (SSU) rRNA locus [19,27] (Figure 1). Electroporation of L. mexicana was undertaken, permitting homologous recombination of the alt-1 and -2 sequences downstream of the strong polymerase I promoter into the sequence for the small sub-unit rRNA, which is known to be transcribed in both stages of the life cycle of the parasite. Following puromycin selection, multiple transgenic lines were isolated for each alt gene, from which representative clones were selected containing the correct insert at an appropriate integration site. Transgenic ALT expression in the free-living culture promastigote stage was confirmed in both lines by Western blot (data not shown) and immunofluorescent staining (Figure 2A–D) of permeabilized parasites with murine anti-ALT antibodies. Not only were ALT proteins found widely distributed in the transgenic protozoa, but staining of the membrane-rich flagella indicated surface expression in the promastigote stage. This was confirmed by flow cytometric analysis of anti-ALT-stained non-permeabilized transgenic promastigotes (Figure 2E, F; negative controls panels G, H).
In vitro infection of macrophages with transfected amastigotes
Bone-marrow-derived macrophages were infected with axenic transformed amastigotes of each line. Both wild-type (Figure 2I) and alt-transfected parasites (Figure 2J) were fully infective to macrophages, and continued to express transgene-encoded protein reactive by immunofluorescence (data not shown). Within 24 hours, ALT-1 and ALT-2 expressing lines were able to infect significantly more host cells (Figure 3A, p < 0.001), a contrast that was sharply accentuated by day 7 of culture (Figure 3B). Moreover, >90% of macrophages infected with alt-transgenic L. mexicana harboured at least 3 parasites, compared to <50% of cells infected with the wild-type, and overall there was a 3-fold difference in mean parasite load per macrophage. This enhancement was manifest in both alt-1 and alt-2 transgenic lines and did not affect the total number of macrophages surviving through the culture period. In contrast, transfectants expressing an unrelated filarial gene, the cystatin Bm-CPI-2 [28], have no effect on survival of L. mexicana in murine macrophages (Figure 3C, D). Thus, the observed effect is gene-dependent and is not an attribute of the transfection system.
Enhanced virulence of ALT-expressing L. mexicana promastigotes
Analysis of transgene function in the more complex setting of in vivo infection was also performed, in order to test more stringently whether ALT products interfere with immunity. In two experiments, the alt-transfectants elicited larger lesions more quickly than either the parental wild-type strain of L. mexicana (Figure 3E), or a transfectant encoding GFP (not shown). In both experiments, the effect of alt transfection was to accelerate lesion development to a plateau after 8–10 weeks, while control parasites reached similar levels only after 12–15 weeks. Recovery of parasites from the footpads of infected mice also showed marked exacerbation: parasites were detected in our assay in 94% (15/16) of animals given transfected L. mexicana compared to 50% (4/8) of those receiving wild-type parasites (p = 0.03, χ2 test) (Figure 3F).
Nitric oxide production and susceptibility to NO-mediated killing
Because nitric oxide generation is known to be a primary factor in the control of parasite survival in macrophages [29,30], we compared levels of the NO-metabolite nitrite from J774 macrophages infected with the different parasite lines. NO production was assayed 24- and 48-hours post-infection, as the differences in parasite numbers are apparent from an early stage. Irrespective of the transgenic status of the parasite, macrophages produced near-identical amounts of NO in culture medium, measured by nitrite, at both time points (Figure 4A); thus the ALT products do not abolish the reactive nitrogen burst. To test whether there was a quantitative reduction in either NO responses or parasite sensitivity to destruction, we infected cells that had been previously stimulated with LPS and a range of doses of IFN-γ. Survival was measured by enumeration of infected macrophages 3 days later. Figure 4B shows that alt-transfected parasites not only achieve higher infection levels in unstimulated macrophages (no IFN-γ), but survive better in cells stimulated with intermediate IFN-γ doses (0.01–0.1 U/ml) than do wild-type organisms. Interestingly, the production of nitrite was comparable at each IFN-γ dose in all parasite types (Figure 4C), indicating that the ALT products are likely to be interfering in an NO-independent pathway of immunity.
Gene expression in infected macrophages
To gain insight into what other mechanisms may be at play, we tested mRNA from macrophages infected with wild-type or transfected L. mexicana against an array of 514 genes associated with immune responsiveness formatted on the Mouse Cytokine Expression Array 1.0 (R & D Systems Inc.). Two independent experiments were performed, and hybridization of [33P]-labelled cDNA was measured by phosphorimaging of spots of interest. Little difference was seen in hybridization for iNOS, IL-10, IL-12 (Table 1) or many other known players in the pro- and counter-inflammatory cascades. The constancy of iNOS expression following transfection is consistent with the measurements of nitric oxide production discussed above. However, other pro-inflammatory players (TLR6, LPS-BP, LTBP3 and TNFSF11 and 12) were down-regulated relative to wild type in both alt -1 and alt -2 transfection, in replicate experiments. Moreover, substantial shifts in mRNA expression were consistently observed with certain products known to modulate the type-1/type-2 balance. Bone-marrow-derived macrophages, infected 7 days earlier with wild-type L. mexicana infection, showed significant ablation of GATA-3 and SOCS-1, but expression was maintained or enhanced in cells infected with alt-transgenic parasites (Figure 5A and 5B). These effects were reproduced with both alt genes in both experiments (Table 1).
Because array hybridization may under certain conditions be non-linear with respect to mRNA concentrations, we performed quantitative RT-PCR with GATA-3- and SOCS-1-specific primers on replicate samples of infected macrophages taken at 24 h (Figure 5C, D) and 7 days (E, F). For GATA-3, these data reinforce the conclusion that while wild-type infection results in a 2–3-fold loss of GATA-3 expression, alt-transgenic infected macrophages maintain the level of this transcription factor throughout the course of in vitro infection (Figure 5C and 5E). Interestingly, SOCS-1 is sharply upregulated by 24 h in all infected macrophages, but elevated expression remains evident only where alt transgenic parasites are present (Figure 5D and 5F). Although it is possible that larger parasite numbers in the alt-transfection system may in themselves alter macrophage gene expression, parasite densities were only slightly shifted at 24 h, when GATA-3 expression was markedly different in wildtype and transfected cell infections.
Up-regulation of GATA-3 and SOCS-1 in filarial infection
We then tested whether GATA-3 and SOCS-1 are up-regulated by live infection with B. malayi larvae in vivo. Mice were injected i.p with 200 larvae, and 7 days later peritoneal cell populations were recovered by lavage with medium. We then performed real time PCR on an adherent macrophage enriched cell population and a non-adherent cell population, predominantly lymphocytes. As shown in Figure 6A and 6C, the macrophage-rich adherent population showed a modest increase in both GATA-3 and SOCS-1 expression, whereas a very substantial rise in both was seen in the non-adherent cells (Figure 6B and 6D). It seems that B. malayi infection results in up-regulation of the same genes as observed with ALT in macrophages, in both macrophage and non-macrophage subsets.
The acidic domain of ALT is essential for biological function
The enhanced virulence phenotype conferred by alt transfection, measured by in vitro infectivity to macrophages, provides a ready and tractable system for submolecular analysis of functional domains of the ALT protein. We investigated whether the N-terminal acidic domain, which is unique to the filarial genes, was important for the immunological activity of the protein. We constructed a truncated mutant of ALT-2, named acidic domain deleted (add), which was cloned into the pSSU vector and electroporated into L. mexicana. These transfectants expressed immuno-reactive protein (Figure 7A–C) and were able to infect bone marrow-derived macrophages. However, add-transfected parasites did not display the greater infectivity seen with alt-transfectants, but showed a phenotype indistinguishable from wild-type parasites (Figure 7D). Thus, the acidic domain is required for functional expression of the immunological effects of the ALT proteins. Similar analyses will permit the future definition of the critical residues required for immunomodulatory activity.
Discussion
Long-lived helminth parasites have evolved highly effective strategies to evade host immunity, requiring both adaptation of existing genes and evolution of new gene families [6]. With genomes that encode many thousands of proteins, these parasites are likely to be repositories of numerous novel 'immune evasion genes' with no or only weak sequence similarity to known products [31]. The imminent completion of genome sequence information for major helminth parasites [32-34] accentuates the problem of how to identify functional immune modulators among numerous novel gene sequences. We hypothesized that transcripts for secreted immunomodulatory proteins would be among the most abundant mRNAs at key points in the parasite life cycle. Two such genes are those that encode the abundant larval transcripts (ALT) proteins, which are released by larval parasites ready to infect the mammalian host and represent 5% of the total mRNA at this stage [9].
To test whether the ALT proteins are functional immune evasion products, we transfected each alt gene into L. mexicana and showed that infection of macrophages in vitro is exacerbated by expression of either ALT protein. Moreover, mice infected with alt-transgenic parasites display more rapid lesion development and higher parasite burdens than controls. Our results also demonstrate that alt-transfected parasites are more resistant to INF-γ-induced killing by macrophages, supporting our hypothesis that ALT proteins act to modify host immune responses in filarial infection. These data validate the transfection strategy in general and, in highlighting changes in key intracellular factors resulting from ALT expression, justify the selection of a protozoal carrier to test the function of a helminth gene.
A further advantage of the system we describe here is the facility with which selected mutants can be analysed with an in vitro read-out of infectivity to macrophages. We constructed a deletion mutant lacking the N-terminal acidic domain, which shows most variation between filarial species and is not present in distantly related genes from free-living nematode organisms. This deletion showed a clear-cut abolition of the alt phenotype, indicating the essential functional nature of this sequence and paving the way for a finer analysis of structure-function relationships in a tractable experimental system.
We have also extended the use of this transfection system to the analysis of other parasite genes that are hypothesised to be immunomodulatory. For example, filarial parasites produce homologues of mammalian macrophage migration inhibitory factor, MIF [39,40], a cytokine generally considered to be an acute pro-inflammatory agent [41,42]. It is, however, paradoxical that long-lived tissue pathogens produce a potentially inflammatory mediator, and we therefore used the Leishmania transfection system to test the hypothesis that long-term MIF production may promote parasite survival. L. mexicana organisms transfected with B. malayi MIF homologues were tested first in vitro, in which setting they were less infective to macrophages, rarely exceeding one parasite per host cell. This result was consistent with an immediate pro-inflammatory action of MIF on macrophages. However, when tested in vivo over a 4- or 8-week-period, MIF-transfected parasites were able to survive better than wild-type, indicating that over the longer term, filarial MIF homologues are able to exert a down-modulatory effect on host immunity (Prieto-Lafuente, manuscript in preparation). We are now using this system to analyse the gene expression profiles of macrophages infected with MIF-transfected parasites in both in vitro and in vivo settings.
These data illustrate the facility with which the Leishmania transfection system can be used in parallel for in vitro and in vivo experiments, and the importance of an in vivo read-out to assay gene effects within the immune system as a whole. In addition, our work has demonstrated that Leishmania transfection for helminth gene analysis is equally applicable for two completely unrelated, but immunologically important, gene families.
The application of this new strategy to transfection of L. mexicana is ideally suited to the study of macrophage modulation by genes predicted to function in this environment. Parallel investigations can also be envisaged by transfection of L. major, which will permit a more thorough analysis of T cell modulation to be undertaken. For example, resistance to L. major is clearly associated with development of a parasite-specific Th1 response, and it is possible that parasite genes that inhibit Th1 differentiation will alter the course of L. major infection in vivo. These studies are now under way.
Macrophages are known to be a critical cell population in the immune response to filarial parasites at successive points in time. First, they participate in innate defences against invading larvae [35,36]; second, if infection becomes established, they evolve an IL-4-dependent "alternatively activated" phenotype, which is broadly immunosuppressive [8,37]; and third, in late stage infection, they clear the bloodstream of microfilarial forms through a nitric oxide-dependent pathway [21,38]. Thus, the reduction in IFN-γ-responsiveness in macrophages harbouring alt-transfected parasites has resonance for initial host susceptibility, and longer-term propensity for chronic infection, as well as the ability to eliminate the blood-borne Mf stage.
A key outcome of the present study is that ALT expression is associated with up-regulation of GATA-3 and SOCS-1. GATA-3 is a pivotal transcription factor for the development and function of the Th2 pathway [43,44] and has not previously been reported in mouse macrophages; hence, at the present time, downstream genes activated by GATA-3 in macrophages have not been defined. Significantly, GATA-3 is required for embryonic development and has recently been shown to be essential for the differentiation [45] and effector function [46] of murine eosinophils. Thus, GATA-3 expression in macrophages may fulfil an important role, possibly in conjunction with the altered phenotype of these cells in chronic parasite infections [8,47].
SOCS-1 is a member of the Suppressor Of Cytokine Signalling family of proteins, which regulate signal transduction by IFN-γ and structurally related cytokines [48,49]. SOCS-1 is particularly important in macrophage responsiveness to inflammatory stimuli [50], countermanding IFN-γ by directly inhibiting the JAK kinase associated with the IFN-γ receptor [51]. SOCS-1 is known to inhibit the IFN-γ-dependent killing of Leishmania parasites, because macrophages from SOCS-1 null mice require 100-fold less IFN-γ to clear infection [52]. Indeed, in the absence of SOCS-1, there is generalised activation of the immune system, causing autoimmune pathology [53,54]. Thus, by up-regulating SOCS-1, cells expressing ALT may have down-shifted responsiveness to inflammatory cytokines, requiring exogenous stimulation to induce Leishmania killing (Figure 5B). In the context of Brugia infection, SOCS-1 induction by ALT proteins could explain why macrophages fail to develop the IFN-γ-activated phenotype, and instead express a counter-inflammatory profile [8].
Overall, these data suggest that the ALT proteins play a role in the evasion strategy of the filarial nematode, by directly amplifying Th2 responses and/or interfering with signals necessary for the development of pro-inflammatory Th1 populations. It is interesting to note that in vivo exposure to live infective larvae of B. malayi induces a prompt Th2 response measurable by 24 h and increasing to 10 days post-infection [13]. We have repeated these experiments by intraperitoneal inoculation of live L3 and find up-regulation of GATA-3 and SOCS-1 in both adherent and non-adherent peritoneal cells (Figure 6).
Conclusion
The novel approach we have described permits, for the first time, the elucidation of gene function for a major group of biologically and medically important parasites, which in the example presented here provides non-intuitive results linking parasite secretions to host cell signalling. The transfection strategy will also accommodate a mutagenesis analysis of structure-function relationships in unique gene families. In conclusion, our system provides a solution to one of the major obstacles facing helminth parasite immunology in the post-genomic era and offers a fascinating insight into the molecular and cellular intricacies of pathogen manipulation of host immune responsiveness.
Methods
Mice and parasites
Six- to eight-week old female C57BL/6 and CBA mice were used. L. mexicana (strain MNYC/BZ/62/M379) promastigotes were cultured in vitro in semi-defined medium/10% heat inactivated foetal calf serum (hiFCS)/1% penicillin-streptomycin (complete SDM) at 26°C. Amastigotes were cultured axenically at 34°C in Schneider's Drosophila medium (Gibco BLR) supplemented with 20% hiFCS and 3.9 g/l 2-(N-morpholino)ethanesulfonic acid (Sigma, U.K.). Transgenic parasites were cultured under the same conditions with the addition of 20 μg/ml puromycin (Sigma, U.K).
Cell culture
The murine macrophage cell line J774 was passaged in DMEM containing 10% hiFCS/1% penicillin-streptomycin/1% L-glutamine and cultured at 37°C in 5% CO2.
Leishmania expression construct for B. malayi ALTs
Primers were designed to amplify the entire coding region of alt-1 and alt-2 from a B. malayi L3 cDNA library. The oligonucleotides used for alt-1 were 5'-CCGCTCGAGATGAACAAATTGCTAATAGCA-3' (sense, initiating codon in bold) and 5'-TGCTCTAGATTACGAGCATTGCCAACTTTC-3' (antisense, terminating codon in bold); and for alt-2, 5'-CCGCTCGAGATGAATAAACTTTTAATAGCA-3' (sense) and 5'-TGCTCTAGACTATGCGCATT GCCAACCTGC-3' (antisense). After an initial denaturation step at 95°C for 5 min the PCR was cycled between 94, 55 and 72°C (1 min each) for 35 rounds, followed by 1 round at 72°C for 10 min. The fragments were digested with XhoI and XbaI and cloned into the pSSU vector (13), yielding pSSU-alt-1 and pSSU-alt-2. Clones were fully sequenced on both strands. DNA was extracted from pSSU-alt-1 and pSSU-alt-2 using the Qiagen Miniprep kit following the manufacturer's instructions
Site directed mutagenesis
An ALT-2 mutant, in which the acidic domain of the protein (amino acids 24–49) were deleted, was generated using the Exite PCR-based site-directed mutagenesis kit (Stratagene, USA) following the manufacturer's instructions. The pSSU-alt-2 construct (see above) was used as the template in a PCR reaction containing two oligonucleotides: 5'-TGATTCTGATACACACGGGAGTGT-3' (antisense, primer phosphorylated) and 5'-TATGTAACCAAAGGGAATTTGTT-3' (sense). The cycling parameters were as follows: 1 cycle of 1 min at 94°C, 4 min at 53°C and 2 min at 72°C; followed by 10 cycles of 1 min at 94°C, 2 min at 55°C (adding 10 s after each cycle) and 1 min at 72°C; and a final cycle of 5 min at 72°C. After the PCR the nonmutated parental plasmids were digested with Dpn restriction enzyme. The undigested linear DNA was then polished with Pfu DNA polymerase and ligated at 37°C for 1 h with T4 DNA ligase. The ligated DNA was then transformed into E. coli, yielding a pSSU-ADD (acidic domain deleted) construct. The insert was sequenced to verify that the intended mutation was correctly constructed.
Transfection of Leishmania
Logarithmic phase promastigotes (4 × 107) were electroporated with 10 μg of PmeI linearized fragments of either pSSU-alt-1, pSSU-alt-2 or pSSU-add. Clones were selected on 24-well plates in complete SDM supplemented with 20 μg/ml of puromycin and further propagated in a culture volume of 10 ml.
Immunofluorescence
Promastigotes were washed in PBS, fixed in 2% paraformaldehyde for 30 min at room temperature, and quenched in NH4Cl for 10 min. Wild-type parasites were incubated with dilution buffer (0.1% saponin, 2% goat serum) in the presence of anti-L. mexicana rabbit serum or anti-ALT mouse serum (from C57BL/6 mice) for 45 min. Following 3 washes with wash buffer (0.1% saponin, PBS) the parasites were incubated with either an anti-rabbit-FITC or an anti-mouse-FITC secondary antibody (DAKO, Denmark) for 45 min. They were then washed 3 times before mounting with Cityfluor (Cityfluor Ltd, UK) for microscopy.
Flow cytometry
Promastigotes were washed in PBS, resuspended at 1 × 106/ml and fixed without permeabilization in 2% paraformaldehyde for 15 min at room temperature. After two washes in PBS, 10% FCS (FACS wash), the parasites were stained with anti-ALT-1 antibody or normal mouse serum diluted in FACS wash. This was followed by incubation with a FITC anti-mouse secondary antibody (DAKO, Denmark). Flow cytometric analysis was performed using a FACScan (Becton Dickinson) and analysed using CellQuest 3.1 software.
Preparation of bone marrow-derived macrophages (BMM)
BMM were obtained from femurs and tibias of 6- to 8-week old CBA mice by flushing the bones with DMEM (Gibco, BLR) containing 10% hiFCS/1% penicillin-streptomycin/1% L-glutamine. Cells were centrifuged and plated out in non-tissue culture Petri dishes at a density of 5 × 105/ml in complete DMEM, supplemented with 20% (v/v) L929 cell-conditioned medium as a source of M-CSF and 20% hiFCS. After 6 days at 37°C, the cells were detached by incubation in PBS containing 3 mM EDTA and 10 mM glucose, plated and cultured in small non-tissue culture Petri dishes at 2.5 × 105/ml in complete DMEM for 24 h at 37°C.
Infection of BMM with L. mexicana
Day 7 BMM were infected for 24 h or 7 days at 34°C with wild-type or alt-transfected amastigotes at a ratio of 10 parasites per macrophage. After the period of infection macrophages were harvested as described above, washed with PBS and fixed and permeabilized using a "Fix & Perm kit" (Pharmigen); 1 × 105 cells were centrifuged on to glass slides with a Cytospin. Intracellular Parasites were detected and counted by immunofluorescence as described above. Between 100 and 200 macrophages were counted for each time-point.
Infections in vivo
For L. mexicana in vivo infections, groups of 8 female C57BL/6 mice were injected subcutaneously in the footpad with 3 × 106 stationary-phase wild-type L. mexicana, alt-1 transfectants and alt-2 transfectants. Lesion size was measured weekly during the course of infection with a dial micrometer and expressed as the difference in size between the infected footpad and the contralateral uninfected footpad.
For B. malayi in vivo infections, groups of 5 female C57BL/6 mice were injected intraperitoneally with 200 infective larvae recovered from crushed Aedes aegypti mosquitoes. After the experimental period the mice were euthanized by terminal anaesthetic, and peritoneal cells (PEC) were harvested by thorough washing of the peritoneal cavity with 15 ml of ice-cold RPMI supplemented with 10% FCS. The harvested PEC were plated in 24-well culture plates at 2 × 106 cells/well. Following 3 h at 37°C to allow cells to adhere, both the non-adherent and the adherent macrophage-enriched cell populations were harvested.
Parasite quantification
The number of parasites in the footpad was estimated by limiting dilution assay. Infected footpads were harvested in cold PBS after removal of the skin. Footpad tissue was dispersed through a cell strainer and resuspended in PBS-1% penicillin/streptomycin. After centrifugation the pellet was resuspended in complete SDM. The cell suspension was then serially diluted in 10-fold steps, in quadruplicate, in 96-well plates. The plates were incubated for 4 days at 27°C; the wells were then observed for parasite growth.
Measurement of nitrite production
J774 macrophages were plated out on 96-well plates (1 × 105/well) and incubated at 37°C for 24 h. The medium was then removed, the cells were washed twice, and L. mexicana promastigotes were added for 4 h at a ratio of 10 parasites per well. IFN-γ (40 U/ml) and LPS (10 ng/ml) were then added, with or without the arginine analogue N-monomethyl-D-arginine (D-NMMA), 1 mM. Nitrite accumulation in medium over the subsequent 24–48 h was used as an indicator of NO production and was assayed by the Griess reaction in which 100 μl of Griess reagent [55] was added to 100 μl of each supernatant in triplicate wells in a 96-well plate. Plates were read at 490 nm against reference wavelength 620 nm using an ELISA plate reader. NaNO2 was used to make a standard curve for each plate reading.
Leishmanicidal assay
BMM (5 × 104) were plated out on glass coverslips in 24-well plates, allowed to adhere for 24 h, and stimulated with 100 ng/ml LPS and the indicated concentrations of IFN-γ. Cells were incubated for 6 h at 37°C before adding stationary phase promastigotes of wild-type L. mexicana or transfected alt-1 and alt-2 parasites at a ratio of 10 parasites per cell. After 72 h at 37°C the cells were stained with Giemsa. The percentage of macrophages infected with parasites was determined by counting 4 samples of 100 cells.
RNA isolation
Total RNA was isolated from non-infected and infected BMM as well as adherent and non-adherent cell populations using TRIzol Reagent (Invitrogen, Life Technologies) according to the manufacturer's instructions. The RNA was subjected to DNase treatment (Ambion, INC) to eliminate genomic contamination, according to the manufacturer's instructions
Gene array analysis
Gene expression was analysed using the Mouse Cytokine Expression Array (R&D systems). The mouse cytokine-specific primers were first annealed to the total RNA, which was then reverse transcribed in the presence of SuperScript II (Invitrogen, Life Technologies) and [α-33P]dCTP. The radiolabelled cDNA probes generated from non-infected and infected cells were hybridized to identical membranes containing the mouse cDNA arrays. Following hybridization, high stringency washes were performed and the membranes were subjected to autoradiography. Quantification was carried out using a PhosphorImager and data analyzed with ImageQuant v1.2.
Real Time RT-PCR analysis
Total RNA was extracted in Trizol, as described above, and single-stranded cDNA was synthesized using MMLV reverse transcriptase (Stratagene). Relative quantification of the expression of the genes of interest was measured by real-time PCR using the LightCycler (Roche Molecular Biochemicals). PCR amplifications were performed in 10 μl volumes containing 1 μl cDNA, 2.5 mM MgCl2, 3 μmM primers and the LightCycler-DNA SYBR Green I mix (Qiagen). The reaction was performed in the following conditions: 15 min activation step at 95°C for one cycle, 15 s denaturation at 95°C, 20 s annealing of primers at 50°C and 15 s elongation at 72°C, for 50 cycles. The fluorescent DNA binding dye SYBR Green was monitored after each cycle at 80°C. Five serial 1:2 dilutions of alt-2 infected macrophages cDNA were used to produce a standard curve in each reaction. The abundances of GATA-3 and SOCS-1 were expressed as ratios of amplified product to the control, mouse S29 ribosomal protein. Primers for RT-PCR analysis were as follows: GATA-3: 5'-CTA CGG TGC AGA GGT ATC C-3' and 5'-GAT GGA CGT CTT GGA GAA GG-3'; SOCS-1: 5'-ACC TTC TTG GTG CGC GAC AGT CGC CAA-3' and 5'-GGA ACT CAG GTA GTC ACG GAG TAC-3'; and S29 ribosomal protein: 5'-ATG GGT CAC CAG CAG CTC TAC-3' and 5'-GTC CAA CTT AAT GAA GCC TAT-3'.
Abbreviations
ALT, abundant larval transcript protein; alt, abundant larval transcript gene or mRNA; BMM, bone marrow-derived macrophages; FACS, fluorescence-activated cell sorter; L3, third-stage infective larva; NO, nitric oxide.
Authors' contributions
NG-E designed and led the experimental work, including molecular constructs, cell transfection, immunological assays, array and RT-PCR analyses. She also drafted the manuscript. CB and LP-L made constructs in pSSU, maintained Leishmania cultures, participated in electroporation procedures, macrophage infection, in vivo infection and dilution assays. TA designed the transfection system and devised the Leishmania transfection methodology. TA, CCB and RMM jointly conceived the transfection strategy for Brugia genes, critically assessed progress, and made revisions to the draft manuscript. The overall study was co-ordinated by RMM who also completed the draft of the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank the Wellcome Trust for grant support, and Adam Balic, David Dresser, Cecilia Fernández, Karen Gilmour, Yvonne Harcus, Eva Malone and Francisca Mutapi for advice and assistance.
Figures and Tables
Figure 1 Maps of the constructs used for homologous recombination. Central map shows the structure of the linearized pSSU vector. 5'SSU and 3'SSU correspond to the sequences required for homologous recombination into an 18s rRNA gene; SL refers to a sequence containing a splice acceptor site; MCS to the multiple cloning site; pac to the puromycin gene and CPB-2.8 IR to the intergenic region of the CPB 2.8 gene of L. mexicana. The top map shows the structure of alt-1 and alt-2 transcripts that were introduced into the MCS of pSSU. The bottom map shows putative intergration into a rRNA locus. The arrow shows the start of transcription from the rRNA genes.
Figure 2 Expression of ALT proteins in L. mexicana cultured promastigotes. (A-D) Immunofluorescent detection of transgenic protein. Paraformaldehyde-fixed wild-type promastigotes were incubated with (A) anti-L. mexicana serum or (B) anti-ALT-1 antibody. (C) Cloned alt-1 transfected and (D) alt-2 transfected fixed parasites were incubated with an anti-ALT-1 antibody, which binds to both ALT-1 and ALT-2, and detected with an anti-mouse-FITC secondary antibody. (E-H) FACS analysis of transfected promastigotes of L. mexicana. Fixed promastigotes were stained, without permeabilization, with anti-ALT-1 antibody and analysed by FACS. (E) alt-1-transfected L. mexicana; (F) alt-2-transfected L. mexicana; (G) wild-type L. mexicana. Positive cells are those within the gate (M1) set above the threshold defined by reactivity of transfected parasites stained with normal mouse serum (H). (I, J) Multiplication of transfectants within macrophages. (I) In vitro-transformed wild-type amastigotes or (J) alt-1 transfected amastigotes were added to bone marrow-derived macrophages from CBA mice at a ratio of 10 parasites per macrophage. After 24 hours at 34°C the infected macrophages were observed by microscopy.
Figure 3 In vitro infection of macrophages with transfected amastigotes. (A, B). Bone marrow derived macrophages from CBA mice were infected for (A) 24 hours or (B) 7 days with wild-type or transfected amastigotes at a ratio of 10 parasites per macrophage. Parasites were detected and counted by immunofluorescence. Between 100 and 200 macrophages were counted for each time-point. The χ2 test showed significant differences in the numbers of infected vs uninfected macrophages between wild-type and either alt-1 or alt-2 transfectants at both time points (p < 0.001). (C, D). In vitro infection of macrophages with L. mexicana expressing the cysteine protease inhibitor-2 (CPI-2) gene of B. malayi (C) after 24 hours and (D) after 7 days. (E, F) Infection of C57BL/6 mice with transfected L. mexicana. (E) Time-course of lesion development in mice. Groups of 8 female C57BL/6 mice were injected subcutaneously in the footpad with 3 × 106 stationary-phase wild-type L. mexicana, alt-1 transfectants or alt-2 transfectants. Lesion size was measured weekly during the course of infection with a dial micrometer and expressed as the difference in size between the infected footpad and the contralateral uninfected footpad. One of two replicate experiments with similar results is shown; because data are not normally distributed (see for example recovery of wild-type parasites in panel F), standard error values cannot be applied. (F) Recovery of parasites from footpads at 15 weeks post-infection. The number of parasites in the footpad was estimated by limiting dilution assay. Results are expressed as log10 of the highest dilution containing parasites.
Figure 4 Nitric oxide production and susceptibility to NO-mediated killing. A. Nitrite production in the presence of ALT expression. J774 cells were incubated with medium alone (-ve), or stimulated with IFN-γ (40 U/ml) and LPS (10 ng/ml) (+ve) with or without the arginine analogue N-monomethyl-D-arginine (D-NMMA, 1 mM), or pre-incubated with L. mexicana promastigotes for 4 h before stimulation. Nitrite accumulation in the medium over the subsequent 24 h or 48 h was used as an indicator of NO production and was assayed by the Griess reaction. Each bar represents mean ± standard deviation. B. Survival of transfected Leishmania in activated macrophages. Bone marrow-derived macrophages stimulated with 100 ng/ml LPS and the indicated concentrations of IFN-γ were infected with stationary phase promastigotes of wild-type L. mexicana or transfected alt-1 and alt-2 parasites at a ratio of 10 parasites per cell. After 72 h at 37°C the cells were stained with Giemsa. The percentage of macrophages infected with parasites was determined by counting 4 samples of 100 cells. Each bar represents mean ± standard error of the mean. C. Nitrite production in response to dose of exogenous IFN-γ. Supernatants from the experiment described above (Figure 3B) were recovered and assayed by the Griess reaction for accumulated nitrite. Mo denotes uninfected macrophages. Each bar represents the mean ± standard deviation.
Figure 5 Expression of GATA-3 and SOCS-1 in infected macrophages. (A,B) Array analysis. Bone marrow-derived macrophages from CBA mice were infected for 7 days with wild-type or transfected amastigotes at a ratio of 10 parasites per macrophage. After the period of infection total RNA was extracted and used as a template to synthesise radiolabelled probes. The mouse cytokine expression array (v.1.0) from R & D Systems, on an 8 cm × 12 cm filter [56], was probed with normal macrophage cDNA, wild-type L. mexicana-infected macrophage cDNA, and cDNA from cells infected with alt-1 or alt-2 transfected L. mexicana. Quantification was carried out using a PhosphorImager and data were analyzed with ImageQuant v1.2. Results from two independent experiments are presented in adjacent bars. (C-F) Real-time RT-PCR analysis. Total RNA from infected and non-infected macrophages was extracted and single-stranded cDNA was synthesised. Relative quantification of the expression of the genes of interest was measured by real-time PCR using the LightCycler. The abundance of GATA-3 and SOCS-1 after 24 hours (C and D, data from n = 4 experiments), and after 7 days (E, n = 7, and F, n = 3) was expressed as a ratio of amplified product to the control, mouse S29 ribosomal protein. Each bar represents mean ± standard error of the mean. One-way analysis of variance showed that for GATA-3, both alt-1 and alt-2 transfectants were significantly higher than wild-type (p < 0.05) at both time points, and that for SOCS-1, both were significantly higher than wild-type (p < 0.01) at day 7.
Figure 6 Up-regulation of GATA-3 and SOCS-1 in filarial infection Peritoneal cells were recovered 7 days after ip infection with 200 L3s. (A, C) Adherent (macrophage-enriched) and (B, D) non-adherent (predominantly lymphocytes) populations were separated and cDNA synthesised from total RNA. The abundance of GATA-3 (A, B) and SOCS-1 (C, D) was determined as in Figure 5. Each bar represents mean ± standard error of the mean. (One way analysis of variance; p = 0.05). Data are representative of two experiments.
Figure 7 Deletion of the Acidic Domain abolishes the functional effect of ALT transfection. (A-C) Immunofluorescent detection of ADD (acidic domain deleted mutant of ALT-2) protein. Paraformaldehyde-fixed wild-type promastigotes (A) and cloned ADD transfected parasites (B) were incubated with mouse antiserum to anti-ALT-2, and detected with an anti-mouse-FITC secondary antibody. (C) Cloned ADD transfected parasites were also incubated with normal mouse serum. (D) Bone marrow-derived macrophages were infected with stationary phase amastigotes of wild-type L. mexicana or transfected alt-2 and ADD parasites at a ratio of 10 parasites per cell. After 7 days, cells were stained with Giemsa. The percentage of macrophages infected with parasites was determined by counting 3 samples (from 3 different wells) of 100 cells. The χ2 test showed significant differences in the number of infected macrophages between wild-type and alt-2 transfectants (p < 0.001), but not between wild-type and add-transfected parasites.
Table 1 Array hybridization intensities for 5 immune-related genes in macrophages infected with filarial alt genes.
Mouse gene Transfectant Experiment 1 Experiment 2
iNOS alt-1 6847.3 2260.4
alt-2 6951.1 2224.4
wild-type 7105.6 2280.1
uninfected 6807.7 2296.3
IL-10 alt-1 605.7 171.3
alt-2 564.2 173.8
wild-type 563.5 169.7
uninfected 579.6 167.3
IL-12 alt-1 150.8 184.2
alt-2 152.8 187.9
wild-type 166.3 178.5
uninfected 197.3 192.5
GATA-3 alt-1 6063.9 7822.8
alt-2 3817.1 3168.4
wild-type 1550.1 2324.6
uninfected 8848.2 8995.3
SOCS-1 alt-1 2517.8 1695.1
alt-2 1837.7 1437.7
wild-type 493.1 429.6
uninfected 752.4 791.6
Data represent Phosphorimager quantification of radioactive probe bound to R&D Systems Mouse Immunological Array v1.0. Two independent experiments are presented.
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| 15788098 | PMC555940 | CC BY | 2021-01-04 16:02:56 | no | BMC Biol. 2005 Mar 23; 3:8 | utf-8 | BMC Biol | 2,005 | 10.1186/1741-7007-3-8 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-281574061510.1186/1471-2164-6-28Research ArticleA comparative sequence analysis reveals a common GBD/FH3-FH1-FH2-DAD architecture in formins from Dictyostelium, fungi and metazoa Rivero Francisco [email protected] Tetsuya [email protected] Ann-Kathrin [email protected] Hideko [email protected] Taro QP [email protected] Chikako [email protected] Center for Biochemistry and Center for Molecular Medicine, Medical Faculty, University of Cologne. Joseph-Stelzmann-Strasse 52, 50931 Köln, Germany2 Gene Function Research Center, Tsukuba Central #4, National Institute of Advanced Industrial Science and Technology (AIST), Higashi 1-1-1 Tsukuba-shi, Ibaraki 305-8562, Japan3 Institute of Biological Science, University of Tsukuba, Tsukuba-shi, Ibaraki 305-8572, Japan2005 1 3 2005 6 28 28 6 10 2004 1 3 2005 Copyright © 2005 Rivero et al; licensee BioMed Central Ltd.2005Rivero 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
Formins are multidomain proteins defined by a conserved FH2 (formin homology 2) domain with actin nucleation activity preceded by a proline-rich FH1 (formin homology 1) domain. Formins act as profilin-modulated processive actin nucleators conserved throughout a wide range of eukaryotes.
Results
We present a detailed sequence analysis of the 10 formins (ForA to J) identified in the genome of the social amoeba Dictyostelium discoideum. With the exception of ForI and ForC all other formins conform to the domain structure GBD/FH3-FH1-FH2-DAD, where DAD is the Diaphanous autoinhibition domain and GBD/FH3 is the Rho GTPase-binding domain/formin homology 3 domain that we propose to represent a single domain. ForC lacks a FH1 domain, ForI lacks recognizable GBD/FH3 and DAD domains and ForA, E and J have additional unique domains. To establish the relationship between formins of Dictyostelium and other organisms we constructed a phylogenetic tree based on the alignment of FH2 domains. Real-time PCR was used to study the expression pattern of formin genes. Expression of forC, D, I and J increased during transition to multi-cellular stages, while the rest of genes displayed less marked developmental variations. During sexual development, expression of forH and forI displayed a significant increase in fusion competent cells.
Conclusion
Our analysis allows some preliminary insight into the functionality of Dictyostelium formins: all isoforms might display actin nucleation activity and, with the exception of ForI, might also be susceptible to autoinhibition and to regulation by Rho GTPases. The architecture GBD/FH3-FH1-FH2-DAD appears common to almost all Dictyostelium, fungal and metazoan formins, for which we propose the denomination of conventional formins, and implies a common regulatory mechanism.
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Background
Eukaryotic cells rely on de novo nucleation mechanisms to generate actin filaments in order to elicit spatial and temporal remodeling of their actin cytoskeleton. Besides the Arp2/3 complex, nucleation activity has been recently demonstrated also for formins (reviewed in [1]). Formins are multidomain proteins conserved from plants to fungi and vertebrates. Their name originates from the mouse limb deformity gene. Mice with mutant alleles fail to form proper limbs and kidneys [2]. Subsequently, homologues were identified in Drosophila (Diaphanous) [3] and yeast (Bni1p and Cdc12p) [4,5]. Due to their pivotal role in the organization of the actin cytoskeleton formins are involved in processes as diverse as formation of filopodia, microspikes and lamellipodia, establishment and maintenance of cell polarity, vesicular trafficking, formation of adherens junctions, cytokinesis, embryonic development and signaling to the nucleus (reviewed in [6]).
The FH2 (formin homology 2) domain is the defining feature of all formins. It is very well conserved and is almost invariably preceded by a proline-rich region, the FH1 (formin homology 1) domain [6,7]. In vitro, the FH2 domain competes with barbed-end capping proteins and is necessary and sufficient to nucleate actin polymerization, but the FH1 domain, which interacts with profilin-actin, funnels actin to the nucleation vicinity and confers full activity to the molecule [1]. Contrary to the Arp2/3 complex, which nucleates a new filament on the side of a preexisting filament, remains attached to the pointed end of the new filament and generates branched networks [8], the FH2 domain binds and stays associated to the barbed end, giving rise to unbranched filaments [9-11]. The crystal structure of the FH2 domain of two formins, Bni1p and mDia1, has been recently solved. Its fold is almost entirely α-helical and forms a ring-shaped flexible but stable dimer that caps the barbed end and allows processive elongation of the actin filament [12,13]. The FH1 domain is also a binding site for diverse SH3-domain containing proteins like Src-like non-receptor tyrosine kinases, WISH (WASP-interacting SH3 protein) and IRSp53 (insulin receptor substrate) in mammals, and Hof1p in yeast [6].
In most fungal and metazoan formins the FH1-FH2 core is accompanied by a less well conserved N-terminal FH3 (formin homology 3) domain involved in targeting [14]. In plants targeting might be mediated by membrane insertion signals or PTEN (phosphatase and tensin)-related domains [15,16]. Some formins, the so called Diaphanous-related formins, are able to interact with activated Rho GTPases through a poorly defined N-terminal Rho GTPase binding domain (GBD) that overlaps with the FH3 domain [6,7]. This binding releases the intramolecular inhibitory interaction between the GBD and a C-terminal Diaphanous autoregulatory domain (DAD) and renders the protein active [10,17].
The social amoeba Dictyostelium discoideum is an attractive model organism to investigate the components of the actin cytoskeleton and the signaling pathways involved in its regulation [18,19]. Dictyostelium amoebae are equipped with a complex actin cytoskeleton that endows the cells with motile behavior comparable to that of human leukocytes. In fact, a genome-wide survey revealed that the repertoire of cytoskeletal components of Dictyostelium is more similar to metazoa followed by fungi than to plants (Eichinger, et al., submitted). In Dictyostelium, nine formins have been previously identified but only three of them have been characterized to some extent [20]. Mutants lacking ForA, ForB or both showed no detectable phenotype, whereas disruption of the gene encoding ForC, which is expressed predominantly at late developmental stages, led to a cell autonomous developmental defect with the formation of aberrant fruiting bodies, suggesting this formin mediates actin remodeling during multicellular stages. In vivo experiments with GFP fusions showed that the N-terminal region of ForC targets the protein to places of active actin reorganization, like macropinosomes, phagocytic cups and cell-to-cell contacts [20].
We have made use of the information released by the Dictyostelium sequencing projects in order to achieve a complete inventory of formin genes. A detailed sequence analysis of the 10 formins identified revealed that, with the exception of ForI and ForC, all other formins conform to the domain structure GBD/FH3-FH1-FH2-DAD present in almost all fungal and metazoan formins, for which we propose the denomination of conventional formins. Our sequence analysis also indicates that the GBD and FH3 domains constitute a single domain also found in two Dictyostelium RasGEFs (guanine nucleotide exchange factors). The expression pattern of formin genes during asexual and sexual development was studied using real-time PCR. Our analysis allows some preliminary insight into the functionality of Dictyostelium formins: all isoforms might display actin nucleation activity and, with the exception of ForI, might also be susceptible to autoinhibition and regulation by Rho GTPases.
Results
Sequence analysis of Dictyostelium formin genes
In a previous publication 9 genes that potentially encode proteins of the formin family were identified in Dictyostelium [20]. For some of the formins (ForA through D and ForF) full length sequences were available, whereas for the rest N- and C-terminal sequences were missing. For a complete analysis of this family in Dictyostelium we sought to exploit the available databases in order to achieve a complete inventory of formin genes in its entire length. The sequences already reported by Kitayama et al. [20] were used as queries for Blast searches of the Dictyostelium genomic DNA database. This allowed assembly of complete genomic sequence for forA through forI. In order to verify the predicted amino acid sequence for each formin, Blast searches were performed against the Dictyostelium EST database. In cases where no EST sequences were available, like forG and forI, introns were verified after RT-PCR.
Inspection of the EST sequences led to the identification of one more formin gene, forJ, whose genomic sequence was also retrieved and inspected. Recent completion of the assembly of the Dictyostelium genome allowed us to confirm our gene predictions and map each formin gene to its corresponding chromosome locus (Eichinger et al., submitted). Formin genes are dispersed all over the six chromosomes (each chromosome harbors at least one formin gene), and in no case two or more genes are placed adjacent to each other (Table 1).
Table 1 Features of Dictyostelium discoideum formins Sequences can be accessed through the Dictybase identifier at
Gene Dictybase ID Chromosome Number of introns Numer of residues
forA DDB0214996 3 5 1218
forB DDB0215000 3 1 1126
forC DDB0191362 5 2* 1158
forD DDB0205290 3 3 1214
forE DDB0190413 1 0 1561
forF DDB0188569 5 1 1220
forG DDB0169087 2 1 1074
forH DDB0186588 4 3 1087
forI DDB0186053 4 2 935
forJ DDB0183855 6 1 2546
* One intron upstream of the start codon.
With the exception of forE, all other formin genes are interrupted by one or more introns, which are generally placed in the 5' half of the sequence, upstream of the region encoding the FH1 domain (Fig. 1, arrowheads). Only in forC is an intron placed in the region encoding the FH2 domain. ForC is also the only case where an intron was identified upstream of the start codon. Dictyostelium formin genes do not appear to undergo alternative splicing, at least within the coding region. This is in contrast to metazoan and plant formins, where alternative splicing gives rise to a large number of variants that frequently differ in their pattern of tissue distribution and interaction with binding partners.
Figure 1 Domain organization of Dictyostelium formins. With few exceptions, Dictyostelium formins conform to the domain structure GBD/FH3-FH1-FH2-DAD. Diagrams have been aligned with the FH2 domain. Regions with high probability of coiled coil structure are depicted as thin gray rectangles. C1 and C2 correspond to protein kinase C conserved regions 1 and 2, respectively. FHA is a forkhead-associated domain. Numbers inside the FH1 boxes indicate the number of XPPPPP motifs. Triangles denote the position of introns. Only introns placed in coding regions are shown. Intron positions shared by two or more genes have been labeled with letters.
Only two intron positions are conserved among Dictyostelium formin genes (Figs. 1 and 4). Intron a is conserved in forA, forB, forD and forH, whereas intron b is conserved in forB and forH. The conserved FH3-FH1-FH2 core domain composition (see below) along with these two intron positions underscore the view that all Dictyostelium formin genes might have arisen from a common ancestor gene. After duplications and divergence from this ancestral formin gene introns were acquired or lost and additional domains and extensions were appended to some genes.
Figure 4 Multiple alignment of the GBD/FH3 domains of Dictyostelium formins and two RasGEFs Amino acid sequences were aligned with ClustalX and the output file was subsequently edited manually. In addition to nine Dictyostelium formins, a GBD/FH3 domain was identified also at the N-terminus of RasGEF-L and RasGEF-V. The sequence of the human DRF3 has been included for reference. Dashes indicate gaps introduced for optimal alignment. In some places extensive repetitive stretches have been removed and replaced by a figure indicating the number of residues omitted. Residues identical or similar in at least 40% of the sequences are boxed in black or gray, respectively. Continuous and discontinuous lines indicate, respectively, the extension of the GBD and FH3 domains as defined in the Pfam database. Short arrows indicate boundaries of the FH3 domain as proposed by Petersen et al. [14]. Conserved intron positions are labeled a and b (see Fig. 1).
Domain structure of Dictyostelium formins: the FH2 domain
The domain structure and topology of all ten Dictyostelium formins was determined by means of bioinformatics tools and visual inspection. Although formins vary considerably in length (935 residues of ForI versus 2546 of ForJ), with few exceptions they have in common a core of about 1100 residues that harbors a GBD/FH3-FH1-FH2-DAD structure characteristic of most fungal and metazoan formins (Fig. 1). To better appreciate the relationships among the members of the Dictyostelium formin family and to analyze the requirements for their function, we have generated multiple alignments of the FH2-DAD domains as well as the GBD/FH3 domain.
The FH2 domain is the best conserved domain of formins (Fig. 2). In general, the FH2 domain is about 400 residues long. Some Dictyostelium formins (ForC, D and I) have one or more stretches of intervening repetitive sequences of variable length rich in Arg, Gln or Ser. Such repetitive sequences are characteristic of many Dictyostelium genes. The crystal structure of the FH2 domain of two formins, Bni1p and mDia1, has been recently solved [12,13]. We will consider the FH2 domains of Dictyostelium formins in the context of these two structures. The FH2 domain fold is almost entirely α-helical. It is a stable dimer that forms a closed parallelogram-shaped ring. The structure of this domain can be subdivided into subdomains. At the N-terminus a so-called lasso is connected to a globular knob (helices α1 to α5 in red in Fig. 2) by a linker of variable length. The knob is followed by a three helix bundle with a coiled-coil structure (α6, α11 and α12 in blue). The C-terminal subdomain (helices α7 to α10 and α13 in green) forms a so-called post. The lasso subdomain of one subunit encircles the post subdomain of the other subunit in a dimer. The post also harbors the GNY/FMN sequence motif that originally defined the FH2 domain (box at the end of helix α7) [21]. Residues of the lasso/post interface are highly conserved, particularly Trp1 and 2 (substituted by Phe in ForB, E and F) that insert into hydrophobic pockets in the post flanked by Gly residues 6 and 8 (Fig. 2).
Figure 2 Multiple alignment of FH2 and DAD domains of Dictyostelium formins. Amino acid sequences were aligned with ClustalX and the output file was subsequently edited manually. The sequence of the human Diaphanous-related formin 3 has been included for reference. Dashes indicate gaps introduced for optimal alignment. In some places extensive repetitive stretches have been removed and replaced by a figure indicating the number of residues omitted. Residues identical or similar in at least 40% of the sequences are boxed in black or gray, respectively. Secondary structure elements as determined for mouse Dia1 core FH2 domain [12] are indicated on top of the aligned sequences. Color coding denotes the N-terminal knob subdomain (red), three-helix-bundle (blue) and FH2 motif post-containg region (green). Regions involved in the formation of the lasso/post dimer interface, as determined for Bni1p [13] are also indicated, as well as the highly conserved GNY/FMN motif (boxed). Conserved residues discussed in the text are indicated by circles and are numbered consecutively. Below the DAD region triangles indicate conserved residues discussed in the text.
All residues of the sequence motif GNY/FMN participate in dimerization. This motif is also highly conserved in almost all Dictyostelium formins (NY is substituted by SI in ForI) but the important methionine residue (Met7) [12] is present only in ForG and ForJ and is substituted by other hydrophobic residues in the rest of the Dictyostelium formins as well as in members of the FHOD (formin homology domain containing protein) and plant class1 subfamilies.
Also very conserved are some residues probably involved in binding to actin, like Ile3 (absolutely conserved) in the N-terminal subdomain and Lys9 in the post region (substituted by Arg in ForB and ForH). Mutation of these residues in Bni1p to Ala and Asp, respectively, abolished actin nucleation and barbed end capping activity of the FH2 domain [13], and replacement of Lys9 and two adjacent Lys residues by Ala abolished alignment of microtubules and bundling of F-actin induced by activated mDia1 [22]. Other conserved residues are Asp4 (or the conservative substitution by Glu in most of the Dictyostelium formins) and Arg5 (substituted by Lys in ForB and ForE). These residues were found mutated in temperature-sensitive yeast mutants [23,24], and they probably participate in stabilization of the knob region [13].
In summary, all essential residues in the FH2 domain revealed by structural and functional studies in metazoan and fungal formins are conserved in Dictyostelium formins, indicating that all ten formins might be functional actin nucleators.
Dictyostelium formins in the context of other organisms
In order to establish the relationship between formins of Dictyostelium and other organisms and to investigate whether different species share subfamilies of formins, we constructed a phylogenetic tree based on the alignment of complete sets of sequences of FH2 domains from selected organisms, including representatives of fungi, plants, invertebrates and vertebrates. We retrieved sequences of already characterized formins and additionally we made a search of further available sequences through the SMART server with the FH2 domain as query. Appart from the ten sequences of Dictyostelium, we collected a total of 62 sequences, 21 from plants, 5 from yeasts, 6 from D. melanogaster, 6 from C. elegans and 14 from human. Taking into account that some genes might not have been predicted accurately and that predicted proteins not supported by EST sequences were not considered for our analysis, further metazoan formins, especially from human, most probably went unidentified in our search.
The phylogenetic tree (Fig. 3) supports the high degree of conservation of the FH2 domain, as becomes evident from the homogeneous branch length for most of the sequences. Yeast formins and some C. elegans members are more divergent. The phylogenetic analysis reveals clustering of most formins into well defined classes. Yeast formins form a separate class whereas plant formins significantly group into any of two classes. Metazoan formins do not constitute a single cluster, rather they distribute into a number of subfamilies. The FHOD, Diaphanous and FMNL (formin in leukocytes) subfamilies have representatives in human, D. melanogaster and C. elegans. The Cappuccino/Formin and DAAM (Dishevelled-asssociated activator of morphogenesis) subfamilies, as well as a novel subfamily, is present in human and D. melanogaster, but seems to be absent in C. elegans. Delphilin constitutes a subfamily with a unique member present only in human. Finally, C. elegans has some additional divergent formins apparently unique to this organism.
Figure 3 Phylogenetic tree of FH2 domains of formins from Dictyostelium and other organisms. Amino acid sequences of the FH2 domains (core and lasso region) were aligned with ClustalX and the output file was subsequently edited manually. A bootstrapped unrooted phylogenetic tree was constructed as described in the Methods section. Dictyostelium members are indicated in red. The other organisms considered are Arabidopsis thaliana (At), Saccharomyces cerevisiae (Sc), Schizosaccharomyces pombe (Sp), Drosophila melanogaster (Dm), Caenorhabditis elegans (Ce) and Homo sapiens (Hs). Nodes supported by either >75% or >50% bootstraps have been marked with red or green circles, respectively. For simplicity, nodes outside of a cluster supported by >50% bootstraps have not been indicated. Asterisks denote novel formin subfamilies. The scale bar indicates percent substitutions.
On average, Dictyostelium formins are 45.5% similar (23.8% identical) to each other, with ForC being only slightly more divergent (40.0%/20.4% similarity/identity to the rest of Dictyostelium formins). A comparable degree of similarity (identity) was found to members of several subfamilies of metazoan formins, and ranged between 40% (20%) and 48% (24%). Similarity (identity) to plant and yeast formins was lower: 38% (19%) and 36% (17%) respectively. Dictyostelium ForC and ForG cluster together with the Cappuccino/Formin group (75% bootstraps), whereas ForI very weakly clusters with the FHOD subfamily (53% bootstraps). However, taking into account that the FH2 domain is highly conserved, the position of these three Dictyostelium formins in the tree does not necessarily mean functional relationship with the mammalian counterpart, because other domains are probably responsible for diversity of localization and function. Bootstrapping does not support a significant clustering of the rest of the Dictyostelium formins, and only few members cluster together with a reasonably high number of bootstraps (ForE, D, A and F, 51% bootstraps).
Domain structure of Dictyostelium formins: FH1, FH3 and other domains
The FH1 domain is a proline-rich region situated immediately upstream of the FH2 domain. It is present in almost all known formins, including that of Dictyostelium, with the notable exception of ForC. The length of the FH1 domains is very variable among formins (10 to >500 amino acids). It constitutes a binding site for the actin monomer binding protein profilin, as well as for SH3 and WW domain containing signaling proteins [25,26]. Binding to profilin is well established for a large number of formin proteins and might take place through type 1 proline-rich motifs with the sequence XPPPPP, where X is usually Gly, Leu, Ile or Ser. Dictyostelium formis have a variable number of these motifs, between 1 in ForD and 8 in ForA (Fig. 1). In most cases Gly occupies the X position. In general the motifs are separated by a short stretch of up to five residues, two or more of them usually glycines. In some formins, like ForA and ForF, the proline-rich motifs might be the product of internal duplications. ForE has one additional short proline-rich region located at the N-terminus of the protein.
The FH3 domain was initially identified and characterized in the yeast formin Fus1p as a region consisting of three blocks of similarity in the same relative order in several formins [14]. It is less well conserved than the FH2 domain and is thought to be important for determining the intracellular localization of formins. Two domains of the Pfam database are recognized in this region that overlap with the FH3 domain of Petersen and co-workers [14], the Diaphanous GTPase-binding domain (PF06371) and the Diaphanous FH3 domain (PF06367). Automatic domain analysis identified a GBD and a FH3 domain in ForA, B, D, E, F and H. In ForC and ForJ a GBD was identified with confidence values slightly below the default threshold of the SMART tool. This was also the case for a FH3 domain in ForG and ForJ. A multiple alignment of the N-terminus of Dictyostelium formins with metazoan and fungal homologues revealed a homology region of approximately 380 residues in all Dictyostelium formins with the exception of ForI (Figs. 1 and 4). We will consider this region as a single GBD/FH3 domain (see discussion). In ForJ this domain is considerably longer due to stretches of intervening repetitive sequences rich in Arg and Ser residues. On average the GBD/FH3 domain of Dictyostelium formins displays 39% similarity to that of human DRF3 taken as reference for figure 4. Interestingly, inspection of the Dictyostelium genome for proteins with a GBD as defined by Pfam PF06371 yielded two genes encoding RasGEF proteins of identical domain composition, RasGEF-L and RasGEF-V. Both proteins harbor a complete GBD/FH3 domain that is 35% similar to that of human DRF3 and constitute the first case where this domain is observed outside of a formin.
We constructed a phylogenetic tree based on a multiple alignment of the GBD/FH3 domain of Dictyostelium formins (except ForI), RasGEFs, fungal formins and members of the Diaphanous, DAAM, FMNL and FHOD subfamilies (Fig. 5). With few exceptions automatic domain analysis identified GBD and FH3 domains in the metazoan and fungal formins. For example, a weak GBD was identified in D. melanogaster and C. elegans FHOD, but not in the human homologs, and conversely, a weak FH3 domain was identified in HsFHOD3 but not in other members of the subfamily. In those cases the missing domain could be reliably identified in multiple alignments. We could not identify a GBD/FH3 domain in members of the cappucino/formin subfamily. DmAE003560 has a FH3 domain and a short piece of a GBD but, interestingly, the human homolog KIAA1727 completely lacks an N-terminal region and starts at the FH1 domain. Inspection of the sequence databases did not allow clearing whether the available sequences correspond to spliced variants of longer proteins. The multiple alignment of the GBD/FH3 domain showed several blocks where similarity is higher among sequences, generally in the central part of the domain (Fig. 4). In many cases these blocks are separated by intervening stretches of variable length in the different subfamilies. We removed these insertions from our alignment prior to calculating the tree. The phylogenetic tree showed significant clustering of members of the respective metazoan subfamilies, and additionally the FMNL and DAAM subfamilies clustered together (73% bootstraps). Bootstrap analysis did not support clustering of fungal or Dictyostelium sequences into distinct classes, but interestingly, ForC and ForG significantly clustered with the FHOD family (92% bootstraps).
Figure 5 Phylogenetic tree of the GBD/FH3 domains of formins and two RasGEFs from Dictyostelium and formins from other organisms. Amino acid sequences of the GBD/FH3 domains were aligned with ClustalX and the output file was subsequently edited manually and intervening sequences between blocks of high similarity were removed. The sequence available for DmAE003560 only contains a FH3 domain and a short part of the GBD. A bootstrapped unrooted phylogenetic tree was constructed as described in the Methods section. Dictyostelium members are indicated in red. The other organisms considered are as in the legend to figure 3. Labeling is also as in the legend to figure 3.
The DAD immediately follows the FH2 domain and is required for autoinhibition by intramolecular interaction with the N-terminus of formins [21]. Inspection of the multiple alignment of the C-terminus of Dictyostelium formins revealed a DAD in all members with the exception of ForI (Figs. 1 and 2). This formin ends abruptly at the last α-helix of the FH2 domain. In all cases the DAD was placed in the vicinity of and no more than approximately 60 residues beyond the FH2 domain. The DAD is composed of two sections, a core leucine-rich sequence and a short stretch of basic residues. Both elements are present in the DAD of most Dictyostelium formins, in particular three hydrophobic residues shown to be required for activity in mouse Dia2 (indicated by triangles in Fig. 2) [17]. In ForJ, where these residues are substituted by polar or charged aminoacids, the DAD might not be functional.
Like metazoan and fungal formins, most Dictyostelium formins have predicted coiled-coil regions adjacent to the FH3 domain that could act as protein-protein interfaces for yet unidentified ligands (Fig. 1). For example, in mammalian formin1 this region constitutes the binding site of α-catenin and is involved in recruitment of formin1 to nascent adherens junctions [27] and in Bni1p the coiled coil region harbors the binding site for Spa2, a protein involved in recruitment of Bni1p to the bud cortex [28]. A few Dictyostelium formins have additional predicted coiled coil regions upstream of the FH3 domain (ForE and ForI) or downstream of the FH2 domain (ForD and ForJ) that might constitute potential protein interaction sites with regulatory or targeting functions.
Three Dictyostelium formins have additional recognizable domains at their N-terminus (Fig. 1). ForA has a C2 (protein kinase C conserved region 2) domain. This domain, present in phospholipases, protein kinases C, synaptotagmins and diverse other proteins, is thought to be involved in calcium-dependent phospholipid binding [29]. ForE has a C1 (protein kinase C conserved region 1) domain, a cysteine-rich region involved in zinc-dependent binding to diacylglycerol [30]. Finally, ForJ has a FHA (forkhead-associated) domain, a phospho-specific protein-protein interaction motif found in nuclear proteins [31]. None of these domains are found in formins from other organisms. ForJ is the only Dictyostelium formin with a long C-terminal extension. Similar extensions, although of unrelated sequence, can be observed in formins from other organisms, like yeast Cdc12 and For3, C. elegans Cyk-1 and AF106580, D. melanogaster AE003560 and human KIAA1727.
Expression analysis
Dictyostelium cells can propagate following either an asexual or a sexual life cycle. Characteristic of the asexual life cycle is the transition from single cell amoebae to a multicellular fruiting body consisting of at least two differentiated cell types. In the sexual life cycle some amoebae become sexually mature under dark and submerged conditions, fuse and form macrocysts. Either life cycle involves coordinated transcription of certain sets of genes. We have used quantitative real-time PCR to study the expression of the formin genes during sexual and asexual development (Fig. 6).
Figure 6 Expression analysis of Dictyostelium formin genes. Expression analysis was performed using quantitative real time PCR on two independently isolated mRNA samples both in sexual and asexual developmental stages. Average and standard deviation of two independent expression ratios obtained from independent cDNA samples are shown. IC, fusion incompetent cells; FC, fusion competent cells; LS, light submerged cells.
The expression patterns observed during asexual development can be classified into two major groups. Expression of forC, D, I and J displayed an increase during transition to multi-cellular stages, and except for forI, levels remained constantly high throughout the rest of development. The rest of genes displayed less marked developmental variations, and expression was either kept at constant levels (forG and H) or gradually increased (forF) or decreased (forA, B and E) after the onset of development.
When expression was analyzed during sexual development only forH and forI displayed a significant increase of about 3-fold in fusion competent cells compared to fusion incompetent cells. Cells cultured in light submerged conditions have a reduced sexual fusion competency [32,33]. In parallel with this, forH and forI were enriched in fusion competent compared to light submerged cells, indicating that this enrichment is related to the acquisition of the fusion competence rather than to the submerged condition that was included to induce the fusion competence.
Discussion
We have performed a detailed sequence and expression analysis of the formin family of Dictyostelium, which in this organism comprises 10 genes. A comparison of the domain composition of formins from diverse phyla allows their grouping into four major classes (Fig. 7). In general, Dictyostelium formins can be grouped within the class of what we designate conventional formins (see below), which includes all fungal and almost all metazoan formins. This is in agreement with a genome wide analysis that places Dictyostelium closer to fungi and metazoa than to plants (Eichinger et al., submitted).
Figure 7 Classification of formins according to structural and functional elements. Most formins of metazoans as well as formins of Dictyostelium and fungi can be classified as conventional formins, with a GBD/FH3-FH1-FH2-DAD structure, although in particular members or alternatively spliced variants a domain (but never the FH2) might be absent. Plant formins can be grouped into one of two classes. Delphilin is an unconventional formin lacking GBD/FH3 and DAD only found in metazoa. Formins are not drawn to scale. SP, signal peptide. TM, transmembrane region.
With very few exceptions all formins have in common a FH2 domain immediately preceded by a FH1 domain. The FH1-FH2 combination constitutes the minimal core that is fully functional in terms of actin nucleation and elongation activity (reviewed in [1]). This FH1-FH2 core is very ancient, and its remarkable degree of conservation points at an essential role within the cell. The diverse formin classes differ in their N-terminal regions, which have regulatory and targeting roles. Plant formins characteristically lack GBD/FH3 and DAD domains and there is no evidence for an interaction with Rop GTPases. In plants the N-terminus is unrelated to that of other organisms. Class 1 plant formins are integral membrane proteins by virtue of a signal peptide or membrane anchor followed by a transmembrane domain, whereas some class 2 plant formins have a PTEN-related domain [15,16]. Conventional formins have characteristically a GBD/FH3 domain at the N-terminus. Together with the DAD region at the very C-terminus this domain confers in most cases regulatable autoinhibition through binding of activated Rho GTPases. Finally, Delphilin is a variation only present in vertebrates. Instead of a GBD/FH3 domain it has a PDZ domain that interacts with a glutamate receptor, and it has been proposed that receptor binding causes activation of this formin [6,34].
The GBD/FH3 domain: a targeting and regulation domain
Our sequence analysis defines a putative GBD/FH3 domain in most Dictyostelium as well as fungal and metazoan formins. The two domains identified in the Pfam database at the N-terminus of several formins, the Diaphanous GTPase-binding domain and the Diaphanous FH3 domain, overlap with the FH3 domain proposed initially by Petersen et al. [14]. This distinction is apparently based on reports on binding of activated Rho GTPases, however the boundaries of each domain have not been defined experimentally. We propose that these two regions constitute a single domain for two reasons. First, when present, these two domains as defined in the Pfam database invariably appear adjacent to each other and are separated by only very few residues. Some cases of sequences where only a FH3 domain is present correspond to alternatively spliced variants of proteins that in their full length possess GBD and FH3 domains. This is the case for example of HsDRF3 [35]. Second, the GBD/FH3 domain appears as a single block in two formin-unrelated proteins of Dictyostelium, indicating that the domain was shuffled as a unit during remodeling of the genome (Fig. 4).
The role of the GBD/FH3 domain appears to be twofold. On one hand the N-terminal region of formins is involved in subcellular localization through interaction with diverse targets. For instance, the N-terminus of yeast Fus-1 is responsible for recruitment to the projection tip during conjugation [14]. In mouse Dia3 the analogous region is required for localization at mitotic spindles [36]. In Dictyostelium an N-terminal fragment that encompasses most of the GBD/FH3 domain of ForC is sufficient for targeting to crowns and macropinosomes [20]. The N-terminus appears thus as a major determinant of localization and therefore function of formins. The low degree of sequence conservation of this region might correlate with the diversity of binding partners, not only Rho GTPases, and subcellular localization patterns described. On the other hand the GBD/FH3 domain is involved in regulation of activation by releasing of an intramolecular interaction between the DAD and the N-terminus, as initially proposed by Watanabe et al. [21]. As already mentioned, the boundaries of the GBD region remain poorly defined and while a CRIB-like (Cdc and Rac interactive binding) region has been described in mammalian DRF [37], such motif cannot be identified in any other formin, whether regulated by Rho GTPases or not.
Although initially not appreciated [6], with very few exceptions a GBD/FH3, a DAD or both can be identified in almost all conventional formins, including all fungal formins, FMNL, FHOD and DAAM ([11,14,38-40] and Figs. 4 and 5). Although a FH3 domain was reported also in cappuccino and in one alternatively splice variant of mouse formin 1 (formin1 IV) [14], we were not able to identify a GBD/FH3 domain in these proteins. We cannot exclude that a strongly divergent GBD/FH3 be present in members of this subfamily. In fact, cappuccino interacts with activated RhoA [41], and the N and C-terminal segments of formin1 (IV) interact with each other [27], two features characteristic of conventinal formins.
The designation Diaphanous-related formin has been applied to those formins that interact with activated Rho GTPases [7]. However, the number of formins shown to posses this property is increasing, and includes to date at least one member of each family of conventional formins as well as Dictyostelium formins (our unpublished data). We therefore propose the use of the name conventional formins for those subfamilies with the general structure GBD/FH3-FH1-FH2, although in particular members or in alternatively spliced variants a domain (but never the FH2) might be absent, indicating whether the protein is Rho-regulated where documented experimentally. The name Diaphanous-related formin should be restricted to the metazoan members of the Diaphanous subfamily, like human DRF1 to 3.
Functionality and roles of Dictyostelium formins
Functional data on Dictyostelium formins is scarce. Only three isoforms, formins A, B and C have been characterized to some extent. Mutants lacking ForA, ForB or both showed no detectable phenotype, whereas deletion of forC led to formation of aberrant fruiting bodies with short stalks and unlifted sori, suggesting this formin mediates actin remodeling during multicellular stages [20]. Dictyostelium formins are expected to be functional according to their highly conserved FH1-FH2 structure; therefore a certain degree of functional redundancy is expected. However, diversity might arise through specific targeting and activation by Rho GTPases conferred by the GBD/FH3 domain, through interaction of specific SH3-domain containing proteins with the FH1 domain and by virtue of unique additional domains. These issues need to be addressed experimentally in the future.
ForC and ForI might be exceptions in terms of regulation. ForC lacks a FH1 domain and consequently does not bind to profilins [20]. Although the FH2 domain is necessary and sufficient for nucleation, FH2-induced nucleation is very slow and requires binding of profilin to the FH1 domain for full functionality [9-11]. While other scenarios are possible, in the case of ForC fueling of the actin polymerization process by profilin-actin might be furnished by heterodimerization with another formin possesing an FH1 domain. Regarding ForI, that lacks GBD/FH3 and DAD domains, it is not clear how this isoform could be regulated.
Three Dictyostelium formins have domains at their N-termini that are not found in other formins and might confer unique additional functions or ways of regulation or targeting. The C2 and C1 domains of ForA and ForE, respectively, might regulate activation or targeting of the molecule through interaction with specific lipids [29,30], while the FHA domain of ForJ might be involved in interactions with components of the cell nucleus [31]. In general, well defined domains others than the ones characteristic of formins are very rare. Most plant class 1 formins carry transmembrane domains and proline-rich regions in their N-termini that together might mediate anchorage of actin nucleation sites to the cell wall across the plasma membrane [15,16] and the PTEN-related domain of some class 2 plant formins might also be involved in membrane anchoring [16]. Apart from Delphilin (see above) we have identified only one more case of additional domains in metazoan formins, CeZ22171. This protein, that also lacks a FH1 domain, has a zinc finger domain and might be involved in nucleic acid interactions. The C-terminal extensions found in ForJ and several other fungal and metazoan formins also probably harbor recognition sites for additional binding partners that remain to be identified.
Functional diversity might also be related to different patterns of local and temporal gene expression. Our gene expression analyses also suggest specific roles during asexual and sexual development. Four genes in particular, forC, D, I and J, displayed an increase in expression during transition to multi-cellular stages. During this phase cells acquire aggregation competence in parallel with maturation of signaling pathways involved in remodeling of the cytoskeleton. At least for forC gene expression data correlate with a developmental role, as mentioned above [20]. ForH and ForI might play specific roles during sexual development, based alone on their patterns of gene expression. Interestingly, expression of rac1b and racF2 was found increased during the analysis of a gamete-enriched cDNA library [33]. It is therefore conceivable that one or more formins, irrespective of their expression pattern, play roles during sexual development upon activation by those GTPases.
Conclusion
The social amoeba Dictyostelium discoideum expresses 10 formins that with few exceptions conform to the domain structure GBD/FH3-FH1-FH2-DAD. This arhitecture and the high degree of conservation of the FH2 domain allow some preliminary conclusions about the functionality of Dictyostelium formins: all isoforms may display actin nucleation activity and, with the exception of ForI, may also be susceptible to autoinhibition and to regulation by Rho GTPases. Although functional redundancy may be expected to occur to some extent among Dictyostelium formins, specific roles may be conferred by the GBD/FH3 domain, which is less well conserved than the FH2 domain, and by specific patterns of gene expression during asexual and sexual development.
We propose four major classes of formins based on a comparison of the domain composition of proteins from diverse phyla. Dictyostelium, fungal and most metazoan formins can be grouped within the class of what we designate conventional formins, characterized by the structure GBD/FH3-FH1-FH2-DAD. The GBD and FH3 domains, whose boundaries had not been defined previously, probably constitute a single domain. The architecture shared by conventional formins implies a common regulatory mechanism based on autoinhibition through intramoleculr interaction of the GBD/FH3 and the DAD domains and activation through release of this interaction upon binding of Rho GTPases. Formins of the other classes (plant formins and Delphilin) lack GBD/FH3 and DAD domains and must therefore have other mechanisms of activation.
Note. While our manuscript was under review a phylogenetic analysis of the FH2 domain by H. N Higgs and K. J. Peterson has been published. These authors used a larger set of FH2 domains that includes only three formins from Dictyostelium. The topology of the phylogenetic tree described in that article and that of our tree are essentially coincident, and all seven metazoan groups identified by their authors can be found in our tree, with the novel subfamily INV comprising our HsKIAA1727, DmAE003560 and CeAF106580 sequences. Higgs and Peterson, however, do not recognize the GBD/FH3 region as a domain present in a larger number of formin subfamilies.
Methods
Sequence analysis
The amino acid or DNA sequences of Dictyostelium formins were used as query for BLAST searches [42] of the Dictyostelium genome project databases at The Welcome Trust Sanger Institute, Baylor College of Medicine, The University of Cologne and the Department of Genome Analysis of the Institute of Molecular Biotechnology in Jena. Nearly all of this data was generated at the aforementioned institutes with a small part of it produced at the Institute Pasteur. After assembly of the genome further analyses were performed through the Dictybase server [43]. BLAST searches against EST sequences were performed at NCBI [44]. Accession numbers for Dictyostelium formins can be found in Table 1.
Accession numbers of the sequences retrieved for phylogenetic analyses are as follows. S. cerevisiae Bni1p, P41832; Bnr1p, P40450; S. pombe Fus1, L37838; Cdc12, 786133; For3, AL035247. D. melanogaster Cappuccino, U34258; Diaphanous, U11288; FHOD, AE003554; FMNL, BT003654; DAAM, AAF45601; a novel formin, AE003560. C. elegans FHOD, U88314; Cyk-1, U40187; FMNL, AC024798; novel formins, Z78013, AF106580 and Z22174. H. sapiens Formin 1, AK127078; Formin 2, XM_351329; FHOD1, AF113615; FHOD3/FHOS2, KIAA1695; DRF1, AF05187; DRF2, Y15909; DRF3, BC034952; Delphilin, XM_353725; FMNL1, AF432213; FMNL2/FHOD2, KIAA1902; WBP3/FMNL3, NM_175736; DAAM1, NM_014992; DAAM2, AL833083; a novel formin KIAA1727. Sequences of plant formins were obtained from Cvrčková et al. [16]. Dictyostelium RasGEF-L and RasGEF-V can be accessed at Dictybase [43] under DDB0217789 and DDB0216586, respectively.
Protein sequences were aligned using the ClustalX [45] program with a BLOSUM62 matrix and default settings, followed by manual edition with the Bioedit program [46]. Phylogenetic trees were constructed using the neighbor-joining algorithms of the ClustalX program with correction for multiple substitutions; positions with gaps were not excluded. Construction of trees was done with TreeView [47]. Bootstrap analysis (1000 bootstraps) was applied to provide confidence levels for the tree topology. The domain analysis was done using the SMART tool [48] and InterProScan [49]. FH1 domains were identified by visual inspection. GBD/FH3 and DAD domains were identified in part by inspection of multiple alignments.
Cell culture
D. discoideum AX2 strain was grown at 21°C in shaking suspension in axenic HL5 medium [50]. AX2 cells were also cultured for sexual gametes in Bonner's salt solution (BSS) as described [33]. In brief, cells were cultured for 15 hours in a dense suspension of K. aerogenes in BSS either in the darkness or in the light. Cells cultured for 15 hours in the dark become fusion-competent cells. However, cells cultured for 15 hours in the light condition exhibit reduced fusion competency, and are designated light submerged cells. Cells on SM agar plates [50] are fusion incompetent cells.
Isolation of total RNA and quantitative real-time PCR
Total RNA was purified from both asexually and sexually developing cells with the TRIZOL reagent (GIBCO BRL, USA). Asexually developing cells on phosphate agar plates [50] were collected every 4 hours. Total RNA was treated with RNase-free DNase to remove contaminating genomic DNA, and then used to synthesize the first strand cDNA using SuperscriptII (Invitrogen, USA). For each time point cDNA was synthesized using two independently isolated mRNA samples. Specific primer sets for each formin gene were designed. To equalize the concentrations of template cDNAs, amplification was conducted using the control primer set for the Ig7 gene, which is expressed constitutively. Quantitative real-time PCR was performed with an ABI 7900HT Sequence Detection System according to the manufacturer's instructions. The amplifications were carried out using Ex Taq R-PCR Version (Takara Bio, JAPAN) and SYBR Green I. Each sample had 2 replicates containing 1-, 4-, or 16-fold diluted cDNA.
Miscellaneous methods
For RT-PCR, first strand cDNA synthesis was performed with M-MLV reverse transcriptase (Promega Corporation, Madison, WI) on poly A+ mRNA purified with the Oligotex system (Qiagen GmbH, Hilden, Germany) from total RNA. PCR fragments were cloned into the pGEM-T Easy vector system (Promega Corporation, Madison, WI) and sequenced. DNA sequencing was done at the service laboratory of the Center for Molecular Medicine, Cologne, using an automated sequencer (ABI 377 PRISM, Perkin Elmer, Norwalk, CO).
Authors' contributions
FR conceived the study, performed the assembly of genomic sequences and the sequence alignments and drafted the manuscript. TM and HU carried out the gene expression studies. AKM performed RT-PCR and cloning. CK and TQPU participated in the design of the study and the assembly of genomic sequences. All authors read and approved the final manuscript.
Acknowledgements
We are grateful to the Dictyostelium cDNA project and the Dictyostelium sequencing consortium genome for allowing access to DNA sequence information. The Dictyostelium discoideum Sequencing Consortium was funded by the Deutsche Forschungsgemeinschaft, the National Institutes of Health, the Medical Research Council and the European Union. The Dictyostelium cDNA Project in Japan is supported by several grants from the Japanese government. We thank Fatima Cvrčková for providing a data set of plant formin sequences ahead of publication and Angelika A. Noegel for critical reading of the manuscript. We thank the Japan Society for Promotion of Science for the fellowship to CK. This work was supported by grants of the Deutsche Forschungsgemeinschaft (RI 1034/2) and the Köln Fortune Program of the Medical Faculty, University of Cologne to FR and by a Grant-in-Aid for Scientific Research on Priority Area C from the Ministry of Education, Culture, Sports, Science and Technology of Japan to HU (#12206001).
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| 15740615 | PMC555941 | CC BY | 2021-01-04 16:39:33 | no | BMC Genomics. 2005 Mar 1; 6:28 | utf-8 | BMC Genomics | 2,005 | 10.1186/1471-2164-6-28 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-331575750910.1186/1471-2164-6-33Research ArticleGene fusions and gene duplications: relevance to genomic annotation and functional analysis Serres Margrethe H [email protected] Monica [email protected] Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, 7 MBL Street, Woods Hole, MA02543-1015, USA2005 9 3 2005 6 33 33 7 9 2004 9 3 2005 Copyright © 2005 Serres and Riley; licensee BioMed Central Ltd.2005Serres and Riley; 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
Escherichia coli a model organism provides information for annotation of other genomes. Our analysis of its genome has shown that proteins encoded by fused genes need special attention. Such composite (multimodular) proteins consist of two or more components (modules) encoding distinct functions. Multimodular proteins have been found to complicate both annotation and generation of sequence similar groups. Previous work overstated the number of multimodular proteins in E. coli. This work corrects the identification of modules by including sequence information from proteins in 50 sequenced microbial genomes.
Results
Multimodular E. coli K-12 proteins were identified from sequence similarities between their component modules and non-fused proteins in 50 genomes and from the literature. We found 109 multimodular proteins in E. coli containing either two or three modules. Most modules had standalone sequence relatives in other genomes. The separated modules together with all the single (un-fused) proteins constitute the sum of all unimodular proteins of E. coli. Pairwise sequence relationships among all E. coli unimodular proteins generated 490 sequence similar, paralogous groups. Groups ranged in size from 92 to 2 members and had varying degrees of relatedness among their members. Some E. coli enzyme groups were compared to homologs in other bacterial genomes.
Conclusion
The deleterious effects of multimodular proteins on annotation and on the formation of groups of paralogs are emphasized. To improve annotation results, all multimodular proteins in an organism should be detected and when known each function should be connected with its location in the sequence of the protein. When transferring functions by sequence similarity, alignment locations must be noted, particularly when alignments cover only part of the sequences, in order to enable transfer of the correct function. Separating multimodular proteins into module units makes it possible to generate protein groups related by both sequence and function, avoiding mixing of unrelated sequences. Organisms differ in sizes of groups of sequence-related proteins. A sample comparison of orthologs to selected E. coli paralogous groups correlates with known physiological and taxonomic relationships between the organisms.
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Background
Eschericia coli remains a useful resource to the genomic community as it provides important knowledge which can be applied to the analysis of most microbial genomes. Its central role devolves from two facts; first, the accumulated results of seven decades of laboratory experimentation have identified the function(s) of over half of its gene products; second being a metabolic generalist, E. coli's metabolic functions are widely shared among other organisms.
Common practices of annotation rely, more than one might realize, on the accuracy of the annotation of E. coli's genes. While searches for sequence matches to unknown genes usually yield a large number of matches, chances are high that firm functional information comes only from experimental studies on E. coli. Because annotations of genes do not always indicate that the assignments are derived, and because derived annotations are used serially for further annotation without experimental confirmation, many genes carry original E. coli annotations. It is therefore important to the entire genome-analyzing community that the data on E. coli gene products be as accurate as possible. Since the original GenBank deposit of E. coli K-12 (U00096), new and updated annotations are available at NCBI (U00096.2) and at more specialized databases including, ASAP [1], coliBASE [2], CyberCell [3], EchoBASE [4], EcoCyc [5], GenProtEC [6], and RegulonDB [7]. An effort is under way to coordinate the current E. coli annotations [8].
Over recent years, our work on the E. coli genome has led us to an appreciation of the pernicious role that gene fusions often play as troublemakers in function assignments and in relating groups of sequence similar proteins [9]. The fusion of two independently functioning genes results in the formation of a composite (multimodular) protein encoding for two independent functions located at separate parts of the protein. This type of fusion is not equivalent to the joining of protein domains, i.e. domains encoding binding sites for a cofactor or a substrate, which is seen in multidomain proteins. An example being the enzyme glyceraldehyde-3-phosphate dehydrogenase which according to the domain databases Pfam [10] and Superfamiliy [11] contains two domains, an NAD binding site and a dehydrogenase catalytic site. In our studies the entire protein including both domains represents one independent functional unit with one activity. Multidomain proteins are more prevalent and most often encode one overall function for the gene product [12].
Annotation involving transfer of function from composite proteins to sequence similar matches requires that the alignment regions be evaluated in order to determine whether all activities or only one of them should be assigned to the matching sequence. Currently fused proteins are themselves not always annotated to reflect that they encode more than one function, and rarely is the location of the separate functions indicated. Different combinations of fused genes are seen in the sequenced genomes, adding potential sources for annotation errors. Errors in functional assignments including those caused by fused genes have been noted years ago [13] and that such proteins may contribute to propagation of annotation errors in databases [14]. The fused proteins also interfere with the generation of sequence related protein groups as they link proteins based on their coexistence in a fused protein and not purely based on sequence similarity. Components of fused genes are often not sequence related, so generating protein groups without taking gene fusions into account may result in "mixed" groups of proteins with different sequence relatedness, functions and evolutionary histories.
Previous work has been published where we identified fused E. coli proteins from partial alignments between proteins encoded in the E. coli genome [15]. This work resulted in the identification of 287 multimodular proteins. As our analysis continued and more genome sequences were incorporated in our studies we realized that most of these identified multimodular proteins actually contained multiple domains and had one overall function. We have therefore revised our method of detecting fused proteins. We are making use of sequence information from 50 genomes including E. coli to detect proteins which are fused in the E. coli genome and are present as individual components in one of the other genomes. We have also made use of published experimental data on E. coli gene fusions. As a result the number of fused proteins in E. coli has been reduced to 109. The number of groups of sequence related proteins was also reduced from 609 to 490 since some of the previously identified groups are made up of protein domains catalyzing only part of an overall reaction. This work represents a revision of the state of fused proteins in the E. coli genome their affect on genome analysis both within E. coli and across genomes.
Results
Multimodular vs. multifunctional proteins
To prevent confusion, we define multimodular proteins as those seeming to result from gene fusion in which two independent proteins are connected. Multimodular proteins encode separate functions in different parts of the molecule. These functions might be the same if two like elements have fused, or as we see more often in E. coli, they differ in sequence and activity. Distinctly different, multifunctional proteins are defined as those that carry out more than one reaction or activity in the same part of the protein. Examples of such multifunctional proteins are encoded by the genes cobU, birA, ubiG, folD, cysG, tesA, and ndk (for details see gene products at GenProtEC [16]).
A protein that illustrates both properties is the FadB protein of E. coli [17,18]. FadB is a multimodular protein with N-terminal and C-terminal modules. Its N-terminal module is multifunctional with three activities that are catalyzed at the same active site and cannot be spatially separated along the length of the protein. The three activities are 3-hydroxybutyryl-CoA epimerase, delta(3)-cis-delta(2)-trans-enoyl-CoA isomerase, and enoyl-CoA hydratase. The C-terminal module of FadB encodes a single function, 3-hydroxyacyl-CoA dehydrogenase. Adding the N-terminal and C-terminal modules, there are 4 activities for the FadB protein.
Identifying multimodular proteins in E. coli
In earlier work, before the genomic sequence of E. coli was completed, we saw that sequence similarity among its proteins was widespread [9,19]. After the entire sequence was available, we identified 287 E. coli proteins as being multimodular and encoded by fused genes [15,20]. The modularity of the proteins was inferred from the alignments among E. coli proteins. However, we have since found that many of these so-called multimodular proteins were proteins containing more than one domain and not more than one protein. Such multidomain proteins may appear to encode two functions but in reality encode two or more conserved motifs (i.e. DNA-binding and effector-binding domains of LysR type transcriptional regulators). By including sequence information from other genomes besides E. coli we were able to better distinguish fusions of complete proteins versus the more common fusions of protein domains. Of the 287 proteins previously identified as multimodulars only 70 remained as fused proteins in this study with the remaining representing domain fusions.
In the present work, some of the fused proteins were identified by searching the literature for experimental data. Examples of E. coli proteins long known to contain multiple functions encoded at separate parts of the proteins include GlnE [21], MetL [22], ThrA [23], and TyrA [24]. We have collected such experimentally verified information over time [9], labeled as multimodular proteins with literature citations in our database GenProtEC [16]. Other multimodular proteins were identified by selected types of alignments between E. coli proteins and proteins encoded in 50 sequenced genomes. The component proteins of a multimodular protein may be unimodular and unfused in another genome. We looked for alignments between the larger potentially multimodular proteins in E. coli and smaller orthologous proteins that are homologous to only one of the modules (Figure 1a). Not all gene fusions of E. coli will be detected by this method. For instance elements of a fused gene may have diverged to the point where the component modules no longer have detectable similarity to their homologous counterparts, or the independently existing modules may have been lost from the gene pool of the 50 genomes analyzed, or the 50 organisms may contain only the multimodular form.
Figure 1 Identification and sequence similarity of multimodular E. coli proteins. (a) An E. coli protein (gi1787250) aligns with two smaller proteins from C. acetobutylicum, histidinol phosphatase (gi15026114) and imidazoleglycerol-phosphate dehydratatase (gi15023840). The E. coli protein represents a fused or multimodular protein encoding the two functions in separate parts of the protein as indicated by the two non-overlapping alignment regions. Based on the alignment regions, the E. coli protein is separated into two separate components, modules. The modules are identified with the extensions "_1" or "_2" to indicate their location in the gene product as N-terminal or C-terminal, respectively. (b) Sequence similarity between modules of the multimodular proteins is shown. No detectable similarity between the joined modules is indicated by a difference in the module patterns in the cartoon. Similarity is measured by Darwin and indicates that the proteins align at a distance of ≤ 200 PAM units over at least 83 amino acid residues or >45% of the length of the proteins. This level of similarity also reflects whether the modules belong to the same paralogous group.
In total we identified 109 E. coli proteins to be multimodular, with 101 containing two modules and 8 containing three modules. The largest number of multimodular proteins joined modules of dissimilar sequence (illustrated in Figure 1b). An abbreviated list of the modules and their functions is shown in Table 1. A complete list of the multimodular E. coli proteins is made available: ' [see Additional file 1]'. The remaining proteins, 97.5 % of the total, were considered to be unimodular. The average length of the multimodular proteins was 637 residues compared to 309 for the remaining proteins in the chromosome (Figure 2). Individual modules from the multimodular proteins were on average 300 residues long, similar to the length of the unimodular proteins. However, the size alone of a protein does not reflect multimodularity as we found many large proteins to be unimodular.
Table 1 Examples of multimodular E. coli proteins.
Gene Module Start End Gty1 Module Function
thrA b0002_1 1 461 e aspartokinase I, threonine sensitive
thrA b0002_2 464 820 e homoserine dehydrogenase I, threonine sensitive
ribD b0414_1 1 143 e diaminohydroxyphosphoribosylaminopyrimidine deaminase
ribD b0414_2 147 366 e 5-amino-6-(5-phosphoribosylamino) uracil reductase
putA b1014_1 1 569 e bifunctional: transcriptional repressor (N-terminal); proline dehydrogenase, FAD-binding (C-terminal)
putA b1014_2 618 1320 e pyrroline-5-carboxylate dehydrogenase
adhE b1241_1 1 400 e acetaldehyde-CoA dehydrogenase
adhE b1241_2 449 891 e iron-dependent alcohol dehydrogenase
thiP b0067_1 1 274 t thiamin transport protein (ABC superfamily, membrane)
thiP b0067_2 285 536 t thiamin transport protein (ABC superfamily, membrane)
mdlA b0448_1 1 310 pt putative transport protein, multidrug resistance-like (ABC superfamily, membrane)
mdlA b0448_2 314 590 pt putative transport protein, multidrug resistance-like (ABC superfamily, ATP_bind)
modF b0760_1 1 260 t molybdenum transport protein (ABC superfamily, ATP_bind)
modF b0760_2 261 490 t molybdenum transport protein (ABC superfamily, ATP_bind)
hrsA b0731_1 1 178 t PTS family enzyme IIA, induction of ompC
hrsA b0731_2 186 454 t PTS family enzyme IIB, induction of ompC
hrsA b0731_3 456 628 t PTS family enzyme IIC, induction of ompC
atoC b2220_1 1 125 r response regulator
atoC b2220_2 145 461 r sigma54 interaction module of response regulator (EBP family)
evgS b2370_1 1 935 e histidine kinase of hybrid sensory kinase
evgS b2370_2 953 1197 r response regulator of hybrid sensory histidine kinase
glnG b3868_1 1 120 r response regulator, two-component regulator with GlnL, nitrogen regulation
glnG b3868_2 139 469 r sigma54 interaction module of response regulator (EBP family)
kefA b0465_1 1 779 o unknown function module of mechanosensitive channel
kefA b0465_2 780 1120 t mechanosensitive channel (MscS family)
argA b2818_1 1 293 o acetylglutamate kinase homolog (inactive)
argA b2818_2 298 442 e N-alpha-acetylglutamate synthase (amino acid acetyltransferase)
ydcR b1439_1 1 117 pr putative transcriptional regulator (GntR family)
ydcR b1439_2 118 468 pe putative amino transferase
rnfC b1629_1 1 448 pc Fe-S binding module of electron transport protein
rnfC b1629_2 450 740 o unknown function module of electron transport protein
1Gene product type: e, enzyme; pe, putative enzyme; r, regulatory protein; pr, putative regulatory protein; t, transport protein; pt, putative transport protein; pc, putative carrier protein; o, unknown function.
Figure 2 Size distribution for multimodular and single module proteins. The protein lengths in amino acid residues are shown for single module proteins (□) and for multimodular proteins (■). On average the multimodular proteins are longer than the unimodular proteins, 637 amino acids versus 314 amino acids. The length of a protein alone does not infer multimodularity and long single module proteins are seen.
Characteristics of multimodular proteins of E. coli
Table 2 shows some characteristics of the modules in the multimodular proteins. The majority of the E. coli modules, 90%, were found to have homologs existing as independent proteins in one of the 50 genomes analyzed. Independent unimodular homologs within E. coli were detected for only 57% of the modules (data not shown). A list of the major types of multimodular proteins is shown in Table 3.
Table 2 Features of multimodular E. coli proteins:
No. Modules
109 multimodular proteins 226
101 bimodular proteins 202
8 trimodular proteins 24
with identity to unfused orthologs 203
without identity to unfused orthologs 23
known function 151
putative function 66
unknown function 9
type of protein1:
enzyme 97
transport protein 85
regulatory protein 26
other 18
1 includes putative assignments
Table 3 Types of multimodular proteins.
Protein type1 Protein names2
Enzyme Aas, AdhE, AegA, ArgA, ArnA, CysG, Dfp, DgoA, DsbD, FadB, FadJ, FtsY, GlcE, GlmU, GlnE, Gsp, HisB, HisI, HldE, HmpA, MaeB, MetL, MrcA, MrcB3, NifJ3, PaaZ, PbpC, PheA, PolA, PurH, PutA, RbbA3, RibD, Rne3, ThrA, TrpC, TrpD, TyrA, YdiF, YfiQ, YgfN, YgfT, YjiR
Transport protein AlsA, AraG, CydC, CydD, DhaH, Ego, FeoB, FhuB, FruA, FruB, FrvB, HrsA3, KefA, MacB, MalK, MalX, ManX, MdlA, MdlB, MglA, ModF, MsbA, MtlA, NagE3, PtsA, PtsG, PtsP, RbsA, ThiP, Uup, XylG, YbhF, YbiT, YddA, YejF, YheS, YjjK, YliA, YnjC, YojI, YpdD3, YphE
Regulatory protein Ada, Aer, ArcB, AtoC, BarA, BglF, CheA, CheB, EvgS, GlnG, KdpD, MalT, RcsC, TorS, YdcR, YfhA, YieN, ZraR
Other InfB, MukB3, RnfC, YegH, YfcK, YoaE
1Gene type includes known and putative functions.
2Protein names derived from gene names.
3Genes encoding three modules.
• Many of the multimodular enzymes function in the biosynthesis or degradation of compounds (amino acids, cofactors, peptidoglycan and fatty acids).
• The majority of the multimodular transport proteins encode fusions of components of the ABC superfamily transporters (ATP-binding and membrane component). Also, fusions of the PTS proteins were detected in different combinations. Thirteen proteins contained two or more PTS components, including Hpr, enzymes I, IIA, IIB, or IIC.
• Among the multimodular regulatory proteins, two-thirds were part of two-component regulatory systems and contained histidine kinases fused to response regulators. Seldom were known domain subdivisions within these modules detected by the rules we applied.
While the fraction of enzymes (39%) is similar to the fraction of enzymes encoded in the genome as a whole (36%), the proportion of multimodular transport proteins (38%) and regulatory proteins (17%) were higher than their proportion genome wide (14% and 8% respectively). The over-representation in transporters and regulators is a reflection of the level of gene duplication seen for these proteins. Large paralogous groups are detected for some of the ABC transporter protein subunits and for components of the two-component regulators.
Pairwise similarity of E. coli single modules
All unimodular proteins, including the modules obtained from multimodular proteins, were tested pairwise for sequence similarity. Matching all single module E. coli proteins to each other using the AllAllDb algorithm of the Darwin package, we collected all aligned pairs with a similarity score of less than or equal to 200 PAM units, with an alignment of at least 83 residues. Altogether 9,626 unique pairs met these criteria (data available at GenProtEC [16]).
Paralogous groups of E. coli protein modules
We used the data on pairwise similarity to assemble groups of proteins of similar sequence that were unlike other proteins in the cell. Besides the PAM less than 200 and alignment length of at least 83 residues, two additional requirements were imposed; that more than 45% of each protein in each pair be aligned, and that a module could not belong to more than one group. A transitive clustering process was used to form the sequence-similar groups [9]. This grouping method requires only that each member of the group have sequence similarity to at least one other member of the group and does not require a detectable similarity among all the members of a group. Both closely related groups and groups with more divergent proteins were found.
We identified 490 sequence-similar or paralogous groups in E. coli ' [see Additional file 2 for a complete list of the sequence-similar E. coli groups and their members]'. Altogether 1946 unimodular proteins belonged to one of the groups. Modules from 94 of the multimodular proteins were present in 61 of the groups. Table 4 shows the power law type of distribution of the number of members in the groups, smaller groups being more abundant than large ones. There were 279 groups of two proteins, and only 10 % of the groups had 7 or more members. As shown in Table 5, the smaller groups tended to be tight groups in which the majority of sequences were related by our criteria to all or most others in the group. Larger groups were more divergent with a minority of members related to all others. At group size 8 and above, no members have the property of relating to all others.
Table 4 Size distribution of paralogous groups.
Group size No. Groups
2 279
3 91
4 32
5 31
6 6
7 18
8 7
9 2
10 2
11 3
12 1
13 2
14 2
18 2
20 1
21 1
22 2
24 1
30 2
40 1
43 1
46 1
51 1
92 1
Table 5 Sequence relationships within paralogous groups.
Group size No. Groups All See All All See Some
3 92 56 36
4 32 21 11
5 31 7 24
6 6 0 6
7 18 2 16
The largest groups of paralogous enzymes, transport proteins and regulatory proteins are shown in Table 6, 7 and 8, respectively. While enzymes represent the largest gene product type in E. coli with known or predicted function, they tend to be present in smaller paralogous groups as compared to the transporters and regulators. Among the larger groups the oxidoreductases and the subunits of oxidoreductases are most common, making up 8 of the top 20 enzyme groups (Table 6).
Table 6 Paralogous enzyme groups in E. coli.
No. Members Group function
20 oxidoreductase, Fe-S-binding
18 oxidoreductase, NAD(P)-binding
18 oxidoreductase1, NAD(P)-binding
13 aldehyde oxidoreductase, NAD(P)-binding
13 oxidoreductase, FAD/NAD(P)-binding
11 sugar kinase
10 terminal oxidoreductase, subunit
9 aldo-keto oxidoreductase, NAD(P)-binding
8 phosphatase
8 nucleoside diphosphate (Nudix) hydrolase
8 acyl-CoA ligase
7 glutathione S-transferase
7 RNA helicase, ATP-binding
7 sugar epimerase/dehydratase, NAD(P)-binding
7 alcohol oxidoreductase
7 acyltransferase
7 aminotransferase, PLP-binding
7 decarboxylase, TPP-binding
7 crotonase
7 acyltransferase
1Contains GroES-like structural domain (SCOP sf50129).
Table 7 Paralogous transport protein groups in E. coli
No. Members Group function
92 ABC superfamily transport protein, ATP-binding component
51 ABC superfamily transport protein, membrane component
40 MFS family transport protein
24 ABC superfamily transport protein, periplasmic binding component/ transcriptional regulator (GalI/LacR family)/
22 APC family transport protein
12 ABC superfamily transport protein, membrane component
11 PTS family transport protein, enzyme IIA
9 ABC superfamily transport protein, periplasmic binding component
8 ABC superfamily transport protein, periplasmic binding component
7 GntP family transport protein
7 RND family transport protein
7 ABC superfamily transport protein, membrane component
5 HAAP family transport protein
5 PTS family transport protein, enzyme IIB
5 PTS family transport protein, enzyme I
5 GPH family transport protein
5 NCS2 family transport protein
5 HAAP family transport protein
5 transport protein
5 PTS family enzyme IIC
5 RhtB family transport protein
5 outer membrane porin
Table 8 Paralogous regulatory protein groups in E. coli.
No. Members Group function
46 LuxR/UhpA or OmpR family transcriptional response regulator of two-component regulatory system
43 LysR family transcriptional regulator
30 GntR or DeoR family transcriptional regulator
22 sensory histidine kinase in two-component regulatory system
14 sigma54 activator protein, enhancer binding protein
14 AraC/XylS family transcriptional regulator
7 ROK family transcriptional regulator/sugar kinase
7 IclR family transcriptional regulator
5 methyl-accepting chemotaxis protein
5 MerR family transcriptional regulator
4 DNA-binding regulatory protein
3 AraC/XylS family transcriptional regulator
3 MarR family transcriptional reguator
3 AsnC family transcriptional regulator
ATP-binding components of the ABC superfamily of transport proteins are highly conserved and make up the overall largest paralogous group in E. coli (Table 7). The other two components of the ABC superfamily transporters are less conserved with membrane components in groups of 52 or less and periplasmic binding components in groups of 9 or less. Components of the PTS system; enzyme IIA, IIB, IIC and I also formed sequence similar groups. One of the groups classified as a group of transporter proteins actually contains both transport proteins (periplasmic binding components of the ABC superfamily) and regulatory proteins (transcriptional regulators of the GalR/LacI family). These two functional types are sequence related, and all of the proteins contain a common structural domain (SCOP sf53822) for the binding of small molecules [25,26]. The difference lies in the presence or absence of a DNA-binding domain.
Response regulators of two-component regulatory systems make up the largest group of regulatory proteins in E. coli (Table 8). Sensory histidine kinases of two-component regulatory systems and the sigma54 activating proteins also constitute paralogous groups. A group almost equal in size to the response regulators is the LysR-family of transcriptional regulators. Other large groups of transcriptional regulators are also present.
Cross genome comparisons of paralogous groups
In addition to using paralogous groups for intra-genomic analyses, the groups were also used in cross genome comparisons (see Table 9). The sizes of selected sequence related groups are shown for three bacteria, the closely related enterics E. coli and Salmonella enterica serovar Typhimurium and the more distantly related organism Bacillus subtilis. The sizes of the groups in the closely related bacteria are similar, whereas there are differences in relation to B. subtilis, a gram positive soil organism. For instance, the largest E. coli enzyme group containing Fe-S-binding oxidoreductases was represented by only one homolog in the B. subtilis genome. However, B. subtilis encodes for 31 oxidoreductases homologous to the group of 18 NAD(P)-binding oxidoreductases of E. coli. The number of homologous sugar kinases, respiratory reductase subunits, and nucleoside diphosphate (Nudix) hydrolases appeared overall to be lower in B. subtilis.
Table 9 Cross genome comparisons of enzyme groups.
Ec1 So2 Bs3 Group function
20 18 1 oxidoreductase, Fe-S-binding
18 14 31 oxidoreductase, NAD(P)-binding
18 13 10 oxidoreductase4, NAD(P)-binding
13 13 11 aldehyde dehydrogenase, NAD(P)-binding
13 11 13 oxidoreductase, FAD/NAD(P)-binding
11 16 6 sugar kinase
10 13 5 respiratory reductase, alpha subunit
9 8 8 aldo-keto reductase, NAD(P)-binding
8 7 5 phosphatase
8 8 2 nucleoside diphosphate (Nudix) hydrolase
1No. proteins in Escherichia coli paralogous group
2No. sequence matches for E. coli paralogous group in Salmonella typhimurium LT2
3No. sequence matches for E. coli paralogous group in Bacillus subtilis
4Contains GroES-like structural domain (SCOP sf50129).
Discussion
Protein modules vs. protein domains
We have attempted to enumerate fused genes in E. coli in earlier work. Although we recognized the difference between independent proteins with complete function, called modules [9], as opposed to parts of proteins such as motifs and domains, we were not successful in our most recent effort in collecting only complete proteins to the exclusion of domains [15,27]. In earlier work we depended on size as a criterion to eliminate domains, but we know now some domains are large and overlap the lower range of sizes of independent proteins [28]. We also limited our previous studies to alignments between E. coli proteins. In this report we make use of information from 50 genomes to detect complete and independent protein homologs for the components of the fused E. coli proteins. The need to make use of additional genome sequences is supported by the fact that only 57% of the modules in fused E. coli proteins had unfused homologs within the E. coli genome while 90% had homologs among the 50 genomes. This result suggests that additional fused E. coli proteins might be detected in the future with more available genome sequences.
The overall effect of changing the methodology has been to reduce the numbers of multimodular proteins identified in E. coli K-12. As a result of reducing the number of fused proteins, the number of paralogous protein groups was also reduced. The grouping process is based on similarity between the sequences hence many parts of the same proteins remained together in the new groups.
The effects of multimodular proteins on annotation of genes
For many years we have known that the E. coli contained fused genes and groups of sequence-similar proteins [19]. Today with the sequence of the entire genome and that of many other microbial genomes, we can quantify the gene fusions in E. coli and apply this information to generate paralogous groups. Even though we find that multimodular proteins are a minor fraction, 2.5%, of the proteins in E. coli K-12 MG1655, they significantly affect the annotation of related genes and the ability to define paralogous genes within a genome.
Examples of the types of errors arising in the annotation of fused proteins are shown in Figure 3a. The multimodular protein ThrA (gi1786183) encodes an aspartokinase in the N-terminal module (aa 1–461) and a homoserine dehydrogenase in the C-terminal module (aa 464–820). A sequence similar protein from Lactococcus lactis, gi12723655, aligning only to the N-terminal module is erroneously annotated as having both aspartokinase and homoserine dehydrogenase activities. The correct annotation should be aspartokinase. In a second example, a protein from Bacillus halodurans, gi10174117, aligns to the aspartokinase module of ThrA but is described as homoserine dehydrogenase. The correct assignment should be aspartokinase.
Figure 3 Annotation and composition of multimodular proteins. (a) Annotation is complicated by multimodular proteins. An E. coli protein (gi1786183) contains two modules, an N-terminal aspartokinase and a C-terminal homoserine dehydrogenase. Two single module proteins from L. lactis and B. halodurans (gi12723655 and gi10174117) align to the N-terminal aspartokinase module of the E. coli protein. Based on the sequence alignments, both of these proteins should be annotated as aspartokinases. However, errors are seen in the annotation of the L. lactis and B. halodurans proteins stemming from transfer of functions between multimodular proteins and partially aligned sequences without taking into account the alignment regions. (b) Different combinations of modules are seen in multimodular proteins of different organisms. While aspartokinase is fused to homoserine dehydrogenase in E. coli it is fused to DAP decarboxylase in X. fastidiosa. In both organisms the fusions are between enzymes of metabolic pathways, threonine biosynthesis for E. coli and lysine biosynthesis in X. fastidiosa.
As shown in Figure 3b, different genes are sometimes fused to the same gene in different organisms. In E. coli an aspartokinase is fused to a homoserine dehydrogenase (gi1766183), while in Xylella fastidiosa, an aspartokinase is fused to a diaminopimelate decarboxylase (gi9106073). One needs to be alert to partial alignments. In this case, the annotation is correct for both activities of the Xylella protein, although the description does not follow the convention of stating the N-terminal activity first, raising the potential for misidentification of the activity of a partial homolog.
Generality of gene fusions and remedies
The details of gene duplication and divergence and of gene fusions have followed different courses in separate lines of descent of bacteria. The fusions of different gene partners to aspartokinase in E. coli and X. fastidiosa connected proteins acting in the same pathway. However, the pathways are different for the two organisms, threonine biosynthesis for E. coli and lysine biosynthesis in X. fastidiosa. Fusions of genes in a pathway have long been known and also the fusions of different genes in different organisms. In the tryptophan biosynthesis pathway of E. coli both the trpC gene (formerly trpC(F)) and the trpD gene (formerly trpG(D)) encode two enzymes as indicated in their former names. In contrast Rhizobium meliloti has a fusion between the trpE and trpG genes, trpE(G) [29]. Such differences not taken into account in annotation have generated errors in assignment of activities in some of the tryptophan synthesis proteins in a number of organisms. The variability in gene fusions among bacteria means that definition of multimodular proteins cannot be transferred from one organism to another, but must be worked out by analyzing the partial homology patterns with smaller independent proteins found in other organisms.
To promote awareness of fused proteins, databases should list such proteins with their separate component activities and the approximate locations of these; either by start and end residues, or by module location (N-terminal, C-terminal, or Middle for proteins with >2 modules). Such a format has been implemented in GenProtEC [16]. When analyzing protein sequence alignments, one should make use of information on the alignment lengths and on the percent of each sequence that is involved in the alignment. Such information may hold clues to detecting fused proteins.
Properties of paralogous groups of E. coli
Groups of unimodular E. coli proteins similar in sequence vary in size from two (simple pairs) up to 92 members (Table 4). From pairs to groups of 8, the number of paralogous groups follows a power law. Above size 8, most sizes are represented by just one or two groups. For the smallest groups, two to four members, the degree of sequence similarity (PAM scores) tend to range widely (Figure 4). As the groups are larger, a clear distribution around PAM 150 emerges. Perhaps the larger groups are ones whose success is reflected in many duplication events over time with a retained function if the sequence drift is held to the range 100 to 200 PAM units. It appears that choosing 200 PAM as the upper ceiling has not eliminated an important number of groups with highly diverged members. Also, the broad range of degree of relatedness among members of paralogous groups (Table 5, Figure 4) suggests that some types of proteins diverge further than others. The cluser around PAM 150 is populated by large successful paralogous groups, some of which are closely related in catalytic function while others have diverged to more distantly related activities.
Figure 4 Sequence similarity of E. coli paralogous protein groups versus the group size. Protein sequences were aligned by the AllAllDb program of Darwin. Multimodular proteins were separated into modules (independent functional units) prior to the Darwin analysis. Alignments with similarities of ≤ 200 PAM units over 83 amino acids and where >45% of the length of both proteins in the pair were aligned were used to generate protein groups. The average PAM distances for the protein pairs in the smaller groups having 2–4 members (▲) and in the larger groups of ≥ 5 members (△) are shown. The smaller groups are more abundant and show a wide range of similarities. The larger groups appear to be more divergent with higher average PAM values clustering around PAM 150.
The largest paralogous groups are transporters and regulators (Tables 7 &8). Paralogous groups of enzymes tend to be smaller (Table 6). The largest enzyme classes tend to be oxidoreductases or subunits of oxidoreductases, and the relationships among members of these groups point in the direction of shared binding capacities accounting for the sequence relatedness, e.g. Fe-S clusters. In earlier work we found that some sequence related enzymes are alike in their ligand-binding characteristics, others are alike in mechanism of the catalytic action [30]. Both types of shared properties are seen in Table 6.
The ABC transporters have been a successful formula in bacterial evolution. The ATP-binding subunits maintain detectable sequence similarity. More divergent are the membrane subunits, and least similar are the periplasmic ligand-binding subunits, perhaps understandably divergent as their binding specificities for each transported compound will differ with the properties of the compounds [31]. One of the groups of periplasmic binding components also contains sequence related transcriptional regulators of the GalR/LacI family, agreeing with previous reports [25,26]. The major difference between these two functions is the presence or absence of a DNA-binding domain. According to Fukami-Kobayashi et al. [26], the regulators in this group are believed to have arisen by the fusion of a DNA binding domain to an ancestral periplasmic binding protein. The substrate specificity is thought to have evolved subsequently. Only a few of the transporters and regulators in this group bind the same substrates; galactose (MglB and GalR), ribose (RbsR and RbsB) and xylose (XylF and XylR).
Among the regulator groups (Table 8), the class of two-component regulators is large. The two major activities of sensory histidine kinase and response regulators separate by the rules for grouping modules, but their known internal structures do not emerge. Many other groups are different kinds of transcriptional regulators. Another example of different functions related by sequence has been reported for a class of repressors and kinases, the ROK family [32]. In this case the two different functions are sequence related via their sugar-binding domains and differ in their DNA-binding or kinase activity.
Cross genome comparisons
Examining comparable paralogous groups among organisms may provide insight into functional and physiological differences among organisms. Illustration of the possibilities is shown in Table 9 where the sizes of comparable paralogous groups are shown for the closely related enteric bacteria E. coli and S. enterica serovar Typhimurium and the distant gram positive soil organism B. subtilis. Major difference is seen for one category of oxidoreductases. The largest enzyme group in E. coli contains 20 FeS-binding proteins whereas the B. subtilis genome has only one protein of this type. Members of the E. coli group include subunits of formate dehydrogenases, hydrogenases 3 and 4, DMSO reductase, and a NADH dehydrogenase. The presence of elements of the formate hydrogen lyase system and of the DMSO reductase in E. coli but not B. subtilis illustrates information on metabolic differences that emerges from such cross-genome comparisons. B. subtilis does not have the diverse anaerobic respiratory capability of E. coli and S. enterica. Duplication and divergence of this common ancestral gene seems to have taken a different course in the two bacterial lineages.
In another example, B. subtilis has made use of one enzyme type to a greater extent than the two enteric organisms. The number of one of the types of NAD(P)-binding oxidoreductases is much larger in B. subtilis (31 proteins) than in the enterics (18 proteins). The B. subtilis enzymes in this group are fatty acid biosynthesis enzymes, agreeing with the known fact that this organism synthesizes a greater variety of fatty acids and has dedicated more of its proteome towards diversifying its fatty acid biosynthetic capabilities [33,34]. Thus sequence similar groups may be used in comparative analysis between genomes, highlighting areas where genetic resources have been expanded, pointing up metabolic differences between organisms.
Conclusion
• Proteins encoded by fused genes, multimodular proteins, require special attention in genome analysis. Such multimodular proteins contain two or more functional components that are located at separate parts of the protein and that may exist as independent proteins in other genomes. Annotation of the multimodular proteins should include the separate functions and their corresponding locations in the gene product. This will improve transfer of function between the fused proteins and sequences matching their entire length or only the length of one of their module components. Current annotation errors involving fused genes can be remedied by introducing this approach.
• The identification of multimodular proteins in E. coli was improved by making use of sequence information from 50 genomes to detect alignments between the fused proteins and smaller, un-fused homologs corresponding to the component modules. The more common multidomain proteins, proteins containing fused sequence domains or motifs that together make up one overall function, were not detected as multimodular proteins by this approach. As a result the current number of fused E. coli proteins was reduced to 109 proteins with 8 containing three modules and 101 containing two modules. The multimodular E. coli proteins consist mainly of enzymes, regulators and transport proteins. Their component modules are often not related by sequence but many are related in that they function in a common pathway or cell role. Components of fused genes appear to vary from genome to genome hence complicating their detection and function assignment.
• Multimodular proteins are different from multifunctional proteins in that the latter catalyze more than one reaction in the same region of the protein.
• The generation of paralogous or sequence related groups is improved when the modules of multimodular proteins are separated and treated as independent proteins for the grouping process. 490 groups of sequence related E. coli proteins ranging in size from 2 to 92 were generated from the new module data. The smaller groups range widely in degree of relatedness while the larger groups have diverged from one another to about the same extent. Transport proteins and regulatory proteins were found in the larger groups while enzyme groups tended to have fewer members.
• Over half of the E. coli proteins belong to paralogous groups, reflecting the prominent role of duplication and divergence in the evolution of the genome. The number and sizes of paralogous groups reflect the distinctiveness of the organisms and they can be used in cross genome comparisons.
Methods
Sequence sources
Protein coding sequences were obtained from GenBank and included the following genomes: Aquifex aeolicus, (AE000657); Archaeoglobus fulgidus, (AE000782); Aeropyrum pernix, (BA000002); Agrobacterium tumefaciens, (AE007869/AE007870); Borrelia burgdorferi, (AE000783); Bacillus halodurans, (BA000004); Bacillus subtilis, (AL009126); Buchnera sp. APS, (BA000003); Campylobacter jejuni, (AL111168); Clostridium acetobutylicum, (AE001437); Chlamydia muridarum, (AE002160); Chlamydophila pneumoniae CWL029, (AE001363); Deinococcus radiodurans, (AE000513/AE001823); Escherichia coli K-12, (U00096); Escherichia coli O157:H7 EDL933, (AE005174); Escherichia coli O157:H7, (BA000007); Haemophilus influenzae, (L42023); Helicobacter pylori 26695, (AE000511); Halobacterium sp. NRC-1, (AE004437); Lactococcus lactis subsp.lactis, (AE005176); Mycobacterium leprae, (AL450380); Mycoplasma genitalium, (L43967); Mycobacterium tuberculosis H37Rv, (AL123456); Methanococcus jannaschii, (LL77117); Mesorhizobium loti, (BA000012); Mycoplasma pneumoniae, (U00089); Mycoplasma pulmonis, (AL445566); Methanobacterium thermoautotrophicum, (AE000666); Neisseria meningitidis MC58, (AE002098); Pseudomonas aeruginosa, (AE004091); Pyrococcus horikoshii, (BA000001); Pasteurella multocida, (AE004439); Pyrococcus abyssi, (AL096836); Rickettsia prowazekii, (AJ235269); Salmonella enterica subsp. enterica serovar Typhi, (NC_003198); Salmonella typhimurium LT2, (AE006468); Shewanella oneidensis MR-1, (NC004347); Sinorhizobium meliloti, (AL591688); Staphylococcus aureus subsp.aureus Mu50, (BA000017); Streptococcus pneumoniae TIGR4, (AE005672); Streptococcus pyogenes M1 GAS, (AE004092); Sulfolobus solfataricus, (AE006641); Synechocystis PCC6803, (AB001339); Thermoplasma volcanium, (BA000011); Thermotoga maritima, (AE000512); Treponema pallidum, (AE000520); Ureaplasma urealyticum, (AF222894); Vibrio cholerae, (AE003852/EC003853); Xylella fastidiosa 9a5c, (AE003849); Yersinia pestis, (AL590842).
Analysis of protein sequence similarities
Pairwise sequence alignments and scores were generated using the AllAllDb program of Darwin (Data Analysis and Retrieval With Indexed Nucleotide/peptide sequence package), version 2.0, developed at the ETHZ in Zurich [35]. Maximum likelihood alignments are generated with an initial global alignment by dynamic programming [36-38] followed by dynamic local alignments [39]. A single scoring matrix is used for these steps. After the initial alignment, the scoring matrix is adjusted to fit the approximate distance between each protein pair to produce the minimum PAM value. PAM units are defined as the numbers of point mutations per 100 residues [37]. The final report includes PAM distances and variances.
For the work reported here, sequence pairs were collected that had alignment lengths of at least 83 amino acids and distances of 200 PAM units or less. We chose the length requirement of 83 residues as it improves the significance of the sequence alignments for the more distantly related protein pairs [40]. The requirement for at least 83 residues also avoids a class of commonly occurring protein domains smaller than 83 residues that appear widely in many otherwise unrelated proteins (such as small binding sites for a type of substrate, cofactor, or regulator). In addition for this study we removed proteins directly involved in horizontal gene transfer (IS proteins, transposases, and known prophage components) from the dataset.
Identification of multimodular proteins
Proteins encoded by fused genes were identified from the E. coli literature and from unequal sequence alignments. The literature was searched for E. coli proteins with more than one function encoded at separate parts of the protein. The locations of the alignment regions in the proteins were analyzed for orthologous and paralogous protein pairs. We identified proteins with two or more non-overlapping alignment regions where each region aligned separately to smaller homologs. Figure 1a illustrates the alignment of two unfused proteins with parts of a fused protein. Multimodular proteins so identified were separated into independent modules. Using the pairwise data, start and end positions of the modules were estimated from the many alignment regions and were set to cover as much of the sequence as possible, not only the most conserved regions of all the alignments. No overlap was allowed between any adjacent modules.
Generation of internal sequence similar groups (paralogs)
The sum of the separated modules from the multimodular proteins and the naturally occurring unimodular proteins of E. coli were aligned against themselves. Protein pairs aligning with >45% of the length of the peptides were used in a transitive grouping process as previously described [15]. The transitive nature of the process ensures sequence similarity to at least one member of the group and does not require all members of the group to have detectable similarity to one another. This type of clustering allows for more divergent sequences to be grouped. The restriction of PAM value to no more than 200 prevents groups from expanding beyond significant similarity.
Authors' contributions
MS designed the study, performed the sequence analysis, and participated in the data analysis and in writing the manuscript. MR participated in the data analysis and in writing the manuscript.
Supplementary Material
Additional File 1
Multimodular E. coli proteins. The table contains a complete list of the multimodular proteins in E. coli. Each module is described by its Gene name, Module Id, Module Start and End positions, Gene type, and Module Product.
Click here for file
Additional File 2
E. coli paralogous groups and their members. The table contains a complete list of the paralogous protein groups in E. coli. The members of the 409 paralogous groups are indicated by their Group Membership, Module Id, Module Start and End Position, Module Product.
Click here for file
Acknowledgements
We would like to thank Daniella Wilmot for assistance in preparation of tables and figures. The research was supported by the Office of Science (BER), U.S. Department of Energy, Grant No. DE-FG02-01ER63202 and by National Aeronautics and Space Administration Astrobiology grant NCC2-1054.
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| 15757509 | PMC555942 | CC BY | 2021-01-04 16:39:33 | no | BMC Genomics. 2005 Mar 9; 6:33 | utf-8 | BMC Genomics | 2,005 | 10.1186/1471-2164-6-33 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-411574353110.1186/1471-2105-6-41SoftwarePHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information Angly Florent [email protected] Beltran [email protected] David [email protected] Pat [email protected] Mya [email protected] Peter [email protected] Ben [email protected] James [email protected] Joseph [email protected] Forest [email protected] Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France2 Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA3 Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA4 Computational Science Research Center, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA5 Center For Microbial Sciences, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA2005 2 3 2005 6 41 41 18 12 2004 2 3 2005 Copyright © 2005 Angly 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
Phages, viruses that infect prokaryotes, are the most abundant microbes in the world. A major limitation to studying these viruses is the difficulty of cultivating the appropriate prokaryotic hosts. One way around this limitation is to directly clone and sequence shotgun libraries of uncultured viral communities (i.e., metagenomic analyses). PHACCS , Phage Communities from Contig Spectrum, is an online bioinformatic tool to assess the biodiversity of uncultured viral communities. PHACCS uses the contig spectrum from shotgun DNA sequence assemblies to mathematically model the structure of viral communities and make predictions about diversity.
Results
PHACCS builds models of possible community structure using a modified Lander-Waterman algorithm to predict the underlying contig spectrum. PHACCS finds the most appropriate structure model by optimizing the model parameters until the predicted contig spectrum is as close as possible to the experimental one. This model is the basis for making estimates of uncultured viral community richness, evenness, diversity index and abundance of the most abundant genotype.
Conclusion
PHACCS analysis of four different environmental phage communities suggests that the power law is an important rank-abundance form to describe uncultured viral community structure. The estimates support the fact that the four phage communities were extremely diverse and that phage community biodiversity and structure may be correlated with that of their hosts.
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Background
Most environmental viruses are phages (a.k.a., bacteriophages) that infect prokaryotic cells, both Bacteria and Archaea. On average there are about ten phage particles per host cell [1]. Extrapolations from the number of prokaryotes [2] make phages the most abundant biological entities in the biosphere with an estimated 1031 viral particles. By killing prokaryotes, phages can strongly impact microbial community biomass [3] and structure [4]. Despite their importance, very little is known about phage biodiversity.
Traditionally, the study of environmental phage diversity, dynamics, and ecology requires growing prokaryotes on microbiology plates and infecting them with phages. However this standard technique is limited by the fact that only a small fraction of environmental microbes are readily cultured [5] and that each phage species generally only has a very narrow number of possible microbial hosts [6]. In addition, even if it is possible to observe phages with an electron microscope, pictures are not sufficient to identify species because of the low taxonomic resolution of viral morphology. Cultivating and observing phages do not permit to assess environmental phage diversity.
Biodiversity is composed of richness, or total number of different species [7], and evenness, expressing the relative abundance of each species [8]. The Shannon-Wiener index quantifies diversity as a single term combining richness and evenness [9]. A high richness and high evenness together represent a high level of diversity.
A new approach to accessing natural microbial diversity is through the creation of shotgun sequence libraries from environmental metagenomes (sum of all genomes) [10-14], so that the genetic information of each genotype of the community is recorded, qualitatively (sequence) and quantitatively (abundance of each sequence). The community is analyzed by sequencing a part of the library. The metagenomic data used here is the contig spectrum, determined by assembly of environmental random shotgun DNA fragments. The contig spectrum is a vector containing the number of contigs (groups of overlapping sequences) of size q (number of sequences in the group) [10]. The stringency of the assembly parameters can be varied so that only sequences belonging to the same genotype overlap. Thus, for one genotype, the bigger the contigs in the contig spectrum, the higher the number of copies and the more abundant this genotype. Based on this, the contig spectrum provides important information about the abundance and diversity of genotypes within a community.
In this work, we present PHACCS (PHAge Communities from Contig Spectrum), an online computational tool to assess the diversity and structure of environmental viral communities from the contig spectrum of shotgun sequence data. The PHACCS program and its predictions are first described and then used to analyze four environmental viral communities.
Implementation
Platform and software
The standalone core mathematics for PHACCS consists of Matlab (MathWorks Inc., Natick, MA.) scripts that are partly based on the previous works [10-12]. A CGI (Common Gateway Interface) script written in PERL (Practical Extraction and Report Language) is used to input and output data from and to an HTML (Hyper Text Markup Language) interface. PHACCS was developed and tested on a Linux-based (2.6.6 kernel) personal computer running PERL 5.8.3 (with CGI module), Matlab 6.5.0, and Apache 2.0.50 web server.
Obtaining a contig spectrum
The input for PHACCS is the contig spectrum, a vector containing the number of q-contigs (groups of q overlapping sequences) from the in silico assembly of random shotgun DNA fragments. Detailed information about the way to get viral metagenomes and their contig spectrum can be found in [10-12]. Briefly, viral communities were isolated via tangential flow filtration and cesium chloride centrifugation, and their DNA was extracted. The DNA was randomly fragmented, used to create a linker amplified shotgun library [15] and clones were sequenced (between 500 and 1200 for studies [10-12]). The sequence assembly program Sequencher (Gene Codes Corp., Ann Arbor, MI.) was used to assemble phage sequences having at least 98% identity on at least 20 bp [10]. The stringency of the assembly parameters was experimentally determined so that only fragments belonging to the same genotype assemble together. Closely related phage genomes (e.g., coliphages T3 and T7) can be discriminated using these parameters [10]. The number of contigs of each size was then recorded to generate the contig spectrum. The number of sequences in the largest contig defines the contig spectrum degree.
Modified Lander-Waterman algorithm
PHACCS uses a modified version of the Lander-Waterman algorithm [16] to predict a contig spectrum from assumed population parameters. The original Lander-Waterman algorithm is a way of predicting the contig spectrum of a randomly fragmented genome (e.g., a single viral species) given: i) the length L of the genome, ii) the number N of DNA fragments studied, iii) the average size s of these fragments, and iv) the minimum overlap length o for the sequence assembly [16]. Given this data, the predicted values of the following quantities are calculated:
• Probability p of an overlap: p = 1 - e-Nx/L with x = s - o
• Probability wq for a fragment to be part of a q-contig (overlap of q fragments):
wq = qpq - 1 (1 - p)2
• Expected number of fragments cq that are part of a q-contig: cq = Nwq
• Contig spectrum:
The modified Lander-Waterman algorithm is a generalization of the original algorithm to a group of M different genotypes (e.g., a whole viral community) [10]. The predicted contig spectrum can be calculated as the sum of the contig spectra for each individual genotype i.
• Expected number of fragments cq part of a q-contig:
wqi is the probability for a fragment to be part of a q-contig for the genotype i and ni is the expected number of fragments for the genotype i.
In this modified algorithm, since there are several genotypes, an assumption about their underlying distribution within the community in terms of abundance has to be made.
Relative rank-abundance forms
PHACCS offers six basic functional forms of relative rank-abundance for biological populations: the power law, logarithmic, exponential, broken stick, niche preemption, and lognormal distributions.
The first three functional forms are empirical models that were designed to describe an asymptotic drop-off in the abundance [17]:
• Power: ni = ai-b for 1 ≤ i ≤ M
• Logarithmic: ni = a(log(i + 1))-b for 1 ≤ i ≤ M
• Exponential: ni = ae-ib for 1 ≤ i ≤ M
The parameter a represents the abundance of the most abundant genotype, b is a parameter related to the evenness, and M is the number of different genotypes in the community.
Two ecological models are based on a partitioning of resources between species [18,19]:
• Broken stick: for 1 ≤ i ≤ M
• Niche preemption: ni = Nk(1 - k)i - 1 and nM = N(1 - k)M - 1 for 1 ≤ i ≤ M - 1
The broken stick function has only one parameter, M, and assumes a random distribution of resources, whereas in the niche preemption function, each species takes only a fraction k of the remaining resources in the environment.
The sixth functional form is the lognormal distribution. It is the most commonly used species distribution, with numerous theoretical justifications in the literature [20,21]. The relationship is specified as species density versus abundance and needs to be transformed to give a rank-abundance relationship. Our rank-abundance form was obtained by dividing the area under the normal distribution with standard deviation σ into M equal area slices and associating an abundance ni with the i-th slice by calculating an average value for the abundance within the slice. The result is:
where erf is the error function and erf1 its inverse.
Modeling the viral community structure
The PHACCS algorithm is represented in Figure 1. The experimentally determined contig spectrum of a sample and the other parameters needed for the modified Lander-Waterman algorithm are the input. For a given rank-abundance function, assumed values of the function parameters (number of different genotypes, as well as b for the power law, logarithmic, exponential and lognormal distributions and k for niche preemption) are used to predict a contig spectrum using the modified Lander-Waterman algorithm. To determine the model fitness, the error between the actual and the predicted contig spectrum is calculated as the variance-weighted sum of squared deviations, L being the contig spectrum vector length and cq' the experimental number of fragments that belong to a q-contig:
The best descriptive model for a community structure is defined as the one with the smallest error. For each rank-abundance function tested, the global minimum for the error is found by optimizing the value of the function parameters.
The values of the error can be roughly interpreted as logarithms of odds ratios of the observed contigs being seen from community distributions of the specified forms. Thus a value of 0.1 for the difference in errors between two models corresponds to an odds ratio of e0.1 which is about 11:10 between the two models. This means that the model with the smallest error is about 10% more likely to give rise to the observed data.
Predicting the viral community diversity
For each rank-abundance form, the best model is used by PHACCS to assess diversity. The richness S is estimated as equal to the number of different genotypes M found in the community structure model. The abundance of the most abundant genotype is also directly determined from the model as the highest rank-abundance value. The Shannon-Wiener index, which is a measure for diversity, is calculated using the relative rank-abundance values ri = ni/N of all individual genotypes i [9]:
• Shannon-Wiener index H' (in nats):
The evenness is derived from H' [18]:
• Evenness E: E = H'/Hmax = H'/ln S
Comparison of four phage communities
As a case study, four viral metagenomes obtained from previous studies and belonging to different ecosystems were tested. Two of these were phage community samples of near-shore surface seawater from Scripps Pier (SP) and Mission Bay (MB), San Diego, California, USA [10]. The two other samples are sediments from Mission Bay (MBSED) [11] and human feces (FEC) [12]. A compilation of the data for these samples is presented in Table 1. These four datasets were analyzed with PHACCS using all six rank-abundance models.
Results
Best abundance forms
The errors obtained from the contig spectrum analysis of the different samples are presented in Table 2. For each sample the best descriptive model of the community structure is the one with the smallest error. The SP community was best described by using the power law (error of 1.84), closely followed by the lognormal (error of 1.93) and logarithmic (error of 2.57) distributions. The exponential and niche preemption distributions had poor fits, with errors of 12.0. The MB community modeling gave qualitatively the same results. Power law was the best fit with an error of 2.15 and exponential and niche preemption were last with an error of 16.2. The FEC community also had the same sequence of best fitting rank-abundance forms. The best model was given by using the power law form (error 9.79). Exponential and niche preemption did a poor job of explaining the data, coming in last with an error of 60.0. For the MBSED community, the power law, lognormal, logarithmic and exponential distributions all tied for the best fit (with an error of 0.0104), whereas broken stick gave the worst fit (error of 0.0157).
Phage community diversity and structure
The different diversity indicators and the rank-abundance curves obtained by using the best descriptive model for each sample are summarized in Figure 2. The MBSED community was the richest with an estimated 7340 different phage genotypes. MB had ~7180 different genotypes, SP ~3350, and FEC was the least rich sample with ~2390 different genotypes. MBSED was the most even community with the maximum possible evenness of 1.00 (flat rank-abundance curve), followed by SP (evenness of 0.932), MB (evenness of 0.900), and FEC (evenness of 0.873). The most abundant genotype represented 4.80% of the total community for FEC, 2.63% for MB, 2.03% for SP and around 0.01% for MBSED. Based on the Shannon-Wiener diversity index, MBSED was overall the most diverse community with 8.90 nats, then MB (7.99 nats), SP (7.57 nats), and finally FEC (6.80 nats), the least diverse community.
Discussion
Using PHACCS
PHACCS is publicly accessible at and the source code is freely available [see Additional file 1]. The biological information PHACCS needs as an input is the viral community's contig spectrum, average genome size, average shotgun DNA sequence length, and the minimum overlap length used for the assembly. PHACCS has two HTML interfaces. The basic interface assumes default values for marine phage communities (average genome size of 50 kb, average fragment length of 650 bp and minimum overlap of 20 bp). All rank-abundance forms (power law, expoential, logarithmic, lognormal, broken stick and niche preemption distributions) are tested for up to 100,000 genotypes. In the advances interface (Figure 3) the user can change all biological and computational parameters.
PHACCS analyses are computer intensive. On a dual-Opteron™ server, the computation for the SP sample takes ~5 minutes. The broken stick and lognormal rank-abundance forms account for most of the computation time (data not shown). Increasing the range of genotypes to search dramatically increases the time needed to complete the analysis (data not shown).
PHACCS estimations about the virus community are: i) structure – best descriptive rank-abundance form, model equation and error, and ii) diversity – richness, evenness, abundance of the most abundant genotype, and Shannon-Wiener index. Graphic representations of the community structure and of the error minimization can also be displayed. The error provides information about which model has the best fit relative to the others for a given contig spectrum. For each type of distribution, the user is informed if the best model (i.e., the error's global minimum) has not been found using the given computation parameters.
Importance of the contig spectrum quality
Predictions by PHACCS are dependent on the quality of the contig spectrum input. The difference in error between two models can be small (Table 2) and using an inappropriate model can change the estimated diversity. For example, the predicted richness for the SP sample is about four times higher for the lognormal distribution than for the power law (data not shown). A useful contig spectrum requires that: i) the same clone be sequenced only once (remove all redundant clones), ii) the sequences be trimmed to remove ambiguities ("N"'s) and, iii) the assembly parameters be sufficiently stringent so that only sequences from the same genotype are part of the same contig (experimental determination by assembly of known sequences). All these experimental problems bias the observed occurrence of the DNA fragments, and thus the contig spectrum. Additionally, accurate community estimations are not to be expected if the contig spectrum only has a small degree (only small contigs) (e.g., MBSED, [1152 2 0 ...]). As a general rule, the higher the contig degree, the better the estimations, because the model fitting is done over a larger number of points. For the same reason, the number of trailing zeros in the contig spectrum is important. Adding zeros at the end of the contig spectrum will improve PHACCS predictions (e.g., 10 trailing zeros were used in the present analyses) but will also increase the computation time.
Limitations
The way the contig spectrum is obtained leads to approximations of the viral diversity. In the samples analyzed here, only the DNA from viruses smaller than 0.22 μm is collected. Larger viruses and RNA viruses are not represented in the shotgun library and in the resulting contig spectrum. The contig spectrum assembly parameters (98% identity on at least 20 bp for phages) are stringent enough to limit the number of false-contigs (contigs between DNA fragments from different genotypes), but may on the other side omit some true-contigs (DNA fragments that are designated as non-overlapping when they actually belong to the same genotype). Additionally, the present implementation of the Lander-Waterman algorithm assumes that all DNA fragments and all the genotypes have the same size. For these reasons, PHACCS estimates should be considered approximations.
Phage community structure and diversity
The comparative analysis of the four phage communities showed that the power law seems overall to be a powerful rank-abundance distribution to model phage community structure (Figure 2). A recent simple predator-prey model based on the observed marine phage-host dynamics explains how a power law distributed phage rank-abundance can be obtained from a modified Lokta-Volterra model [23]. Before analyzing the viral samples with the contig spectrum approach, the number of viral genotypes in an environment was totally unknown. The viral communities turned out to be extremely diverse with estimated Shannon-Wiener diversity indices between 6.8 nats (fecal sample) and 8.9 nats (sediment sample) (Figure 2), representing diversity levels higher than for most bacterial communities [11]. Because phages are specific predators, the structure and diversity of phage communities could be directly correlated to the structure and diversity of the coexisting microbial communities [2]. Some facts seem to support this hypothesis.
First, the extreme diversity of the sediment viral community may reflect the higher diversity of the microbial communities found in sediments using automated rRNA intergenic spacer analysis (ARISA) [24] in comparison with seawater. Also, only a few hundred different bacterial species were reported in the human colon intestinal flora [25] using the 16s ribosomal DNA methodology, which could account for the relatively low phage richness in the fecal sample.
Conclusion
PHACCS is a web-based service that predicts community structure and diversity using the contig spectrum from metagenomic random shotgun sequence data. This methodology allows PHACCS to determine the mathematical model that most accurately reflects the underlying genotype abundance distribution (i.e., power law, logarithmic, exponential, broken stick, niche preemption, or lognormal distributions) and use it to makes estimates about the diversity of the communities, (i.e., richness, evenness, Shannon-Wiener index and abundance of most abundant genotype). Using uncultured environmental viral samples, PHACCS has been used to confirm that phage biodiversity is higher than in any previously observed community and that the structure of viral communities may closely follow that of their hosts. PHACCS is designed for biologists to mathematically analyze their viral shotgun libraries and gain insights about viral ecology and population dynamics.
Availability and requirements
• Project name: PHACCS – PHAge Communities from Contig Spectrum
• Project home page:
• Operating system(s): Unix based system for PHACCS and its web interface. Platform independent for PHACCS core.
• Programming language: Matlab (for the core scripts) and Perl
• Other requirements: For the interface: CGI.pm Perl module, ppmtogif, webserver program (to use PHACCS as a web service)
• License: GNU GPL
Authors' contributions
FA developed the PHACCS main program and its interface. BRB helped with the programming. BRB, DB, PMN, PS, BF, JN and JM developed the modified Lander-Waterman algorithm and implemented it with Matlab. FR and MB helped write the manuscript and provided the test datasets. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
This file contains the script files part of PHACCS. These files are either standard text or picture files.
Click here for file
Acknowledgements
This work was supported by NSF 0316518 (FR) and an EPA STAR fellowship (MB). We thank Ines Thiele, Steve Rayhawk and Cynthia Steiner for useful suggestions and comments.
Figures and Tables
Figure 1 Flowchart of PHACCS. *The rank-abundance functions and the range of genotypes to use can be defined by the user. **This parameter represents b for the power law, logarithmic, lognormal and exponential distributions and k for the niche preemption. This parameter is not applicable to the broken stick.
Figure 2 Comparison of the structure and diversity of the different viral communities using PHACCS. The graphics represent rank-abundance curves, where the abundance of each genotype is plotted versus its abundance rank, the genotype of rank one being the most abundant. The curves were obtained by plotting the PHACCS rank-abundance values of the different communities on the same axis. *The predicted community structure for MBSED was the same for the lognormal, logarithmic, power and exponential rank-abundance forms. As a consequence, the diversity predictions were also the same.
Figure 3 Screenshot of PHACCS' advanced web interface.
Table 1 Test data used for the study of the phage communities with PHACCS [10-12]. The average fragment sequence length was determined using Sequencher after sequence trimming (maximum one ambiguity on 99 bp at each extremity). A 98% identity for a minimal overlap length of 20 bp was used for sequence assembly with Sequencher to obtain the contig spectra. The average genome length was determined by pulse field gel electrophoresis [12, 22].
Community SP (Scripps Pier) MB (Mission Bay) MBSED (Mission Bay Sediments) FEC (Fecal)
Contig spectrum * 1021 17 2 0 ... 841 13 2 0 ... 1152 2 0 ... 482 18 2 2 0 ...
Avg. community genome size 50 kb 50 kb 50 kb 30 kb
Avg. shotgun fragment length 663 bp 663 bp 570 bp 699 bp
* The number of trailing zeros was set to 10 for each contig spectrum.
Table 2 Best descriptive rank-abundance form for the viral communities as determined by PHACCS. The error represents the variance weighted sum squared deviation between the experimental and the predicted contig spectra. For each community, the best descriptive function is the one that minimizes the error. The best fit obtained for each rank-abundance form was ranked according to the error in ascending order.
SP MB MBSED FEC
Rank Model Error Rank Model Error Rank Model Error Rank Model Error
1 Power law 1.84 1 Power law 2.15 1 Power law 0.0104 1 Power law 9.79
2 Lognormal 1.93 2 Lognormal 2.36 1 Lognormal 0.0104 2 Lognormal 10.2
3 Logarithmic 2.57 3 Logarithmic 2.88 1 Logarithmic 0.0104 3 Logarithmic 10.3
4 Broken stick 10.7 4 Broken stick 14.6 1 Exponential 0.0104 4 Broken stick 52.2
5 Exponential 12.0 5 Exponential 16.2 5 Niche preemption 0.0139 5 Exponential 60.0
5 Niche preemption 12.0 5 Niche preemption 16.2 6 Broken stick 0.0157 5 Niche preemption 60.0
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| 15743531 | PMC555943 | CC BY | 2021-01-04 16:02:49 | no | BMC Bioinformatics. 2005 Mar 2; 6:41 | utf-8 | BMC Bioinformatics | 2,005 | 10.1186/1471-2105-6-41 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-481575751710.1186/1471-2105-6-48SoftwareA Taxonomic Search Engine: Federating taxonomic databases using web services Page Roderic DM [email protected] Division of Environmental and Evolutionary Biology, Institute of Biomedical and Life Sciences, Graham Kerr Building, University of Glasgow, Glasgow G12 8QQ, UK2005 9 3 2005 6 48 48 13 12 2004 9 3 2005 Copyright © 2005 Page; 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 taxonomic name of an organism is a key link between different databases that store information on that organism. However, in the absence of a single, comprehensive database of organism names, individual databases lack an easy means of checking the correctness of a name. Furthermore, the same organism may have more than one name, and the same name may apply to more than one organism.
Results
The Taxonomic Search Engine (TSE) is a web application written in PHP that queries multiple taxonomic databases (ITIS, Index Fungorum, IPNI, NCBI, and uBIO) and summarises the results in a consistent format. It supports "drill-down" queries to retrieve a specific record. The TSE can optionally suggest alternative spellings the user can try. It also acts as a Life Science Identifier (LSID) authority for the source taxonomic databases, providing globally unique identifiers (and associated metadata) for each name.
Conclusion
The Taxonomic Search Engine is available at and provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names.
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Background
Biological taxonomy provides the central link between diverse items of information about an organism. Given the scientific name of an organism, a researcher can query a wide range of databases for information on that organism's genome, development, morphology, geographic distribution, behaviour, phylogeny, and conservation status. However, the utility of taxonomic names as keys to accessing information is hampered by several factors, notably the lack of a single authoritative list of all taxonomic names [1,2]. In the absence of such a list, databases that make use of taxonomic names have no ready means of validating those names. Consequently, there is no guarantee that taxonomic names stored in different databases will be mutually consistent.
In the absence of a single database of names, one solution is to use a federated approach [3] where multiple, independent databases are queried. Numerous taxonomic databases exist, although each tends to have limited taxonomic and geographic scope, and the degree of interoperability among these databases varies greatly. The NIH/NIAID/Wellcome Trust Workshop on Model Organism Databases [4] defines the minimum level of interoperability as providing a FTP dump of the database contents. The only taxonomic databases currently meeting even this minimum level are the Integrated Taxonomic Information Service (ITIS) [5] and the NCBI Taxonomy [6] databases. Greater degrees of interoperability require the availability of an explicit Application Programming Interface (API) that clients can use to query the database. Each taxonomic database provider has developed their own interface which is typically aimed at a single user with a web browser. Few databases provide an API, or better still, a documented API. Taxonomic names themselves have limitations as identifiers in databases [7] due to the existence of multiple names (synonyms) for the same taxon, and the use of the same name to refer to different taxa. For example, the genus Morus applies to both an animal (the gannet) and a plant (the mulberry tree). Even species names can be identical – a species of wasp and a species of conifer both share the name Agathis montana. Hence, using names alone to link different data sources can be prone to error. As an example, at the time of writing NCBI's LinkOut feature [8] mistakenly links the catfish genus Loricaria (tax_id = 52085) to the TreeBASE [9] taxon Loricaria (TaxonID = 1305), which is a plant genus (family Compositae).
To avoid ambiguity some form of identifier other than a taxonomic name needs to be employed, such as Digital Object Identifiers (DOIs) [10] or Life Science Identifiers (LSIDs) [11,12]. Given such an identifier a user can unambiguiously refer to a name, and at the same time discover the provenance of that name (i.e., the source database). The use of globally unique identifiers in taxonomy is in its infancy: the use of DOIs has been explored in the context of prokaryote taxonomy [13], but LSIDs have yet to be employed for taxonomic names. Instead most efforts to link taxonomic databases use URLs (e.g., Species 2000 [14]) and NCBI Linkout [8]). However link integration using URLs has serious limitations [15].
Given the lack of a central list of names, and the limitations of names as identifiers, there is a clear need for a taxonomy name service that can validate names and provide unique identifiers [2]. The SPICE project [16,17] has explored the utility of a federated approach to querying taxonomic databases. For each database, SPICE requires that a wrapper is installed on the computer hosting that database. This wrapper communicates natively with the local database to perform a standard set of queries. The central query engine then communicates with each instance of the wrapper using a consistent protocol (e.g., CGI). This approach places much of the burden of interoperability on the source database, which must adapt and install the SPICE wrappers.
This paper describes the Taxonomic Search Engine (TSE), which takes federated approach to the problem of searching for taxonomic names. Unlike the SPICE project, the TSE relies solely on the interfaces made available by the data source. A wrapper is created for each source database, but this resides on the same machine as the TSE. In this way, no special demands are made of the source database. The TSE searches multiple databases for a name, and returns the result in a consistent format. For each name, TSE also creates a LSID, so that each name from each source database has a globally unique identifier.
Implementation
Source databases
The TSE uses five data providers: ITIS, Index Fungorum, IPNI, uBIO, and the NCBI.
ITIS
The Integrated Taxonomic Information System (ITIS) [5] was established in the mid 1990's by a consortium of United States federal agencies tasked with to providing a database of taxonomic information for North American taxa. In addition to the original site in the United States [5], there is a French language version hosted by the Canadian Biodiversity Information Facility [18], and a Spanish language version hosted in Mexcio [19]. The Canadian site can serve data in XML format, and users can search for a name, or retreive details about an individual record using a simple URL API. A Document Type Definition (DTD) file for the XML format is available from the ITIS web site.
ITIS provides a classification of taxonomic names (i.e., a parent-child hierarchy), and where more than one name exists for a taxon, ITIS specifies which name it regards as correct (termed the "accepted" name if the taxon is an animal, and "valid" if it is a plant). Every name in the database, regardless of taxonomic status or position in the hierarchy is assigned a unique identifier (its "taxon serial number"). The database schema is fully documented, and the entire database is available for downloading by FTP as a SQL schema with the data in delimited text files. As a consequence, ITIS is frequently used as the de facto source of taxonomic data in biodiversity informatics projects.
IPNI
The International Plant Names Index (IPNI) [20] combines data from three sources: Index Kewensis (Royal Botanic Gardens, Kew), the Gray Card Index (Harvard University Herbaria), and the Australian Plant Names Index (Australian National Herbarium), and contains some 1.6 million records. It provides names and associated basic bibliographical details for vascular plants. The IPNI web site provides web forms for querying the database, and data can be returned in HTML, "%" delimited text, or XML. However, the XML is a serialisation of IPNI database objects, rather than a format designed to be handled by end users. There are plans to support emerging standards, such as the Taxonomic Concept Transfer Schema [21]. IPNI aims to be a catalogue of all names that have been applied to vascular plants. However, where more than one name for a taxon exists, IPNI does not specify which name should be used, that is, it does not indicate an "accepted name" for a taxon. In this sense it is That is, it is a nomenclatural database rather than a taxonomic database. However, if two names are nomenclatural synonyms, the HTML output specifies the nature of synonymy, such as "basionym" (one name is the original name for the taxon), "nomenclatural synonym" (one or other of the names is the basionym, or the names share a basionym), or "replaced synonym" (one name has been created to replace another). IPNI provides a minimal classification, in that genera are assigned to families, but no higher-level classification is given.
Index Fungorum
IndexFungorum [22] is a database of over 370,000 names of fungi, primarily at species level. The database can be searched through a web interface or through a SOAP web service which returns an XML document. If more than one name exists for a fungus, Index Fungorum designates one name as the "current name." It also reports the basionym (first recorded name) for that taxon. Index Fungorum does support a detailed hierarchical classification in the form of a lineage, but higher level taxa are not assigned records in the database (unlike, for example, ITIS). In fungal taxonomy, names are often assigned to the asexual state (anamorph) of a fungus for which the sexual state (telomorph) is unknown. Names for anamorphs are flagged as such in the database.
uBio
The Universal Biological Indexer and Organizer (uBio) [23] is a product of the science library community, and is motivated by the information retrieval problem posed by the lack of long term stability of many taxonomic names [2]. Presently it is the single largest electronic catalogue of scientific names (1,396,868 as of 13 November 2004). In addition to a web interface uBio provides a SOAP web service which returns a nested array data structure.
NCBI
The NCBI Taxonomy database [6] is a curated database of the names of all organisms for which sequences have been submitted to GenBank [24]. Each taxon regardless of taxonomic level is assigned a unique identifier (the "taxid"), and the NCBI taxonomy provides a single classification for all taxa in its database. If a taxon has more than one scientific name, each name has name has the same taxid, but only one is indicated as the "scientific name" [25]. The other names are flagged as synonyms, common names, etc. The NCBI taxonomy is not intended to be an authoritative source of taxonomic information, but is a rapidly grouping database that contains many taxa that are not found in other databases. Although every sequence in NCBI is assigned to an organism, in many cases the exact identity of that organism may be unknown. Sequences obtained from environmental sampling are typically unidentified, and the number of such sequences is likely to increase with the advent of large scale environmental genomics [26]. The NCBI taxonomy database can be queried via the Entrez Utilities [27] using wither a URL or a SOAP interface. The entire database is also available for download by FTP.
Architecture
The basic architecture of the TSE is summarised in Fig. 1. For each database a wrapper (implemented as a class in the PHP scripting language) is responsible for communicating with the database, using either the HTTP GET protocol (using the Net HTTP Client [28] library) or SOAP (using the NuSOAP library [29]). The wrapper takes the query string supplied by the user, and constructs a suitable query for the corresponding database, such as a URL or a SOAP call. The wrapper is also responsible for handling the response. If databases return a XML document this is transformed using an XSLT style sheet into the XML format used by TSE. Other formats such as text or SOAP data structures are converted into XML by the wrapper.
Each wrapper is derived from the same base class which provides some generic routines for creating XML documents and for caching results (see next section). The wrapper class supports three methods, IsAlive, NameSearch, and GetDataForID, which must be overridden in descendant classes. The IsAlive method queries whether the data source is available. The NameSearch method queries a data source for a given string. If one or more names are found, NameSearch returns basic information about that name, including the identifier used by the data source. This identifier is used by the GetDataForID method to query the data source for more details about the name.
Caching results
In order to improve the responsiveness of the search engine, the results of queries to each source database are cached for 24 hours. The results of the query are stored in the format returned by the database (i.e., XML or delimited text), except for uBio where the SOAP response is serialised to disk.
Approximate string matching
The Taxonomic Search Engine seeks exact matches to the user supplied query. In order to accommodate spelling mistakes the web interface to the search engine supports approximate string matching using two techniques. The first employs agrep [30] to search for a match amongst a flat file list of names obtained from the ITIS and NCBI databases. Names showing no more than two character differences from the query string are returned as suggested alternative spellings. To supplement agrep, the TSE calls Google's spelling suggestion web service [31] and adds the result of that query (if any) to the list of suggested spellings.
Interface
The TSE has a simple web interface (Fig. 2). The user types in a query, and has the option to specify whether TSE should look for alternative spellings. Clicking on the "Go" button starts the search. The XML summary of the search is transformed into HTML using an XSLT transformation. The user can click on a name to get more information, including a link to the original database source for the name, and a LSID for the name.
Web service
The TSE has a SOAP web service that is described by a Web Services Description Language (WSDL) file available at . The service provides two operations: NameSearch which queries the source databases for a user-supplied name, and SpellingSuggestion, which suggests alternative spellings for a name. Hence users can write web service clients that can use the TSE as part of their own applications. The TSE web site provides source code for two simple clients written in perl.
Life Science Identifiers
A LSID is a Uniform Resource Name (URN) comprising five parts: the Network Identifier ("lsid"), the root DNS name of the issuing authority, a namespace, an object identifier, and optionally a revision id to indicate the version [11]. TSE generates LSIDs by concatenating the name of the source web server with the suffix "lsid.zoology.gla.ac.uk" to generate the authority. The namespace is the name given to the identifier in the source database, and the object identifier is the identifier used by the source database. For example, the record for Homo sapiens in the ITIS database would have the LSID:
urn:lsid:itis.usda.gov.lsid.zoology.gla.ac.uk:tsn:180092
where "tsn" is the "taxonomic serial number" used by ITIS as a unique identifier for each taxonomic name, and "180092" is the tsn for Homo sapiens.
The TSE uses the perl library distributed by IBM's Life Science Identifier project [11] to create a LSID authority for each of the source databases. Hence, any software that can resolve LSIDs (such as LaunchPad [11] or the BioPathways Consortium Web Resolver [32]) can view the metadata associated with an LSID generated by TSE. For ITIS this metadata is constructed by querying a local copy of the ITIS database, but for the remaining databases the LSID metadata is generated using the same combination of GET/HTTP and SOAP calls used to query the source databases by TSE (although these calls are implemented in perl).
Performance evaluation
The 2004 edition of the Species 2000 CD-ROM [14] was used as a source of names with which to query the TSE. This database comprises 583,469 names provided by 18 taxonomic databases, two of which (ITIS and Index Fungorum) are also source databases for TSE. In addition, uBio currently includes names from the 2003 edition of the Species 2000 CD-ROM in its database. Hence, most names in the Species 2000 list are likely to be found by TSE.
To create a test dataset, 1000 names were selected at random from the Species 2000 dataset. Each name was sent to the TSE web service by a perl script which recorded the time taken for each source database to respond to the query, and whether that source database contained the name. The time recorded is from the time the query was made until the time the response was returned – post processing by the TSE is not included in the measurement. For this experiment, the cache feature was turned off so that for each query the TSE went to the external source database, rather than using a local copy of the query result.
Results and discussion
Performance
The results of the simple performance benchmarks are shown in Table 1. Most of the names were found in uBio (887 of the 1000 names), which is as expected given that uBio has harvested all the names in the previous (2003) edition of the Species 2000 CD-ROM. ITIS is a major contributor to both uBio and Species 2000, and just over half the names in the test set are present in ITIS. The Species 2000 CD-ROM contains some names from Index Fungorum, and none from IPNI, hence its coverage of plants and fungi is somewhat limited. That only 10% of the query names were found in the NCBI database suggests there is little overlap between the taxa being catalogued by taxonomic databases and those being sequenced. Amongst the five source databases, ITIS had the slowest median response time (0.915 seconds) and Index Fungorum was the quickest (0.132 seconds). The IPNI database was the second slowest, and occasionally took up to a minute to respond – on 20 occasions no response was obtained at all. It is difficult to generalise about these results as the performance of a data source will depend on a number of factors, such as the server hardware and software, the database design, and the load other users are placing on the system. For the five data sources currently queried, the operating systems being used include both Linux and Windows 2000, the web servers are Apache, Oracle HTTP server, and Microsoft IIS (determined by NetCraft [33]), and the database vendors include Microsoft, Oracle, and MySQL. However, it is encouraging that five such disparate systems all have a median response time of less than a second.
Extensibility
The TSE can be extended to handle additional data sources simply by deriving a new wrapper class from the base class. To date wrappers have only been written for data sources which can return plain text, XML, or SOAP messages. There are many more taxonomic databases that could be queried if wrappers were written to handle HTML output ("screen scraping"). However, this would make the wrapper very vulnerable to changes in web page design [34]. Of course, a change in a data source's API would also break the wrapper. This is a general problem in integrating disparate databases [34], and in the long term a better solution would be for each taxonomic database to support a standard API that services such as the TSE can query.
Scalability
Despite the reasonable performance of TSE, there are obvious limitations in the current design and implementation. The PHP language does not support threads, so each source database is queried sequentially. As additional source databases are added the time to complete the search will get progressively longer. If the performance of additional databases is comparable to those already being queried (Table 1), then each new source will add at least 0.5 – 1.0 seconds to the time required for TSE to return a result (not counting the additional overhead of pre- and post-processing the query). If the search engine is to scale to handle a large number of databases it is likely that these databases will need to be queried in parallel.
Query filtering
Some source databases have broad taxonomic coverage such as ITIS, NCBI, and uBio, whereas others are restricted to particular groups, such as fungi (Index Fungorum) and vascular plants (IPNI). Hence, it makes little sense to query Index Fungorum or IPNI for an animal name (especially as this will could 1–2 seconds onto the time taken to complete the search). An option to select the databases to query could be easily added to the TSE web interface. However, it would be more efficient if the TSE could determine which databases were relevant to the user's query. If the TSE knew that the query string was the name of a fungus, it could send the query to the appropriate database. In practice, however, this is problematic. In order to know what organism a name refers to the TSE would have to have access to a databases of names and their classification – the very lack of such a database is the motivation behind the TSE in the first place. Furthermore, as discussed above, the same name can apply to different organisms. A user searching using the term "Morus" might be looking for a plant name, or an animal name (or perhaps both). There is some scope for more intelligent querying, such as looking for aspects of the name that are specific to one of the codes of nomenclature (e.g., most plant family names end in "-aceae"), but any such effort needs to be done with care – for example, "Compositae" is a family of plants.
Conclusion
The Taxonomic Search Engine is a simple tool for querying multiple taxonomic databases. Typically, results of querying five major databases are returned in a few seconds. In addition to providing basic information about a name, the TSE acts as a LSID authority, providing globally unique identifiers for each name. The TSE provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names.
Availability and requirements
The source code for the TSE, the web site, and the LSID authorities is available from the TSE site .
System requirements
TSE requires a web server and the PHP scripting language. It has been developed and tested under Red Hat Linux 8.0 with the Apache web server version 2.0.40 and PHP version 4.2.2, and Mac OS X 10.2.8 with Apache version 1.3.29 and PHP version 4.3.4. If PHP does not have the XSLT extension enabled then the user will either have to recompile PHP, or install the Sablotron toolkit [35]. The code makes use of various PHP libraries including NuSOAP [29], Net HTTP Client [28], Php.XPath [36], and phpdomxml [37]. The approximate string matching feature requires agrep to be installed (available from ), and a developer key from Google [31].
Acknowledgements
I thank Sally Hinchcliffe (IPNI), and Guy Baillargeon and Derek Munro (ITIS) for quickly fixing minor problems I encountered when querying their databases. Paul Kirk (Index Fungorum) kindly told me about the Index Fungorum web service before it was publicly released, and Sally Hinchcliffe provided helpful feedback on the approximate string matching feature. Bob Morris (University of Massachusetts) alterted me to incompatibilities between the original TSE WSDL file and the Apache Axis toolkit. Iain Bryson (University of Glasgow Computing Service) kindly added the necessary records to the University of Glasgow DNS server in order to support the LSID authority.
Figures and Tables
Figure 1 Architecture of the Taxonomic Search Engine. The user's query is passed to each database using either the HTTP GET protocol or SOAP, and the results (which may be in XML format, delimited text, or a SOAP data structure) are combined and returned as an XML document.
Figure 2 Screen shot of the Taxonomic Search Engine. The web browser displays the results of searching for a name in five external databases. For each database that returns a "hit" the page displays some information about that name. The user can click on the name to obtain further information about the name, including a link to the original database record, and a Life Science Identifier (LSID) for that record.
Table 1 Performance of each source database used by the Taxonomic Search Engine. Each database was queried for 1000 taxonomic names taken at random from the 2004 edition of the Species 2000 CD-ROM. The table displays the number of times each database contained the name (n), and median, mean, standard deviation, and best and worst times taken for a database to respond to a query. The number of times a query failed to return a response is also recorded.
Response time (in seconds)
Source n Median Mean StdDev Best Worst Failed
ITIS 513 0.915 1.151 0.802 0.808 6.593 0
Index Fungorum 73 0.132 0.250 0.562 0.108 9.379 6
IPNI 153 0.356 1.055 3.264 0.143 59.653 20
uBio 887 0.295 0.384 0.544 0.259 8.710 0
NCBI 101 0.252 0.369 0.561 0.225 8.983 0
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| 15757517 | PMC555944 | CC BY | 2021-01-04 16:02:51 | no | BMC Bioinformatics. 2005 Mar 9; 6:48 | utf-8 | BMC Bioinformatics | 2,005 | 10.1186/1471-2105-6-48 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-321575751610.1186/1471-2164-6-32Research ArticleMice have a transcribed L-threonine aldolase/GLY1 gene, but the human GLY1 gene is a non-processed pseudogene Edgar Alasdair J [email protected] Department of Craniofacial Development, King's College, London, UK2005 9 3 2005 6 32 32 24 12 2004 9 3 2005 Copyright © 2005 Edgar; licensee BioMed Central Ltd.2005Edgar; 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 pathways of L-threonine catabolism. The enzyme L-threonine aldolase (TA) has been shown to catalyse the conversion of L-threonine to yield glycine and acetaldehyde in bacteria, fungi and plants. Low levels of TA enzymatic activity have been found in vertebrates. It has been suggested that any detectable activity is due to serine hydroxymethyltransferase and that mammals lack a genuine threonine aldolase.
Results
The 7-exon murine L-threonine aldolase gene (GLY1) is located on chromosome 11, spanning 5.6 kb. The cDNA encodes a 400-residue protein. The protein has 81% similarity with the bacterium Thermotoga maritima TA. Almost all known functional residues are conserved between the two proteins including Lys242 that forms a Schiff-base with the cofactor, pyridoxal-5'-phosphate. The human TA gene is located at 17q25. It contains two single nucleotide deletions, in exons 4 and 7, which cause frame-shifts and a premature in-frame stop codon towards the carboxy-terminal. Expression of human TA mRNA was undetectable by RT-PCR. In mice, TA mRNA was found at low levels in a range of adult tissues, being highest in prostate, heart and liver. In contrast, serine/threonine dehydratase, another enzyme that catabolises L-threonine, is expressed very highly only in the liver. Serine dehydratase-like 1, also was most abundant in the liver. In whole mouse embryos TA mRNA expression was low prior to E-15 increasing more than four-fold by E-17.
Conclusion
Mice, the western-clawed frog and the zebrafish have transcribed threonine aldolase/GLY1 genes, but the human homolog is a non-transcribed pseudogene. Serine dehydratase-like 1 is a putative L-threonine catabolising enzyme.
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Background
Elucidating the factors involved in threonine homoeostasis is important for the development of nutritional strategies in human clinical diets for treating patients suffering from wasting diseases. In farmed animals the regulation of livestock feed is required to ensure optimal growth and to reduce nitrogen excretion which poses environmental disposal problems. Threonine is required for protein synthesis and the removal of excess threonine by oxidation is needed to prevent its accumulation both intracellularly and in the circulation. The rate of catabolism of many amino acids, including threonine, increases when dietary protein exceeds the body's requirements. Gluconeogenesis occurs mainly in the liver where it helps maintain blood glucose homeostasis in mammals. During starvation amino acid catabolism increases to support gluconeogenesis. Glucocorticoids and glucagon hormones are known to up regulate and insulin down regulate the gene expression of many amino acid-catabolising enzymes [1].
There are three L-threonine (L-alpha-amino-beta-hydroxybutyric acid) degradation pathways in living organisms; via L-threonine aldolase (L-TA)(EC 4.1.2.5)(gene abbreviation GLY1), via L-serine/threonine dehydratase (SDH)(EC 4.2.1.16)(gene abbreviation SDS)(in bacteria also called L-threonine deaminase) and via L-threonine 3-dehydrogenase (EC 1.1.1.103)(TDH) [2-5]. L-threonine is broken down by; L-TA to yield glycine and acetaldehyde, by SDH to yield NH4+ and 2-ketobutyrate and TDH to yield 2-amino-3-ketobutyrate. The subsequent reaction between 2-amino-3-ketobutyrate and coenzyme A to form glycine and acetyl-CoA is catalysed by 2-amino-3-ketobutyrate coenzyme A ligase (KBL)(EC 2.3.1.29), also called glycine acetyltransferase (gene abbreviation GCAT).
Together with the cofactor, pyridoxal-5'-phosphate (PLP), SDH uses threonine and serine as substrates to generate glycine which is used in gluconeogenesis. Serine dehydratase-like 1 gene (SDH1) is a second SDH gene found in vertebrates, but has yet to be characterised. I suggest that it is also a putative L-threonine catabolising enzyme.
Vitamin B6-dependant enzymes can be grouped according to their fold type. L-TA belongs to fold type I. L-TA enzymes are unrelated to D-TA enzymes which possess type III folds [6]. In vertebrates, the TA enzyme has not been purified by protein fractionation, only assayed in homogenised tissue fractions and isolated hepatocytes. In vertebrates most L-threonine degradation occurs via the enzymatic activities of serine/threonine dehydratase and threonine dehydrogenase. However, the presence of threonine aldolase enzymatic activity has been demonstrate in rat liver extracts [7-14]. Threonine aldolase contributes 1–3% of total threonine degradation under a variety of nutritional states in both rat and quail [4,15].
L-TAs from a number of species of bacteria and fungi have been isolated and characterized (reviewed in [16]). In the yeast, Saccharomyces cerevisiae, the glycine synthase-1 gene, GLY1 was identified as threonine aldolase [17,18]. Previously, gene ablation studies had shown that the GLY1 pathway is a major source of glycine [19]. But it only plays a minor role in Candida albicans [20]. In a number of bacteria species such as Escherichia coli, Aeromonas jandaei, Pseudomonas and Thermatoga maritima the GLY1 gene has been cloned and their enzymatic activity characterised [21-24]. In thale cress, Arabidopsis thaliana, there are two threonine aldolase genes (THA1 and THA2). THA1 has been shown to play a role in seed nutritional quality [25]. Putative GLY1 genes have been also identified in nematodes and flies [21]. Recently, the X-ray crystal structures of L-threonine aldolase from the bacteria Thermotoga maritima have been determined as the apo-enzyme, bound to L-allo-threonine and to glycine [21].
These GLY1/threonine aldolases are distinct from the serine hydroxymethyltransferases (EC 2.1.2.1)(SHMT). However, some SHMT also possess some threonine aldolase enzymatic activity. SHMT from E. coli and the yogurt bacterium, Streptococcus thermophilus, have TA activity [26,27]. SHMT isolated from rabbit liver has been shown to possess weak TA activity [28]. Consequently, it has been thought that the minor threonine aldolase activity in liver extracts was due solely to SHMT, and that mammals lack a true threonine aldolase, but this has been questioned [29]. Here I report that TA genes are present in vertebrates.
Results
Analysis of murine L-threonine aldolase cdnas
I conducted a search of the GenBank database for a putative mouse L-threonine aldolase gene using the sequence of the E. coli TA protein [22]. PCR primers were designed to the 5' and 3' ends of EST sequences that matched the genomic DNA sequence of the putative L-threonine aldolase gene. These primers were used to amplify the cDNA from murine liver RNA by RT-PCR. The amplicons were electrophoresised on an agarose gel. Two bands of similar intensity were obtained. Both bands were excised from the gel, cloned and sequenced. The upper band encoded an 1855 bp murine L-threonine aldolase cDNA sequence. It has a 127 bp 5'UTR containing an in-frame stop codon, an ORF which encodes a 400 residue protein and has an ATTAAA polyadenylation signal at 1822–1827 (GenBank accession No. AY219871)(Fig. 1). The predicted protein has a 43,496 Da molecular mass and an isoelectric point 6.73. The lower band encoded a second cDNA clone that was identical to the first clone except that it skipped exon 3. On translation, this results in a frame shift in the ORF that would encode a severely truncated protein of 124 residues that would not be expected to have any enzymatic activity (GenBank accession No. AY219872). Both cDNA sequences matched the mouse genomic DNA sequence. The mouse L-threonine aldolase/Gly1 gene is located on chromosome 11 band E2 (clone RP23-268N22, EMBL accession No. AL591433, Sanger Institute, UK) towards the telomere, between the baculoviral IAP repeat-containing 5 (Birc5) and suppressor of cytokine signalling 3 (Socs3) genes. The L-threonine aldolase gene spans 5.6 kb, consisting of 7 exons (Fig. 2). All splice donor/acceptor sites have consensus GT/AG dinucleotides. There is a 507 bp CpG island (66% GC) encompassing exon 1. Such CpG islands are generally associated with active housekeeping genes [30]. The predicted start of transcription, CCAT, on the genomic DNA is just 2 bp upstream of the cDNA sequence suggesting that the clone is almost full-length.
Figure 1 The cDNA sequence and translation of murine L-threonine aldolase. A potential polyadenylation signals (aataaa at 1822–1827) is shown in bold and underlined with the polyadenylation sites indicated by a. An * indicates the tga stop codon. The underlined nucleotide pairs indicate the positions of the exon/exon boundaries. The in-frame stop codon in the 5'UTR is indicated, tag, (coloured red).
Figure 2 Chromosomal localisation and the gene structure of murine L-threonine aldolase gene. (A) The gene is located on chromosome 11, band E2 (accession No. AL591433, the Sanger Institute, UK). (B) The 7-exon gene spans 5.6 kb. There is a CpG island spanning the 5' untranslated exon. The ORF is indicated by closed boxes. The sizes, in bp, of the exons and introns are indicated.
Predicted secondary structure of the murine threonine aldolase protein
A comparison of the predicted secondary structure of the murine TA protein with the known secondary structure of T. maritima [21] is shown (Fig. 3). The proteins have 44% identity and 81% similarity and are similar throughout their length. Overall there is good correspondence between the position of the predicted α-helices and β-sheets in the murine protein with those determined from the crystal structure of T. maritima. However, the mouse protein has an additional putative amino-terminal mitochondrial import leader peptide. Given long evolutionary distance between mouse and bacteria this high degree of homology strongly suggests that this murine protein is also a threonine aldolase. Most functional residues are conserved between the two proteins. By homology with the T. maritima protein, Lys242 is expected to form a Schiff-base with the cofactor, pyridoxal-5'-phosphate (PLP), with Asp211 and Arg214 expected to interact with PLP. Those residues that contact the ligands L-allo-threonine and glycine, Ser45, His123, Tyr127, Arg214 and Arg372, are conserved. T. maritima His125 from the second subunit is predicted to bind the hydroxyl group of L-threonine. This residue is homologous to murine Tyr168, a conservative substitution since both residues are polar and aromatic. In other TA proteins from diverse phyla this residue is mainly histidine, but in rice it is a tyrosine also. At the catalytic dimer interface electrostatic interactions occur among the side chains of Arg44-Glu71, Thr47-Asp66 and Arg274-Ser241. These residues are conserved. Residues involved in ion coordination are also conserved with Ala246, Thr49 and Ser241 contacting Ca2+ with Arg112 contacting a chloride ion. In the Arabidopsis THA1 enzyme a Gly114 to Arg mutation, located between two beta-sheets, results in loss of enzymatic activity [25]. This residue, Gly149 in mouse, is conserved in all four vertebrate TA enzymes.
Figure 3 Comparison of the predicted secondary structure of the murine threonine aldolase protein with that of the crystal structure of threonine aldolase from the bacteria Thermotoga maritima. The labels are: mouse predicted secondary structure, MmTA_PSSM; mouse protein sequence, MmTA_seq; T. maritima protein sequence, TmTA_seq and T. maritima secondary structure 1JG8_ss; alpha-helix, H, highlighted in light blue; beta-sheet, E, highlighted in yellow; c = turn, coil or loop. Identical residues in both proteins are illustrated with a "+" indicating positive equivalence and a "-" a negative equivalence. The PLP-binding lysyl residues are indicated with a pink asterisk and those residues that interact with PLP are indicated with a black asterisk. Those residues that contact the substrates, L-threonine and L-allo-threonine, and the product, glycine, are indicated with a green hash. Residues involved in electrostatic interactions in the catalytic dimer interface are indicated with an ampersand. Residues making contact with calcium ions are indicated with a plus sign and those contacting a chloride ion with a negative sign.
Sequence homology to other vertebrate threonine aldolase proteins
Database searches revealed the presence of other L-threonine aldolase genes in other vertebrates (Fig. 4). There is a single seven exon gene in the Japanese puffer fish genome (Takifugu rubripes)(accession No. BK005561). The exon/exon boundaries on the proteins are highly conserved between mouse and Japanese puffer fish with only one being displaced slightly. The Japanese puffer fish gene encodes a 421-residue protein that has 46% identity and 74% similarity to the murine protein. Similar L-threonine aldolase cDNAs for the western-clawed frog and the zebrafish were identified (accession Nos. BK005562 and AAH72718 respectively). The proteins are of similar lengths with the functional residues identified in T. maritima being well conserved. Homology extends throughout their lengths, apart from the amino-terminal regions. Despite their low sequence identity in the amino-terminal region all four proteins contain putative mitochondrial import leader peptides, being positively charged. They possess also a predicted cleavage site that would be utilised during their import into mitochondria. After cleavage of the mitochondrial import sequence the mature murine TA enzyme would have a mass 39,778 Da and pI 6.11. Recently, two other vertebrate GLY1 genes have been sequenced. In the dog there is a complete gene (GenBank accession number NW_140385) that would encode a 391-residue protein with 82% identity and 94% similarity to murine TA. Three overlapping unassigned genomic DNA sequences from the freshwater puffer fish, Tetraodon nigroviridis, would encode a gene encoding a 431-residue protein with 44% identity and 73% similarity to murine TA. Additionally, homologous coding ESTs from mammals (rat, pig and cow), birds (chicken), amphibians (African clawed frog) and fish (little skate, rainbow trout, Atlantic salmon, channel catfish and Japanese medaka) were identified, indicating that L-threonine aldolase expression in vertebrates is widespread.
Figure 4 Comparison of vertebrate L-threonine aldolase protein sequences. The L-threonine aldolase sequences are: mouse, Mus musculus, MmTA; pufferfish, Takifugu rubripes, TrTA; (BK005561); western clawed frog, Xenopus tropicalis, XtTA (BK005562) and zebrafish, Danio rerio, DrTA (AAH72718). Predicted cleavage sites during import into mitochondria are indicated by a backwards slash (\). Where known, the locations of the exon/exon boundaries are shown on the translated protein as underlined residues. Stop codons are indicated by a hash. Conserved residues are indicated by a (*), strongly similar residues by a (:) and weakly similar residues by a (.). Residues are colour coded: basic, DE, red; acidic, KR, pink; polar, CGHNQSTY, green and hydrophobic, AFILMPVW, red.
Human GLY1/threonine aldolase gene is a pseudogene
A database search identified 17q25 as the location for the human GLY1 gene. All exon/intron boundaries found in the murine threonine aldolase gene are conserved in man. The mouse to human conserved synteny map shows that both GLY1 genes have the synaptogyrin 2 (SYNGR2), baculoviral IAP repeat-containing 5 (BIRC5) and soluble thymidine kinase 1 (TK1) genes as near neighbours. Starting with human liver RNA, I was neither able to amplify any threonine aldolase transcripts using a variety of 5' and 3' RACE methods nor to detect any transcripts in a wide range of tissue and cell line cDNAs by RT-PCR. A search of the human EST database identified five potential EST transcripts scattered throughout the threonine aldolase gene, but they lack supporting evidence that they are truly transcribed sequences, being unspliced singletons. There is a potential polyadenylation signal site in the human threonine aldolase gene with good homology to the murine site. However, corresponding 3'UTR ESTs and SAGE tags are conspicuously absent from the databases leading to the conclusion that the GLY1 threonine aldolase gene is not transcribed in man.
If the human GLY1 threonine aldolase gene were transcribed there are two single nucleotide deletions that would cause frame-shifts. They are the equivalent of murine nucleotide 55 in exon 4 (Fig. 5A) and the equivalent of murine nucleotide 198 in exon 7 (Fig. 5B). Both these deletions are found in genomic DNA clones from two individuals showing that these deletions are not sequencing errors (accession Nos. AC032035 and AC010532, MIT Center for Genome Research, USA and DOE Joint Genome Institute, USA, respectively). The presence of the frame-shift in exon 4 would create a truncated ORF of 144 residues that does not include the PLP-binding lysine residue, consequently the protein would not be functional (Fig. 5C). Also there is a premature in-frame stop codon towards the carboxy-terminal. Even if the frame-shifts in the human GLY1 gene were not present then the translated human TA protein would not function due to the mutation of four important residues. These four residues have remained conserved during evolution since the last common ancestor of the bacteria, T. maritima, and vertebrates. One residue that would be expected to interact with the PLP ligand, murine Arg214, would be mutated to Gln in man. Murine residue Arg372 that would be expected to interact with threonine is mutated to Ala. The side chains of two residues that form electrostatic interactions at the catalytic dimer interface are also mutated, murine Thr47 to Lys, and murine Arg274 to His. If the frame-shifts were not present, the mouse and human proteins would have 66% identity and 85% similarity. Likewise, the chimpanzee threonine aldolase gene is a pseudogene possessing the same frame-shifts as the human gene. Additionally, it has lost the splice donor site in exon 1 and, by comparison with the mouse gene, has a 64 bp deletion in exon 7 (Fig. 5C).
Figure 5 Comparison of the mouse threonine aldolase cDNA and ORF with the human and chimpanzee genes. (A) There is a cytidine deletion in exon 4 of the human threonine aldolase gene resulting in a frame-shift. (B) There is a guanosine deletion in exon 7 of the human threonine aldolase gene resulting in a frame-shift. (C) Comparison of the mouse protein with a translation of human and chimpanzee genes shows that the presence of the frame-shift in exon 4 creates a truncated ORF of 144 residues that does not include the PLP-binding lysine residue (pink K); consequently the protein would be non-functional. All exon/exon boundaries are conserved and shown on the translated protein as black underlined residues except that of chimpanzee exon 1 which is shown as a red underlined residue. Stop codons are indicated by red hashes. ORF residues generated by frame-shifts are shown in lower case. Conserved residues are indicated by a (*), strongly similar residues by a (:) and weakly similar residues by a (.). Abbreviations: mouse, Mus musculus, Mm; Homo sapiens, Hs; Pt, Pan troglodytes; exon 4, Ex4; exon 7, Ex7; translations in frame 1, F1; frame 2, F2 and frame 3, F3.
Homology of serine/threonine dehydratase and serine dehydratase like-1 proteins in vertebrates
The sequences of murine SDH and SDH-1 cloned cDNAs matched those of reference sequences (accession numbers NM_145565 and NM_133902 respectively). In mammals, these two genes are adjacent, being arranged in a 5' to 5' orientation. Database searches identified both the SDH and SDH1 genes in man, rat, freshwater puffer fish and the Western and African clawed frogs. But in the chicken only the SDH1 gene is present since SDH is absent from the draft genome and all expressed sequences. A comparison of vertebrate SDH and SDH1 proteins with the crystal structure of rat SDH [31] suggests that SDH1 is also a serine/threonine dehydratase because residues with important functions are conserved (Fig. 6). By homology, Lys48 of murine SDH1 is the PLP binding residue forming a Schiff base and the amino acid sequence around Lys48 SxKIRG is well-conserved in other SDHs from vertebrates, plants, yeasts and bacteria [32-35]. Two other conserved amino acid sequences, S(A/G)GNA and GGGG(L/M) and Cys309 (murine SDH1 numbering) form hydrogen bonds with PLP. In SDH1 a potassium ion near the active site would be expected to be coordinated by six oxygen atoms, five of which are from conserved residues; Gly174, Glu200, Ala204, Ser206, Leu229, but Ala231 replaces Val225 of rat SDH.
Figure 6 Comparison of vertebrate SDH and SDH1 proteins. The species are: house mouse, Mus musculus, Mm; Norway rat, Rattus norvegicus, Rn; human, Homo sapiens, Hs; chicken, Gallus gallus, Gg; western clawed frog, Xenopus tropicalis, Xt; African clawed frog, Xenopus laevis, Xl; freshwater puffer fish and Tetraodon nigroviridis, Tn. By comparison with the crystal structure of rat SDH, important conserved residues found in SDH enzymes are conserved also in SDH1 and are shown above the sequence alignment. The amino acid sequence SFKIRG (blue), around the PLP binding Lys41 (pink), is conserved in SDH and SDH1. Two other conserved amino acid sequences, SAGNA (brown) and GGGGL (purple), form hydrogen bonds with PLP, as does Cys303 (green). A potassium ion near the active site is coordinated by six oxygen atoms from Gly168, Ala198, Leu223, Val225, Glu194, and Ser200 (orange). Conserved residues are indicated by a (*), strongly similar residues by a (:) and weakly similar residues by a (.). Residues are colour coded: basic, DE, red; acidic, KR, pink; polar, CGHNQSTY, green and hydrophobic, AFILMPVW, red. Exon/exon boundaries determined from genomic DNA are indicated on the proteins by black underlining. Unknown sequences are indicated by xx.
Expression of threonine aldolase, serine/threonine dehydratase and serine dehydratase like-1 mRNA in mouse tissues
To identify those tissues which are likely to contribute to TA activity in the mouse, the expression of TA mRNA in adult tissues was examined by RT-real time PCR normalised to the expression of the housekeeping genes, β-actin and glyceraldehyde-3-phosphate dehydrogenase (G3PDH). Low levels of TA mRNA were detected in all tissues examined. They varied 20-fold between tissues, being highest in prostate, heart and liver (Fig. 7A). In contrast, the mRNA levels of SDH, another enzyme that catabolises L-threonine, has a very specific tissue distribution. It is expressed highly in the liver at a level similar to the two housekeeping genes. It is over 300 fold more abundant in liver than heart, the second highest expressing tissue (Fig. 7B). Low levels of SDH1 mRNA were found also in all tissues. Like SDH, SDH1 was most abundant in the liver with moderate levels being found in testis, heart, kidney and spleen (Fig. 7C).
Figure 7 Expression of TA, SDH and SDH1 mRNA in mouse tissues by real time PCR. The expression levels in each tissue were normalised to that of the housekeeping genes beta-actin and G3PDH. (A) For TA the expression levels in all tissues were standardised to that of prostate, which was taken as 100; (B) and for SDH and SDH1 the expression levels were standardised to that of liver, which was taken as 100.
Expression of threonine catabolic enzymes in mouse embryos
The mRNA expression of threonine catabolic enzymes was examined by real time PCR in cDNAs derived from whole mouse embryos from days 7, 11, 15 and 17 (Fig. 8). Overall, TA, TDH and SDH expression were low prior to E-15, but increased more then four-fold by E-17. KBL expression was low at E-7, but increased earlier than the other enzymes. SDH1 did not change substantially with increasing embryonic age.
Figure 8 Expression of TA, SDH, SDH1, TDH and KBL mRNA in whole mouse embryos by real time PCR. Their expression levels in each embryonic stage were normalised to that of the housekeeping genes beta-actin and G3PDH. Each gene was standardised to its expression level at embryo day 17, which was taken as 100. The gene abbreviations are: threonine aldolase, TA; serine/threonine dehydratase, SDH; serine dehydratase like-1, SDH1; threonine dehydrogenase, TDH and 2-amino-3-ketobutyrate coenzyme A ligase, KBL.
Discussion
In vertebrates, L-threonine is one of the indispensable amino acids. It is obtained from protein in the diet, typically being the second or third limiting amino acid in herbivorous diets. Some of it is utilized in synthesising new protein, but the rest is converted to other amino acids by oxidative catabolism by three different enzymes that are found in most organisms; TDH, TA and SDH. Both the TDH and TA pathways produce glycine. However, the TDH pathway occurs in two steps, requiring KBL as the second step. Using protein homology searches of the mouse genome with the bacterial enzymes has allowed me to identify and clone TDH and KBL cDNAs [36,37]. GLY1/TA genes have been identified previously in bacteria, fungi and plants [16,21,25]. Here I describe the first TA cDNA found in vertebrates. The murine TA cDNA encodes a 400-residue protein that is highly similar to that from T. maritima with an expect value of 2e-73, being clearly distinct from glycine dehydrogenase, the second most closely related protein, with an expect value of 0.002. This remarkable conservation, over billions of years of evolution since the last common ancestor, shows the general importance of these metabolic pathways. However, the presence of some abnormal TA mRNA splicing in mouse, the low levels of mRNA found in mouse tissues, together with the low levels of TA enzymatic activity found in rat liver [4,15] plus the loss of a functional TA gene in humans suggests that TA has reduced importance in mammals.
The L-TA enzymes can act on the stereoisomers, L-threonine and L-allo-threonine. These can be divided into three types based on the stereospecificity towards the β-carbon of threonine. Low-specificity L-TA can use both L-threonine and L-allo-threonine as substrates. L-TA only acts on L-threonine and L-allo-TA is specific to L-allo-threonine [16]. Murine L-TA is likely to be a low-specificity L-TA with a preferences for the allo isomer in a manner similar to the T. maritima enzyme, because Tyr127 (Tyr87 in T. maritima) in the TA active site is conserved, a residue which appears to be involved in discriminating L-threonine from L-allo-threonine [21].
All the vertebrate TA proteins have putative amino-terminal mitochondrial import sequences, suggesting that the mitochondrion is its intracellular localisation. In contrast, fractionation studies in the yeast, S. cerevisiae, revealed a cytosolic localisation for TA [38]. Additionally, the yeast TA protein does not possess an amino-terminal mitochondrial import sequence. In vertebrates, threonine catabolism is mostly confined to the liver when the mass of the organ is taken into consideration. Expression of SDH, TDH and KBL mRNA are highest in liver [36,37,39]. However, low levels of murine TA expression were found in a wide range of tissues suggesting a role in housekeeping metabolism in all tissues. Generally, during embryogenesis, expression of threonine catabolic enzymes increased with maturation of the developing liver.
Humans have lost two of the three enzymes of threonine catabolism with both GLY1 and TDH [40] genes being defective, both pathways produce glycine from threonine. In man, the loss of a functional GLY1 gene appears to be a more ancient event than the loss of TDH because GLY1 genes in both man and chimpanzees have a number of frame-shifts and mutations of functional amino acid residues, whereas the mutated exon 6 splice-acceptor site in human TDH is intact in chimpanzees (data not shown). This suggests that GLY1 has been lost prior to, and TDH after, the divergence of man and chimpanzees, about 6–8 million years ago. Consequently, humans may not be as metabolically well equipped as other species to cope with diets high in threonine/protein. Perhaps a reduction in the rate of threonine catabolism in man's ancestors would have conferred a selective advantage on those individuals with these defective genes under conditions of protein starvation. Although humans have lost both the glycinergic pathways of threonine catabolism their gut microbial flora will have both TA and TDH enzymes, therefore, gut microbial flora may make significant contributions to human threonine catabolism.
With the loss of TA and TDH genes in humans this leaves serine/threonine dehydratase as our only major threonine catabolic enzyme. However, vertebrates also have a second SDH gene, called SDH-like-1 which, by homology, is likely to function also as a serine/threonine dehydratase since all the residues that bind the PLP co-factor are conserved between the two proteins. Only the SDH1 gene is present in the chicken, therefore the serine dehydratase activity found in chick livers [41] must be due to SDH1.
The SHMT enzymes are members of the α-class of pyridoxal phosphate enzymes, catalyzing the reversible interconversion of serine and glycine. Mammals have two SHMT genes. One encodes a cytosolic and the other a mitochondrial enzyme. Purified SHMT enzyme from rat liver possesses some threonine aldolase activity [28] and both SHMT genes may also contribute to threonine catabolism in vertebrates.
With the identification of murine TA and SDH-1 mRNA the way is open to study their enzymatic activity in vitro and relative contribution to threonine catabolism under different physiological states in vivo. Changes in TA and SDH-1 mRNA expression in response to diet have yet to be examined, but rats fed a high protein diet or fasted showed an increase in TA enzymatic activity [42]. In contrast, quails and rats fasted or on threonine enriched diets did not show any statistically significant changes in TA enzymatic activity [15].
Conclusion
I have shown that GLY1/TA genes are present in vertebrates. TA genes and enzymatic activities have been previously isolated from bacteria, fungi and plants. These enzymes are distinct from the serine hydroxymethyltransferases. The mouse GLY1 gene is located on chromosome 11, band E2 and the 1855 bp cDNA from this gene encodes a 400-residue threonine aldolase. The presence of a positively-charged amino-terminal import leader peptide sequence in mammalian, amphibian and fish TA proteins, that are not present in bacterial proteins, suggests that the vertebrate TA enzymes are mitochondrial. Man and chimpanzees have lost a functional GLY1 gene. Vertebrates also have a second SDH gene, SDH1, that by homology to the crystal structure of SDH may function as a threonine dehydratase and contribute to threonine catabolism.
Methods
Molecular cloning of murine L-threonine aldolase
Total RNA was extracted from mouse liver using guanidine thiocyanate and treated with DNase-I to remove any contaminating genomic DNA (SV total RNA isolation system, Promega, UK). Total RNA was reversed transcribed with AMV RNase H- reverse transcriptase (ThermoScript, Life Technologies, UK) at 50°C using an oligo-dT primer. The cDNA was amplified by touchdown PCR using the Advantage cDNA polymerise mix (Clontech, UK) on a Perkin-Elmer 2400 thermocycler. Amplification conditions for the first 10 cycles were 94°C for 5 sec, 72°C less 0.4°C per cycle for 3 min and for the next 20 cycles 94°C for 5 sec, 68°C for 10 sec, 72°C for 3 min per cycle using primers (100 nM) derived from the sequence of the mouse genomic DNA from clone RP23-268N22 (accession number AL591433 forward 5'-ATAGTGCCCCGGGCTTGC-3' and, first reverse 5'-TTTTTTTTTTTTTTTGTGCCTTCAGTATTT-3' and (Amersham-Pharmacia Biotech, UK). PCR amplicons were electrophoresised in a low-melting point agarose gel stained with ethidium bromide. They were excised from the gel. The agarose digested with agarase (Promega, U.K.). These PCR amplicons were cloned into pCR-II-TOPO, a T-A vector (Invitrogen, The Netherlands) and sequenced in both directions using the big dye terminator cycle sequencing ready reaction kit with AmpliTaq DNA polymerase FS on an ABI 373XL Stretch Sequencer (both from PE Applied Biosystems, UK). SDH and SDH-1 cDNAs were cloned in a similar manner and were used as positive controls in RT-PCR assays.
Gene expression in mouse tissues by real time PCR
Quantitative PCR was carried out on a GeneAmp 5700 Sequence Detection System (AB Applied Biosystems) and a Rotor Gene 3000 utilising a CAS-1200 robotic precision liquid handling system (Corbett Research, Australia) using a SYBR Green I double-stranded DNA binding dye assay (Applied Biosystems, UK). For the determination of TA, SDH, SDH1, TDH and KBL mRNA expression in adult mouse tissues and whole mouse embryos, cDNAs were generated from polyA+ selected RNA by reverse transcriptase using an oligo-dT primer (BD Clontech, UK). Approximately 8.0 ng of cDNA were used for each PCR. Tissue master mixes were divided into gene specific mixes and primers were added to a final concentration of 200 μM. The primers were: TA, CCCAGAGATTCGCTAAAGGACTC (exon 6/7) and CACGGCCAGTCTGAGCCAC (exon 7), which produced a 171 bp amplicon; SDH, TTTTACGAACACCCCATTTTCTC (exon 7) and AGAATCTTCTCATCGTCCACGAA (exon 8/7), which produced an 89 bp amplicon; SDH1, CCTGCCAGACATCACCAGTGT (exon 6/7) and GCGCTCATCGTCCAGGAA (exon 8/7), which produced a 154 bp amplicon; G3PDH, TCCCACTCTTCCACCTTCGA and GTCCACCACCCTGTTGCTGTA, which produced a 111 bp amplicon. Primers for beta-actin, TDH and KBL have been described previously [36]. Amplification conditions were; a 10 min hot start to activate the polymerase followed by up to 50 cycles of 95°C for 15 sec and 60°C for 1 min. The number of cycles required for the fluorescence to become significantly higher than background fluorescence (termed cycle threshold [Ct]) was used as a measure of abundance. A comparative Ct method was used to determine gene expression. Expression levels in each tissue cDNA sample were normalised to the average expression levels of the housekeeping genes beta-actin and G3PDH (ΔCt). Ratios of gene of interest mRNA/housekeeping mRNA from each tissue were standardised to that of the highest expressing tissue for that gene which was taken as 100% (ΔΔCt). Formula E-ΔΔCt was used to calculate relative expression levels where E is the efficiency of the PCR per cycle. Amplification specificity was confirmed by melting curve analysis and agarose gel electrophoresis.
Bioinformatics
The predicted start site of transcription of murine TA mRNA was determined using the program Eponine [43]. The predicted secondary structure of the protein was determined using the Psi-Pred program [44] aligned with that of the crystal structure of TA from the bacteria T. maritima [21] using 3D-PSSM [45]. Mitochondrial locations were predicted for the TA proteins using MITOPRED [46]. Cleavage-sites in the mitochondrial targeting peptides were identified using PSORT [47].
Authors' contributions
A.J.E. initiated and carried out the molecular genetic studies, drafted the manuscript and approved the final manuscript.
Acknowledgements
I thank Cameron Tristan Edgar, Chloe Fiona Edgar and Maria-athina Milona for technical help with PCR and cloning, and June Edgar for her constructive comments on the manuscript, and the Advanced Biotechnology Centre, Charing Cross Campus, Imperial College for DNA sequencing.
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| 15757516 | PMC555945 | CC BY | 2021-01-04 16:39:32 | no | BMC Genomics. 2005 Mar 9; 6:32 | utf-8 | BMC Genomics | 2,005 | 10.1186/1471-2164-6-32 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-6-131573056410.1186/1471-2202-6-13Research ArticleGlutamate-induced apoptosis in primary cortical neurons is inhibited by equine estrogens via down-regulation of caspase-3 and prevention of mitochondrial cytochrome c release Zhang YueMei [email protected] Bhagu R [email protected] Department of Obstetrics and Gynecology, University of Toronto, Toronto, Canada2 Institute of Medical Sciences, University of Toronto, Toronto, Canada3 Department of Obstetrics and Gynecology, St. Michael's Hospital, Toronto, Canada2005 24 2 2005 6 13 13 15 10 2004 24 2 2005 Copyright © 2005 Zhang and Bhavnani; 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
Apoptosis plays a key role in cell death observed in neurodegenerative diseases marked by a progressive loss of neurons as seen in Alzheimer's disease. Although the exact cause of apoptosis is not known, a number of factors such as free radicals, insufficient levels of nerve growth factors and excessive levels of glutamate have been implicated. We and others, have previously reported that in a stable HT22 neuronal cell line, glutamate induces apoptosis as indicated by DNA fragmentation and up- and down-regulation of Bax (pro-apoptotic), and Bcl-2 (anti-apoptotic) genes respectively. Furthermore, these changes were reversed/inhibited by estrogens. Several lines of evidence also indicate that a family of cysteine proteases (caspases) appear to play a critical role in neuronal apoptosis. The purpose of the present study is to determine in primary cultures of cortical cells, if glutamate-induced neuronal apoptosis and its inhibition by estrogens involve changes in caspase-3 protease and whether this process is mediated by Fas receptor and/or mitochondrial signal transduction pathways involving release of cytochrome c.
Results
In primary cultures of rat cortical cells, glutamate induced apoptosis that was associated with enhanced DNA fragmentation, morphological changes, and up-regulation of pro-caspase-3. Exposure of cortical cells to glutamate resulted in a time-dependent cell death and an increase in caspase-3 protein levels. Although the increase in caspase-3 levels was evident after 3 h, cell death was only significantly increased after 6 h. Treatment of cells for 6 h with 1 to 20 mM glutamate resulted in a 35 to 45% cell death that was associated with a 45 to 65% increase in the expression of caspase-3 protein. Pretreatment with caspase-3-protease inhibitor z-DEVD or pan-caspase inhibitor z-VAD significantly decreased glutamate-induced cell death of cortical cells. Exposure of cells to glutamate for 6 h in the presence or absence of 17β-estradiol or Δ8, 17β-estradiol (10 nM-10 μM) resulted in the prevention of cell death and was associated with a significant dose-dependent decrease in caspase-3 protein levels, with Δ8, 17β-E2 being more potent than 17β-E2. Protein levels of Fas receptor remained unchanged in the presence of glutamate. In contrast, treatment with glutamate induced, in a time-dependent manner, the release of cytochrome c into the cytosol. Cytosolic cytochrome c increased as early as 1.5 h after glutamate treatment and these levels were 5 fold higher after 6 h, compared to levels in the untreated cells. Concomitant with these changes, the levels of cytochrome c in mitochondria decreased significantly. Both 17β-E2 and Δ8, 17β-E2 reduced the release of cytochrome c from mitochondria into the cytosol and this decrease in cytosolic cytochrome c was associated with inhibition of glutamate-induced cell death.
Conclusion
In the primary cortical cells, glutamate-induced apoptosis is accompanied by up-regulation of caspase-3 and its activity is blocked by caspase protease inhibitors. These effects of glutamate on caspase-3 appear to be independent of changes in Fas receptor, but are associated with the rapid release of mitochondrial cytochrome c, which precedes changes in caspase-3 protein levels leading to apoptotic cell death. This process was differentially inhibited by estrogens with the novel equine estrogen Δ8, 17β-E2 being more potent than 17β-E2. To our knowledge, this is the first study to demonstrate that equine estrogens can prevent glutamate-induced translocation of cytochrome c from mitochondria to cytosol in rat primary cortical cells.
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Background
High concentrations (mM) of the excitatory neurotransmitter glutamate can accumulate in the brain and are thought to be involved in the etiology of a number of neurodegenerative disorders including Alzheimer's disease [1-4]. A number of in vitro studies indicate that at high concentrations, glutamate is a potent neurotoxin capable of destroying neurons [5,6].
The mechanisms by which glutamate-induced neurotoxicity or excitotoxicity is mediated, has not been established, however, a substantial body of evidence suggests that glutamate toxicity involves oxidative stress and apoptosis (programmed cell death) [2,7-9]. This latter form of cell death is characterized by DNA degradation that results by cleaving DNA at internucleosomal sites [10]. Apoptosis is a gene-directed process and an increasing number of genes and their proteins are involved in this process [11,12]. We have previously reported that in a stable mouse hippocampal neuronal cell line (HT22), glutamate-induced cell death is associated with DNA fragmentation and up-regulation of the pro-apoptotic protein Bax and down-regulation of the anti-apoptotic protein Bcl-2, however, in this cell line, the apoptotic process did not appear to involve caspase-3 [13]. In contrast, recent studies demonstrate that a family of cysteine proteases (caspases) play an important role in apoptotic cell death observed in some neurodegenerative diseases [14-16]. Caspase-3 is considered to be the central and final apoptotic effector enzyme responsible for many of the biological and morphological features of apoptosis [15-17]. Caspase-3 usually exists in the cytosolic fraction of cells as an inactive precursor that is activated proteolytically by cleavage at a specific amino acid sequence to form the active enzyme [18] which is capable of cleaving several proteins that culminate in apoptotic cell death [19]. Although these observations strongly indicate that caspase-3 is essential for apoptosis in mammalian cells, the mechanisms involved in caspase-3 regulation of the neuronal system remain to be elucidated. Many signal transduction pathways such as Fas receptor-mediated signaling pathway via caspase-8, via activation of granzyme B, or the damage of mitochondria that results in cytochrome c release, have been implicated in the initiation of caspase-3 cascade [20-25].
A number of studies have demonstrated that estrogens are potent antioxidants capable of inhibiting some of the neurotoxic effects of oxidative stress [7,26,27]. We, and others have shown that estrogens can increase cell survival and attenuate in vitro cell death induced by various neurotoxins [28-31]. We have also previously demonstrated in HT22 cells and in the neuronal-like PC12 cell line derived from rat adrenal pheochromocytoma cells that the neurotoxic effects of glutamate can be inhibited differentially by various equine estrogens [31]. The data further indicated that the less estrogenic (uterotropic) Δ8-estrogens were the most effective neuroprotectors [31]. We further suggested that the increased potency of these Δ8-estrogens may to some extent, be due to their greater antioxidant properties [28,29,31]. However, the mechanism(s) involved in estrogen mediated neuroprotection are not fully understood. In the present study, we have determined whether glutamate-induced neuronal apoptosis in primary cortical cells and its inhibition by estrogen, involves changes in caspase-3 protease and whether this process is mediated by Fas receptor and/or mitochondrial signal transduction pathways involving release of cytochrome c. The estrogens selected for this study were 17β-estradiol (17β-E2) which has high affinity for both estrogen receptors ERα and ERβ and Δ8, 17β-estradiol (Δ8, 17β-E2), an estrogen that is a more potent antioxidant than 17β-E2 and whose activity appears to be mediated to a greater extent via ERβ [31-34].
Results
Effects of various concentrations of glutamate on cortical cell viability
Cortical cells (2.5 × 104) cultured for 7 days in 96-well plates were treated with increasing concentrations (0.2 – 100 mM) of glutamate for 18 h. As depicted in Figure 1, increasing concentrations of glutamate resulted in a dose-dependent decrease in cell viability measured by the MTS (3-[4,5-dimethylthiazol-2 yl]-5 [3 carboxymethoxyphenyl] 2 H-tetrazolium, inner salt) cell proliferation assay as described under "Methods". Generally as the amount of glutamate increased, cell death increased progressively, however, there was no significant difference between 1 and 25 mM glutamate in 18 h. Similar results were obtained after treatment with glutamate for 6 h (data not shown). Based on these preliminary data, all subsequent experiments were carried out for 6 h at glutamate concentrations between 1 to 20 mM. At these concentrations, the mean percent cell death was approximately 20%.
Demonstration of apoptosis in cortical cells treated with glutamate
Cortical cells cultured in poly-lysine coated 6-well plates for up to 7 days were treated with 1 mM glutamate for 18 h. DNA was extracted, purified and subjected to agarose gel electrophoresis as described under "Methods". The results indicate (Figure 2), that in DNA isolated from untreated cortical cells prior to culture (Figure 2, lane 2), untreated cells in culture for 1 day (Figure 2, lane 3) no DNA fragmentation was detectable. However, after 2 and 7 days in culture, DNA fragmentation was detectable (Figure 2, lanes 4 and 6). The extent of DNA fragmentation was potentiated in cells treated for 18 h with 1 mM glutamate (Figure 2, lanes 5 and 7). These results indicate that cortical cells in culture for longer than 2 days display characteristic DNA fragmentation or laddering that is associated with apoptosis mediated via caspase-3 activation. Glutamate treatment as can be seen in Figure 2, lane 7 induced a much greater DNA fragmentation than untreated cortical cells. However, these are qualitative data and should be interpreted with caution, particularly since some DNA fragmentation occurs in untreated cells. Glutamate-induced enhancement of apoptosis was also detectable by characteristic morphological changes observed by using phase contrast microscopy (Figure 3). After 6 h in culture, untreated cortical cells retained normal morphology of neuronal cortical cells and their cellular extensions (dendrites) and membranes were clearly visible. An occasional degenerated cell was also visible (Figure 3A). In contrast, after a 6 h incubation with 1 mM glutamate degenerated, dead or apoptotic cells were clearly visible in these cultures (Figure 3B), and the cellular extensions seen in untreated cortical cells (Figure 3A), were retracted, and cells appeared rounded. Similar changes were also observed with 5 mM glutamate (data not shown). In the presence of 1 mM glutamate and 1 μM, 17β-E2 (Figure 3C) and Δ8, 17β-E2 (Figure 3D), the cells retained the normal morphology of untreated cells, and only an occasional degenerated cell was visible. These results clearly confirm that glutamate induces and enhances apoptosis. These morphological changes and cell death were prevented by both estrogens. Taken together, these data indicate that cortical cells in the presence of glutamate undergo apoptotic changes in culture.
Effects of glutamate and estrogens on cell death
Cortical cells were cultured in 6-well plates (1 × 106 cells/well) for 7 days. The medium was then changed and the effects of various concentrations (1–20 mM) of glutamate on lactate dehydrogenase (LDH) release were measured for 6 h. The results indicate that glutamate induced a significant (30–40%) increase in LDH release compared to the control untreated cells. No significant difference in LDH release was observed in 6 h at glutamate concentration between 1 to 20 mM, (data not shown). Based on these observations and the cell viability data (Figure 1), all subsequent experiments were done in the presence of 1 or 5 mM glutamate to avoid potential necrosis which may occur at higher concentration of glutamate. In order to follow the kinetics of glutamate cytotoxicity, the effect of 5 mM glutamate on LDH release as a function of time was measured for up to 24 h. The results shown in Figure 4 indicate that glutamate toxicity varied markedly during the course of culture. Significant increase (40–60%) in cytotoxicity was observed after 6 to 24 h exposure of cells to glutamate. During this period, LDH release in untreated control cells remained the same for up to 8 h and significantly increased (P < 0.05) after 24 h (Figure 4). Next, cortical cells were treated with 5 mM glutamate for 6 h in the presence or absence of various concentrations (0.01–10 μM) of 17β-E2 and Δ8, 17β-E2. Release of LDH from cells was measured and the results are summarized in Figure 5. Both 17β-E2 and Δ8, 17β-E2 inhibited glutamate-induced cell death in a dose-dependent manner, with Δ8, 17β-E2 being more potent. Thus, 0.1 μM Δ8, 17β-E2 and 1 μM 17β-E2 significantly reduced cell death compared to glutamate alone (Figure 5). However, even at the highest concentration of estrogens tested, cell death induced by 5 mM glutamate was not fully preventable. In contrast, when cell death was induced by lower concentration (1 mM) of glutamate, 10 μM of Δ8, 17β-E2 completely inhibited cell death and the release of LDH returned to control levels (data not shown).
Effects of glutamate and estrogens on caspase-3 protein levels
Following measurement of LDH, cortical cells were harvested, lysed and processed for Western blot analysis as described under "Methods". The results (Figure 6) indicate that both anti-caspase-3 antibodies detected the presence of caspase-3 Mr 32 kDa protein band (precursor protein), but not the p 20 and p 11 active fragments of caspase-3. The results further indicate that the exposure of cells to glutamate resulted in an increase in caspase-3 precursor protein in a dose (Figure 6) and time (Figure 7), dependent manner. Thus, 1 to 5 mM glutamate increased caspase-3 protein levels by 45 to 66% respectively, however, higher concentrations of glutamate (10–20 mM) did not result in any further increase in caspase-3 protein levels (Figure 6). The kinetics of glutamate effects on caspase-3 protein levels indicate that a significant increase in the levels occurred by 3 h of glutamate (5 mM) exposure and reached maximum levels observed at 6 h (54% increase) (Figure 7). The levels were significantly lower at 24 h (Figure 7), most likely due to decreased transcription of caspase-3. These results further indicate that changes in levels of caspase-3 occur soon after induction of apoptosis. Lack of further increase in caspase-3 protein levels suggest that cell death at this late stage may be due to necrosis and therefore all subsequent experiments were carried out for up to 6 h only. These glutamate-induced changes were reversed in the presence of estrogens. Thus, increasing concentrations of 17β-E2 and Δ8, 17β-E2 in the presence of 5 mM glutamate for 6 h resulted in a decrease in caspase-3 levels in a dose-dependent manner (Figure 8). As depicted in Figure 8, glutamate (5 mM) alone increased the levels of caspase-3 by 60% and in the presence of 1 to 10 μM 17β-E2, the levels of caspase-3 protein decreased gradually and returned to control values with 10 μM 17β-E2. Similar results were obtained with Δ8, 17β-E2, however, significant decrease (30%) in the levels of caspase-3 occurred at 10 times lower concentration (1 μM) (Figure 8). When cell death was induced with lower concentration (1 mM) of glutamate, 0.1 μM 17β-E2 and Δ8, 17β-E2 completely inhibited the changes in caspase-3 protein and the levels returned to control values (Figure 9). Whether the ability of low concentrations of estrogens to fully protect the cortical cells against neurotoxicity induced by 1 mM glutamate was due to the cell death resulting via predominantly apoptosis rather than necrosis or a mixture of the two processes remains to be investigated.
Effects of caspase inhibitors, z-DEVD and z-VAD, on glutamate-induced cell death
To further confirm the role of caspase-3 in glutamate-induced cell death, cortical cells were pre-incubated with 100 μM of z-DEVD or z-VAD prior to induction of cell death by 1 mM glutamate. Glutamate alone caused a significant increase in cell death over control (Figures 10 A, B), while pre-incubation with the caspase-3 inhibitors prior to glutamate exposure resulted in a 50% reduction in cell death (Figures 10 C, D). In absence of glutamate, both inhibitors had no effect on cell viability (Figures 10 E, F). Similarly, DMSO, the vehicle used in the preparation of the inhibitors had no effect on cell viability (Figure 10 G). These data confirm that in primary culture of rat cortical cells glutamate induces cell death via the apoptotic pathway that involves changes in caspase-3 protease.
Activation of caspase-3
(a) Effects of caspase inhibitors on proleolytic cleavage of protein kinase c (PKC) in untreated primary cortical cells and cells treated with glutamate
During apoptosis, activation of caspases including caspase-3 can result in the generation of breakdown products (BDPs) of PKC [35,36]. Primary cortical cells were either treated with glutamate alone or first pretreated with caspase-3 specific inhibitor z-DEVD or pan-caspase inhibitor z-VAD as described under "Methods". The immunoblots are depicted in Figure 11. Glutamate treatment resulted in an increase in the formation of two BDPs (48 kDa and 45 kDa) in both the cytosol and cell lysates. Pretreatment with z-DEVD and z-VAD reduced the amounts of the two BDPs to levels seen in the untreated primary cortical cells (Figure 11). These results clearly indicate that pro-caspase-3 is activated and that active caspase-3 is present in primary cortical cells and cells treated with glutamate.
(b) Detection of active caspase-3 in primary cortical cells treated with glutamate
Western blot analysis had indicated the presence of pro-caspase-3 in primary cortical cells treated with glutamate (Figure 12). To detect the presence of active caspase subunits, highly specific active caspase-3 antibody raised against amino acids 163 to 175 (p 18 subunit) of murine caspase-3 was obtained from Stratagene. Primary cortical cells were treated with 1 mM glutamate for 3 and 6 h and processed as described under "Methods". The results (Figure 12) indicate that glutamate treatment for 3 h, did not significantly change the levels of caspase-3 active form. However, as can be seen (Figure 12) after 6 h of glutamate treatment, the levels of caspase-3 active form increased over 2 fold compared to untreated cells (Figure 12). These results further confirm that caspase-3 is activated in apoptosis induced by glutamate in primary cortical cells.
Effects of glutamate on Fas receptor protein expression
To determine whether the regulation of caspase-3 in glutamate-induced cell death in cortical cells is modulated by Fas receptor mediated apoptotic pathway, expression of Fas receptor protein was evaluated by Western blot analysis. The results indicate (Figure 13) that anti-Fas receptor antibody reacted specifically with Fas receptor protein, however, no significant changes in the levels of this protein were observed following treatment with various concentrations (1–20 mM) of glutamate (Figure 13) for up to 24 hours (Figure 14) compared to control levels.
Effects of glutamate and estrogen on the release of mitochondrial cytochrome c into the cytosol
To determine whether the activation of caspase-3 in glutamate-induced cell death requires cytochrome c, primary cultures of cortical cells were first incubated with 1 mM glutamate for up to 6 hours. The cells were then processed for Western blot analysis as described under "Methods". The results depicted in Figure 15 clearly indicate that prior to glutamate treatment, the bulk of cytochrome c is localized in the mitochondria and barely detectable levels were observed in the cytosol. In contrast, exposure of cells to glutamate resulted in a rapid release of cytochrome c from the mitochondria into the cytosol (Figure 15). Significant increase in cytosolic cytochrome c occurred as early as 1.5 h, and after 3 h, the levels of cytochrome c were higher in the cytosol compared to the mitochondria (Figure 15). By 6 h, the levels of cytochrome c in the cytosol were 2.5 times higher than those in the mitochondria. The cytosolic levels of cytochrome c between 1.5 hours to 6 hours were 2 to 5 fold higher in the cytosol prepared from glutamate treated cells compared to untreated cells (Figure 15). Concomitant with these changes in the cytosol, the mitochondrial levels of cytochrome c decreased significantly. Similar results were also observed when 5 mM glutamate was used (data not shown). Exposure of cortical cells to 1 mM glutamate and 1 μM 17β-E2 or Δ8, 17β-E2 for 6 hours resulted in a significant decrease in the release of cytochrome c into the cytosol (Figure 16). Thus, the levels of cytochrome c in the cytosol from estrogen-treated cells were 30 to 50% lower than the corresponding levels in the glutamate alone treated cells (Figure 16).
Discussion
In the present study, we used primary fetal rat cortical cell cultures to demonstrate that glutamate can induce neuronal cell death by apoptotic mechanisms and that the process can be reversed or inhibited by equine estrogens such as 17β-E2 and Δ8, 17β-E2. Our results further indicate that glutamate-induced cell death appears to result, to some extent, by a mechanism that involves DNA fragmentation and morphological changes characteristic of apoptosis. These changes are similar to those we and others have reported previously with other neuronal cell models [7,13]. Unlike the mouse hippocampal cell line HT22 [13], the primary cultures of rat cortical cells after day two in culture appear to undergo some degree of apoptotic cell death in absence of glutamate (Figure 2), however, the extent of DNA fragmentation is significantly enhanced after treatment with glutamate. Moreover, compared to the HT22 cells, the primary rat cortical cells appear to be resistant and require higher concentration of glutamate to induce apoptosis. Similar differences were also observed between HT22 cells and PC12-neuronal cells derived from rat adrenal pheochromocytoma [31]. The glutamate induced DNA fragmentation and subsequent cell death was associated with characteristic morphological changes also noted previously in HT22 cells following treatment with β-amyloid and glutamate [13,37]. The results from the present study indicate that these morphological changes associated with apoptosis were prevented by 17β-E2 and Δ8, 17β-E2 (Figure 3). To quantitatively estimate the extent of cell death induced by glutamate, we measured cell viability by MTS assay and cell death by LDH activity released in the media during culture. Results from the MTS assay clearly indicated that cell viability was dependent on the dose of glutamate (Figure 1). Thus, increasing concentrations of glutamate resulted in a dose-dependent decrease in cell viability. Because in the MTS proliferation assay, cells cannot be reused, subsequent quantitative experiments were carried out using LDH release assay. The LDH assay had previously been validated using the MTS assay [29,31].
In the present study, we further observed that glutamate induces cell death of primary rat cortical cells and involves changes in caspase-3 protease. Exposure to glutamate up-regulated caspase-3 protein levels while caspase inhibitors blocked this apoptotic process. These observations indicated that cell death induced by glutamate in primary cortical neurons was via apoptosis.
Caspase-3 was one of the cysteine proteases that played an essential role in apoptosis by cleaving several key cellular proteins such as poly (ADP-ribose) polymerase (PARP), sterol-regulatory element-binding protein (SREBPs), PKC, DNA-dependent protein kinase, DNA-fragmentation factor (DFF) and a number of others [16,19,21,24,25,35,36]. Our studies show that up-regulation of caspase-3 expression preceded neuronal cell death, supporting the possibility that glutamate-induced apoptotic cell death was the consequence of up-regulation of caspase-3 gene in cortical neurons. These observations are consistent with up-regulation of precursor caspase-3 in frontal neuronal cortex of subjects with Alzheimer's disease [5]. This enzyme has been proposed to activate death effector molecules resulting in the fragmentation of genomic DNA and was associated with morphological and structural changes characteristic of apoptosis [16,19,21,25,38].
When cells undergo apoptosis, caspase-3 is initially present as pro-enzyme (32 kDa precursor protein) that is subsequently transformed into the active heterodimeric complexes through a cascade of proteolytic events [14,18]. The active form of caspase-3 is composed of two subunits of Mr 20 kDa (p20) and 11 kDa (p11), which are derived from proteolytic processing of the 32 kDa precursor during apoptosis [21,24,25]. In our study, these p 20 and p11 forms of caspase-3 were not detected in glutamate-induced cell death by immunoblotting analysis with three different kinds of antibodies: i. a polyclonal goat antibody caspase-3 p11 (K-19) against the p11 fragment of caspase-3, ii. a polyclonal rabbit antibody against caspase-3 (H-227) and iii. a monoclonal mouse antibody (E-8) (data not shown with this latter antibody). These observations suggest that either our antibodies are incapable of detecting these fragments or that cells may clear them rapidly as been observed in other studies [39]. Since we observed that glutamate-induced cell death was effectively blocked by both caspase-3 specific inhibitor z-DEVD and pan-caspase-inhibitor z-VAD, these data further provide evidence that caspase-3 protease is not only up-regulated, but is also activated during glutamate-induced cell death. That activation of pro-caspase-3 does indeed occur in glutamate-induced apoptotic cell death of primary rat cortical cells was confirmed by the demonstration that PKC, an endogenenous substrate of active caspase-3, was cleaved into two 45 kDa and 48 kDa BDPs [35,36]. The formation of these BDPs is characteristic of caspase-3 like protease activity [35,36]. The formation of the two BDPs was inhibited by caspase-3 specific inibitor z-DEVD and pan-caspase-inhibitor z-VAD). These results strongly suggest that proleolytic cleavage of PKC involves active caspase-3. These observations were further supported by the direct immunodetection of activated caspase-3 in the primary cultures of rat cortical cells treated with glutamate (Figure 12). The primary antibody was raised against amino acids 163 to 175 of murine caspase-3 and this neo-epitope is present on the p18 subunit of cleaved caspase-3. This antibody recognizes the p18 subunit but not the inactive pro-caspase-3 [40]. Taken together these data strongly indicate that in our neuronal cell cultures, glutamate induces apoptosis that involves active caspase-3. In contrast, it has been suggested that glutamate-induced cell death in HT22 mouse hippocampal cells appears to occur by apoptotic mechanisms that are independent of caspase-3 [13]. Since in HT22 cells, glutamate also induced DNA laddering, mitochondrial proteins such as apoptosis inducing factor (AIF) and endonuclease G (Endo G) released in response to death signals may also play a role in the HT22 neuronal cell line [13]. Whether similar caspase-3 independent pathways are also involved in glutamate-induced apoptosis in primary cultures of rat cortical cells remains to be investigated.
Several signaling pathways are implicated in the initiation of the caspase-3 cascade; one of the well defined pathways for activation of apoptosis is Fas receptor-mediated pathway [41-43]. Fas is a cell surface antigen and a member of tumor necrosis factor (TNF) receptor family [44]. Activation of Fas by its ligand or an agonistic anti-Fas antibody can transmit apoptotic signal by activation of caspase-3 and induces apoptosis in T-lymphocytes and malignant cells [26,45,46]. In vitro, cross-linking of Fas antibody to its antigen increases caspase-3 activity and induces apoptotic cell death in Jurkat cells [47]. However, in primary cortical cells, we did not observe any change in Fas receptor protein levels following glutamate treatment. These results suggest that activation of caspase-3 in neuronal cortical cells and their apoptotic demise induced by glutamate is independent of Fas receptor mediated pathway. However, the Fas mediated pathway is complex and further studies are required to establish whether this pathway is or not involved in glutamate-induced cell death in cortical cells. In contrast to glutamate, chronic morphine administration has been shown to induce up-regulation of Fas receptor and caspase-3 precursor protein in the rat neuronal cells [48,49]. Thus, whether Fas receptor mediated pathway plays a role in up-regulation of caspase protein or caspase-3 activation and apoptotic cell death may depend on the nature of neurotoxic agents being used.
Another recently characterized mechanism for pro-caspase-3 activation involves translocation of the respiratory chain protein, cytochrome c (Apaf-2), from mitochondria to the cytosol during the induction of apoptosis by a variety of different agents in non-neuronal and neuronal cells such as cerebellar granule cells [21,50]. Cytosolic cytochrome c binds to Apaf-1 in the presence of dATP and this leads to the recruitment and activation of caspase-9 and subsequent activation of caspase-3 [23,25]. In mitochondria cytochrome c resides in the inter-membrane space and matrix as a soluble protein and it functions as an electron carrier in oxidative phosphorylation [21]. The mechanism responsible for the translocation of cytochrome c from mitochondria into cytosol is not known. Cytochrome c release can occur upon interaction of pro-apoptotic protein with the outer mitochondria membrane protein such as Bax [51,52] or can be induced by interaction with elevated calcium or reactive oxygen species (ROS) with mitochondria. This results in mitochondrial dysfunction and a reduction in mitochondrial trans-membrane potential [53-55]. Our present studies demonstrate that glutamate-induced cell death in primary cortical cells is accompanied by the early release of cytochrome c into the cytoplasm that precedes changes in caspase-3 protease. Although we were unable to directly detect by Western blot analysis, the active caspase-3 fragments p20 and p11, the presence of active caspase-3 was confirmed as discussed above. Since there was a close correlation between the time course of cytochrome c release from the mitochrondria and changes in caspase-3 protease levels; it provides a possible mechanism whereby caspase-3 protease is not only up-regulated, but also activated by cytochrome c during glutamate-induced apoptotic cell death of primary cortical cells. Whether cytochrome c release from the mitochondria to cytosol is involved in the regulation of caspase-3, remains to be investigated.
We have previously reported that a number of equine estrogens, which are components of the drug CEE (conjugated equine estrogen) used extensively for management of vasomotor symptoms and osteoporosis in postmenopausal women, are potent antioxidants and protect neuronal cells against cell death induced by oxidized LDL and glutamate [28,29,56,57]. In keeping with these observations, the results from the present study indicate that in primary cortical cells, these equine estrogens prevent cell death by reducing glutamate-induced cytochrome c release from mitochondria and caspase-3 protein levels. To our knowledge, this is the first study that demonstrates that equine estrogens can prevent glutamate-induced translocation of cytochrome c from mitochondria to the cytosol in the rat primary cortical cells. Previous studies have shown that 17β-E2 induces release of cytochrome c from heart mitochondria [58] and also decreases cytosolic cytochrome c levels in hippocampus following global ischemia [59]. These observations indicate that equine estrogens protect against glutamate-induced apoptosis of primary cortical cells at least in part by inhibiting caspase-dependent apoptotic pathway. However, the mechanism by means of which estrogens reduce cytochrome c release from mitochondria is not yet fully understood. Some have suggested that the mechanism of estrogen-mediated neuroprotection involves regulation of mitochondrial calcium (Ca2+) and Bcl-2 expression [59]. Exposure of glutamate has also been associated with an increase of cytosolic Ca2+ in cortical cells [3,53] and up-regulation of pro-apoptotic protein Bax in neuronal cell line, HT22 [13]. Whether these changes induced by glutamate are involved in the mitochondria release of cytochrome c into the cytosol, remains to be investigated.
The cortical neuronal cells used in the present study contain both estrogen receptors ERα and ERβ (Bhavnani and Zhang, unpublished data), and therefore whether the mechanism by means of which estrogens exert their neuroprotective effects remain to be elucidated. In general, the current evidence indicates that there are at least two mechanisms for estrogen action: i) the genomic mechanism mediated via two nuclear receptors ERα and ERβ, and ii) the non-genomic mechanism mediated via putative membrane receptors and these have been recently reviewed [32]. Although the concentrations of estrogens that protect the cortical neurons from glutamate toxicity are pharmacological doses, these levels can however, be potentially attained in a postmenopausal women taking daily 0.625 mg of (CEE) [61]. These estrogen effects are most-likely mediated to some extent by the non-genomic mechanism, and may be related to the estrogens' antioxidant property [28,29]. This is in keeping with our previous observations that although 17β-E2 has higher affinity for ERα and ERβ, it is possible that some of these effects are due to the antioxidant properties of estrogens. However, we have also demonstrated [33] that Δ8, 17β-E2 expresses its biological effects to a two fold greater extent via ERβ than 17β-E2 [33]. Since the localization of ERα and ERβ in the brain is differential [[32] and references therein] further detailed studies are needed to elucidate the molecular mechanisms involved in the neuroprotective aspects of estrogens.
Recent studies further report that estrogen receptor (ERβ) is localized in the mitochondrial membrane and estrogen can directly modulate the mitochondrial content of Ca2+ and mitochondrial trans-membrane potential [61,62]. The role of the novel estrogen membrane receptors in apoptotic cell death induced by glutamate remains to be investigated. We along with others, have previously demonstrated that estrogens prevent cell death induced by glutamate [13] or β-amyloid [63] via modulation of Bcl-2 family of proteins. Thus, it is possible that in the neuronal cell model, estrogens decrease glutamate-induced cytochrome c release from mitochondria by protecting mitochondrial trans-membrane potential via binding estrogen receptor on mitochondrial membrane as well as up-regulation of anti-apoptotic protein Bcl-2. Studies to further delineate these mechanisms are being initiated.
Conclusion
In the primary cortical cells, glutamate-induced apoptosis is accompanied by up-regulation of caspase-3 that can be blocked by caspase protease inhibitors. These effects of glutamate on caspase-3 appear to be independent of changes in Fas receptor, but are associated with the rapid release of mitochondrial cytochrome c, which precedes changes of caspase-3 protein levels leading to apoptotic cell death. This process was differentially inhibited by estrogens with the novel equine estrogen Δ8, 17β-E2 being more potent than 17β-E2, To our knowledge, this is the first study to demonstrate that equine estrogens can prevent glutamate-induced translocation of cytochrome c from mitochondria to cytosol in rat primary cortical cells.
Methods
Materials
All primary and secondary antibodies and other reagents were purchased from commercial sources: rabbit polyclonal antibody to caspase-3 (H-227: sc 7148) and goat polyclonal antibody to caspase-3 p11 (K-19: sc-1224). Both of these antibodies react specifically with precursor protein: Mr 32 kDa and the active caspase-3: Mr 20 (p 20) and 11 kDa (p 11) fragments. Rabbit polyclonal antibody to protein kinase c (P4334) (PKC, crossreacts with PKCα /and PKC δ) purchased from Sigma. Goat polyclonal antibody to Actin (I-19: sc1616) (all from Santa Cruz, California), mouse monoclonal antibody to Fas raised against Fas extracellular domain (CD95:AF435) (R&D System, Inc. Minnepolis, MN), mouse monoclonal antibody cytochrome c (7H8.2C12) (BD PharMingen, Mississauga, ON). Secondary antibodies were obtained from Sigma (Saint Louis, Missouri). SuperSignal® West Pico Chemiluminescent Substrate (Pierce, Rockford, IL). Z-Val-Ala-DL-Asp (OMe)-fluoromethylketone (z-VAD-fmk) and z-Asp (OMe)-Glu (OMe)-Val-DL-Asp (OMe)- fluoromethylketone (z-DEVD-fmk) (Bachem Bioscience Inc., King of Prussia, PA) and the Cytotox 96 Non-radioactive Cytotoxicity Assay Kit (Promega G1780, from VWR, Toronto, Ontario, Canada). Neurobasal, B27, Hanks' buffered salt solution (HBSS) and antibiotics were from Gibco Life technologies (Burlington, Ontario, Canada). Active Caspase-3 Detection FITC kit # 280000 was obtained from Stratagene (La Jolla, CA).
Animals
Timed pregnant Sprague-Dawley rats were obtained from commercial sources. All procedures were reviewed and approved by the Institutional Animal Care and Use Committee at St. Michael's Hospital, University of Toronto, in accordance with the guidelines of the Canadian Council on Animal Care.
Cell culture
Eighteen to twenty day old pregnant Sprague-Dawley rats were sacrificed by cervical dislocation. The fetuses were delivered and fetal skulls were carefully opened and the meninges were removed and the brain processed essentially as described by Brewer et al. [64]. Briefly, two pieces of cortex (0.5–1.5 × 1–2 mm) were cut from the posterior dorsal surface and dissociated in HBSS (Ca2+ and Mg2+ free). Following dissociation, cells were suspended in 2 X volume of HBSS with Ca2+ and Mg2+ and allowed to settle for 3 to 5 minutes. The supernatant was transferred to new tubes and centrifuged at 1100 rpm (200 g) for 1 min. The pellets were suspended in HBSS buffer. Fresh cell suspensions were plated onto poly-L-lysine-coated 96-well plates or 6-well plates containing neurobasal medium supplemented with 2% B27, 0.5 mM L-glutamine, 25 μM glutamic acid, 120 mg/L penicillin and 260 mg/L streptomycin and incubated at 37 C in a humidified incubator with 5% CO2/95% air. Culture medium was replaced every other day. Experiments were performed on days 7 to 8 of cultures in neurobasal medium containing 2% antioxidant free B27 supplement. Under these serum-free culture conditions, only neuronal cells survive.
Determination of glutamate-Induced cell cytotoxicity
During the development of this work, the lactate dehydrogenase (LDH) cytotoxicity assay (Promega G1780, VWR Toronto, Ontario, Canada) and the Cell Titer 96® Aqueous Non-radioactive cell proliferation assay (MTS assay, Promega G5430) were used to assess cell death and cell viability respectively [31]. In MTS (3-[4,5-dimethylthiazol-2 yl]-5 [3-carboxymethoxyphenyl]-2H-tetrazolium, inner salt) the formation of a formazan product occurs only in live cells, however, once treated with MTS, the cells are not useable for other measurements. We have previously demonstrated [31] a strong correlation between the MTS and LDH assays [31]. Since we wanted to use the cells for various determinations, the LDH assay for cell death was selected as in this assay only the supernatant is used and the cells can be used for other measurements. Thus after the initial determination of the glutamate dose response curve, only the LDH assay was used in all subsequent experiments. Cell death was measured by the Cytotox 96 Assay kit, which quantitatively measures the release of lactate dehydrogenase into the medium following cell lysis or cell death as described previously [13]. In all experiments, cultures were also treated with 0.1% Triton X-100 to lyse the cells, and LDH levels measured under these conditions, were taken as the maximal LDH release (100% cell death). The results were expressed as a percentage of maximum LDH release.
Assessment of apoptosis by DNA fragmentation
Apoptotic cells produce characteristic DNA ladders made up of nucleotide fragments which are visualized by staining with ethidium bromide following DNA-agarose gel electrophoresis. We used DNA fragmentation as one of the criteria for apoptotic cell death and this was determined as described previously [13]. Briefly, primary cortical cells isolated from 18 to 20 day old fetal rat brains were cultured for up to 7 days and then treated with 1 mM glutamate for 18 h. The cell monolayers were washed with ice cold TBS (20 mM Tris-HCl, pH 7.6, 137 mM NaCl) and then DNA was isolated as described previously [13]. The DNA (5 μg) was electrophoresed on 1.5% agarose gel for 1.5 h at 100 V. The DNA fragments were visualized by staining with ethidium bromide and detected under UV transillumination.
Western blot analysis
Protein levels of Caspase-3, Fas or PKC were determined by Western blotting using a polyclonal and monoclonal antibody raised against caspase-3 or Fas or PKC as described previously [13]. In brief, cells were washed twice with cold PBS, harvested using a cell scraper, and lysed in buffer (9 mM Na2PO4, 1.7 mM NaHPO4 150 mM NaCl, pH 7.4), containing 1% Nonidet P-40 (Sigma, Toronto, Canada), 0.5% sodium deoxycholate, 0.1% SDS and 1 mM phenyl methyl sulfonyl fluoride) for 20 min on ice. Cell lysates were centrifuged at 10,000 g at 4 C for 10 min. The protein concentration was determined by Bradford method (BioRad, Toronto, Canada). Cell lysates containing 10 to 20 μg protein were added in equal volume of 2 X reducing sample buffer (100 mM Tris-CI pH 6.8, 200 mM dithiothretiol, 4% SDS, 20% glycerol, and 0.2% Bromophenol blue) and heated at 100 C for 3 min. The samples were electrophoresed on discontinuous 10% polyacrylamide gel electrophoresis under constant current (14–15 mA). Separated proteins were electrotransfered onto a Protan nitrocellulose membrane (Schleicher & Schuell Inc., Keene N.H). The blots were blocked with 5% non-fat milk in TBST (20 mM Tris-HCl pH 7.6, 137 mM NaCl, 0.05% Tween-20) at 4 C overnight and then incubated with primary antibody for 1 to 2 h at room temperature, washed three times with TBST and incubated (1–2 h) with appropriate horse radish peroxidase (HRP)-conjugated second antibody. The membranes were washed three times with TBST and the immunoblots were visualized on X-ray films after exposure to enhanced chemiluminescence reagent (ECL) (Amersham, Toronto, Canada). Actin bands were monitored on the same blot to verify consistency of protein loading. Briefly, the Immunoblots were stripped with TBST containing 0.04% sodium azide for 30 min at room temperature. The blots were probed with anti-actin primary antibody and second antibody (anti-goat) as described above. The molecular size of protein was determined by running pre-stained protein markers in an adjacent lane, omitting primary antibody as a procedural control. The band intensity was measured from scanned images using UN-SCAN IT Gel Automated Digitizing system, Version 5.1 (Silk scientific, Inc, Orem, Utah, USA), software.
Statistics
Each experiment was repeated at least three times and combined data were compared using the Student's paired t- test. Analysis of variance (ANOVA) when appropriate, was used for multiple comparisons. Significance was set at P < 0.05.
Measurement of cytochrome c translocation
The cell homogenates, mitochondria and cytosolic fractions were prepared essentially as described by Atlante et al. [54]. The cells were washed twice with ice-cold PBS, pH 7.4 and then suspended in cold isotonic buffer (250 mM sucrose, 20 mM Hepes-KOH, 10 mM KCI, 1.5 mM MgCI2, 1 mM Na-EDTA, 1 mM Na-EGTA, 1 mM dithiothretiol and protease inhibitors-complete cocktail). After 15 min incubation on ice, cells were homogenized using a glass homogenizer (25 stokes) at 4 C. Cell homogenates were spun at 750 g for 10 minutes and the pellets containing the nuclei and unbroken cells were discarded. The supernatant was then centrifuged at 10,000 g for 15 minutes. The pellets containing mitochondria was stored at -80 C until processed. The 10,000 supernatant was further centrifuged at 100,000 g for 1 hour at 4 C to obtain the cytosolic fraction (S-100). The intactness of the mitochondrial membranes was checked by assaying mitochondrial specific enzyme glutamate dehydrogenase in mitochondria, mitochondria treated with Triton X-100 and in cytosol (S-100) as described previously [54]. GDH was only detectable in mitochondria and mitochondria treated with Triton X-100. It was undetectable in cytosol. Thus, the cytosol fraction was free of mitochondrial contamination. Following analysis of protein concentration, the levels of cytochrome c in these two fractions were analyzed by Western blot on 12% SDS-polyacrylamide gel. Cytochrome c was detected using murine anti-cytochrome c antibody as described above.
Assessment of the effects of caspase inhibitors
Caspase inhibitors were used as described previously [65] caspase-3-inhibitor z-DEVD-fmk and a pan-caspase-inhibitor z-VAD-fmk. The concentrations of inhibitors tested were 25, 50 and 100 μM. Both were prepared as 20 mM stocks in DMSO and stored at -80 C. Primary cultures were incubated with the inhibitors for 30 min prior to the addition of glutamate. Effects of inhibitors on glutamate-induced cell death were measured with LDH release assay as described above.
Detection of active caspase-3 by immunofluorescence
The primary cortical cells were cultured for 7 days in 96-well microplates and then treated with or without glutamate for 3 h and 6 h. Cells were then processed essentially as described by the manufacturer of the assay kit (Stratagene). Briefly, the microplates were centrifuged (200 × g) for 2 minutes and the supernatant discarded. The cells were fixed for 15 minutes following which the fixative was aspirated and the cells washed twice with phosphate buffered saline (PBS) containing 1% saponin. The cells were then incubated with the primary anti-cleaved caspase-3 antibody in PBS + 1% saponin for 30 minutes at room temperature. The cells were then rewashed and incubated for 30 minutes with secondary FITC-goat anti-rabbit antibody and then washed twice and the relative fluorescence measured using a microplate reader (excitation 490 nm; emission 520 nm).
Authors' contributions
YMZ is a postdoctoral fellow who participated in the development of the hypothesis, study design and carried out most of the experimental work and preparation of the manuscript.
BB conceived the study and participated in the development of the hypothesis, the study design, and overall direction of the study and preparation of the manuscript.
Acknowledgements
This work was supported by the Medical Research Council of Canada Grant MT-11929 and a basic research grant from Women's Health Care Research, Wyeth Pharmaceuticals, Philadelphia, PA, USA. We wish to thank XiaoFeng Lu, Joel Perrella and Julie Khang for carrying out some of the experiments.
Figures and Tables
Figure 1 Effects of various concentrations of glutamate on cell viability. Primary cortical cells isolated from 18–20 day old fetal rat brains were cultured for 7 days and then treated with various concentrations of glutamate or in absence of glutamate (control) for 18 h. Cell viability was determined using the MTS cell proliferation assay as described under "Methods". Cell viability is expressed as the mean (± SEM) from three experiments. * P < 0.05 (1-100 mM)compared with untreated control cells.
Figure 2 Detection of glutamate induced DNA fragmentation (DNA ladder) Primary cortical cells isolated from 18–20 day old fetal rat brains were cultured for 7 days and treated with 1 mM glutamate for 18 h. The cells were processed as described under "Methods". The isolated DNA was electrophoresed on TBE agarose gel for 1.5 h at 100 V. The DNA fragments were visualized by staining with ethidium bromide. Lane 1 = 1 kb DNA ladder; lane 2 = DNA from untreated cortical cells prior to culture; lane 3 = DNA from untreated control cells cultured for 1 day; lane 4 = DNA from untreated control cells cultured for 2 days; lane 5 = DNA from cells cultured for 2 days and treated with glutamate for 18 h; lane 6 = DNA from untreated control cells cultured for 7 days; lane 7 = DNA from cells cultured for 7 days and then treated for 18 h with glutamate. Note the potentiation of DNA fragmentation in presence of glutamate.
Figure 3 Glutamate-induced toxicity in rat primary cortical cells as assessed by phase contrast microscopy (100×). Primary cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and then treated with 1 mM glutamate alone or in the presence of estrogens for 6 h. Degenerated cells are depicted by arrowheads. A = untreated control cells; B = cells treated with 1 mM glutamate for 6 h; C = cells treated with 1 mM glutamate and 1 μM 17β-E2; D = cells treated with 1 mM glutamate and 1 μM Δ8, 17β-E2. Note after glutamate treatment, dead cells with loss of dendrites are clearly visible in the cultures. In contrast, either untreated control cells or cells treated with glutamate in presence of estrogens retain their normal morphology. Dendrites and membranes are clearly visible and only a few degenerated cells are present in these cultures (A,C,D).
Figure 4 Kinetics of glutamate-induced cell death in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 5 mM glutamate for the indicated times in a neurobasal medium containing 2% antioxidant free B27. LDH release (% maximum) was measured after 3,6,8 and 24 h of treatment. Data are the mean ± SEM values from at least 3 experiments. *P < 0.05 compared with control ** P < 0.05 compared to 3,6,8 h control.
Figure 5 Effect of various concentrations of 17β-E2 and Δ8, 17β-E2 on glutamate (5 mM) induced cell death in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 5 mM glutamate alone or in the presence of estrogens in neurobasal medium containing 2% antioxidant free B27. LDH release (% maximum) was measured after 6 h treatment. Data are the mean ± SEM values from at least 3 experiments. a = P < 0.05 compared with untreated control cells; b = P < 0.05 compared with glutamate alone.
Figure 6 Effects of various concentrations of glutamate on caspase-3 protein levels in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 1,5,10 and 20 mM glutamate for 6 h in a neurobasal medium containing 2% antioxidant free B27. Cells were then harvested and total protein was extracted. Caspase-3 protein levels were assessed by Western blot analysis as described under "Methods". Actin was used as loading control. The bars depict densitometric analyses of Western blots from at least three experiments (± SEM). *P < 0.05 compared with untreated control cells.
Figure 7 Kinetics of glutamate (5 mM) treatment on caspase-3 protein levels in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 5 mM glutamate for the indicated times in a neurobasal medium containing 2% antioxidant free B27. Cells were then collected and total protein was extracted. Caspase-3 protein levels were determined by Western blot analysis as described in Figure 6. The bars depict densitometric analyses of Western blots from at least three experiments (± SEM). *P < 0.05 compared to untreated control cells.
Figure 8 Effects of estrogens on caspase-3 protein levels during glutamate (5 mM) induced cell death in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 5 mM glutamate alone or in the presence of 17β-E2 and Δ8, 17β-E2 for 6 h. Cells were then harvested and total protein was extracted. Caspase-3 protein levels were determined by Western blot analysis as described under Figure 6. The bars depict densitometric analyses of Western blots from at least three experiments (± SEM). a = P < 0.05 compared with untreated control cells, b = P < 0.05 compared with glutamate alone.
Figure 9 Effects of estrogens on caspase-3 protein levels during glutamate (1 mM) induced cell death in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 1 mM glutamate alone or in the presence of 17β-E2 and Δ8, 17β-E2 for 6 h. Cells were then harvested and total protein was extracted. Caspase-3 protein levels were determined by Western blot analysis as described under Figure 6. The bars depict densitometric analyses of Western blots from at least three experiments (± SEM). a = P < 0.05 compared with untreated control cells, b = P < 0.05 compared with glutamate alone.
Figure 10 Effects of caspase inhibitors on glutamate-induced cell death in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days. Cell death was measured by LDH release assay 6 h after incubation with 1 mM glutamate with or without 30 min pretreatment with 100 μM caspase-3 specific inhibitor z-DEVD or a pan-caspase inhibitor z-VAD (A,B,C and D). Inhibitors alone (E and F) or DMSO (G) had not effect on LDH release. Data are the mean ± SEM for 3 experiments performed. a = P < 0.05 compared with control, b = P < 0.05 compared with glutamate alone.
Figure 11 Effect of caspase inhibitors on proteolytic cleavage of PKC in primary cortical cells treated with glutamate. Primary cortical cells were cultured for 7 days and then treated with 1 mM glutamate with or without 30 minutes of pre-treatment with 100 μM caspase-3 specific inhibitor z-DEVD or a pan-caspase inhibitor z-VAD. After 6 h cells were harvested and total protein was extracted from the cytosol and cell lysates. PKC and its fragments were assessed by Western blot analysis as described under "Methods". Immunoblots indicate the presence of PKC (80 kDa) and its 48 kDa and 45 kDa breakdown products (BDPs). Note that glutamate treatment results in the formation of greater amounts of the 2 BDPs (lanes 2 and 4) in the cytosol and cell lysates compared to the untreated controls (lanes 1 and 3). Pre-treatment of cortical cells with 100 μM z-DEVD or z-VAD prior to glutamate inhibits the formation of PKC to its BDPs to levels seen in untreated control cells (lanes 7,8).
Figure 12 Detection of active caspase-3 by immunofluorescence. Primary cortical cells were cultured for 7 days in 96-well microplates and then treated with or without 1 mM glutamate for 3 h and 6 h. The cells were fixed and then incubated first with primary rabbit anti-cleaved caspase-3 antibody and then with FITC-goat anti-rabbit secondary antibody as described under "Methods". After aspiration, the cells were washed twice with PBS + saponin and the relative fluorescence measured using a Labsystem Fluroskan Accent FL microplate reader (excitation 490 nm; 520 nm emission). The bars depict relative fluorescence units from 3 measurements (± SEM). P < 0.05 compared to control untreated cells.
Figure 13 Effects of various concentration of glutamate on Fas receptor protein levels in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 1,5,10 and 20 mM glutamate for 6 h. Cells were harvested and total protein was extracted. Fas receptor protein levels were assessed by Western blot analysis as described under Figure 6. The results show that glutamate had no significant effect on Fas receptors. Data are the mean (± SEM) from 3 experiments.
Figure 14 Kinetics of glutamate (5 mM) treatment on Fas protein levels in primary cortical cells. Cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 5 mM glutamate for the indicated times. Cells were collected and total protein was extracted. Fas receptor protein levels were determined by Western blot analysis as described under Figure 6. The results show that glutamate has no significant effects on Fas receptors up to 24 h of treatment. Data are the mean (± SEM) from 3 experiments.
Figure 15 Kinetics of glutamate (1 mM) induced Cyto c release from mitochondria into cytosol in primary cortical cells. Primary cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and then treated with 1 mM glutamate for the indicated times. Mitochondrial and cytosol fractions were isolated as described in "Methods". Protein levels of Cyto c in mitochondria and cytosol were determined by Western Blot analysis as described under Figure 6. Actin bands were also used to monitor the same blot to verify consistency of cytosolic protein loading. The content of Cyto c was measured by densitometric scanning of the film. The results are the mean (± SEM) of at least three experiments. *P < 0.05 compared with control.
Figure 16 Effects of 17β-E2 and Δ8, 17β-E2 on glutamate-induced Cyto c release into cytosol in primary cortical cells. Primary cortical cells isolated from 19 day old fetal rat brains were cultured for 7 days and treated with 1 mM glutamate alone or in the presence of 17β-E2 and Δ8, 17β-E2. After 6 h, cells were collected and cytosol fraction was prepared as described under "Methods". Cytosolic protein levels of Cyto c were determined by Western blot analysis as described under Figure 6. Actin bands were used to monitor the same blot to verify consistency of protein loading. The content of Cyto c was measured by densitometric scanning of the film. The results are the mean (± SEM) of at least three experiments. a = P < 0.05 compared with control; b = P < 0.05 compared with glutamate treated cells.
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| 15730564 | PMC555946 | CC BY | 2021-01-04 16:03:47 | no | BMC Neurosci. 2005 Feb 24; 6:13 | utf-8 | BMC Neurosci | 2,005 | 10.1186/1471-2202-6-13 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-5-111575242710.1186/1471-2334-5-11DebateCould flies explain the elusive epidemiology of campylobacteriosis? Ekdahl Karl [email protected] Bengt [email protected] Yvonne [email protected] Department of Epidemiology, Swedish Institute for Infectious Disease Control (SMI), SE-17182 Solna, Sweden2 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-17177 Stockholm, Sweden3 County Medical Office for Communicable Disease Control, SE-581 91 Linköping, Sweden2005 7 3 2005 5 11 11 14 1 2005 7 3 2005 Copyright © 2005 Ekdahl et al; licensee BioMed Central Ltd.2005Ekdahl 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
Unlike salmonellosis with well-known routes of transmission, the epidemiology of campylobacteriosis is still largely unclear. Known risk factors such as ingestion of contaminated food and water, direct contact with infected animals and outdoor swimming could at most only explain half the recorded cases.
Discussion
We put forward the hypothesis that flies play a more important role in the transmission of the bacteria, than has previously been recognized. Factors supporting this hypothesis are: 1) the low infective dose of Campylobacter; 2) the ability of flies to function as mechanical vectors; 3) a ubiquitous presence of the bacteria in the environment; 4) a seasonality of the disease with summer peaks in temperate regions and a more evenly distribution over the year in the tropics; 5) an age pattern for campylobacteriosis in western travellers to the tropics suggesting other routes of transmission than food or water; and finally 6) very few family clusters.
Summary
All the evidence in favour of the fly hypothesis is circumstantial and there may be alternative explanations to each of the findings supporting the hypothesis. However, in the absence of alternative explanations that could give better clues to the evasive epidemiology of Campylobacter infection, we believe it would be unwise to rule out flies as important mechanical vectors also of this disease.
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Background
Campylobacter infection is a zoonotic disease, observed in most parts of the world. The disease is caused by Campylobacter jejuni, or less commonly Campylobacter coli. It is estimated to cause 5–14% of diarrhoea, worldwide [1], and also in the Western world Campylobacter infection has emerged to be the most important bacterial cause of gastrointestinal infection. Animals (variety of fowl, swine, cattle, sheep, dogs, cats, and rodents) are the major reservoir for the bacteria. Campylobacter does not easily grow in food, but the critical infective dose is low [2]. Unlike salmonellosis with well-known routes of transmission, the epidemiology of campylobacteriosis is still largely unclear [3]. Known risk factors for the disease include ingestion of undercooked meat, contaminated food and water or raw milk, direct contact with pets, farm animals and small children, and swimming in lakes, but also travel abroad [2,4-6]. Direct person-to-person transmission between adults appears to be uncommon. In temperate regions, campylobacteriosis has a distinct seasonal pattern, with the peak incidence in the summer months [3,5,7,8]. Identified risk factors for Campylobacter infections, that may coincide with the summer peaks in the temperate regions include direct animal contact, eating barbecued meals, swimming in lakes, and drinking untreated water from streams and other natural sources [4-6,9]. However, all these factors could at most explain 50% of the sporadic cases [3]. Instead we put forward the hypothesis that flies play a more important role in the transmission of the bacteria, than has previously been recognized.
Discussion
The fly hypothesis
The common houseflies (Musca domestica) and other muscid flies thrive in excreta and other filth. They could act as mechanical vectors, by carrying bacteria on the hairs and surface of their bodies or on the glandular hairs on their feet, but they could also act as biological vectors by passage through the alimentary tract, where pathogens have opportunity to multiply [10]. The houseflies are important mechanical vectors in the transmission of many infectious diseases with low infective dose, such as shigellosis, typhoid fever and E. coli infection [11,12]. Fly control has shown to be effective in preventing childhood diarrhoea in Pakistan and The Gambia [13,14], and shigellosis in Israeli Army personnel [15]. Already in 1983, Rosef and Kapperud postulated that flies might play a linking role by transmitting Campylobacter from animals to human food [16]. Since then several researchers have unravelled the role of flies in the epidemiology of avian campylobacteriosis [17-19], but the idea of flies as important vectors for human Campylobacter infection has been largely neglected [20]. Six factors speak in favour of our hypothesis.
1. Infective dose
The infective dose of Campylobacter can be as low as 800 bacteria [21], which is in the same magnitude as that of Shigella spp, Salmonella Typhi, and E. coli, pathogens that are known to be transmitted by flies [11,12,15], and much lower than the infective dose of Vibrio cholerae (108 bacteria), and non typhoidal Salmonella species (105-1010 bacteria). Although less tolerant to desiccation than some other food-borne pathogens [22], Campylobacter can survive on dry surfaces for at least seven days [23], thus enabling the bacteria to survive for several days both on the body of the fly and in desiccated fly faeces.
2. Flies as a possible vector
Studies have shown that Campylobacter could easily be transmitted from the environment to flies [17,24], and thus making flies a reservoir for the bacteria. Campylobacter could also be transmitted from flies to chickens [19]. In a recent study, Campylobacter could be isolated from 4 of 49 (8%) of flies caught outside a broiler house in Denmark. Furthermore, Wright showed that Campylobacter could be isolated from five of 210 (2.4%) living flies, isolated from three different locations [25]. From these results the author drew the conclusion that the health hazard from the transmission of Campylobacter from animals to human food is small. On the contrary, giving the numerous contacts between flies and human food, we find it highly likely that if one out of every 40 flies carries Campylobacter the health hazard would be significant.
3. Presence of the pathogen in the environment
Shigella is a strict human pathogen, while the major source of Campylobacter is the faeces of both humans and animals such as chickens, cattle and pigs, which are often kept in close proximity of humans. Stanley and Jones have previously shown the importance of cattle and sheep farms as reservoirs of Campylobacter [26]. Campylobacter is also common in the droppings from wild birds [27,28], and ubiquitous in the environment. Campylobacter spp have been isolated from sewage contaminated water [29], contaminated soil [30] and aquatic sediments [31], and in sand from bathing beaches [32]. There are therefore likely considerably more Campylobacter than Shigella in the close vicinity of humans. Since flies have been shown to be an important mechanical vector of shigellosis, it would be surprising if they could not also be so for campylobacteriosis. Direct transmission from the soil could probably account for some of the cases in children, but less likely for adult cases.
4. Seasonality of the disease
The distinct seasonality in the temperate regions [3,5,7,8,33] fits well with the fly hypothesis. The summer is the only season in temperate countries when people are in close contact with flies – often while having picnics or otherwise eating outdoor in close proximity of cattle and other environmental sources of Campylobacter. Some of the recorded association between barbeque and campylobacteriosis could very well be due to contamination of the food by flies, rather than undercooked meat or cross-contaminations, as has previously been postulated. A recent study from the UK has shown a close temporal association between the incidence of campylobacteriosis and fly density [34]. Although there is a seasonal pattern in the density of flies in the tropics, flies are present year round [13,14]. Therefore, if our hypothesis holds true, there should not be the same distinct seasonal peaks in the tropics. However seasonal data on campylobacteriosis from tropical regions are largely lacking. Instead we have recently compared Swedish notification data on travel-related campylobacteriosis with an extensive database on the travel patterns of Swedish residents (denominator for monthly risks per region). While a distinct seasonal pattern, as previously described, could be discerned in travellers from all temperate regions, the risk of campylobacteriosis in travellers from the tropics were more dispersed over the year [35]. Lack of detailed data on seasonal fly density and quite large geographical regions for our risk estimates of campylobacteriosis, prevented us from making any correlations between risk of campylobacteriosis and the presence of flies in the tropical regions.
5. Age profile
Small children are less able to protect themselves from flies than older children and adults, and are more likely to have their hands on fly-soiled surfaces. In the tropics, the Campylobacter infection is largely confined to children below the age of two years, and the decreasing incidence thereafter has been attributed to a lasting immunity [20]. On the contrary, in Sweden and other Western countries, the highest incidence is seen in young adults, with a smaller peak in pre-school age children [20,36]. Then, how about western travellers going to the tropics? If the major transmission route of Campylobacter was ingestion of contaminated food, one would expect the infection to be relatively evenly distributed among the largely non-immune westerners coming to high prevalence countries. Again we turned to the risk estimates for campylobacteriosis in returning Swedish travellers. While, the incidence pattern in travellers returning from temperate countries closely mimicked the age pattern of indigenous Swedish cases, we noted that among travellers returning from tropical areas of Africa and Asia, the youngest children had twice as high risk as young adults, and more than four times the risk compared to older children [35]. This age pattern thus suggests other major routes of transmission than food or water, e.g. direct or indirect transmission from environmental sources. The flies would fit well in this concept.
6. Dominance of solitary cases
If intake of chicken and undercooked meat (or cross-contamination from these food items) was a major route of transmission, clusters of cases within the same family should be common. Instead a striking feature of indigenous campylobacteriosis in Sweden is that the cases (except for in a few larger outbreaks) are solitary. A survey of notification data in one Swedish county over several years showed that it was exceptionally rare that cases shared the same address [37]. Information on the notification form indicating symptomatic cases around the notified patient was also very rare, even though this is specifically asked for. Solitary cases are instead more compatible with circumstance where an infected fly defecates on the plate of one family member, leaving the rest of the family unexposed.
Testing the hypothesis
The fly hypothesis needs to be backed by further experimental and epidemiological studies. The best evidence would be if controlled intervention studies could show an effect on the incidence of Campylobacter infection by fly control, as has previously been done for shigellosis and diarrhoea [13-15]. Such studies could only be done in high incidence areas, and would require good laboratory support. In temperate regions such intervention studies would be less feasible. Instead, questions focusing on the exposure to flies, and possible nearby environmental Campylobacter sources, e.g. cattle farms, sewage treatment or fowls, should be included in forthcoming case-control studies on campylobacteriosis. This has been a neglected line of questioning so far. More data on the carriage of Campylobacter by flies in different settings where people could be exposed are also needed. An alternative, and more innovative, approach would be to combine information on the likely place/s of infection with data on environmental sources, in analytic studies using geographical information systems (GIS) tools.
Conclusion
All the evidence in favour of the fly hypothesis is circumstantial and there may be alternative explanations to each of the findings supporting the hypothesis. However, in the absence of alternative explanations that could give better clues to the evasive epidemiology of Campylobacter infection, we believe it would be unwise to rule out flies as important mechanical vectors also of this disease.
Summary
We put forward the hypothesis that flies play a more important role in the transmission of the bacteria, than has previously been recognized. Factors supporting this hypothesis are: 1) the low infective dose of Campylobacter; 2) the ability of flies to function as vectors; 3) a ubiquitous presence of the bacteria in the environment; 4) a seasonality of the disease with summer peaks in temperate regions and a more evenly distribution over the year in the tropics; 5) an age pattern for campylobacteriosis in western travellers to the tropics suggesting other routes of transmission than food or water; and finally 6) very few family clusters. The hypothesis should be further tested with experimental and epidemiological studies
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Bengt Normann raised the original idea and has studied the (lack of) family clustering. Yvonne Andersson contributed with in depth knowledge of campylobacteriosis. Karl Ekdahl did the literature search, looked into the seasonality and age distribution of the disease in domestic cases and returning travellers, and prepared the first draft of the manuscript. All authors have participated in revising the draft manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-5-241577747110.1186/1471-2458-5-24Research ArticleTime trends in the impact factor of Public Health journals López-Abente Gonzalo [email protected]ñoz-Tinoco Concha [email protected] National Center for Epidemiology, Carlos III Institute of Health, Sinesio Delgado 6, 28029 Madrid, Spain2 Ramón y Cajal Hospital Library, Ctra. de Colmenar km 9.100, 28034 Madrid, Spain2005 18 3 2005 5 24 24 14 10 2004 18 3 2005 Copyright © 2005 López-Abente and Muñoz-Tinoco; licensee BioMed Central Ltd.2005López-Abente and Muñoz-Tinoco; 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
Journal impact factor (IF) is linked to the probability of a paper being cited and is progressively becoming incorporated into researchers' curricula vitae. Furthermore, the decision as to which journal a given study should be submitted, may well be based on the trend in the journal's overall quality. This study sought to assess time trends in journal IF in the field of public, environmental and occupational health.
Methods
We used the IFs of 80 public health journals that were registered by the Science Citation Index from 1992 through 2003 and had been listed for a minimum period of the previous 3 years. Impact factor time trends were assessed using a linear regression model, in which the dependent variable was IF and the independent variable, the year. The slope of the model and its statistical significance were taken as the indicator of annual change.
Results
The IF range for the journals covered went from 0.18 to 5.2 in 2003. Although there was no statistical association between annual change and mean IF, most of the fastest growing journals registered mean IFs in excess of 1.5, and some represented emerging areas of public health research. Graphs displaying IF trends are shown.
Conclusion
In view of the delay between the publication of IFs and that of any given paper, knowing the trend in IF is essential in order to make a correct choice of journal.
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Background
Scientific journal impact factor (IF) is directly linked to the probability of a paper being cited. It is currently accepted that a higher IF indicates a better journal quality, existing a correlation among IF and quality indicators at least in some health disciplines [1]. As a result these indices are progressively becoming incorporated into researchers' curricula vitae. In Spain, publication in top-IF journals has occupational implications in terms of academic careers and obtaining research grants [2]. The most widespread and important bibliometric indicators are those referring to the repercussion of scientific activity, and among these, IF has a leading role [3].
This pressure means that when it comes to having to select a journal in which to publish their studies, researchers turn to journals with IF, and assess the possibility of publishing in those that have the highest IF possible. Journals having the top IF within each medical specialty tend to be those with the greatest international prestige and highest profile, i.e., the most widely read by researchers and most in demand to publish their studies. However, the publication of extraordinary relevant scientific findings are concentrated in a small list of core journals, most of them not directly related with the specialty of the author [1,4]
Positive evaluation of IF which failed to take account of its trend over time would tend to favor publications of recent, but not necessarily lasting, interest. In contrast, publications of steadily growing interest and stable impact would be undervalued, though there may be differences of opinion about this [5].
Hence, in addition to journal quality, the decision as to which journal a manuscript should be submitted may be based on the trend in its IF over time. The aim of this study was to analyze IF time trends of journals in the "Public, Environmental and Occupational Health" category, thereby furnishing a new criterion on which to base the choice of journal for publication.
Methods
For study purposes, we selected 80 journals that: were included in the "Public, Environmental and Occupational Health" category of the hard copy version of the Journal Citation Reports (JCR) from 1992 through 2003 [6]; were listed for a minimum period of 3 consecutive years; and had IFs in the JCR for 2003. We consulted the 2003 JCR-IFs via the ISI Web of Knowledge [7].
The impact factor is one of the quantitative tools provided by JCR for ranking, evaluating, categorizing, and comparing journals. The annual impact factor of a journal is calculated by dividing the number of current year citations to the source items published in that journal during the previous two years [6].
Impact factor time trends were assessed using a linear regression model, in which the dependent variable was IF and the independent variable, the year. The slope of the model (index of annual change-IAC) and its statistical significance were taken as the indicator of year-to-year variation.
Results
Shown in Table 1 is a list of all journals included, ranked by their impact factor in 2003. This table also shows the index of annual change (slope of the model) and its statistical significance. Table 2 shows the same list, but ranked this time according to the IAC. This method of ranking can prove useful, since, by comparing the IF trends between two journals with similar IFs, the better choice would be the journal with the better index of annual change.
Table 1 Journals ranked by Impact Factor (IF) in 2003
Title IAC IF(2003) mean p-value
ANNU. REV. PUBL. HEALTH 0.225 5.179 3.158 0.010
CANCER. EPIDEMI. BIOMAR 0.214 4.720 3.475 0.001
AM. J. EPIDEMIOL 0.099 4.486 3.788 0.000
EPIDEMIOLOGY 0.250 4.220 3.093 0.000
AM. J. PUBLIC. HEALTH 0.076 3.363 3.057 0.010
EPIDEMIOL. REV -0.175 3.306 3.203 0.076
INT. J. EPIDEMIOL 0.125 3.289 1.820 0.001
AM. J. PREV. MED 0.232 3.256 1.440 0.000
TOBACC. CONTROL 0.509 3.164 2.052 0.181
MED. CARE 0.105 3.152 2.379 0.004
ENVIRON. HEALTH. PERSP 0.220 3.038 2.192 0.000
DRUG. SAFETY 0.238 2.971 2.059 0.000
CANCER. CAUSE. CONTROL 0.087 2.726 2.623 0.061
B. WORLD. HEALTH. ORGAN 0.115 2.442 1.838 0.001
ANN. EPIDEMIOL 0.141 2.345 1.995 0.001
J. EPIDEMIOL. COMMUN. H 0.075 2.332 1.679 0.003
PSYCHIATR. SERV 0.138 2.274 1.658 0.004
GENET. EPIDEMIOL 0.018 2.265 1.681 0.544
J. CLIN. EPIDEMIOL 0.065 2.227 1.872 0.001
TROP. MED. INT. HEALTH 0.181 2.156 1.477 0.003
TR. ROY. SOC. TROP. MED. H 0.064 2.114 1.553 0.001
AM. J. TROP. MED. HYG 0.019 2.105 1.950 0.058
QUAL. LIFE. RES -0.171 2.000 2.089 0.149
INFECT. CONT. HOSP. EP 0.081 1.951 2.074 0.035
PREV. MED 0.013 1.889 1.540 0.568
J. MED. SCREEN -0.033 1.867 1.815 0.696
OCCUP. ENVIRON. MED 0.100 1.847 1.755 0.013
SCAN. J. WORK. ENV. HEA 0.075 1.816 1.433 0.001
NEUROEPIDEMIOLOGY 0.095 1.762 1.411 0.001
J. ADOLESCENT. HEALTH 0.082 1.674 1.361 0.000
PAEDIATR. PERINAT. EP 0.132 1.673 1.176 0.005
J. WOMEN. HEALTH. GEN. B 0.388 1.561 0.928 0.007
AM. J. IND. MED 0.049 1.542 1.256 0.001
EPIDEMIOL. INFECT 0.023 1.509 1.594 0.199
J. OCCUP. ENVIRON. MED 0.081 1.472 1.349 0.121
QUAL. HEALTH. CARE 0.056 1.466 1.232 0.221
J. AEROSOL. MED 0.056 1.459 0.818 0.006
ENVIRON. RES 0.058 1.452 1.390 0.068
INT. ARCH. OCC. ENV. HEA 0.026 1.388 1.086 0.072
ANN. OCCUP. HYG 0.070 1.357 1.041 0.002
J. URBAN. HEALTH 0.316 1.286 0.723 0.002
EUR. J. PUBLIC. HEALTH 0.002 1.281 1.044 0.983
J. EXPO. ANAL. ENV. EPID 0.124 1.263 1.033 0.001
PALLIATIVE. MED -0.103 1.185 1.627 0.060
PUBLIC. HEALTH. REP 0.025 1.139 1.012 0.192
STAT. MED 0.030 1.134 1.238 0.094
PATIENT. EDUC. COUNS 0.103 1.130 0.774 0.001
COMUNITY. DENT. ORAL 0.069 1.100 0.976 0.002
INT. J. HYG. ENVIR. HEAL 0.302 1.085 0.822 0.142
J. OCCUP. HEALTH -0.040 1.047 1.049 0.445
SCAN. J. PUBLIC. HEALT 0.207 1.018 0.714 0.044
ANN. TROP. MED. PARASIT 0.052 1.010 0.837 0.000
J. PUBLIC. HEALTH. DENT -0.010 1.000 0.787 0.568
J. PUBLIC. HEALTH. MED 0.032 0.973 0.805 0.014
EUR. J. EPIDEMIOL 0.022 0.972 0.676 0.080
AVIAT. SPACE. ENVIR. MD 0.075 0.946 0.681 0.007
FLUORIDE 0.034 0.907 0.560 0.018
ANN. HUM. BIOL 0.026 0.885 0.787 0.001
ARCH. ENVIRON. HEALTH -0.054 0.878 1.391 0.028
J. SCHOOL. HEALTH 0.039 0.868 0.688 0.185
ANN. AGR. ENV. MED 0.216 0.827 0.590 0.065
HEALTH. PHYS 0.012 0.777 0.865 0.385
J. ENVIRON. SCI. HEAL. B -0.034 0.758 0.718 0.131
INT. J. TECHNOL. ASSESS 0.013 0.754 0.922 0.686
SOZ. PREVENTIV. MED 0.170 0.750 0.525 0.013
PUBLIC. HEALTH 0.025 0.697 0.522 0.010
OCCUP. MED. OXFORD 0.041 0.693 0.464 0.010
BIOMED. ENVIRON. SCI -0.036 0.609 0.557 0.596
AIHAJ 0.180 0.601 0.449 0.166
INT. J. ENVIRON. HEAL. R 0.094 0.588 0.419 0.088
ENVIRON. GEOCHEM. HLTH 0.027 0.565 0.369 0.082
INDOOR. BUILT. ENVIRON 0.085 0.525 0.496 0.542
TOXICOL. IND. HEALTH 0.106 0.508 1.051 0.206
REV. EPIDEMIOL. SANTE 0.024 0.485 0.401 0.003
IND. HEALTH 0.015 0.474 0.497 0.262
TROP. DOCT 0.022 0.347 0.326 0.089
J. ENVIRON. HEALTH 0.021 0.341 0.228 0.007
J. PUBLIC. HEALTH. POL -0.023 0.314 0.615 0.675
WILD. ENVIRON. MED -0.019 0.280 0.339 0.822
B. SOC. PATHOL. EXOT -0.092 0.183 0.262 0.154
IAC = index of annual change
Table 2 Journals ranked in descending order, by index of annual change (IAC).
Title IAC IF(2003) mean p-value
TOBACC. CONTROL 0.509 3.164 2.052 0.181
J. WOMEN. HEALTH. GEN. B 0.388 1.561 0.928 0.007
J. URBAN. HEALTH 0.316 1.286 0.723 0.002
INT. J. HYG. ENVIR. HEAL 0.302 1.085 0.822 0.142
EPIDEMIOLOGY 0.250 4.220 3.093 0.000
DRUG. SAFETY 0.238 2.971 2.059 0.000
AM. J. PREV. MED 0.232 3.256 1.440 0.000
ANNU. REV. PUBL. HEALTH 0.225 5.179 3.158 0.010
ENVIRON. HEALTH. PERSP 0.220 3.038 2.192 0.000
ANN. AGR. ENV. MED 0.216 0.827 0.590 0.065
CANCER. EPIDEMI. BIOMAR 0.214 4.720 3.475 0.001
SCAN. J. PUBLIC. HEALT 0.207 1.018 0.714 0.044
TROP. MED. INT. HEALTH 0.181 2.156 1.477 0.003
AIHAJ 0.180 0.601 0.449 0.166
SOZ. PREVENTIV. MED 0.170 0.750 0.525 0.013
ANN. EPIDEMIOL 0.141 2.345 1.995 0.001
PSYCHIATR. SERV 0.138 2.274 1.658 0.004
PAEDIATR. PERINAT. EP 0.132 1.673 1.176 0.005
INT. J. EPIDEMIOL 0.125 3.289 1.820 0.001
J. EXPO. ANAL. ENV. EPID 0.124 1.263 1.033 0.001
B. WORLD. HEALTH. ORGAN 0.115 2.442 1.838 0.001
TOXICOL. IND. HEALTH 0.106 0.508 1.051 0.206
MED. CARE 0.105 3.152 2.379 0.004
PATIENT. EDUC. COUNS 0.103 1.130 0.774 0.001
OCCUP. ENVIRON. MED 0.100 1.847 1.755 0.013
AM. J. EPIDEMIOL 0.099 4.486 3.788 0.000
NEUROEPIDEMIOLOGY 0.095 1.762 1.411 0.001
INT. J. ENVIRON. HEAL. R 0.094 0.588 0.419 0.088
CANCER. CAUSE. CONTROL 0.087 2.726 2.623 0.061
INDOOR. BUILT. ENVIRON 0.085 0.525 0.496 0.542
J. ADOLESCENT. HEALTH 0.082 1.674 1.361 0.000
INFECT. CONT. HOSP. EP 0.081 1.951 2.074 0.035
J. OCCUP. ENVIRON. MED 0.081 1.472 1.349 0.121
AM. J. PUBLIC. HEALTH 0.076 3.363 3.057 0.010
AVIAT. SPACE. ENVIR. MD 0.075 0.946 0.681 0.007
J. EPIDEMIOL. COMMUN. H 0.075 2.332 1.679 0.003
SCAN. J. WORK. ENV. HEA 0.075 1.816 1.433 0.001
ANN. OCCUP. HYG 0.070 1.357 1.041 0.002
COMUNITY. DENT. ORAL 0.069 1.100 0.976 0.002
J. CLIN. EPIDEMIOL 0.065 2.227 1.872 0.001
TR. ROY. SOC. TROP. MED. H 0.064 2.114 1.553 0.001
ENVIRON. RES 0.058 1.452 1.390 0.068
J. AEROSOL. MED 0.056 1.459 0.818 0.006
QUAL. HEALTH. CARE 0.056 1.466 1.232 0.221
ANN. TROP. MED. PARASIT 0.052 1.010 0.837 0.000
AM. J. IND. MED 0.049 1.542 1.256 0.001
OCCUP. MED. OXFORD 0.041 0.693 0.464 0.010
J. SCHOOL. HEALTH 0.039 0.868 0.688 0.185
FLUORIDE 0.034 0.907 0.560 0.018
J. PUBLIC. HEALTH. MED 0.032 0.973 0.805 0.014
STAT. MED 0.030 1.134 1.238 0.094
ENVIRON. GEOCHEM. HLTH 0.027 0.565 0.369 0.082
ANN. HUM. BIOL 0.026 0.885 0.787 0.001
INT. ARCH. OCC. ENV. HEA 0.026 1.388 1.086 0.072
PUBLIC. HEALTH 0.025 0.697 0.522 0.010
PUBLIC. HEALTH. REP 0.025 1.139 1.012 0.192
REV. EPIDEMIOL. SANTE 0.024 0.485 0.401 0.003
EPIDEMIOL. INFECT 0.023 1.509 1.594 0.199
EUR. J. EPIDEMIOL 0.022 0.972 0.676 0.080
TROP. DOCT 0.022 0.347 0.326 0.089
J. ENVIRON. HEALTH 0.021 0.341 0.228 0.007
AM. J. TROP. MED. HYG 0.019 2.105 1.950 0.058
GENET. EPIDEMIOL 0.018 2.265 1.681 0.544
IND. HEALTH 0.015 0.474 0.497 0.262
INT. J. TECHNOL. ASSESS 0.013 0.754 0.922 0.686
PREV. MED 0.013 1.889 1.540 0.568
HEALTH. PHYS 0.012 0.777 0.865 0.385
EUR. J. PUBLIC. HEALTH 0.002 1.281 1.044 0.983
J. PUBLIC. HEALTH. DENT -0.010 1.000 0.787 0.568
WILD. ENVIRON. MED -0.019 0.280 0.339 0.822
J. PUBLIC. HEALTH. POL -0.023 0.314 0.615 0.675
J. MED. SCREEN -0.033 1.867 1.815 0.696
J. ENVIRON. SCI. HEAL. B -0.034 0.758 0.718 0.131
BIOMED. ENVIRON. SCI -0.036 0.609 0.557 0.596
J. OCCUP. HEALTH -0.040 1.047 1.049 0.445
ARCH. ENVIRON. HEALTH -0.054 0.878 1.391 0.028
B. SOC. PATHOL. EXOT -0.092 0.183 0.262 0.154
PALLIATIVE. MED -0.103 1.185 1.627 0.060
QUAL. LIFE. RES -0.171 2.000 2.089 0.149
EPIDEMIOL. REV -0.175 3.306 3.203 0.076
The IF range for the journals covered went from 0.18 to 5.2 in 2003. Although there was no statistical association between annual change and mean IF, most of the fastest growing journals registered mean IFs in excess of 1.5 (Journal of Womens Health and Gender Based Medicine IAC = 0.388 p = 0.007, Journal of Urban Health IAC = 0.316 p = 0.002, Epidemiology IAC = 0.250 p < 0.001, Drug Safety IAC = 0.238 p < 0.001), and some represented emerging areas of public health research (Table 2).
Figure 1 depicts IF trends on a multiple graph, thereby allowing a quick idea to be formed of the evolution of the indicator in all the journals included.
Figure 1 Time trends in the impact factor of Public Health journals by alphabetical order.
If journals that publish review papers are excluded, then the "American Journal of Epidemiology" ranked first in 1991, a position occupied in 1992 by a recently created publication with a fairly specific content matter, the "Cancer Epidemiology Biomarkers & Prevention", in tandem with a journal of similar orientation and seniority, viz., "Cancer Causes and Control". The rise of both these journals may be indicative of current priorities in epidemiologic research [8,9]. This upward trend pattern among journals addressing cancer epidemiology remained in evidence throughout the study period.
In 2003 the epidemiology journals with the highest IF ranged from 1.5 through 4.5, though it should be stressed that special caution is called for when dealing with the individual journal lists for the respective medical specialties.
Discussion
The use of IF as an indicator of a journal's profile or prestige has become widespread among researchers, editors, libraries, and even among the agencies that fund research. Nevertheless, this indicator has a number of limitations that have been extensively debated in the literature [5,10]. Thus, for instance, journals that publish review papers receive a high number of citations and their impact factors are particularly high. It should be pointed out here that the ISI seeks to offer an overview of international science, with the result that journals covering topics or disciplines of more local interest are scarcely covered. Within each category, therefore, it is frequent for journals that are more basic -and thus of universal interest- to be associated with a higher impact factor than those that are more applied -and so of more local interest- given that the latter's circulation is more restricted. The publication of extraordinary relevant scientific findings are concentrated in a small list of core journals, most of them not directly related with the specialty of the author [1,4]. The mere quality of the documents published by a journal, albeit essential, will not suffice for it to be cited. The number of citations can be enhanced using management techniques, such as expanding a journal's international circulation, raising its profile in databases and on web pages, and increasing the number of papers. It becomes necessary for journals to be known among the international community and attain sufficient prestige to be subsequently cited. The policy pursued in Spain in recent years aimed at fostering high-quality, competitive science has induced Spanish scientists to bypass Spanish journals and send their publications instead to journals enjoying a wide international circulation, something that is often associated with journals having the greatest IF.
Our results suggest that it would be of interest to add the index of annual change to the criteria used for selecting a journal for publication. Studies conducted in another JCR area have shown the importance of analyzing IF trends by category [11]. IF trend might, however, be determined by certain factors that should be discussed.
The journals analyzed may be assigned to more than two categories in the SCI-JCR. The Public Health category changed names in 1996, and from 1997 onwards was called "Public, Environmental and Occupational Health", thus explaining why the number of titles jumped from 61 to 73 from one year to the next (Table 3).
Table 3 Trend in the number of journals classified in the Public, Environmental and Occupational Health category.
Year 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
No. of journals 58 60 58 60 61 73 80 84 87 88 90 89
Similarly, until the year 2000, the "Epidemiology" category came within the umbrella of "Public Health", thereafter disappearing and falling within the category of study without any specific entry. The categories covered by the JCR have changed in name and number over the years and, logically, this is equally true of the journals that comprise them.
In addition to the comments regarding the list of journals included in the category of study, it would be a wise decision on the ISI's part if allocation of journals to categories were made in agreement with the researchers [9]. However, it is not the intention of this paper to dwell on the use and abuse of the IF or the arbitrariness of the study-category journal list, an aspect previously analyzed in connection with the public health sphere [10].
This study solely included journals listed for 3 years in the JCR. Recently launched journals with a policy geared to novel publication, such as the BMC group, were not taken into consideration. The "BMC Public Health" journal has been listed for two years, with IFs of 0.29 in 2002 and 0.93 in 2003, and plots a growth pattern similar to that of journals addressing emerging issues.
In general terms and in view of Figure 1, the linear model seems to be adequate, inasmuch as there are very few journals with trends that display evident turning points. Many of the journals maintain their trend over the years. Most of them (60%) add 0.03 points or more to their IFs every year and there are very few that register a decline with time; indeed this is statistically significant in only one instance. A number of categories can be drawn up based on the trend pattern, namely: long-standing journals with the top IF, which maintain their trend; new journals focused on emerging issues, which seem to enjoy good acceptance; journals that maintain a very stable intermediate ranking; and a small group that has witnessed a decrease in their respective IFs.
The reasons why a journal changes its IF trend has been commented before and some of them does not have any relationship with a better quality of its papers. Probably a better or worst management of the journal also is related with the changes in the citations trend and could deserve some study.
In the publication of a scientific paper, a long time elapses between deciding upon a journal and the date of publication: on average, more than one year can go by. In view of the stability of the indicators in the area of study targeted, relying upon IF time trends in order to choose a particular journal might perhaps not be very relevant. Yet it may be a critical aspect in other areas of science where there are increases of around one point per year. For researchers/authors who know only too well how costly it can prove to see their paper published and are, moreover, aware that it is going to be evaluated on the basis of concepts as abstract as the impact and number of their publications, this decision is important.
Conclusion
Leaving aside the speculative components of the choice, and given the delay that accumulates between the publication of IFs and that of any given paper, knowing the trend in the IF is yet another factor that will help authors make the correct choice of journal.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CMT was responsible for the development of intellectual content and the study design, collected the data, data coding and entry, interpretation of the results, manuscript drafting and the critical revisions of manuscript. GLA was responsible for the development of intellectual content, statistical analyses, interpretation of the results and manuscript drafting. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Study supported in part by ISCIII-RCESP C03/09 (Spanish Network for Cooperative Research in Epidemiology and Public Health).
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| 15777471 | PMC555948 | CC BY | 2021-01-04 16:28:55 | no | BMC Public Health. 2005 Mar 18; 5:24 | utf-8 | BMC Public Health | 2,005 | 10.1186/1471-2458-5-24 | oa_comm |
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BMC GenetBMC Genetics1471-2156BioMed Central London 1471-2156-6-171577747010.1186/1471-2156-6-17Research ArticleISSR markers show differentiation among Italian populations of Asparagus acutifolius L Sica Maria [email protected] Graziella [email protected] Stefania [email protected] Luciano [email protected] Serena [email protected] Dipartimento di Genetica, Biologia generale e molecolare, Università degli Studi di Napoli Federico II, via Mezzocannone 8, 80134 Napoli, Italy2005 18 3 2005 6 17 17 23 9 2004 18 3 2005 Copyright © 2005 Sica et al; licensee BioMed Central Ltd.2005Sica 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
Asparagus acutifolius L. is a dioecious and native plant species, widely distributed in the Mediterranean Basin. It is known for its fine flavour and could represent an important resource for cultivation programs in desert areas. Few molecular studies have been performed on this species. In the present paper, the ISSR technique was employed to study genetic diversity in Italian A. acutifolius.
Results
Twenty-three primers produced a total of 228 polymorphic fragments used to evaluate genetic variation. FST (0.4561) and Theta B (0.4776) values indicate a wide genetic variation among the samples examined. The distance UPGMA tree grouped together the genotypes strictly according to their geographical origin, showing that each sample is genetically structured and can be considered a distinct population. AMOVA analysis further confirmed genetic structuring of the populations. Population-specific fragments were also detected.
Conclusion
The results suggest that ISSR markers are useful in distinguishing the populations of A. acutifolius according to geographical origin, and confirm the importance of genetic studies for designing germplasm conservation strategies.
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Background
The availability of a variety of DNA markers, such as restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), simple sequence repeat (SSR) and intersimple sequence repeat (ISSR) has enabled researchers to investigate genetic diversity among various plant species across natural populations [1-5]. Among these, PCR-based techniques of random multilocus analysis (RAPD, AFLP, ISSR) have been successfully used in genotyping, genome mapping and phylogenetic studies in horticultural crops such as strawberry [6], soybean [7], and potato [8].
Local populations of traditional cultivars provide a valuable resource for plant breeding as well as for the preservation of genetic diversity [9]. The exploration, evaluation, and conservation in situ and ex situ of genetic diversity in natural populations is imperative to guarantee sustainable development [10].
Asparagus acutifolius L. (Liliaceae) is a native, perennial plant species widely distributed throughout the Mediterranean areas, whose flowers are classified as dioecious and are mainly bee-pollinated; it generally does not reproduce by self-pollination. It grows in bushy and semi-dry places, sunny or semi-shade, mainly on limestone.
This species is known for its strong taste compared to the cultivated A. officinalis and does not require rich soils for cultivation; for these reasons, it could be an economically important resource for the recovery of arid rural areas where controlled introduced programs could be achieved.
To date, there is little information available on the genetic variability of this species. At present, the most widely studied species is A. officinalis, for which many molecular markers have been characterized (RAPD, RFLP, AFLP) [11]. The few molecular data regarding A. acutifolius are drawn from RAPD analyses [12] and the identification of microsatellite loci [13]. The ISSR technique is similar to that for RAPD, except that ISSR primers consist of a di- or trinucleotide simple sequence repeat with a 5' or 3' anchoring sequence of 1–3 nucleotides. Compared with RAPD primers, the ISSR primers sequence is usually larger, allowing for a higher primer annealing temperature which results in greater band reproducibiliy than RAPD markers [14]. They have been successfully used to assess genetic variation in plants such as citrus [15], Viola pubescens [14], potato [8], and Oryza [16].
In this study we used ISSR markers to analyse the genetic diversity of Italian A. acutifolius collecting samples in eight different scattered rural areas: six continental and one each from the Italian islands of Sardinia and Sicily.
Results
Figure 1 and Table 1 show the eight different Italian sites where A. acutifolius was collected and their characteristics.
Figure 1 Collection sites of A. acutifolius.
Table 1 Populations of A. acutifolius.
Site Abbr Characteristics N
Hab Alt Exp Ped Assn
Caserta Vecchia (Caserta) CAS Clay hill 400 m South, sunny Acidic ground Rubus 15
Colli al Volturno (Isernia) ISE Limestone hill 380 m Semi-shadow Alkaline ground Quercus, Genista 16
Lotrine (Livorno) LIV Hedges 100 m South-west, sunny Alkaline ground Quercus, Rubus 14
Recco (Genova) REC Torrent levee 5–10 m West, shadow Not recorded Rubus 6
Sassari (Sardinia) SAS Coast 10–15 m North, shadow Neutral ground Pinus 18
Spoleto (Perugia) SPO Cultivated fields 600 m South- west, sunny Alkaline ground Quercus, Rubus, Genista 14
Vignacastrisi (Lecce) LEC Dry walls 50–100 m North-west, shadow Neutral ground Quercus, Rubus 11
Vizzini (Catania, Sicily) CAT Ground cover 500 m North, shadow Neutral ground Quercus, Rubus 15
Abbr, abbreviation; N, number of individuals; Hab, habitat; Alt, altitude; Exp, exposure; Ped, pedology; Assn, ecological association.
Among the 42 primers tested, 23 proved useful to characterize the samples (Table 2), whereas 19 were excluded due to absence of amplification (9 primers) or to amplification of the same single fragment in all samples (10 primers). The 23 useful primers gave a total of 228 polymorphic fragments, ranging from 150 to 1100 bp, with 100% repeatability. Fragments of the same molecular weight were considered as the same locus [17]. The validity of ISSRs in assessing genetic variability in the eight samples of Italian A. acutifolius is summarized in Table 3.
Table 2 ISSR primers useful for the amplification of the eight populations of A. acutifolius.
Primer Name Sequence 5'-3' AT (°C)
3 (CA)8AT 50
4 (CA)8AC 51.7
5 (CA)8GT 51.7
8 (CA)8GAC 54.7
15 GGTC(AC)7 56
16 CGTC(AC)7 56
17 CAGC(AC)7 56
21 CAGC(TC)7 56
23 GAG(TC)8 56
8082 (CT)9G 57
8564 (CAC)7T 58
8565 GT(CAC)7 58
BEC (CA)7YC 54
CHR (CA)7YG 54
DAT (GA)7RC 54
HAD CT(CCT)3CRC 54
MAN CA(CCA)3CRC 54
OH (GAG)7RG 66.7
TE GT(GGT)3GRC 54
W7 (CT)8RG 52.8
W814 (CT)8TG 52
W844 (CT)8RC 52.8
W902 (GT)6AY 39
AT, annealing temperature.
Table 3 Genetic variability among the populations of A. acutifolius.
a)
CAS CAT ISE LEC LIV REC SAS SPO
3 0.1554 0.2111 0.1018 0.1811 0.2157 0.0428 0.3172 0.0727
4 0.0599 0.3073 0.1664 0.1685 0.0000 0.0000 0.2753 0.1102
5 0.0581 0.1603 0.0823 0.1032 0.1059 0.1502 0.1037 0.0518
8 0.0698 0.1182 0.0667 0.1209 0.0621 0.0000 0.0563 0.1454
15 0.2086 0.2980 0.1330 0.2445 0.2612 0.1695 0.1734 0.1865
16 0.1661 0.1646 0.2129 0.1870 0.0470 0.1616 0.1820 0.1560
17 0.0846 0.1034 0.2370 0.0938 0.1660 0.2356 0.1616 0.1346
21 0.2490 0.2258 0.1345 0.1251 0.2058 0.1145 0.1185 0.1551
23 0.0711 0.1478 0.2607 0.0930 0.2663 0.0749 0.2746 0.2650
8082 0.1558 0.1163 0.1106 0.0127 0.0672 0.1474 0.1398 0.1395
8564 0.2033 0.0000 0.1453 0.2546 0.1567 0.0145 0.0825 0.1299
8565 0.2316 0.0924 0.1752 0.0783 0.1579 0.0856 0.1337 0.1001
BEC 0.0907 0.0860 0.1107 0.0475 0.1847 0.1471 0.1855 0.1250
CHR 0.2614 0.1785 0.0198 0.1980 0.0156 0.0530 0.0240 0.1161
DAT 0.1202 0.0676 0.1308 0.1706 0.1434 0.1699 0.1472 0.0905
HAD 0.1248 0.2192 0.1576 0.1725 0.1492 0.2171 0.2668 0.0529
MAN 0.2177 0.1119 0.2266 0.2135 0.0584 0.1394 0.0617 0.1455
OH 0.2527 0.2122 0.2099 0.2512 0.2150 0.1953 0.2615 0.1010
TE 0.0233 0.0559 0.0555 0.2077 0.0923 0.0525 0.1646 0.1333
W7 0.1613 0.1383 0.1502 0.1638 0.0738 0.1324 0.1987 0.1577
W814 0.1575 0.3503 0.2574 0.2106 0.0000 0.0000 0.3189 0.2179
W844 0.1063 0.2670 0.0962 0.0000 0.0000 0.2641 0.1253 0.0687
W902 0.2554 0.2413 0.2115 0.3393 0.1764 0.1636 0.1896 0.0895
Mean ± SD 0.1512 ± 0.1887 0.1539 ± 0.1940 0.1489 ± 0.1875 0.1625 ± 0.1964 0.1173 ± 0.1744 0.1180 ± 0.1771 0.1618 ± 0.1903 0.1259 ± 0.1842
P 50.44% 45.61% 46.49% 46.93% 39.91% 35.09% 51.75% 39.91%
Number of specific bands 3 M 9 P 4 P 4 P 2 P 2 P 3 P 1 M 3 P
b)
HT HS DST FST Theta-B
Mean ± SD 0.2618 ± 0.0240 0.2859 ± 0.0155 0.1424 ± 0.0084 0.1619 ± 0.0026 0.1194 0.4561 0.4766 ± 0.0173
a) Gene diversity for each primer set and population and over-all populations, and percentage of polymorphic loci (P) per population; the first column indicates the primer name; the last row indicates the number of population specific fragments (M = monomorphic, P = polymorphic);b) Mean values ± SD of total heterozygosity (HT), intrapopulation heterozygosity (HS) (Left column, POPGENE result; right column, Hickory result), diversity among population (DST), fixation index (FST), Theta-B.
A high level of genetic variation was observed using ISSR markers, with 100% polymorphic loci at the species level. The highest number of polymorphic loci (51.75%) was exhibited in the Sassari and the lowest (35.09%) in the Recco samples.
Genetic structuring was evident due to the detection of specific bands in each sample examined. Spoleto and Caserta samples showed one and three fixed specific fragments, respectively, found to be statistically significant (P < 0.0001). For the other samples, 27 ISSR specific polymorphic fragments were detected, with a varying degree of statistical significance ranging from P < 0.0400 to P < 0.0001.
Genetic distances [18] were examined for all pairwise comparisons between the sub-populations (Table 4). The mean distance for all comparisons was 0.1680, ranging from 0.0916 (between Isernia and Lecce) and 0.2865 (between Recco and Caserta). The Mantel test showed no correlation between the genetic and geographic data (-0.220).
Table 4 Genetic (below diagonal) and geographic (above diagonal) distances among the eight populations of A. acutifolius.
LIV SPO LEC ISE CAT SAS REC CAS
LIV *** 1.89 7.39 3.87 7.84 3.46 1.46 4.30
SPO 0.1354 *** 5.53 2.12 6.53 4.19 3.27 2.65
LEC 0.1687 0.1134 *** 3.58 4.17 8.13 8.80 3.33
ISE 0.1498 0.1257 0.0916 *** 4.62 4.84 5.31 0.60
CAT 0.1180 0.1288 0.1321 0.1326 *** 6.67 9.25 4.02
SAS 0.1265 0.1528 0.1404 0.1255 0.1096 *** 4.12 4.86
REC 0.1388 0.1702 0.1821 0.1759 0.1267 0.1257 *** 5.76
CAS 0.2591 0.2751 0.2591 0.2573 0.2421 0.2549 0.2865 ***
Samples collected at different geographic site grouped together, as shown in the UPGMA tree (Fig. 2), and the AMOVA analysis revealed significant genetic structuring (p = 0.001).
Figure 2 UPGMA tree of the 109 A. acutifolius samples used in the ISSR analysis. The numbers indicate the bootstrap values.
The values of gene diversity are summarized in Table 3a. For some primers, the value was 0.0000, and the highest value (0.3503) was found for the primer W814 in the Catania sample. This explains the high standard deviation values observed.
As summarized in Table 3b, the total variation (HT) was 0.2618 ± 0.0240 and the average variation within samples (average HS) was 0.1424 ± 0.0084. The mean diversity among the samples (DST) was 0.1194. The fixation index FST = (HT-HS)/HT was 0.4561, indicating a reduction of genetic diversity of about 45%. The Theta-B value obtained by Hickory analysis is an estimate of FST under a random-effects model of population sampling. Its mean value is 0.4766 ± 0.0173; the HT and HS values are, respectively, 0.2859 ± 0.0115 and 0.1619 ± 0.0026 showing that there is a general agreement between the results obtained using the two different approaches.
Discussion
ISSR markers can be used in population genetic studies of plant species as they effectively detect very low levels of genetic variation [19]. They also may have potential for analysing biogeographic patterns among populations of a single plant species. In this study, we have shown that these markers revealed genetic variation among geographically separated samples of A. acutifolius in an Italian population. ISSRs also revealed diversity within each sub-population. The results obtained are in accordance with the principle that the number of individuals used to estimate average heterozygosity can be very small if a large number of loci is studied [18].
The gene diversity values (Table 3a) ranged from 0.0525 (TE-Recco) to 0.3503 (W814-Catania). As expected, some primer-sample combinations showed no diversity (0.0) for two reasons: i) the combination primer-sample produced the same amplification pattern in all the samples (primer 4, Livorno and Recco; primer 8, Recco; primer W814, Livorno and Recco); ii) the combination primer-sample produced no measurable fragments (primer 8564, Catania; primer W844, Lecce and Livorno). These primers were not excluded from the analysis because in some cases they produced sample-specific fragments (e.g.: primer 8564 in Caserta and Lecce; W844 in Catania and Recco; primer 8 in Caserta, Catania, Isernia, Sassari, and Spoleto).
The fixation index is 0.4561, which indicates a substantial reduction of genetic diversity (about 45%), probably due to the high genetic isolation of samples analysed. The FST and the Theta-B value (0.4766) demonstrated a very great genetic differentiation among samples, possibly caused by random genetic drift. Further statistical support to the genetic structuring of the samples examined comes from the AMOVA analysis. Despite the continuous distribution of A. acutifolius, the eight samples represent genetically differentiated populations.
In each population it was possible to identify ISSR specific fragments. As shown in Table 3a, the Caserta and Spoleto populations had specific fixed fragments that distinguished them from all others. Although the Isernia specific fragments are not statistically significant (P > 0.05), population-specific fragments were detected for all the other populations, with varying levels of intra-population polymorphism, ranging from 0.147 to 0.764. Thus, we have identified reproducible markers that distinguish the geographical origin of the A. acutifolius populations.
The high degree of genetic differentiation is confirmed by the UPGMA tree topology, in which all accessions from the same population grouped together (Fig. 2). The populations showed genetic distances ranging from 0.0916 and 0.1821 with the exception of the Caserta population that is more distant from the others (from 0.2421 with Catania to 0.2865 with Recco, Table 4). This result is probably due to the high number of specific ISSR fragments found in the Caserta population (12, including monomorphic and polymorphic) and can be attributed to genetic drift. In particular, the high distance between Caserta and Isernia (0.2573) is unexpected because of the short geographical distance separating the regions, and could explain the absence of correlation between genetic and geographical data matrices obtained with the Mantel test. The high genetic structuring of the eight populations shows that despite the continuous distribution of A. acutifolius throughout the Italian peninsula, there is poor gene flow through the isolates. The high genetic differentiation of the A. acutifolius populations examined might be attributed to the kind of pollinators (mainly bees) that can act at short distances, preventing the gene flow, and to the effects of anthropogenic habitat fragmentation.
The results obtained using ISSR markers are in agreement with the RAPD analysis that also identified population-specific fragments in different Italian A. acutifolius populations [12].
Conclusion
Information about the spatial organization of genetic variability is essential for the conservation of genetic resources [20]. Our results provide an important contribution toward confirming that A. acutifolius has well-differentiated populations, despite their morphological low variability. These results show that to maintain genetic diversity within A. acutifolius it is necessary to conserve many populations.
Methods
Plant materials
A total of 109 samples of A. acutifolius collected in the eight different locations in Italy (listed in Table 1 and showed in Fig. 1) were used for the analysis. Although they are only a tiny fraction of the A. acutifolius Mediterranean distribution, they are representative of the Italian population.
ISSR amplification
DNA was extracted from silica gel dried cladodes following the Doyle and Doyle protocol [21].
A total of 42 ISSR primers were tested on the eight populations of A. acutifolius. The polymerase chain reaction was conducted in a 9600 Perkin Elmer Thermal Cycler using the following reaction conditions: 2–5 ng DNA, 1.5 mM MgCl2, 0.2 mM dNTPs, 0.6 μM primer, 1.5 UE Taq polymerase (BIOLINE) and 1X Taq DNA polymerase buffer, in a total volume of 25 μL. The amplification programme was 1.5 min at 94°C; 35 × 40 s at 94°C, 45 s at the primer annealing optimal temperature (see Table 2), 1.5 min at 72°C; 45 s at 94°C, 45 s at the annealing temperature, 5 min at 72°C.
Following PCR, the samples were loaded onto a 1.5% agarose gel in TAE 1X buffer, stained with ethidium bromide. Additionally, 100 bp ladder (Promega) and negative and positive controls were loaded and run at constant voltage (150 V) for 2 hours. After running, the gels were UV visualised and recorded using a Kodak Digital Science dS1D DC40/DC120 Camera. To verify the repeatability of the results, each DNA extraction, PCR amplification, and gel running was repeated twice.
Data analysis
Unequivocally scorable and consistently reproducible amplified DNA fragments were transformed into binary character matrix (1 = presence, 0 = absence).
Genetic variation within and among sub-populations was analysed on the basis of the banding profile using various parameters such as percentage polymorphism (P), total heterozygosity (HT), heterozygosity within population (HS), diversity among populations (DST), fixation index (FST), and genetic distance [18,22-24], using POPGENE software [25]. Since ISSR are dominant markers, data were also analysed using Hickory software [26] based on a Bayesian method that does not require prior decisions about the breeding system and Hardy-Weinberg equilibrium; the analysis was conducted under the f-free model.
AMOVA analysis, implemented in Arlequin [27], was conducted to document the degree of genetic structure among sub-populations.
The Mantel test of genetic and geographic distances was carried out to evaluate the correlation between the two data matrices.
The UPGMA tree was generated using the PAUP*4.0 software [28], and Bootstrap analysis was conducted using 1000 replicates.
Authors' contributions
MS carried out part of the sample collection, designed ISSR primers and carried out ISSR work; GG did part of DNA extraction; SM carried out part of the sample collection and DNA extraction; LG participated in the manuscript preparation and revision; SA conceived the study, carried out data analysis, co-ordination and interpretation of the results.
Acknowledgements
The Authors are grateful to Mrs. Terracciano for technical support, and to Prof. A. Parente and Dr. A. Farina for help in plant field collection. This work was supported by a SCRIGNO grant from the National Research Council, Italy.
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| 15777470 | PMC555949 | CC BY | 2021-01-04 16:38:18 | no | BMC Genet. 2005 Mar 18; 6:17 | utf-8 | BMC Genet | 2,005 | 10.1186/1471-2156-6-17 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-6-101574545310.1186/1471-2121-6-10Research ArticleBacillus subtilis actin-like protein MreB influences the positioning of the replication machinery and requires membrane proteins MreC/D and other actin-like proteins for proper localization Defeu Soufo Hervé Joël [email protected] Peter L [email protected] Biochemie, Fachbereich Chemie, Hans-Meerwein-Straße, Philipps-Universität Marburg, 35032 Marburg, Germany2 Institut für Mikrobiologie, Biologie II, Universität Freiburg, Stefan-Meier-Str. 19, 79104 Freiburg, Germany2005 3 3 2005 6 10 10 29 11 2004 3 3 2005 Copyright © 2005 Soufo and Graumann; licensee BioMed Central Ltd.2005Soufo and Graumann; 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
Bacterial actin-like proteins have been shown to perform essential functions in several aspects of cellular physiology. They affect cell growth, cell shape, chromosome segregation and polar localization of proteins, and localize as helical filaments underneath the cell membrane. Bacillus subtilis MreB and Mbl have been shown to perform dynamic motor like movements within cells, extending along helical tracks in a time scale of few seconds.
Results
In this work, we show that Bacillus subtilis MreB has a dual role, both in the formation of rod cell shape, and in chromosome segregation, however, its function in cell shape is distinct from that of MreC. Additionally, MreB is important for the localization of the replication machinery to the cell centre, which becomes aberrant soon after depletion of MreB. 3D image reconstructions suggest that frequently, MreB filaments consist of several discontinuous helical filaments with varying length. The localization of MreB was abnormal in cells with decondensed chromosomes, as well as during depletion of Mbl, MreBH and of the MreC/MreD proteins, which we show localize to the cell membrane. Thus, proper positioning of MreB filaments depends on and is affected by a variety of factors in the cell.
Conclusion
Our data provide genetic and cytological links between MreB and the membrane, as well as with other actin like proteins, and further supports the connection of MreB with the chromosome. The functional dependence on MreB of the localization of the replication machinery suggests that the replisome is not anchored at the cell centre, but is positioned in a dynamic manner.
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Background
Actin provides vital functions as a cytoskeletal component in eukaryotic and in prokaryotic cells. In eukaryotes, actin filaments give mechanical strength to cells in form of a dynamic cytoskeleton, and are structural fibers in muscle contraction. Additionally, actin proteins have motor like functions [1-3], most notably in cell migration through pushing of membranes. Motility receptors turn on WASP family proteins, which binds to and activate the Arp2/3 complex. The latter induces branching and growth of actin filaments [3]. In vitro, actin filaments can deform vesicles and thus push membranes, providing the force to elongate cellular extensions such as pseudopods [4,5]. Listeria monocytogenes cells move within macrophages through propelling by actin bundles that extend at only one pole of the bacterial cells, due to the ActA protein that is present at one cell pole and that induces rapid polymerisation of actin. Bacterial cells also possess several different actin-like proteins [6]. MreB is essential for cell viability, its depletion leads to a defect in chromosome segregation, and ultimately to the formation of round cells, i.e. to loss of rod cell shape. Except for plasmid encoded ParM protein, which actively partitions plasmids [7], the true mode of action and regulation of bacterial actins is still rather unclear.
During the depletion of Bacillus subtilis MreB or of Mbl, the second actin ortholog, or of Caulobacter crescentus MreB, origin regions on the chromosomes fail to separate properly, leading to a severe (or in case of Mbl more moderate) segregation defect [8,9], likewise to overproduction of a dominant negative mreB allele in E. coli [10]. It is unclear, if actin proteins have a direct role, e.g. as an active segregation motor, or an indirect influence on the segregation of chromosomes. In support of an active role, MreB appears to be associated with the nucleoids, in contrast to Mbl [8], which is thought to be involved in the insertion of new cell wall material into the growing peptide glycan layer [11]. Plasmid encoded E. coli ParR protein binds to a specific cis site on the duplicated plasmids, which are located close to the cell centre, and induces polymerisation of the ParM actin homolog [12]. ParM filaments contain plasmids at their pole ward ends, so two oppositely orientated ParM filaments appear to push plasmids towards each cell pole [7]. On the other hand, C. crescentus MreB has been shown to also affect cell shape and the localization of cell wall synthesizing proteins [13], and to play an important role in determining the global polarity of the cell, i.e. by affecting the localization of proteins to the cell pole [9]. MreB and Mbl form helical filaments just underneath the cell membrane [13-16], which in B. subtilis are highly dynamic. MreB and Mbl move along helical tracks, with a speed of about 0.1 μm/s, providing potential motor like force [17]. Actin polymerises into a two stranded right handed helix through addition of ATP-bound actin monomers. Actin movement arises through growth at the barbed end of the filament, while actin is released from the pointed end following ATP hydrolysis (a process termed treadmilling). Active pushing is thought to occur through binding of actin monomers to the tip of the filament when the object moves away, thus preventing backward movement, such that the object is driven by Brownian diffusion, with the actin filament dictating a single direction (polymerisation rachett) [1].
Bacterial chromosome segregation is a highly organized process, depending on several essential protein complexes. DNA polymerase localizes to the cell centre throughout most of the cell cycle [18]. During replication, the chromosome moves through this stationary replisome, and duplicated regions are rapidly moved towards opposite cell poles [19], where they are bound and organized by the SMC complex [20]. It is still unknown if the localization of the replisome involves an anchor, or which factors are involved in the central positioning.
We have investigated the role of MreB and Mbl in the positioning of the replication factory, and investigated the role of other actin like proteins and membrane proteins for the localization of MreB. We have found that B. subtilis MreC and MreD proteins localize to the cell membrane, and affect the localization of MreB, likewise to Mbl and MreBH, showing that an intricate interplay exists between actin orthologs and MreCD membrane proteins.
Results
MreB and MreC have distinct functions in cellular growth and in control of cell shape
The depletion of MreB has been shown to lead to a strong defect in chromosome segregation, followed by a loss of rod shape, while the depletion of MreC results in a defect in cell shape, but not in a segregation defect [8,14]. Thus, it is clear that the chromosome segregation defect is due to the lack of MreB. However, repression of transcription of mreB may also lead to the depletion of MreC, because the mreC gene lies directly downstream of mreB, so, it was unknown if depletion of MreB also affects cell shape directly, and if so, to which extent, or if the observed defect in cell shape is due to a polar effect on mreC. Since both genes are essential [8,21,22], the lethal defect in cell shape could be caused by the loss of either gene product. To clarify this point, we depleted MreB in the presence of continued synthesis of MreC and MreD (the mreD gene lies downstream of mreC, and loss of MreD also leads to a defect in cell shape [8]). We introduced a second, IPTG inducible copy of mreCD at an ectopic site on the chromosome, which fully complemented the loss of the original mreCD genes. Depletion of mreB during continued synthesis of mreCD from the ectopic site resulted in cessation of growth (Fig. 1, compare first tube with second and fourth), in the formation of round cells (Fig. 2A, upper panels), and in the described defect in chromosome segregation (data not shown). This finding shows that MreB has indeed a dual function, in the formation of rod shaped cells, as well as in chromosome segregation. However, depletion of MreB in the absence of ectopically expressed MreC and MreD led to a considerably different phenotype compared to continued expression of MreCD. In the absence of de novo MreCD synthesis, depletion of MreB resulted in a much more rapid growth arrest (Fig. 1, compare second and fourth tubes with third and fifth), cell growth was abolished after about 4–5 doubling times. Moreover, round cells appeared already 2–3 doubling times after the onset of depletion (Fig. 2A, lower left panel), when cells with continued expression of MreCD still had wild type cell morphology [Fig. 2A, upper left panel, cells look indistinguishable from wild type cells (data not shown)]. Additionally, the cell shape was differentially affected during each condition. MreB depleted cells which continued to express MreCD became highly enlarged (Fig. 2A, upper middle and right panel), but were still mostly rod shaped, whereas MreBCD depleted cells became round and entirely lost rod shape (Fig. 2A, lower middle and right panel). Likewise, the depletion of MreC is followed by the rapid formation of small round cells, and an early arrest in growth [8]. Thus, the depletion of MreC has a much more immediate effect on growth compared with MreB, and MreB affects the formation of rod shaped cells in a manner distinct from MreC.
Figure 1 Synthesis of MreB, MreC and MreD is continued or repressed during the exponential growth phase. Depletion of MreB in the presence of MreC and MreD leads to an arrest in growth, compared to cells with continued synthesis of all the proteins, as indicated above the tubes. Depletion of MreB, C and D results in a more rapid cessation of growth.
Figure 2 Depletion of MreB in the presence of MreC and of MreD affects both cell shape and segregation of chromosomes, and affects localization of the replication factory. Fluorescence microscopy of exponentially growing Bacillus subtilis cells. A) MreB is depleted in the presence (+IPTG, upper panels) or in the absence (-IPTG, lower panels) of MreC and MreD, 2–3, 4–5 and 5–6 doubling times indicate the time after the onset of depletion. B) Localization of DnaX-CFP in wild type cells, or C) 2–3 doubling times after depletion of MreB, or D) 2–3 doubling times after depletion of Mbl. Arrowheads indicate the proper positioning of DnaX-CFP in wild type cells, and its abnormal loacalization during depletion of actin orthologs. E) Localization of GFP-MreB in wild type cells, or F) in smc mutant cells, G) localization of GFP-MreC, overlay of GFP-MreC (green) and DNA stain (red), H) localization of MreD, overlay of GFP-MreD (green) and DNA (red). White lines indicate ends of cells, white bars 2 μm.
Depletion of MreB leads to the loss of mid cell localization of the replication machinery
We wished to further investigate the function of MreB in chromosome partitioning. An important cell biological question is which factors are implicated in the localization of the replication machinery to the cell centre in B. subtilis and in E. coli cells. To investigate a possible role of actin like proteins in this positioning, we depleted MreB or Mbl in cells expressing DnaX-CFP, the tau subunit of the replication DNA polymerase core machinery. In wild type cells showing clear foci (92%), 67% contained a single focus that was positioned close to the cell centre (< 0.2 μm distance, Fig. 2B, and Fig 3A), 7% had a focus >0,2 μm away from the cell centre, and 26% had two foci that were mostly located around at the cell quarter positions (300 cells have been monitored). The latter cells were the larger cells (> 2.7 to 2.8 μm), as has been described before [23]. Of note, 7% of the double-DnaX-CFP foci were present in smaller cells (<2.7 μm), indicating that the replication forks can also move apart, and come back together (under the growth conditions used, a new round of replication can only occur very late in the cell cycle). Contrarily, 3–4 hours after depletion of MreB, when most cells still retained their rod shape (which is lost after 4–5 doubling times, see above), DnaX-CFP foci were placed at irregular positions within the cells. Only 14% of the cells contained single central foci, while 86% of the foci were off centre (that is more than 0.2 μm away from the cell centre), and were present at random places on the nucleoids (Fig. 2C, Fig. 3C). Additionally, 6% of the MreB-depleted cells contained 3 foci, which was observed in only 1% of the wild type cells, 5% contained two foci within one cell half (never found in wild type cells), and in 3% of the cells, foci were even seen close to a cell pole, which was also never found for wild type cells. However, Fig. 3C shows that in spite of the loss of mid cell positioning, DnaX-CFP foci were still mostly absent from the cell poles, which is due to the fact that there is rarely any DNA at these subcellular places (Fig. 2C). Thus, the replication machinery persists for a long time during depletion of MreB, but is located at random sites on the nucleoids, as illustrated in Fig. 3C.
Figure 3 Graphical representation of the position of the replication machinery within wild type or actin-depleted cells. The distance of DnaX-CFP foci to the nearest cell pole was measured and plotted relative to cell size. A) wild type cells, B) cells 2–3 doubling times after depletion of Mbl, C) cells 2–3 doubling times after depletion of MreB. ◇ single focus of focus closest to a pole, □ second focus, Δ third focus.
The depletion of Mbl also had an effect on the positioning of the replication machinery, however, to a much milder extent compared with MreB. 3–4 doubling times after the onset of depletion, 38% of the cells contained a single, mid cell-positioned focus, and 30% two foci in each cell half (roughly at the quarter positions), whereas in 32% of the cells, the DnaX-CFP signals were more than 0.4 μm away from the cell centre (Fig. 2D). Nevertheless, it is apparent from Fig. 3B that although the scatter of DnaX-CFP around the cell centre (and around the quarter positions) is larger in Mbl depleted cells compared with wild type cells, the replication machinery is still largely retained close to the cell centre, contrarily to MreB depleted cells. These results show that directly or indirectly, MreB has a major effect on the positioning of the replisome.
MreB appears to form several discontinuous helices within each cell
To obtain a more detailed view on the nature of the helical MreB filaments, Z sections were taken through the cells, and 3D image reconstruction was performed on the stacks of fluorescent images. Fig. 4 shows representative reconstructions (cells are turned around 180°, as indicated by the grey arrows, such that the MreB filaments can be seen from 15° angle turns around a 180° view), which clearly show that MreB filaments have a helical path underneath the cell membrane. However, the filaments were not continuous; rather, the cells appeared to contain a variable number of distinct, apparently unconnected filaments. The longest filaments were observed to be only little longer than a full turn around the cell diameter, (indicated by arrowheads in Fig. 4A and 4B), while half turn and much shorter filaments were also present within the cells. Thus, MreB appears to be present as a number of unrelated, membrane-associated very short filamentous structures. However, the reconstructions do not rule out that MreB is organized into longer helices with linkers between the short fragments that are difficult to visualize. It is also apparent from Fig. 4A and 4B, that the fluorescence intensity of the filaments is different within a single cell (compare filaments indicated by arrowheads with other filaments in the respective cell), which was highly reproducible. Thus, MreB helices are heterogeneous within cells, and apparently, do not form cytoskeletal fibres extending continuously throughout the cell. These data are in agreement with our finding, that several MreB filaments or bundles of filaments rapidly move along helical tracks [17], and support our findings that these filaments form independent dynamic structures.
Figure 4 3D reconstruction of stacks of Z sections taken through B. subtilis cells expressing MreB-GFP. 180° view of cells (panels are tilted 15° relative to each other as indicated by the grey arrows next to the panels). A) Horizontally turned view of two cells (ends are indicated by white lines, arrow indicates clearly visible helical filament), B) horizontal (upper panel) and vertical (lower panel) view on a single cell (white arrow indicates helical filament, grey arrow half turn filament). The cartoons indicate the rotation, the cartoon on top for the first two panels, the cartoon on the right for the third panel. All images are scaled identically, grey bar 2 μm.
The localization of MreB is affected by the state of the nucleoids
It has been shown that MreB is closely associated with DNA, because no helical filaments are visible in anucleate cells, in contrast to Mbl or MreBH filaments that are found in anucleate cells [17]. However, MreB filaments were present in cells containing nucleoids during depletion of Topo IV, which leads to a block in full separation of the chromosomes. To investigate, if MreB filaments might be affected by the shape of the nucleoids, we moved the GFP-MreB fusion into spo0J mutant cells, which have slightly decondensed DNA, or into smc mutant cells, in which the nucleoids are highly decondensed, and which contain less negatively supercoiled DNA compared to wild type cells [24]. Wild type cells contained different numbers of distinct MreB filaments at cellular positions that also contained DNA, but not close to the cell poles, which are devoid of DNA (Fig. 2E). Contrarily, MreB formed somewhat abnormal long filaments in spo0J mutant cells (data not shown), and highly aberrant elongated filaments throughout smc mutant cells (Fig. 2F), that is the filaments extended right to the cell poles, had fewer gaps than in wild type cells, and the spacing between individual turns was much shorter compared with wild type cells. These findings indicate that the formation of proper MreB filaments is influenced by the state of the chromosomes. In agreement with earlier results, GFP-MreB filaments were not observed in all of the 35 anucleate smc mutant cells monitored (forming about 15% anucleate cells [25]).
Formation of MreB filaments is influenced by MreC and MreD membrane proteins, and by other actin proteins
MreB is upstream of mreC and mreD genes, whose depletion leads to formation of round cells (see above, [8,22,26]). Both gene products are highly hydrophobic, and MreD is predicted to form at least 5 membrane spanning helices (data not shown). N-terminal GFP fusions to both proteins were fully functional, and showed a uniform staining of the cell membrane (Fig. 2G and 2H). Thus, both proteins are associated with the cell membrane. Lee and Stewart have used immuno-gold labelling to show that MreC is predominantly found at the septum between cells [22]. It is clear from Fig. 2G and 2H that MreC and MreD fluorescence is highest at the septum, because two membranes are closely adjacent to each other, which is most likely the explanation for why immuno-gold labels were enriched at this location.
We wished to investigate if formation of helical filaments of MreB depends on the other two actin proteins, or on MreC and MreD, which could provide membrane association of the helical filaments. We moved a gfp-mreB copy to the amylase locus under control of the hyperspank promoter that is induced by IPTG, while mbl, mreBH, mreC or mreD genes were driven by the xylose promoter that is induced by xylose (in fructose medium), and is repressed in glucose medium lacking xylose. GFP-MreB filaments were observed in 85–90% of exponentially growing cells in the presence of IPTG (Fig. 5A). After 1–2 generation times of growth of pxyl-mreC cells in the absence of xylose, 65% of the cells contained GFP-MreB foci, rather than filaments, and only 20% of the cells showed GFP-MreB filaments, while the cell morphology was still normal (Fig. 5B). When cells started to become round and ceased to grow after 3–4 generation times, only 15% of the cells showed MreB filaments, and after more than 6 generation times, when most cells had a cocci like morphology, only 5% contained visible GFP-MreB helices, while most cells contained GFP-MreB foci (Fig. 5C). Depletion of MreD led to a similar albeit much less drastic phenotype (data not shown). Thus, MreC and MreD are required for the formation of proper helical filaments of MreB.
Figure 5 Fluorescence microscopy of Bacillus subtilis cells expressing GFP-MreB from an ectopic site on the chromosome. A) wild type cells (helical filaments), B) 2 or C) 4 doubling times after depletion of MreC (loss of filaments), D) 2 or E) 6 doubling times after depletion of Mbl (abnormal filaments and later loss of filaments), F) 2 or G) 6 doubling times after depletion of MreBH (abnormal filaments). Grey arrows point out extended GFP-MreB filaments, and the white arrow indicates GFP-MreB foci. Grey bars 2 μm.
As opposed to depletion of MreC or MreD, depletion of Mbl or of MreBH leads to a high number of cells, in which MreB filaments extended throughout the entire cell (about 40% of the cells 2 doubling times after depletion of Mbl or of MreBH, indicated by grey arrows, Fig. 5D and 5F), or in which only foci were visible (25%, white arrow, Fig. 5F). In Mbl depleted cells (which are bulgy and twisted [8,14,27]), only highly aberrant and weak GFP-MreB filaments were detectable (Fig. 5E), while the more vibrio-shaped MreBH depleted cells contained highly irregular MreB filaments (Fig. 5G). Thus, formation of proper MreB filaments is affected by Mbl and MreBH. However, even the highly abnormal MreB filaments in Mbl depleted cells are able to support cell viability, albeit at a highly reduced level (mbl deleted cells grow extremely slowly [14,27]).
Discussion
This work provides several important conclusions on the function and localization of the B. subtilis actin ortholog MreB. Our experiments establish that MreB has a dual function, it is vital for the formation of proper rod shape of the cells, and for regular chromosome segregation. However, its function in cell shape is different from that of MreC, or of MreD. The depletion of MreC and MreD leads to rapid cessation of growth and to the formation of small round cells, whereas the sole depletion of MreB results in the formation of large oval shaped cells, and a slower occurring growth arrest. Interestingly, though, we found a connection between MreC and MreB, because during depletion of MreCD, MreB formed fewer and usually abnormally shaped helical filaments. Similar observations have recently been made in E. coli cells [28]. Our experiments show that MreC and MreD localize throughout the B. subtilis membrane, establishing a link between MreB and the membrane. We speculate that MreC and MreD might provide low affinity binding sites for MreB, such that the filaments extend underneath the membrane in a regular helical pattern. Our results also suggest a dual function for MreC, because its deletion affects the localization of MreB (which is apparently not severe enough to strongly interfere with chromosome segregation), as well as cell shape (in a manner distinct from MreB).
An important, if not crucial function of MreB is the positioning of the replication machinery in B. subtilis cells. Soon after the depletion of MreB, the replisome lost its central position in the cell, before a change in cell shape was apparent. The depletion of Mbl had only a minor effect on the localization of the replisome, showing that MreB also affects the positioning of an intracellular protein assembly. Our results do not distinguish between the possibilities that the lack of MreB activity results in the loss of central localization of the replisome, which in turn leads to a segregation defect, or that a more direct defect in chromosome segregation due to the lack of MreB might cause mislocalization of the replisome. However, it is tempting to speculate that MreB could actively push DNA away from the central replisome towards opposite cell poles, and that the net result of this simultaneous pushing of ejected DNA towards opposite directions might lead to a balanced positioning of the replisome towards the cell centre, without any need for an anchor. This is in agreement with recent data showing that the replication machinery is highly mobile around the cell centre [29,30].
An intriguing property of bacterial actin orthologs is the formation of highly dynamic helical filaments underneath the cell membrane that for some members of this protein family are thought to extend through the entire cell length [14,16]. Three dimensional image reconstructions have helped to resolve the nature of the helical MreB filaments in live cells. MreB does not form a closed cytoskeleton like structure, but different forms of filaments within a single cell. These filaments can stretch along a half turn up to a full turn underneath the membrane, but are not clearly connected with each other. This is in agreement with findings showing that several MreB filaments move continuously along helical tracks [31], generating motor-like intracellular movement.
We also provide evidence that the formation of MreB filaments is affected by the nature of the nucleoids, and by the other actin like proteins. In smc mutant cells, MreB filaments are abnormally spaced and extended, and to a much lesser extent in spo0J mutant cells. This further supports and extends our earlier findings that a connection exists between MreB and the nucleoids. Interestingly, smc mutant cells are elongated and frequently twisted and wider than wild type cells [32], which might be due to the effect on MreB. Likewise, the depletion of Mbl or of MreBH interfered with formation of proper MreB filaments, revealing a tight link between the three actin orthologs. It will be interesting to investigate how these proteins localize relative to each other within a single cell, and if they even physically interact with each other.
Conclusion
Our findings show that an intricate interplay exists between MreB, membrane associated MreC and MreD proteins, other actin orthologs, the replication machinery and the nucleoids, shedding light on the question why the depletion of MreB affects both, chromosome segregation and cell shape. What remains to be investigated are several important questions, e.g. what is the mode of interaction between MreB and the MreCD proteins or with Mbl and MreBH, and to identify the link between MreB and the nucleoids or the replisome, to distinguish between the causality of defects caused by the loss of MreB activity. Also, it will be highly revealing to identify the possible load MreB might be pushing, if its dynamic movement indeed constitutes a motor function within the prokaryotic cell.
Methods
Growth conditions
Escherichia coli XL1-Blue (Stratagene) or B. subtilis strains were grown in Luria-Bertani (LB) rich medium supplemented with 50 μg/ml ampicillin or other antibiotics, where appropriate. For induction of the hyperspank promoter, the culture media were supplemented with 0.1 to 1 mM isopropyl-β-D-thiogalactopyranoside (IPTG). For induction of xylose promotor, glucose in S750 medium was exchanged for 0.5% fructose and xylose was added up to 0.5%.
Constructions of plasmids
Gfp mut1 including MCS was amplified from pSG1729 [33] and was cloned into pSG1164 [33] in which the gfp mut1 for C-terminal fusion had been excised using KpnI and SpeI. The resulting plasmid pHJDS1 was used to generate N-terminal GFP fusions at the original gene locus. To obtain inducible N-terminal GFP fusion alleles of mreC or of mreD at the original locus, the 5' prime regions (350 to 500 bp) of the genes were PCR amplified and inserted in the EcoRI and ApaI sites of plasmid pHJDS1 to generate pJS14 or pJS15, respectively. To create a fusion of GFP to the N-terminus of mreB, mreC or mreD at an ectopic site on the chromosome, the entire sequences of theses genes were PCR amplified and inserted into the EcoRI and ApaI sites of plasmid pSG1729 [33] to generate pJS17, pJS19, or pJS20, respectively. To generate an IPTG inducible copy of gfp-mreB (pJS22) or of mreCD (pJS23) at the amylase locus, gfp-mreB was PCR amplified from pJS17 and mreCD from B. subtilis PY79 chromosomal DNA, and the products were cloned as HindIII-SphI or as SalI-SphI fragments, respectively, immediately downstream of the hyperspank promotor in plasmid pDR111 (kind gift of D. Rudner, Harvard Medical School).
Bacterial strains
To express GFP-MreC or GFP-MreD at their original locus in Bacillus, pJS14 or pJS15 plasmids were transformed into wild type B. subtilis (PY79) selecting for chloramphenicol resistance (Cm, 5 μg/ml) to generate strains JS14 (Pxyl-gfp-mreC) or JS15 (Pxyl-gfp-mreD), respectively. For GFP N-terminal fusions at the amy locus, plasmids pJS19 and pJS20 for mreC and mreD were transformed into PY79 selecting for spectinomycin resistance (spec, 25 μg/ml) to generate strains JS19 (Pxyl-gfp-mreC::amy) and JS20 (Pxyl-gfp-mreD::amy), respectively. Strain JS32, in which mreB can be depleted in the presence or absence of mreCD, was created by transforming compentent JS1 cells with chromosomal DNA of JS32. To examine the subcellular localization of GFP-MreB in spo0J or in smc null cells, strain JS19 was transformed with chromosomal DNA from strains AG1468 [34] or PGΔ388 [25], generating strains JS23 (Pxyl-gfp-mreB::amy, ΔspoOJ) and JS24 (Pxy-gfp-mreB::amy, smc::kan) respectively. To be able to visualize the localisation patterns of labelled MreB helices in cells depleted of MreC, MreD, Mbl and MreBH cells, chromosomal DNA from strains JS3 (Pxyl-mreC), JS4 (Pxyl-mreD), JS2 (Pxyl-mbl) and JS5 (Pxyl-mreBH) was used to transform strain JS25 (Phyperspank-gfp-mreB::amy)selecting for Cm and spec, generating strains JS29 (Phyperspank-gfp-mreB::amy, Pxyl-mreC), JS30 (Phypespank-gfp-mreB::amy, Pxyl-mreD), JS28 (Phyperspank-gfp-mreB::amy, Pxyl-mbl), and JS31 (Phyperspank-gfp-mreB::amy, Pxyl-mreBH). To express DnaX-CFP in MreB or Mbl depleted cells, chromosomal DNA from JS1 and JS2 was used to transform PG24 competent cells.
Image acquisition
For microscopic analysis, Bacillus strains were grown in S750 defined medium [35] complemented with 1% casamino acids. Fluorescence microscopy was performed on an Olympus AX70 microscope. Cells were mounted on agarose gel pads containing S750 growth medium on object slides. Images were acquired with a digital CCD camera; signal intensities and cell length were measured using the Metamorph 4.6 program (Universal Imaging Corp., USA). For and 3D reconstruction, 10 to 12 images (spacing between 0.2 to 0.38 μm) were taken through the focal plane, and processed in Metamorph 6 program. DNA was stained with 4',6-diamidino- 2-phenylindole (DAPI; final concentration 0.2 ng/ml) and membranes were stained with FM4-64 (final concentration 1 nM).
Authors' contributions
H J D S performed all experiments, PLG helped with 3D image reconstructions, conceived the study, and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
We would like to thank the reviewers for their helpful comments. This work was supported through a Heisenberg fellowship of the Deutsche Forschungsgemeinschaft.
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Popul Health MetrPopulation Health Metrics1478-7954BioMed Central London 1478-7954-3-31576929810.1186/1478-7954-3-3ReviewMortality registration and surveillance in China: History, current situation and challenges Yang Gonghuan [email protected] Jianping [email protected] Ke Quin [email protected] Jeimin [email protected] Chalapati [email protected] Alan D [email protected] Chinese Academy of Medical Sciences, Peking Union Medical University, 5, Dong Dan San Tiao, Beijing 100005, China2 Center for Health Statistics Information, Ministry of Health, 1, Nanlu, Xizhimenwai, Xicheng District, Beijing 100044, China3 School of Population Health, University of Queensland, Public Health Building, Herston Road, Herston, Qld 4006, Australia2005 16 3 2005 3 3 3 9 12 2004 16 3 2005 Copyright © 2005 Yang et al; licensee BioMed Central Ltd.2005Yang 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
Mortality statistics are key inputs for evidence based health policy at national level. Little is known of the empirical basis for mortality statistics in China, which accounts for roughly one-fifth of the world's population. An adequate description of the evolution of mortality registration in China and its current situation is important to evaluate the usability of the statistics derived from it for international epidemiology and health policy.
Current situation
The Chinese vital registration system currently covers 41 urban and 85 rural centres, accounting for roughly 8 % of the national population. Quality of registration is better in urban than in rural areas, and eastern than in western regions, resulting in significant biases in the overall statistics. The Ministry of Health introduced the Disease Surveillance Point System in 1980, to generate cause specific mortality statistics from a nationally representative sample of sites. Currently, the sample consists of 145 urban and rural sites, covering populations from 30,000 – 70,000, and a total of about 1 % of the national population. Causes of death are derived through a mix of medical certification and 'verbal autopsy' procedures, applied according to standard guidelines in all sites. Periodic evaluations for completeness of registration are conducted, with subsequent corrections for under reporting of deaths.
Conclusion
Results from the DSP have been used to inform health policy at national, regional and global levels. There remains a need to critically validate the information on causes of death, and a detailed validation exercise on these aspects is currently underway. In general, such sample based mortality registration systems hold much promise as models for rapidly improving knowledge about levels and causes of mortality in other low-income populations.
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Introduction
Data on the causes, levels and patterns of mortality are critical to support the development of evidence-based health policy. Cause of death statistics represent the longest historical series of data on the health of populations, in some cases extending back well over 150 years [1]. Yet, complete vital registration systems, which have traditionally generated these data, are often difficult and expensive to establish and maintain in developing countries. China is no exception. With 1.3 billion people, complete registration and medical certification of deaths is logistically and financially unattainable at present. However, mortality registration systems have been established in China that provide useful data on the health status of all Chinese, and how it is changing. In this paper, we review the history of vital registration in China, and describe the establishment and operation of the Chinese Disease Surveillance Points (DSP) system. We focus on vital registration because we consider it as the 'gold standard' for mortality statistics, since it provides population level data on causes of death on an annual basis. There are other sources of mortality data such as the census series and annual surveys of population change [2], large scale retrospective household surveys conducted in the periods 1973–75 and 1986–88 [3], and the National Maternal and Child Health Surveillance System [4]. However, censuses and annual population change surveys do not provide routine information on causes of death, retrospective surveys could be affected by recall bias, and the child mortality surveillance system does not inform about causes of death or adult mortality. As a result, owing to these shortcomings, statistics on causes of death from the DSP have been the principal data source for estimating burden of disease in China [5]. Nonetheless, despite the current utility of the DSP system for generating evidence for health policy, there are several challenges yet to be overcome, as discussed in this paper. The primary aim of this paper is to describe the operation, strengths and weaknesses of mortality registration systems in China. Little is known outside of China about the characteristics of such systems and their potential application to other low-income countries.
History and Function of Vital registration in China
Prior to 1950 vital registration hardly functioned in China, and even then only yielded reports on causes of death for the cities of Beijing and Nanjing[6]. The reported crude death rates ranged from about 18 to 21 per 1000, and the only causes of death reported were tuberculosis, measles, acute infectious disease, 'infant disease', respiratory disease, heart disease, urinary disease, digestive disease, stroke, and ill-defined causes. Cancer was not listed. In 1957, vital registration was expanded to several other large cities, including Shanghai, Tianjin, Harbin, and Wuhan. Thereafter, the vital registration system was extended to include more cities and counties.
In 1976, a nationwide mortality survey was undertaken yielding information on the causes of about 20 million deaths [7]. Data on symptoms experienced by the deceased were collected through retrospective enquiry of all households in China, and based on this information, the cause of death was assigned by a team of physicians. Although this survey was primarily targeted to collect information on cancer mortality, the survey vastly increased the level of appreciation in China about the utility of reliable cause of death data for health planning, and gave impetus to the expansion of vital registration to its current extent. Subsequently, in 1987, the Ministry of Health established a vital registration system to record the fact and cause of death. At present, the vital registration system covers 41 cities (15 large cities and 21 middle/small cities) and 85 counties, among which 25 are 'suburban' counties adjacent to large cities, such as Beijing, Tianjin, and Shanghai. The other counties are located in 12 provinces, mostly in the eastern and central areas of China. Only a few are located in the western regions. The total population covered by this vital registration system in 2000 was about 110 million, half in cities and half living in rural counties. Half of the population covered lives in the eastern region, 40% in the central area, and 10% in the western regions [8].
In brief, the system functions as follows. When a person dies, family members report the death to the registration office of the nearest township hospital in order to get a death certificate, then to the police station to deregister permanent residence and obtain ratification for the burial procedure. Staff in the vital registration office fill in the death certificate based on information from family members, and available medical records or documents. One copy of the death certificate is kept in the registration office, and another is sent to the county Center for Disease Control (CDC), where the cause of death is coded. At the time of inception of the coding system in 1987, a specially designed Chinese Classification of Diseases List consisting of over 500 specific diseases or injuries was used, which could be translated into codes from the Ninth Revision of the ICD [9]. From 1990 onwards, coding was directly based on ICD – 9.
The county CDC then uses a prescribed summary tabulation format to submit monthly reports of deaths by age, sex and cause to the Center of Health Information and Statistics, a Department within the Ministry of Health. This is known as the Ministry of Health – Vital Registration (MOH-VR) system, the annual compilations of which are submitted to the World Health Organization and published by WHO. Since 1996, several cities have started producing local tabulations for internal use.
Quality control measures, including training of staff and development of guidelines and regulations for death registration vary between different areas. Registration in cities is better than in counties, and is better in eastern than western areas. An annual review of data is conducted, and areas that reported implausibly low death rates are excluded from statistical tabulations produced by the MOH-VR system.
Assessment of Chinese Vital Registration Data
A good test to assess the quality of the vital registration data is to examine trends in cause-specific death rates [10]. For example, the reported death rate from cancer fluctuated improbably in rural areas between 1975 and 1989, then remained relatively stable during the 1990 s (Figure 1). This fluctuation belies an expectation of steady change in cancer mortality over time. Factors causing the fluctuations in cancer mortality trends reported in the vital registration data might include an increase in population coverage with poor quality of data from new reporting areas, or a change in proportions of people accessing health facilities. Another possible reason is that data cleaning was arbitrary, without explicit standards for exclusion of data from specific sites.
Figure 1 Trends in reported cancer mortality in urban and rural areas of China, 1973–2000
An additional limitation of the data system is that birth registration was not included. This undoubtedly contributed to the implausibly low death rates reported for infants in both the MOH-VR data and the DSP system (Table 1), since there was no mechanism for linking infant deaths to births. In comparison, the Census and the National Maternal and Child Health Surveillance system reported much higher infant death rates for the same points in time (see Table 1). Hence, although routine death registration systems provide important information on causes of death, they need to be strengthened to provide a more complete picture on the levels and causes of mortality.
Table 1 Unadjusted death rates during infancy (per 1000 population) reported from various data sources in China, 1991 and 2000.
Data source Total population Urban Rural
1991 2000 1991 2000 1991 2000
Census 32.9 32.0 - - - -
NMS - - 16.5 8.0 25.4 15.2
DSP 21.4 13.5 8.2 7.7 24.6 14.5
CMSS 50.2 32.2 - 11.9 - 36.4
Source: [4]
More importantly, the coverage of the MOH-VR system is biased towards the more urban and better-off populations of eastern China. Death rates from infectious disease are lower in the MOH-VR system than those reported by the more representative DSP system, which includes populations in poor rural areas (Figure 2). Similarly, the rates from non-communicable diseases are higher in the MOH-VR system than the DSP system. These observations suggest that the data from the vital registration system are not a true reflection of the mortality profile in China. This concern led to the establishment of the Disease Surveillance Points system as described below
Figure 2 Comparison of age standardized mortality rates* due to broad cause groups, from MOH -VR and DSP systems in China. * Standardized onto WHO World Population [19]
National Diseases Surveillance Points System (DSP)
In order to improve the usability of data from the vital registration system, the Peking Union Medical University/Chinese Academy of Medical Sciences put forward a proposal in 1978 to develop a sample based Disease Surveillance Point (DSP) system. The system was designed primarily to collect data on births, causes of death, and the incidence of infectious diseases. A pilot study was carried out in East Town and Tongxian counties of Beijing in 1978. The Ministry of Health then instructed departments of health at province and county levels to recognize disease surveillance as an important public health task, and set up disease surveillance points under the technical guidance of the Chinese Academy of Preventive Medicine. By 1989, there were 71 DSPs scattered throughout 29 provinces in the country, with a standard working procedure for data collection, management, analysis and dissemination. However, the system was not representative of the national population [11]. In 1990, the Chinese Academy of Preventive Medicine established a nationally representative population sample of 145 points based on random sampling. This revision of the DSP was an activity under the 'Health I plus' project, supported by a loan from the World Bank.
Revised DSP Sampling Plan
Based on the principle that the characteristics of the population under surveillance should be similar to that of the general population in different geographic areas, a multi-stage cluster probability sampling was designed with stratification at three levels.
The first level of stratification was according to 7 geographic regions (Northeast, North, East, South, Southwest, Northwest and Central areas) and 3 municipalities (Beijing, Tianjin and Shanghai) in China. The second level was based on the urban and rural location of primary sampling units. Within rural areas, a third level of stratification was based on a classification of rural sites into four socio economic strata, based on the 1982 Census returns about average levels of variables such as literacy rates, GDP per capita, and dependency ratios. Also, urban areas were re-classified according to population size into big cities, with over 1 million population, middle sized cities with 0.5 -1 million population and small cities with 0.2–0.5 million population.
The primary cluster unit in urban areas was the city, and in rural areas, the county. Probability proportionate to population size sampling (PPS) was used to select a city or county, using 1982 Census data. In the second stage cluster, in selected cities or counties, the unit of sampling was a 'neighborhood' (Jiedao) within cities, or 'townships' (Xiang) in rural areas. Both the 'Jiedao' and the 'Xiang' represent a community with a primary government, with a population ranging from 30 000 – 100 000. PPS sampling was again used for selection of units at the second stage, such that the probability of selection was according to population size of the neighborhood or township[12] The resultant new DSP system consists of 145 points, which are scattered over the 31 provinces or autonomous regions or municipalities of China (Figure 3).
Figure 3 Distribution of sample points in DSP system, China, 2000
A population of about 10 million resides in the areas covered by the system (a little under 1% of the Chinese population). Based on national data on public health indicators used for stratification, the selected DSP sites are representative of the national population[12], and the socioeconomic characteristics of these sites derived from the 2000 Census data are shown in Table 2. As expected, a general gradient can be observed in socioeconomic status across the different rural strata, ranging from 1 (best off) to 4 (worst off).
Table 2 Socio economic characteristics of sites representing different strata in the DSP (Rural 1 best off; Rural 4 worst off)
Socioeconomic characteristic Urban Rural 1 Rural 2 Rural 3 Rural 4
Average GDP* (Million RMB per site) 5098 5108 2602 2054 552
Average literacy rate (%) 91.6 79.5 80.6 78.5 60.5
Average dependency ratio(%) 32.6 44.2 48.4 50.1 57.8
Average Infant mortality rate (per 1000 live births) 9.9 15.8 26.5 42.6 67.8
Source: Chinese Academy of Medical Sciences, 2004, based on data from the 2000 Census
* GDP derived from 1982 Census data on county specific gross agricultural and industrial products. The GDP for each strata was calculated as an average of GDPs for it constituent counties.
Mortality registration in the DSP
Since 1990, the system has covered natality, mortality, and the incidence of 35 notifiable diseases. In this section, we describe the process of mortality registration within the DSP system, and comment on aspects regarding quality control of data, particularly with respect to completeness of reporting, and the use of the data for public policy.
In each DSP site, there is at least one township hospital, and the 'Disease Prevention Unit' in these hospitals is responsible for vital registration. The detailed working procedure for mortality registration is described in the guidelines for surveillance in the DSP[9]. In urban areas, almost half of all deaths occur in health facilities, and there are standard protocols for death registration that are closely adhered to. For deaths occurring at home, the attending physician issues a medical certificate of cause of death, in compliance with the registration protocol. Here, we describe briefly the procedure for death registration in rural sites of the DSP.
In rural areas, about 80% of adult deaths occur at home, with few occurring at the township hospital, or other tertiary hospitals in the vicinity. Even for those deaths that occur at home, there is often clinical evidence available from recent consultations with medical staff at township or other hospitals. The procedure for collection and compilation of cause of death data is as follows:
• For deaths occurring at home, a village health worker reports the event to the Prevention Unit at the township hospital. A staff member from the Unit visits the household, and completes a death certificate based on a description of symptoms from family members, and available documents from recent contact with health services.
• For deaths occurring in the township hospital, the DSP staff collect the death certificate from the hospital, completed by the physician who attended the death.
• For deaths occurring in other hospitals, relatives of the deceased submit physician-certified death certificates to the Prevention Unit at the township hospital.
• In the event of a childhood death, or deaths in women of maternal age, the Maternal and Child Health Unit at the township hospital undertakes the investigation of the cause of death, and screens death certificates for such deaths from other hospitals for accuracy.
• Data cleaning and compilation is done at the county or provincial level, and following computerization, an electronic data-file is transferred to the Chinese Academy of Preventive Medicine.
• ICD coding of the underlying cause of death and subsequent tabulation and publication of results is done at the central level in Beijing. Annual reports of deaths by causes, age and sex have been published in Chinese by the Chinese Academy of Medical Science since 1990, and a public access website for these data is currently under development.
There are instances where the above procedures are not strictly adhered.to. In situations where there is a delay in the household investigation by the Prevention Unit staff, family members or neighbours visit the unit to deregister the residential status of the deceased, and obtain the necessary documentation for corpse disposal and other legal purposes. Such instances can promote improper assignment of cause of death in individual cases, since the respondents in these cases may not be familiar with the disease and related conditions experienced by the deceased.
Data quality control and improvement
Within the DSP system, there are two methods employed for controlling data quality. The first is an internal procedural check system, which evaluates timeliness of death registration, completeness of entries in the registration form, and the accuracy of data entry. Errors detected from these checks are corrected through re- enquiry, and enhance the usability of the datasets.
At a second level, the datasets are evaluated using statistical measures. The completeness and accuracy of population enumeration in the DSP has been evaluated using the standard United Nations Age Sex Accuracy Index [13], and the results of the evaluation in 1999 are shown in Table 3. The index for almost all regions is around 20, suggestive of accurate age-sex data in the DSP population enumeration (see footnote to Table 3).
Table 3 UN Age Sex accuracy Index* for DSP population, by region, 1999
Region UN Index
North China 17.7
Northeast China 15.2
East China 21.9
Central China 19.7
South China 22.9
Northwest China 20.5
South West China 23.0
Source: Chinese Academy of Medical Sciences, 2004
The United Nations Index measures the quality of population data as follows: < 20 = accurate; 20 to 40 = inaccurate; > 40 = highly inaccurate [13]
While, there is no mechanism for evaluating the completeness of death registration in the MOH-VR system, the DSP evaluates completeness of both birth and death registration. This is done through independent resurveys, and statistical techniques based on "capture – mark – recapture" methods are used to estimate the completeness of registration [14]. These surveys are conducted once every three years, on a sample of 5000 households in each province.
Results from three such surveys conducted in 1992, 1995 and 1998 are presented in Table 4, for infant deaths and deaths at all ages separately[15,16]. These data suggest that the coverage of infant deaths remains problematic, and as might be expected, is lower than the coverage of adult deaths. Somewhat surprisingly, the extent of undercount was similar in both urban and rural areas, and has shown no improvement in successive surveys. . Although the overall completeness in 1998 was 86 %, there has been no such survey since then, and there is an urgent need to assess coverage in recent years, to ascertain current levels of completeness.
Table 4 Estimated under registration of deaths (%) in the DSP system during the 1990 s
Region 1992 1995 1998
Infant deaths All ages Infant deaths All ages Infant deaths All ages
Urban - 10.9 25.8 15.1 20.5 13.2
Rural 25.4 13.1 35.6 13.0 21.9 14.9
National 16.0 12.8 34.7 13.5 20.7 14.1
Source: Calculated from 1992, 1995 and 1998 completeness surveys carried out by the Chinese Academy of Preventive Medicine.
Discussion
In this paper, we have described in detail for the first time in English, the Disease Surveillance Points System that operates in China, and provides critical information on the health of one-fifth of the world's population, from a sample of less than 1 % of the Chinese population. This is a remarkable achievement, and perhaps the most cost-effective system of data collection to inform health policies and programs ever devised. Yet, the performance, even the existence of the system is not widely appreciated outside of China, despite its obvious implications for rapidly improving knowledge about causes of death in several other low income populations.
Undoubtedly, complete vital registration of deaths with full medical certification is the most appropriate means to monitor the health of populations, but to establish, and particularly to maintain such a system will be outside the realms of possibility for most developing countries for decades to come. Meanwhile, novel, affordable and sustainable approaches to data collection on mortality that is representative of populations are required. The Chinese DSP system described in this paper has many advantages. Almost all countries conduct censuses at least every ten years, and hence the socio-demographic information on which to select a representative sample of surveillance sites is available. Adequate information can be obtained from relatively small samples (≈1% in China and India), and substantial progress has been made with the development of 'verbal autopsy' instruments and procedures to have sufficient confidence in the utility of cause of death data that they produce, at least for broad causes of death. While this may not be sufficient for specific disease or injury control programmes, field experience in Tanzania suggests that the data are useful for determining the need for priority health programs [17].
The data from the DSP have been used to monitor the emergence of tobacco-caused mortality in China[18], and to assess the global and regional burden of disease [5]. Certainly, from a national perspective, much insight has been gained from these data into the levels and patterns of mortality in China over the past decade or so. However, any system that is not based on complete registration and medical certification is of questionable validity for two reasons. Firstly, any undercount of deaths is likely to bias the overall cause of death patterns, with communicable diseases more likely to be missed in poorer segments of the population. Hence, complete registration or at least an assessment of completeness is absolutely necessary in a system like the DSP. Secondly, 'verbal autopsies' are a blunt instrument and can never be expected to capture the full medical history of the deceased. Data generated by systems such as the DSP in China require periodic validation to calibrate the degree of uncertainty in cause of death statistics and to suggest appropriate adjustment factors for specific causes of death.
The authors are currently undertaking such a validation study based on a sample of 2900 deaths in six cities and 3500 deaths in nineteen rural counties in China. In the urban sites of the study, two arms of the project are being implemented. Firstly, medical records of the sampled deaths are being reviewed to develop a reference 'gold standard' diagnosis of the underlying cause of death, using the international form of medical certificate of cause of death. For the same deaths, the diagnosis from the routine registration system is compared and validated against this reference diagnosis, to assess the validity of the routine system. Secondly, for each of these deaths, a verbal autopsy interview was conducted to derive a cause of death, and this will be compared with the reference diagnosis to establish the validity and operational characteristics of the verbal autopsy procedure to be used in the DSP system.
In rural areas, the same standard procedures for verbal autopsy are being introduced, and diagnoses from these standard procedures will be compared with the diagnoses from the routine registration system to measure the reliability of cause of death ascertainment in rural China.
It is envisaged that the results from these studies, as well a proposed under-reporting survey in 2005, will enable correction of datasets from mortality registration systems in China to improve knowledge of cause specific mortality at the population level. The research will also provide the evidence base to strengthen mortality registration in China by identifying structural weaknesses and areas for development, which will minimize undercount and misclassification of deaths in the future. In addition, this evaluation research will build capacity that will result in long term improvements in data quality from the DSP, given the recent changes in the funding, management and coordination of activities within the system. In particular, opportunities to build on existing networks, such as the family planning services system, will need to be more effectively exploited in future to accelerate the implementation of vital registration nationwide.
Acknowledgements
The authors acknowledge financial support for this project from the US National Institute on Aging (Research Grant no. PO1 AG17625)
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| 15769298 | PMC555951 | CC BY | 2021-01-04 16:37:42 | no | Popul Health Metr. 2005 Mar 16; 3:3 | utf-8 | Popul Health Metr | 2,005 | 10.1186/1478-7954-3-3 | oa_comm |
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Curr Control Trials Cardiovasc MedCurrent Controlled Trials in Cardiovascular Medicine1468-67081468-6694BioMed Central 1468-6708-6-41579041310.1186/1468-6708-6-4ResearchFluvastatin in the therapy of acute coronary syndrome: Rationale and design of a multicenter, randomized, double-blind, placebo-controlled trial (The FACS Trial)[ISRCTN81331696] Ostadal Petr [email protected] David [email protected] Petr [email protected] Jiri [email protected] Martin [email protected] Peter [email protected] Josef [email protected] Milan [email protected] Jiri [email protected] Martin [email protected] Ondrej [email protected] Josef [email protected] Eduard [email protected] Frantisek [email protected] Marek [email protected] Milan [email protected] Jana [email protected] Department of Cardiology, University Hospital Motol and Charles University, 2nd Faculty of Medicine, Prague, Czech Republic2 Department of Internal Medicine, University Hospital Motol and Charles University, 2nd Faculty of Medicine, Prague, Czech Republic3 Department of Cardiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic4 Department of Cardiology, Na Homolce Hospital, Prague, Czech Republic5 Department of Medicine, Hospital Kolin, Kolin, Czech Republic6 1st Department of Medicine, Hospital Na Frantisku, Prague, Czech Republic7 Department of Cardiology, Regional Hospital Liberec, Liberec, Czech Republic8 Internal Medicine & Clinical Pharmacology Department, Faculty Hospital Nitra, Nitra, Slovakia9 Institute of Biology and Medical Genetics, University Hospital Motol and Charles University, 2nd Faculty of Medicine, Prague, Czech Republic10 Department of Clinical Biochemistry and Pathobiochemistry, University Hospital Motol and Charles University, 2nd Faculty of Medicine, Prague, Czech Republic2005 24 3 2005 6 1 4 4 26 1 2005 24 3 2005 Copyright © 2005 Ostadal et al; licensee BioMed Central Ltd.2005Ostadal 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 inflammatory pathways plays an important contributory role in coronary plaque instability and subsequent rupture, which can lead to the development of acute coronary syndrome (ACS). Elevated levels of serum inflammatory markers such as C-reactive protein (CRP) represent independent risk factors for further cardiovascular events. Recent evidence indicates that in addition to lowering cholesterol levels, statins also decrease levels of inflammatory markers. Previous controlled clinical trials reporting the positive effects of statins in participants with ACS were designed for very early secondary prevention. To our knowledge, no controlled trials have evaluated the potential benefits of statin therapy, beginning immediately at the time of hospital admission. A previous pilot study performed by our group focused on early initiation of cerivastatin therapy. We demonstrated a highly significant reduction in levels of inflammatory markers (CRP and interleukin-6). Based on these preliminary findings, we are conducting a clinical trial to evaluate the efficacy of another statin, fluvastatin, as an early intervention in patients with ACS.
Methods
The FACS-trial (Fluvastatin in the therapy of Acute Coronary Syndrome) is a multicenter, randomized, double-blind, placebo-controlled study evaluating the effects of fluvastatin therapy initiated at the time of hospital admission. The study will enroll 1,000 participants admitted to hospital for ACS (both with and without ST elevation). The primary endpoint for the study is the influence of fluvastatin therapy on levels of inflammatory markers (CRP and interleukin-6) and on pregnancy associated plasma protein A (PAPP-A). A combined secondary endpoint is 30-day and one-year occurrence of death, nonfatal myocardial infarction, recurrent symptomatic ischemia, urgent revascularization, and cardiac arrest.
Conclusion
The primary objective of the FACS trial is to demonstrate that statin therapy, when started immediately after hospital admission for ACS, results in reduction of inflammation and improvement of prognosis. This study may contribute to new knowledge regarding therapeutic strategies for patients suffering from ACS and may offer additional clinical indications for the use of statins.
statinfluvastatinacute coronary syndromeC-reactive proteininterleukin 6pregnancy-associated plasma protein A
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Background
During the past decade, inflammation has often been cited as a major factor in the pathogenesis of atherosclerosis and its clinical sequelae, including ischemic heart disease. It was found that traditional risk factors such as hypertension, hypercholesterolemia, diabetes, and smoking could not fully account for the development of coronary stenosis in all patients suffering from ischemic heart disease. Intensive study of the pathogenesis of coronary plaque development and rupture led to the hypothesis that inflammatory factors contribute to this process. For example, T-lymphocytes and monocytes/macrophages have been repeatedly identified in plaque lesions; elevated levels of acute phase proteins (C-reactive protein, serum amyloid A, fibrinogen), cytokines (interleukin 1, interleukin 6, interleukin 8, tumor necrosis factor), and adhesive molecules (ICAM-1) correlate with the worse prognoses in patients with ischemic heart disease [1-5]. Furthermore, the increased level of C-reactive protein (CRP) is now widely recognized as being an independent risk factor for a higher incidence of non-fatal and fatal coronary events in patients with chronic ischemic heart disease and acute coronary syndromes [6-9].
Activation of the immune reaction in acute ischemic heart disease likely derives from: (i) pathological events occurring in the arterial wall where the lesion develops, leading to plaque rupture and the subsequent clinical consequences and (ii) myocardial necrosis, which triggers processes involved in removal of the necrotic mass and replacement with scar tissue. Whereas activation of plaque inflammation (as noted above) serves as a marker for plaque instability, elevation of inflammatory factors from the second source correlates with the extent of myocardial necrosis.
Statins, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, lower cholesterol levels by decreasing the production of low-density lipoproteins (LDL) and up-regulating the expression of the LDL-receptor. These drugs are widely used in patients with hypercholesterolemia for primary and secondary prevention of coronary artery disease because of their efficacy in reducing cardiovascular morbidity and mortality [10-12]. Surprisingly, statin therapy also improves prognosis in patients with normal or low cholesterol levels[13]. Evaluation of the non-lipid effects of statins reveals a possible beneficial effect mediated by the reduction of inflammatory markers, namely, CRP. This effect seems to be independent of cholesterol level [1,14]. The mechanisms by which statins inhibit inflammation are not fully understood. It has been reported that they suppress production of monocyte chemotactic protein-1 (MCP-1) [15], as well as matrix metalloproteinases (MMPs) [5,16,17]. Statins also decrease macrophage expression of soluble ICAM-1 and secretion of IL-1, IL-6, TNF-alpha [5,18-23].
A number of large clinical trials have been designed to investigate the effect of statins in treating acute coronary syndromes (ACS) [24-30] Because patients in these studies were randomized later after being admitted to hospital, often after they had been clinically stabilized, these randomized, double blind trials focused more on early secondary prevention, as opposed to evaluating earlier therapy to target plaque instability in ACS. Furthermore, broad exclusion criteria in some of these trials, including coronary intervention during the index hospitalization visit, deter from the generalizability of their results to the majority of patients treated according to current clinical practice [24]. Nevertheless, these studies have shown promising results, despite the fact that the statins were administered after activation of the immune mechanisms was completed and after the inflammatory reaction was already fully developed.
Little information is available on the efficacy of statins in treating ACS at an earlier phase, i.e., at the time of hospital admission. Recently published data from experimental projects [31-35] and from small clinical trials [36-38]) have shown a positive effect of statins when they are administered in the acute phase of ACS. Our preliminary results with cerivastatin treatment in patients with non-ST segment elevation ACS starting at the time of hospital admission have shown the safety of such a strategy as well as a decrease in inflammatory markers (CRP, IL-6) by 24-hour follow-up, as compared to the non-treated group [39]. Based on these pilot data, we are conducting a clinical trial to evaluate fluvastatin therapy administered to patients with ACS immediately at the time of admission (Figure 1).
Figure 1 Design of the FACS trial in comparison to other trials evaluating statins in ACS patients. In the Pravastatin in Acute Coronary Treatment (PACT) trial statin, therapy was initiated within 24 hours of onset of ACS. In the Myocardial Ischemia Reduction With Aggressive Cholesterol Lowering (MIRACL) trial, patients were randomized 24 to 96 hours after ACS. In the Z-phase of the A-to-Z trial, simvastatin therapy was initiated within 5 days of the onset of ACS, after clinical stabilization. In the Pravastatin or Atorvastatin Evaluation and Infection Therapy (PROVE IT) trial, patients were randomized up to 10 days after ACS. In the Lipid-Coronary Artery Disease (L-CAD) trial, statin therapy was initiated up to ten days following the onset of ACS. In the Fluvastatin on Risk Diminishing After Acute Myocardial Infarction (FLORIDA) trial, patients were randomized up to two weeks after ACS.
Methods
Objectives
The objectives of the FACS trial are to determine:
(i) Whether initiation of fluvastatin therapy in patients with ACS immediately after hospital admission decreases levels of CRP, IL-6, and pregnancy-associated plasma protein A/ proform eosinophilic major basic protein (PAPP-A/proMBP), which represent indirect markers of plaque instability and indicators of poor prognosis; and
(ii) Whether initiation of fluvastatin therapy decreases the occurrence of ischemic events (death, nonfatal myocardial infarction, recurrent symptomatic ischemia, urgent revascularization, cardiac arrest) in patients with ACS.
Overview
This is a prospective, 30-day, multicenter, randomized, double-blind, placebo-controlled study in 1,000 patients with ACS. Patients are enrolled from 10 sites in the Czech Republic and Slovakia. At each institution, the protocol and the informed consent form are reviewed and approved by the institutional ethics committee before study initiation. Eligible patients are randomized to one of two treatment groups immediately after hospital admission (within one hour). One group is assigned 80 mg/day fluvastatin (Lescol XL), with the other group receiving placebo. Participants are followed on an intention-to-treat basis. The primary endpoint relates to levels of CRP, IL-6, and PAPP-A/proMBP. The secondary endpoint is the occurrence of an ischemic event, defined as death, nonfatal myocardial infarction (MI), recurrent symptomatic myocardial ischemia, cardiac arrest with resuscitation, and urgent revascularization.
Study population
This study will enroll high-risk patients admitted to the hospital for ACS. Eligible patients with ST elevation ACS must have resting chest pain less than 12 hours before admission and either ≥ 1 mm ST-segment elevation in 2 or more continuous leads or new left bundle branch block on ECG. Those with non-ST elevation ACS must have resting chest pain during the previous 48 hours and either ≥ 1 mm ST segment depression or negative T waves in 2 or more continuous leads.
Exclusion criteria
Subjects are excluded from study participation if they are <18 years of age or if they have concomitant active liver disease or persistent elevation of transaminases (> 3 times the upper limit of normal), a history of lipid-lowering therapy less than 30 days before the index event or a known allergy to fluvastatin or to any additives present in the drug. Other exclusions include inability to ingest oral medication, unwillingness to be followed for the duration of the study, muscle disease (e.g., myositis), and creatine kinase ≥ 5 times the upper limit of normal due to conditions other than myocardial infarction. Women of childbearing potential who are pregnant, nursing or who are not using effective contraception will also be excluded.
Follow-up
After obtaining informed consent, blood samples are taken from patients for examination of inflammatory markers (CRP, IL-6, and PAPP-A/proMBP). Patients are then randomized to 80 mg fluvastatin (Lescol XL) or to placebo immediately p.o. Medical history and physical examination, standard 12-lead ECG, blood lipid profile, and liver function tests are performed as part of participants' routine admission care. Fluvastatin 80 mg or placebo are then taken once daily for 30 days. Follow-up measurement of inflammatory markers (CRP, IL-6, and PAPP-A/proMBP) is performed on day 2 and day 30. Follow-up visits are scheduled at pre-discharge, day 30, 90, 180 and 360. Blood liver function and creatine kinase tests are done at pre-discharge and at the 30-day visits. At day 30, the lipid profile is also examined, and study medication is withdrawn. All visits include assessment of ischemic events and recent medical history since the last follow-up visit, including use of concomitant medications (Figure 2).
Figure 2 Study design of the FACS Trial. Patients admitted with acute coronary syndrome (ACS) are randomized to either fluvastatin 80 mg or placebo for 30 days. Patients are then followed for one year. Assessments of CRP, IL-6, and PAPP-A/proMBP (LAB) are performed at admission, on day 2, and day 30.
During follow-up, no specific recommendations are made with respect to diagnostic and therapeutic strategy, except that other lipid-lowering drugs should not be given after randomization until day 30. All management decisions are left to the discretion of each patient's treating physician.
Safety
The principal safety concerns are hepatic dysfunction and myopathy. If a patient's serum transaminase levels are persistently elevated to > 3 times the upper limit of normal, the study medication is discontinued. Similarly, study medication is stopped if the patient develops muscle pain, weakness, or tenderness in association with a serum creatine kinase level > 10 times the upper limit of normal.
Sample size
The trial will enroll 1,000 patients, to ensure adequate power to detect significant treatment benefit of 80 mg fluvastatin (Lescol XL) with respect to the primary endpoint (30-day decrease of CRP and IL-6) and the combined secondary endpoint (death, nonfatal myocardial infarction, recurrent symptomatic ischemia, urgent revascularization, cardiac arrest). With 500 patients randomized to 80 mg fluvastatin (Lescol XL) and 500 patients randomized to placebo, the trial will have more than 80 % power to detect a decrease in CRP level by 1.36 μg/L and a decrease in IL-6 level by 1.09 ng/L. Calculations are based on a two-sample t-test. The estimated combined secondary endpoint rate at 30-days is 20 %. Based on comparison of proportions with p = 0.05 test significance, the trial will have more than 80 % power to detect a 33% decrease in the combined secondary endpoint.
Conclusion
The FACS trial is the first multicenter, randomized, double-blind, placebo-controlled trial investigating the effects of fluvastatin therapy started immediately after hospital admission in patients with ACS.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PO has made substantial contributions to the concept and design of the trial and has drafted the manuscript. DA, PH, JVej, JK, MMat, and JVes have made substantial contributions to the concept and design of the study and have been involved in revising the manuscript. PB, MW, OA, JS, EN, FH, MR, and JC have been involved in the acquisition and analysis of data. MMac and MK have given final approval of the version to be published.
Acknowledgements
Laboratory investigation of CRP, IL-6, and PAPP-A/proMBP is supported by a grant from the Czech Ministry of Health, No. 00000064203. Study medication and clinical monitoring were kindly sponsored by Novartis Pharma CR s.r.o.
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| 15790413 | PMC555952 | CC BY | 2021-01-04 16:47:32 | no | Curr Control Trials Cardiovasc Med. 2005 Mar 24; 6(1):4 | utf-8 | Curr Control Trials Cardiovasc Med | 2,005 | 10.1186/1468-6708-6-4 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-6-121575242510.1186/1471-2121-6-12Research ArticlecAMP controls cytosolic Ca2+ levels in Dictyostelium discoideum Lusche Daniel F [email protected] Karen [email protected] Kathrin [email protected] Christina [email protected] Faculty for Biology, University of Konstanz, 78457 Konstanz, Germany2005 7 3 2005 6 12 12 7 12 2004 7 3 2005 Copyright © 2005 Lusche et al; licensee BioMed Central Ltd.2005Lusche 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
Differentiating Dictyostelium discoideum amoebae respond upon cAMP-stimulation with an increase in the cytosolic free Ca2+ concentration ([Ca2+]i) that is composed of liberation of stored Ca2+ and extracellular Ca2+-influx. In this study we investigated whether intracellular cAMP is involved in the control of [Ca2+]i.
Results
We analyzed Ca2+-fluxes in a mutant that is devoid of the main cAMP-phosphodiesterase (PDE) RegA and displays an altered cAMP metabolism. In suspensions of developing cells cAMP-activated influx of extracellular Ca2+ was reduced as compared to wild type. Yet, single cell [Ca2+]i-imaging of regA- amoebae revealed a cAMP-induced [Ca2+]i increase even in the absence of extracellular Ca2+. The cytosolic presence of the cAMP PDE inhibitor 3-isobutyl-1-methylxanthine (IBMX) induced elevated basal [Ca2+]i in both, mutant and wild type cells. Under this condition wild type cells displayed cAMP-activated [Ca2+]i-transients also in nominally Ca2+-free medium. In the mutant strain the amplitude of light scattering oscillations and of accompanying cAMP oscillations were strongly reduced to almost basal levels. In addition, chemotactic performance during challenge with a cAMP-filled glass capillary was altered by EGTA-incubation. Cells were more sensitive to EGTA treatment than wild type: already at 2 mM EGTA only small pseudopods were extended and chemotactic speed was reduced.
Conclusion
We conclude that there is a link between the second messengers cAMP and Ca2+. cAMP-dependent protein kinase (PKA) could provide for this link as a membrane-permeable PKA-activator also increased basal [Ca2+]i of regA- cells. Intracellular cAMP levels control [Ca2+]i by regulating Ca2+-fluxes of stores which in turn affect Ca2+-influx, light scattering oscillations and chemotactic performance.
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Background
Starving Dictyostelium discoideum amoebae form a multicellular organism by chemotactic aggregation. The signaling molecule that mediates aggregation and development is cAMP. Aggregation proceeds in a rhythmic fashion; cAMP is secreted periodically by cells in the center of the aggregate. Cells in the neighbourhood respond by an oriented inward movement and secrete cAMP themselves to relay the signal. In cell suspensions periodic synthesis and release of cAMP leads to rhythmic shape changes that cause alterations in light transmittance and spike-shaped and sinusoidal light scattering oscillations [1]. The marked rhythmic behaviour of the cell population is also apparent by oscillations of other parameters, e.g. extracellular concentrations of Ca2+, K+ or H+ (for review see [2]). Recently, changes in [Ca2+]i were postulated to comprise the (or at least a part of the) master oscillator controlling oscillation patterns [3,4]. A short [Ca2+]i-transient induced by addition of CaCl2 or calmodulin antagonists alters light scattering oscillations and can even reset the oscillation phase [3]. The height of the [Ca2+]i-increase determines whether light scattering and the accompanying cAMP oscillations are abolished or augmented: large [Ca2+]i-transients inhibit cAMP and light scattering oscillations [3] whereas small [Ca2+]i-elevations enhance oscillations of both parameters [4]. From these experiments it was concluded that Ca2+ exerts a dual control over the production of the first messenger cAMP (for a detailed model see [4]). cAMP controls its own synthesis as binding of the agonist to cell surface receptors induces a transient [Ca2+]i-elevation [5-7]. However, until now the question as to whether there is an interaction between cAMP acting intracellularly as second messenger and [Ca2+]i in D. discoideum has not been resolved. In other cell systems such as nerve cells crosstalk between the cAMP and the Ca2+ signaling pathway exists that is necessary to generate oscillations of both parameters [8].
In order to gain insight into a possible connection between intracellular cAMP and [Ca2+]i we used a mutant defective in the phosphodiesterase RegA. RegA is one out of two cAMP-specific phosphodiesterases (for an overview of classes of PDEs in Dictyostelium see [9]) that is inhibited by IBMX and comprises part of an eukaryotic phospho-relay system [10,11]. RegA- mutants are rapid developers; their differentiation is shifted towards the stalk pathway [12,13]. Chemotactic migration is characterized by an increased frequency of lateral pseudopod extension as compared to wild type amoebae [14]. We found that the mutant displayed an altered [Ca2+]i-response pattern upon stimulation with cAMP with an augmentation of Ca2+-release from stores and a concomitant decrease of extracellular Ca2+-entry. Light scattering oscillations and the underlying cAMP oscillations were drastically reduced in regA- cells. Chemotaxis was influenced by the extracellular presence of EGTA. We conclude that indeed, intracellular cAMP signaling and the regulation of [Ca2+]i are linked at the level of Ca2+-storage compartments.
Results
Extracellular and intracellular [Ca2+]-recordings
To test whether the absence of the main cAMP-specific phosphodiesterase affects regulation of [Ca2+]i we analyzed extracellular Ca2+-fluxes in cell suspensions and studied [Ca2+]i in single amoebae. cAMP-induced Ca2+-influx in suspensions of regA- cells occurred with a similar time course as in wild type. Yet, influx was reduced by approximately 40% (Fig. 1). The loss of RegA should lead to an altered cAMP metabolism. Indeed, the basal total amount of cAMP was increased fourfold (13 ± 3 pmol/107 regA- cells; mean ± s.e.m. of 16 determinations in 7 independent experiments vs. 2.8 ± 0.3 pmol/107 wild type cells; mean ± s.e.m. of 11 determinations in 6 independent experiments). Addition of the PDE inhibitor IBMX (up to 200 μM) to wild type cells affected neither the amount nor the characteristics of cAMP-activated extracellular Ca2+-fluxes.
Figure 1 Ca2+-influx after cAMP stimulation is reduced in regA- cells. The amount of influx (pmol/107 cells) after addition of 1 μM cAMP is plotted versus extracellular [Ca2+]. Average influx was higher in wild type than in regA- amoebae (mean ± s.d. from at least 6 determinations in 3 independent experiments each).
IBMX does not inhibit extracellular PDE [15] but affects cAMP hydrolysis intracellularly, so we compared basal [Ca2+]i and cAMP-activated [Ca2+]i-changes of regA- to wild type cells in the absence and intracellular presence of IBMX. The inhibitor should affect the activity of both cAMP phosphodiesterases, RegA and PDE-E [16,17]. Without IBMX, basal [Ca2+]i was similar in both strains (Table 1). However, cAMP-addition induced a [Ca2+]i-transient in regA- cells in nominally Ca2+-free medium (Fig. 2, Table 1). In wild type, cAMP-activated [Ca2+]i-changes were observed after preincubation with 1 mM Ca2+ for 10–15 min only (see also [18]). After loading of IBMX into the cytosol both, basal [Ca2+]i and cAMP-induced [Ca2+]i-changes were altered. Basal [Ca2+]i in the presence and absence of extracellular Ca2+ was significantly increased in regA-; the height of the [Ca2+]i-transient after cAMP-stimulation was comparable to the control situation. In wild type, basal [Ca2+]i was elevated and a [Ca2+]i-change was also observed after cAMP addition in nominally Ca2+-free medium (Fig. 3, Table 1). In summary, increasing cAMP levels augmented cAMP-induced [Ca2+]i-transients at concomitantly reduced levels of Ca2+-influx; the increase in basal intracellular cAMP caused by the absence of RegA was sufficient. Alteration of basal [Ca2+]i required an even higher concentration of cAMP. This was achieved by inhibition of RegA and of PDE-E via loading of IBMX into the cytosol. In wild type where both enzymes are present basal [Ca2+]i was not elevated in the presence of external Ca2+ which indicates that the amount of cAMP had just reached a threshold value and that basal [Ca2+]i is more tightly controlled than agonist activated [Ca2+]i-changes.
Figure 2 Measurement of cAMP activated [Ca2+]i-changes in wild type and mutant amoebae. Cells were stimulated with 1 μM cAMP in the presence or absence of 1 mM external CaCl2. In wild type amoebae a [Ca2+]i-transient was observed in the presence of external Ca2+. The graph shows the average increase (mean ± s.e.m.).
Figure 3 Measurement of cAMP activated [Ca2+]i-transients in wild type and mutant amoebae in the cytosolic presence of IBMX. IBMX led to an elevation of basal [Ca2+]i. Upon stimulation with 1 μM cAMP in the absence of external CaCl2 a [Ca2+]i-transient was observed in both, mutant and wild type amoebae (mean ± s.e.m.).
Table 1 Basal [Ca2+]i and the increase over basal [Ca2+]i after cAMP-addition in wild type and regA- cells in the absence and presence of IBMX. 1 μM cAMP was added to wild type at t7–t8 and to regA- at t4 because the mutant develops more rapidly. [Ca2+]i was determined by ratiometric imaging in single cells either in nominally Ca2+-free buffer (- Ca2+) or in buffer containing 1 mM Ca2+. Values are mean ± s.e.m. and numbers in brackets indicate the numbers of cells tested in at least 3 determinations in at least 2 independent experiments each.
Strain Condition Basal [Ca2+]i cAMP-induced [Ca2+]i-change
- IBMX + IBMX - IBMX + IBMX
regA-
- Ca2+ 55 ± 1 (131) 97 ± 1 (111) 71 ± 8 (30) 81 ± 9 (25)
+ 1 mM Ca2+ 54 ± 1 (85) 96 ± 2 (66) 79 ± 6 (52) 85 ± 8 (35)
Wild type
- Ca2+ 53 ± 1 (94) 98 ± 1 (148) no increase 132 ± 9 (58)
+ 1 mM Ca2+ 50 ± 1 (185) 53 ± 1 (127) 125 ± 6 (83) 155 ± 10 (55)
The effect of the increased basal cAMP concentration on the [Ca2+]i-regulation in regA- amoebae might be caused by a change in the characteristics of Ca2+-fluxes of internal stores. A positive influence of cAMP via PKA-mediated phosphorylation of both, IP3-receptors and ryanodine receptors on release of stored Ca2+ has been reported (for review see [19]). We therefore tested the response of regA- amoebae upon stimulation with cAMP in the presence of the chelator BAPTA. We found that even after the addition of 1 mM BAPTA cAMP activated a transient increase in [Ca2+]i (Fig. 4). The elevation was smaller than that observed in nominally Ca2+-free medium and amounted to an average of 44 ± 3 nM above basal [Ca2+]i (mean ± s.e.m. of 18 determinations in 2 independent experiments). In wild type amoebae a cAMP-stimulated [Ca2+]i-increase is not detectable in the presence of BAPTA; the occurrence of a transient [Ca2+]i-elevation in regA- cells indicates an augmented release of Ca2+ from stores in the mutant. Support for an effect of cAMP via PKA came from experiments where we incubated cells with the membrane permeant activator of PKA, Sp-5,6-DCl-cBIMPS [20,21]. Basal [Ca2+]i was increased in regA- cells upon treatment with 30 μM Sp-5,6-DCl-cBIMPS for 60 min (139 ± 2 nM; mean ± s.e.m. of 15 determinations in 3 independent experiments); agonist-induced [Ca2+]i-transients in nominally free Ca2+-buffer were unaltered in height (87 ± 8 nM; mean ± s.e.m.) as compared to control cells. In addition, we found that preincubation of wild type amoebae with 30 μM Sp-5,6-DCl-cBIMPS reduced cAMP-activated Ca2+-influx in cell suspensions by 26 ± 8% (mean ± s.e.m. of 3 independent experiments).
Figure 4 [Ca2+]i-changes in regA- cells in the presence of a Ca2+-chelator. Amoebae in nominally Ca2+-free medium were challenged with 1 mM BAPTA (final concentration) and subsequently with 1 μM cAMP. Arrows indicate the time point of addition of agents. The graph shows the average increase (mean ± s.e.m.).
Light scattering and extracellular Ca2+ oscillations depend on internal cAMP levels
We had shown previously that artificial changes of [Ca2+]i, either by affecting Ca2+-stores or by activating Ca2+-influx alter light scattering oscillations [3,4]. When light scattering was analyzed in regA- suspensions two types of responses were observed. On one hand, regular oscillations with a phase length of 4.3 ± 1 min (mean ± s.d. of 61 determinations in 6 independent experiments) occurred (Fig. 5A). The amplitude of these oscillations was reduced as compared to wild type (Fig. 5B), i.e. by 78%. On the other hand, irregular light scattering changes were detected (Fig. 5C). Determination of cAMP levels revealed that cAMP scarcely oscillated in regA- (Fig. 5D) and increased on average by a factor of 2.9 ± 0.6 (mean ± s.e.m. of 5 independent experiments). The response upon addition of cAMP was also different: after an increased first light scattering peak and the occurrence of a second peak light scattering did not return to the baseline as in wild type suspensions but fell well below (Fig. 6). The alteration in light scattering responses in the mutant might be due to a shift in sensitivity to cAMP. As a control we tested the reaction upon stimulation with cAMP and found that regA- cells reacted when 3 nM cAMP was added (not shown) which indicates that the mutant strain is practically as sensitive as wild type. Measurement of [Ca2+]e in regA- cell suspensions revealed irregular [Ca2+]e oscillations, similar to the results obtained for light scattering (Fig. 7).
Figure 5 Light scattering and [Ca2+]e oscillations of regA- cells. Light scattering and [Ca2+]e were recorded as outlined in Methods. (a, b) Regular light scattering oscillations with a phase length of roughly 4–5 min but with strongly reduced amplitude as compared to wild type oscillations (see also [3]). (c) Irregular light scattering changes. (d) Oscillations of cAMP levels in the regA- strain were less pronounced than in the wild type; the graph shows examples of one cAMP oscillation each, determined during one spike of light scattering oscillations.
Figure 6 Light scattering response upon addition of 1 μM cAMP. (a) Wild type cells displayed two peaks of light scattering which subsequently returned to the baseline. (b) In regA- cells there was a strong decrease in light scattering after the second peak. One out of 7 independent experiments is shown.
Figure 7 [Ca2+]e oscillations in wild type and regA- cell suspensions. (a) Regular [Ca2+]e oscillations were recorded in wild type cell suspensions (see also [2]). (b) Similar to light scattering oscillations the pattern of [Ca2+]e oscillations in regA- was irregular. One out of 5 independent experiments is shown.
Chemotaxis of regA- amoebae
It had been reported previously that regA- cells have a reduced capacity to suppress lateral pseudopod formation [14]. In accordance with the data presented by Wessels et al. [14] we also observed augmented lateral pseudopod extension upon challenge of aggregation competent amoebae with a cAMP filled glass capillary (not shown). The reduction in chemotactic polarization was reflected by a decrease in the average chemotactic speed as compared to wild type amoebae (Fig. 8). Pretreatment with EGTA to empty Ca2+-storage compartments dose-dependently inhibited chemotaxis of regA- and wild type. The EGTA-incubated cells were rounded and extended only small pseudopods towards the capillary tip (not shown); in both strains chemotactic velocity was reduced. The effect was more pronounced in regA-: already in the presence of 2 mM EGTA cells chemotaxed more slowly than under control conditions (velocity of EGTA-treated amoebae was significantly lower at all concentrations of EGTA tested (P < 0.001) as compared to control cells; Mann Whitney rank sum test). Wild type cells were unaffected by preincubation with 5 mM EGTA for up to 1 hour whereas at 10 mM EGTA chemotaxis was reduced.
Figure 8 Chemotactic speed of wild type and regA- amoebae. The effect of preincubation with EGTA for 30 min was assayed. Chemotactic velocity of amoebae was affected dose dependently by EGTA treatment; when compared to the wild type the speed of the regA- strain was significantly reduced at lower concentrations of EGTA. Velocity of wild type and regA- cells is shown (median of at least 2 independent experiments).
Discussion
The cytosolic concentration of Ca2+ was demonstrated to control light scattering oscillations by affecting the synthesis of cAMP; depending on the height of an artificial [Ca2+]i-transient the production of cAMP which in this case serves as first messenger was either augmented or blocked [3,4]. The results presented in this study provide evidence for a reciprocal influence of the second messengers cAMP and Ca2+ in Dictyostelium cells. We observed altered agonist-induced Ca2+-fluxes and [Ca2+]i-transients in the regA- mutant cell line where the absence of the main cAMP-hydrolyzing PDE led to a fourfold increased basal cAMP level. One could argue that the effect on [Ca2+]i was not a consequence of the increased basal concentration of cAMP but rather due to a potentially altered pattern of gene expression in the mutant strain. Indeed, this is possible and could result in a different signal perception and/or processing. However, we consider an alteration in gene expression unlikely to be responsible for the augmented [Ca2+]i-transients upon cAMP-stimulation since the same effect could be evoked in wild type amoebae by loading of the PDE inhibitor IBMX into the cytosol. In addition, IBMX evoked an increase in basal [Ca2+]i in both, wild type and mutant cells. In regA- the inhibitor should act on the additional cAMP-PDE (PDE-E) [16,17] and therefore increase cAMP levels even further. In wild type amoebae hydrolysis of cAMP should be retarded as well. Yet, the threshold of the cAMP concentration required to increase basal [Ca2+]i might not be achieved as consistently as in the mutant since IBMX must act on both PDEs.
The sensitizing effect of the increased amount of cAMP on [Ca2+]i could be caused by several factors. Ca2+-flux characteristics can be changed by influencing Ca2+-channels and/or Ca2+-ATPases located on both, the plasma membrane and membranes of internal stores. When we analyzed Ca2+-fluxes with a Ca2+-sensitive electrode influx was reduced in the mutant while the rates of influx and efflux were unchanged. If the activity of the plasma membrane Ca2+-ATPase (PMCA) was altered then flux rates should be affected. Moreover, the reduced amount of Ca2+-influx precludes activation of a plasma membrane Ca2+-channel. In other cell systems activation of the PMCA and of Ca2+-channels by an increase in cAMP levels was shown [22-24] but our data argue against a stimulating effect on plasma membrane Ca2+-channel or PMCA activity in Dictyostelium amoebae.
The second target of action of cAMP are intracellular stores. Indeed, we showed for the first time that in Dictyostelium a cAMP-activated [Ca2+]i-elevation occurred in the extracellular presence of the Ca2+-chelator BAPTA. This argues for an alteration of Ca2+-uptake into and/or Ca2+-release from stores. An as yet unknown negative regulation of Ca2+-sequestration could cause accumulation of Ca2+ in the cytosol; until now, however, activation of SERCA-type Ca2+-ATPases was found only (for review see [19]). On the other hand, release of Ca2+ could have been augmented by the high basal cAMP level in the mutant. cAMP-dependent phosphorylation of the IP3-receptor by PKA results in increased sensitivity for IP3 in pancreatic acinar cells [25]; the same holds true for the ryanodine receptor [19]. Stimulation of PKA activity is plausible since pretreatment with the PKA-activator Sp-5,6-DCl-cBIMPS elevated basal [Ca2+]i and reduced agonist-evoked Ca2+-entry. Membrane permeable Sp-5,6-DCl-cBIMPS was shown to be virtually ineffective in inducing gene expression and to be highly selective for PKA vs cAMP receptor activation at the concentration employed [21]. In summary, we propose the following model: in the mutant sensitivity of the Ca2+-release system is enhanced by an augmented PKA-mediated phosphorylation which is due to increased basal cAMP levels. This results in larger amounts of Ca2+ being liberated upon stimulation. In Dictyostelium release of Ca2+ from stores was also found after addition of calmidazolium [26] which was shown to inhibit calmodulin-dependent and independent activity of calcineurin [27]. Calcineurin in turn was proposed to be responsible for termination of Ca2+-release by dephosphorylating the IP3-receptor [28]. In regA- augmented release of Ca2+ leads to a reduction of Ca2+-entry across the plasma membrane as a negative feedback.
We suggest the alteration in [Ca2+]i to be responsible for the irregular light scattering and extracellular [Ca2+]-oscillations of regA- cells. Previously, Wessels et al. [14] have shown that the mutant cannot propagate a cAMP wave since wild type amoebae no longer aggregated correctly when mixed with mutant cells. Indeed, we found that peak cAMP levels during light scattering oscillations were very low in regA- as compared to wild type. This effect is plausible, as the increased sensitivity of the Ca2+ second messenger system exerts a negative feedback on cAMP synthesis: large [Ca2+]i-transients inhibit production of cAMP [3]. An interplay of cAMP and [Ca2+]i-oscillations and their mutual dependence has also been shown in neurons: absence of either, cAMP or [Ca2+]i-oscillations resulted in failure of the other component to oscillate [8]. In Dictyostelium the strong decrease in peak cAMP oscillation levels affected [Ca2+]e-oscillations which were irregular. The basis is probably an influence on [Ca2+]i-oscillations. Such oscillations were suggested to occur but have not been demonstrated in single cells until now, presumably due to the small size of the amoebae and the characteristics of the wave [29].
With respect to chemotaxis, reduced suppression of lateral pseudopod formation was shown in regA- cells and an essential role of RegA for a correct response in a natural cAMP wave and chemotactic migration was assigned [14]; subsequently, a similar result was found in a mutant expressing a constitutively active PKA [30]. When we analyzed chemotaxis towards a cAMP-filled glass capillary we observed the same behaviour as described by Wessels et al. [14]. In principle, it is possible that the reduced capacity of regA- cells to polarize was due to a difference in the developmental stage as compared to wild type cells. However, regA- develops much faster than wild type which suggests an even more efficient chemotaxis as this response increases during differentiation to aggregation competence. Alternatively, an altered or dampened signaling response caused by a lower number of cAMP receptors present on the cell surface could have caused the reduced chemotactic response. We consider this to be unlikely for the following reason. Aggregation-competent Dictyostelium amoebae possess roughly 50.000 cAMP receptors at the cell surface [31]. Yet, for chemotactic orientation and polarization in a cAMP gradient the difference in receptor occupancy between the front and the rear end of the amoebae is important rather than the absolute number of stimulated receptors [31]. So even if regA- expressed less receptors than wild type this should not influence the accuracy of the response. We propose the reduced polarization capacity of regA- amoebae to be caused by their altered [Ca2+]i-regulation. In the mutant strain the threshold for generation of an agonist-induced [Ca2+]i-increase is lower than in wild type. The [Ca2+]i-elevation is not as tightly controlled and occurs even in the presence of BAPTA. The characteristics of a [Ca2+]i-increase are important for the resulting cytoskeletal rearrangements and whether pseudopods are formed correctly. Indeed, artificial induction of a small global [Ca2+]i-transient by incubation with calmidazolium caused overall pseudopod protrusion [26]. In migrating cells the establishment of a [Ca2+]i-gradient at the rear end was shown [5,32] which indicates the presence of a highly organized spatial [Ca2+]i-pattern during chemotaxis. By contrast, a role of the [Ca2+]i-elevation for the chemotactic response was questioned by Traynor et al. [33] because a mutant disrupted in a gene bearing similarity to IP3-receptors of higher eukaryotes aggregated and differentiated almost normally but displayed no cAMP-activated global [Ca2+]i-change; yet, the existence of localized, small [Ca2+]i-transients in this particular mutant cell line that had escaped detection could not be excluded [33].
When we analyzed the influence of pretreatment with EGTA on chemotactic behaviour of wild type and regA- cells we found that the mutant was more sensitive. When compared to wild type, lower doses of EGTA were sufficient to reduce chemotactic speed. The effect of EGTA treatment is most probably due to emptying of the storage compartments [34]; the presence or absence of extracellular Ca2+ affects the Ca2+-content of stores [35,36]. RegA- cells are more sensitive than wild type amoebae because of the lower threshold for Ca2+ release and thus a more rapid depletion of Ca2+ in the cells.
Conclusion
Abnormal basal levels of cAMP impair chemotactic performance by augmenting agonist-activated [Ca2+]i-elevations which in turn lead to uncontrolled pseudopod extension. [Ca2+]i regulates cAMP acting as first messenger in a negative feedback loop: when the [Ca2+]i response is increased the amount of cAMP synthesized upon stimulation is low as observed in regA- cells devoid of the phosphodiesterase RegA. The low level of cAMP relay results in improper light scattering oscillations. We conclude that intracellular cAMP acts on [Ca2+]i via PKA: phosphorylation of the system responsible for release of Ca2+ from stores leads to a greater sensitivity facilitating Ca2+ liberation. The cAMP activated [Ca2+]i-increase is due to Ca2+-release from internal stores which triggers subsequent extracellular Ca2+-entry. The fraction of the [Ca2+]i-elevation that is mediated by liberation of Ca2+ is thus larger in the mutant.
Methods
Materials
Fura2-dextran and BAPTA were from MoBiTec (Göttingen, FRG). IBMX was purchased from Sigma (Munich, FRG) and cAMP was from Boehringer (Mannheim, FRG). Sp-5,6-DCl-cBIMPS was from Biomol (Hamburg, FRG).
Cell culture
D. discoideum axenic wild type Ax2 was grown as described [4]; the mutant regA- (kindly provided by Dr. P. Thomason) was grown in the presence of blasticidinS. Cells were washed by repeated centrifugation and resuspension of the cell pellet in cold Sørensen phosphate buffer (17 mM Na+/K+-phosphate, pH 6.0; SP-buffer). Amoebae were shaken at 2 × 107 cells/ml, 150 rpm and 23°C until use. The time, in hours, after induction of development is designated tx.
Recording of light scattering
At t2.5–t4 2 ml of cell suspension was pipetted into cuvettes and aerated. Light scattering oscillations were recorded at 500 nm with a photometer as described [4].
Determination of cAMP
The total amount of cAMP was determined using the cAMP enzyme immuno assay (Biotrak, Amersham Pharmacia Biotech, Freiburg, FRG) according to the manufacturer's instructions. Samples were prepared as outlined previously [4].
Extracellular [Ca2+]-measurements
The extracellular Ca2+-concentration ([Ca2+]e) was measured in 2 ml of cell suspension (5 × 107 cells/ml in 5 mM Tricine, 5 mM KCl, pH 7.0) with a Ca2+-sensitive electrode (Möller, Zürich, Switzerland) as described [18]. [Ca2+]e-oscillations were measured at a cell density of 1 × 108 cells/ml.
Single cell [Ca2+]i-imaging
Cytosolic [Ca2+]-imaging was done as outlined in [6]. Cells (5 × 107 cells/ml; 20 μl) were loaded at t3 with the Ca2+-indicator fura2-dextran (concentration in the loading solution: 5 mg/ml SP-buffer + 1 mM CaCl2) by electroporation (0°C, 850 V, 3 μF, 200 Ω). Immediately after electroporation, 80 μl of cold 5 mM MgCl2 was added and cells were incubated for 10 min on ice. Then cells were washed 3× with 5 mM Hepes, pH 7.0 (H5-buffer). Washed cells (2–5 μl) were placed on glass coverslips and incubated in a humid chamber until use. When experiments were done in nominally Ca2+-free medium, 85–88 μl of H5-buffer was added 1 min before the [Ca2+]-imaging experiment. To test the response of amoebae in the presence of BAPTA, 75–78 μl of H5-buffer was pipetted to the cells; 10 μl of 10 mM BAPTA was added during the [Ca2+]-imaging experiment and 10–12 sec later cAMP was given. When the response of cells was to be analyzed in the presence of extracellular CaCl2, H5-buffer (85–88 μl) with 1 mM CaCl2 was added to the cells 15 min before the [Ca2+]-imaging experiment to load stores (see also [18]). cAMP-stimulation was done by adding 10 μl of 10 μM cAMP (± 1 mM CaCl2) to the cells. To load cells with IBMX, they were electroporated with fura2-dextran in the presence of 250 μM of the inhibitor. The cytosolic concentration of IBMX is in the range of maximally 2–5% of the concentration present during electroporation [6]. Measurement of regA- was done at t4 and wild type [Ca2+]-imaging was done at t7–8. In another series of experiments we treated regA- cells with Sp-5,6-DCl-cBIMPS, a membrane permeant activator of PKA [20]. Incubation was done with 37 μM of the activator for 60 min prior to the [Ca2+]-imaging experiment.
Chemotaxis of regA- cells
Chemotactic performance of the amoebae depends on the degree of differentiation, so their shape was checked prior to the chemotaxis assay. 200 μl of cells at 2 × 107 cells/ml were placed on a coverslip and allowed to settle for at least 30 min. The morphology of the cells was controlled microscopically: when elongated and thus aggregation competent cells were present, an aliquot of cells from the suspension was diluted for the chemotaxis assay. RegA- was tested at t4–t5, wild type was measured at t7–t10. 250 μl of cells in 5 mM Hepes, pH 7.0 (1 × 105 cells/ml) were placed on glass coverslips. After 30 min cells were challenged with a cAMP (100 μM) filled glass capillary and chemotaxis was recorded for 40–45 min either on vidoe tape or images were stored directly on a hard disk. In addition, experiments were done with cells incubated with 2–10 mM EGTA for 30 min to empty Ca2+-storage compartments. Analysis of chemotaxis was done as outlined previously [34].
List of abbreviations
Cytosolic free Ca2+ concentration: [Ca2+]i
Phosphodiesterase: PDE
3-isobutyl-1-methylxanthine: IBMX
cAMP-dependent protein kinase: PKA
Plasma membrane Ca2+-ATPase: PMCA
Authors' contributions
DFL performed extracellular [Ca2+] recordings and light scattering experiments. He also determined cAMP levels and designed the study. KBR did chemotaxis experiments at different external conditions. KH carried out [Ca2+]i-measurements. CS did [Ca2+]i-imaging experiments, designed the study and wrote the manuscript. All authors read and approved the manuscript.
Acknowledgements
The authors wish to thank Dieter Malchow for many helpful discussions and critical reading of the manuscript. This work was supported by the Deutsche Forschungsgemeinschaft and the FAZIT foundation.
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| 15752425 | PMC555953 | CC BY | 2021-01-04 16:39:11 | no | BMC Cell Biol. 2005 Mar 7; 6:12 | utf-8 | BMC Cell Biol | 2,005 | 10.1186/1471-2121-6-12 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-121575532510.1186/1477-7525-3-12ReviewPatient-Reported Outcome and Quality of Life Instruments Database (PROQOLID): Frequently asked questions Emery Marie-Pierre [email protected] Laure-Lou [email protected] Catherine [email protected] Mapi Research Trust, 27 rue de la Villette, 69003 Lyon, France2 Mapi Research Institute, 27 rue de la Villette, 69003 Lyon, France2005 8 3 2005 3 12 12 4 3 2005 8 3 2005 Copyright © 2005 Emery et al; licensee BioMed Central Ltd.2005Emery 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 exponential development of Patient-Reported Outcomes (PRO) measures in clinical research has led to the creation of the Patient-Reported Outcome and Quality of Life Instruments Database (PROQOLID) to facilitate the selection process of PRO measures in clinical research. The project was initiated by Mapi Research Trust in Lyon, France. Initially called QOLID (Quality of Life Instruments Database), the project's purpose was to provide all those involved in health care evaluation with a comprehensive and unique source of information on PRO and HRQOL measures available through the Internet.
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Review
In clinical research it has become increasingly common to assess the patients' perspective of their symptoms and their impact on their daily life as a tool for determining treatment and a means of evaluating the outcome of the treatment chosen [1,2]. The added value of measuring Patient-Reported Outcomes (PRO) starts to be recognized by key players in the field of clinical research [3]. How patients perceive their health, and the impact of their treatment on their life can provide insight to clinicians previously unavailable [4-6].
However the successful application of PRO studies is dependant on the selection of the appropriate questionnaires for a given application [7,8]. They must be selected according to the domains they measure and the populations and pathologies for which they are designed. Practical issues, such as the availability of different translations, copyrights, and access to instruments are also major criteria in the choice of instruments.
The search for the most appropriate instruments is hindered by the substantial increase of PRO questionnaires developed in the past ten years. A recent search on PubMed, matching "quality of life" and "questionnaires" shows a striking growth of 450% between the last two decades (Figure 1).
Figure 1 PubMed Search: Number of references with Quality of Life-AND-questionnaire from 1966 to 2004
In order to facilitate the selection process the project of a Patient-Reported Outcome and Quality of Life Instruments Database (PROQOLID) was initiated by Mapi Research Trust in Lyon, France. Initially called QOLID (Quality of Life Instruments Database), the project's purpose was to provide all those involved in health care evaluation with a comprehensive and unique source of information on PRO and Health-Related Quality of Life (HRQOL) measures available through the Internet. In collaboration with Dr. Marcello Tamburini (Director, Unit of Psychology, National Cancer Institute, Milan, Italy), the developer of the QLMed.org web site, PROQOLID was launched at the beginning of 2002.
PROQOLID was created by the systematic collection of over 470 validated HRQOL and PRO instruments and their subsequent ordering into categories (e.g. pathologies, conditions, population). Through the structured presentation of synthesized, reliable and constantly updated data on PRO instruments, the PROQOLID database aims to present an overview of existing PRO instruments including all relevant and updated information on each. By providing this information the PROQOLID database facilitates access to the instruments and their developers and eases the process of selecting the instrument appropriate for a given application. Instruments can be chosen through a powerful interactive search function that will allow for the rapid selection of an appropriate instrument. What follows is an overview of the proper usage of the PROQOLID web site through a series of FAQ's collected directly from users of the web site. The PROQOLID database can be accessed on the Internet at .
Access
What are the differences in access to PROQOLID's database between members and guests?
The access to PROQOLID is organized in two levels.
The guest or free level is available to all visitors at no charge. This level provides brief information for each instrument, including
• Full name of the instrument and acronym
• Author(s)
• Objective
• Pathology
• Disease
• Type of instrument: Coping, Disability/physical functioning, Health status, Psychosocial/psychological, Quality of life, Satisfaction, Social functioning, Symptom/functioning, Utility, Work
• Population: Adolescent, Adult, All, Caregivers, Female, Geriatrics, Male, Pediatrics, Terminal patients
• Mode of administration: Caregiver-administered, Interviewer-administered, Nurse-rated, Physician-rated, Proxy-administered, Self-administered, Telephone-administered
• Number of items
• Original language
• List of existing translations
• Existence of a database: Yes / No
• Time recall
Since 1995, Mapi Research Trust has been a non-profit organization promoting the use and development of Patient Reported Outcomes. In an effort to accomplish this, a significant part of PROQOLID has been made accessible to all users free of charge.
In order to further develop and improve the database and provide additional information on the available instruments, Mapi Research Trust requests a financial participation in order to access PROQOLID'S advanced (or members') level. By subscribing to PROQOLID, you are supporting the continuous collection and update of this unique PRO resource. Membership options are available for pharmaceutical or commercial companies, non-profit organizations, universities, individual academic researchers and students. Benefits to members include a greater degree of practical information on the instruments and, when available, includes the review copy of the instrument, its translations and the user manual, most of them in PDF format.
Detailed information of the advanced level available include for each instrument
• Name of the instrument (full and abbreviated)
• Name and contact information of the Authors
• Contact person for information on, or permission to use, the instrument in its original language
• Copyright information
• Detailed conditions of use (e.g. fee, written permission, user agreement etc.)
• Review copy of the original instrument (when possible). Depending on the author's wish, original instruments may be used under specific conditions such as an access fee or signed agreement
• Bibliographic references of the original instrument
• Contact person for information on, or permission to use the translations
• Review copy of the available translations (when possible)
• Bibliographic references of the available translations (when possible)
• Dimensions covered by the instrument
• Time for completion
• Age range
• Scoring: response options, available scores, weighting, score direction and Minimal Important Difference (MID) or Minimal Clinically Important Difference (MCID)
• Existence of a user manual and copy of the user manual (when possible)
• Link to the PRO database identification form, when available
• Methodology of development
• Internal consistency reliability
• Related websites
• Other bibliographic references
Users
Who uses PROQOLID?
The web site is available to anyone having an interest in the development, availability and use of Patient-Reported Outcomes (PRO). Through the power of the Internet the PROQOLID project intends to provide this information to the world. Every major pharmaceutical company, non-profit organizations such as the US Food and Drug Administration, the NIH's National Cancer Institute, the Veterans Administration as well as dozens of Universities, researchers and students worldwide subscribe to the advanced level of PROQOLID on a yearly basis. The PROQOLID database is routinely visited by over 800 users per day, thereby educating clinicians, researchers, students, and the world about the availability and proper usage of PRO instruments.
Content
How are the instruments organized in the PROQOLID database?
The PROQOLID database was created in an effort to provide a means to facilitate the search process for and provide more efficient searches of any given PRO instrument. By organizing instruments in the PROQOLID database by several easy to understand categories, both time and energy are saved by the user. The different categories can be located on the Search page of the web site or by accessing directly from the tool bar at the top of the page. The different categories are as follows:
Alphabetical
The purpose of the Alphabetical list is to provide an overview of all existing PRO instruments. Over 1000 instruments are listed in alphabetical order according to their abbreviated name (or acronym). Some of the instruments are only listed, and these are displayed in standard font. For the remaining instruments access is available by simply clicking on the green link containing the abbreviated name of the instrument. Instruments can be accessed through an interactive letterbox at the top of the page. For example if the instrument begins with "D", simply click on the "D" at the top of the page and all instruments beginning with that letter will be displayed alphabetically.
Generic Instruments
The generic instruments are listed by alphabetic order on a separate web page.
Pathology/disease
A specific web page is dedicated to each pathology, and the instruments are listed either as generic instruments of the pathology or as disease-specific. The classification is structured based on Medline's Medical Subject Headings (MeSH) to ensure that the concepts are widely accepted. Please note that some diseases may be part of several pathologies. For example the disease "dementia" is part of both the pathologies "Neurology" and "Psychiatry/Psychology".
Population
The web page lists the instruments as they apply to specific populations including Adolescent, Adult, All, Caregivers, Female, Geriatrics, Male, Pediatrics, and Terminal patients.
Author's name
Instruments are grouped alphabetically according to the author's name, and as in the alphabetical list a letterbox is provided at the top of the page.
Search engine
You may search for instruments according to 10 criteria, including the name of the instrument, the pathology, the population or the available languages. The various criteria may be crossed referenced using the following Boolean Operators: AND, OR, NOT.
How many instruments are contained in PROQOLID?
The PROQOLID database was developed and is updated in close collaboration with the instruments' developers. It currently describes over 470 PRO instruments according to a structured format. The list is currently growing at the rate of fifty instruments per year. The database also includes review copies of 350 original instruments and 350 translations, most of them in PDF format. In order to determine the available translations simply access the instrument on the web site and all existing translations are conveniently listed. Also available through PROQOLID are over 125 associated User Manuals, and the description of 80 separate PRO databases. A fifth update of the whole database is underway and will include new information for each questionnaire on the reproducibility (or test-retest reliability) and clinical validity. In addition the PROQOLID website contains links to 150 external Internet resources relevant to the field of PRO usage and development. A general question and answer section on PRO is also included as well as on line full text articles on the development, validation, and linguistic adaptation of many of the PRO instruments published in medical journals.
What are the instruments criteria to be eligible for inclusion in PROQOLID?
To be eligible for inclusion in the database an instrument must be the subject of a publication that describes its development and/or validation. There is no charge to authors who wish to insert their instruments in PROQOLID nor are authors paid for their participation in this program. The Mapi Research Trust in Lyon, France determines ultimate decision for inclusion.
Search
How does the Search Engine work?
Besides the search by categories of instruments listed above (i.e. alphabetical list, generic, pathology/disease, population and author's name), an interactive search engine is included in the PROQOLID web site. All of the instruments contained on the web site can be accessed from this location. Searches can be made by:
• Abbreviated Name
• Full Name
• Author
• Pathology
• Disease
• Type of Instrument
• Population
• Mode of Administration
• Inclusion of a PRO Database
• Language
In an effort to increase the effectiveness of each search the ability exists to include up to nine (9) sub-parameters per search with the stipulation of "AND" or "OR". For example one could enter Author "=" Anderson J "OR" Author "=" Anderson R "AND" Type of Instrument "=" Quality of Life, "AND" Language "=" French, or any other combination that would suit the users needs. Through this function a user is able to drastically narrow the number of instruments displayed in the results window, thereby saving time and effort. If questions exist on the functioning of the search engine, or any questions about the PROQOLID database a contact page is provided with names and e-mail addresses for Mapi Research Trust and individual site managers in both Europe and North America. Additionally a video including a demo of PROQOLID can be seen on the home page.
Update
Who maintains the PROQOLID database?
Mapi Research Trust has maintained the PROQOLID database for three years. The information contained in the website for each instrument is updated at least once a year in collaboration with the instruments' developers and over fifty new instruments are added to the website each year. Mapi Research Trust has listened to the needs of the Pharmaceutical Industry, Industry Regulators, health care professionals, and patients. With the passing of time the organisation has developed into an intricate team of professionals whose single goal is to define and unite the various requirements of each of these groups in order to provide better communication and understanding of each groups needs. PROQOLID achieves to translate these objectives into a concrete application which ultimate goal is the improvement of the patients' quality of life and health outcomes.
Conclusion
The Patient-Reported Outcome and Quality of Life Instruments (PROQOLID) database aims to present an overview of existing PRO instruments. PROQOLID currently describes more than 470 PRO instruments in a structured format. It includes review copies of over 350 original instruments, 120 user manuals and 350 translations. Most are available in PDF format. The database is updated in close collaboration with the instruments' authors on a regular basis. Fifty or more new instruments are added annually. By providing this information the PROQOLID database facilitates access to the instruments and their developers and eases the process of selecting the most appropriate instrument for a given application. Instruments can be chosen through a powerful interactive search function. The PROQOLID database can be accessed on the Internet at .
Authors' contributions
MPE conceived the database and participated in its implementation and helped to draft the manuscript. CA helped in the conception of the database and drafted the manuscript. LLP has contributed in the design, coordination and follow up of the database. All authors read and approved the final manuscript.
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The ERIQA Group Assessing Treatment Impact Using Patient-Reported Outcomes (PROs): Challenges in Study Design, Conduct and Analysis. Meeting Report (Paris, May 10-11, 2004) Patient-Reported Outcomes Newsletter 2005 3 1 16
Muller-Buhl U Engeser P Klimm HD Wiesemann A Quality of life and objective disease criteria in patients with intermittent claudication in general practice Fam Pract 2003 20 36 40 12509368 10.1093/fampra/20.1.36
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Eton DT Fairclough DL Cella D Yount SE Bonomi P Johnson DH Eastern Cooperative Oncology Group Early change in patient-reported health during lung cancer chemotherapy predicts clinical outcomes beyond those predicted by baseline report: results from Eastern Cooperative Oncology Group Study 5592 J Clin Oncol 2003 21 1536 1543 12697878 10.1200/JCO.2003.07.128
Chassany O Sagnier P Marquis P Fulleton S Aaronson N Patient Reported Outcomes and Regulatory Issues: the Example of Health-related Quality of Life – A European Guidance Document for the Improved Integration of HRQL Assessment in the Drug Regulatory Process Drug Inf J 2002 36 209 238
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| 15755325 | PMC555954 | CC BY | 2021-01-04 16:38:15 | no | Health Qual Life Outcomes. 2005 Mar 8; 3:12 | utf-8 | Health Qual Life Outcomes | 2,005 | 10.1186/1477-7525-3-12 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-5-141577401810.1186/1471-2334-5-14Research ArticleEmergence of unusual species of enterococci causing infections, South India Prakash Vittal P [email protected] Sambasiva R [email protected] Subash C [email protected] Department of Microbiology, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Pondicherry, India2 Vice-Chancellor, NTR University of Health Sciences, Vijayawada, India2005 17 3 2005 5 14 14 22 12 2004 17 3 2005 Copyright © 2005 Prakash et al; licensee BioMed Central Ltd.2005Prakash 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
Enterococci tend to be one of the leading causes of nosocomial infections, with E. faecalis and E. faecium accounting up to 90% of the clinical isolates. Nevertheless, the incidence of other species of enterococci from clinical sources shows an alarming increase with the properties of intrinsic resistance to several antibiotics including beta-lactams and glycopeptides. Thus proper identification of enterococci to species level is quintessential for management and prevention of these bacteria in any healthcare facility. Hence this work was undertaken to study the prevalence of unusual species of enterococci causing human infections, in a tertiary care hospital in South India.
Methods
The study was conducted in a tertiary care hospital in South India from July 2001 to June 2003. Isolates of enterococci were collected from various clinical specimens and speciated using extensive phenotypic and physiological tests. Antimicrobial susceptibility testing were performed and interpreted as per NCCLS guidelines. Whole cell protein (WCP) fingerprinting of enterococci were done for species validation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and analyzed computationally.
Results
Our study showed the prevalence of unusual (non-faecalis and non-faecium enterococci) and atypical (biochemical variant) species of enterococci as 19% (46 isolates) and 5% (12 isolates) respectively. The 7 unusual species (46 isolates) isolated and confirmed by phenotypic characterization includes: 15 E. gallinarum (6.2%), 10 E. avium (4.1%), 6 E. raffinosus (2.5%), 6 E. hirae (2.5%), 4 E. mundtii (1.7%), 3 E. casseliflavus-including the two atypical isolates (1.2%) and 2 E. durans (0.8%). The 12 atypical enterococcal species (5%) that showed aberrant sugar reactions in conventional phenotyping were confirmed as E. faecalis, E. faecium and E. casseliflavus respectively by WCP fingerprinting. The antimicrobial susceptibility testing depicted the emergence of high-level aminoglycoside and beta-lactam resistance among different species apart from intrinsic vancomycin resistance by some species, while all the species tested were susceptible for linezolid and teicoplanin.
Conclusion
Our study reveals the emergence of multi-drug resistance among unusual species of enterococci posing a serious therapeutic challenge. Precise identification of enterococci to species level enables us to access the species-specific antimicrobial resistance characteristics, apart from knowing the epidemiological pattern and their clinical significance in human infections.
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Background
Enterococci, generally regarded as normal flora of gastrointestinal and genitourinary tract of humans, have emerged as the etiogen of several nosocomial as well community-acquired infections since last two decades. Globally, many studies have revealed that enterococci tend to be one of the leading causes of several nosocomial infections, with the emergence and spread of multi drug resistance among isolates [1-3]. Since the inception of separate genus Enterococcus, there are 23 species of enterococci with clinical significance to date [4], of which Enterococcus faecalis and Enterococcus faecium accounts up to 90% of clinical isolates belonging to this genus [1]. Nevertheless, the incidence of other species of enterococci from clinical sources shows an alarming increase with the properties of intrinsic resistance to several antibiotics including beta-lactams and glycopeptides [5,6]. But the incidence of non-faecalis and non-faecium enterococci is underestimated because of frequent misidentification. On several instances only one phenotypic character differentiates one species from another, and to further complicate some strains of enterococci do not posses the exact phenotypic character of the type strains, and there comes confusion over their exact taxonomic status [7]. Thus proper identification of enterococci to species level is quintessential for management and prevention of these bacteria in any health care facility. Many studies focus on the two most common species E. faecalis and E. faecium, and only few reports or studies of non-faecalis and non-faecium enterococci are prevalent [5,6]. Hence the aim of our study was to check the prevalence of unusual and atypical species of enterococci causing human infections, in a tertiary care hospital in South India over a time period.
Methods
i. Bacterial isolates and conventional phenotypic characterization of enterococci
The study was conducted in a 900-bedded tertiary care hospital at Pondicherry, South India from July 2001 to June 2003. Isolates of enterococci were collected over the time period from various clinical specimens such as blood, urine, wound swabs and pus (surgical and non-surgical), catheters, ascitic fluid, synovial fluid, by plating them on 5% Sheep Blood agar and Mac-conkey agar, as well on Bile esculin azide agar (Hi-media, Mumbai, India) as per nature of the specimen. Extensive phenotypic and physiological characterization was carried out by the conventional tests devised by Facklam et al [3,8]. Carbohydrate fermentation tests were performed using 1% sugar discs in Brain heart infusion (BHI) broth with Andrade's indicator (Hi-media, Mumbai, India) as per manufacturer's instructions. The following sugars were tested for fermentation by isolates using commercial discs: mannitol, sorbitol, inulin, arabinose, melibiose, sucrose, raffinose, trehalose, lactose, glycerol, salicin, maltose, adonitol, and xylose, while sorbose and ribose were added to a final concentration of 1% to the broth base directly after sterilization (due to non-availability of discs). Group D antigen was detected using a commercial latex agglutination kit (The Binding site limited, Birmingham, B29 6AT) as per manufacturer's™ instructions.
ii. Antimicrobial susceptibility testing
Antibiotic susceptibility testing of the clinical isolates along with the quality control strains were performed using BHI agar instead of Muller Hinton agar by disk diffusion method (for the antibiotics: penicillin [10 units], ampicillin [10 μg], gentamicin-high content [120 μg], streptomycin-high content [300 μg], ciprofloxacin [5 μg], nitrofurantoin-for urinary isolates only [300 μg], vancomycin [30 μg], teicoplanin [30 μg] and linezolid [30 μg]), standard agar dilution (for the antibiotics mentioned in Table-1) and agar screening methods (for vancomycin and high-level aminoglycoside resistance) and interpreted as per NCCLS guidelines [9]. Production of β-lactamase was determined by using nitrocefin discs (BBL Microsystems) as per manufacturer's™ instructions.
Table 1 Analysis of MIC ranges of unusual species of enterococci.
Species tested (no.of.isolates) Antibiotic Tested No. of isolates at specified MIC, in μg/mL Susca,%
2 4 8 16 32 ≥ 64
E. avium (10) Pen. 9 9 9 9 7 6 10
Amp. 9 6 6 6 6 0 40
Van. 4 2 0 0 0 0 100
Te. 0 0 0 0 0 0 100
Va.Scr. NA NA NA NA NA NA 100
HLGm. NA NA NA NA NA NA 10
HLStr. NA NA NA NA NA NA 50
E. casseliflavus (3) Pen. 0 0 0 0 0 0 100
Amp. 0 0 0 0 0 0 100
Van. 3 3 0 0 0 0 NA
Te. 0 0 0 0 0 0 100
Va.Scr. NA NA NA NA NA NA 66.6
HLGm. NA NA NA NA NA NA 100
HLStr. NA NA NA NA NA NA 100
E. durans (2) Pen. 2 1 1 1 0 0 50
Amp. 1 1 1 1 0 0 50
Van. 2 1 0 0 0 0 100
Te. 1 0 0 0 0 0 100
Va.Scr. NA NA NA NA NA NA 50
HLGm. NA NA NA NA NA NA 50
HLStr. NA NA NA NA NA NA 0
E. gallinarum (15) Pen. 9 9 8 8 8 8 46.6
Amp. 8 8 8 8 7 6 46.6
Van. 13 9 2 0 0 0 NA
Te. 0 0 0 0 0 0 100
Va.Scr. NA NA NA NA NA NA 53.3
HLGm. NA NA NA NA NA NA 46.6
HLStr. NA NA NA NA NA NA 66.6
E. hirae (6) Pen. 3 3 2 2 0 0 66.6
Amp. 6 2 2 2 0 0 66.6
Van. 0 0 0 0 0 0 100
Te. 0 0 0 0 0 0 100
Va.Scr. NA NA NA NA NA NA 100
HLGm. NA NA NA NA NA NA 100
HLStr. NA NA NA NA NA NA 100
E. mundtii (4) Pen. 1 1 1 0 0 0 100
Amp. 0 0 0 0 0 0 100
Van. 2 2 0 0 0 0 100
Te. 0 0 0 0 0 0 100
Va.Scr. NA NA NA NA NA NA 50
HLGm. NA NA NA NA NA NA 100
HLStr. NA NA NA NA NA NA 50
E. raffinosus (6) Pen. 4 4 4 4 4 4 33.3
Amp. 4 4 4 4 4 0 33.3
Van. 0 0 0 0 0 0 100
Te. 0 0 0 0 0 0 100
Va.Scr. NA NA NA NA NA NA 100
HLGm. NA NA NA NA NA NA 66.6
HLStr. NA NA NA NA NA NA 66.6
NOTE:
a Interpretations based on NCCLS guidelines,
NA-not applicable; Susc- Susceptiblity,
Pen-Penicillin; Amp-Ampicillin; Van-Vancomycin; Te-Teicoplanin,
Va. Scr- Vancomycin resistance (6 μg/mL) agar screening,
HLGm- High-level gentamicin resistance (500 μg/mL) agar screening,
HLStr- High-level streptomycin resistance (2000 μg/mL) agar screening,
iii. Molecular phenotyping of enterococci
Whole cell protein (WCP) analysis of the enterococcal isolates, including atypical biochemical variants of enterococci and the reference/type strains of enterococci (a kind gift from Dr. Richard.R.Facklam, CDC, Atlanta, GA. USA) were done using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) as described previously with minor modifications [10,11] for species identification, as well confirmation of species identities of atypical strains. Briefly, enterococcal test strains were grown for 18 hours at 37°C on Trypticase soy agar with 5% sheep blood. The samples were prepared by removing the bacterial growth from the surface of agar plate carefully with a sterile disposable loop, and suspended in 5 ml of sterile saline solution in order to obtain a turbidity equal to that of No.8 MacFarland density standard, centrifuged, and resuspended in 0.5 ml of an aqueous lysozyme solution (10 mg/ml). The suspensions were incubated in a water bath preset at 37°C for 2 hours. The WCP extracts were obtained by mixing one part of whole cell extract to one part of sample loading buffer, and boiled for 5 minutes and separated by SDS-PAGE along with a broad range molecular weight marker (New England Biolabs Inc.,) as per standard procedure [11]. The SDS-PAGE was performed using 5% stacking gel and 10% separating gel at a constant current of 20 mA using a mini-gel electrophoresis system (Bangalore Genei, India) and stained with Coomassie brilliant blue. Visual comparisons of the gels were made, and documentation done using a Gel doc system (Vilber loubert, France) for further analysis. The gel images were analyzed and dendrogram constructed using appropriate software (Bionumerics, version 2.5, Applied Maths, Kortrijik, Belgium) for validating the taxonomy of the enterococcal species studied.
Results
Conventional and Molecular phenotyping of enterococci
We isolated a total of 242 enterococci during our 2-year study period from different clinical samples. The biochemical phenotyping results revealed 46 isolates (19%) belonging to 7 different unusual species of enterococci (excluding E. faecalis and E. faecium-data not shown) which included 15 E. gallinarum (6.2%), 10 E. avium (4.1%), 6 E. raffinosus (2.5%), 6 E. hirae (2.5%), 4 E. mundtii (1.7%), 3 E. casseliflavus (1.2%) and 2 E. durans (0.8%). The distribution by site of isolation for the 46 unusual enterococcal species included 30 isolates- 12 E. gallinarum, 6 E. avium, 3 each of E. hirae, E. casseliflavus and E. raffinosus, 2 E. mundtii and 1 E. durans (65.2%) from bloodstream, 6 isolates- 3 E. raffinosus, 2 E. avium and 1 E. mundtii (13 %) from surgical and non-surgical wound swabs, 10 isolates- 3 each of E. hirae and E. gallinarum, 2 E. avium and 1 each of E. durans and E. mundtii (21.8%) from miscellaneous sites, including muscle tissues sent for anaerobic culture, catheter tips, peritoneal fluid, ear swab and urine. Of the 46 persons from whom unusual enterococci were obtained, 56.5% were males and 43.5% were females including newborn/neonates. The infections were polymicrobial in 6 (13%) of the 46 cases from which unusual enterococci were isolated, including 2 (6.7%) of 30 bloodstream infections. The 12 atypical enterococcal strains (5%) showing aberrant sugar reactions included 6 mannitol negative variant E. faecalis like species, 1 arginine negative variant E. faecalis like species, 3 mannitol negative variant E. faecium like species and, 2 arginine negative variant E. casseliflavus like species. The WCP analysis by SDS-PAGE confirmed the species identities of seven different species. Atypical strains showed a similar banding pattern like the reference strains from CDC (E. faecalis SS-1273, E. faecium SS-1274, E. casseliflavus SS-1229) except for minor quantitative differences, with no qualitative difference. The computational analysis of the WCP gel images of atypical strains were performed by Dice coefficient, and the dendrogram constructed using unweighted pair group method using arithmetic averages (UPGMA) as shown in Figure-1, and validated their exact taxonomic status as E. faecalis, E. faecium and E. casseliflavus respectively. The 2 (atypical) isolates of arginine negative variant E. casseliflavus like species after taxonomic validation were included as an unusual species of enterococci accounting to 3 E. casseliflavus isolated overall.
Figure 1 Cluster analysis of atypical strains of Enterococci using Dice coefficient and UPGMA method (Bionumerics, Applied Maths, Belgium). Note : SS- Designation of CDC standard strains, E. porcinosus is currently designated as E. villorum, E. pseudoav.- E. pseudoavium; E. malodorat.- E. malodoratus, E. casselifla.- E. casseliflavus, MNV- Mannitol negative variant; ANV- Arginine negative variant.
Antimicrobial susceptibility testing
The antimicrobial susceptibilities of the isolates given in Table-1 depict the ranges of MICs for various antimicrobial agents tested by standard agar dilution, and agar screening methods. The E. gallinarum and E. casseliflavus isolates showed reduced susceptibility to lesser concentrations of vancomycin ranging 2–8 μg/ml. Other species were highly susceptible for vancomycin and teicoplanin except one isolate of E. durans. High-level aminoglycoside resistance for gentamicin and streptomycin was found absent only in E. casseliflavus and E. hirae, while other species exhibited variable susceptibilities ranging 0 – 66.7% for either aminoglycoside tested. The disk diffusion testing showed 100% susceptibility for linezolid and teicoplanin by all isolates tested, while E. casseliflavus and E. mundtii showed 100% susceptibility for penicillin and ampicillin. Only 37% of unusual enterococcal isolates were susceptible to ciprofloxacin, with resistance exhibited by 9 E. avium (n = 10), 2 E. durans (n = 2), 11 E. gallinarum (n = 15), 2 E. hirae (n = 6), 2 E. mundtii (n = 4) and, 3 E. raffinosus (n = 6). Only E. casseliflavus (n = 3) exhibited 100% susceptibility to ciprofloxacin. None of the 46 isolates was positive for β-lactamase, but resistance for β-lactam agents were prevalent variably among different species. The results of MICs for penicillin, ampicillin, high-level gentamicin and high-level streptomycin resistance were in accordance with the disk diffusion testing results except for vancomycin. Disk diffusion testing showed vancomycin resistance for 6 isolates (1 E. durans, 2 E. mundtii, 3 E. gallinarum), but the agar screening method exhibited vancomycin resistance for 11 isolates (2 E. mundtii, 1 E. casseliflavus, 1 E. durans, 7 E. gallinarum)(including 5 isolates-4 E. gallinarum and 1 E. casseliflavus, which showed susceptibility to vancomycin by the disk diffusion method).
Discussion
Our study reveals that the prevalence of unusual species of enterococci as 19% in our clinical setup in South India. Many studies and reviews show the prevalence of non-faecalis and non-faecium enterococci as 2–10% [3,6,12]. Previous studies from India have reported E. faecalis and E. faecium as the only prevalent species [13-16], which may not reflect the true incidence rate. From our perspective the real incidence tends to be higher which in part can be explained as, misidentification of species due to exhibition of aberrant sugar reactions by some enterococci or, due to lack of application of the complete range of tests to identify non-faecalis and non-faecium enterococci [7,17]. The prevalence rate (19%) of our study was partly in accordance with another Indian study [18] showing 14.8% (excluding E. faecalis and E. faecium) prevalence of unusual species of enterococci from catheterized patients with urinary tract infections. E. mundtii and E. durans were not reported in their study, whose prevalence was 1.7% and 0.8% respectively in our study. E. gallinarum (6.2%) and E. avium (4.1%) were the most commonly identified species, which markedly differs in isolation rate (0.3–1.2%) from other studies [6,19,20]. The incidence of infections caused by unusual enterococcal species is of serious concern, since 43.5% of the isolates were from cases of septicemia without endocarditis. Apart from septicemia, the unusual species of enterococci were isolated frequently from cases of urinary tract infections, surgical and non-surgical wound infections and peritonitis. Most of the patients with the bloodstream infections had a peripheral or central catheter. Further, only 13% of enterococcal infections were polymicrobial, with majority from non-bloodstream isolates that underscores the clinical significance of these unusual enterococcal species. Although the unusual species of enterococci were isolated at regular intervals throughout our study period, we could find clustering of specific species during a specific time period from specific units/wards. Interestingly, 10 among the 15 isolates of E. gallinarum isolated during our study period were from pediatrics unit, while 7 of the 10 isolates exhibiting a similar antibiotype were isolated from the same ward within a span of 2 months. The remaining 3 of the 10 E. gallinarum were isolated from the same ward in the preceding 3 months, one of which showed an antibiotype similar to the cluster of 7 isolates. The same was the case of 3 E. casseliflavus isolated from the same pediatrics unit within a span of 2 months in the preceding year. Most of these (8 of 10 E. gallinarum, and all 3 E. casseliflavus) isolates were from cases of septicemia. Although molecular epidemiological studies have not been done to compare the genetic similarities of these isolates, the data depicts the nosocomial spread of these species. WCP analysis by SDS-PAGE had been proven to assist in validating the species identities as well, to identify strains that do not exhibit phenotypic characteristics identical to the type strains of each species [4,10,21]. We were able to validate the authenticity of the unusual species, and the exact taxonomic status of the atypical phenotypic variant strains identified by conventional biochemical testing as shown in Figure-1, using WCP fingerprinting by SDS-PAGE.
Ciprofloxacin resistance was 63% among isolates (excepting E. casselifalvus) which proves that it may be successful only in treating enterococcal urinary tract infections [1,9], since most of our isolates were from bloodstream and other related specimens. None of the isolates were β-lactamase producer, but penicillin and ampicillin resistance were exhibited by 54.3% and 45.7% isolates. We suggest penicillin binding protein modification based resistance for our isolates as a basis for β-lactam resistance, as depicted previously [22,23], but markedly differs from other Indian studies [15,24] showing up to 50% β-lactamase associated resistance. The prevalence of high-level gentamicin resistance (43.4%) and high-level streptomycin (37%) among unusual enterococcal isolates from our study partially correlates with studies from Japan [25] and United States [12,26]. In our study, most strains with high-level gentamicin resistance lacked high-level streptomycin resistance, and vice versa, thus facilitating the combination therapy (cell wall inhibitor plus aminoglycoside) treatment options for serious enterococcal bloodstream infections [1,9]. The prevalence of vancomycin resistance was 24% by agar screening /agar dilution method and 13% by disk diffusion. The difference may be attributed to the intrinsic low level vancomycin resistance (van C genotype), exhibited by 4 E. gallinarum and, 1 E. casseliflavus isolates, which may go undetected by disk diffusion testing [27]. Of serious concern was the low-level vancomycin resistance exhibited by one E. durans and two E. mundtii (MIC ≤ 6 μg/ml). The genotypic basis of vancomycin resistance for these 3 isolates yet to be studied, will give us a definitive picture regarding its clinical significance, since studies have reported the prevalence of vancomycin resistance in these two species, and its transferable nature from E. durans to E. faecium [28-30].
Conclusion
Precise identification of enterococci to species level enables us, to access the species-specific antimicrobial resistance characteristics, apart from knowing the epidemiological pattern and their clinical significance in human infections. The difficulty in detecting (intrinsic) low-level vancomycin resistance by disk diffusion testing [28] emphasizes the necessity for including agar screening methods as per NCCLS guidelines in routine susceptibility testing of all enterococci isolated from clinical specimens [9]. Further as shown in our study, the increase in the rate of prevalence of the unusual and atypical species and the emergence of multidrug resistance among them, highlights the significance of rapid and accurate identification of enterococci to the species level for initiating appropriate therapeutic regimen, and reemphasizes the importance of the implementation of appropriate infection control measures to limit the nosocomial spread of these unusual species in any nosocomial setting.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PVP designed the study and carried out the experimental works and analysis, and drafted the manuscript. RSR supervised and participated in the design of the study and coordination, and helped to draft the manuscript. SCP participated in the coordination of the study and helped to draft the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We are grateful to Dr. Richard.R.Facklam, CDC, Atlanta, GA. USA for providing us the reference/type strains of enterococci used in this study. We acknowledge Dr. SL.Hoti and Mr. Thangadurai for their technical expertise in computational analysis of the gel images, and the support of all technical staff of our laboratory during the study period.
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| 15774018 | PMC555955 | CC BY | 2021-01-04 16:28:14 | no | BMC Infect Dis. 2005 Mar 17; 5:14 | utf-8 | BMC Infect Dis | 2,005 | 10.1186/1471-2334-5-14 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-3-121577177610.1186/1479-5876-3-12ResearchInfluence of immunomagnetic enrichment on gene expression of tumor cells Woelfle Ute [email protected] Elisabeth [email protected] Klaus [email protected] Institute of Tumor Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany2005 16 3 2005 3 12 12 9 12 2004 16 3 2005 Copyright © 2005 Woelfle et al; licensee BioMed Central Ltd.2005Woelfle 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
Metastasis is the leading cause of cancer-related death. Bone marrow (BM) is a frequent site for the settlement of disseminated tumor cells which occurs years before overt metastases signal incurability.
Methods
Here we describe a new method to assess the initial stage of metastasis development in cancer patients. By immunomagnetic selection with HER2/neu and EpCAM as catcher antigens single disseminated tumor cells can be enriched from BM samples. To examine whether the immunomagnetic enrichment technique may change gene expression in the selected tumor cells, we performed differential expression profiling with the breast cancer cell lines MCF-7 and BT474 as models. The profiles were performed using 1.2 Cancer Arrays (Clontech) containing 1176 cDNAs that can be grouped into different functional categories, such as signal transduction, cell cycle, adhesion, cytoskeleton plasticity, growth factors and others.
Results
The reproducibility of the gene expression profiling between duplicate cDNA-array experiments was assessed by two independent experiments with MCF-7 breast cancer cells. Scatter blot analysis revealed a good reproducibility of the cDNA array analysis (i.e. less than 10% difference in the gene expression between the arrays). Subsequent comparative cDNA-array analyses of immunobead-selected and unselected MCF-7 and BT474 cancer cells indicated that the antibody incubation during the immunomagnetic selection procedure did not considerably alter the gene expression profile.
Conclusion
The described method offers an excellent tool for the enrichment of micrometastatic tumor cells in BM without largely changing the gene expression pattern of these cells.
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Introduction
Solid tumors derived from epithelial organs are the main form of cancer in industrialized countries. The first phase of the metastatic development consists of local tumor cell invasion, followed by tumor cell circulation in the blood and homing to secondary distant organs [1,2]. As indicator organ for early systemic dissemination of epithelial tumor cells to distant sites, BM has played a prominent role [3]. BM can easily be aspirated from the iliac crest and single metastatic cells are already present in 20–40% of patients with epithelial tumors (e.g., breast, lung or colon carcinomas) years before overt distant metastases occur in the skeleton or other distant organs [3-5]. The molecular description of these cells has been, however, hampered by the low concentration of these cells (e.g., 10-5-10-6 per BM cell). To predict and monitor therapeutic responses the assessment of the gene expression profile of disseminated tumor cells seems to be of utmost importance. However, it is uncertain to which extent incubation with antibodies used for immunomagnetic isolation of these cells might affect their expression profile. We have addressed this aspect, using monoclonal antibodies against two prominent antigens, EpCAM and HER2/neu that are frequently and independently expressed on micrometastatic tumor cells [3].
Materials and methods
Ficoll density gradient centrifugation and immunocytochemistry
The enrichment of tumor cells from BM by Ficoll density gradient centrifugation and the immunocytochemical detection of epithelial tumor cells in cytological BM preparations has been described elsewhere in detail [5,6].
Immunomagnetic cell separation and immunocytochemistry of BM samples
Two ml of a BM sample usually containing 2 × 106 mononuclear cells were washed with Hank's Salt Solution (Biochrom KG, Germany). The pellet was resuspended in 2 ml Hanks and 3.2 × 107 (80 μl) CELLection™ and pan-mouse immunomagnetic beads (Dynal, Oslo, Norway) coated with anti-EpCAM (MAb 3B10) and anti-HER2/neu (MAb 7C1) antibodies (Micromet, Munich, Germany) were added. All solutions and cell preparations were kept at 4°C during the whole procedure to avoid nonspecific binding of immunomagnetic beads. After an incubation time of 30 min at 4°C and 20 min at room temperature on a rotating mixer the magnetically labeled cells were isolated in a magnetic particle concentrator and resuspended in 200 μl bead removing buffer (40 mM Tris, 10 mM MgSO4, and 1 mM CaCl2, pH7.4, prewarmed to room temperature). The immunomagnetic beads were removed by DNase treatment with 15 μl DNase (50 U/μl) at room temperature for 15 min. After separation in a magnetic particle concentrator the supernatant was collected and centrifuged onto glass slides. Tumor cells were identified by immunostaining with monoclonal anti-cytokeratin antibody A45-B/B3 according to the manufacturer's instruction (Micromet, Munich, Germany). Cytokeratins are specific constituents of the epithelial cytoskeleton and they have become the marker antigen of choice for the detection of disseminated epithelial tumor cells in mesenchymal organs such as BM [3,7]. To avoid unspecific binding of the antibody via Fc-receptors present on leukocytes, we used Fab fragments of A45-B/B3 that were directly conjugated to the marker enzyme alkaline phosphatase.
Cell culture and antibody incubation
MCF-7 cells and BT474 cells were maintained in RPMI (Invitrogen, Karlsruhe, Germany) supplemented with 5 % glutamine (Invitrogen) and 10 % FCS (C-C Pro, Neustadt, Germany). MCF-7 and BT474 cells (ATCC HTB-22 and HTB-20) were allowed to reach a logarithmic growth phase in culture. At 90 % confluency, the cells were incubated for 30 min at 4°C and 20 min at room temperature in Hanks containing 1 μg/ml of anti-EpCAM (MAb 3B10) as well as anti-HER2/neu (MAb 7C1) antibodies according to the immunomagnetic cell separation protocol. In a negative control experiment, cells were suspended in Hanks devoid of antibodies.
cDNA probe preparation and hybridization
Total RNA was isolated using the peqGold TriFast™ (Peqlab, Erlangen, Germany) according to the manufacturer's instruction. In order to remove genomic DNA contamination, a DNase step was included using the DNA-free™ kit (Ambion, Cambrigeshire, England) according to manufacturer's instructions. RNA was dissolved in RNase-free H2O with 1 U/μl RNase inhibitor (SUPERase. IN™, Ambion). 5 μg purified total RNA was used for [α-33P] dATP (3000 Ci/mmol, 10 μl; Amersham, Freiburg, Germany) labeled cDNA synthesis as previously described [8]. The cDNA probe was purified with nucleotide removal columns (Qiagen, Hilden, Germany). The Atlas Human 1.2 Cancer Arrays (Clontech, Heidelberg, Germany) were hybridized according to the manufacturer's protocol.
cDNA- array data analysis
The membranes were exposed to phosphoimager plates (Raytest Isotopen-Meβgeräte, Straubenhardt, Germany) for 3 days, and plates were scanned with the phosphoimager Fuji Bas (Raytest) at a 100 μm-resolution. The images were analyzed using the Imagene 5.5 software (Biodiscovery, CA, USA). The data of the arrays were normalized on the basis of the genes ubiquitin, HLAC and beta actin. The ratio between antibody-treated and non-treated cells was calculated for each gene. Ratios lower than 0.5 or higher than 2 were considered as differentially expressed if at least one sample showed an expression above 0.5. We performed duplicates of each experiment and created scatter blots with the SPSS software for windows.
Results
In a recent work [9] we demonstrated that our immunomagnetic separation works on clinical samples (Figure 1) and is superior to the standard Ficoll density centrifugation technique, used in most previous studies on cancer micrometastasis [4,5,7,10].
Figure 1 CK-positive cells detected after immunomagnetic enrichment of BM from breast cancer patients. One single cell and one 2-cell cluster is shown in a 400× magnification.
To test whether the antibody incubation during the immunomagnetic enrichment approach affects gene expression in the selected cells, we applied cDNA-array analysis. We subsequently evaluated whether the immunomagnetic enrichment method affected gene expression in the selected cells. This aspect is of utmost importance for further molecular description of disseminated tumor cells and has not been addressed before. The profiles were performed using 1.2 Cancer Arrays (Clontech) containing 1176 cDNAs that can be grouped into different functional categories, such as signal transduction, cell cycle, adhesion, cytoskeleton plasticity, growth factors and others [11].
As models, we used the breast cancer cell lines MCF-7 and BT474 (ATCC HTB-22 and HTB-20), because they express heterogeneous levels of the target antigens HER2/neu and EpCAM comparable to micrometastatic breast cancer cells in vivo [3]. The reproducibility of the gene expression profiling between duplicate cDNA-array experiments was assessed by two independent experiments with MCF-7 breast cancer cells. As shown in Figure 2A, scatter blot analysis revealed a good reproducibility of the cDNA array analysis (i.e. less than 10% difference in the gene expression between the arrays). We plotted the data of the antibody-treated and untreated MCF-7 (B) or BT474 (C) cells two dimensionally in a scatter plot; y-axis represents the data of untreated cells and the x-axis represents the data of cells treated with anti-HER2/neu or anti-EpCAM. For both cell lines, the scatter plots show that expressed genes in antibody-treated versus untreated cells (Figure 2B, C) was in principal within the range observed in the duplicate experiments with MCF-7 cells (Figure 2A). However, subtle changes in the expression of individual genes after antibody incubation were observed in particular in BT474 cells. In this cell line 38 genes were strongly differentially expressed (ratio >3) in antibody-treated versus untreated cells (Table 1). Most of these genes play a role in extracellular matrix remodeling, signal transduction and replication, as well as repair and transcription. MCF-7 cells showed in this experimental approach 31 differentially expressed genes with a ratio of over 3 (data not shown). Although similar group of genes were affected, only 3 common genes (CDC7, SGI and KIR) were differentially expressed in both cell lines after antibody incubation.
Figure 2 Representative scatter blots of breast cancer cells using the 1.2 Cancer Array for expression analysis. (A) untreated MCF-7 breast cancer cells (results of duplicate experiments), (B) antibody-treated versus untreated MCF-7 cells, and (C) antibody-treated versus untreated BT474 breast cancer cells.
Table 1 Genes differentially expressed in antibody-treated versus untreated BT474 cell
Genes GenBank Accession# Ratio*
Extracellular matrix remodeling:
COL11 J04177 - 6.2
MMP17 X89576 - 4.7
MMP16 D50477 -4.6
SPARC J03040 5.7
Adhesion:
PKD1 U24497 - 7.3
NCAM AF002246 7.3
M-cadherin D83542 3.5
Cytoskeleton plasticity:
SPTA1 M61877 7.4
Signal transduction:
RGS4 U27768 - 10.1
GAS L13720 - 7.1
BMP1 U50330 - 7.0
FGFR4 L03840 - 6.5
PMEL17 M77348 - 6.5
ETS-1 - 5.9
ERBB2 M95667 - 5.8
TGF-beta X02812 - 5.2
N-ras X02751 - 4.8
HRS D84064 - 4.7
KIR U10550 9.0
BMP6 M60315 7.5
BIN1 U68485 7.4
SGI Y00064 4.1
CDC7 AF015592 3.8
CNTF S72921 3.4
SH3BP2 AF000936 3.1
Apoptosis:
CD27BP U82938 6.0
DR5 F016268 4.8
Metabolism:
PPAT U00238 6.3
HPRT P00492 5.4
Immune response:
MHC class I U65416 8.4
Replication/repair/transcription:
CHAF1A U20979 - 12.3
NEK3 Z29067 - 5.6
BTG U72649 8.9
HRC1 M91083 6.2
TOP1 J03250 4.9
CLK1 L29222 4.0
Functionally unclassified
PIG7 AF010312 - 4.9
menin U93236 3.1
*Ratio of normalized data from antibody-treated versus untreated BT474 cells as described in the Materials and Methods section. Negative values indicate downregulated and positive values upregulated genes.
Discussion
Here, we investigated whether an immunomagnetic enrichment procedure for micrometastatic cancer cells present in BM aspirates leads to significant changes in the gene expression pattern of the enriched tumor cells. In order to mimic the biological conditions of a tumor type with frequent BM involvement, we used two breast cancer cell lines (MCF-7 and BT474). Both cell lines expressed the target antigens, EpCAM and HER2/neu, for immunomagnetic separation at different levels [12] and they were incubated with the anti-EpCAM and anti-HER2/neu antibodies according to the same immunomagnetic enrichment protocol used for the BM samples from cancer patients analyzed recently [9]. It has been shown by other groups that some of the mAb directed against HER2/neu (e.g., HerceptinR) can specifically block cell proliferation and affect gene expression in HER2/neu-positive breast cancer cells [13,14]. Furthermore the incubation of human cells with anti-EpCAM-specific mAbs (e.g. KS1/4 mAb) can induce considerable changes in the expression of insulin and glucagons [15]. However, our present results suggest that the two antibodies against EpCAM and HER2/neu used for the immunomagnetic selection process did not considerably influence the gene expression pattern of the enriched cells, although the HER2/neu- positive cell line showed a slightly increased number of differentially expressed genes. These genes are involved in extracellular matrix remodeling, signal transduction and replication, repair and transcription, and they were either up or downregulated after antibody incubation. For example, HER2/neu gene expression was downregulated after antibody binding, as expected from reports in the literature [16]. Taken together, we cannot exclude subtle changes in the expression of individual genes after antibody incubation, but we observed no obvious shift in the expression pattern that exceeds the normal variability of duplicate experiment.
Thus, we conclude that the immunomagnetic selection protocol described here might be useful for experimental approaches aimed to determine the gene expression profile and genome of disseminated CK-positive cells [17,18]. As we performed our study only on two breast cancer cell lines, larger series of similar experiments with further cancer cell lines as well as with enriched tumor cells from the blood or BM must be investigated to draw firm conclusions. The detection and characterization of micrometastatic cancer cells will provide new insights into the biology of the metastatic process in cancer patients. This will lead to an improved molecular staging of cancer patients and to the identification of new biological targets for adjuvant systemic therapies aimed to eradicate micrometastatic disease before the onset of overt metastasis signals incurability.
Acknowledgements
We appreciate the excellent technical help of Kathrin Baack and Antje Andreas and thank Dr. Marcus Otte for helpful discussions. This work was supported by the Deutsche Forschungsgemeinschaft (Pa-341-12-1), Bonn, Germany.
The abbreviations used are: CK, cytokeratin; EpCAM, epithelial cell adhesion molecule; BM, bone marrow
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| 15771776 | PMC555956 | CC BY | 2021-01-04 16:39:29 | no | J Transl Med. 2005 Mar 16; 3:12 | utf-8 | J Transl Med | 2,005 | 10.1186/1479-5876-3-12 | oa_comm |
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J Negat Results BiomedJournal of Negative Results in Biomedicine1477-5751BioMed Central London 1477-5751-4-21574545210.1186/1477-5751-4-2ResearchPreprandial ghrelin is not affected by macronutrient intake, energy intake or energy expenditure Paul David R [email protected] Matthew [email protected] Donna G [email protected] William V [email protected] U.S. Department of Agriculture, Agricultural Research Service, Diet and Human Performance Laboratory, Beltsville Human Nutrition Research Center, Beltsville, MD 20705, USA2005 3 3 2005 4 2 2 20 10 2004 3 3 2005 Copyright © 2005 Paul et al; licensee BioMed Central Ltd.2005Paul 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
Ghrelin, a peptide secreted by endocrine cells in the gastrointestinal tract, is a hormone purported to have a significant effect on food intake and energy balance in humans. The influence of factors related to energy balance on ghrelin, such as daily energy expenditure, energy intake, and macronutrient intake, have not been reported. Secondly, the effect of ghrelin on food intake has not been quantified under free-living conditions over a prolonged period of time. To investigate these effects, 12 men were provided with an ad libitum cafeteria-style diet for 16 weeks. The macronutrient composition of the diets were covertly modified with drinks containing 2.1 MJ of predominantly carbohydrate (Hi-CHO), protein (Hi-PRO), or fat (Hi-FAT). Total energy expenditure was measured for seven days on two separate occasions (doubly labeled water and physical activity logs).
Results
Preprandial ghrelin concentrations were not affected by macronutrient intake, energy expenditure or energy intake (all P > 0.05). In turn, daily energy intake was significantly influenced by energy expenditure, but not ghrelin.
Conclusion
Preprandial ghrelin does not appear to be influenced by macronutrient composition, energy intake, or energy expenditure. Similarly, ghrelin does not appear to affect acute or chronic energy intake under free-living conditions.
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Background
Ghrelin, a peptide secreted by endocrine cells in the gastrointestinal tract, is thought to play a significant role in the regulation of energy balance due to its effects on the stimulation of food intake [1,2] and weight gain [1-3] in rodents. It has been suggested that ghrelin may also play a role in meal initiation in humans, since the concentration of ghrelin increases immediately prior to a meal [4] and decreases after eating [4-6]. Furthermore, ghrelin infusions are associated with feelings of hunger and increased energy intake during a buffet-style lunch [7].
Despite the evidence indicating a role in acute food intake, little is known about the factors regulating ghrelin and its effects on long-term energy balance in humans. One hypothesis is that ghrelin secretion is up-regulated in periods of negative energy balance and down-regulated in periods of positive energy balance [8]. Since energy balance is a function of both energy intake and expenditure, ghrelin concentrations should increase or decrease with fluctuations in food intake (macronutrient composition and/or energy intake) and/or energy expenditure. In turn, increased ghrelin concentrations should be associated with higher food intake. However, the effects of daily fluctuations in food intake and energy expenditure on ghrelin have not been investigated in humans.
The purpose of the present study was to determine how changes in macronutrient composition, energy intake, and energy expenditure affect preprandial ghrelin concentrations, and ghrelin's subsequent effects on food intake.
Results
Body weight and composition
Ghrelin was negatively related to body fat percentage (r = -0.46, P < 0.05) and BMI (r = -0.18, P < 0.02), but not body weight (r = -0.16, P > 0.45). There were no significant body weight changes during the seven day observation periods (2)(data not shown, P > 0.40).
Effect of treatment on macronutrient and energy intake
The composition of the treatment beverages and their contribution to daily food intake is listed in Table 1. Overall, macronutrient intake during the seven day observation periods was primarily determined by the composition of the treatment beverages (Table 2).
Table 1 Macronutrient composition of treatment beverages for one day, and their proportion of total daily macronutrient and energy intake during the seven day treatment periods.
Hi-CHO Hi-PRO Hi-FAT
Composition of Treatment
Energy (MJ/d) 2.13 2.11 2.11
Carbohydrate 113 83 8
Protein (g/d) 6 34 7
Fat (g/d) 4 4 50
Percentage of Total Daily Intake
Energy (%) 17.5 ± 4.0 17.1 ± 3.2 17.8 ± 3.4
Carbohydrate (%) 25.8 ± 4.4 20.2 ± 4.3 2.5 ± 0.7
Protein (%) 4.5 ± 12.9 28.3 ± 4.8 7.7 ± 2.3
Fat (%) 5.1 ± 2.4 5.0 ± 1.8 41.5 ± 9.9
presented as means ± SD
Hi-CHO = carbohydrate treatment beverage
Hi-PRO = protein/carbohydrate treatment beverage
Hi-FAT = fat treatment beverage
Table 2 Effect of the treatment beverages on macronutrient and energy intake
Hi-CHO Hi-PRO Hi-FAT
Macronutrient Intake (% of daily total)
Carbohydrate (%) 60.4 ± 6.1a 56.4 ± 6.9b 47.0 ± 8.3c
Protein (%) 13.5 ± 2.3a,c 16.6 ± 3.2b 13.5 ± 3.3c
Fat (%) 26.3 ± 5.7a 26.0 ± 5.6a 39.1 ± 6.3b
Macronutrient (g/day) and Energy (MJ/d) Intake
Carbohydrate (g) 450.9 ± 80.4a 427.3 ± 83.3b 347.3 ± 102.3c
Protein (g) 100.6 ± 22.0a,c 123.4 ± 19.4b 97.5 ± 22.3c
Fat (g) 91.0 ± 34.2a 88.8 ± 27.6a 126.9 ± 29.8b
Energy (MJ/d) 12.6 ± 2.7 12.7 ± 2.3 12.2 ± 2.5
presented as means ± SD
different letters in the row denote statistical significance (mixed model ANOVA)
Hi-CHO = carbohydrate treatment beverage
Hi-PRO = protein/carbohydrate treatment beverage
Hi-FAT = fat treatment beverage
Energy expenditure and macronutrient intake effects on preprandial ghrelin
Average 24 hour energy expenditure (24EE; uncorrected activity log alone) was 13.9 ± 1.9 MJ/d compared to 12.6 ± 1.6 MJ/d for total energy expenditure (TEE; doubly labeled water), which is an average over-reporting of energy expenditure of 11%. Thus, our assumption that subjects would likely misreport energy expenditure and the values would require adjustment was valid.
The mean preprandial ghrelin concentrations during the last week of each treatment period were 2501.4 ± 438.0 pg·mL-1 for Hi-CHO, 2869.5 ± 817.3 pg·mL-1 for Hi-PRO, and 2688.2 ± 755.5 pg·mL-1 for Hi-FAT (Figure 1). These values are higher than reported in similar investigations. This discrepancy is explained by the use of the Linco Research Total Ghrelin RIA kit, which produces values that are approximately 10-fold higher than the most commonly used kit (Phoenix Pharmaceuticals)[9]. In a side-by-side comparison, both kits have been found to be analytically acceptable despite the differences in values obtained [9]. Furthermore, the ghrelin concentrations of at least two studies using the same kit were very similar to those we measured [10,11]. The within- and between-subject coefficients of variation for the two observation periods (seven days per period) were 12.9% and 23.0%, respectively.
Figure 1 Effect of covert manipulation of macronutrient intake on preprandial ghrelin over the course of one week. Hi-CHO = carbohydrate treatment beverage Hi-PRO = protein/carbohydrate treatment beverage Hi-FAT = fat treatment beverage 1 = Monday 2 = Tuesday 3 = Wednesday 4 = Thursday 5 = Friday 6 = Saturday 7 = Sunday There were no significant treatment effects (mixed model ANOVA). Data are shown on the original scale (see text for details)
Preprandial ghrelin was not influenced by treatment, 24EE, macronutrient composition, and selected (without treatment beverages) and total (including treatment beverages) energy intake (breakfast or entire day), or the interactions between these variables (previous or same day)(all P between 0.40 to 0.80). As a further test, we included energy intake for seven days prior to- and two days after each ghrelin value. None of these days were significant (all P between 0.40 to 0.90). Individual day and mean 24EE up to the prior 4 four days before each ghrelin measurement was also not significant (all P between 0.10 to 0.90).
Effect of ghrelin and energy expenditure on macronutrient and energy intake
Selected and total energy intake for the entire day were significantly influenced by treatment period (P < 0.02), Monday/Friday effect (P < 0.003), Sunday effect (P < 0.03), and 24EE (P < 0.008) (Table 3a). Classifying energy intake into the three macronutrients, the only macronutrient influenced by 24EE was total and selected carbohydrate intake (P < 0.03, and P < 0.02, respectively) (Table 3b). There was no significant effect of ghrelin on total or selected energy intake for breakfast or entire day (all P between 0.80 to 0.90).
Table 3 Determinants of total energy intake (log10) (A) and carbohydrate intake (log10) (B)
A Independant Variable Slope SE P
Intercept -0.04 0.23 0.85
Treatment Period 0.13 0.05 <0.02
Sunday effect 0.18 0.08 <0.02
Monday/ Friday effect -0.18 0.06 <0.003
24EE (log10) 1.53 0.57 <0.001
B Independant Variable Slope SE P
Intercept 3.94 0.09 <0.0001
Treatment Period 0.10 0.03 <0.003
24EE (log10) 0.53 0.23 <0.03
Treatment Period = first 8 wk treatment vs. second 8 wk treatment period
Sunday effect = Sunday vs. other days of the week
Monday/ Friday effect = Monday and Friday vs. the other days of the week
24EE = daily energy expenditure
Power analyses
The partial correlation between breakfast energy intake and ghrelin was 0.07. At 80% power, we could have detected a ghrelin effect if the true partial correlation was a small as 0.36. For powers of 90% and 95%, the true partial correlations would have had to be 0.40 and 0.43, respectively. The partial correlation between total energy intake and ghrelin was even lower than that with breakfast energy intake (r = 0.003). Note that, for a partial correlation of 0.40, ghrelin would have only been explaining about 16% (0.402) of the variation in energy intake, still a relatively small percentage of explained variation for a hormone purported to exert a large influence on intake.
Discussion
Of the variables related to energy balance measured in this study (daily macronutrient and energy intake, energy expenditure, and body weight and composition), none appear to play a role in preprandial ghrelin regulation. Similarly, ghrelin did not significantly predict macronutrient or energy intake, despite a power analysis indicating that we would have detected even a moderate effect of ghrelin on intake.
Most of the evidence linking food intake and ghrelin comes from single meal, short-term studies. The ingestion of amino acids or a protein meal results in a post-prandial increase in ghrelin [12-14], whereas high- [14,15] or moderate carbohydrate [4,5,16], and fat [14] meals decrease ghrelin. Carbohydrate meals may result in a greater post-prandial suppression of ghrelin than fat [16,17]. However, it has been reported that preprandial ghrelin is unrelated to macronutrient intake in a large (118 subjects) cross-sectional study [18] and a 12 week longitudinal study [19]. Similarly, three weeks of a high fat diet has been shown to have no effect on fasting ghrelin [20]. Based on the results of the current study and others [18-20], it appears that macronutrient intake does not affect preprandial ghrelin, and any macronutrient-specific effects are limited to the post-prandial period.
Wren et al.[7] were the first investigators to demonstrate that the infusion of ghrelin acutely results in an increase energy intake in humans. The lack of an energy-intake stimulating effect of ghrelin on food intake in the present study when compared to Wren et al.[7] may be related to the amount of ghrelin that was infused (resulting in concentrations twice that under fasted conditions), and the non-free living nature of the subjects. However, other studies have also failed to detect an increase in hunger after ghrelin infusion [21,22]. Ghrelin concentrations do not predict the timing of a meal request or meal size [23], and are unaffected by energy-restricted diets [10,18,24] and when appetite is increased [10]. Interestingly, it has also been shown that fasting ghrelin is negatively associated with energy intake [25]. In this same study [25], Caucasians had ghrelin concentrations that were approximately double that of Pima Indians, yet there was no difference in food intake between the groups.
Although body weight typically increases by ≈ 4.5 kg in men and ≈ 7.3 kg in women over the course of 30 years [26], the human body regulates energy balance rather well (within 1% over the course of 20 years)[27]. The strength of the relationship between total energy intake and 24EE measured in this study reflects this regulation, but our data indicate that 24EE does not influence ghrelin. One other study has shown that ghrelin does not appear to be influenced by exercise, regardless of exercise intensity [28]. This longitudinal study (three months) of normal weight young women indicated that ghrelin increases in response to an exercise regimen, but only when exercise induces weight loss. Therefore, it appears that ghrelin is not influenced by changes in energy expenditure alone.
Conclusion
In conclusion, it appears that macronutrient and energy intake, and energy expenditure have no effect on preprandial ghrelin. None of the variables measured in this study explain the high daily variability in preprandial ghrelin observed over the course of two-seven day periods. In turn, this study fails to detect the energy intake-stimulating effect of ghrelin, despite carefully measured food intake that lasted more than a week and a study powered to detect even a moderate effect of ghrelin.
Methods
Subjects
Twelve healthy, non-smoking men were recruited from the Beltsville, MD area to participate in this study (Table 4). All subjects were weight-stable, and not using any medications known to affect food intake, appetite or water balance. The John Hopkins Bloomberg School of Public Health Committee on Human Research approved the study protocol. Subjects provided written informed consent and received a medical evaluation by a physician that included measurement of blood pressure and analysis of fasting blood and urine samples to screen for presence of metabolic disease.
Table 4 Characteristics of the subjects (n = 12)
Mean SD
Age (yr) 39 9
Height (m) 1.81 0.07
Weight (kg) 79.9 8.3
Body Mass Index (kg·m-2) 24.1 1.4
Body Fat (%) 18.1 1.7
Ad libitum feedings
Voluntary food intake was studied continuously for 16 weeks, whereby subjects consumed only foods provided by the Human Studies Facility (HSF) at the Beltsville Human Nutrition Research Center (BHNRC). Subjects choose foods ad libitum from the menus, and could consume any part or all of a food item, then return the remaining portion to be weighed. BHNRC staff that came into contact with the subjects provided no guidance as to the quantities and/or types of food items chosen. During weekdays, subjects reported to the BHNRC in the morning to eat breakfast, pack selected food items for lunch, then return again in the evening for dinner. Any food taken from the HSF that was subsequently not eaten (all or partial quantities), was returned the next day, and weighed and recorded. On Friday evenings, subjects were provided with coolers packed with a large amount of food for weekend meals. The weekend coolers provided a wide variety of foods in excess quantities, and subjects were allowed to request additional food items be included. Weekend food could be consumed on either day as long as the subjects logged which day each food item was eaten. All uneaten weekend food was returned on Monday, and weighed and recorded. Although subjects were instructed to consume only food items provided by HSF, they were allowed free access to beverages including caloric, noncaloric and alcoholic beverages. Detailed records of the amount, composition and name brand of beverages was submitted daily. In addition to beverages provided on the menu (milk and juice), both regular and decaffeinated coffee and tea were available at meals.
Menus
Food items offered in the morning (breakfast and lunch) were presented in a cafeteria-style setting as three different rotating menus, each lasting seven days (Table 5). Some food items remained on all three menus (e.g. milk and orange juice). In the evening, breakfast and lunch items were also available. A typical dinner was presented cafeteria-style as one or two entrée selections with optional gravies or sauces, and a minimum of three vegetables and side dishes. A garden salad with a variety of additional toppings and dressings was also available. Fifteen different dinner menus were rotated daily (Table 5).
Table 5 Representative food offerings during breakfast and lunch (one of three weekly rotations), and one dinner (1 of 15 daily rotations).
BREAKFAST AND LUNCH DINNER
Beverages Cereals Bread Meat, Dairy, Eggs Snack Packaged Foods Produce #15
2 % milk Hot (6) English muffin Ham Fig bars Vegetable soup Apple Turkey
Skim milk Cold (10) Waffle Chicken salad Granola bar (LF) Beef w/veg soup Orange Chicken gravy
Orange juice Honey bun Salami Popcorn Clam chowder Banana Mashed potatoes
Apple juice Bread (4) Provolone cheese Short bread cookies Noodle soup Grapes Mixed
Vegetable juice Pita bread American cheese Brownie Pizza Peaches Citrus salad
Buttery cracker Scrambled egg Strawberry twist Pocket sandwhich Dates Cranberry sauce
Saltine cracker Bacon Chocolate bar (2) Sausage biscuit Garden salad Sourdough bread
Yogurt (FF) Peanuts Lettuce Macaroni & cheese
Cottage cheese Peanut butter Tomato
Parmesan chesse Carrots
Cucumber
Celery
(#) = number of items available in a category
LF = low fat
FF = fat free
The goals of the menu design were to allow detection of macronutrient selection by offering a wide range of carbohydrate, fat- and/or protein-rich foods, and to provide a variety of commonly available foods typical of what many Americans eat. In a research setting it is impossible to duplicate the degree of food choice available in real life. However, more than 300 food items were used to develop menus for this study, and specific requests for food items were incorporated into the menus whenever possible.
Recording and tracking of food intake
After each subject selected his desired foods, he presented them to a staff member that recorded the identity and weight of each food item by hand and on a computer (combination of bar code recognition of the food item and hand-entering of the weight). Upon termination of feeding, each subject presented his tray to a staff member that weighed any uneaten food. The accuracy of the food item recording process was verified by comparing the information on the computer with the hand-entered logs. This verification procedure was followed daily, and repeated at the end of the study with all food records. Energy and macronutrient composition were determined by consultation with the USDA Nutrient Database for Standard Reference [29].
Covert manipulation of macronutrient composition
During the 16 weeks of ad libitum intake, subjects were randomly assigned to two of three treatments. Each treatment lasted 8 weeks with no break between the periods. The treatments consisted of a daily beverage that contained ≈ 2 MJ/day of predominantly carbohydrate (Hi-CHO), fat (Hi-FAT), or a combination of protein and carbohydrate (Hi-PRO) (Table 1). The daily beverage was divided into three equal portions, and subjects consumed them with each of the three primary meals. The protein drink was designed to provide half the daily Recommended Daily Allowance (RDA) [30] of protein, with the balance carbohydrate. The drinks were formulated using sucrose, heavy whipping cream, and egg white as the principle source of carbohydrate, fat, and protein, respectively. Water, fat free non-dairy creamer, and aspartame were used to provide volume, adjust texture and add sweetness. Cocoa was added to all drinks to provide a uniform taste and appearance. Subjects were blinded to the treatments and the three drinks were judged to be indistinguishable by a taste panel conducted in our laboratory.
Ghrelin analysis
Each morning for the last seven days of each treatment period, subjects reported to the laboratory after a 10–12 hr fast, provided a blood sample, then reported to the HSF to eat breakfast. Blood was collected in tubes containing EDTA, centrifuged, and stored at -80°C until analysis. Plasma ghrelin was analyzed using a commercially available radioimmunoassay kit (Total Ghrelin, Linco Research, Inc.). The intra- and interassay coefficients of variation (CV) were 5.6% and 7.3%, respectively.
Body weight and composition
Before breakfast and after voiding, body weight was determined weekly on an electronic balance to the nearest 0.01 kg. Body composition was measured by Dual-energy X-ray Absorptiometry (DEXA; QDR 4500, Hologic, Inc, Waltham, MA).
Total and 24 hr energy expenditure (24EE)
To "capture" daily variations in energy expenditure, we combined a self-reported activity log [31] and doubly labeled water measurements. Although doubly labeled water is the "gold standard" measure of free-living energy expenditure, its use is limited by the production of a single value that is assumed to represent average energy expenditure over the course of the dosing period (seven days in this study). This seven day value for energy expenditure is not useful to compare with daily variation in ghrelin and food intake (macronutrient composition and energy intake). Since self-reported measures of energy expenditure (that can provide a daily energy expenditure value) may be misreported by subjects [32,33], we adjusted the daily numbers using doubly labeled water measurements (see below).
Twenty-four hour energy expenditure (24EE) was estimated using a daily recording log method, modified from Bouchard et al. [31]. Briefly, subjects recorded their daily activities in a log every 15 min over the course of the last seven days of each treatment period. Activities were entered in as a number (1–9), corresponding to example activities listed in the log. Each activity assumed a pre-determined energy expenditure score, thus energy expenditure was calculated as time spent in that activity times the energy expenditure rate.
Total energy expenditure (TEE) was concurrently measured by the doubly labeled water method as described by Speakman [34], which provided an estimate of energy expenditure during the last seven days of each treatment period. Subjects reported to the BHNRC between 6:30 and 9:00 a.m., at which time they received an oral dose of H2 18O (0.16 g/kg body weight) and 2H2O (0.30 g/kg body weight). Urine samples were collected immediately before the dose and on every morning (second void) for the last seven days of the treatment period. The first sample was collected approximately 24 hr after the dose. Enrichments of 2H and 18O in urine samples were measured by infrared spectroscopy and isotope ratio mass spectrometry, respectively. TEE was calculated using the equations of Weir [35].
Individual daily 24EE values were corrected using the ratio adjustment (notation denoting subjects is suppressed),
24EEdayx, corrected = 24EEdayx × (TEE/24EEday 1–7), where
24EEdayx is the uncorrected daily energy expenditure value from the activity log for one of the seven days (day X),
TEE is the daily mean energy expenditure estimate using doubly labeled water. Represents a single value during the seven days of measurement (of which 24EEdayx is one), and
24EEday 1–7 is the mean of the seven days of uncorrected 24EE values corresponding to TEE, of which 24EEdayx is one.
To simplify the notation, the 24EEdayx, corrected value for day X will subsequently be referred to as 24EE.
Data transformation
To check the assumption of homogeneous variances necessary for valid F-tests and correct P-values, we used the standard technique of plotting the standard deviations (SD's) against the means for selected energy intake, grouping observations by subject and treatment period. The results of this scatter plot revealed a strong positive linear relationship (r = 0.67, P < 0.001). The relationships between the SD and mean for macronutrient and energy intake (total and selected), and 24EE were also positive and significant. This indicated that the SD's (variances) were a function of the mean and that the data needed to be transformed. We followed methods described by Draper and Smith [36], and used a family of transformations based on logarithms. For selected energy intake, this transformation was log (b0 + b1yi), where b0 and b1 are the estimated coefficients of the line fit by regressing the SDs on the means, and yi represents the energy intake data. The other variables were transformed using this same family of transformations. This procedure resulted in homogeneous variances for all variables once transformed, satisfying ANOVA assumptions. We present the data on the original scale in tables and figures for ease of interpretation (unless indicated otherwise).
Due to the free-living nature of the subjects, there were three observations (of 168) where (for unknown reasons) a subject's food intake differed greatly from habitual intake due to a skipped meal or meals with low energy intake. For this reason, these observations were not used in the analyses. Additionally, a preliminary sensitivity analysis and residual diagnostics (e.g., restricted likelihood distance, Cook's D; optional output of Proc Mixed, new in version 9.1, in [37]) suggested they were outliers.
Statistical analysis
The experimental design was an incomplete block crossover design, with two of the three drink treatments given sequentially to each subject. Data were analyzed in the mixed linear models framework, using the Proc Mixed procedure in SAS (version 9.1)[37]. Subject-to-subject variation was modelled as a random effect. Repeatedly measuring each subject over the seven days induced an autoregressive covariance structure we modelled as AR(1). Other design effects we retained in our modelling were a two level period effect ((first 8 week treatment period (1) vs. the second 8 week period (2)), and two day-of-the-week variables, found in a preliminary analysis to account for day-of-the-week effects. Each of these day-of-the-week variables classify days into two groups: (1) Sunday (0 vs. 1 for other days of the week) and (2) Monday/Friday (0 vs. 1 for other days of the week). They allow for the major differences in food intake and energy expenditure due to day-of-the-week effects. Some subject-specific variables, such as body weight, were included as covariates as appropriate. The treatment effects (Hi-CHO, Hi-PRO, and Hi-FAT) were included in all models.
For models predicting ghrelin concentration, we included 24EE, energy intake, and the interaction between 24EE and energy intake. We also considered prior day (up to 7 days) and subsequent day (up to 2 days) values for energy intake and ghrelin, and their interactions as candidate covariates. Values for up to 4 prior days for 24EE were used to predict ghrelin. For models predicting daily energy intake, we included preprandial ghrelin concentrations, 24EE, the interaction between ghrelin and 24EE, and additionally considered as candidate covariates the prior (seven days) and subsequent (two days) days for these two variables and their interactions. We explored models that included other variables and interactions, but none of those variables appeared useful. Data are presented as total intake (intake including treatment drinks) and/or selected intake (intake without treatment drinks). Values are presented as means ± SD unless indicated otherwise.
Since a preliminary analysis suggested that the effect of ghrelin on energy intake was small or negligible, we conducted a power analysis to determine our ability to detect an effect of ghrelin if the effect was small. This was accomplished by Monte-Carlo simulation (creating simulated data sets based on the data we collected) and, starting with no effect of ghrelin (a true coefficient of zero for ghrelin in a regression context), determining how large the true coefficient needed to be to obtain significance for most of the simulations, at powers of 80%, 90%, and 95%, with 1000 simulations for each coefficient value. These results are most easily interpreted as how large a partial correlation between ghrelin and energy intake (adjusting for all other fixed and random effects, other than ghrelin) would be necessary for us to detect it. We conducted this analysis for both total energy intake and breakfast energy intake (the latter was the meal most likely to be influenced by preprandial ghrelin because of the timing of the blood draw).
Authors' contributions
MK was responsible for statistical analysis and interpretation. DR was responsible for supervising the food intake portion of the study. WR conceived the study, and supervised the data collection and analysis. DP was responsible for ghrelin analysis, data collection, statistical analysis and manuscript preparation. All authors read and approved the final manuscript.
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| 15745452 | PMC555957 | CC BY | 2021-01-04 16:37:37 | no | J Negat Results Biomed. 2005 Mar 3; 4:2 | utf-8 | J Negat Results Biomed | 2,005 | 10.1186/1477-5751-4-2 | oa_comm |
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BMC Pregnancy ChildbirthBMC Pregnancy and Childbirth1471-2393BioMed Central London 1471-2393-5-71583314710.1186/1471-2393-5-7Study ProtocolAntiplatelet agents for prevention of pre-eclampsia and its consequences: a systematic review and individual patient data meta-analysis The Perinatal Antiplatelet Review of International Studies (PARIS) Collaboration Steering Group on behalf of the PARIS Collaboration [email protected] PARIS Collaboration [email protected] Centre for Perinatal Health Services Research, Building DO2, University of Sydney, NSW, 2006, Australia2005 18 3 2005 5 7 7 24 2 2005 18 3 2005 Copyright © 2005 Askie and The Perinatal Antiplatelet Review of International Studies (PARIS) Collaboration Steering Group on behalf of the PARIS Collaboration; licensee BioMed Central Ltd.2005Askie and The Perinatal Antiplatelet Review of International Studies (PARIS) Collaboration Steering Group on behalf of the PARIS Collaboration; 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 is now good evidence that antiplatelet agents (principally low dose aspirin) prevent pre-eclampsia, a leading cause of morbidity and mortality for pregnant women and their babies. A Cochrane Review identified moderate, but clinically important, reductions in the relative risks of pre-eclampsia (19%), preterm birth (7%) and perinatal mortality (16%) in women allocated antiplatelets, rather than placebo or no antiplatelet.
Uncertainty remains, however, about whether some women (in terms of risk) benefit more than others, what dose of aspirin is best and when in pregnancy treatment should ideally start. Rather than undertake new trials, the best way to answer these questions is to utilise existing individual patient data from women enrolled in each trial.
Methods/Design
Systematic review with meta-analysis based on individual patient data. This involves the central collection, validation and re-analysis of thoroughly checked data from individual women in all the available randomised trials.
The objective is to confirm that antiplatelet agents, given during pregnancy, will reduce the incidence of pre-eclampsia. The review will then determine the size of this effect, and whether antiplatelets delay the onset of pre-eclampsia or its impact on important outcomes for women and their babies. It will also explore whether the effect of antiplatelets differs by womens' risk profile; when commenced during pregnancy; and/or by dose.
Discussion
The PARIS (Perinatal Antiplatelet Review of International Studies) Collaboration has been formed to undertake the review. This will be the first individual patient data review in the perinatal field. Final results should be available by 2006–7.
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Background
Clinical significance of pre-eclampsia
World-wide, over half a million women die each year of pregnancy related causes with 99% of these occurring in low resource countries [1,2]. An estimated 10–15% of these maternal deaths are associated with hypertensive disorders of pregnancy [3].
High blood pressure is common during pregnancy: approximately one in ten women will have their blood pressure recorded as above normal at some point before delivery [4]. For women who develop raised blood pressure but have no other complications, pregnancy outcome is similar to that for women with normal blood pressure. Pre-eclampsia, a multi-system disorder of pregnancy usually associated with high blood pressure (hypertension) and proteinuria, complicates 2–8% of pregnancies [5]. It can affect the mother's organs, leading to problems in liver, kidneys and brain, and to abnormalities of the clotting system. As the placenta is also involved, there are increased risks for the baby. The most common problems are poor fetal growth due to inadequate blood supply through the damaged placenta, and prematurity, related either to the spontaneous onset of pre-term labour or the need for an early, elective delivery.
Although the outcome for most women is good, pre-eclampsia and eclampsia (the rare occurrence of seizures superimposed on the syndrome of pre-eclampsia) are major causes of maternal mortality. In low resource countries pre-eclampsia and eclampsia account for 10–15% of maternal deaths [3] whilst in high resource countries pre-eclampsia is consistently a leading cause of maternal mortality [6,7]. Perinatal mortality is also increased [8,9]. There is little good quality information about morbidity for either mother or baby, but it is likely that this too is high. For example, pre-eclampsia accounts for about one fifth of antenatal admissions [10], two thirds of referrals to day care assessment units [11] and a quarter of obstetric admissions to intensive care units [12]. Pre-eclampsia is an antecedent for up to 19% of pre-term births [13] and 12% of growth restricted babies [14]. Also of note is the high rate of intrauterine growth restriction (20–25%) in pregnancies complicated by pre-eclampsia and the possible lifelong health effects due to prenatal programming [15].
Pre-eclampsia is a multi-factorial condition. Although its aetiology remains unclear, there have been significant advances in the understanding of the pathophysiology of the disorder. The primary lesion is thought to be deficient trophoblast invasion of the maternal spiral arteries in the second trimester, leading to underperfusion of the uteroplacental circulation and placental ischaemia [16]. The resulting placental damage is thought to lead to release of factors into the maternal circulation, which are responsible for the maternal syndrome. Activation of platelets and the clotting system may occur early in the course of the disease, before clinical symptoms develop [17,18]. Deficient intravascular production of prostacyclin, a vasodilator, with excessive production of thromboxane, a platelet-derived vasoconstrictor and stimulant of platelet aggregation [19,20], have also been demonstrated to occur in pre-eclampsia. These observations have led to the hypothesis that antiplatelet agents, low dose aspirin (<300 mg/day) in particular, might prevent or delay the development of pre-eclampsia or reduce its severity and the risk of adverse events.
It is further hypothesised that the effect of antiplatelets may be different if treatment is started before placental implantation is complete. [21]. If this hypothesis were correct, the greatest benefit should be seen in women who started treatment before 16 weeks gestation, with the effect attenuating with later onset of treatment. Similarly, it remains unclear as to the most appropriate dose of antiplatelet therapy for the prevention of pre-eclampsia in order to maximise benefits whilst minimising harms [22]. It has been suggested that low doses of aspirin may selectively inhibit the cyclo-oxygenase pathway in platelet production but not in vessel wall endothelium thereby diminishing the synthesis of thromboxane but not of prostacycline. A higher dose may inhibit both thromboxane and prostacycline thereby neutralising the effect of the intervention [20]. However, there is also limited evidence from randomised trials that a higher dose of aspirin may effect a greater reduction in the risk of pre-eclampsia [23].
Although it is known that pre-eclampsia is a multi-system disorder, the relationship between the placental pathology and maternal endothelial response is not fully understood. Numerous maternal factors can predispose to the disorder, such as previous pre-eclampsia, diabetes, renal disease, chronic hypertension or other risk factors [24]. The syndrome known as pre-eclampsia may also be more than one disease, each with distinct origins, pathologic characteristics and natural history, rather than one fundamental process with varying degrees of clinical severity [25,26]. Hence, the ability to assess the affect of antiplatelets on women with individual risk factors, or a series of risk factors, is of great relevance to clinicians and women. A meta-analysis based on data from individual women will enable the exploration of these hypotheses.
Randomised trials of antiplatelet agents
The effects of antiplatelet agents were first evaluated in small randomised trials, which reported striking reductions in the risk of hypertension and proteinuria [27-29]. These trials were too small to provide reliable information about other more substantive outcomes, such as perinatal mortality and preterm birth. Also, there was no information about the potential hazards of antiplatelet therapy, such as a possible increased risk of bleeding for both the woman and her baby, or possible effects on infant and child development. The promising results of these early trials led to several large studies around the world. Before these could be completed, however, the use of low dose aspirin had already become relatively widespread for women considered at increased risk of pre-eclampsia. Results of the larger trials were disappointing, as they failed to confirm any statistically significant reductions in substantive outcomes [30]. Nevertheless, the first Cochrane Review of these trials demonstrated that, when taken together, there are modest, but clinically important benefits [31,32].
Summary of systematic review of aggregate data in 2004
The updated Cochrane Review identified 51 trials with over 36,500 pregnant women evaluating antiplatelet agents, principally low dose aspirin, for the prevention of pre-eclampsia [23]. Nine of these trials included over 1000 women, and 15 involved less than 50 women. Fifty-one studies were excluded, mostly due to the non-availability of clinically relevant data. Aggregate data from the included trials demonstrated a 19% reduction (RR 0.81, 95% CI 0.75–0.88) in the risk of developing pre-eclampsia associated with the use of antiplatelet agents, rather than placebo or no antiplatelet. There was also a small (7%) reduction in the risk of pre-term birth (RR 0.93, 95% CI 0.89–0.98) and a 16% reduction in the risk of the baby dying (RR 0.84 95% CI 0.74–0.96). Based on the average risk of women included in these trials, about 70 women would have to be treated to prevent one case of pre-eclampsia, and 240 to prevent one baby death.
These effects are much smaller than had initially been hoped for but, nevertheless, potentially they have considerable public health importance. The conclusion from this aggregate data review was that for most low-risk women large numbers of women would need to be treated with antiplatelets agents to prevent one episode of either pre-eclampsia or perinatal death. Whether there are specific high risk sub-groups of pregnant women for whom there might be greater benefits, remains unclear, as does the best time to initiate treatment, and at what dose. The aim is now to extend this review based on aggregate data, to utilise the available data for every individual woman in each trial to help address these remaining questions. The use of individual data for each woman will allow for more powerful and flexible analysis of both subgroups and outcomes [33,34]. This review will therefore provide more specific information to guide the care of women at risk of pre-eclampsia.
Limitations of the review using published, aggregate data
• Many studies were excluded because publications did not report sufficient information to allow them to be included in the review.
• The published aggregate data is variable in the completeness of outcome reporting and in definitions used between trials.
• The aggregate data meta-analyses were restricted to analysing outcomes for complete trials. As many trials included women with a wide range of risk profiles at trial entry, trials could only be defined by average values. Such aggregated outcomes such as 'proportion of women developing pre-eclampsia' conceal a range of severity of disorder and so it was not possible to explore fully whether the effectiveness of antiplatelet therapy differed according to risk.
• It was also difficult to make precise recommendations about when to start treatment. While most trials could generally be classified as starting treatment at earlier or later in gestational ages, there was a wide within-study variation that could not be explored with aggregate data analyses.
• There is a markedly skewed distribution of effect estimates for each trial around the summary effect on pre-eclampsia, with more small positive trials than small negative trials. This suggests the possibilities of publication bias or that the differences in the characteristics of the women enrolled in small and large studies has an important influence on the effects of antiplatelet agents.
Ways of overcoming these limitations by using individual patient data
• Obtaining individual patient data from previously excluded trials will allow assessment of whether these trials are eligible for inclusion, both in terms of available outcomes and methodological quality. This will potentially increase the power and scope of the analyses.
• Trial level information obtained by direct discussion with the trialists enables clarification of the definitions and measurements used. Data for measured, but previously unreported, outcomes can also often be obtained and trialists, who are part of a Collaborative group, may be able to provide missing outcome data. Furthermore, we will be able to apply common definitions across trials based on each woman's baseline and clinical data. For example, where possible, we will define pre-eclampsia based on a set of uniform criteria.
• Subgroup analyses will be performed for a number of different risk factors using individual patient's specific risk data, rather than using the aggregated risk profile of all women enrolled in a particular trial.
• By obtaining gestational age at randomisation for each individual woman, more precise patient-based analyses can be performed exploring whether gestation at treatment commencement alters the effectiveness of antiplatelets.
• Formation of a Collaborative group, is likely to lead to a more complete identification of all relevant trials, including those previously unpublished. This may help overcome the potential for publication bias. The patient-level data will also allow us to explore whether there were important differences in the characteristics of women enrolled in small and large trials.
Methods/design
Objectives
The objective of this review is to confirm that antiplatelet agents, given during pregnancy, reduce the incidence of pre-eclampsia. The review will then determine the size of this effect, and whether antiplatelets reduce the severity of pre-eclampsia and/or its impact on important outcomes for women and their babies. It will also explore whether the effect of antiplatelets differs by womens' risk profile, when commenced during pregnancy, and/or by dose.
The main questions to be addressed in this review are:
• Do antiplatelet agents, primarily low dose aspirin, have clinically important benefits for women at risk of developing pre-eclampsia and their babies?
Investigation of this hypothesis will also explore whether the treatment effect differs, in a clinically meaningful way, between women with different risk factors such as those with a history of early onset pre-eclampsia, renal disease, diabetes, chronic hypertension, or autoimmune disease.
• Does the planned dose of aspirin affect outcome in terms of preventing or delaying the onset of pre-eclampsia or other adverse outcomes, such as preterm birth or perinatal death?
• Do the effects of aspirin differ according to gestation at onset of treatment?
Identifying studies
The search strategy to identify potentially eligible studies will include a search of the register of trials developed and maintained by the Cochrane Collaboration Pregnancy and Childbirth Review Group. Details of how this register is maintained are available elsewhere [35,36], but it involves extensive searching of bibliographic databases such as MEDLINE, The Cochrane Controlled Trials Register and hand searching of relevant journals. Trialists will be asked if they know of any further studies. [See Additional file 1] for the list of trials potentially eligible for inclusion. In addition, all members of the Collaborative Group will be asked to notify any unpublished trials of which they are aware.
Inclusion and exclusion criteria for studies
The inclusion and exclusion criteria for the types of study designs, participants, interventions and data completeness to be included in the review are listed below. Each potentially eligible study will be assessed independently by two members of the Secretariat, unblinded to the trial's identity. Any differences of opinion regarding the assessment of the inclusion criteria will be resolved by discussion between the two assessors. If differences cannot be resolved, a third member of the Secretariat will be asked to assess the study. If individual patient data are unavailable from an eligible trial, the trial will remain included in the review and aggregate data will be used.
a. Study design
Studies will be included in the review if they were randomised trials. Quasi-random study designs, such as those using alternate allocation, will be excluded. The level of allocation concealment within each trial will be assessed according to the criteria outlined in the Cochrane Handbook [37], and classified as either adequate, unclear or clearly inadequate. These assessments will be made together with the outcomes of thorough data checking procedures.
b. Participants
Participants will be pregnant women at risk of developing pre-eclampsia. Women who started treatment postpartum will be excluded, as will those who already have a diagnosis of pre-eclampsia at trial entry (defined as hypertension with new onset proteinuria after 20 weeks gestation, not due to renal disease).
c. Interventions
The interventions will be any comparisons of an antiplatelet agent (such as low dose aspirin or dipyridamole), or any combination of antiplatelet agents, compared with placebo or no antiplatelet agent. This is regardless of dose, mode of administration and irrespective of whether the antiplatelet is in combination with another drug. Trials that assessed only physiological outcomes following a short duration of intended therapy will be excluded.
d. Completeness of follow-up
The main analyses will include all trials that fulfill the previous inclusion criteria, regardless of completeness of follow-up. Sensitivity analyses will be undertaken to assess the effect of the inclusion of data from trials where only small numbers of enrolled participants have available outcome data. The threshold for an acceptable level of data completeness may vary by outcome. For example, for long-term follow-up of women and children, data may be included if follow-up was less than 80% provided that substantive bias between the groups was unlikely. Other outcomes from each trial may only be included in the analysis if available for 80% or more of women.
Data collection, data management and confidentiality
The individual patient data provided by the Collaborators will be de-identified, re-coded as required and stored in a custom-designed Microsoft ACCESS database. It will not include any patient identifying information such as names or addresses. Electronic data will be located on a secure, password protected network server. Copies of hardcopy data will be stored in locked filing cabinets until converted into electronic format, and will then be securely destroyed. Only authorised personnel will have access to the data. All data will be securely stored and archived according to the policies of the major funder, the Australian National Health and Medical Research Council.
The data will be checked with respect to range, internal consistency, consistency with published reports and missing items. Trial details such as randomisation methods, and dose and timing of the interventions will be cross-checked against any published reports, trial protocols and data collection sheets. Integrity of the randomisation process will be examined by reviewing the chronological randomisation sequence and pattern of assignment, as well as the balance of prognostic factors across treatment groups (taking into account stratification factors). Inconsistencies or missing data will be discussed with the individual trialists and attempts will be made to resolve any problems by consensus. Each trial will be analysed individually, and the resulting analyses and trial data will be sent to the trialists for verification.
Data items requested from the trialists
There has been extensive consultation with the PARIS Collaborative Group regarding what data items to collect for each woman in the analyses. The following section contains the list of data items requested from trialists, which has been compiled following this consultation. More detailed definitions for the data items listed below can be found in Tables 1 and 2. Details of the suggested coding for each of the following variables can be found in [see Additional file 2]. A formal request for the provision of the individual patient data was sent in April 2004 [see Additional file 3].
Table 1 Key definitions for enrolment characteristics
Variable Definition
gestational hypertension de novo systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg after 20 weeks' gestation, without proteinuria
severe hypertension systolic BP ≥ 160 mmHg and/or diastolic BP ≥ 110 mmHg
proteinuria ≥ 1+ on dipstick, or ≥ 300 mg/24 hours, or spot urine protein/creatinine ratio ≥ 30 mg/mmol
pre-eclampsia (for women normotensive at trial entry) de novo systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg after 20 weeks' gestation with new-onset proteinuria as described above
pre-eclampsia (for women with chronic hypertension at trial entry) new-onset proteinuria as described above
pre-eclampsia (for women with chronic hypertension and proteinuria at trial entry) signs and symptoms of superimposed pre-eclampsia after 20 weeks' gestation, for example worsening of hypertension or proteinuria
early onset proteinuria proteinuria as defined above, occurring ≤ 33 weeks + 6 days of gestation
early onset pre-eclampsia hypertension and early onset proteinuria as described above
chronic hypertension Essential hypertension: BP ≥ 140/90 mmHg pre-conception or in first half of pregnancy without an underlying cause, or Secondary hypertension: hypertension associated with renal, renovascular, cardiac and endocrine disorders
intrauterine growth restriction (IUGR) or small for gestational age (SGA) growth below the 3rd centile, or as defined in the individual trial
miscarriage / fetal death any death in utero
perinatal death death in utero or within the first 7 days of life
neonatal death live born and any reported death within the first 28 days
Table 2 Key definitions for outcome measures
Main outcomes Definition
pre-eclampsia as defined in Table 1
pregnancy loss / neonatal death miscarriage, fetal death or death of a liveborn infant before hospital discharge
pre-term birth pre-term birth: ≤ 37 weeks + 6 days of gestation moderately pre-term birth: ≤ 33 weeks + 6 days of gestation extremely pre-term birth: ≤ 27 weeks + 6 days of gestation
small for gestational age (SGA) infant infant with birth-weight below the 3rd centile, or as defined in the individual trial
pregnancy with serious adverse outcome pregnancy with any of the above main outcomes for the woman or any fetus/baby, or the death of the woman. If sufficient data available, severe maternal morbidity will also be included in this definition.
Other outcomes Definition
early onset pre-eclampsia as defined in Table 1
maternal death death during pregnancy or up to 42 days after termination of the pregnancy
antepartum haemorrhage any vaginal bleeding before the onset of labour
placental abruption clear evidence of placental separation
severe maternal morbidity including eclampsia, HELLP syndrome, DIC, pulmonary oedema, liver failure, renal failure or CVA/stroke
infant death live born and any reported death from 29 days to 1 year of life or after hospital discharge
neonatal bleeding abnormal bleeding in the neonatal period including periventricular haemorrhage, gastrointestinal, umbilical or other sites
a. Characteristics of trials
1 informed consent
2 dates trial opened and closed to accrual
3 total number of women randomised
4 treatments used in each arm of the trial
5 intended duration of treatments
6 definitions of key outcomes used in the trial
7 method of random allocation
8 stratification factors used
9 methods of allocation concealment
b. Characteristics of enrolled women at trial entry
1 unique identifier for the enrolled woman, coded for anonymity
2 date of randomisation
3 gestational age at randomisation, or best estimate of expected date of delivery or last menstrual period
4 woman's date of birth or age
5 any previous pregnancy
6 blood pressure (systolic, diastolic, whether diagnosed as raised)
7 presence of proteinuria
8 presence of oedema
9 risk factors – multiple pregnancy, autoimmune disease, renal disease, diabetes, chronic hypertension, previous gestational hypertension or pre-eclampsia/eclampsia (including early onset disease), family history of pregnancy-related hypertensive disorders, previous fetal growth restriction, previous perinatal death, abnormal uterine artery Doppler flow and abnormalities on other diagnostic tests
c. Maternal data items
1 hypertension during pregnancy (highest blood pressure, diagnosis of severe hypertension)
2 proteinuria during pregnancy (including date and/or gestation at onset)
3 oedema during pregnancy
4 pre-eclampsia
5 drug treatment for pre-eclampsia or hypertension
6 severe maternal morbidity (including eclampsia, renal failure, disseminated intravascular coagulation, liver failure, HELLP syndrome, stroke)
7 onset of labour (spontaneous or induced or pre-labour caesarean section)
8 mode of delivery (vaginal, vaginal assisted, caesarean section)
9 antepartum haemorrhage (all, placental abruption)
10 postpartum haemorrhage and/or estimated blood loss at delivery
11 maternal mortality
d. Fetal / neonatal / child data items (for each fetus)
1 gestational age at birth and/or date of birth
2 birthweight
3 gender
4 small for gestation age (as defined within each trial: including centile charts and cut-off point used)
5 miscarriage or stillbirth: date and/or gestational age at death/loss
6 neonatal or infant death: date and/or age at death
7 admission to special care baby unit or neonatal intensive care unit
8 use of assisted ventilation
9 number of days in hospital or date of hospital discharge
10 neonatal bleeding (for example, periventricular haemorrhage)
11 child growth and development (such as cerebral palsy, blindness, deafness, significant cognitive delay – as defined within each trial)
Planned analyses
This section contains a summary of the planned analyses. The full, detailed analysis plan will be discussed and agreed upon by the Collaborators before any data have been analysed.
Analysis will aim to be of all women ever randomised and will be based on intention to treat. In the main analyses a two stage approach will be taken. Outcomes will be analysed in their original trial and then these separate results will be combined to give an overall measure of effect. A fixed effect model will be used and the assumption of homogeneity of treatment effects will be tested using the chi squared test. The I2 statistic will also be used to assess consistency of results.
1. Outcomes to be analysed
The main analyses comparing the effect of antiplatelet agents with placebo or no antiplatelet agents will be undertaken for all outcomes listed below, for the woman and any fetus/baby. The planned sub-group and sensitivity analyses will be restricted to the designated main outcomes listed as follows:
a. Main outcomes
• pre-eclampsia
• pregnancy loss / neonatal death
• pre-term birth
• small for gestational age infant
• pregnancy with serious adverse outcome
b. Other outcomes
• early onset pre-eclampsia
• maternal death
• severe maternal morbidity
• antepartum haemorrhage
• placental abruption
• induction of labour
• Caesarean section delivery
• postpartum haemorrhage
• gestation at delivery
• infant admission to special care or neonatal intensive care unit
• infant required assisted ventilation
• neonatal bleeding
• infant death
2. Planned sub-group analyses
The planned sub-group analyses will be restricted to the designated main outcomes unless there are clear indications for expanding the analyses further.
a. Trial-level characteristics
The effect of antiplatelet therapy may vary across the trials in the meta-analysis because they have used different agents in different ways. To explore this further, analyses are planned whereby trials will be grouped according to the agent used and by dose. These analyses will focus on the main outcomes. Trials will be classified into subsets based on the following:
(i) type of antiplatelet(s)
Trials will be grouped by the type of antiplatelet agent (trials that used aspirin alone, trials that used other antiplatelet agents, trials that used both aspirin and other antiplatelets agents) given as the active treatment.
(ii) daily dose of aspirin
In trials that used aspirin alone, trials will be grouped by planned aspirin dose (<75 mg, 75–149 mg, ≥ 150 mg).
b. Patient-level characteristics
One of the strengths of individual patient data reviews is that it allows us to assess eligibility and outcome using individual women's characteristics. Subgroup analyses will explore whether any particular risk factors act as effect modifiers. That is, are there any particular types of women who benefit more or less from antiplatelet agents? These analyses will take account each individual woman's own characteristics, rather than relying on summary measures of the 'average' risk profile of all participants in an individual trial.
Analyses will be undertaken to explore whether there are any particular types of women who benefit more or less from antiplatelet agents based on the following criteria:
(i) risk factor profile for pre-eclampsia at trial entry
Women normotensive at trial entry:
- previous hypertensive disorders of pregnancy: previous early onset (≤33 weeks + 6 days gestation) pre-eclampsia or eclampsia / previous pre-eclampsia / previous gestational hypertension / no previous hypertensive disorders of pregnancy / no previous pregnancy but family history of hypertensive disorders of pregnancy / no previous pregnancy and no family history of hypertensive disorders of pregnancy
- diabetes: pre-existing diabetes at enrolment / no pre-existing diabetes at enrolment
- renal disease: pre-existing renal disease / no pre-existing renal disease
- autoimmune disease: autoimmune disease / no autoimmune disease
- multiple pregnancy: multiple pregnancy / singleton pregnancy
- maternal age: <20 years / 20–35 years / >35 years. Maternal age may also be analysed as a continuous variable.
- diagnostic test results: abnormal uterine artery Doppler scan / other diagnostic test abnormalities / no abnormal diagnostic test results
- previous SGA: previous small for gestational age infant / previous infant not small for gestational age / no previous infant
- primigravida: first pregnancy with no other risk factors / first pregnancy with one or more risk factors / second or subsequent pregnancy with one or more risk factors / second or subsequent pregnancy with no risk factors
Women with hypertension at trial entry:
- hypertension: gestational hypertension / chronic hypertension
(ii) gestation at trial entry
To determine whether antiplatelet agents are differentially effective if given earlier in pregnancy and to determine the magnitude of any difference, gestational age at randomisation will be primarily analysed as a continuous variable in regression analyses. However, a subgroup analysis with women classified according to the following categories may also be performed: <16 weeks, 16–19 completed weeks, 20–23 completed weeks, 24–27 completed weeks, ≥28 weeks gestation. If numbers are insufficient for any category, categories will be combined.
3. Planned sensitivity analyses
a. To assess whether results are robust to the inclusion or exclusion of particular types of trials or patients, the following sensitivity analyses will be conducted:
• exclusion of trials that did not use a placebo for the control group
• exclusion of trials of small size
• exclusion of poor quality trials (assessed by adequacy of allocation concealment, blinding, completeness of follow-up and other data checking procedures)
b. To assess whether results are robust to different methods of analysis or different definitions of pre-eclampsia [38,39], the following sensitivity analyses will be conducted:
• comparison of analyses using random effects and fixed effect models
• comparison of analyses using individual patient data (IPD) only with analyses using individual patient data and aggregate data where IPD unavailable
• comparison of analyses using different definitions of pre-eclampsia
These sensitivity analyses will be carried out for the main outcomes.
4. Additional analyses
Depending on what data are available, the level of heterogeneity encountered and available time a one-stage modeling approach may also be undertaken to further explore important key outcomes as appropriate.
Ethical considerations
Participants in the individual trials have previously given informed consent to participate in their respective trial. The data for this project are to be used for the purpose for which they were originally collected and are available through an agreement between all trialists of the PARIS Collaboration. These trialists remain the custodians of their original individual trial data at all times. Data are provided on the stipulation that all trials have received ethical clearances from their relevant bodies.
Project management
Membership of the PARIS Collaboration will be representative(s) from each of the trials contributing data to the review with an accompanying project coordination and data management structure as described in this section.
The membership and responsibilities of each of these management groups is as follows:
a. Steering Group
The Steering Groupwill be responsible for project management decisions and will meet approximately 4–6 times per year, usually via teleconference. Membership: D Henderson-Smart1 (co-chair), L Duley2 (co-chair), L Askie1 (project coordinator), M Showell1 (project administrator), B Farrell2, L Stewart3, M Clarke4, J King5, C Roberts.1 The first six members act as the Secretariat.
1 Centre for Perinatal Health Services Research, University of Sydney, Australia;
2 Resource Centre for Randomised Trials, University of Oxford, Oxford, UK;
3 Medical Research Council Clinical Trials Unit, London, UK;
4 UK Cochrane Centre, Oxford, UK;
5 Royal Women's Hospital, Melbourne, Australia.
b. Advisory Group
The aim of the Advisory Group is to facilitate representative input from the Collaborative Group to the Steering Group. Membership of the Advisory Group will include people who have contributed to the trials included in the project and other international experts. Each trial that recruited over 1000 women will be invited to have a representative on the Advisory Group. This group will not have regular meetings, but may be consulted from time to time by means of email or teleconference, and may have occasional ad hoc meetings. Co-chairs of the Advisory Group: C Redman, C Roberts.
c. Collaborative Group
All potentially eligible trialists will be contacted and invited to become members of the Collaborative Group. The corresponding author for each study will be contacted in the first instance. If there is no response, the associated statistician, data manager and/or other authors will be contacted. This process will be updated annually for the duration of the project, to ensure that new trialists are offered the opportunity to join the project and contribute their data.
d. Project coordination centre
The project will be coordinated from the Centre for Perinatal Health Services Research (CPHSR), University of Sydney, NSW, Australia. The coordination centre will be responsible for the daily management of the project including correspondence, newsletter production, maintaining current trialist contact information and meeting/teleconference organisation.
e. Data management centre
The data management centre, based at the UK Cochrane Centre, will be responsible for the receipt, storage, and analysis of project data as directed by the Collaborative Group via the Steering Group.
f. Collaborators' meetings
All members of the Collaboration, including the Steering Group, the Advisory Group, and representatives of each participating trial, will be invited to attend regular Collaborators' meetings. These meetings will be scheduled, where possible, to coincide with the biennial International Society for the Study of Hypertension Pregnancy (ISSHP) congresses. The meetings will be designed to allow maximum input from the participating trialists into the design, conduct, analysis and reporting of the project's results. The final Collaborators' meeting, at which the results will be presented for discussion, is scheduled for 2006 in Oxford, UK. The discussion at this meeting will provide the basis for the paper publication.
Funding
The National Health and Medical Research Council (NHMRC) of Australia have provided funding for the project through the Centre for Perinatal Health Services Research, University of Sydney. These funds are: a three year project grant (ID: 253636) for the overall project administration base in Sydney, and a Sidney Sax Public Health Postdoctoral Fellowship (ID: 245521), based in Oxford and Sydney. Additional support is being provided by the Resource Centre for Randomised Trials and the UK Cochrane Centre, located in Oxford, UK, and the Medical Research Council Clinical Trials Unit in London, UK.
Publication policy
The results of the project's analyses will be presented to, and discussed with, the Collaborative Group before publication. The aim of publication will be presentation of the results, rather than their interpretation. The main manuscript will be prepared by the Secretariat, and then circulated to the Steering and Advisory Groups for comment and revision. The revised draft paper then will be circulated to all members of the Collaborative Group for comment before publication. All publications using these data will be authored in the name of the PARIS Collaboration, as follows: Perinatal Antiplatelet Review of International Studies (PARIS) Collaboration.
Discussion
Despite good evidence that antiplatelet agents (principally low dose aspirin) reduce the incidence of pre-eclampsia and its consequences, such as preterm birth and perinatal mortality, uncertainty remains regarding whether some women (in terms of risk) benefit more than others, when in pregnancy treatment should ideally start, and whether treatment effectiveness is dependent on antiplatelet dose.
The best way to answer these questions is to utilise existing individual patient data from all women enrolled in trials that have addressed this question. This approach has been described as the 'gold standard' of systematic review methodology as it allows for more powerful and flexible analysis of both subgroups and outcomes.
The PARIS Collaboration has been formed to undertake a systematic review of all available trials, with meta-analysis based on individual patient data, to answer these important clinical questions. This will be the first individual patient data review in the perinatal field. Provision of data by the participating Collaborators commenced in 2004, and results will be ready for presentation in 2006. Following consultation and discussion with the Collaborative Group, the main publication is expected in early 2007.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors, the named members of the PARIS Collaboration Steering Group, contributed to the development of the protocol, and read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
List of eligible trials text document listing potentially eligible trials
Click here for file
Additional File 2
Suggested coding sheet table listing variables and suggested coding
Click here for file
Additional File 3
Data provision form form to collect trial level data and data provision procedures
Click here for file
Acknowledgements
In addition to the named members of the PARIS management groups, the following people have contributed to the success of the Collaboration: Lynn Hampson, Sonja Henderson, Patsy Spark, Jayne Tierney and Sylvaine Verhulst.
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| 15833147 | PMC555958 | CC BY | 2021-01-04 16:32:05 | no | BMC Pregnancy Childbirth. 2005 Mar 18; 5:7 | utf-8 | BMC Pregnancy Childbirth | 2,005 | 10.1186/1471-2393-5-7 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-3-91573723410.1186/1477-7827-3-9ResearchPlacentation in the paca (Agouti paca L) Bonatelli Marina [email protected] Anthony M [email protected] Marcia R Fernandes [email protected] Oliveira Moacir F [email protected] Lima Marcelo Cardoso [email protected] Maria Angelica [email protected] Department of Surgery, School of Veterinary Medicine, University of Sao Paulo, Sao Paulo, Brazil2 Department of Physiology and Pharmacology, University of Southern Denmark, Odense, Denmark3 Paulista State University, Jaboticabal, Sao Paulo, Brazil4 Mossoró Superior School of Agriculture, Rio Grande do Norte, Brazil2005 28 2 2005 3 9 9 1 12 2004 28 2 2005 Copyright © 2005 Bonatelli et al; licensee BioMed Central Ltd.2005Bonatelli 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 paca is a South American rodent with potential as a commercial food animal. We examined paca placenta as part of a wider effort to understand the reproductive biology of this species.
Methods
Thirteen specimens between midgestation and term of pregnancy were studied by light and transmission electron microscopy.
Results
The placenta is divided into several lobes separated by interlobular trophoblast. Maternal arterial channels and fetal veins are found at the centre of each lobe. In the labyrinth, maternal blood flows through trophoblast-lined lacunae in close proximity to the fetal capillaries. The interhaemal barrier is of the haemomonochorial type with a single layer of syncytiotrophoblast. Caveolae occur in the apical membrane of the syncytiotrophoblast and recesses in the basal membrane, but there is no evidence of transtrophoblastic channels. The interlobular areas consist of cords of syncytiotrophoblast defining maternal blood channels that drain the labyrinth. Yolk sac endoderm covers much of the fetal surface of the placenta. The subplacenta comprises cytotrophoblast and syncytiotrophoblast. There are dilated intercellular spaces between the cytotrophoblasts and lacunae lined by syncytiotrophoblast. In the junctional zone between subplacenta and decidua, there are nests of multinucleated giant cells with vacuolated cytoplasm. The entire placenta rests on a pedicle of maternal tissue. An inverted yolk sac placenta is also present. The presence of small vesicles and tubules in the apical membrane of the yolk sac endoderm and larger vesicles in the supranuclear region suggest that the yolk sac placenta participates in maternal-fetal transfer of protein.
Conclusion
The paca placenta closely resembles that of other hystricomorph rodents. The lobulated structure allows for a larger exchange area and the development of precocial young.
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Introduction
The paca (Agouti paca, L) is a South American rodent that lives in forested habitats near water and feeds largely on fallen fruit. It is hunted for its meat, which is considered a delicacy, and is an important source of animal protein for rural populations. This has led to indiscriminate exploitation, resulting in a significant reduction in the population density of this species in Brazil.
We here describe the morphology of paca placenta as revealed by light microscopy and transmission electron microscopy. This study is part of a wider effort to document the reproductive physiology of paca. It is hoped that the information obtained will contribute to a rational strategy for conservation of the species and possibly for production, as paca has great potential as a commercial food animal [1].
Materials and methods
The observations are based on placentae collected from 13 pregnant females. One was in early gestation, two in midgestation and nine near term of pregnancy. This material was collected at Paulista State University, Jaboticabal, Sao Paulo, Brazil. The research was authorized by the Brazilian Institute of the Environment and Renewable Natural Resources (IBAMA). The experimental protocol was approved by the Bioethics Committee of the School of Veterinary Medicine, University of Sao Paulo.
The animals were sedated with azaperone (Stresnil, Janssen Pharmaceutica, Brazil; 0.1 mg/kg I.M.) and given atropine (0.5 mg I.M.). Anaesthesia was induced with xylazine (Coopazine, Coopers Brazil, Sao Paulo, S.P., Brazil; 1.5 mg/kg I.M.) and ketamine (Holliday Scott S.A., Brazil; 20 mg/kg I.M.). Hemihysterectomy was then performed under aseptic conditions during inhalation anaesthesia with halothane (Hoechst, Frankfurt, Germany). Postoperatively the animals were treated with benzyl penicillin and streptomycin (Pentabiotico®, Fort Dodge, Campinas, S.P., Brazil; 8,000–24,000 IU/kg I.M.) and an analgetic (buprenorphine, Temgesic®, Schering-Plough, S.P., Brazil). A detailed description of the anaesthesia and surgical procedures has been published elsewhere [2].
Pieces of ten placentae were fixed in Bouin's solution or 10% formaldehyde and processed by standard histological procedures for embedding in paraplast, then sectioned at 7 μm (automatic microtome, Model RM2155, Leica, Germany). Sections were stained with haematoxylin and eosin, Masson's trichrome or Gomori's trichrome or by the periodic acid Schiff (PAS) reaction with haematoxylin as counterstain.
Seven placentae were processed for transmission electron microscopy. Small samples were fixed in 2.5% glutaraldehyede in 0.1 M phosphate buffer, pH 7.4, washed in buffer and post-fixed in 1% osmium tetroxide (Polysciences, Warrington, PA, USA). They were then dehydrated, washed with propylene oxide and embedded in Spurr's resin (Spurr's Kit, Electron Microscopy Sciences, CO, U.S.A.). 60 nm sections were made and stained with 2% uranyl acetate (5 minutes) and 0.5% lead citrate (10 minutes). The ultrastructural observations were made with a transmission electron microscope (JEOL 1010, Peabody, MA, U.S.A).
Results
The overall plan of the paca placenta is shown schematically in Figure 1. The labyrinth is divided into lobes separated by interlobular trophoblast. Beneath this is the subplacenta, a structure unique to hystricomorph rodents. The junctional zone between these structures and the maternal decidua contains nests of giant cells. The placenta is attached to the uterus by a pedicle of maternal tissue. In addition, there is an inverted yolk sac placenta, which connects with the fetal surface of the chorioallantoic placenta.
Figure 1 Schematic drawing of the paca placenta. The labyrinth is divided into lobes separated by interlobular trophoblast. Beneath it is found the subplacenta and then the decidua. There is a tenuous attachment to the uterine wall, the pedicle or mesoplacenta. An inverted yolk sac placenta is present throughout gestation.
Histology
The centres of the lobes, the labyrinth and the interlobular regions are clearly defined (Figure 2A). The central region of each lobe contains fetal and maternal vessels around which there is a considerable quantity of connective tissue (fetal mesenchyme; Figure 2B). The vessels carrying maternal arterial blood lack endothelium and they are lined by trophoblast cells (Figure 2C).
Figure 2 The labyrinth of the paca placenta. (A) The central region of a lobe (cl), a labyrinthine region (lab) and interlobular regions (in). Haematoxylin and eosin. (B) Central region of a placental lobe, showing the presence of fetal veins (fv), maternal arterial blood spaces (ma) lined by trophoblast cells, and the mesenchyme (mes) surrounding these structures. Haematoxylin and eosin. (C) Detail to show the trophoblastic lining of maternal blood spaces (tr) and the intact walls of small fetal veins. PAS. (D) The placental labyrinth, showing the radial disposition (←) of the trophoblastic columns, the fetal capillaries (fc) and maternal blood spaces (mbs). Haematoxylin and eosin. Scale bars: 500 μm (A), 200 μm (B); 50 μm (C, D).
The most extensive portion of the lobe is the labyrinth. Due to close proximity between maternal and fetal blood vessels, it is the region where most maternal-fetal exchange takes place. The maternal blood spaces or lacunae are not lined by endothelium; they are defined by trophoblastic cell columns or cords. The cell columns are radially arranged as is apparent when the lobe is seen in cross section (Figure 2D). The syncytial nature of the trophoblastic columns is indicated by the close proximity of their nuclei.
The interlobular regions comprise cords of syncytiotrophoblast with abundant basophilic cytoplasm. These cords define maternal blood channels. The channels converge upon larger blood spaces, still without an endothelial lining, that receive blood from two or more adjacent lobes. Thus, each interlobular region is common to several lobes (Figure 3A). The interlobular regions also contain fetal arteries (Figure 3B) that give rise to the capillaries of the labyrinth.
Figure 3 The interlobium of the paca placenta. (A) Channels in the interlobium (in) drain the maternal blood spaces of the labyrinth (lab) and converge on larger venous blood spaces (mv). Haematoxylin and eosin. (B) Fetal artery (fa) at the border between an interlobular region and the labyrinth at the periphery of a lobe. Haematoxylin and eosin. Scale bars: 100 μm (A); 50 μm (B).
Placental surface
Most of the surface of the placental disk is covered by an epithelium formed by the endoderm of the parietal yolk sac (Figure 4A–C). An almost continuous Reichert's membrane can be demonstrated (Figure 4B). Beneath it is a layer of spongiotrophoblast. These cells differ from those that occur in the marginal and interlobular trophoblast. They are larger in size and have a rounded and vacuolated appearance (Figure 4C). The centre of the fetal surface of the placenta, including the attachment of the cord, is covered by a layer of connective tissue (Figure 4D).
Figure 4 Fetal surface of the paca placenta. (A) The visceral yolk sac (vys) attaches to the surface of the chorioallantoic placenta. Just before attachment it forms the fibrovascular ring (fvr). Left of the point of attachment, the endoderm continues as the parietal yolk sac (pys). To the right, the surface is covered by connective tissue. Masson's trichrome. (B) The parietal yolk sac endoderm (endo) forms a layer of epithelial cells that rests on Reichert's membrane (Rm). Immediately below the membrane are scattered connective tissue cells (ct). Masson's trichrome. (C) Beneath the endoderm and Reichert's membrane are spongiotrophoblast cells (sp tr), with a vacuolated appearance, marginal syncytium (ma tr) and a portion of the labyrinth (lab). Haematoxylin and eosin. (D) The centre of the placental disk is covered by connective tissue. Beneath this are marginal trophoblast and labyrinth. Haematoxylin and eosin. Scale bars: 500 μm (A); 10 μm (B); 100 μm (C-D).
Subplacenta, junctional zone and pedicle
The subplacenta is organized as folded lamellae of cytotrophoblasts supported on a thin layer of mesenchyme carrying fetal vessels (Figure 5A). The cytotrophoblast is multilaminar and mitotic figures are common here in early and midgestation. Beneath this layer is syncytiotrophoblast, which contains vacuoles and PAS-positive material. In late gestation this region becomes more compact and the entire subplacenta undergoes a process of degeneration.
Figure 5 Subplacenta, junctional zone and pedicle of the paca placenta. (A) Subplacenta. A layer of cytotrophoblasts (cy tr) is supported on lamellae of fetal mesenchyme (fm). Beneath it is the subplacental syncytiotrophoblast (sy tr). Gomori's trichrome. (B) Groups of multinucleated giant cells with a finely granular cytoplasm are found between the subplacenta and decidua. They are bordered by connective tissue. Masson's trichrome. (C) Middle portion of the placental pedicle, showing a large number of maternal blood vessels (mbv) and connective tissue fibers (ct). Haematoxylin and eosin. Scale bars: 20 μm (A-B); 200 μm (C).
Groups of giant cells are found in the junctional zone between the subplacenta and decidua (Figure 5B). They are multinucleated and have a finely granular, basophilic cytoplasm. They are PAS-positive.
The placenta is attached to the uterus by a placental pedicle made up of uterine tissue. In the upper region of this pedicle, a fine and discontinuous layer of connective tissue is interposed between the maternal tissue and fetal trophoblast. In the middle portion of the pedicle, a large number of vessels pass to or from the placenta. This region is characterized by dense fibres of connective tissue externally and of looser connective tissue around the vessels (Figure 5C). A layer of squamous epithelial cells with simple, round nuclei, lightly condensed chromatin and clear cytoplasm covers the entire pedicle (not shown).
Yolk Sac Placenta
There is an inverted yolk sac placenta, which is attached to the fetal surface of the chorioallantoic placenta. Just before attachment it forms the fibrovascular ring (Figure 4A). The yolk sac exhibits numerous digitiform projections (Figure 6A), which sometimes are branched. They consist of a mesenchymal core covered by a layer of endoderm. The latter forms a simple columnar epithelium of cells with apically situated cell nuclei (Figure 6B).
Figure 6 Yolk sac placenta of the paca. (A) The visceral yolk sac is complexly folded with columnar epithelial cells of endodermal origin (endo) supported by fetal mesenchyme (fm). Haematoxylin and eosin. (B) The mesenchyme (stained blue) contains vitelline blood vessels (vit bv). Masson's trichrome. Scale bars: 50 μm (A); 10 μm (B).
Ultrastructure
The paca placenta is of the syncytial haemomonochorial type. A single trophoblast layer can be identified between the blood in the maternal blood spaces of the labyrinth and the endothelium of the fetal capillaries (Figure 7A–B). This trophoblast is syncytial in nature without cell boundaries and with large nuclei, often in close proximity to one other. Microvilli project from the apical membrane into the maternal blood space. Caveolae are seen in the apical membrane and the syncytiotrophoblast contains coated vesicles and larger vacuoles. There are recesses in the basal membrane. However, we saw no evidence of transtrophoblastic channels. There is an abundant amount of rough endoplasmic reticulum, a Golgi apparatus, lysosomes and numerous mitochondria.
Figure 7 Ultrastructure of the labyrinth, interlobium and parietal yolk sac endoderm. (A-B) The interhaemal barrier. Only a single layer of syncytiotrophoblast is interposed between the blood flowing in the maternal blood spaces (mbs) and the endothelium of the fetal capillary (fc). The apical membrane contains invaginations (arrowheads) and the cytoplasm includes small coated vesicles (cv) and larger vacuoles. There are recesses in the basal membrane (arrows). (C) Interlobium. Most of the trophoblast is syncytial (sy tr) with abundant rough endoplasmatic reticulum. There are many microvilli where it is in contact with the maternal blood space. Cytotrophoblast cells with large nuclei are found in some regions of the interlobium. Desmosomes are found between the lateral membranes of adjacent cells (arrowheads). (D) Parietal yolk sac endoderm. These columnar cells have numerous microvilli at the apical surface. The supranuclear cytoplasm contains vacuoles and vesicles, tubular mitochondria and rough endoplasmic reticulum. Desmosomes are found between the lateral membranes of adjacent cells (arrowheads). Scale bars: 1 μm (A-B); 5 μm (C); 2 μm (D).
In the interlobular areas, the syncytiotrophoblast bordering the maternal blood spaces has numerous microvilli (Figure 7C). The cytoplasm has abundant rough endoplasmic reticulum, mitochondria and electron-dense droplets. Cytotrophoblast cells occur within the syncytium. Desmosomes are present between adjacent cytotrophoblast cells as well as between cytotrophoblasts and the overlying syncytium.
The cells of the parietal yolk sac endoderm form a columnar epithelium (Figure 7D). These cells are irregular in shape with basally situated nuclei. The apical membrane, which faces the uterine lumen, has microvilli and caveolae. The supranuclear cytoplasm contains vacuoles and vesicles, tubular mitochondria and rough endoplasmic reticulum. Desmosomes are found between the lateral membranes of adjacent cells.
Subplacenta and junctional zone
The cytotrophoblast layer is multilaminar with dilated intercellular spaces (Figure 8A). The cytoplasm of the syncytium has mitochondria, rough endoplasmic reticulum, numerous electron-dense granules (Figure 8B) and large accumulations of electron-dense material. The cytoplasm has electron transparent patches, which gives it a vacuolated appearance. There are lacunae within the syncytiotrophoblast, lined by microvilli and containing material of moderate electron density (Figure 8B). Desmosomes occur between adjacent cytotrophoblast cells and between the plasma membranes of these cells and that of the syncytial trophoblast.
Figure 8 Ultrastructure of the subplacenta and junctional zone of the paca placenta. (A) The cytotrophoblast layer (cy tr) is multilaminar with dilated intercellular spaces. The cells are characterized by their large nuclei. They rest on a thin basement membrane (bm) which separates them from the fetal mesenchyme. (B) The syncytiotrophoblast (sy tr) contains electron-dense droplets (dd). Microvilli project from the syncytiotrophoblast into lacunae containing material of moderate electron density (arrows). (C) Multinucleated giant cells from the junctional zone. The cytoplasm has electron transparent areas, giving it a vacuolated appearance. (D) Two giant cells (gc) separated by intercellular matrix into which they send processes (arrows). Scale bars: 5 μm (A-C); 1 μm (D).
In the junctional zone between the decidua and the lateral aspect of the subplacenta, there are nests of multinucleated giant cells (Figure 8C). Their morphology is variable. The cytoplasm has extensive electron transparent areas. The organelles tend to be confined to the perinuclear and marginal areas (Figure 8D) and include mitochondria, rough endoplasmic reticulum and electron-dense granules. The giant cells are separated by electron-dense material into which they send processes (Figure 8D).
Yolk sac placenta
The apical surface of the endoderm cells has numerous microvilli of relatively uniform length (Figure 9A). Small vesicles and tubules are present in the most apical regions of the cytoplasm (Figure 9B). The supranuclear cytoplasm contains a number of larger vesicles and vacuoles with a variable amount of electron-dense content. The perinuclear cytoplasm also houses a small Golgi complex. The cytoplasm has mitochondria and rough endoplasmic reticulum. Desmosomes and terminal bars are present between the lateral membranes of the cells.
Figure 9 Ultrastructure of the inverted yolk sac placenta of the paca. (A) The apical surface of the endodermal cells bears numerous microvilli. The supranuclear cytoplasm contains many larger vesicles or vacuoles with a small amount of electron dense content. (B) Invaginations (arrowheads), small coated vesicles (cv) and tubules are present in the most apical regions of the cytoplasm. Scale bars: 4 μm (A); 1 μm (B).
Discussion
As in other hystricomorph rodents [3,4], the placenta of paca consists of several lobes separated by interlobular trophoblast. The center of each lobe contains maternal arteries from which blood flows to the periphery through the trophoblastic channels of the labyrinth. In the fetal capillaries, blood flows from the periphery towards the center, allowing for countercurrent exchange [5,6]. The interlobular regions are made up of cords of syncytiotrophoblast, which define maternal blood spaces. We show here that each interlobular region drains several lobes.
In the labyrinth, the trophoblast is bathed directly by maternal blood and is separated from the fetal capillaries by a single layer of syncytiotrophoblast. Thus the placental barrier is syncytial haemomonochorial, as in the guinea pig [7-9], chinchilla [10], cane rat [11], degu [12] and rock cavy [13]. The apical membrane of this trophoblast, which is in contact with maternal blood, is well supplied with microvilli. There are recesses in the basal membrane and caveolae can occasionally be seen at the apical membrane. However, we did not observe transtrophoblastic channels such as those described in the degu [12].
In the interlobular region, the trophoblast contained mitochondria and rough endoplasmic reticulum. The surface in contact with maternal blood bore numerous microvilli. The interlobular trophoblast is the functional equivalent of the spongy zone of murid rodents [14]. In the guinea pig it has been identified as the principal site of progesterone synthesis [15]. In addition, it is thought to synthesize progesterone-binding protein [16], which is found in the plasma throughout gestation and is unique to hystricomorph rodents.
Much of the surface of the placental disk is covered by the endoderm of the parietal yolk sac. This epithelium is largely columnar and rests on Reichert's membrane. Beneath the membrane are trophoblast cells that differ from those in other regions of the placenta in their larger size, rounder form and vacuolated appearance. An appropriate designation for these cells is spongiotrophoblast. It has been shown in the guinea pig that maternal protein can cross the spongiotrophoblast and Reichert's membrane. It can then pass into the uterine lumen through the intercellular spaces between the endoderm cells, as they are not sealed by tight junctions [17]. In theory the protein could then be taken up by the visceral yolk sac, but whether maternal-fetal exchange occurs by this circuitous route is open to speculation.
The subplacenta is a unique feature of hystricomorph rodents [3]. Characteristic of the subplacenta of paca were the large intercellular spaces between the cytotrophoblasts and the lacunae within the syncytiotrophoblast. As in the guinea pig [18] and chinchilla [10], the lacunae in the syncytium were lined by microvilli and contained electron-dense material. It seems likely that the lacunae intercommunicate, but this requires further investigation. Wolfer and Kaufmann [19] suggested that the subplacenta might be a highly active area from a metabolic point of view. They pointed out that the structure had been carefully described, but that little was known about its function, except that it might have endocrine activity. Recently it was proposed that the subplacenta is an important source of invasive trophoblast in the guinea pig, chinchilla, capybara and degu [20].
We found multinucleated giant cells in the junctional zone between the subplacenta and decidua. Intriguingly, the cytoplasm of these cells contained electron-dense granules reminiscent of those found in the subplacental syncytium. The cytoplasm of the giant cells had areas of low electron density, a feature also shared by the subplacental syncytium. These cells were PAS-positive and may store glycogen or glycoprotein.
The placenta of paca is attached to the uterus by a prominent structure, formed largely of maternal tissue, that we have denoted the placental pedicle. It was first described by Strahl [21] and named by him the mesoplacenta. A similar structure occurs in the agouti [4], chinchilla [22] and nutria [5]. The equivalent formation in the capybara and guinea pig placenta is the placental stalk [23]. Trophoblast is found in the walls of the maternal vessels that pass through the pedicle to supply the placenta [4].
Like other hystricomorph rodents, paca has an inverted yolk sac placenta that persists until term. This visceral yolk sac displays folds and complex villi. The numerous digitiform projections are sometimes branched. They consist of a mesenchymal axis covered by a simple columnar epithelium of endodermal cells. The cells seem to have a high level of endocytotic activity. Similar characteristics are found in the yolk sac endoderm of the guinea pig [24], chinchilla [10] and rock cavy [13]. In the guinea pig it has been shown experimentally that immunoglobulin G is taken up from the uterine lumen to coated pits. The endocytotic vesicles thus formed are transported to the lateral membrane and empty into the intercellular spaces by exocytosis [25]. From here the immunoglobulins are presumed to reach fetal capillaries. Protein cannot move directly into the intercellular spaces because of the tight junctions near the apex of the cells [25]. In addition to this mechanism for conferring passive immunity to the fetus, there is nonspecific uptake of protein from the uterine lumen. Many endocytotic vesicles fuse with larger vacuoles that form part of the cell's lysosomal apparatus [26]. The protein they contain is thought to provide amino acids to the fetus. Given the similarity in ultrastructure, these mechanisms are likely to operate in the yolk sac placenta of paca.
In conclusion, the placenta of the paca conforms to patterns previously described for hystricomorph rodents [3,14]. Common features include the lobulation of the placenta and the presence of a subplacenta. At the ultrastructural level they comprise the haemomonochorial nature of the interhaemal barrier and the pinocytotic apparatus of the visceral yolk sac endoderm. The lobulated structure of the placenta allows for a larger exchange area and the development of precocial young [14].
Recently it was argued that more attention should be given to the hystricomorph rodents as models in human medicine. They bear a closer genetic similarity to humans than do murid rodents, such as the mouse and rat, because the latter have undergone a very high rate of gene mutation [27]. Since the paca carries a singleton fetus with a birth weight of 640–900 g, it deserves particular consideration as a potential model of fetal growth and development [28].
Acknowledgements
This study was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). We are grateful to Dr. Fabrício Singaretti de Oliveira (UNESP) for ultrasound examination of the animals. Marina Bonatelli wishes to thank Professors José Manoel dos Santos (FM-ABC), Idercio Luiz Sinhorini (USP) and Áureo Tatsumi Yamada (UNICAMP) for their guidance.
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| 15737234 | PMC555959 | CC BY | 2021-01-04 16:37:11 | no | Reprod Biol Endocrinol. 2005 Feb 28; 3:9 | utf-8 | Reprod Biol Endocrinol | 2,005 | 10.1186/1477-7827-3-9 | oa_comm |
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Lipids Health DisLipids in Health and Disease1476-511XBioMed Central London 1476-511X-4-61574545710.1186/1476-511X-4-6ResearchHuman macrophages limit oxidation products in low density lipoprotein Hultén Lillemor Mattsson [email protected]öm Christina [email protected] Alexandra [email protected] Reyk David [email protected] Stefan L [email protected] Claes [email protected] Olov [email protected] Wallenberg Laboratory for Cardiovascular Research, Sahlgrenska University Hospital, SE-413 45 Göteborg, Sweden2 Department of Health Sciences, University of Technology, Sydney, N.S.W. 2007, Australia3 Medical Biosciences, Clinical Chemistry, Umeå University Hospital, SE-901 85 Umeå, Sweden4 Phagocyte Research Laboratory, Department of Rheumatology and Inflammation Research, University of Göteborg, SE-413 46 Göteborg, Sweden2005 4 3 2005 4 6 6 11 2 2005 4 3 2005 Copyright © 2005 Hultén et al; licensee BioMed Central Ltd.2005Hultén 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.
This study tested the hypothesis that human macrophages have the ability to modify oxidation products in LDL and oxidized LDL (oxLDL) via a cellular antioxidant defence system. While many studies have focused on macrophage LDL oxidation in atherosclerosis development, less attention has been given to the cellular antioxidant capacity of these cells.
Compared to cell-free controls (6.2 ± 0.7 nmol/mg LDL), macrophages reduced TBARS to 4.42 ± 0.4 nmol/mg LDL after 24 h incubation with LDL (P = 0.022). After 2 h incubation with oxLDL, TBARS were 3.69 ± 0.5 nmol/mg LDL in cell-free media, and 2.48 ± 0.9 nmol/mg LDL in the presence of macrophages (P = 0.034). A reduction of lipid peroxides in LDL (33.7 ± 6.6 nmol/mg LDL) was found in the presence of cells after 24 h compared to cell-free incubation (105.0 ± 14.1 nmol/mg LDL) (P = 0.005). The levels of lipid peroxides in oxLDL were 137.9 ± 59.9 nmol/mg LDL and in cell-free media 242 ± 60.0 nmol/mg LDL (P = 0.012). Similar results were obtained for hydrogen peroxide. Reactive oxygen species were detected in LDL, acetylated LDL, and oxLDL by isoluminol-enhanced chemiluminescence (CL). Interestingly, oxLDL alone gives a high CL signal. Macrophages reduced the CL response in oxLDL by 45% (P = 0.0016). The increased levels of glutathione in oxLDL-treated macrophages were accompanied by enhanced catalase and glutathione peroxidase activities.
Our results suggest that macrophages respond to oxidative stress by endogenous antioxidant activity, which is sufficient to decrease reactive oxygen species both in LDL and oxLDL. This may suggest that the antioxidant activity is insufficient during atherosclerosis development. Thus, macrophages may play a dual role in atherogenesis, i.e. both by promoting and limiting LDL-oxidation.
MacrophagesLDLlipid peroxidesantioxidant enzymes
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Introduction
Oxidative modification of low density lipoprotein (LDL) plays a major role in the pathogenesis of atherosclerosis. The first stage of atherogenesis is characterized by an influx and accumulation of LDL in the intima, followed by recruitment of blood-derived monocytes and lymphocytes to the developing lesion [1]. Subsequently, LDL is oxidatively modified by free radicals that are either secreted from cells within lesions or generated extracellular in the arterial wall [2]. Oxidatively modified LDL (oxLDL) induces a multitude of cellular responses which lead to vascular dysfunction [3]. Much attention has sofar been devoted to the mechanisms by which cells oxidize LDL, since interventions targeting these mechanisms could prevent or retard the disease process.
However, cells may also provide a protective effect by reducing oxidation products present in LDL and oxLDL. Murine macrophages effectively block LDL oxidation by mechanisms which include metal ion sequestration [4]. Recent studies show that macrophages decrease cholesteryl ester hydroperoxide levels in LDL, an antioxidant action that is proportional to cell number [5,6]. In addition, endothelial cells prevent accumulation of lipid hydroperoxides in LDL [7]. Human hepatic cells show a protective role by selective uptake and detoxification of cholesterol ester hydroperoxides present in high density lipoprotein [8]. Enzymes associated with antioxidant defense, such as manganese superoxide dismutases, catalase, and glutathione peroxidases are induced by oxidants in vitro [9-11].
Four selenium-dependent glutathione peroxidases (GPx) have been identified sofar: cytosolic GPx (cGPx), gastrointestinal GPx (GI-GPx), plasma GPx (pGPx), and phospholipid hydroperoxide GPx (PHGPx) [12,13]. The PHGPx reduces hydroperoxides present in complex lipids such as phospholipids and cholesteryl esters [14,15]. Interestingly, increased glutathione levels are present in macrophages derived from the human monocytic cell line THP-1, as well as in mouse peritoneal macrophages after incubation with oxLDL [16,17]. An increased activity of both glutathione peroxidase and superoxide dismutase occurs in the arterial wall of cholesterol-fed rabbits [18]. Furthermore, lipid-laden macrophages within atherosclerotic vessels express an extracellular form of superoxide dismutase (EC-SOD) [19].
This study tested the hypothesis that human macrophages have the ability to modify oxidation products in LDL and oxLDL. We also analyzed the activity of cellular antioxidant defenses such as catalase, glutathione peroxidase, and superoxide dismutase in these cells. We used early macrophages as cell culture model to mimic newly recruited macrophages into the intima.
Materials and methods
Cell isolation
Mononuclear cells were isolated by the Ficoll-Hypaque procedure (Pharmacia, Uppsala, Sweden) [20] from buffy coats obtained from the blood of healthy donors from the Blood Bank at Sahlgrenska University Hospital, Göteborg. Monocytes in RPMI 1640 medium (Life Technologies, Paisley, Scotland), supplemented with non-essential amino acids, 2 mM sodium pyruvate, 100 U/mL penicillin, and 100 μg/mL streptomycin were seeded in 6 well plates at 4 × 106 cells per well. Non-adherent cells were removed after 1 h. RPMI 1640 containing 100 μg/mL LDL or oxLDL was incubated at 37°C in 5% CO2 in the presence or absence of macrophages. By definition, monocytes are denoted macrophages when they are attached, thus the cells used in this study are considered early human monocyte-derived macrophages (HMDM).
For chemiluminescence experiments, monocytes were allowed to adhere to cell culture flasks for 1 h. Adhered macrophages were then detached by incubation with PBS containing 5 mM EDTA and 2% fetal calf serum for 20 minutes at +4°C [21]. Cells were collected, washed, and resuspended to a density of 5 × 106 cells/mL in Krebs-Ringer Bicarbonate buffer supplemented with glucose (KRG) (Sigma, St. Louis, Missouri). To obtain non-viable macrophages, cells were stored at +4°C for 16 h. Trypan blue exclusion test confirmed that 100% of the cells were non-viable.
Lipoproteins
Fresh human EDTA-plasma was obtained from healthy male donors after overnight fasting. LDL (density 1.019–1.063 g/L) was isolated by sequential ultracentrifugation [22]. Before oxidation, native LDL was desalted on a PD-10 column equilibrated with PBS containing 100 μg/mL penicillin and 100 μg/ml streptomycin (PEST) using PBS-PEST as elution buffer. The LDL was oxidized at 37°C for 2–24 hours by 12.5 μmol CuSO4/mg LDL. Oxidation was terminated through the addition of 0.5 mmol/L EDTA. The oxLDL was purified on a PD-10 column with PBS as elution buffer and sterilized by filtration through a 0.22 μm filter. Native LDL was acetylated as described [23]. Oxidation of LDL was determined as the relative electrophoretic mobility (REM), i.e. the ratio between the distance oxLDL and native LDL migrate on a 0.5% agarose gel. The LDL in this study was oxidized for 2 h and had a REM ranging from 1.06 to 1.32 and TBARS values between 3 and 8 nmol MDA/mg LDL protein. Lipoprotein concentrations were determined with the BioRad protein assay using γ-globulin as standard.
Chemiluminescence
The chemiluminescence (CL) assay was performed at 37°C and the CL detected for at least 100 minutes with a luminescence counter (Bio Orbit Luminometer 1251, Turku, Finland) [21]. The CL response was detected in a total volume of 1.0 mL, containing 10 μg isoluminol (Sigma), 4 U horseradish peroxidase (Roche AB, Stockholm, Sweden), and 100 μg of either LDL, oxLDL, or acLDL in KRG in the presence or absence of 5 × 105 cells. As a specific inhibitor of hydrogen peroxide, 30 μg catalase (Roche AB) was added in some experiments to oxLDL without cells.
Measurement of oxidation products
Thiobarbituric acid-reactive substances (TBARS) were determined by the method of Yagi [24]. Fluorescence was measured at 553 nm with 515 nm excitation. Lipid peroxides (LPO) were determined by the Lipohydrox assay from Wak-Chemie Medical (Bad-Soden, Germany). Lipid peroxides are reduced to hydroxyl derivatives in the presence of hemoglobin, and the chromogen 10-N-methylcarbamyl-3, 7-dimethylamino-10H-phentiazine is oxidatively cleaved to form methylene blue. Lipid peroxides are quantitated by colorimetric measuring of the methylene blue at 675 nm.
Levels of hydrogen peroxide equivalents (H2O2eq) were analyzed in the LDL-containing media incubated with or without macrophages. The assay is based on the oxidation of ferrous ions to ferric ions by hydrogen peroxide at acidic pH (OXIS International Inc., Portland, Oregon). The ferric ion binds to the indicator dye xylenol-orange to form a stable complex which is measured at 560 nm.
Apolipoprotein B (Apo B) concentration was determined in cell culture media by immunoprecipitation enhanced by polyethylene glycol at 340 nm (Thermo Clinical Labsystems, Espoo, Finland). ApoB analyses were performed on a Konelab 20 autoanalyser (Thermo Clinical Labsystems).
Analysis of antioxidant properties of macrophages
The intracellular levels of glutathione and the activity of GPx were measured in crude extracts from macrophages incubated with either LDL or oxLDL. The cells were washed twice with ice-cold PBS and harvested in 0.5 mL lysis buffer. For glutathione peroxidase measurements, this buffer contained 50 mM Tris-HCl, pH 7.5, 5 mM EDTA, 1 mM dithiothreitol. For glutathione measurements, the cells were lysed in 0.5 mL ice-cold 5% metaphosphoric acid. The lysate was spun down at 3000 × g, and the supernatants stored at -80°C.
The presence of glutathione peroxidase was determined with a colorimetric assay (Bioxytech GPX-340) and glutathione (GSH) was measured with Bioxytech GSH-400, both from OXIS International Inc. The assay GPX-340 measures the functional activity of the GPx. In functional terms, all four types of GPx (cGPx, GI-GPx, pGPx, and PHGPx) appear similar in catalytic activity [12,25].
ROOH + 2GSH ----GPx----> ROH + GSSG(oxidized glutathione)+ H2O
GSSG + NADPH+ + H+ --------> 2GSH + NADP+
Catalase activity in macrophages was quantified by the method of Aebi [26]. Decomposition of H2O2 was measured in cell lysates at 240 nm. One unit of catalase activity was defined as the rate constant for the reaction using purified catalase (Roche AB) as standard.
In the cell lysates, the CuZn-superoxide dismutase (SOD) and Mn-SOD enzymatic activities were measured with a direct spectrophotometric method [27]. Extracellular SOD protein was determined by ELISA essentially as previously described [28].
Data analyses
Data were expressed as means ± standard error. Statistical analysis was performed using Student's paired t-test or ANOVA. P values < 0.05 were considered statistically significant.
Results
Macrophages diminish oxidation products in LDL and oxLDL
To study the effect of macrophages on LDL oxidation, we measured TBARS, LPO, and H2O2 in LDL-containing culture media after 2 h and 24 h incubation with or without macrophages.
Compared to cell-free controls (6.2 ± 0.7 nmol/mg LDL), there was a significant reduction of TBARS by macrophages to 4.42 ± 0.4 nmol/mg LDL after 24 h incubation with LDL (P = 0.022) (Fig. 1A). After 2 h incubation with oxLDL, TBARS was 3.69 ± 0.5 nmol/mg LDL in cell free media, and 2.48 ± 0.9 nmol/mg LDL in the presence of macrophages (P = 0.034). Although a time-dependent increase of TBARS in the presence of cells is seen, this change was not statistically significant. Lipid peroxide levels are unaffected in LDL and oxLDL incubated in cell-free wells during 2 h to 24 h (Fig. 1B). In the presence of cells, a reduction of lipid peroxides in LDL (33.7 ± 6.6 nmol/mg LDL) was found after 24 h compared to cell-free incubation (105.0 ± 14.1 nmol/mg LDL) (P = 0.005). In oxLDL, the cell-mediated loss of lipid peroxides was significant compared to cell-free media after 24 h. The levels of lipid peroxides in oxLDL were 137.9 ± 59.9 nmol/mg LDL and in cell-free media 242 ± 60.0 nmol/mg LDL (P = 0.012). In LDL-containing media incubated in cell-free wells, the levels of H2O2eq increase with time from 116 ± 31 nmol/mg LDL after 2 h, to 270 ± 46 nmol/mg LDL after 24 h (P = 0.009) (Fig. 1C). Levels of H2O2eq increase from 303 ± 14 nmol/mg LDL to 357 ± 24 nmol/mg LDL in oxLDL-containing media (P = 0.011), suggesting both LDL and oxLDL are oxidized during culture conditions. H2O2eq is significantly decreased in both LDL (P = 0.038) and oxLDL (P = 0.035) after incubation with macrophages. In macrophage-treated LDL, the H2O2eqcontent is decreased to 14.8 ± 2.9 nmol/mg LDL after 24 h, which is similar to levels found in non-treated LDL (0 h). Taken together, these results suggest a cell-mediated loss of oxidation products in both LDL and oxLDL in the presence of macrophages.
Figure 1 Effect of macrophages on oxidation products in LDL and oxLDL. RPMI 1640 containing 100 μg/mL of LDL or oxLDL (oxidized for 2 h) was incubated in cell culture wells at 37°C with and without macrophages (HMDM) for 2 h and 24 h. Values are expressed as nmol/mg LDL, TBARS (n = 5) (A), lipid peroxides (n = 6) (B), H2O2 (n = 6), (C). Values of Apo B are expressed as % change compared to cell free control incubations (n = 4) (D).
Apo B was analysed to study if the cell mediated decrease in oxidation products was due to a decrease of LDL or oxLDL in the cell culture media. In the presence of macrophages, the content of apo B in native LDL is reduced by 7 % after 2 h (P = 0.013) and 11 % after 24 h (P = 0.025) (Fig. 1D). In the oxLDL medium, no significant change of apo B in the medium is found after 2 h, and the content of apo B in oxLDL decreases 18% after 24 h incubation with macrophages compared to cell-free control (P = 0.006). This result suggests that the decrease in oxidation products can not be fully explained by an increased uptake of LDL or oxLDL by macrophages.
To further test the capacity of macrophages to decrease oxidation products in LDL, we used isoluminol-enhanced chemiluminescence to detect reactive oxygen species in LDL. OxLDL, acLDL, or LDL was incubated with macrophages (5 × 105cells) or without cells at 37°C. OxLDL alone had a CL response of approximately 400 mV after 2 h (Fig. 2). AcLDL shows a low but increasing CL up to 30 mV after 2 h as does native LDL, but the response is lower than that of oxLDL. In repeated experiments, incubation of macrophages with oxLDL leads to a significant reduction in the maximum peak value to 260 ± 45 mV compared to 462 ± 127 mV for oxLDL alone (P < 0.01) (n = 6). Adding catalase to oxLDL leads to a decrease of the CL signal by 37%, suggesting peroxides in the oxLDL.
Figure 2 Production of reactive oxygen species from LDL, oxLDL and macrophages as measured by isoluminol-enhanced chemiluminescence. The incubation mixture of 1.0 mL KRG contained 5 × 105 cells (HMDM), 10 μg isoluminol, 4 U horseradish peroxidase, and 100 μg of LDL, oxLDL (oxidized for 2 h), or acLDL. The chemiluminescence was measured every 2 minutes for a total of 100 minutes at 37°C (n = 5).
LDL oxidized by Cu2+ for 2, 8, or 20 h, shows similar maximum peak values, however a lag time of 10 min is observed with the shorter oxidation times. Incubation of oxLDL with macrophages leads to a reduction in the CL signal (Fig. 3). Different dilutions of oxLDL lead to a dose dependent increase in CL (Fig. 4). The addition of macrophages results in a 45% lower CL maximal peak value at the different concentrations of oxLDL (P = 0.0016).
Figure 3 Production of reactive oxygen species from oxLDL and macrophages as measured by isoluminol-enhanced chemiluminescence. The incubation mixture of 1.0 mL KRG, containing 10 μg isoluminol, 4 U horseradish peroxidase, and 100 μg of oxLDL (oxidized for either 2 h, 8 h or 20 h), was used alone or in combination with 5 × 105 macrophages (HMDM) (n = 3).
Figure 4 Dose-response effect of oxLDL on production of reactive oxygen species as measured by isoluminol-enhanced chemiluminescence. The incubation mixture of 1.0 ml KRG contained 10 μg isoluminol, and different concentrations of oxLDL oxidized for 2 h, alone and in combination with HMDM (5 × 105 cells). Data are shown as means of maximal peak values in mV ± SE for triplicate determinations within a single experiment and are representative of two independent experiments. Results of oxLDL in cell-free incubations versus oxLDL in the presence of macrophages were analyzed by ANOVA.
To exclude the possibility that quenching contributes to the macrophage effect, oxLDL was incubated with non-viable macrophages. No reduction in CL signal is seen (Fig. 5), which suggests that no quenching occurred and that viable cells are necessary for antioxidative activity.
Figure 5 Production of reactive oxygen species from oxLDL and non-viable macrophages as measured by isoluminol-enhanced chemiluminescence. The incubation mixture of 1.0 mL KRG, containing 10 μg isoluminol, 4 U horseradish peroxidase, and 100 μg of oxLDL (oxidized for 2 h), was used alone or in combination with 5 × 105 non-viable macrophages. Data are shown from a typical experiment with 2 different cell donors.
LDL affects cellular antioxidant defences in macrophages
Since the levels of peroxides are elevated in oxLDL compared to LDL, and the cellular defences against peroxides excess are catalase and GPx, we investigated the cellular activity of these enzymes in macrophages. Macrophages incubated with oxLDL for 2 h have increased intracellular activity of catalase (P = 0.006) and GPx (P = 0.0002) (Figs 6A and 6B). In contrast, no significant increase of the intracellular activity of these enzymes occurs in macrophages incubated with LDL. In addition, the expression of glutathione, which is a cofactor for the GPx enzymes when H2O2 is detoxified, is enhanced in macrophages treated with oxLDL for 2 h (P = 0.0048) (Fig. 6C).
Figure 6 Effect of LDL and oxLDL on the intracellular antioxidant defenses in macrophages. The intracellular activity of catalase (A), glutathione peroxidase (B), and the levels of glutathione (C) were measured in crude extracts from macrophages (n = 6) incubated with LDL or oxLDL (oxidized for 2 h). Control cells were incubated in the absence of LDL. Results were analyzed by ANOVA.
Neither LDL nor oxLDL significantly affect the CuZn-SOD or Mn-SOD activity in macrophages (Table I). However, the Mn-SOD activity is enhanced after 24 h compared to 2 h incubations. Although an increase in secreted EC-SOD is seen at 24 h, this change was not statistically significant. Neither LDL nor oxLDL affect this secretion. These observations suggest that macrophages respond to oxLDL by increasing their enzyme activity of catalase and glutathione peroxidase.
Table 1 Effect of LDL and oxLDL on superoxide dismutase isoenzymes in macrophages.
CuZnSOD
U/mg cell protein MnSOD
U/mg cell protein EC-SOD
secreted ng/mg cell protein
Control 2 h 81.1 ± 7.5 8.1 ± 0.7 0.61 ± 0.28
LDL 2 h 70.9 ± 8.1 8.2 ± 0.7 0.59 ± 0.23
oxLDL 2 h 71.8 ± 9.0 7.4 ± 0.4 0.45 ± 0.15
Control 24 h 79.3 ± 12.1 18.6 ± 1.8 (P = 0.0016) 0.86 ± 0.35
LDL 24 h 80.8 ± 9.9 15.4 ± 0.7 (P = 0.0004) 0.85 ± 0.31
oxLDL 24 h 91.2 ± 20.6 17.1 ± 1.9 (P = 0.0025) 0.80 ± 0.30
Values are means ± SE (n = 4). CuZn-SOD and Mn-SOD enzymatic activities were measured in cell lysates, and EC-SOD protein was determined in culture media. P values indicate the comparison between the effect of LDL/oxLDL on superoxide dismutase at 2 h and 24 h.
Discussion
Oxidation of LDL is a crucial event in the pathophysiology of atherosclerosis. Reactive oxygen species such as H2O2 participate in the oxidation of LDL [29]. Although the mechanisms are not fully understood, aortic cells such as endothelial cells, smooth muscle cells, and macrophages have the capacity to oxidize LDL in vitro. We have recently shown that hypoxia enhances both macrophage-mediated LDL oxidation and the expression of the putative LDL-oxidizing enzyme 15-lipoxygenase-2 [30]. While many studies have focused on cellular oxidation of LDL, less attention has been given to the cellular antioxidant capacity of macrophages.
This study shows that macrophages play an important role in limiting lipid oxidation products that accumulate in LDL and oxLDL. Since early macrophages are used in this study, this may resemble newly recruited macrophages entering into the arterial intima. Regarding oxLDL, macrophages decrease TBARS levels by about 30%, LPO decreases 43%, and the H2O2eqcontent decreases 64% in cell culture media after 24 h compared to cell-free controls. These results are in agreement with an earlier study where human macrophages reduce the content of cholesteryl ester hydroperoxides in LDL by 43% [5]. When the apo B content was analyzed in the culture media, we found that the levels of apo B in oxLDL were 18% reduced after 24 h incubation with macrophages compared to cell free control. For LDL the corresponding figure was 11%, which implies that the decrease in oxidation products cannot be entirely explained by a higher uptake of LDL or oxLDL by macrophages. Our results suggest that macrophages respond to oxidative stress by an endogenous antioxidative activity, which is sufficient to decrease reactive oxygen species; i.e. TBARS, LPO and H2O2 both in LDL and oxLDL. Our data also suggest that oxidation products accumulate in LDL and in oxLDL during regular cell culture conditions in the absence of cells. This has to be taken into consideration when cell culture data using LDL are interpreted.
The chemiluminescence technique is generally used to detect cellular ROS production. The amplifying molecule isoluminol reacts with ROS to produce an excited state intermediate that emits light upon relaxation to the ground state [31]. Our data show that oxLDL contains reactive oxygen metabolites that have the capacity to induce CL. Lipid hydroperoxides, the major oxidizing species in oxLDL, are likely to cause the CL. In the presence of transition metal ions, they generate Fenton-type oxidants, which may induce the chemiluminescence. The CL response is reduced when LDL is oxidized longer than 24 h. This agrees with previous data concerning the kinetics of LDL oxidation where lipid hydroperoxides are formed during the propagation phase and are decreased during the decomposition phase [32]. In the presence of macrophages, the CL response in oxLDL is reduced by 45%, which suggests that these cells exhibit antioxidant activity. Our results further suggest that cellular viability is necessary for antioxidant activity.
There are a number of cellular defences against oxidative stress, such as superoxide dismutase, catalase, and glutathione-related enzymes. In this study we sought to define the antioxidative activity of macrophages. All glutathione peroxidases reduce H2O2 or soluble alkyl peroxides, by coupling its reduction of H2O with oxidation of glutathione [11]. We found increased glutathione peroxidase activity that coincided with enhanced glutathione levels in oxLDL-treated macrophages. Only PHGPx reduces hydroperoxy groups of lipids together with those of phospholipids and cholesteryl esters when present in lipoproteins [15]. Interestingly, overexpression of PHGPx inhibits H2O2-induced oxidation and activation of NFκB in transfected rabbit smooth muscle cells [33]. Our results may have in vivo relevance, since an increased activity of glutathione peroxidase is found in the artery wall of cholesterol-fed rabbits [18].
Catalase is a representative antioxidant enzyme and previous studies show that oxidants such as H2O2 and lipid peroxides induce catalase gene expression in cultured rabbit endothelial cells, rabbit macrophages, and human smooth muscle cells [10]. This study provides further evidence that lipid hydroperoxides in oxLDL induce antioxidant defences in macrophages, since human macrophages also upregulated their catalase activity. Human macrophages induce catalase activity in response to oxidative stress [34]. Addition of catalase to oxLDL alone reduced the CL response by 37%, suggesting that peroxides are detected by the CL technique.
EC-SOD occurs in high concentration in both non-diseased [35] and atherosclerotic arterial walls [19]. In non-diseased arteries, the enzyme is primarily secreted by smooth muscle cells, whereas in atherosclerotic lesions it is also expressed by macrophages [19]. Higher levels of EC-SOD are present in cell culture media after 24 h than after 2 h, but the presence of LDL or oxLDL does not effect EC-SOD expression. Similar results of EC-SOD expression have been described in human fibroblasts [36]. The expression of CuZn-SOD is neither affected by LDL, oxLDL nor time. This was not unexpected since CuZn-SOD is generally regarded as a constitutively expressed enzyme. Mn-SOD activity increases with time, but there is no additive effect of LDL or oxLDL on its activity.
Cells can tolerate mild oxidative stress, which triggers the antioxidant defence system in an attempt to restore the oxidant- antioxidant balance [37]. We did not find an increase in antioxidant enzyme activity in LDL-treated macrophages, which suggests that the basal levels of catalase and GPx activity are sufficient to remove the oxidation products in LDL. In contrast, the augmented catalase and GPx activity suggests that oxLDL induces cellular adaptation when macrophages are exposed to increased oxidative stress.
This study suggests that oxidative stress induced by oxLDL could be balanced by a cellular antioxidant defence by newly recruited macrophages to sites of LDL oxidation. As oxidation of LDL is implicated in the development of atherosclerosis, and oxidation products of LDL are present in advanced atheromatous lesions, this may suggest that the antioxidant activity is insufficient in vivo. Thus, it is evident that macrophages play a dual role in atherogenesis, i.e. both by promoting and limiting LDL-oxidation. It remains to be determined during which stage of lesion development these individual characteristics pertain.
A strategy to intervene with the development of atherosclerosis would be to increase the endogenous intracellular antioxidant capacity of cells, which would remove and detoxify oxidized LDL. Interestingly, recent results show that Lovastatin increases hepatic catalase activity in cholesterol-fed rabbits [38]. In light of the response-to-retention hypothesis of atherosclerosis, subendothelial retention of atherogenic LDL is the initiating event of the disease [39,40]. It is tempting to speculate that macrophages are present in the atherosclerotic intima because of their antioxidant activity, which detoxifies and removes cytotoxic products in the retained LDL.
Authors' contributions
All authors have contributed to the design of the study, the data analysis, and the writing of the manuscript. The final version of the manuscript has been read and approved by all authors prior to submission. Each author's specific contribution was as follows; LMH.: study design, macrophage cell culture, CL analysis, LDL treatment. C.U.: macrophage cell culture, TBARS, LPO, GpX, and catalase analyses. A.K.: data interpretation, writing, and editing. D.v.R: CL analysis. S.L.M: SOD analyses. C.D.: setting up the CL method. O.W.: study coordination and data interpretation.
Acknowledgements
We thank Emilia Markström and Sofia Martinsson for excellent technical assistance. This work was supported by grants from the Swedish Heart-Lung Foundation, the Swedish Research Council (grant no. 13488 to OW and grant 14816 to AK), and the Swedish Society of Medicine. Dr. David van Reyk, a visiting scientist at the Wallenberg Laboratory, Sahlgrenska University Hospital, was kindly supported by the Heart Research Institute and the Sydney Free Radical Group.
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| 15745457 | PMC555960 | CC BY | 2021-01-04 16:39:18 | no | Lipids Health Dis. 2005 Mar 4; 4:6 | utf-8 | Lipids Health Dis | 2,005 | 10.1186/1476-511X-4-6 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-2-171573723310.1186/1743-422X-2-17ResearchPhosphorylation of HIV Tat by PKR increases interaction with TAR RNA and enhances transcription Endo-Munoz Liliana [email protected] Tammra [email protected] David [email protected] Nigel AJ [email protected] Centre for Immunology and Cancer Research, University of Queensland, Princess Alexandra Hospital, Brisbane, Australia2 Queensland Institute of Medical Research, Royal Brisbane Hospital, Brisbane, Australia2005 28 2 2005 2 17 17 30 11 2004 28 2 2005 Copyright © 2005 Endo-Munoz et al; licensee BioMed Central Ltd.2005Endo-Munoz 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 interferon (IFN)-induced, dsRNA-dependent serine/threonine protein kinase, PKR, plays a key regulatory role in the IFN-mediated anti-viral response by blocking translation in the infected cell by phosphorylating the alpha subunit of elongation factor 2 (eIF2). The human immunodeficiency virus type 1 (HIV-1) evades the anti-viral IFN response through the binding of one of its major transcriptional regulatory proteins, Tat, to PKR. HIV-1 Tat acts as a substrate homologue for the enzyme, competing with eIF2α, and inhibiting the translational block. It has been shown that during the interaction with PKR, Tat becomes phosphorylated at three residues: serine 62, threonine 64 and serine 68. We have investigated the effect of this phosphorylation on the function of Tat in viral transcription. HIV-1 Tat activates transcription elongation by first binding to TAR RNA, a stem-loop structure found at the 5' end of all viral transcripts. Our results showed faster, greater and stronger binding of Tat to TAR RNA after phosphorylation by PKR.
Results
We have investigated the effect of phosphorylation on Tat-mediated transactivation. Our results showed faster, greater and stronger binding of Tat to TAR RNA after phosphorylation by PKR. In vitro phosphorylation experiments with a series of bacterial expression constructs carrying the wild-type tat gene or mutants of the gene with alanine substitutions at one, two, or all three of the serine/threonine PKR phosphorylation sites, showed that these were subject to different levels of phosphorylation by PKR and displayed distinct kinetic behaviour. These results also suggested a cooperative role for the phosphorylation of S68 in conjunction with S62 and T64. We examined the effect of phosphorylation on Tat-mediated transactivation of the HIV-1 LTR in vivo with a series of analogous mammalian expression constructs. Co-transfection experiments showed a gradual reduction in transactivation as the number of mutated phosphorylation sites increased, and a 4-fold decrease in LTR transactivation with the Tat triple mutant that could not be phosphorylated by PKR. Furthermore, the transfection data also suggested that the presence of S68 is necessary for optimal Tat-mediated transactivation.
Conclusion
These results support the hypothesis that phosphorylation of Tat may be important for its function in HIV-1 LTR transactivation.
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Background
Since its isolation in 1983 [1,2], human immunodeficiency virus type 1 (HIV-1) continues to cause 5 million new infections each year, and since the beginning of the epidemic, 31 million people have died as a result of HIV/AIDS [3]. One of the major mechanisms employed by the immune system to counteract the effects of viral infections is through an antiviral cytokine – type 1 interferon (IFN). However, while IFN is able to inhibit HIV-1 infection in vitro [4], it has not been effective in the treatment of HIV-1 infections in vivo. Furthermore, the presence of increasing levels of IFN in the serum of AIDS patients while viral replication continues and the disease progresses [5-7] indicates that HIV-1 must employ a mechanism to evade the antiviral effects of IFN.
In response to viral infection, IFN induces a number of genes including the dsRNA-dependent protein kinase R (PKR). PKR exerts its anti-viral activity by phosphorylating the alpha subunit of translation initiation factor 2 (eIF2α), which results in the shut-down of protein synthesis in the cell [8]. The importance of PKR in the host antiviral response is suggested by the fact that most viruses including vaccinia [9], adenovirus [10], reovirus [11], Epstein-Barr virus [12], poliovirus [13], influenza [14], hepatitis C [15,16], human herpes virus [17-19], and SV40 [20], employ various mechanisms to inhibit its activity. HIV-1 is no exception and we and others have shown that PKR activity is inhibited by HIV via the major regulatory protein, Tat [21-23]. Productive infection by HIV-1 results in a significant decrease in the amounts of PKR [23] and HIV-1 Tat protein has been shown to act as a substrate homologue of eIF2α, preventing the phosphorylation of this factor and allowing protein synthesis and viral replication to proceed in the cell [21,22]. During the interaction between Tat and PKR the activity of the enzyme is blocked by Tat and Tat itself is phosphorylated by PKR [21] at serine 62, threonine 64 and serine 68 [22].
HIV-1 Tat is a 14 kDa viral protein involved in the regulation of HIV-1 transcriptional elongation [24-26] and in its presence, viral replication increases by greater than 100-fold [27,28]. It functions to trigger efficient RNA chain elongation by binding to TAR RNA, which forms the initial portion of the HIV-1 transcript [29]. The interaction between Tat and TAR is critical for virus replication and mutations in Tat that alter the RNA-binding site result in defective viruses. Furthermore, virus replication can be strongly inhibited by the overexpression of TAR RNA sequences that act as competitive inhibitors of regulatory protein binding [30].
While a number of reports have shown that PKR and Tat protein interact, and furthermore, that Tat is phosphorylated by PKR, none have yet addressed the issue of the functional consequences for the phosphorylation of the Tat protein. Here we examine the phosphorylation of Tat by PKR and its effect on TAR RNA binding and HIV-1 transcription, and show that the phosphorylation of Tat results in Tat protein binding more strongly to TAR RNA. Removal of the residues reported to be phosphorylated by PKR resulted in decreased Tat phosphorylation and a significant loss of Tat-mediated transcriptional activity.
Results
The phosphorylation of HIV-1 Tat by PKR increases its interaction with TAR RNA
We first confirmed the capability of our PKR preparation immunoprecipitated from HeLa cells to phosphorylate synthetic Tat protein (aa 1–86) (Figure 1a), and we determined the optimal phosphorylation time of Tat by PKR as 60 minutes (Figure 1b). We also confirmed that Tat was not phosphorylated by PKR in the absence of ATP, or by ATP alone (data not shown).
Figure 1 Phosphorylation of HIV-1 Tat86 by PKR. (a) PKR was immunoprecipitated from HeLa cell extracts and activated with synthetic dsRNA in the presence of γ-32P-ATP. This activated 32P-PKR was used to phosphorylate 0.5, 1 and 5 μg of synthetic Tat86 in the presence of γ-32P-ATP, at 30°C for 15 minutes. Proteins were separated by 15% SDS-PAGE. (b) PKR immunoprecipitated from HeLa cell extracts, and activated with dsRNA and ATP, was used to phosphorylate 2 μg of synthetic Tat86 at 30°C for the times indicated.
To address the issue of the consequences of PKR phosphorylation on Tat function we investigated the ability of phosphorylated Tat (herein called Tat-P) and normal Tat (Tat-N) to bind to HIV-1 TAR RNA. Synthetic Tat protein (aa 1–86) was phosphorylated in vitro using PKR previously immunoprecipitated from HeLa cells. An electrophoretic mobility shift assay (EMSA) was performed to observe any difference in the binding of Tat-N and Tat-P to TAR RNA (Figure 2a). It can be seen that Tat-N was able to form a specific Tat-TAR complex that could be effectively competed off using a 7.5-fold excess of cold TAR RNA. Tat-P was also able to form a specific Tat-TAR complex that clearly contained more TAR RNA than non-phosphorylated Tat. This complex could also be competed off using cold TAR but some residual complex was left suggesting that the Tat-P-TAR complex was more resistant to competition with cold TAR than the Tat-N-TAR complex.
Figure 2 EMSA of Tat-N, Tat-P and TAR RNA showing dissociation of the Tat-TAR complex with increasing salt concentration. (a) PKR immunoprecipitated from HeLa cell extracts, and activated with dsRNA and ATP, was used to phosphorylate 2 μg of synthetic Tat86 at 30°C for 1 h, in the presence (Tat-P) or absence (Tat-N) of γ-32P-ATP. TAR RNA was synthesized in vitro from pTZ18TAR80 using a commercial kit, and either γ-32P-dCTP or unlabelled dCTP. The Tat-TAR RNA binding reaction was allowed to proceed in binding buffer at 30°C for 10 minutes. Each reaction contained 200 ng of either Tat-N or Tat-P, and approximately 70 000 cpm of 32P-TAR RNA (lanes 1 and 2), or approximately 70 000 cpm of 32P-TAR RNA and 7.5 × the volume of unlabelled TAR RNA (lanes 3 and 4). The Tat-TAR complexes formed were resolved on a 5% acrylamide/0.25X TBE gel. (b) The Tat-TAR binding reactions were performed at 30°C for 10 minutes in binding buffer containing various concentrations of NaCl: 25 mM (lanes 2 and 8), 50 mM (lanes 3 and 9), 100 mM (lanes 4 and 10), 200 mM (lanes 5 and 11), 500 mM (lanes 6 and 12), and 1000 mM (lanes 7 and 13). Lanes 2–7 show the dissociation of the Tat-N-TAR complex, and lanes 8–13 show the dissociation of the Tat-P-TAR complex. Lane 1 is TAR RNA only.
As Tat-P appeared to bind more readily to TAR, we next investigated the differences in the binding efficiency of Tat-N and Tat-P with TAR RNA. EMSA were performed in the presence of increasing concentrations of NaCl (from 25–1000 mM). The progressive dissociation of the Tat-N-TAR RNA complex with increasing concentrations of salt in the buffer was observed (Figure 2b, lanes 2–7) while Tat-P-TAR complexes under the same conditions were clearly more stable (lanes 8–13). For example, at 500 mM NaCl the Tat-N-TAR complex was almost completely dissociated (lane 6) while the Tat-P-TAR complex was still clearly observed (lane 12). Even at the maximum salt concentration (1000 mM), the Tat-P-TAR complex can still be seen (lane 13), while the Tat-N-TAR complex was completely dissociated. These results suggest that Tat86 phosphorylated by PKR binds TAR RNA more efficiently and more strongly than normal Tat.
Efficient phosphorylation of Tat requires particular residues
Brand et al. [22] reported that PKR was able to phosphorylate Tat at amino acids serine-62, threonine-64 and serine-68. We therefore wished to know if any of these residues were critically important in the ability of Tat to bind TAR RNA. To this end, we created a series of Tat proteins containing mutations of all possible combinations of S62, T64 and T68 and investigated the phosphorylation of the resulting mutant Tat protein. A series of seven Tat mutants were made using alanine scanning (Figure 3a) and cloned into the bacterial expression vector pET-DEST42, which contains a C-terminal 6 × His tag to allow purification using metal affinity chromatography. The resulting constructs were validated by sequencing before the mutant Tat proteins were expressed and purified (Figure 3b). Protein yields varied between 40–170 g/mL and all mutants were full length, as confirmed by western blotting using an anti-His antibody (data not shown).
Figure 3 Construction of HIV-1 Tat phosphorylation mutants. (a) Amino acid sequence of HIV-1 Tat wild-type and mutants. Changes to alanine at serine 62, threonine 64 and serine 68 are indicated for each mutant, and compared to the wild-type protein. Mutations were introduced by site-directed mutagenesis into pET-DEST42-HIS-Tat86. (b) Competent BL21(DE3)pLysS cells, transformed with pET-DEST42-HIS-Tat86 wild-type or mutants, were grown and lysed with 6 M guanidine-HCl, pH 8.0. The suspension was cleaned of cell debris and loaded onto a packed metal affinity resin. The resin was washed and the HIS-tagged Tat proteins were eluted with 6 M guanidine-HCl, pH 4.0. The fractions collected were dialysed in 0.1 mM DTT and then analysed by 15% SDS-PAGE and stained with Coomassie blue. Tat lanes show fractions containing HIS-tagged Tat proteins; M lanes, 14 kDa marker; C lanes, BL21(DE3)pLysS cell extract.
Activated PKR was used to phosphorylate each of the Tat mutants as above and the reaction was allowed to proceed for 2, 5, 10, 15, 30, 45 and 60 minutes. The phosphorylated proteins were analyzed by SDS-PAGE and visualized by autoradiography (Figure 4). As can be seen from the figure, the phosphorylation of each protein by PKR varied and was the most efficient for wild-type Tat and the least efficient for the triple mutant, Tat S62A.T64A.T68A, where no sites for PKR phosphorylation were available. Scanning densitometry and non-linear regression analysis was performed and the extent of phosphorylation after 15 minutes was measured for each protein and expressed as a percentage of the wild-type protein (which is set to 100%) (Figure 5a). This time was chosen from non-linear regression analysis of the wild-type protein that indicated enzymatic phosphorylation of the wild-type protein was active at this time point. Non-linear regression analysis was performed to calculate the maximal phosphorylation for each protein (Pmax), and the time required to reach half-maximal phosphorylation (K0.5) (Figure 5b).
Figure 4 PKR phosphorylation of HIV-1 Tat wild-type and mutants. HIV-1 Tat wild-type and mutant proteins were expressed in BL21(DE3)pLysS cells from pET-DEST-42 expression clones, and purified by passage through a TALON™ cobalt affinity resin. PKR was immunoprecipitated from HeLa cell extracts, and activated with dsRNA in the presence of ATP. The phosphorylation reactions contained 2 μg of Tat protein, 6 μL of activated PKR suspension, and DBGA to a final volume of 12 μL. Phosphorylation was preformed at 30°C for the times indicated, in the presence of 2 μCi of γ-32P-ATP. Protein samples were analyzed by 15% SDS-PAGE. This figure only shows one representative gel out of three separate phosphorylation experiments performed for each protein.
Figure 5 PKR phosphorylation of HIV-1 Tat wild-type and mutants after 15 minutes and phosphorylation kinetics. (a) Proteins were phosphorylated by activated PKR at 30°C for 15 minutes in the presence of γ-32P-ATP. The reaction was stopped by the addition of protein loading buffer and incubation at 4°C. Samples were analyzed by 15% SDS-PAGE. Graph shows the results for three separate experiments. (b) Non-linear regression analysis of PKR phosphorylation curves of wild-type and mutant proteins was performed using a one-site binding hyperbola, which describes the binding of a ligand to a receptor and follows the law of mass action. K0.5 is the time required to reach half-maximal phosphorylation.
Phosphorylation of the single mutants was rapid and specific with maximal phosphorylation values (Pmax) for S62, T64 and T68 of 98.6%, 87.5% and 81.6% respectively compared to the wild type (Pmax = 82.8%) and K0.5 values of 10.9 min, 5.2 min and 0.8 min (wild-type = 5.5 min). This observation was also applicable to the Tat S62A.T64A mutant, which exhibited 87% phosphorylation (Figure 5a) (Pmax = 82.1%, K0.5 = 5.5 min). However, the percentage of phosphorylation at 15 minutes for the other double mutants and for the triple mutant decreased to 68% for Tat T64A.S68A, 48% for Tat S62A.S68A, and 56% for Tat S62A.T64A.S68A. These values also correlated well with the higher Pmax values (172.8%, 256.8% and 189.7% respectively) and K0.5 values (54.9 min, 109.7 min and 62.2 min respectively) for each mutant, indicating slower, less efficient and non-specific phosphorylation.
The phosphorylation of HIV-1 Tat by PKR enhances viral transcription
To examine the effect of Tat phosphorylation on its transactivation ability mammalian expression constructs containing the Tat mutants were prepared and transfected into HeLa cells. To measure Tat-specific transcription, we co-transfected with pHIV-LTR-CAT as well as with β-actin-luciferase to normalize for transfection efficiency. The transfection reaction was optimized for DNA concentration, transfection reagent concentration, and time. The results for three separate transfections are shown in Figure 6 and expressed as percentage of wild-type Tat. As expected, no transactivation of the HIV-1 LTR was observed in the untransfected control or in the absence of pHIV-LTR-CAT, and basal transcription was present at low levels (0.08-fold) in the absence of Tat. We observed significant decreases in transactivation with mutant Tat, even when a single phosphorylation site was mutated. There was a general trend to low activity as more mutations were introduced. Thus, the average transactivation by the single mutants, Tat 62A, T64A and S68A, was 58%, transactivation by the double mutants, Tat S62A.T64A, T64A.S68A and S62A.S68A, was 41%, while the triple mutant, Tat S62A.T64A.S68A, exhibited only 24% transactivation.
Figure 6 Transactivation of the HIV-1 LTR by HIV-1 Tat wild-type and mutants. Duplicate wells of confluent HeLa cells were transfected for 6 h with pcDNA3.2-DEST-Tat, pHIV-LTR-CAT and β-actin luciferase. Cells were harvested 24 h post transfection and assayed for CAT activity, luciferase activity and protein concentration. The graph shows the results of three separate experiments.
The differences in LTR activation observed for the individual single mutants were not large, indicating that the absence of any one of these phosphorylation residues reduced the ability of Tat to activate the HIV-1 LTR but that no single residue was more important than the other. As in the phosphorylation data, Tat S62A.T64A behaved similarly to the single mutants. The mutations that had the greatest effect were the T64A.S68A, S62A.S68A, and the triple mutant. Of the three residue combinations, the absence of T64 and S68 together had the greatest negative effect on transactivation, inducing a 3-fold decrease, which was comparable to that observed for the triple mutant (4-fold).
The absence of S62 in combination with S68 also had a marked effect on transactivation, reducing it 2.5-fold. On the other hand, the absence of S62 in combination with T64 reduced transactivation 1.8-fold. This suggests that the absence of S62 and T64 either singly or in combination is not as important for Tat-mediated transactivation as when these residues are absent in combination with S68, and may indicate a more important role for S68 in Tat transactivation. These data correlate with observations previously obtained in PKR phosphorylation experiments with these Tat mutants.
Discussion
HIV-1 inhibits the antiviral effects of IFN by the direct binding of its Tat protein to PKR [21]. In the infected cell, Tat blocks the inhibition of protein synthesis by PKR, thus allowing viral replication to proceed. As a consequence of this interaction, Tat becomes phosphorylated at S62, T64 and S68 [22]. Here we have examined the consequences of this phosphorylation on Tat function and have shown that it results in increased and stronger binding of Tat to TAR RNA. Tat protein is an essential regulatory protein during viral transcription and binds to the positive elongation factor B (P-TEFb), through its cyclin T1 subunit, and to TAR RNA to ensure elongation of viral transcripts [31]. Since protein phosphorylation is a well-known regulatory mechanism for the control of transcription by a number of eukaryotic and viral proteins, and since phosphorylation of Rev, the other major regulatory protein of HIV-1, increases its ability to bind to RNA [32], it was important to determine if phosphorylation of Tat also resulted in the modification of its function.
The binding of Tat and TAR RNA is a necessary step for Tat to mediate viral transcription elongation [33-35]. In electrophoretic mobility shift assays, we show that Tat-P bound more TAR RNA than Tat-N, and the Tat-P-TAR complex was more resistant to competition by excess unlabelled TAR RNA. Moreover, when the NaCl concentration in the binding buffer reached 1000 mM, the dissociation of the Tat-N-TAR complex was approximately 5 times greater than that of the Tat-P-TAR complex. Together, these observations appear to indicate faster, greater and stronger binding of Tat to TAR RNA after phosphorylation by PKR. Interestingly, phosphorylated HIV-1 Rev protein has been shown to bind RNA seven times more strongly than non-phosphorylated protein, and the non-phosphorylated Rev-RNA complex dissociates 1.6 times more rapidly than the phosphorylated complex [32].
However, the precise mechanism by which phosphorylated Tat accomplishes this remains to be elucidated. It may be that the phosphorylation of Tat changes its secondary structure. This may result in an increased net positive charge by either exposing basic amino acids or masking negative amino acids, and this increases the attraction to negatively charged RNA, as in the case of cAMP response element binding protein (CREB) phosphorylation by protein kinase A and glycogen synthase kinase-3 [36]. On the other hand, phosphorylation of Tat may change the conformation of the adjacent RNA-binding domain of Tat, as observed with the phosphorylation of proteins such as HIV-1 Rev [32] and serum response factor (SRF) [37].
We examined the effect of phosphorylation on Tat-mediated transactivation of the HIV-1 LTR in vivo with a series of mammalian expression constructs carrying the wild-type tat gene or mutants of the gene with alanine substitutions at one, two, or all three of the serine/threonine PKR phosphorylation sites. Firstly, we investigated the in vitro phosphorylation of Tat by PKR using Tat proteins expressed and purified from analogous bacterial expression constructs. These were subject to different levels of phosphorylation by PKR and displayed distinct kinetic behaviour. Nonlinear regression analysis of the proteins indicated that PKR could not phosphorylate S62 or T64 alone in the absence of S68. These results suggest a cooperative role for the phosphorylation of S68 in conjunction with S62 and T64, although the mechanism involved and the reason for cooperation require further investigation. Overall, a gradual reduction in phosphorylation was observed as the number of mutated phosphorylation sites increased, and any phosphorylation observed with the triple mutant was shown to be non-specific, thus confirming previous published results identifying S62, T64 and S68 as the only PKR phosphorylation sites [22]. However, these findings do not exclude the possibility that there could be other sites within Tat that could be subject to phosphorylation by other kinases.
Co-transfection experiments with the mammalian expression constructs showed a 4-fold decrease in LTR transactivation with the Tat triple mutant which could not be phosphorylated by PKR. A gradual reduction in transactivation was observed as the number of mutated phosphorylation sites increased – a 2-fold reduction with the removal of one site, and 2.5-fold with the removal of two sites. Furthermore, the transfection data also suggested that the presence of S68 is necessary for optimal Tat-mediated transactivation, since its absence in conjunction with one or both of the other residues yielded the lowest levels of transcription. These results were in agreement with the in vitro phosphorylation data and support the hypothesis that phosphorylation of Tat may be important for its function in HIV-1 LTR transactivation.
It is relevant to note that even in the absence of all three PKR phosphorylation sites the level of transcription was still 3-fold above baseline. This may imply that Tat can still transactivate in the absence of PKR phosphorylation, although at much reduced efficiency, and/or that the protein may be phosphorylated by other kinases at other sites, for example, PKC which phosphorylates Tat at S46 [38]. Alternatively, it may be that phosphorylation could be progressive between PKR and one or more other kinases as in the case of CREB protein [36]. Furthermore, the identification of a phosphatase in enhanced Tat-mediated transactivation [39] could point to a possible, finely tuned interplay and balance between kinases and phosphatases in Tat-mediated HIV-1 transcription.
The mechanism by which the absence or presence of phosphorylation affects transactivation still requires further investigation. It could be that the introduction of an increasing number of mutations in the region 62–68 which lies next to the nuclear localization signal (aa 49–58) leads to conformational changes that prevent the protein from entering the nucleus. However, HIV-1 subtype C viruses which are rapidly expanding, carry mutations in Tat R57S and G63Q within and close to the basic domain, and yet exhibit increased transcriptional activity [40]. On the other hand, the phosphorylation of serines and threonines may facilitate the rapid folding and conformation of the protein necessary for full function as in the case of HIV-1 Rev [32]. Rev from the less pathogenic HIV-2 contains alanines in place of the serines required for phosphorylation [41,42]. It is possible to envisage a similar situation for Tat, where phosphorylation of the protein by PKR and possibly by other kinase(s) may also lead to rapid folding and changes in conformation. These changes may allow it to bind to more TAR RNA, more strongly, which in turn may lead to the formation of a stronger and more stable Tat-TAR-P-TEFb complex ensuring hyperphosphorylation of the RNAPII CTD and subsequent, successful viral transcript elongation.
Conclusion
Overall, these results suggest that the phosphorylation of Tat by PKR plays a key role in the ability of Tat to transactivate the HIV-1 LTR, allowing the virus to use the natural antiviral responses mediated by interferon to further its own replication. This may, in part, explain the observation of increasing IFN levels in patients with advanced AIDS. The gradual reduction in transactivation observed with the decreasing absence of phosphorylation residues suggest that the presence of all PKR phosphorylation sites within the protein may be required for the optimal function of Tat in transactivation, and that the absence of S68, especially when in combination with T64, has a greater negative impact on transactivation.
Methods
Plasmids and proteins
The plasmid, pTZ18-TAR80 was a kind gift from Dr. E. Blair, and was used for in vitro transcription of TAR RNA after digestion with HinD III. A β-actin luciferase reporter gene plasmid was used as a transfection control to normalize transfection efficiency and was provided by Assoc. Prof. Nick Saunders, CICR, University of Queensland, Brisbane. The pHIV-LTR-CAT construct used in transfection experiments, the destination vector, pET-DEST42 (Invitrogen, CA, USA), and the pET-DEST42-Tat86 construct were a gift from Dr. David Harrich, QIMR, Brisbane. The mammalian expression vector, pcDNA3.2-DEST was purchased from Invitrogen (CA, USA) and was used as the destination vector for the construction of the Tat86 wild-type and mutant constructs.
Synthetic HIV-1 Tat(1–86) protein was a gift from Dr. E. Blair. The protein is a chemically synthesized, full-length HIV-1(Bru) Tat (amino acids 1–86). Histidine-tagged HIV-1 Tat86 was expressed in BL21(DE3)pLysS cells (Invitrogen, CA, USA) and purified in the laboratory of Dr. David Harrich, QIMR, Brisbane. Histidine-tagged HIV-1 Tat86 phosphorylation mutants were prepared as described elsewhere in this method.
PKR was prepared as described elsewhere in this method.
Preparation of histidine-tagged HIV-1 Tat86 phosphorylation mutants
Bacterial expression constructs were prepared using the prokaryotic expression vector, pET-DEST42-Tat86. Mutations were introduced in the tat gene at the three PKR phosphorylation sites: serine 62, threonine 64 and serine 68, by site-directed mutagenesis using complementary synthetic oligonucleotide primers (Proligo, Genset Pacific, Lismore, Australia) encoding the mutation of the residue, or residues, to alanine. The reaction for site-directed mutagenesis contained 32 μL distilled water, 5 μL Pfu I 10X reaction buffer (Promega, USA), 100 ng pET-DEST42-Tat86, 5 μL 5' oligonucleotide primer at a concentration of 25 ng/μL, 1 μL 10 mM dNTP mix, and 3 Units Pfu I DNA polymerase (Promega, USA). The reaction was subjected to PCR with the following cycling conditions: 95°C for 30 seconds, 18 cycles at 95°C for 30 seconds/55°C for 1 minute/68°C for 15 minutes, hold at 4°C. Electrocompetent JM109 cells were prepared in the laboratory and transformed with 2 μL of PCR reaction. Minipreps were prepared from selected ampicillin-resistant colonies and sequenced to confirm the mutation in the construct.
Mammalian expression constructs were prepared using Gateway Cloning Technology (Invitrogen, USA) to transfer the mutated tat genes from pET-DEST42-Tat86 wild type and mutants to the mammalian expression vector, pcDNA3.2-DEST, according to the protocol supplied by the manufacturer.
Expression and purification if HIS-tagged Tat mutant proteins
Competent BL21(DE3)pLysS cells (Dr. David Harrich, QIMR, Brisbane, Australia) were transformed with 1 μL of pET-DEST42-His-Tat86 wild-type or mutants, and plated. A single ampicillin resistant colony was resuspended in 10 mL of LB broth/amp and incubated overnight at 37°C. This culture was added to 500 mL of LB broth/amp and incubated in an orbital shaker, at 37°C until the OD600 was 0.6. The culture was inoculated with IPTG (Roche, Germany) to a final concentration of 200 μg/mL and incubation was continued for a further 2 hours. Cells were pelleted; the pellet was resuspended in 2 volumes of 6 M guanidine-HCl, pH 8.0 and incubated at room temperature overnight. The suspension was centrifuged at 14500 × g for 20 minutes, and the supernatant was centrifuged at 100 000 × g for 30 minutes. The supernatant was loaded onto a 1 mL equilibrated, packed resin (TALON™ Metal Affinity Resin, BD Biosciences Clontech, USA). To equilibrate, the resin was washed twice with 10 mL of Milli-Q water and charged by incubating with 5 mL of 0.3 M CoCl2 at room temperature for 5 minutes. The resin was then washed extensively with water, and equilibrated in 6 M guanidine-HCl, pH 8.0. The HIS-tagged protein was allowed to bind to the resin by incubation on a rocking platform, at room temperature, for 1 hour. The resin was then sedimented at 700 × g for 2 minutes, and washed with 6 M guanidine-HCl, pH 8.0 for 5 minutes. The resin was sedimented as above and washed with 6 M guanidine-HCl, pH 6.0 for 5 minutes. The resin was loaded onto an empty column (Poly-Prep ion exchange column, Bio-Rad, USA), and the wash allowed to flow through. The HIS-tagged protein was eluted with 4 mL of 6 M guanidine-HCl, pH 4.0, and collected in 500 μL fractions. Fractions were dialysed in 0.1 mM DTT in PBS, at room temperature, overnight, and then centrifuged at 14500 × g for 2 minutes. To identify fractions containing the HIS-tagged protein, 5–20 μL aliquots were analysed by 15% SDS-PAGE and stained with Coomassie blue. Fractions containing protein were assayed for protein concentration (Bio-Rad Protein Assay Dye Reagent Concentrate, Bio-Rad, USA), and by Western blot against a 1:1000 dilution of monoclonal anti-poly HISTIDINE Clone HIS-1 antibody (Sigma Aldrich, USA). Aliquots of fractions were stored at -80°C in 10 mM DTT in PBS.
In vitro phosphorylation assays
PKR was purified from HeLa cell extracts as described previously [43]. Briefly, confluent HeLa cells in 75 cm2 flasks were lysed in 1 mL of Buffer 1 (20 mM Tris, pH 7.6, 50 mM KCl, 400 mM NaCl, 1 mM EDTA, 1% Triton X-100, 20% glycerol, 200 μM PMSF, 5 mM mercaptoethanol), and centrifuged at 13500 × g for 30 minutes at 4°C. The supernatant was incubated in ice, for 30 minutes, with 2 μL of a 1:10 dilution of specific monoclonal antibody 71/10 (Dr. A. Hovanessian, Pasteur Institute, France), and then at 4°C overnight with 65 μL of protein G-sepharose (Amersham Biosciences, Sweden), with continuous rotation. Protein G-sepharose-PKR was sedimented, washed three times with Buffer 1, and three times with DBGA (10 mM Tris, pH 7.6, 50 mM KCl, 2 mM magnesium acetate, 20% glycerol, 7 mM β-mercaptoethanol). PKR was activated by incubating 120 μL of this suspension with 80 μL of DBGB (DBGA + 2.5 mM MnCl2), synthetic dsRNA (Sigma Aldrich, USA) to a final concentration of 0.5 μg/mL, and 20 μL of 2 mg/mL ATP (Sigma Aldrich, USA), at 30°C for 15 minutes.
Phosphorylation reactions for Tat proteins contained 2 μg of HIV-1 Tat, unless otherwise indicated in the figure legend, 6 μL of activated PKR suspension, and DBGA to a final volume of 12 μL. Phosphorylation was performed at 30°C for 1 hour, unless otherwise stated, in the presence of 2 μCi of γ-32P-ATP (Perkin-Elmer, USA). For measuring the extent of phosphorylation of the mutant Tat proteins, phosphorylation was stopped after 2, 5, 10, 15, 30, 45, and 60 minutes by the addition of protein loading buffer. Samples were analysed by 15% SDS-PAGE, and proteins were visualized by autoradiography, and scanning densitometry in a STORM 860 phosphorimager with ImageQuant® software (Molecular Dynamics, USA).
Electrophoretic mobility shift assay (EMSA)
TAR RNA was synthesized from 0.8 μg of pTZ18TAR80 using a commercial in vitro transcription system (MAXIscript™ T7 kit, Ambion, USA) according to the protocol supplied with the kit. HIV-1 Tat was phosphorylated (Tat-P) with activated PKR for 1 hour, as described above, or in the absence of γ-32P-ATP (Tat-N). Tat-P and Tat-N were allowed to equilibrate at 30°C for 10 minutes in Binding Buffer (10 mM Tris, pH 7.6, 1 mM DTT, 1 mM EDTA, 50 mM NaCl, 0.05% glycerol, 0.09 μg/μL BSA), before incubating at 30°C for 10 minutes with 2.5 × 105 cpm of 32P-TAR RNA. The Tat-TAR RNA complexes were separated on a 5% acrylamide/0.25X TBE gel (0.45 M Tris, 0.45 M boric acid, 0.1 M EDTA, pH 8.0), for 3–4 hours, at 10 mA, and visualized by autoradiography.
Transfection assays
Transfections were performed in duplicate in 6-well plates. HeLa cells were diluted in Modified Eagle's Medium (Invitrogen, USA) supplemented with 10% foetal bovine serum (Trace Scientific, Melbourne, Australia), antibiotics and glutamine (Invitrogen, USA), to yield 5 × 105 cells/mL. Each well was seeded with 2 mL of this cell suspension, and incubated at 37°C/5% CO2 for 24 hours or until the cell monolayer was 80–90% confluent. A solution of 625 μL of serum-free medium and 10 μg of total DNA (3.3 μg β-actin-luciferase, 3.3 μg pcDNA3.2-DEST-Tat, 3.3 μg pHIV-LTR-CAT) was mixed with 600 μL of serum-free medium containing 25 μL of Lipofectamine 2000 (Invitrogen, USA), and incubated at room temperature for 20 minutes. The cells were washed twice with serum-free medium, inoculated with the DNA-Lipofectamine mixture, and incubated at 37°C for 6 hours. The DNA solution was replaced with complete medium and the cells wee incubated as above for 24 hours. The cells were harvested and assayed for CAT activity using the CAT ELISA kit (Roche, Switzerland) according to the protocol supplied with the kit, for luciferase activity using the Luciferase Assay System (Promega, USA) according to the supplied protocol, and for protein concentration (Bio-Rad Protein Assay Dye Reagent Concentrate, Bio-Rad, USA).
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
LEM was responsible for the experiments described and contributed to the drafting of the manuscript. TW performed the optimization experiments for the phosphorylation of Tat by PKR. DH participated in the design of the study, provided reagents and critically read the manuscript. NAJM conceived and coordinated the study, and contributed to the drafting of the manuscript.
Acknowledgements
This work was supported by grants from the National Health and Medical Research Council and the Princess Alexandra Hospital Research Foundation. LEM was supported by a Dora Lush Postgraduate Research Scholarship from the National Health and Medical Research Council and a University of Queensland Completion Scholarship.
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| 15737233 | PMC556014 | CC BY | 2021-01-04 16:39:00 | no | Virol J. 2005 Feb 28; 2:17 | utf-8 | Virol J | 2,005 | 10.1186/1743-422X-2-17 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573697510.1371/journal.pbio.0030060Research ArticleAnimal BehaviorNeuroscienceZoologyInsectsPerceptual and Neural Olfactory Similarity in Honeybees Olfactory Similarity in HoneybeesGuerrieri Fernando
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Schubert Marco
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Sandoz Jean-Christophe
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Giurfa Martin [email protected]
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1Centre de Recherches sur la Cognition Animale, CNRSUniversité Paul-Sabatier (UMR 5169), ToulouseFranceChittka Lars Academic EditorUniversity of London, Queen Mary CollegeUnited Kingdom4 2005 22 2 2005 22 2 2005 3 4 e6012 10 2004 14 12 2004 Copyright: © 2005 Guerrieri et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Cracking the Olfactory Code
The question of whether or not neural activity patterns recorded in the olfactory centres of the brain correspond to olfactory perceptual measures remains unanswered. To address this question, we studied olfaction in honeybees Apis mellifera using the olfactory conditioning of the proboscis extension response. We conditioned bees to odours and tested generalisation responses to different odours. Sixteen odours were used, which varied both in their functional group (primary and secondary alcohols, aldehydes and ketones) and in their carbon-chain length (from six to nine carbons).The results obtained by presentation of a total of 16 × 16 odour pairs show that (i) all odorants presented could be learned, although acquisition was lower for short-chain ketones; (ii) generalisation varied depending both on the functional group and the carbon-chain length of odours trained; higher generalisation was found between long-chain than between short-chain molecules and between groups such as primary and secondary alcohols; (iii) for some odour pairs, cross-generalisation between odorants was asymmetric; (iv) a putative olfactory space could be defined for the honeybee with functional group and carbon-chain length as inner dimensions; (v) perceptual distances in such a space correlate well with physiological distances determined from optophysiological recordings of antennal lobe activity. We conclude that functional group and carbon-chain length are inner dimensions of the honeybee olfactory space and that neural activity in the antennal lobe reflects the perceptual quality of odours.
Training thousands of bees uncovers the chemical characteristics they use to discriminate between odours and reveals how the perception of odour correlates with specific neural activity in their brain
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Introduction
Stimulus discrimination and generalisation constitute two major abilities exhibited by most living animals. Discrimination allows treating different signals as distinct, while generalisation allows treating different but similar stimuli as equivalents [1,2,3]. Similarity along one or several perceptual dimensions determines the degree of generalisation between stimuli [2]. Determining such dimensions is fundamental for defining an animal's perceptual space. This objective remains, however, elusive in the case of the olfactory modality in which the dimensions along which odours are evaluated are not well known. Characteristics such as the functional chemical group or the carbon-chain length of a chemical substance may influence olfactory perception. It is known that at least some features of odorant molecules influence olfactory perception. For instance, some enantiomers can be discriminated by humans and nonhuman primates [4]. If and how chemical group and carbon-chain length are integrated as inner dimensions into an olfactory perceptual space remains unknown.
Vertebrate and invertebrate nervous systems show important functional as well as anatomical similarities in the way in which olfactory signals are detected and processed in their brains, particularly at the level of their first olfactory centres, the olfactory bulb in the case of vertebrates and the antennal lobe (AL) in the case of insects [5,6,7]. Insects are useful models for studying olfaction, as their behaviour heavily relies on the use of olfactory cues. The honeybee Apis mellifera is one such model in which behavioural and neurobiological studies have been performed to unravel the basis of olfaction [8,9,10,11]. Honeybee foragers are ‘flower constant' and learn and memorise a given floral species that they exploit at a time as long as it is profitable. Floral cues, among which odours play a prominent role, are then associated with nectar or pollen reward [12,13]. However, under natural conditions, the blends of volatiles emitted by floral sources vary widely in quantity and quality both in time and in space [14,15]. To cope with such changes in an efficient way, a ‘flower constant' forager should be able to generalise its choice to the same kind of floral sources despite fluctuations in their volatile emissions.
In a pioneering investigation, von Frisch [16] trained freely flying bees to visit an artificial feeder presenting several essential oils (odour mixtures). Using a set of 32 odour mixtures, von Frisch observed that after learning that a blend was associated with sucrose solution, bees tended to prefer this odour blend, but they sometimes visited other blends that were similar (to the human nose) to the rewarded one. Olfactory generalisation in honeybees was mainly studied on restrained honeybees using the conditioning of the proboscis extension reflex (PER) [17,18]. In this paradigm, harnessed honeybees are conditioned to odours associated with a sucrose reward. When the antennae of a hungry bee are touched with sucrose solution, the animal reflexively extends its proboscis to reach out towards and to lick the sucrose. Odours presented to the antennae do not usually release such a reflex in naive animals. If an odour is presented immediately before sucrose solution (forward pairing), an association is formed and the odour will subsequently trigger the PER in a subsequent unrewarded test. This effect is clearly associative and involves classical conditioning [18]. Thus, the odour can be viewed as the conditioned stimulus (CS), and sucrose solution as an appetitive unconditioned stimulus (US). Bees conditioned to individual odours or to olfactory mixtures can generalise PER to a wide range of different olfactory stimuli. Using the PER paradigm, Vareschi [19] showed that bees generalise most often between odours with similar carbon-chain lengths and between odours belonging to the same functional group. However, Vareschi conditioned odours in a differential way, with two rewarded and many unrewarded odours, so that several generalisation gradients (excitatory and inhibitory) may have interacted in an unknown way to determine the generalisation responses exhibited by the bees [19]. Using a similar approach and a restricted (6 × 6) set of odour combinations, Smith and Menzel [20] confirmed that bees generalise among odours with the same functional group, but their analysis did not detail the results obtained with individual odour combinations, thus rendering impossible the analysis of generalisation between odours with similar carbon-chain lengths. Free-flying bees trained in a differential way to a rewarded odour presented simultaneously with multiple unrewarded odours also generalise between odours with similar functional groups [21]. As for Vareschi's study [19], such an experimental design makes it difficult to interpret the generalisation responses due to unknown interactions between excitatory and inhibitory generalisation gradients.
Recently, optical imaging studies facilitated our understanding of how olfactory stimuli are detected and processed in the bee brain [22,23,24,25,26]. The first relay of the bee's olfactory system involves the ALs, which receive sensory input from the olfactory receptor neurons of the antennae within a number of 160 functional units, the glomeruli [27,28,29]. Within each glomerulus, synaptic contacts are formed with local interneurons and projection neurons (PNs). PNs send processed information from the ALs to higher brain centres such as the mushroom bodies and the lateral protocerebrum [30]. Stimulation with an odour leads to a specific spatiotemporal pattern of activated glomeruli, as shown, using in vivo calcium imaging techniques that employ fluorescent dyes to measure intracellular calcium in active neurons [22,24,31]. The odour-evoked activity patterns are conserved between individuals and constitute therefore a code [23,24]. Odours with similar chemical structures tend to present similar glomerular activity patterns [23]. Furthermore, it is believed that the neural code of odour-evoked glomerular patterns measured in the bee brain actually represent the perceptual code, although this idea was never tested directly.
In the present work, we studied behavioural olfactory generalisation, using the PER conditioning paradigm, with 16 odorants varying in two chemical features, functional group and chain length. The odours belonged to four chemical categories: alcohols with the functional group on the first or second carbon of the carbon chain (henceforth primary and secondary alcohols, respectively), aldehydes, and ketones. They possessed therefore three functional groups (alcohol, aldehyde, ketone). Their chain length ranged from six to nine carbon atoms (C6, C7, C8, and C9). The pairwise combination of 16 odours defined a 16 × 16 matrix. These odours are well discriminated by free-flying bees [21] and give consistent odour-evoked signals in optical imaging studies [23]. Using a behavioural approach, we measured similarity between odours and calculated their perceptual distances in a putative olfactory space. These perceptual distances were correlated with physiological distances measured in optical imaging experiments [23]. The correlation between both datasets was highly significant, thus indicating that odours that are encoded as physiologically similar are also perceived as similar by honeybees. Although other studies have addressed the issue of perceptual correlates of neural representations [32,33], we show for the first time that neural olfactory activity corresponds to olfactory perception defined on the basis of specific dimensions in a putative olfactory space, a finding that is of central importance in the study of the neurobiology of perception.
Results
We trained 2,048 honeybees along three trials in which one of the 16 odours used in our experiments was paired with a reward of sucrose solution (conditioned odour). Afterwards, each bee was tested with four odours that could include or not include the trained odour.
Acquisition Phase
The level of PER in the first conditioning trial was very low (between 0% and 8.60%) for all odours (Figure 1). All the 16 odours were learnt but not with the same efficiency. An overall (trial × odour) analysis of variance (ANOVA) showed a significant increase in responses along trials (F
2, 4064 = 2215.50, p < 0.001) and a significant heterogeneity among odours (F
15, 2032 = 8.80, p < 0.001). Responses to the CS in the last conditioning trial reached a level of approximately 70% for primary and secondary alcohols, 80% for aldehydes, and 61% for ketones.
Figure 1 Acquisition Curves for Primary Alcohols, Secondary Alcohols, Aldehydes, and Ketones
The ordinate represents the percentage of proboscis extensions to the training odour (CS). The abscissa indicates the conditioning trials (C1, C2, C3) and the test with the CS (T). The curves correspond to molecules with 6 (white triangles), 7 (white diamonds), 8 (black circles) and 9 carbons (black squares); (n = 128 bees for each curve). As not all 128 bees were tested with the odour used as CS, the sample size in the tests was smaller (n = 32). Different letters (a, b, c) indicate significant differences either between acquisition curves for different chain-length molecules (in the case of the ketones) or between test responses (post hoc Scheffé tests).
In the case of aldehydes and primary and secondary alcohols, no significant chain-length effect within functional groups was found over the whole conditioning procedure (chain length × trial ANOVA; chain-length effect for primary alcohols: F
3, 508 = 0.18, p > 0.05; secondary alcohols: F
3, 508 = 1.47, p > 0.05; and aldehydes: F
3, 508 = 1.26, p > 0.05). In contrast, bees conditioned to ketones showed a significant chain-length effect in the acquisition (chain length × trial ANOVA; chain-length effect: F
3, 508 = 20.00, p < 0.005). Scheffé post hoc comparisons showed that acquisition was significantly better for nonanone (81.25% responses in the last conditioning trial) than for all other ketones. Octanone (68.75% responses in the last conditioning trial) was also better learned than hexanone and heptanone (45.31% and 48.44% responses in the last conditioning trial, respectively) (Figure 1, bottom right). The effect over trials was significant in all cases (p < 0.05) as bees learned all odours.
The analysis of acquisition for each chain length separately revealed that it varied significantly depending on the functional group (functional group × trial ANOVA; C6: F
3, 508 = 18.89; p < 0.005; C7: F
3, 508 =10.78; p < 0.005; C8: F
3, 508 = 3.84; p < 0.01; C9: F
3, 508 = 2.73, p < 0.05). Scheffé post hoc comparisons generally showed that this effect was mainly due to ketones being less well learned than aldehydes and alcohols. Generally, the longer the carbon chain, the lower the heterogeneity in acquisition between functional groups. Thus, apart from short-chain ketones, all odours were learned similarly (reaching a level of acquisition between 60% and 80% in the last conditioning trial).
Test Phase
When the conditioned odour was presented in a test (Figure 1, grey panels), the level of PER recorded corresponded mainly to that found in the last acquisition trial (McNemar tests [2 × 2 Table]: in all cases p > 0.05). To compare generalisation after conditioning, and because acquisition levels were heterogeneous between odours, we built a generalisation matrix in which only bees responding to the CS at the end of training (3rd conditioning trial) were considered (Figure 2). The number of individuals included in the statistical analysis varied within each ‘training odour/test odour' pair. The number of bees completing the tests varied between 17 and 28 for primary alcohols, between 13 and 29 for secondary alcohols, between 23 and 30 for aldehydes, and between 11 and 31 for ketones. The responses to the CS in the tests ranged between 70% and 100% in the generalisation matrix. All further analyses were carried out on this matrix. In the following sections, we will use the matrix data to analyse generalisation within and between functional groups, within and between chain lengths, and the asymmetries in olfactory generalisation.
Figure 2 Olfactory Generalisation Matrix
The generalisation matrix represents the percentage of PER in the tests performed by bees that actually learned the CS, that is, bees that responded to the CS at the third conditioning trial (n = 1,457). Upper part: percentages recorded. Lower part: colour-coded graphic display grouping the level of responses in ten 10% response categories. Red, maximal response; light blue, minimal response.
Generalisation within Functional Groups
Figure 3A shows the percentage of PER to odours having different (white quadrants) or the same (grey quadrants) functional group as the conditioned odour. High levels of PER to odours different from the trained one correspond to high generalisation. In order to better visualise generalisation as depending on functional groups, we pooled all the observed responses within each quadrant of Figure 3A (i.e., not considering chain length) and calculated the resulting percentage of PER (Figure 3B). Grey bars correspond to generalisation to the same functional group; white bars correspond to generalisation to different functional groups. Generalisation mainly occurred within a given functional group (grey bars). This pattern was clearest for aldehydes (Figure 3B, 3rd row) because bees conditioned to aldehydes responded with a high probability to other aldehydes but showed lower responses to any other odour (see also the clear aldehyde “response block” in Figure 2).
Figure 3 Generalisation Depending on Functional Groups
(A) Data of the generalisation matrix (see Figure 2) represented as two-dimensional graphs for each conditioned odour. The right ordinate represents the CSs categorised in four functional groups, primary alcohols, secondary alcohols, aldehydes, and ketones (from top to bottom). The abscissa represents the test odours aligned in the same order as the conditioned odours (from left to right). The left ordinate represents the percentage of proboscis extensions to the test odours after being trained to a given odour. Each quadrant in the figure represents generalisation responses to one functional group after training for the same (grey quadrants) or to a different functional group (white quadrants).
(B) Same data as in (A), but the observed responses within each quadrant were pooled and the resulting percentage of responses per quadrant was calculated. The abscissa and the right ordinate represent the four functional groups. The left ordinate represents the percentage of proboscis extensions to each of these groups after being trained to a given group. Grey bars correspond to grey quadrants in (A) and represent generalisation to the same functional group as the conditioned one. White bars correspond to white quadrants in (A) and represent generalisation to a functional group different from the conditioned one: 1-ol, 2-ol, al, and one mean primary alcohol, secondary alcohol, aldehyde, and ketone, respectively. Asterisks indicate significant differences along a row or a column (p < 0.001)
(C) Within-functional group generalisation, depending on chain length. The abscissa represents the functional groups tested. The ordinate represents the percentage of proboscis extensions to the functional groups tested after being trained to a given chain-length (lines). Thus, for instance, the first point to the left for C9 molecules (black circles) represents generalisation to 1-hexanol, 1-heptanol, and 1-octanol after conditioning to 1-nonanol. A significant heterogeneity was found in within-functional group generalisation for C8 and C9 but not for C6 and C7 molecules.
(D) Generalisation within-functional groups. The figure shows results from pooling the data of (C) corresponding to each functional group. Each point shows the percentage of proboscis extensions to odours of the same functional group as the conditioned odour. Within-group generalisation was significantly heterogeneous (asterisks, p < 0.001). Pairwise comparisons showed that generalisation within aldehydes was significantly higher than within primary alcohols or ketones and marginally higher than within secondary alcohols (different letters indicate significant differences).
We analysed within-functional group generalisation as depending on chain length (see Figure 3C). To this end we represented generalisation from C6, C7, C8, and C9 molecules having a given functional group to the other compounds having the same functional group (e.g., Figure 3C, black circle curve, first data point: generalisation to 1-hexanol, 1-heptanol, and 1-octanol after conditioning to 1-nonanol). A significant heterogeneity appeared for C8 and C9 molecules (χ2 = 12.60 and 14.30, respectively, p < 0.01 in both cases, n = 67–85) but not for C6 and C7 molecules (p > 0.05). In the case of C8 and C9 molecules, generalisation was significantly higher within aldehydes (p < 0.05).
When comparing within-group generalisation over all four functional groups (Figure 3D), a significant heterogeneity appeared (χ2 = 14.40, df = 3, p < 0.01, n = 276–316). Pairwise comparisons (using a corrected threshold for multiple comparisons: α′ = 0.017) showed that generalisation within aldehydes was significantly higher than within primary alcohols (χ2 = 11.80, df = 1, p < 0.0006) and ketones (χ2 = 9.90, df = 1, p < 0.005) and close to significance in favour of aldehydes when compared to secondary alcohols (χ2 = 4.40, df = 1, 0.017 < p < 0.05).
Generalisation within Chain Lengths
Figure 4A shows the generalisation responses of bees to odours having different (white quadrants) or the same (grey quadrants) chain length as the conditioned odour. In order to better visualise generalisation as depending on chain length, we pooled all the observed responses within each quadrant of Figure 4A and calculated the resulting percentage of PER (Figure 4B). Grey bars correspond to generalisation to the same chain length; white bars correspond to generalisation to different chain lengths. Generalisation was highest in the case of odours with the same or similar chain length.
Figure 4 Generalisation Depending on Chain Length
(A) Data of the generalisation matrix (see Figure 2) represented as two-dimensional graphs for each conditioned odour. The right ordinate represents the CSs categorised in four chain lengths, C6, C7, C8, and C9 molecules (from top to bottom). The abscissa represents the test odours aligned in the same order as the conditioned odours (from left to right). The left ordinate represents the percentage of proboscis extensions to the test odours after being trained for a given odour. Each quadrant in the figure represents generalisation responses to one chain length after training for the same (grey quadrants) or to a different chain length (white quadrants).
(B) Same data as in (A), but the observed responses within each quadrant were pooled and the resulting percentage of responses per quadrant was calculated. The abscissa and the right ordinate represent the four chain-length categories. The left ordinate represents the percentage of proboscis extensions to each of these categories after being trained for a given chain-length category. Grey bars correspond to grey quadrants in (A) and represent generalisation to the same chain length as the conditioned one. White bars correspond to white quadrants in (A) and represent generalisation to a chain length different from the conditioned one: C6, C7, C8, and C9 mean chain length of 6, 7, 8, and 9 carbons, respectively. Asterisks indicate significant differences along a row or a column (p < 0.001).
(C) Within chain-length generalisation as depending on functional group. The abscissa represents the chain lengths tested. The ordinate represents the percentage of proboscis extensions to the same chain length after being trained to a given functional group (lines). Thus, the first point to the left for ketones (red circles) represents generalisation to 1-hexanol, 2-hexanol, and hexanal after conditioning to 2-hexanone; the second point represents generalisation to 1-heptanol, 2-heptanol, and heptanal after conditioning to 2-heptanone. A significant heterogeneity was found in within-chain-length generalisation for aldehydes and ketones.
(D) Generalisation within-chain lengths. The figure results from pooling the data of (C) corresponding to each chain length. Each point shows the percentage of proboscis extensions to odours of the same chain length as the conditioned odour. Within-chain-length generalisation was significantly heterogeneous (asterisks, p < 0.001). Pairwise comparisons showed that generalisation within C9 molecules was significantly higher than within C7 and C6 molecules and marginally higher than within C8 molecules (different letters indicate significant differences).
We analysed within-chain length generalisation as depending on functional group (Figure 4C). To this end we represented generalisation from primary alcohols, secondary alcohols, aldehydes, or ketones of a given chain length to the other compounds having the same chain length (e.g., Figure 4C, red circle curve, first data point: generalisation to 1-hexanol, 2-hexanol, and hexanal after conditioning to 2-hexanone). Generalisation within-chain length was generally higher for longer than for shorter chain lengths. This effect was significant for aldehydes (χ2 = 28.70, df = 3, p < 0.01, n = 75–80) but not for primary and secondary alcohols (χ2 = 5.20 and 3.4, df = 3, p > 0.05, n = 67–73 and n = 61–66, respectively). For ketones, a significant heterogeneity was found (χ2 = 10.00, df = 3, p < 0.05, n = 40–79), but generalisation was more important between C8 than between C7 molecules. The generalisation corresponding to other chain lengths fell in between.
When comparing within-chain length generalisation over all four chain-length groups (Figure 4D, i.e., not considering functional group), a significant heterogeneity appeared χ2 = 23.2, df = 3, p < 0.001, n = 247–293). Pairwise comparisons (using a corrected threshold for multiple comparisons: α′ = 0.017) showed that within-chain length generalisation was significantly higher within C9 than within C6 (χ2 = 18.50, df = 1, p < 0.0001) and C7 molecules (χ2 = 15.00, df = 1, p < 0.0001). Generalisation within C8 molecules was close to significance when compared to generalisation within C9 molecules (χ2 = 5.00, df = 1, 0.017 < p < 0.05), and it was significantly higher than generalisation within C6 molecules (χ2 = 4.3, df = 1, 0.017 < p < 0.05).
Generalisation between Functional Groups
To analyse generalisation between groups, we took into account the responses to functional groups different from the conditioned one (see white bars in Figure 3B). Bees showed heterogeneous patterns of generalisation (all vertical and horizontal comparisons in Figure 3B were significant: χ2 > 37.70, df = 3, p < 0.001, in all eight cases). We found high between-group generalisation for primary and secondary alcohols: bees conditioned to secondary alcohols responded preferentially to primary alcohols, somewhat less to aldehydes, and even less to ketones (see Figures 3A and 3B, second row). A similar but less obvious response gradation was found for bees conditioned to primary alcohols Figures 3A and 3B, first row). In fact, the overall generalisation patterns were very similar for primary and secondary alcohols sharing the same chain length (see, for instance, the very close relationship between the two sets of blue [primary alcohol] and green curves [secondary alcohols] in Figure 4A).
As indicated before, bees conditioned to aldehydes generalised very little to odours belonging to other functional groups (see Figure 3B, third row). Contrarily, bees conditioned to other functional groups highly generalised to aldehydes (see third column ‘al' in Figure 3B). This shows that generalisation between aldehydes and odours belonging to other functional groups was asymmetrical. The topic of asymmetric generalisation will be considered below in more detail.
Generalisation between Chain Lengths
To analyse generalisation between chain lengths, we took into account the responses to chain lengths that differed from the conditioned one (see white bars in Figure 4B). In general, responses to molecules with different chain lengths followed a clear decreasing gradient, depending on the difference in the number of carbon atoms between the molecules considered (see Figure 4B; all horizontal and vertical comparisons were significant, χ2 > 16.3, df = 3, p < 0.001 in all eight cases). For instance, when conditioned to a C9 molecule (see Figure 4B, fourth row), bees responded in 53%, 31%, and 23% of the cases to C8, C7, and C6 molecules, respectively, while they responded to C9 molecules in 67% of the cases. This gradient was also evident when generalisation took place between functional groups: for instance, after training with 2-nonanol (see Figure 3A, second row), the response of bees to odours of different functional groups (solid lines in white boxes) always followed a similar decreasing tendency with the same (C9) or similar (C8) chain length on top.
Asymmetry in Olfactory Generalisation
As previously mentioned, some groups like aldehydes induced asymmetrical cross-generalisation (i.e., bees responded less to other functional groups after training for aldehydes than to aldehydes after training for other functional groups). We analysed this asymmetrical generalisation and built an asymmetry matrix (Figure 5A). To this end, we calculated for each odour pair (A and B) the difference (in percentage) between generalisation from A to B and generalisation from B to A. Such differences were ranked in 10% categories from −55% to 55%. White boxes indicate no asymmetries. Blue shades in Figure 5A indicate that cross-generalisation was biased towards odour A (i.e., conditioning to A resulted in lower generalisation to B while conditioning to B resulted in higher generalisation to A); red shades indicate that cross-generalisation was biased towards odour B (i.e., conditioning to A resulted in higher generalisation to B while conditioning to B resulted in lower generalisation to A). This representation showed that some odours induced generalisation while other odours diminished it. For instance, hexanal was well learnt but induced low generalisation to other odours, except to other aldehydes. On the other hand, bees conditioned to other odours very often generalised to hexanal. Thus, a clear blue row (or a red column) corresponds to hexanal in the asymmetry matrix. Conversely, 2-hexanone induced high generalisation to other odours but received few responses as a test odour. Thus a red row (or a blue column) corresponds to 2-hexanone in the asymmetry matrix. Most odours, however, showed little or no asymmetry. Figure 5B presents the mean asymmetry found for each training odour. In six cases, the mean asymmetry deviated significantly from zero, which represents a theoretically perfect symmetry (t-test). Two odours (red bars) significantly induced generalisation (2-hexanone and 2-hexanol, t-test, df = 14, p < 0.001 and p < 0.01, respectively), while four odours (blue bars) diminished it significantly (hexanal, heptanal, and octanal, and 2-nonanone, t-test, df = 14, p < 0.001 for the former and p < 0.01 for the three latter odours).
Figure 5 Asymmetric Generalisation between Odours
(A) The asymmetry matrix depicts asymmetric cross-generalisation between odours. For each odour pair (A and B), the difference (percentage) between generalisation from A to B and generalisation from B to A was calculated. Such differences were ranked in 10% categories varying from blue (−55%) to red (55%). Blue shades indicate that cross-generalisation was biased towards odour A (i.e., conditioning to A resulted in lower generalisation to B, while conditioning to B resulted in higher generalisation to A); red shades indicate that cross-generalisation was biased towards odour B (i.e., conditioning to A resulted in higher generalisation to B, while conditioning to B resulted in lower generalisation to A). For this reason, each odour pair (A and B) appears twice in the matrix, once in the upper-left of the black diagonal line, and once in the lower-right of the black diagonal line, with opposite values. See, for example, the two cells outlined in green for the pair 2-hexanone/2-octanol.
(B) Mean generalisation induced or diminished by each odour A in (A). Each bar represents the mean asymmetry of the respective horizontal line in the asymmetry matrix. Red bars show that an odour induced more generalisation than it received, while blue bars show the opposite. Significant generalisation asymmetries were found in six out of 16 cases (**, p < 0.01; ***, p < 0.001).
Olfactory Space
In order to define a putative olfactory space for the honeybee, we performed a principal component analysis (PCA) on our data to represent in a limited number of dimensions the relative relationships between odorants in a 16-dimension perceptual space (Figure 6A). The first three factors represented 31%, 29%, and 15% of overall variance in the data (total of the first three factors: 75%). The analysis showed a clear organisation of odours depending on their chemical characteristics. First, chain length was very clearly represented by the first factor (see upper-right graph in Figure 6A), from C6 to C9 molecules from the right to the left. On the other hand, the chemical group was mostly represented by factors 2 and 3. Whereas factor 2 separated mostly aldehydes from alcohols, with ketones falling between them, factor 3 segregated ketones from all other odours (lower-right graph, Figure 6A). None of these factors separated primary and secondary alcohols. This analysis indicates that the chemical features of molecules (chain length and functional group), which are sometimes thought of as artificial perceptual (psychophysical) dimensions determined by experimenters [34] can be considered as true inner dimensions of the bees' perceptual space. Cluster analyses performed on the data segregated odours mostly according to their chain length. In the first group (Figure 6B, upper part), we found two subgroups, short-chain alcohols (C6 and C7, primary and secondary alcohols) and short-chain ketones (C6 to C8). On the other hand (Figure 6B, lower part), three clear subgroups were formed: short-chain aldehydes (C6 and C7), long-chain alcohols (C8 and C9, primary and secondary alcohols), and a last group with long-chain aldehydes (C8 and C9) and 2-nonanone. Very similar results were obtained using Euclidian or city-block metrics.
Figure 6 A Putative Honeybee Olfactory Space
(A) Left: The olfactory space is defined on the basis of the three principal factors that accounted for 76% of overall data variance after a PCA performed to represent the relative relationships between odorants. Primary alcohols are indicated in blue, secondary alcohols in green, aldehydes in black, and ketones in red. Different chain-lengths are indicated as C6, C7, C8, and C9, which corresponds to their number of carbon atoms. For each functional group, arrows follow the increasing order of carbon-chain lengths. Right: Chain length was very clearly represented by factor 1. C6 to C9 molecules are ordered from right to left. The chemical group was mostly represented by factors 2 and 3. Whereas factor 2 separated mostly aldehydes from alcohols, with ketones falling between them, factor 3 separated ketones from all other odours. None of these three factors separated primary and secondary alcohols.
(B) Euclidean cluster analysis. The analysis separated odours mostly according to their chain length. Linkage distance is correlated to odour distances in the whole 16-dimension space. The farther to the right two odours/odour groups are connected, the higher the perceptual distance between them (odour colour codes are the same as in [A]).
Correlation between Optophysiological and Behavioural Measures of Odour Similarity
We asked whether optophysiological measures of odour similarity, obtained using calcium imaging techniques at the level of the honeybee AL [22,23,24,35], correspond to perceptual odour similarity measures as defined in our putative honeybee olfactory space. We thus calculated the Euclidian distance between odour representations in our 16-dimension “behavioural” space for all odour pairs (120 pairs). We then calculated distances between odours in optical imaging experiments, using the odour maps by Sachse et al. [23]. A correlation analysis was performed between both datasets. This analysis was possible because both the study by Sachse et al.[23] and our study used the same set of odours delivered under the same conditions. Figure 7A presents the correlation obtained, including all 120 odour pairs. Both sets of data were highly significantly correlated (r = 0.54, t
118 = 7.43, p < 2.10–10), a result that shows that odours, which were found to be physiologically similar in the optical imaging study, were also evaluated as similar in behavioural terms. Note, however, that data points cluster quite broadly around the main trend line, showing that many exceptions were found. In order to use a more exact measure of physiological odour similarity, we used the correlation results between primary and secondary alcohol maps provided by Sachse et al. [23]. By correlating this more exact value of physiological similarity with our behavioural data, we also found a highly significant relationship between physiological and behavioural data (Figure 7B; r = 0.82, t
26 = 7.83, p < 7.10–8). The correlation coefficient achieved with this second method was significantly higher than that achieved with the first method (Z = 2.52, p < 0.05). A better fit between the two datasets was thus found, although outliers were still present in the data. These two analyses show that optophysiological and behavioural measures of odour similarity correlate well using the methods described here. Thus, in the case of the honeybee, olfactory neural activity corresponds to olfactory perception.
Figure 7 Correspondence between Perceptual and Physiological Odour Similarity
(A) Correlation between optophysiological measures of odour similarity (carried out using calcium imaging recordings [23]) and our behavioural measures of odour similarity. Euclidian distance between odour representations in our 16-dimension “behavioural” space for all odour pairs (120 pairs, x axes) and distances between odours in optical imaging experiments, using the odour category maps displayed by Sachse et al. [23] (also 120 pairs, y axes) were calculated. This correlation, including all 120 odour pairs, was highly significant (r = 0.54, p < 0.001). Odours found to be similar in the optical imaging study were also similar in the behaviour. Data points cluster quite broadly around the main trend line, showing that many exceptions were found.
(B) Correlation between measures of optophysiological similarity carried out using the optical imaging technique [23] and our behavioural measure of odour similarity. Using the exact data given for primary and secondary alcohols [23], a much better correlation between the two datasets was achieved than in (A) (r = 0.82, p < 0.001), although outliers were still found in the data.
Discussion
In the present work, we have studied perceptual similarity among odorants in the honeybee, using an appetitive-conditioning paradigm, the olfactory conditioning of the PER [17,18]. We showed that all odorants presented could be learned, although acquisition was lower for short-chain ketones. Generalisation varied, depending both on the functional group and on the carbon-chain length of odours trained. Generalisation was very high among primary and secondary alcohols, being high from ketones to alcohols and aldehydes and low from aldehydes to all other tested odours; thus, in some cases, cross-generalisation between odorants was asymmetric. Some odours, like short-chain ketones or aldehydes, induced more asymmetries than other odours. Higher generalisation was found between long-chain than between short-chain molecules. Functional group and carbon-chain length constitute orthogonal inner dimensions of a putative olfactory space of honeybees. Perceptual distances in such a space correlate well with physiological distances determined from optophysiological recordings performed at the level of the primary olfactory centre, the AL [23] such that olfactory neural activity corresponds to olfactory perception.
Previous studies have attempted to describe olfactory generalisation in honeybees and to study structure–activity relationships [19,20,36,37,38]. These studies generally supported the view that generalisation mainly happens when odours belong to the same chemical group. Moreover, they also suggested that the rules underlying olfactory learning and perception of different chemical classes [20] or of particular odorants (e.g., citral [20,37]) may vary. However, these studies used differential training, thus inducing several generalisation gradients (excitatory and inhibitory) that make the interpretation of generalisation responses difficult [21,36]. Furthermore, these studies were carried out on a rather discrete number of odour pairs [37], did not detail the results obtained with individual odour combinations [20], or used a very reduced number of bees per conditioned odour ([21]; two bees per odorant).Thus, the present study is the first one to provide (i) generalisation data based on absolute conditioning (i.e., only one odour conditioned at a time), (ii) a systematical test of all odour combinations, (iii) robust sample sizes for each experimental situation, and (iv) important generalisation gradients. These are in our view crucial prerequisites to describe odour perception and similarity in a precise way.
Chemical Group and Chain Length
Several studies in other species have shown the importance of functional group and carbon-chain length of the odour molecules for behavioural responses to odours. Differences in the response between molecules of diverse aliphatic and aromatic homologue odour classes (i.e., differing in functional group, chain length, and overall molecule form) were investigated in moths [39,40], cockroaches [41], rats [42], squirrel monkeys [4,43] and humans [38,44,45]. These studies show that both functional group and chain length affect the perceived quality of an odorant. Concerning chain length, the greater the difference in the number of carbons between odours, the easier the discrimination and the lower the generalisation ([21,40,42,44] and present study).
In our study, both chemical group and chain length of odour molecules determined the bees' generalisation responses. Bees mostly generalised to other odours when these shared the same functional group. This effect was observed for all functional groups (see Figure 3B) but was strongest for aldehydes. Other studies have found that aldehydes induced high within-group generalisation [20,21,36]. Thus, aldehydes may represent a behaviourally relevant chemical class for honeybees. Between-functional group generalisation depended on the functional group considered. It was high between primary and secondary alcohols, which appear therefore perceptually similar to the bees, and low between other chemical groups. Bees clearly generalised between odours that shared the same chain length. Increasing chain length promoted generalisation. Moreover, generalisation to other chain lengths decreased if the difference in the number of carbons between odours increased. This suggests a perceptual continuum between different chain lengths (but see below). Thus, the chemical structure of the odorants is critical for determining the amount of generalisation.
A Putative Olfactory Space for the Honeybee
We found that the two controlled physical characteristics of odour molecules used in this study, functional group and chain length, correspond to internal dimensions in the bees' olfactory perceptual space such as the three most important factors extracted in our PCA analysis, one mainly represented chain length and the other two were mostly influenced by functional group. Cluster analyses allowed separating odours in clusters according to their functional groups and their chain length. Interestingly, C6 and C7 molecules and C8 and C9 molecules were mainly grouped together, so that, for instance, all short-chain primary and secondary alcohols were grouped on one side, and all long-chain alcohols on the other side. The same happened for aldehydes, and in a different way for ketones (C9 separated from the rest). This discrepancy suggests that, although chain length appears mostly as a perceptual continuum in the PCA analysis, there may be a perceptual “jump” between short-chain and long-chain molecules.
Neural Bases of Odour Perception
Both in vertebrates and in invertebrates, studies quantifying the neural responses to structurally similar odours in the first relay of the olfactory pathway have been performed (olfactory bulb: e.g., [46,47,48,49]; AL: [23,50]). These studies show that activity patterns are more similar when the difference in the number of carbons between molecules is small. It was hypothesised that such a physiological similarity is the basis for olfactory discrimination and generalisation as measured behaviourally. This has indeed been reported for mucosal activity in mice [51], electrical mitral cell activity [42], and/or radiolabelled 2-deoxyglucose uptake in the rat olfactory bulb [32]. Also, in Manduca sexta, qualitative similarities were observed between the degree of behavioural generalisation according to chain length [40] and the degree of overlap between electrophysiological temporal patterns of activity across AL neurons [50].
Several correspondences, but also discrepancies, can be found between our behavioural results and the physiological results obtained at the level of the bee AL [23]. First, within the regions of the AL accessible to optical imaging (about 25% of the glomeruli), patterns of glomerular activity for different odours are highly dependent on chain length, but much less so on chemical group. Thus, most active glomeruli respond to several functional groups as long as the chain length corresponds, but respond differentially to different chain lengths. Glomeruli T1–28 and T1–52 are specialised in short-chain molecules (respectively C5–C7 and C6–C7), whilst glomeruli T1–33 and T1–17 are specialised in long-chain molecules (respectively C7–C9 and C8–C9). These glomeruli also respond to most functional groups but in a graded way. For instance, glomerulus T1–17 responds more to alcohols in the intermediate range than to aldehydes or ketones, whereas T1–52 generally responds more to ketones in the short range, more to aldehydes in the long range, and overall little to alcohols. No individual glomerulus was found that responds specifically to a chemical group. However, it should be kept in mind that some regions of the ALs are not yet accessible to calcium imaging techniques (about 75% of the lobe; see below). Thus, a possible explanation is that glomeruli responding to specific chemical groups (or with responses more dependent on chemical groups than on chain length) were not imaged.
Second, primary and secondary alcohols induce extremely similar activation patterns in the AL, but subtle differences could be found, so that for a given chain length, the representation of a secondary alcohol was between that of the primary alcohol of the same chain length and that with one less carbon atom (see Figure 6B in Sachse et al. [23]). We found a similar arrangement of alcohol representations, with primary and secondary alcohols alternating on a common axis (see Figure 6A).
Third, optical imaging data showed that higher chain lengths support more similarity between patterns (see Figure 6C in Sachse et al. [23]). Our finding that longer chain lengths induce more generalisation agrees with the imaging data. These last two points suggest that the general rules governing odour similarity at the neural and the behavioural level are similar.
The Correspondence between Perceptual and Physiological Odour Similarity
We aimed at comparing behavioural and physiological data in a more precise way, using correlation analyses between our behavioural similarity matrix, in which distances between two odour points represent psychological distances between stimuli, and a physiological similarity matrix obtained from optophysiological recordings of glomerular activation patterns [23]. Comparing distances between odours in these two matrixes resulted in a good correlation. This means that glomerular activity patterns recorded in the brain could predict behavioural responses and vice versa.
The optophysiological dataset of Sachse et al. [23] has nevertheless some limitations with respect to the objectives of our work: (i) bath application measurements of AL activity using calcium green as a dye [23] record the combined activity of several neuronal populations of the AL, among which primary-afferent activity seems to have the most important contribution [52]; (ii) such measurements survey only the dorsal part of the AL, which constitutes 25% of the neuropile studied; and (iii) learning alters odour representations in the AL [35,53,54] such that there could be a mismatch between our data collected after olfactory conditioning and the dataset of Sachse et al. [23], which was obtained from naive bees.
With respect to the first point, it could be argued that the AL circuitry transforms the primary-afferent representations of odours [25] such that recordings where primary-afferent receptor activity is predominant are not very useful for evaluating optophysiological similarity. However the very fact that we found a significant correlation between our behavioural data and the imaging data by Sachse et al. [23], strongly suggests that the perceptual quality of odorants mostly appears at the peripheral level. Clearly, this correlation was not perfect, and odour quality is most probably refined by further processing within the AL, and/or at higher stages of the olfactory pathway, such as in the mushroom bodies or the lateral protocerebrum. In honeybees, new methods have been developed, which allow recording selectively the activity of the efferent PNs [25]. However, the two studies published using this method [25,26] do not provide an extensive odorant matrix as that provided by Sachse et al. [23]. In this sense the study on which we based our correlation analysis is certainly the only one of its kind published to date. However, in the future, a careful comparison of our behavioural data with both bath-applied imaging data emphasising receptor neuron input (as done here) and selective imaging of PNs would be extremely helpful in understanding to what extent AL processing shapes odour perceptual quality.
With respect to the second point, calcium imaging recordings of AL activity are certainly limited to the dorsal part of the AL, which is the region accessible when the head capsule is opened in order to expose the brain for recordings. This is an inherent limitation of the method that the use of two-photon microscopy during calcium imaging measurements will soon allow us to overcome, as shown already by recordings obtained in the fruit fly Drosophila melanogaster [55].
Finally, with respect to the third point, it is known that learning alters odour representations in the AL, when bees are trained in a differential conditioning procedure, with one odour rewarded and another odour unrewarded [53]. This is not the conditioning procedure used in our work, which was absolute (only one odour rewarded at a time). In the bee, changes in the olfactory code due to absolute conditioning seem to be difficult to detect (C. G. Galizia, personal communication), such that this point may not be so critical for our correlation analysis. In any case, if there are changes in odour representations due to conditioning, recording glomerular activity patterns after conditioning would only improve our correlation analyses.
Generalisation Asymmetries between Odours
We have found a number of asymmetries in olfactory cross-generalisation, with bees responding more to odour B after learning odour A than in the reverse situation. Previous studies have observed such a phenomenon, but it was mostly related to olfactory compounds with pheromonal value (aggregation pheromone citral [20,37] and alarm pheromones 2-heptanone and isoamyl acetate [56]). In the present study, we found that six out of the 16 odours used induced significant generalisation asymmetries over the whole matrix; none of these six odours was related to any known pheromone (see Table 1). Generalisation asymmetries seem to be a general feature of honeybee olfaction.
Table 1 Chemical and Biological Characteristics of the Odours Used
The odours were listed by functional groups (primary alcohols, secondary alcohols, aldehydes, and ketones) and purity. Odour vapour pressure values (VP), pheromone characteristics and occurrence in floral scents (after Knudsen et al. [66]) are also given
aNotation: *1, releases altering at hive entrance and stinging, repels clustering bees, inhibits scenting, repels foragers (sting chamber); *2, releases altering at hive entrance, inhibits foraging activity, repels foragers (sting chamber); *3, repels at hive entrance, releases stinging, encourages foraging activity (sting chamber); *4, releases stinging, inhibits foraging activity, repels foragers (mandibular glands)
Odour concentration can affect stimulus salience. In our work, generalisation asymmetries could not be directly explained by differences in odour concentration (through differences in vapour pressure), because, for instance, the two odours with the highest vapour pressure in our sample (2-hexanone and hexanal) produced totally opposite results: 2-hexanone induced important generalisation, while hexanal strongly reduced generalisation. Also, although we used 16 different odours with a range of different vapour pressures, we found that acquisition was very similar for most odours, except for the short-chain ketones, which were less easily learned. This suggests that almost all odours used had a good salience for bees. Wright and Smith [57] studied the effect of odour concentration in generalisation in honeybees. They found that discrimination increased with concentration for structurally dissimilar odours but not for similar odours. Further experiments using odorants at different concentrations should be carried out to determine the effect of odour concentration on generalisation asymmetries.
Generalisation asymmetries could be due to innate or experience-dependent differences in the salience of odours for honeybees, such that more salient odours would induce higher generalisation than less salient odours. This interpretation implies that most aldehydes (hexanal, heptanal, and octanal) are highly salient odours for honeybees, because aldehydes showed a clear “functional group” effect, which could reveal a certain bias of the olfactory system towards these odours. Ketones, on the other hand, showed a heterogeneous effect, as 2-hexanone seemed to have a low salience (it was not well learnt) and induced a high generalisation to other odours, while 2-nonanone consistently reduced generalisation to other odours. In the group of alcohols, only 2-hexanol induced generalisation to other odours. Therefore, only aldehydes showed a clear group effect on generalisation asymmetry. This effect could be due to innate odour preferences [58,59] or to previous odour exposure within the hive [60,61]. Innate odour preferences could be related to natural, floral odours that were more consistently associated with food resources [20,62]. It is thus important to investigate whether or not such ecological trends exist in the natural flora associated with the honeybee and whether or not other bee species also present such clear biases, in particular towards aldehydes.
Conversely, asymmetries could be the result of the conditioning procedure. This would be the case if conditioning modifies odour representation in an asymmetric way. Indeed, experience-induced modifications of odour representations have been found at the level of the honeybee AL. Thus, odour-evoked calcium signals in the AL can be modified by elemental [53] and nonelemental olfactory learning paradigms [35] such that the representations of odours that have to be discriminated become more distinct and uncorrelated as a result of learning. In the fruit fly D. melanogaster, new glomeruli become active after olfactory learning [54], while in the moth M. sexta new neuronal units in the AL are recruited after olfactory learning [63]. These elements suggest that modifications of odour representation after learning two different odours could indeed be asymmetrical: if, for instance, the neuronal representation of A after conditioning becomes A′, which is slightly farther away from B than A in the bee's olfactory space, and if the perceptual representation of B becomes B′ after conditioning, which is closer to A than B, then bees would show less generalisation in behavioural tests from A to B than from B to A. On the level of the AL network, glomeruli are connected via lateral inhibitory interneurons [25,64,65]. Due to this, glomerular activation by an odour A will transiently inactivate parts of the network and possibly parts encoding a subsequent odour B. Optical imaging experiments have shown that inhibition between glomeruli may be asymmetric [25]. In our case, glomeruli activated by odour A may inhibit glomeruli coding for odour B, while glomeruli coding for odour B may not inhibit those coding for odour A. In this hypothesis, asymmetric cross-generalisation could reflect a sensory phenomenon. Nevertheless, we believe that inhibitions at the level of the AL are rather short-lived such that a purely sensory priming effect seems improbable. If, however, the strength of lateral inhibitions between glomeruli can be modified by learning as proposed by Linster and Smith [65], then asymmetrical generalisation would come from the fact that inhibitory lateral connections are modified. In order to determine the physiological mechanisms underlying asymmetrical cross-generalisation and the possible role of AL networks in it, future work will aim at visualising the evolution of glomerular activity patterns during and after olfactory conditioning with odours that showed asymmetries in our study.
Conclusion
We have shown that the two odorant physical dimensions that varied in our study, functional group and chain-length, correspond to internal dimensions of the bees' olfactory space. Generalisation was mainly due to these two characteristics with generalisation within functional group being more important. Such generalisation was particularly high for aldehydes, a fact that suggests that these odours may have an intrinsic value for bees. Generalisation between functional groups was mostly found between primary and secondary alcohols. Furthermore, a gradient in generalisation was found with respect to chain length. Asymmetric cross-generalisation was found in the case of certain odorants. Such asymmetries were neither strictly linked to chain length nor to functional group, but depended on particular odorants.
The 16 odours used in our work represent a small part of the odorants that bees may encounter in nature (see Knudsen et al. [66]). For a complete description of the bees' olfactory perceptual space, more odours having other molecular features have to be studied. New dimensions in the bees' perceptual space could then be found.
Finally, and most important, the perceptual distance between odours can be predicted on the basis of the differences in the patterns of glomerular activation in the first relay of the olfactory pathway: the AL, and vice versa. This emphasises the relevance of studying activity patterns in the brain in imaging studies and trying to relate them to perceptual tasks. Our work shows that this objective, which is at the core of cognitive neurosciences, can be achieved using an invertebrate model such as the honeybee.
Materials and Methods
Insects
Every experimental day, honeybees were captured at the entrance of an outdoor hive and were cooled on ice for 5 min until they stopped moving. Then they were harnessed in small metal tubes in such a way that only the head protruded. The mouthparts and the antennae could move freely. Harnessed bees were left for 3 h in a resting room without disturbance. Fifteen minutes before starting the experiments, each subject was checked for intact PER by lightly touching one antenna with a toothpick imbibed with 50% (w/w) sucrose solution without subsequent feeding. Extension of the proboscis beyond the virtual line between the open mandibles was counted as PER. Animals that did not show the reflex were not used in the experiments.
Stimulation apparatus
The odours were delivered by an odour cannon, which allowed the presentation of up to seven different odours, and a clean airstream [67]. Each odour was applied to a filter paper placed within a syringe (see below) that was connected to the cannon. An airstream was produced by an air pump (Rena Air 400, Annecy, France) and directed to the relevant syringes with electronic valves (Lee Company, Voisins-le-Bretonneux, France) controlled by the experimenter via a computer. In the absence of odour stimulation, the airstream passed through a syringe containing a clean filter paper piece (clean airstream). During odour stimulation, the airstream was directed to a syringe containing a filter paper loaded with odour. After a 4-s stimulation, the airstream was redirected to the odourless syringe until the next stimulation.
Stimuli
Sixteen odours (Sigma Aldrich, Deisenhofen, Germany) were used in our work as CS and test stimuli (see Table 1). Racemic mixtures were used in the case of molecules that had chiral carbons. These odours are present in flowers and some in pheromones (see Table 1). Pure odorants (4 μl) were applied to 1-cm2 filter paper pieces, which were transferred to 1-ml syringes, cut to 0.7 ml to make them fit into the odour cannon. Fifty percent sugar solution was used throughout as US.
Experimental design
Our work was designed to obtain a generalisation matrix with 16 different odours. Ideally, after conditioning each of the 16 odours as CS, the response to each odour (including the CS) should be measured (i.e., 16 × 16 = 256 cells). However, testing 16 odours implies presenting them without reward, a situation that may result in extinction of the learned response due to the repeated unrewarded odour presentations. Preliminary experiments were performed in which four groups of 180 bees were trained along three trials to 1-hexanol, 2-octanol, linalool, and limonene, respectively. Training was followed by tests with the four different odours, including the conditioned one. These experiments showed that after three conditioning trials, the response of the bees to the CS in the four tests remained at the same level, independently of the order of occurrence of the CS such that it was not influenced by extinction. We thus kept this protocol for the 16 × 16 matrix. Each of the 2,048 bees used in this study was thus subjected to three conditioning trials with their respective CS, and to four test trials, each with a different odour chosen among the 16 possible odours. Intertrial intervals of 10 min were used throughout. A randomisation schedule (detailed below) was developed for the test phase to reduce any possible day- and odour-combination effects.
Conditioning trials
One bee at a time was placed into the conditioning setup. The total duration of each trial was 37.5 s After 15 s of familiarisation to the experimental context, the CS was presented to the bee for 4 s. Three sec after onset of the CS, the antennae were stimulated with the US, leading to a proboscis extension. The bee was allowed to feed for 3 s. Stimulus overlap was 1 s (interstimulus interval, 3 s). The bee was left in the conditioning place for 17.5 s and then removed.
Test trials
The procedure was similar to that for conditioning trials but no US was given after odour delivery. After the four test trials, PER to the US was checked once again. Animals unable to show PER at this point were not considered for the analyses. Overall, less than 2% of the bees died during the experiment, and less than 1% of the survivors showed no US reaction at the end of the tests.
Randomisation schedule
On each day, two to three experimenters worked in parallel, each training 16 bees at a time. In the training phase, the 16 bees were divided into four groups of four bees, and each group was trained to one of the 16 different odours. In the test phase, four out of 16 odours were presented to each of the 16 bees. The combination of four odours tested together changed in each experiment, so that any effect of having particular odours in the same test combination was suppressed. The whole experiment was planned in such a way that in any of our experimental groups, two given odours appeared at least once, but a maximum of three times together in a test sequence. This was possible by carefully picking out eight of the 16! (2.1 × 1013) possible experimental plans. Additionally, within each group, the testing order for the four test odours was determined randomly.
Data analysis and statistics
During the experiments, we recorded the response to the presented odour, that is, whether bees extended their proboscis after the onset of the odour and before the presentation of the sucrose solution in the case of reinforced trials, such that the anticipatory response recorded was due to the odour and not to the US. Multiple responses during a CS were counted as a single PER. The percentages of PER recorded during acquisition were used to plot acquisition curves (see Figure 1). To test whether bees learnt the different odours in a similar way, ANOVAs for repeated measurements were used both for between-group and for within-group comparisons. Monte Carlo studies have shown that it is permissible to use ANOVA on dichotomous data only under controlled conditions [68], which are met by the experiments reported in this study: equal cell frequencies and at least 40 df of the error term. The α level was set to 0.05 (two-tailed).
To ensure that we analysed a true generalisation response in the tests, and hence built a true generalisation matrix, we kept only those bees which had actually learnt the CS (71% of the bees used in this work). We therefore performed new analyses that only included those bees that responded to the CS before the presentation of the US in the third conditioning trial. A lack of response to an odour in the tests could be due either to the fact that the bees had not made any association between CS and US or because their motivational level was low. For all odours tested, we observed that responses to the CS in the third conditioning trial were equivalent to responses to the CS in the tests (McNemar test; see Results). We represented the responses of the selected bees to the test odours (see Figure 2). As the numbers of bees were now heterogeneous in the different groups, we could not use ANOVAs to analyse the responses in the tests (see above). We thus used χ2 tests for all further between-group comparisons. In the case of multiple two-by-two comparisons, the significance threshold was corrected using the Dunn–Sidak correction [α′ = 1 − (1 − α)1/k where k is the number of two-by-two comparisons in which each dataset is used] in order to reduce the type I errors. Alpha values between α′ and 0.05 were considered as near significant.
Olfactory space
To observe the relationships between odours in a reduced number of dimensions, we performed a PCA, which identified orthogonal axes (factors) of maximum variance in the data, and thus projected the data into a lower-dimensionality space formed of a subset of the highest-variance components. We calculated the three factors, which accounted for most of the observed variance. Calculating distances between odours in the resulting putative olfactory space allowed the evaluation of their perceptual similarity, not only based on direct generalisation between these odours (i.e., generalisation from odour A to odour B and vice versa), but also including responses to these odours after conditioning to other odours (e.g., C, D, E, etc.). We performed cluster analyses to group odours, according to their respective distance in the olfactory space, using both Euclidian and city-block metrics, with Ward's classification method. Both metrics gave very similar results, so we later used only Euclidian metrics. Euclidian (i.e., direct) distances in the 16-dimensional space are defined as
with i and j indicating odours, p the number of dimensions—that is, conditioning groups—and Xik the response of bees to odour i after conditioning to odour k. These distances were used in correlation analyses with optical imaging data (see below).
Correlation analysis between perceptual and optophysiological similarity measures
We studied whether or not physiological similarity between odours as determined by optical imaging studies of AL activity [22,23,35] actually reflects perceptual odour similarity for the bees. To this end, we performed correlation analyses between published optical imaging data that were obtained using the same set of odours as in our work [23] and our behavioural data. We used two sets of physiological data. First, to perform such a correlation on the whole dataset (including all 16 odours), we transcribed the activation maps presented by Sachse et al. [23] (see Figure 7) into activation levels for each glomerulus from zero to three, according to the following signal scale: dark blue (0%–20%) and light blue (>20%–40% activity), zero; green (>40%–60% activity), one; yellow (>60%–80% activity), two; and red (>80% activity), three. As the activity under 40% was less accurately separated from noise, activation levels between 0% and 40% were ranked as 0. Scaling the physiological data in this way instead of using the original imaging activation data, gave a good overview of physiological similarity between odours for imaging data (see Results). To provide a more precise correlation analysis between behavioural and imaging data, albeit on a more limited odour dataset (eight odours), we used exact correlation data ([23], Table 1). Each correlation value C, as calculated by Sachse et al. [23] between activity patterns for all pairs of primary and secondary alcohols, was converted into physiological distances by the operation 100 − C. All linear correlations were assessed by calculating Pearson's r, and using Student's t-test. Comparison between correlation coefficients obtained with the two methods was carried out statistically using a Z test as in [69].
We thank Nina Giotto for her help with the experiments and Giovanni Galizia, Monique Gauthier, Christiane Linster, and three anonymous referees for useful comments on previous versions of this work. M. Giurfa acknowledges the support of the Human Frontier Science Program (Young Investigator Award), the Fondation pour la Recherche Médicale, the Action Concertée Incitative Neurosciences Computationnelles (French Research Ministry), the Region Midi-Pyrenees, and the Institut Universitaire de France. J. C. Sandoz was supported by the Human Frontier Science Program, the Fyssen Foundation, and the Centre National de la Recherche Scientifique. F. Guerrieri was partially supported by the Programa “José Estenssoro,” Fundación YPF (Argentina), and M. Schubert was supported by the Graduiertenkolleg 120 “Signal Cascades in Living Systems” (Germany). M. Schubert thanks Jochen Erber for support.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. MG and JCS conceived and designed the experiments. FG and MS performed the experiments. FG, MS, and JCS analyzed the data. MG, FG, MS, and JCS wrote the paper.
Citation: Guerrieri F, Schubert M, Sandoz J-C, Giurfa M (2005) Perceptual and neural olfactory similarity in honeybees. PLoS Biol 3(4): e60.
Abbreviations
ALantennal lobe
ANOVAanalysis of variance
CSconditioned stimulus
PCAprincipal component analysis
PERproboscis extension reflex
PNsprojection neurons
USunconditioned stimulus
==== Refs
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| 15736975 | PMC1043859 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Apr 22; 3(4):e60 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030060 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1572311610.1371/journal.pbio.0030085Research ArticleBioinformatics/Computational BiologyCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyEukaryotesDrosophilaPrinciples of MicroRNA–Target Recognition Principles of MicroRNA-Target RecognitionBrennecke Julius
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Stark Alexander
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Russell Robert B
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Cohen Stephen M [email protected]
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1European Molecular Biology LaboratoryHeidelbergGermany3 2005 15 2 2005 15 2 2005 3 3 e8521 9 2004 4 1 2005 Copyright: © 2005 Brennecke et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Seeds of Destruction: Predicting How microRNAs Choose Their Target
MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression in plants and animals. Although their biological importance has become clear, how they recognize and regulate target genes remains less well understood. Here, we systematically evaluate the minimal requirements for functional miRNA–target duplexes in vivo and distinguish classes of target sites with different functional properties. Target sites can be grouped into two broad categories. 5′ dominant sites have sufficient complementarity to the miRNA 5′ end to function with little or no support from pairing to the miRNA 3′ end. Indeed, sites with 3′ pairing below the random noise level are functional given a strong 5′ end. In contrast, 3′ compensatory sites have insufficient 5′ pairing and require strong 3′ pairing for function. We present examples and genome-wide statistical support to show that both classes of sites are used in biologically relevant genes. We provide evidence that an average miRNA has approximately 100 target sites, indicating that miRNAs regulate a large fraction of protein-coding genes and that miRNA 3′ ends are key determinants of target specificity within miRNA families.
MicroRNA target site recognition falls into two broad categories: 5' dominant sites that require little support from microRNA 3' end; and 3' compensatory sites that require strong 3' pairing to function
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Introduction
MicroRNAs (miRNAs) are small non-coding RNAs that serve as post-transcriptional regulators of gene expression in plants and animals. They act by binding to complementary sites on target mRNAs to induce cleavage or repression of productive translation (reviewed in [1,2,3,4]). The importance of miRNAs for development is highlighted by the fact that they comprise approximately 1% of genes in animals, and are often highly conserved across a wide range of species (e.g., [5,6,7]). Further, mutations in proteins required for miRNA function or biogenesis impair animal development [8,9,10,11,12,13,14,15].
To date, functions have been assigned to only a few of the hundreds of animal miRNA genes. Mutant phenotypes in nematodes and flies led to the discovery that the lin-4 and let-7 miRNAs control developmental timing [16,17], that lsy-6 miRNA regulates left–right asymmetry in the nervous system [18], that bantam miRNA controls tissue growth [19], and that bantam and miR-14 control apoptosis [19,20]. Mouse miR-181 is preferentially expressed in bone marrow and was shown to be involved in hematopoietic differentiation [21]. Recently, mouse miR-375 was found to be a pancreatic-islet-specific miRNA that regulates insulin secretion [22].
Prediction of miRNA targets provides an alternative approach to assign biological functions. This has been very effective in plants, where miRNA and target mRNA are often nearly perfectly complementary [23,24,25]. In animals, functional duplexes can be more variable in structure: they contain only short complementary sequence stretches, interrupted by gaps and mismatches. To date, specific rules for functional miRNA–target pairing that capture all known functional targets have not been devised. This has created problems for search strategies, which apply different assumptions about how to best identify functional sites. As a result, the number of predicted targets varies considerably with only limited overlap in the top-ranking targets, indicating that these approaches might only capture subsets of real targets and/or may include a high number of background matches ([19,26,27,28,29,30]; reviewed by [31]). Nonetheless, a number of predicted targets have proven to be functional when subjected to experimental tests [19,26,27,29].
A better understanding of the pairing requirements between miRNA and target would clearly improve predictions of miRNA targets in animals. It is known that defined cis-regulatory elements in Drosophila 3′ UTRs are complementary to the 5′ ends of certain miRNAs [32]. The importance of the miRNA 5′ end has also emerged from the pairing characteristics and evolutionary conservation of known target sites [26], and from the observation of a non-random statistical signal specific to the 5′ end in genome-wide target predictions [27]. Tissue culture experiments have also underscored the importance of 5′ pairing and have provided some specific insights into the general structural requirements [29,33,34], though different studies have conflicted to some degree with each other, and with known target sites (reviewed in [31]). To date, no specific role has been ascribed to the 3′ end of miRNAs, despite the fact that miRNAs tend to be conserved over their full length.
Here, we systematically evaluate the minimal requirements for a functional miRNA–target duplex in vivo. These experiments have allowed us to identify two broad categories of miRNA target sites. Targets in the first category, “5′ dominant” sites, base-pair well to the 5′ end of the miRNA. Although there is a continuum of 3′ pairing quality within this class, it is useful to distinguish two subtypes: “canonical” sites, which pair well at both the 5′ and 3′ ends, and “seed” sites, which require little or no 3′ pairing support. Targets in the second category, “3′ compensatory” sites, have weak 5′ base-pairing and depend on strong compensatory pairing to the 3′ end of the miRNA. We present evidence that all of these site types are used to mediate regulation by miRNAs and show that the 3′ compensatory class of target sites is used to discriminate among individual members of miRNA families in vivo. A genome-wide statistical analysis allows us to estimate that an average miRNA has approximately 100 evolutionarily conserved target sites, indicating that miRNAs regulate a large fraction of protein-coding genes. Evaluation of 3′ pairing quality suggests that seed sites are the largest group. Sites of this type have been largely overlooked in previous target prediction methods.
Results
The Minimal miRNA Target Site
To improve our understanding of the minimal requirements for a functional miRNA target site, we made use of a simple in vivo assay in the Drosophila wing imaginal disc. We expressed a miRNA in a stripe of cells in the central region of the disc and assessed its ability to repress the expression of a ubiquitously transcribed enhanced green fluorescent protein (EGFP) transgene containing a single target site in its 3′ UTR. The degree of repression was evaluated by comparing EGFP levels in miRNA-expressing and adjacent non-expressing cells. Expression of the miRNA strongly reduced EGFP expression from transgenes containing a single functional target site (Figure 1A).
Figure 1 Complementarity to the miRNA 5′ End Is Important for Target Site Function In Vivo
(A) In vivo assay for target site regulation in the wing imaginal disc. The EGFP reporter is expressed in all cells (green). Cells expressing the miRNA under ptcGal4 control are shown in red. Functional target sites allow strong GFP repression by the miRNA (middle). Non-functional target sites do not (right). Yellow boxes indicate the disc region shown in (B) and later figures.
(B) Regulation of individual target sites by miR-7. Numbers in the upper left of each image indicate the mismatched nucleotide in the target site. Positions important for regulation are shown in red, dispensable positions in green. Regulation by the miRNA is completely abolished in only a few cases.
(C) Summary of the magnitude of reporter gene repression for the series in (B) and for a second set involving miR-278 and a target site resembling the miR-9 site in Lyra [26]. Positions important for regulation are shown in red, dispensable positions in green. Error bars are based on measurements of 3–5 individual discs.
In a first series of experiments we asked which part of the RNA duplex is most important for target regulation. A set of transgenic flies was prepared, each of which contained a different target site for miR-7 in the 3′ UTR of the EGFP reporter construct. The starting site resembled the strongest bantam miRNA site in its biological target hid [19] and conferred strong regulation when present in a single copy in the 3′ UTR of the reporter gene (Figure 1B). We tested the effects of introducing single nucleotide changes in the target site to produce mismatches at different positions in the duplex with the miRNA (note that the target site mismatches were the only variable in these experiments). The efficient repression mediated by the starting site was not affected by a mismatch at positions 1, 9, or 10, but any mismatch in positions 2 to 8 strongly reduced the magnitude of target regulation. Two simultaneous mismatches introduced into the 3′ region had only a small effect on target repression, increasing reporter activity from 10% to 30%. To exclude the possibility that these findings were specific for the tested miRNA sequence or duplex structure, we repeated the experiment with miR-278 and a different duplex structure. The results were similar, except that pairing of position 8 was not important for regulation in this case (Figure 1C). Moreover, some of the mismatches in positions 2–7 still allowed repression of EGFP expression up to 50%. Taken together, these observations support previous suggestions that extensive base-pairing to the 5′ end of the miRNA is important for target site function [26,27,29,32,34].
We next determined the minimal 5′ sequence complementarity necessary to confer target regulation. We refer to the core of 5′ sequence complementarity essential for target site recognition as the “seed” (Lewis et al. [27]). All possible 6mer, 5mer, and 4mer seeds complementary to the first eight nucleotides of the miRNA were tested in the context of a site that allowed strong base-pairing to the 3′ end of the miRNA (Figure 2A). The seed was separated from a region of complete 3′ end pairing by a constant central bulge. 5mer and 6mer seeds beginning at positions 1 or 2 were functional. Surprisingly, as few as four base-pairs in positions 2–5 conferred efficient target regulation under these conditions, whereas bases 1–4 were completely ineffective. 4mer, 5mer, or 6mer seeds beginning at position 3 were less effective. These results suggest that a functional seed requires a continuous helix of at least 4 or 5 nucleotides and that there is some position dependence to the pairing, since sites that produce comparable pairing energies differ in their ability to function. For example, the first two duplexes in Figure 2A (4mer, top row) have identical 5′ pairing energies (ΔG for the first 8 nt was −8.9 kcal/mol), but only one is functional. Similarly, the third 4mer duplex and fourth 5mer duplex (middle row) have the same energy (−8.7 kcal/mol), but only one is functional. We thus do not find a clear correlation between 5′ pairing energy and function, as reported in [34]. These experiments also indicate that extensive 3′ pairing of up to 17 nucleotides in the absence of the minimal 5′ element is not sufficient to confer regulation. Consequently, target searches based primarily on optimizing the extent of base-pairing or the total free energy of duplex formation will include many non-functional target sites [28,30,35], and ranking miRNA target sites according to overall complementarity or free energy of duplex formation might not reflect their biological activity [26,27,28,30,35].
Figure 2 The Minimal miRNA Target Site
(A) In vivo tests of the function of target sites with 6mer, 5mer, and 4mer seeds complementary to the first eight nucleotides of the miRNA. Sites were designed to have optimal support from 3′ pairing. The first 4mer seed site shows that extensive complementarity to the miRNA 3′ region is not sufficient for regulation in vivo.
(B) Regulation of 8mer, 7mer, and 6mer seed sites lacking complementarity to the miRNA 3′ end. The test UTR contained one site (first column) or two identical sites (second column).
To determine the minimal lengths of 5′ seed matches that are sufficient to confer regulation alone, we tested single sites that pair with eight, seven, or six consecutive bases to the miRNA's 5′ end, but that do not pair to its 3′ end (Figure 2B). Surprisingly, a single 8mer seed (miRNA positions 1–8) was sufficient to confer strong regulation by the miRNA. A single 7mer seed (positions 2–8) was also functional, although less effective. The magnitude of regulation for 8mer and 7mer seeds was strongly increased when two copies of the site were introduced in the UTR. In contrast, 6mer seeds showed no regulation, even when present in two copies. Comparable results were recently reported for two copies of an 8mer site with limited 3′ pairing capacity in a cell-based assay [34]. These results do not support a requirement for a central bulge, as suggested previously [29].
We took care in designing the miRNA 3′ ends to exclude any 3′ pairing to nearby sequence according to RNA secondary structure prediction. However, we cannot rule out the possibility that extensive looping of the UTR sequence might allow the 3′ end to pair to sequences further downstream in our reporter constructs. Note, however, that even if remote 3′ pairing was occurring and required for function of 8- and 7mer seeds, it is not sufficient for 5′ matches with less than seven complementary bases (all test sites are in the same sequence context; Figure 2B). In addition, pairing at a random level will occur in any sequence if long enough loops are allowed. However, whether the ribonucleoprotein complexes involved in translational repression require 3′ pairing, and whether they are able to allow extensive looping to achieve this, remains an open question. Computationally, remote 3′ pairing cannot be distinguished from random matches if loops of any length are allowed. On this basis any site with a 7- or 8mer seed has to be taken seriously—especially when evolutionarily conserved.
From these experiments we conclude that (1) complementarity of seven or more bases to the 5′ end miRNA is sufficient to confer regulation, even if the target 3′ UTR contains only a single site; (2) sites with weaker 5′ complementarity require compensatory pairing to the 3′ end of the miRNA in order to confer regulation; and (3) extensive pairing to the 3′ end of the miRNA is not sufficient to confer regulation on its own without a minimal element of 5′ complementarity.
The Effect of G:U Base-Pairs and Bulges in the Seed
Several confirmed miRNA target genes contain predicted binding sites with seeds that are interrupted by G:U base-pairs or single nucleotide bulges [17,19,26,36,37,38,39]. In most cases these mRNAs contain multiple predicted target sites and the contributions of individual sites have not been tested. In vitro tests have shown that sites containing G:U base-pairs can function [29,34], but that G:U base-pairs contribute less to target site function than would be expected from their contribution to the predicted base-pairing energy [34]. We tested the ability of single sites with seeds containing G:U base-pairs and bulges to function in vivo. One, two, or three G:U base-pairs were introduced into single target sites with 8mer, 7mer, or 6mer seeds (Figure 3A). A single G:U base-pair caused a clear reduction in the efficiency of regulation by an 8mer seed site and by a 7mer seed site. The site with a 6mer seed lost its activity almost completely. Having more than one G:U base-pair compromised the activity of all the sites. As the target sites were designed to allow optimal 3′ pairing, we conclude that G:U base-pairs in the seed region are always detrimental.
Figure 3 Effects of G:U Base-Pairs and Bulges
(A) Regulation of sites with 8mer, 7mer, or 6mer seeds (rows) containing zero, one, two, or three G:U base-pairs in the seed region (columns).
(B) Regulation of sites with bulges in the target sequence or in the miRNA.
Single nucleotide bulges in the seed are found in the let-7 target lin-41 and in the lin-4 target lin-14 [17,36,37]. Recent tissue culture experiments have led to the proposal that such bulges are tolerated if positioned symmetrically in the seed region [29]. We tested a series of sites with single nucleotide bulges in the target or the miRNA (Figure 3B). Only some of these sites conferred good regulation of the reporter gene. Our results do not support the idea that such sites depend on a symmetrical arrangement of base-pairs flanking the bulge. We also note that the identity of the bulged nucleotide seems to matter. While it is clear that some target sites with one nucleotide bulge or a single mismatch can be functional if supported by extensive complementarity to the miRNA 3′ end, it is not possible to generalize about their potential function.
Functional Categories of Target Sites
While recognizing that there is a continuum of base-pairing quality between miRNAs and target sites, the experiments presented above suggest that sites that depend critically on pairing to the miRNA 5′ end (5′ dominant sites) can be distinguished from those that cannot function without strong pairing to the miRNA 3′ end (3′ compensatory sites). The 3′ compensatory group includes seed matches of four to six base-pairs and seeds of seven or eight bases that contain G:U base-pairs, single nucleotide bulges, or mismatches.
We consider it useful to distinguish two subgroups of 5′ dominant sites: those with good pairing to both 5′ and 3′ ends of the miRNA (canonical sites) and those with good 5′ pairing but with little or no 3′ pairing (seed sites). We consider seed sites to be those where there is no evidence for pairing of the miRNA 3′ end to nearby sequences that is better than would be expected at random. We cannot exclude the possibility that some sites that we identify as seed sites might be supported by additional long-range 3′ pairing. Computationally, this is always possible if long enough loops in the UTR sequence are allowed. Whether long loops are functional in vivo remains to be determined.
Canonical sites have strong seed matches supported by strong base-pairing to the 3′ end of the miRNA. Canonical sites can thus be seen as an extension of the seed type (with enhanced 3′ pairing in addition to a sufficient 5′ seed) or as an extension of the 3′compensatory type (with improved 5′ seed quality in addition to sufficient 3′ pairing). Individually, canonical sites are likely to be more effective than other site types because of their higher pairing energy, and may function in one copy. Due to their lower pairing energies, seed sites are expected to be more effective when present in more than one copy. Figure 4 presents examples of the different site types in biologically relevant miRNA targets and illustrates their evolutionary conservation in multiple drosophilid genomes.
Figure 4 Three Classes of miRNA Target Sites
Model of canonical (left), seed (middle) and 3′ compensatory (right) target sites. The upper diagram illustrates the mode of pairing between target site (upper line) and miRNA (lower line, color). Next down in each column are diagrams of the pattern of 3′ UTR conservation. The vertical black bars show stretches of at least six nucleotides that are conserved in several drosophilid genomes. Target sites for miR-7, miR-4, and miR-10 are shown as colored horizontal bars beneath the UTR. Sites for other miRNAs are shown as black bars. Furthest down in each column the predicted structure of the duplex between the miRNA and its target site is shown; canonical base-pairs are marked with filled circles, G:U base-pairs with open circles. The sequence alignments show nucleotide conservation of these target sites in the different drosophilid species Nucleotides predicted to pair to the miRNA are shown in bold; nucleotides predicted to be unpaired are grey. Red asterisks indicate 100% sequence conservation; grey asterisks indicate conservation of base-pairing to the miRNA including G:U pairs. The additional sequence alignment for the miR-10 target site in Scr in Tribolium castanaeum, Anopheles gambiae, and Bombyx mori strengthens this prediction. Note that the reduced quality of 3′ compensation in these species is compensated by the presence of a better quality 7mer seed. A. ga, Anopheles gambiae; B. mo, B. mori; D. an, D. ananassae; D. me, D. melanogaster; D. ps, D. pseudoobscura; D. si, D. simulans; D. vi, D. virilis; D. ya, D. yakuba; T. ca, T. castanaeum.
Most currently identified miRNA target sites are canonical. For example, the hairy 3′ UTR contains a single site for miR-7, with a 9mer seed and a stretch of 3′ complementarity. This site has been shown to be functional in vivo [26], and it is strikingly conserved in the seed match and in the extent of complementarity to the 3′ end of miR-7 in all six orthologous 3′ UTRs.
Although seed sites have not been previously identified as functional miRNA target sites, there is some evidence that they exist in vivo. For example, the Bearded (Brd) 3′ UTR contains three sequence elements, known as Brd boxes, that are complementary to the 5′ region of miR-4 and miR-79 [32,40]. Brd boxes have been shown to repress expression of a reporter gene in vivo, presumably via miRNAs, as expression of a Brd 3′ UTR reporter is elevated in dicer-1 mutant cells, which are unable to produce any miRNAs [14]. All three Brd box target sites consist of 7mer seeds with little or no base-pairing to the 3′ end of either miR-4 or miR-79 (see below). The alignment of Brd 3′ UTRs shows that there is little conservation in the miR-4 or miR-79 target sites outside the seed sequence, nor is there conservation of pairing to either miRNA 3′ end. This suggests that the sequences that could pair to the 3′ end of the miRNAs are not important for regulation as they do not appear to be under selective pressure. This makes it unlikely that a yet unidentified Brd box miRNA could form a canonical site complex.
The 3′ UTR of the HOX gene Sex combs reduced (Scr) provides a good example of a 3′ compensatory site. Scr contains a single site for miR-10 with a 5mer seed and a continuous 11-base-pair complementarity to the miRNA 3′ end [28]. The miR-10 transcript is encoded within the same HOX cluster downstream of Scr, a situation that resembles the relationship between miR-iab-5p and Ultrabithorax in flies [26] and miR-196/HoxB8 in mice [41]. The predicted pairing between miR-10 and Scr is perfectly conserved in all six drosophilid genomes, with the only sequence differences occurring in the unpaired loop region. The site is also conserved in the 3′ UTR of the Scr genes in the mosquito, Anopheles gambiae, the flour beetle, Tribolium castaneum, and the silk moth,Bombyx mori. Conservation of such a high degree of 3′ complementarity over hundreds of millions of years of evolution suggests that this is likely to be a functional miR-10 target site. Extensive 5′ and 3′ sequence conservation is also seen for other 3′ compensatory sites, e.g., the two let-7 sites in lin-41 or the miR-2 sites in grim and sickle [17,26,36].
The miRNA 3′ End Determines Target Specificity within miRNA Families
Several families of miRNAs have been identified whose members have common 5′ sequences but differ in their 3′ ends. In view of the evidence that 5′ ends of miRNA are functionally important [26,27,29,42], and in some cases sufficient (present study), it can be expected that members of miRNA families may have redundant or partially redundant functions. According to our model, 5′ dominant canonical and seed sites should respond to all members of a given miRNA family, whereas 3′ compensatory sites should differ in their sensitivity to different miRNA family members depending on the degree of 3′ complementarity. We tested this using the wing disc assay with 3′ UTR reporter transgenes and overexpression constructs for various miRNA family members.
miR-4 and miR-79 share a common 5′ sequence that is complementary to a single 8mer seed site in the bagpipe 3′ UTR (Figure 5A and 5B). The 3′ ends of the miRNAs differ. miR-4 is predicted to have 3′ pairing at approximately 50% of the maximally possible level (−10.8 kcal/mol), whereas the level of 3′ pairing for miR-79 is approximately 25% maximum (−6.1 kcal/mol), which is below the average level expected for random matches (see below). Both miRNAs repressed expression of the bagpipe 3′ UTR reporter, regardless of the 3′ complementarity (Figure 5B). This indicates that both types of site are functional in vivo and suggests that bagpipe is a target for both miRNAs in this family.
Figure 5 Target Specificity of miRNA Family Members
(A) Diagrams of 3′ UTR conservation in six drosophilid genomes (horizontal black bars) and the location of predicted miRNA target sites. Above is the 3′ UTR of the myogenic transcription factor bagpipe (bap) showing the predicted target site for the Brd box miRNA family, miR-4 and miR-79 (black box below the UTR). Alignment of miR-4 and miR-79 illustrates that they share a similar seed sequence (except that mir-4 has one extra 5′ base) but have little 3′ end similarity. Below are the conserved sequences in the3′ UTRs of the pro-apoptotic genes grim and sickle. Predicted target sites for the K Box miRNAs miR-11, miR-2b, and miR-6 are shown below the UTR. Alignment of miR-11, miR-2b, and miR-6 illustrates that they share the same family motif but have little similarity in their 3′ ends.
(B) The bagpipe (bap) 3′ UTR reporter gene is regulated by miR-4 and miR-79. Alignments of the two miRNAs to the predicted target site show good 8mer seed matches (left). Overexpression of miR-4 or miR-79 under ptcGal4 control downregulated the bagpipe 3′ UTR reporter (right).
(C) Left: Alignment of K box miRNAs with the single predicted site in the grim 3′ UTR and regulation by overexpression of miR-2 (top), but not by miR-6 (middle) or miR-11 (bottom). Right: Alignment of K box miRNAs with the two predicted sites in the sickle 3′ UTR. Regulation by overexpression of miR-2 was strong (top), regulation by miR-6 was weaker (middle), and miR-11 had little effect (bottom).
(D) Effect of clones of cells lacking dicer-1 on expression of UTR reporters for predicted miRNA-regulated genes. Mutant cells were marked by the absence of β-Gal expression (red). EGFP expression is shown in green. Both channels are shown separately below in black and white. Mutant clones are indicated by yellow arrows. Expression of a uniformly transcribed reporter construct lacking miRNA target sites was unaffected in dicer-1 mutant cells (first column). The UTR reporter for the bantam miRNA target hid was upregulated in the mutant cells (second column). The bagpipe (bap) UTR reporter was upregulated in dicer-1 clones (third column). The grim (fourth column) and sickle (fifth column) UTR reporters were upregulated.
To test whether miRNA family members can also have non-overlapping targets, we used 3′ UTR reporters of the pro-apoptotic genes grim and sickle, two recently identified miRNA targets [26]. Both genes contain K boxes in their 3′ UTRs that are complementary to the 5′ ends of the miR-2, miR-6, and miR-11 miRNA family [26,32]. These miRNAs share residues 2–8 but differ considerably in their 3′ regions (Figure 5A). The site in the grim 3′ UTR is predicted to form a 6mer seed match with all three miRNAs (Figure 5C, left), but only miR-2 shows the extensive 3′ complementarity that we predict would be needed for a 3′ compensatory site with a 6mer seed to function (−19.1 kcal/mol, 63% maximum 3′ pairing, versus −10.9 kcal/mol, 46% maximum, for miR-11 and −8.7 kcal/mol, 37% maximum, for miR-6). Indeed, only miR-2 was able to regulate the grim 3′ UTR reporter, whereas miR-6 and miR-11 were non-functional.
The sickle 3′ UTR contains two K boxes and provides an opportunity to test whether weak sites can function synergistically. The first site is similar to the grim 3′ UTR in that it contains a 6mer seed for all three miRNAs but extensive 3′ complementarity only to miR-2. The second site contains a 7mer seed for miR-2 and miR-6 but only a 6mer seed for miR-11 (Figure 5C, right). miR-2 strongly downregulated the sickle reporter, miR-6 had moderate activity (presumably via the 7mer seed site), and miR-11 had nearly no activity, even though the miRNAs were overexpressed. The fact that a site is targeted by at least one miRNA argues that it is accessible (e.g., miR-2 is able to regulate both UTR reporters), and that the absence of regulation for other family members is due to the duplex structure. These results are in line with what we would expect based on the predicted functionality of the individual sites, and indicate that our model of target site functionality can be extended to UTRs with multiple sites. Weak sites that do not function alone also do not function when they are combined.
To show that endogenous miRNA levels regulate all three 3′ UTR reporters, we compared EGFP expression in wild-type cells and dicer-1 mutant cells, which are unable to produce miRNAs [14]. dicer-1 clones did not affect a control reporter lacking miRNA binding sites, but showed elevated expression of a reporter containing the 3′ UTR of the previously identified bantam miRNA target hid (Figure 5D). Similarly, all 3′ UTR reporters above were upregulated in dicer-1 mutant cells, indicating that bagpipe, sickle, and grim are subject to repression by miRNAs expressed in the wing disc. Taken together, these experiments indicate that transcripts with 5′ dominant canonical and seed sites are likely to be regulated by all members of a miRNA family. However, transcripts with 3′ compensatory sites can discriminate between miRNA family members.
Genome-Wide Occurrence of Target Sites
Experimental tests such as those presented above and the observed evolutionary conservation suggest that all three types of target sites are likely to be used in vivo. To gain additional evidence we examined the occurrence of each site type in all Drosophila melanogaster 3′ UTRs. We made use of the D. pseudoobscura genome, the second assembled drosophilid genome, to determine the degree of site conservation for the three different site classes in an alignment of orthologous 3′ UTRs. From the 78 known Drosophila miRNAs, we selected a set of 49 miRNAs with non-redundant 5′ sequences. We first investigated whether sequences complementary to the miRNA 5′ ends were better conserved than would be expected for random sequences. For each miRNA, we constructed a cohort of ten randomly shuffled variants. To avoid a bias for the number of possible target matches, the shuffled variants were required to produce a number of sequence matches comparable (±15%) to the original miRNAs for D. melanogaster 3′ UTRs. 7mer and 8mer seeds complementary to real miRNA 5′ ends were significantly better conserved than those complementary to the shuffled variants. This is consistent with the findings of Lewis et al. [27] but was obtained without the need to use a rank and energy cutoff applied to the full-length miRNA target duplex, as was the case for vertebrate miRNAs. Conserved 8mer seeds for real miRNAs occur on average 2.8 times as often as seeds complementary to the shuffled miRNAs (Figure 6A). For 7mer seeds this signal was 2:1, whereas 6mer, 5mer, and 4mer seeds did not show better conservation than expected for random sequences. To assess the validity of these signals and to control for the random shuffling of miRNAs, we repeated this procedure with “mutant” miRNAs in which two residues in the 5′ region were changed. There was no difference between the mutant test miRNAs and their shuffled variants (Figure 6A). This indicates that a substantial fraction of the conserved 7mer and 8mer seeds complementary to real miRNAs identify biologically relevant target sites.
Figure 6 Computational Analysis of Target Site Occurrence
(A) Genome-wide occurrence of conserved 5′ seed matches. Histogram showing the ratio of 5′ seed matches for a set of 49 5′ non-redundant miRNAs and the average of their ten completely shuffled variants for different seed types (black bars). A ratio of one (red line) indicates no difference between the miRNA and its shuffled variants. The same ratio for mutated miRNAs and their shuffled variants shows no signal (white bars). The inset depicts shuffling of the entire miRNA sequence (wavy purple line).
(B) Target site conservation between D. melanogaster and D. pseudoobscura. Histogram showing the average conservation of the 3′ UTR sequence (16 nt) upstream of a conserved 8mer seed match that would pair to the miRNA 3′ end. All sites were binned according to their conservation, and the percentage of sites in each bin is shown for sites identified by 49 5′ non-redundant miRNA sequences (grey) and their shuffled control sequences (black, error bars indicate one standard deviation).
(C) 3′ pairing preferences for miR-7 target sites. Histogram showing the distribution of 3′ pairing energies for miR-7 (red bars) and the average of 50 3′ shuffled variants (black bars) for all sites identified genome-wide by 6mer 5′ seed matches for miR-7. The inset illustrates shuffling of the 3′ end of miRNA sequence only (wavy purple line). Because the miRNA 5′ end was not altered, the identical set of target sites was compared for pairing to the 3′ end of real and shuffled miRNAs.
(D) 3′ pairing preferences for miRNA target sites. Histograms showing the ratio of the top 1% 3′ pairing energies for the set of 58 3′ non-redundant miRNAs and their shuffled variants. The y-axis shows the number of miRNAs for each ratio. Real miRNAs are shown in red; mutant miRNAs are shown in black. Left are shown combined 8- and 7mer seed sites. Right are shown combined 5- and 6mer seed sites. For combined 8- and 7mer seeds, 1% corresponds to approximately ten sites per miRNA; for combined 6- and 5mer, to approximately 25 sites. The difference between the real and mutated miRNAs improves if fewer sites per miRNA are considered.
(E) Non-random signal of 3′ pairing. Plot of the ratio of the number of target sites for the set of 58 3′ non-redundant miRNAs and their shuffled miRNA 3′ ends (y-axis) with 3′ pairing energies that exceed a given pairing cutoff (x-axis). 100% is the pairing energy for a sequence perfectly complementary to the 3′ end. As the required level of 3′ pairing energy increases, fewer miRNAs and their sites remain to contribute to the signal. Plots for the real miRNAs extended to considerably higher 3′ pairing energies than the mutants, but as site number decreases we observe anomalous effects on the ratios, so the curves were cut off when the number of remaining miRNAs fell below five.
3′ compensatory and canonical sites depend on substantial pairing to the miRNA 3′ end. For these sites, we expect UTR sequences adjacent to miRNA 5′ seed matches to pair better to the miRNA 3′ end than to random sequences. However, unlike 5′ complementarity, 3′ base-pairing preference was not detected in previous studies looking at sequence complementarity and nucleotide conservation because UTR sequences complementary to the miRNA 3′ end were not better conserved than would be expected at random [27].
On this basis, we decided to treat the 5′ and 3′ ends of the miRNA separately. For the 5′ end, seed matches were required to be fully conserved in an alignment of orthologous D. melanogaster and D. pseudoobscura 3′ UTRs (we expected one-half to two-thirds of these matches to be real miRNA sites). We first investigated the overall conservation of UTR sequences adjacent to the conserved seed matches and found that overall the sequences are not better conserved than a random control with shuffled miRNAs (Figure 6B). For both real and random matches, the number of sites increases with the degree of 3′ conservation (up to the 80% level), reflecting the increased probability that sequences adjacent to conserved seed matches will also lie in blocks of conserved sequence (Figure 6B). For real 7mers and 8mers we found a slightly higher percentage of sites between 30% and 80% identity than we did for the shuffled controls. In contrast, the ratio of sites with over 80% sequence identity was smaller for real 7- or 8mers than for random ones, meaning that in highly conserved 3′ UTR blocks (>80% identity) the ratio of random matches exceeds that of real miRNA target sites. This caused us to question whether the degree of conservation for sequences adjacent to seed matches correlates with miRNA 3′ pairing as would be expected if the conservation were due to a biologically relevant miRNA target site. Indeed, we found that the best conserved sites adjacent to seed matches (i.e., those with zero, one, or two mismatches in the 3′ UTR alignment) and the least conserved sites (i.e., those with only three, two, or one matching nucleotides) are not distinguishable in that both pair only randomly to the corresponding miRNA 3′ end (approximately 35% maximal 3′ pairing energy, data not shown). The observation that miRNA target sites do not seem to be fully conserved over their entire length is consistent with the examples shown in Figure 4 in which only the degree of 3′ pairing but not the nucleotide identity is conserved (miR-7/hairy), or at least the unpaired bulge is apparently not under evolutionary pressure (miR-10/Scr). Although this result obviously depends on the evolutionary distance of the species under consideration (see [43] for a comparison of mammalian sites), it shows that conclusions about the contribution of miRNA 3′ pairing to target site function cannot be drawn solely from the degree of sequence conservation.
We therefore chose to evaluate the quality of 3′ pairing by the stability of the predicted RNA–RNA duplex. We assessed predicted pairing energy between the miRNA 3′ end and the adjacent UTR sequence for both Drosophila species and used the lower score. Use of the lower score measures conservation of the overall degree of pairing without requiring sequence identity. Figure 6C shows the distribution of the 3′ pairing energies for all conserved 3′ compensatory miR-7 sites identified by a 6mer seed match, compared to the distribution of 50 miR-7 sequences shuffled only in the 3′ part, leaving the 5′ unchanged. This means that real and shuffled miRNAs identify the same 5′ seed matches in the 3′ UTRs, which allows us to compare the 3′ pairing characteristics of the adjacent sequences. We also required 3′ shuffled sequences to have similar pairing energies (±15%) to their complementary sequences and to 10,000 randomly selected sites to exclude generally altered pairing characteristics. The distributions for real and shuffled miRNAs were highly similar, with a mean of approximately 35% of maximal 3′ pairing energy and few sites above 55%. However, a small number of sites paired exceptionally well to miR-7 at energies that were far above the shuffled averages and not reached by any of the 50 shuffled controls. This example illustrates that there is a significant difference between real and shuffled miRNAs for the sites with the highest 3′ complementarity, which are likely to be biologically relevant. Sites with weaker 3′ pairing might also be functional, but cannot be distinguished from random matches and can only be validated by experiments (see Figure 5). To provide a global analysis of 3′ pairing comprising all miRNAs and to investigate how many miRNAs show significantly non-random 3′ pairing, we considered only the sites within the highest 1% of 3′ pairing energies.
The average of the highest 1% of 3′ pairing energies of each of 58 3′ non-redundant miRNAs was divided by that of its 50 3′ shuffled controls. This ratio is one if the averages are the same, and increases if the real miRNA has better 3′ pairing than the shuffled miRNAs. To test whether a signal was specific for real miRNAs, we repeated the same protocol with a mutant version of each miRNA. The altered 5′ sequence in the mutant miRNA selects different seed matches than the real miRNA and permits a comparison of sequences that have not been under selection for complementarity to miRNA 3′ ends with those that may have been. Figure 6D shows the distribution of the energy ratios for canonical (left) and 3′ compensatory sites (right) for all 58 real and mutated 3′ non-redundant miRNAs. Most real miRNAs had ratios close to one, comparable to the mutants. But several had ratios well above those observed for mutant miRNAs, indicating significant conserved 3′ pairing.
A small fraction of sites show exceptionally good 3′ pairing. If we use 3′ pairing energy cutoffs to examine site quality for all miRNAs, we expect sites of this type to be distinguishable from random matches. The ratio of the number of sites above the cutoff for real versus 3′ shuffled miRNAs was plotted as a function of the 3′ pairing cutoff (Figure 6E). For low cutoffs the ratio is one, as the number of sites corresponds to the number of seed matches (which is identical for real and 3′ shuffled miRNAs). For increasing cutoffs, the ratios increase once a certain threshold is reached, reflecting overrepresentation of sites that pair favorably to the real miRNA 3′ end but not the 3′ shuffled miRNAs. The maximal ratio obtained for mutated miRNAs never exceeded five, which we used as the threshold level to define where significant overrepresentation begins. For 8mer seed sites overrepresentation began at 55% maximal 3′ pairing; for 7mer seed sites, at 65%; for 6mer seed sites, at 68%; and for 5mer seed sites, at 78%. There was no statistical evidence for sites with 4mer seeds.
We also tested whether sequences forming 7mer or 8mer seeds containing G:U base-pairs, mismatches, or bulges were better conserved if complementary to real miRNAs. We did not find any statistical evidence for these seed types. Analysis of 3′ pairing also failed to show any non-random signal for these sites. This suggests that such sites are few in number genome-wide and are not readily distinguished from random matches. Nonetheless, our experiments do show that sites of this type can function in vivo. The let-7 sites in lin-41 provide a natural example.
Most Sites Lack Substantial 3′ Pairing
The experimental and computational results presented above provide information about 5′ and 3′ pairing that allows us to estimate the number of target sites of each type in Drosophila. The number of 3′ compensatory sites cannot be estimated on the basis of 5′ pairing, because seed matches of four, five, or six bases cannot be distinguished from random matches, reflecting that a large number of randomly conserved and non-functional matches predominate (Figure 6A). Significant 3′ pairing can be distinguished from random matches for 6mer sites above 68% maximal 3′ pairing energy, and above 78% for 5mers (Figure 6E). Using these pairing levels gives an estimate of one 3′ compensatory site on average per miRNA. The experiments in Figure 5 provide an opportunity to assess the contribution of 3′ pairing to the ability of sites with 6mer seeds to function. The 6mer K box site in the grim 3′ UTR was regulated by miR-2 (63% maximal 3′ pairing energy), but not by miR-11, which has a predicted 3′ pairing energy of 46%. Similarly, the 6mer seed sites for miR-11 in the sickle 3′ UTR had 3′ pairing energies of approximately 35% and were non-functional. We can use the 63% and 46% levels to provide upper and lower estimates of one and 20 3′ compensatory 6mer sites on average per miRNA. For 5mer sites, the examples in Figure 1 show that sites with 76% and 83% maximal 3′ pairing do not function. At the 80% threshold level, we expect less than one additional site on average per miRNA, suggesting that 3′ compensatory sites with 5mer seeds are rare. The predicted miR-10 site in Scr (see Figure 4) is one of the few sites with a 5mer seed that reaches this threshold (100% maximum 3′ pairing energy; −20 kcal/mol). It is likely that other sites in this group will also prove to be functionally important.
The overrepresentation of conserved 5′ seed matches (see Figure 6A) suggests that approximately two-thirds of sites with 8mer seeds and approximately one-half of the sites with 7mer seeds are biologically relevant. This corresponds to an average of 28 8mers and 53 7mers, for a total of 81 sites per miRNA. We define canonical sites as those with meaningful contributions from both 5′ and 3′ pairing. Given that 7- and 8mer seed matches can function without significant 3′ pairing, it is difficult to assess at what level 3′ pairing contributes meaningfully to their function. The range of 3′ pairing energies that were minimally sufficient to support a weak seed match was between 46% and 63% of maximum pairing energy (see Figure 5C). If we take the 46% level as the lower limit for meaningful 3′ pairing, over 95% of sites would be considered seed sites. This changes to 99% for pairing energies that can be statistically distinguished from noise (55% maximal; see Figure 6E) and remains over 50% even for pairing energies at the average level achieved by random matches (30% maximal). It is clear from this analysis that the majority of miRNA target sites lack substantial pairing in the 3′ end in nearby sequences. Indeed the 3′ pairing level for the three seed sites for miR-4 in Brd are all less than 25% (i.e., below the average for random matches) and Brd was thus not predicted as a miR-4 target previously [26,28,35].
Again, we note the caveat that some of sites that we identify as seed could in principle be supported by 3′ pairing to more distant upstream sequences, but also that such sites would be difficult to distinguish from background computationally and that it is unclear whether large loops are functional. If there were statistical evidence for 3′ pairing that is lower than would be expected at random for some sites, this would be one line of argument for a discrete functional class that does not use 3′ pairing and would therefore suggest selection against 3′ pairing. Although the overall distribution of 3′ pairing energies for real miRNA 3′ ends adjacent to 8mer seed matches is very similar to the random control with 3′ shuffled sequences (Figure 7; R
2 = 0.98), we observed a small but significant overrepresentation of real sites on both sides of the random distribution, which leads to a slightly wider distribution of real sites at the expense of the peak values around 30% pairing. Bearing in mind that one-third of 8mer seed matches are false positives (see Figure 6A), we can account for the noise by subtracting one-third of the random distribution. We then see two peaks at around 20% and 35% maximum pairing energy, separated by a dip. Subtracting more (e.g., one-half or two-thirds) of the random distribution increases the separation of the two peaks, suggesting that the underlying distribution of 3′ pairing for real 8mer seed sites might indeed be bimodal. This effect is still present, though less pronounced, if 7mer seed matches are included. No such effect is seen for the combined 5- and 6mer seed matches. In addition, we see no difference between a random (noise) model that evaluates 3′ pairing of 3′ shuffled miRNAs to UTR sites identified by real miRNA seed matches and a random model that pairs the real (i.e., non-shuffled) miRNA 3′ end to randomly chosen UTR sequences, thus excluding bias due to shuffling. Overall, these results suggest that there might indeed be a bimodal distribution due to an enrichment of sites with both better and worse 3′ pairing than would be expected at random. We take this as evidence that seed sites are a biologically meaningful subgroup within the 5′ dominant site category.
Figure 7 Distribution of 3′ Pairing Energies for 8mer Seed Matches
Shown is the distribution (number of sites versus 3′ pairing) for 8mer seed matches identified genome-wide for 58 3′ non-redundant miRNAs (black) compared to a random control using 50 3′ shuffled miRNAs per real miRNA (grey). Note that the distribution for real miRNAs is broader at both the high and low end than the random control and has shoulders close to the peak. The red, blue, and green curves show the effect of subtracting background noise (random matches) from the real matches at three different levels, which reveals the real matches underlying these shoulders.
Overall, these estimates suggest that there are over 80 5′ dominant sites and 20 or fewer 3′ compensatory sites per miRNA in the Drosophila genome. As estimates of the number of miRNAs in Drosophila range from 96 to 124 [44], this translates to 8,000–12,000 miRNA target sites genome-wide, which is close to the number of protein-coding genes. Even allowing for the fact that some genes have multiple miRNA target sites, these findings suggest that a large fraction of genes are regulated by miRNAs.
Discussion
We have provided experimental and computational evidence for different types of miRNA target sites. One key finding is that sites with as little as seven base-pairs of complementarity to the miRNA 5′ end are sufficient to confer regulation in vivo and are used in biologically relevant targets. Genome-wide, 5′ dominant sites occur 2- to 3-fold more often in conserved 3′ UTR sequences than would be expected at random. The majority of these sites have been overlooked by previous miRNA target prediction methods because their limited capacity to base-pair to the miRNA 3′ end cannot be distinguished from random noise. Such sites rank low in search methods designed to optimize overall pairing energy [16,17,26,27,28,30,35]. Indeed, we find that few seed sites scored high enough to be considered seriously in these earlier predictions, even when 5′ complementarity was given an additional weighting (e.g., [28,43]. We thus suspect that methods with pairing cutoffs would exclude many, if not all, such sites.
In a scenario in which protein-coding genes acquire miRNA target sites in the course of evolution [4], it is likely that seed sites with only seven or eight bases complementary to a miRNA would be the first functional sites to be acquired. Once present, a site would be retained if it conferred an advantage, and sites with extended complementarity could also be selected to confer stronger repression. In this scenario, the number of sites might grow over the course of evolution so that ancient miRNAs would tend to have more targets than those more recently evolved. Likewise, genes that should not be repressed by the miRNA milieu in a given cell type would tend to avoid seed matches to miRNA 5′ ends (“anti-targets” [4]).
Although a 7- to 8mer seed is sufficient for a site to function, additional 3′ pairing increases miRNA functionality. The activity of a single 7mer canonical site is expected to be greater than an equivalent seed site. Likewise, the magnitude of miRNA-induced repression is reduced by introducing 3′ mismatches into a canonical site. Genome-wide, there are many sites that appear to show selection for conserved 3′ pairing and, interestingly, many sites that appear to show selection against 3′ pairing. In vivo, canonical sites might function at lower miRNA concentrations and might repress translation more effectively, particularly when multiple sites are present in one UTR (e.g., [42]). Efficient repression is likely to be necessary for genes whose expression would be detrimental, as illustrated by the genetically identified miRNAs, which produce clear mutant phenotypes when their targets are not normally repressed (“switch targets” [4]). Prolonged expression of the lin-14 and lin-41 genes in Caenorhabditis elegans mutant for lin-4 or let-7 causes developmental defects, and their regulation involves multiple sites [17,36,37]. Similarly, multiple target sites allow robust regulation of the pro-apoptotic gene hid by bantam miRNA in Drosophila [19]. More subtle modulation of expression levels could be accomplished by weaker sites, such as those lacking 3′ pairing. Sites that cannot function efficiently alone are in fact a prerequisite for combinatorial regulation by multiple miRNAs. Seed sites might thus be useful for situations in which the combined input of several miRNAs is used to regulate target expression. Depending on the nature of the target sites, any single miRNA might not have a strong effect on its own, while being required in the context of others.
3′ Complementarity Distinguishes miRNA Family Members
3′ compensatory sites have weak 5′ pairing and need substantial 3′ pairing to function. We find genome-wide statistical support for 3′ compensatory sites with 5mer and 6mer seeds and show that they are used in vivo. Furthermore, these sites can be differentially regulated by different miRNA family members depending on the quality of their 3′ pairing (e.g., regulation of the pro-apoptotic genes grim and sickle by miR-2, miR-6, and miR-11). Thus, members of a miRNA family may have common targets as well as distinct targets. They may be functionally redundant in regulation of some targets but not others, and so we can expect some overlapping phenotypes as well as differences in their mutant phenotypes.
Following this reasoning, it is likely that the let-7 miRNA family members differentially regulate lin-41 in C. elegans [17,45]. The seed matches in lin-41 to let-7 and the related miRNAs miR-48, miR-84, and miR-241 are weak, and only let-7 has strong 3′ pairing. On this basis, it seems likely that lin-41 is regulated only by let-7. In contrast, hbl-1 has four sites with strong seed matches [38,39], and we expect it to be regulated by all four let-7 family members. As all four let-7-related miRNAs are expressed similarly during development [6], their role as regulators of hbl-1 may be redundant. let-7 must also have targets not shared by the other family members, as its function is essential. lin-41 is likely to be one such target.
The idea that the 3′ end of miRNAs serves as a specificity factor provides an attractive explanation for the observation that many miRNAs are conserved over their full length across species separated by several hundreds of millions of years of evolution. 3′ compensatory sites may have evolved from canonical sites by mutations that reduce the quality of the seed match. This could confer an advantage by allowing a site to become differentially regulated by miRNA family members. In addition, sites could retain specificity and overall pairing energy, but with reduced activity, perhaps permitting discrimination between high and low levels of miRNA expression. This might also allow a target gene to acquire a dependence on inputs from multiple miRNAs. These scenarios illustrate a few ways in which more complex regulatory roles for miRNAs might arise during evolution.
A Large Fraction of the Genome Is Regulated by miRNAs
Another intriguing outcome of this study is evidence for a surprisingly large number of miRNA target sites genome-wide. Even our conservative estimate is far above the numbers of sites in recent predictions, e.g., seven or fewer per miRNA [27,28,29]. Our estimate of the total number of targets approaches the number of protein-coding genes, suggesting that regulation of gene expression by miRNAs plays a greater role in biology than previously anticipated. Indeed, Bartel and Chen [46] have suggested in a recent review that the earlier estimates were likely to be low, and a recent study by John et al. [43], published while this manuscript was under review, predicts that approximately 10% of human genes are regulated by miRNAs. We agree with these authors' suggestion that this is likely an underestimate, because their method identifies an average of only 7.1 target genes per miRNA, with few that we would classify as seed sites lacking substantial 3′ pairing. A large number of target sites per miRNA is also consistent with combinatorial gene regulation by miRNAs, analogous to that by transcription factors, leading to cell-type-specific gene expression [47]. Sites for multiple miRNAs allow for the possibility of cell-type-specific miRNA combinations to confer robust and specific gene regulation.
Our results provide an improved understanding of some of the important parameters that define how miRNAs bind to their target genes. We anticipate that these will be of use in understanding known miRNA–target relationships and in improving methods to predict miRNA targets. We have limited our evaluation to target sites in 3′ UTRs. miRNAs directed at other types of targets or with dramatically different functions (e.g., in regulation of chromatin structure) might well use different rules. Accordingly, there may prove to be more targets than we can currently estimate. Further, there may be additional features, such as overall UTR context, that either enhance or limit the accessibility of predicted sites and hence their ability to function. For example, the rules about target site structure cannot explain the apparent requirement for the linker sequence observed in the let-7/lin-41 regulation [48]. Further efforts toward experimental target site validation and systematic examination of UTR features can be expected to provide new insight into the function of miRNA target sites.
Materials and Methods
Fly strains
ptcGal4; EP miR278 was provided by Aurelio Teleman. The control, hid, grim, and sickle 3′ UTR reporter transgenes, and UAS-miR-2b are described in [19,26]. For UAS constructs for miRNA overexpression, genomic fragments including miR-4 (together with miR-286 and miR-5) and miR-11 were amplified by PCR and cloned into UAS-DSred as described for UAS-miR-7 [26]. Details are available on request. UAS-miR-79 (also contains miR-9b and miR-9c) and UAS-miR-6 (miR-6–1, miR-6–2, and miR-6–3) were kindly provided by Eric Lai. dcr-1
Q1147X is described in [14].
Clonal analysis
Clones mutant for dcr-1
Q1147X were induced in HS-Flp;dcr-1 FRT82/armadillo-lacZ FRT82 larvae by heat shock for 1 h at 38 °C at 50–60 h of development. Wandering third-instar larvae were dissected and labeled with rabbit anti-GFP (Torrey Pines Biolabs, Houston, Texas, United States; 1:400) and anti-β-Gal (rat polyclonal, 1:500).
Reporter constructs
The bagpipe 3′ UTR was PCR amplified from genomic DNA (using the following primers [enzyme sites in lower case]: AAtctaga
AGGTTGGGAGTGACCATGTCTC and AActcgag
TATTTAGCTCTCGGGTAGATACG) and cloned downstream of the tubulin promoter and EGFP (Clontech, Palo Alto, California, United States) in Casper4 as in [26].
Single target site constructs
Oligonucleotides containing the target site sequences shown in the figures were annealed and cloned downstream of tub>EGFP and upstream of SV40polyA (XbaI/XhoI). Clones were verified by DNA sequencing. Details are available on request.
EGFP intensity measurements
NIH image 1.63 was used to quantify intensity levels in miRNA-expressing and non-expressing cells from confocal images. Depending on the variation, between three and five individual discs were analyzed.
3′ UTR alignments
For each D. melanogaster gene, we identified the D. pseudoobscura ortholog using TBlastn as described in [26]. We then aligned the D. melanogaster 3′ UTR obtained from the Berkeley Drosophila Genome Project to the D. pseudoobscura 3′ adjacent sequence (Human Genome Sequencing Center at Baylor College of Medicine) using AVID [49]. For individual examples, we manually mapped the D. melanogaster coding region to genomic sequence traces (National Center for Biotechnology Information trace archive) of D. ananassae, D. virilis, D. simulans, and D. yakuba by TBlastn and extended the sequences by Blastn-walking. These 3′ UTR sequences were then aligned to the D. melanogaster and D. pseudoobscura 3′ UTRs using AVID.
miRNA-sequences
Drosophila miRNA sequences were from [44,50,51] downloaded from Rfam (http://www.sanger.ac.uk/Software/Rfam/mirna/index.shtml). The 5′ non-redundant set (49 miRNAs) comprised bantam, let-7, miR-1, miR-10, miR-11, miR-100, miR-124, miR-125, miR-12, miR-133, miR-13a, miR-14, miR-184, miR-210, miR-219, miR-263b, miR-275, miR-276b, miR-277, miR-278, miR-279, miR-281, miR-283, miR-285, miR-287, miR-288, miR-303, miR-304, miR-305, miR-307, miR-309, miR-310, miR-314, miR-315, miR-316, miR-317, miR-31a, miR-33, miR-34, miR-3, miR-4, miR-5, miR-79, miR-7, miR-87, miR-8, miR-92a, miR-9a, and miR-iab-4–5p. Additional miRNAs in the 3′ non-redundant set were miR-2b, miR-286, miR-306, miR-308, miR-311, miR-312, miR-313, miR-318, and miR-6.
miRNA shuffles and mutants
For the completely shuffled miRNAs, we shuffled the miRNA sequence over the entire length and required all possible 8mer and 7mer seeds within the first nine bases to have an equal frequency (±15%) to the D. melanogaster 3′ UTRs (i.e., same single genome count). For the 3′ shuffled miRNAs, we shuffled the 3′ end starting at base 10 and required the shuffles to have equal (±15%) pairing energy to a perfect complement and to 10,000 randomly chosen sites. For each miRNA we created all possible 2-nt mutants (exchanging A to T or C, C to A or G, G to C or T, and T to A or G) within the seed (nucleotides 3–6) and chose the one with the closest alignment frequencies to the real miRNA in D. melanogaster 3′ UTRs and in the conserved sequences in D. melanogaster and D. pseudoobscura 3′ UTRs.
Seed matching and site evaluation
For each miRNA and seed type we found the 5′ match in the D. melanogaster 3′ UTRs and required it to be 100% conserved in an alignment to the D. pseudoobscura ortholog allowing for positional alignment errors of ±2 nt. When searching 7mer to 4mer seeds we masked all longer seeds to avoid identifying the same site more than once. For each matching site we extracted the 3′ adjacent sequence for both genomes, aligned it to the miRNA 3′ end starting at nucleotide 10 using RNAhybrid [35], and took the worse energy.
Supporting Information
Accession Numbers
The miRNA sequences discussed in this paper can be found in the miRNA Registry (http://www.sanger.ac.uk/Software/Rfam/mirna/index.shtml). NCBI RefSeq (http://www.ncbi.nlm.nih.gov/RefSeq/) accession numbers: bagpipe (NM_169958), Brd (NM_057541), grim (NM_079413), hairy (NM_079253), hid (NM_079412), lin-14 (NM_077516), lin-41 (NM_060087), and Scr (NM_206443). GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) accession numbers: sickle (AF460844) and D. simulans hairy (AY055843).
We thank Ann-Mari Voie for cheerfully producing the large number of transgenic strains used in this work. We are grateful to Marc Rehmsmeier for providing us with the RNAhybrid program prior to publication, to Eric Lai for providing unpublished fly strains, to Aurelio Teleman for comments on the manuscript, and to Lars Juhl Jensen for helpful discussions on the statistics.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. JB, AS, and SMC conceived and designed the experiments. JB and AS performed the experiments and analyzed the data. JB, AS, RBR, and SMC wrote the paper.
Citation: Brennecke J, Stark A, Russell RB, Cohen SM (2005) Principles of microRNA–target recognition. PLoS Biol 3(3): e85.
Abbreviations
Brd
Bearded
EGFPenhanced green fluorescent protein
miRNAmicroRNA
Scr
Sex combs reduced
Academic Editor: James C. CarringtonOregon State University
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| 15723116 | PMC1043860 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 15; 3(3):e85 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030085 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030114SynopsisBioinformatics/Computational BiologyCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyEukaryotesDrosophilaSeeds of Destruction: Predicting How microRNAs Choose Their Target Synopsis3 2005 15 2 2005 15 2 2005 3 3 e114Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Principles of MicroRNA-Target Recognition
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Compare the gene number of fruitfly (13,000) to human (20,000), and it's pretty clear that complexity emerges not just from gene number but from how those genes are regulated. In recent years, it's become increasingly clear that one class of molecules, called microRNAs (miRNAs), exert significant regulatory control over gene expression in most plant and animal species. A mere 22 nucleotides long, miRNAs control a cell's protein composition by preventing the translation of protein-coding messenger RNAs (mRNAs). When a miRNA pairs with an mRNA, through complementary base pairing between the molecules, the mRNA is either destroyed or is not translated.
Hundreds of miRNAs have been found in animals, but functions for just a few have been identified, mostly through genetic studies. Many more functions could be assigned if miRNA targets could be predicted. This approach has worked in plants, because miRNAs and their targets pair through the near perfect complementarity of their base pairs. But the molecules follow different rules in animals—duplexes contain just short stretches of complementary sequence interrupted by gaps and mismatches—which makes predicting miRNA targets a challenge.
In a new study, Stephen Cohen and his colleagues at the European Molecular Biological Laboratory in Germany establish basic ground rules for miRNA–mRNA pairing using a combination of genetics and computational analyses, and identify different classes of miRNA targets with distinct functional properties. Although the miRNA is only 22 nucleotides long, its 5′ and 3′ ends seem to have distinct roles in binding. Cohen and colleagues show that miRNA functional targets can be divided into two broad categories: those that depend primarily on pairing to the miRNA's 5′ end (called 5′ dominant sites), with varying degrees of 3′ pairing, and those that also need the miRNA's 3′ end (called 3′ compensatory sites). Surprisingly, miRNAs can regulate their targets simply by strong pairing with so-called seed sites that consist of just seven or eight bases complementary to the miRNA 5′ end. Target sites with weaker 5′ complementarity need supplemental pairing with the miRNA's 3′ end to function. The finding that so little sequence complementarity is needed means that there are many more target sites than had been previously recognized.
The miRNA 3′ end, while not essential, is expected to confer some function, since it tends to be conserved in animals—miRNA 3′ ends provide an additional measure of regulatory control by permitting the function of target sites that have only limited complementarity to the miRNA 5′ end. The authors speculate that seed sites might be the first functional sites acquired by protein-coding genes that require repression, and that additional sites might be acquired to promote stronger repression.
Based on their experimental results, Cohen and colleagues searched the Drosophila genome for biologically relevant targets, and estimate that the fly has about 100 sites for every miRNA in its genome. Since the fruitfly has anywhere from 96 to 124 miRNAs, that means it has 8,000 to 12,000 target sites (in the 11,000 genes sampled). This indicates that miRNAs regulate a large fraction of protein-coding genes. Of the known animal miRNAs, many regulate critical developmental processes. This new approach to predicting targets should help reveal just how much regulatory control actually flows from these tiny bits of RNA.
| 0 | PMC1043861 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 15; 3(3):e114 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030114 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030122SynopsisAnimal BehaviorNeuroscienceZoologyInsectsCracking the Olfactory Code Synopsis4 2005 22 2 2005 22 2 2005 3 4 e122Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Perceptual and Neural Olfactory Similarity in Honeybees
Perception Space-The Final Frontier
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For Proust, a taste of cookie was enough to trigger vivid recollections of his childhood, the first of a long string of reveries that he fashioned into his famous memoir Remembrance of Things Past. For many animals, too, tastes and smells are evocative and play a crucial role in finding food, allowing them to build on past successes and to learn how to find their next meal.
To locate blooming flowers, for example, honeybees rely heavily on scent. They can associate a whiff of an aldehyde, say, with a nectar-filled orchid. Then later they'll seek out the same or similar scents. To succeed in the wild, they must be able to distinguish relevant scents at varying concentrations, and within complex milieus of other scents. But to find food in varied conditions and adapt to new situations, they also have to generalize from past experience.
Through both physiological and behavioral studies, scientists have investigated the response to smell in a wide range of organisms and have suggested that two key properties of scent-inducing chemicals are the functional class, such as alcohol or aldehyde, and the carbon-chain length. Bees trained to associate a particular chemical with a reward, for example, can then generalize to some extent to other chemicals with the same functional groups or similar carbon-chain lengths. In these situations, bees are surprisingly consistent in both in their behavior (extending their proboscis to an odor previously associated with food) and in their brains (brain activity in smell-processing centers). Each set of data, behavioral and neural, can be thought of as a “code” underlying the bee's response: present a scent, and a bee's brain and body will tend to react in a certain way.
Linking smellperception and neural activity in the bee (Image: Axel Brockmann)
A new study of smell perception in honeybees (Apis mellifera) published in PLoS Biology gives a more comprehensive picture of how bees react to a suite of scents and also shows a remarkable correspondence between the codes for the insects' behavior and brain activity. The researchers, led by Martin Giurfa, first trained bees to associate a specific chemical, such as the alcohol 1-nonanol, with a sucrose reward. Then the researchers tested the bees' response to a set of other chemicals, varying in carbon-chain length from six to nine, and with four different functional groups: aldehydes, ketones, and primary and secondary alcohols.
By watching how often the bees generalized—that is, how often they responded positively to a particular scent when they'd been trained on another—the researchers could assign perceptual “distances” between pairs of chemicals. Drawing together all these distances, they created a preliminary map of the bees' “perceptual space,” similar to how surveyors measure distances between landmarks to map a landscape. From this comparison they found, for example, that the bees generalized more by functional group than by carbon-chain length.
Previously, Giovanni Galizia's group, which works closely with Giurfa's group, had recorded bees' brain responses to the same pairs of scents, assigning distances within centers of activity for each scent. Giurfa's team compared these two sets of data and found that the perceptual and neural distances correlated well, which suggests there's a species-specific code that ties together the insects' brain and behavior.
The brain recordings covered only a quarter of the bees' main smell-processing center, the antennal lobe. Future studies with new methods of microscopy that visualize more of the brain and which focus on the olfactory message sent by the antennal lobe to higher-order brain centers should only improve our ability to investigate the correlations between brain and behavior, the authors say. Such studies would go even further toward cracking the codes underlying animals' perception and memory.
| 15736988 | PMC1043862 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Apr 22; 3(4):e122 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030122 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573697610.1371/journal.pbio.0030063Research ArticleEcologyEvolutionGenetics/Genomics/Gene TherapyPlantsAnimalsRecombination Difference between Sexes: A Role for Haploid Selection Sex Dimorphism in RecombinationLenormand Thomas [email protected]
1
Dutheil Julien
2
1UMR 5175Centre d'Ecologie Fonctionnelle et Evolutive, MontpellierFrance2UMR 5171Université Montpellier II, MontpellierFranceHurst Laurence D. Academic Editor University of Bath3 2005 22 2 2005 22 2 2005 3 3 e639 6 2004 15 12 2004 Copyright: © 2005 Lenormand and Dutheil.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Selection on Sex Cells Favors a Recombination Gender Gap
Why the autosomal recombination rate differs between female and male meiosis in most species has been a genetic enigma since the early study of meiosis. Some hypotheses have been put forward to explain this widespread phenomenon and, up to now, only one fact has emerged clearly: In species in which meiosis is achiasmate in one sex, it is the heterogametic one. This pattern, known as the Haldane-Huxley rule, is thought to be a side effect, on autosomes, of the suppression of recombination between the sex chromosomes. However, this rule does not hold for heterochiasmate species (i.e., species in which recombination is present in both sexes but varies quantitatively between sexes) and does not apply to species lacking sex chromosomes, such as hermaphroditic plants. In this paper, we show that in plants, heterochiasmy is due to a male-female difference in gametic selection and is not influenced by the presence of heteromorphic sex chromosomes. This finding provides strong empirical support in favour of a population genetic explanation for the evolution of heterochiasmy and, more broadly, for the evolution of sex and recombination.
Measuring the opportunity for natural selection on gametes provides the first empirical evidence for a theory explaining why recombination at meiosis varies between males and females
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Introduction
Sex differences in recombination were discovered in the first linkage studies on Drosophila [1,2] and Bombyx (Tanaka [1914] in [3]) almost one century ago. However, this observation remains today largely unexplained despite several attempts. Based on very limited observations (see Table 1), especially of Bombyx, in which the female is heterogametic, Haldane [3] suggested, as far as “these facts are anything more than a coincidence,” that the lower autosomal recombination rate in the heterogametic sex may reflect a pleiotropic consequence of selection against recombination between the sex chromosomes. Later, Huxley [4] showed that Gammarus males also recombined less than females. He gave the same evolutionary explanation, although he restricted it to cases of a marked sex difference.
Table 1 Data on Which the Haldane-Huxley Rule is Based
Listed are the data available to Haldane [4] when he proposed the Haldane-Huxley rule
a
rm represents recombination in males
b
rf represents recombination in females
c Plus and minus symbols indicate the direction of heterochiasmy, and zero indicates achiasmy
This conjecture has now been confirmed for achiasmate species (i.e., species in which only one sex recombines) and is referred to as Haldane-Huxley rule: Nei [5] showed theoretically that tight linkage should evolve on Y or W chromosomes, and Bell [6] compiled a large dataset showing that achiasmy evolved 29–34 times independently, each time with no recombination in the heterogametic sex.
However, for heterochiasmate species, three problems with the Haldane-Huxley pleiotropy explanation were discovered [7,8]. The first problem arose when substantial variation in male-female differences in recombination rate was found between pairs of autosomes within mice [8] and Tribolium [9,10], and between genotypes for the same pair of autosomes [11]. The second problem was the discovery that hermaphrodite species (the platyhelminth Dendrocoelum [12] and the plant Allium [13]) may present strong heterochiasmy between male and female meiosis despite having no sex chromosomes or even sex-determining loci. The third problem was the discovery of species in which the heterogametic sex recombines more than the homogametic one (e.g., in some Triturus species) [14]. Because of these contradictory observations, variation in heterochiasmy has remained difficult to explain because of the absence of an alternative theory as well as the lack of a clear pattern in the data.
In 1969, Nei [5] worked out the first “modifier” model to study the evolution of sex differences in recombination, and concluded for autosomes that “the evolutionary mechanism of these sex differences is not known at present.” Surveying an updated dataset, Bell [6] concluded that “female gametes experience more crossing over among hermaphroditic plants (and perhaps animals), but this is not invariably the case among gonochoric animals (…) certainly (this) has never received any explanation.” The idea that heterochiasmy may be explained by a sex rather than by a sex chromosome effect, which was ignored by Haldane because of Bombyx, was reconsidered. This led Trivers [15] to suggest that, because only males with very good gene combinations reproduce (relative to females, for whom reproduction success is often less variable), they should recombine less to keep intact these combinations. He accounted for exceptions by variation in the regime of sexual selection. The idea was criticized by Burt et al. [16], who also questioned the correlations—with an updated dataset—between heterochiasmy and either sex or heterogamety. These authors tried to correlate the level of heterochiasmy with the amount of “opportunity for sex-specific selection,” but failed to find an effect. They were tempted to advocate neutrality, but were puzzled by the positive correlation between male and female recombination rate and by evidence showing compensation (e.g., female mice tend to recombine more on the X, as if they were compensating for no recombination in males; similarly, no species is known with achiasmy in both sexes [16]). In 1994, Korol et al. [17] insisted on a possible role for gametic selection but did not give evidence in favour of this claim. Recently, Lenormand [18], using Nei's modifier approach, showed that it is very difficult to explain heterochiasmy by sex-specific diploid selection. Rather, a sex difference in selection during haploid phase, or a sex difference in diploid selection on imprinted genes, is a more likely explanation. He predicted that, as far as haploid selection is concerned, the sex experiencing the more intense haploid selection should recombine less. Indeed, when allelic effects interact to determine fitness (i.e., when there is “epistasis,” either negative or positive), recombining decreases mean fitness in the population of the next generation [19]. This effect occurs because recombination breaks up combinations of genes that have previously been built up by selection. For a given average recombination rate between sexes and for a given average epistasis between male and female haploids, it is always advantageous for the haploid population (male or female) with the greatest absolute value in epistasis to be produced with the lowest amount of recombination. In this way, the “recombination load” that the haploid population is exposed to is minimized.
In this paper, we would like to come up with a more quantitative evaluation of the possible role of haploid selection in shaping heterochiasmy. For that purpose, we first updated the dataset of Burt et al. [16] on heterochiasmy, focusing on genetic maps that have become available over the last 15 years. We then determined how fast heterochiasmy evolves, in order to measure the amount of phylogenetic inertia on this trait. Finally, we determined whether variables such as gender, heterogamety, or the opportunity for selection in the haploid phase, could explain variation in heterochiasmy. If there is selection with substantial epistasis on some genes during the haploid phase, we expect the sex with the greater opportunity for haploid selection to show less recombination. Alternatively, if selection during the haploid phase is weak or without substantial epistasis, we do not expect it to produce a directional bias in the amount of recombination displayed by either sex.
Results/Discussion
Sex Chromosomes
Heterochiasmy is a fast-evolving trait, and phylogenetic inertia does not satisfactorily explain its distribution. In contrast to achiasmy, we found that heterochiasmy is not influenced by the nature of the sex chromosomes. This is interesting, because it suggests that achiasmy and heterochiasmy are influenced by qualitatively different evolutionary forces, although they seem to differ only quantitatively. It would be useful to determine whether achiasmy evolved to reduce the average recombination rate or to change the relative amount of recombination between the sexes. The two situations may be discriminated by determining whether the homogametic sex in achiasmate species tends to recombine more than in closely related chiasmate species. Evidence for such compensation would indicate that achiasmy did not evolve to reduce the average recombination rate. In the absence of such compensation, however, achiasmy may simply reflect selection for tight linkage. In such a situation, we propose that Haldane-Huxley rule may be caused by the converse argument to the one previously considered: The presence of achiasmy only in the heterogametic sex may reflect selection to maintain nonzero recombination rate on X or Z chromosomes in the homogametic sex. In species in which the average autosomal recombination rate is selected against (i.e., towards a lower equilibrium value), loss-of-function (recombination) mutations with an effect restricted to one sex may spread only if they affect the heterogametic sex, because mutations suppressing recombination in the homogametic sex completely suppress recombination on the X or Z chromosome. The same argument applies to XO species and may explain why achiasmy is associated only with the heterogametic sex. In addition, this hypothesis does not require the existence of genes suppressing recombination between the sex chromosomes with autosomal pleiotropic effects. Under this hypothesis, there is no reason to find an effect of the presence of heteromorphic sex chromosome on the amount of heterochiasmy, as originally envisioned by Haldane and Huxley. Overall, this hypothesis would explain why heterochiasmy and achiasmy differ qualitatively and why we do not observe any effect of sex chromosomes on heterochiasmy.
Heterochiasmy in Animals
In animals, male-female dimorphism in haploid selection may also contribute to heterochiasmy. In general, there is no female haploid phase in animals, because meiosis is completed only at fertilisation. As far as at least some genes are expressed and under selection during the male haploid phase, this would tend to bias towards tighter linkage in males. Sets of genes responsible for male-specific meiotic drive systems would be good candidates and are often found in tight linkage. Measuring the opportunity for haploid selection in animals may be possible within some groups. Imprinting may, however, act as a confounding effect in many groups of animals while trying to measure the opportunity for “haploid” selection. Within-species comparisons of imprinted regions or of regions with sex-specific recombination using high-resolution maps [20] may be more fruitful to discriminate among potential causes of heterochiasmy in animals. In particular, there is evidence in humans that the reduction in crossing-over associated with imprinting is in the direction that theory predicts, even if this pattern is consistent with other explanations [21]. Finally, understanding exceptions within groups (e.g., male marsupials, contrarily to most mammals, recombine more than females of the species [22]) may also shed light on the different hypotheses.
Heterochiasmy in Plants
We found that plant heterochiasmy is correlated with the opportunity for male and female haploid selection. Female meiosis tends to exhibit lower recombination rates relative to male meiosis when selection is intense among female gametophytes (e.g., in Pinaceae) or mild among male gametophytes (e.g., in highly selfing species). This pattern is expected if heterochiasmy is determined by the relative magnitude of haploid selection in male and female individuals. Finding a pattern consistent with this general population genetic prediction is, of course, not firm evidence that male-female dimorphism in haploid selection is the evolutionary force generating heterochiasmy. Other correlates of selfing rates might have to be closely examined [23]. However, we consider this explanation the most parsimonious so far. Our finding provides, therefore, the first empirical evidence for a theory explaining male-female differences in the amount of recombination and contributes to our understanding of contradictory observations that have puzzled geneticists for almost a century. It also indicates that the amount of recombination may be shaped by indirect selection, and, therefore, corroborates theories based on selection and variation for the evolution of sexual reproduction.
Materials and Methods
An extended dataset
We measured heterochiasmy as the log of the male/-to-female ratio (ρ) of autosomal recombination rate measured either with chiasma number or map length. We log-transformed the ratio to avoid bias due to measurement error in the denominator. Chiasma-count data for different species were compiled by Burt et al. [16], and we used their dataset, adding a few recent studies. We compiled genetic map data and linkage studies in animals and plants for which both a male and a female map were available. Only homologous fragments (i.e., between shared markers) in male and female maps were considered (especially in low-resolution maps). Heterochiasmy data were available for 107 species, with 46 sets of data based on genomic maps (Table 2).
Table 2 Dataset Pooled by Species with Levels of Phylogenetic Grouping Used in the Analysis
Note that references given in Burt et al. [17] were not repeated here
a K, kingdom. Numeric indicators in this column are: 1, Animalia; 2, Plantae
b P, phylum. Numeric indicators in this column are: 1, Arthropoda; 2, Chordata; 3, Embryophyta; 4, Platyhelminthes
c C, class. Numeric indicators in this column are: 1, Actinopterygii; 2, Amphibia; 3, Magnoliopsidae (subclass asterids); 4, Aves; 5, Coniferopsida; 6, Insecta; 7, Liliopsida; 8, Mammalia; 9, Magnoliopsidae (subclass rosids); 10, Trematoda; 11, Turbellaria
d Data refers to linkage map (LM) or chiasma count (CC)
e Male and female indicate the value for the chiasma count or map length for each sex
f Ratio refers to male/female recombination rate
g
Vsc refers to the presence or absence of sex chromosome (see Materials and Methods, “Sex chromosome effect”)
h Data were obtained from maps DBNordic2 and NIAIJapan (http://www.genome.iastate.edu/pig.html) [54,55]
ND, no data
Table 2 Continued
Phylogenetic inertia
Heterochiasmy may evolve so slowly that there is important phylogenetic inertia. Alternatively, it may be so fast-evolving that the amount of heterochiasmy takes on nearly independent values among related species. In the same way, heterochiasmy may be so variable between genotypes within a species that it may be difficult to measure and irrelevant to analyse species specific effects. In order to get a picture of phylogenetic inertia on heterochiasmy, we estimated the phylogenetic autocorrelation of ρ using Moran's I spatial autocorrelation statistic [24]. When standardized, values of Moran's I vary from −1 to 1. Positive values indicate that heterochiasmy is more similar than random within a taxonomic level, whereas negative values indicate that it is more different. Because a few species had multiple estimates of heterochiasmy, we also estimated the within-species correlation. The resulting correlogram is shown in Figure 1. We found that heterochiasmy is a fast-evolving trait: Genotypes tend to be correlated within a species (I/Imax = 0.38, p = 7.9%), but this correlation is lower among species within genera (I/Imax = 0.18, P-value = 13%), and very low when comparing genera within families (I/Imax = 0.039, p = 63%). This pattern is very different from the one observed for highly autocorrelated traits using the same method (for instance, mammalian body size [25]). This analysis indicates that there is very little phylogenetic inertia overall on heterochiasmy, but that the species level is appropriate for our dataset. However, this low level of inertia may nevertheless inflate type-I error while testing the effect of independent variables on heterochiasmy. In order to avoid this problem, we tested the association between different variables and heterochiasmy using a generalized estimating equations linear model correcting for the full phylogeny (see below) [26].
Figure 1 Phylogenetic Correlogram of Heterochiasmy and Selfing Rate
The y-axis represents Moran's I rescaled to enable comparisons between each taxonomic level for heterochiasmy (ρ, solid line) and selfing rate (Vm, dashed line). The x-axis represents the taxonomic level: /S is the correlation within species, S/G is the correlation of species within genera, etc. F, family; O, order; C, class; P, phylum; K, kingdom. Filled points indicate significance at p = 0.05.
Sex chromosome effect
For each species, we reported the presence of sex chromosomes. We defined the variable Vsc with the following values: −1 for XY/XX species, −1/2 for XO/XX or XY/XX without pseudoautosomal regions (marsupials), 0 for species without sex-chromosomes, and +1 for ZZ/ZW species. We distinguished the −1 and −1/2 cases to reflect the fact that, in the latter, recombination does not occur between sex chromosomes, so we expect a lower current selection pressure to suppress recombination. Under the Haldane-Huxley hypothesis, the presence of sex chromosomes is supposed to favour reduced recombination rate in the heterogametic sex. We therefore expect a positive effect of the variable Vsc on ρ. We did not find such an effect in animals or plants (the linear effect of Vsc on ρ is not significantly different from zero [p = 0.75 in animals and p = 0.52 in plants], assuming species were independent), and this result is unchanged if the −1 and −1/2 cases are not distinguished. Given this negative result, there was no need to do a phylogenetic correction.
Gametic selection
In animals from our dataset, there is no female haploid phase because the completion of meiosis occurs only at fertilisation (sperm triggers the end of meiosis). In male gametes, very few genes are expressed, and sperm phenotype is determined mostly either by the diploid genotype of the paternal tissue or by its mitochondrial genome. Imprinted genes, which can also affect the evolution of heterochiasmy [18,21], may be as numerous as haploid-expressed genes and act as a confounding factor while evaluating the “opportunity” for male or female gametic selection. As a consequence, we did not attempt to evaluate the opportunity for haploid selection in animals. Rather, we focused on plants, in which there is both a male (pollen) and female (ovule) haploid phase and during which many genes are expressed (e.g., as many as 60% of genes may be expressed in the male gametophyte [27,28]).
In order to evaluate the effect of the “opportunity for selection” for male haploid phase on ρ, we used selfing rate as an indirect variable estimating the degree of pollen competition. We assume that with high selfing rates, there is less genetic variation among competing pollen grains and, therefore, less scope for haploid selection. We defined Vm (the degree of male gamete competition in plants) using three values depending on the amount of selfing: 0 for dioecious, self-incompatible or largely outcrossing (less than 5% selfing reported) species; 1 for species exhibiting low selfing rates (less than 30% reported); and 2 for other species. We used these three broad categories to reflect the fact that selfing rate is often variable within species and that it is often measured indirectly and with low precision. We therefore expect a positive effect of the variable Vm on ρ if the opportunity for male gametic selection favours smaller ρ values, as predicted by the modifier model [18]. We tested this effect using the 57 species for which we were able to estimate Vm (Table 3). We used a linear model in R [29] assuming that all species are either independent or phylogenetically related. In the latter case, we used a generalized estimating equations linear model [26] with a plant phylogenetic tree to the family level using data from Davies et al. [30], and several calibration points, including the Picea/Pinus divergence approximately 140 million years ago [31], that are not included in the Davies et al. dataset. We found an effect in the right direction with or without correcting for the phylogeny (linear effect of ρ on Vm, p < 0.0002 in both cases, Figure 2). The fact that selfing plants exhibit higher recombination rates than their outcrossing relatives has been mentioned previously in the literature [32,33]. However, in most cases, recombination was measured only in male meiosis. It would be valuable to reexamine this trend in the light of our results that recombination in male meiosis is typically greater than in female meiosis among selfers.
Figure 2 Logarithm of Male-Female Ratio in Recombination Rate in Plants
Mean and 95% confidence interval of ρ is shown for different groups of plants, assuming normality and independent data points The number of species in each group is indicated next to the mean.
Table 3 Plant Species Used to Test the Effect of Male and Female Opportunity for Selection
a Ratio refers to male-to-female recombination rate
LM, linkage map; CC, chiasma count; n, haploid number of chromosomes; Vm, measure of male opportunity for haploid selection; Vf, measure of female opportunity for haploid selection
In order to evaluate the effect of the “opportunity for selection” during the female haploid phase on ρ in plants, we contrasted angiosperms with gymnosperms. In angiosperms, ovules do not compete much with each other on a mother plant, because resource accumulation starts after fertilisation (i.e., during fruit development in the diploid phase). In Pinus (three species in our dataset; see Table 2), male meiosis, female meiosis, and pollination occur in the year prior to fertilisation, but the pollen tube stops growing until the next spring, while the female gametophytes continue to accumulate resources and compete with each other over the course of the year. The same situation occurs in Picea, although the period between female meiosis and fertilisation is only 2–3 mo [34]. Perhaps more importantly, the endosperm (which is the organ managing resources for the zygote) is haploid in Pinaceae, in contrast to the double fertilisation that occurs in angiosperms to produce at least a diploid (typically triploid) endosperm [35,36]. We therefore expect that ρ should be greater in Pinaceae, compared to angiosperms. We assigned Vf (the degree of female gamete competition in plants) the values 1 for gymnosperms and −1 for angiosperms. We expected a positive effect of the variable Vf on ρ according to the modifier model. An effect in the right direction was indeed detected (linear effect of Vf on ρ, p = 0.011 and p = 0.0001, with and without correcting for the phylogeny as above, respectively; see Figure 2).
We thank G. Besnard, J. Britton-Davidian, J.-B. Ferdy, S. Glémin, L. D. Hurst, P. Jarne, O. Judson, M. Kirkpatrick, S. P. Otto, J.-M. Prosperi, and C. Vosa for helpful comments, information, and stimulating discussions. This study was supported by the Centre National de la Recherche Scientifique and French Ministry of Research.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. TL and JD conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, and wrote the paper.
Citation: Lenormand T, Dutheil J (2005) Recombination difference between sexes: A role for haploid selection. PLoS Biol 3(3): e63.
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| 15736976 | PMC1044830 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 22; 3(3):e63 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030063 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573697710.1371/journal.pbio.0030064Research ArticleBioinformatics/Computational BiologyDevelopmentSystems BiologyDrosophilaEngineering Gene Networks to Emulate Drosophila Embryonic Pattern Formation Emulating Drosophila Embryonic PatterningIsalan Mark [email protected]
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Lemerle Caroline
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Serrano Luis
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1European Molecular Biology Laboratory, Structures and BiocomputingHeidelbergGermanyArias Alfonso Martinez Academic EditorCambridge UniversityUnited Kingdom3 2005 22 2 2005 22 2 2005 3 3 e6420 7 2004 15 12 2004 Copyright: © 2005 Isalan et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Engineering Gene Networks to Probe Embryonic Pattern Formation in Flies
Pattern formation is essential in the development of higher eukaryotes. For example, in the Drosophila embryo, maternal morphogen gradients establish gap gene expression domain patterning along the anterior-posterior axis, through linkage with an elaborate gene network. To understand the evolution and behaviour of such systems better, it is important to establish the minimal determinants required for patterning. We have therefore engineered artificial transcription-translation networks that generate simple patterns, crudely analogous to the Drosophila gap gene system. The Drosophila syncytium was modelled using DNA-coated paramagnetic beads fixed by magnets in an artificial chamber, forming a gene expression network. Transient expression domain patterns were generated using various levels of network connectivity. Generally, adding more transcription repression interactions increased the “sharpness” of the pattern while reducing overall expression levels. An accompanying computer model for our system allowed us to search for parameter sets compatible with patterning. While it is clear that the Drosophila embryo is far more complex than our simplified model, several features of interest emerge. For example, the model suggests that simple diffusion may be too rapid for Drosophila-scale patterning, implying that sublocalisation, or “trapping,” is required. Second, we find that for pattern formation to occur under the conditions of our in vitro reaction-diffusion system, the activator molecules must propagate faster than the inhibitors. Third, adding controlled protease degradation to the system stabilizes pattern formation over time. We have reconstituted transcriptional pattern formation from purified substances, including phage RNA polymerases, ribonucleotides, and an eukaryotic translation extract. We anticipate that the system described here will be generally applicable to the study of any biological network with a spatial component.
To understand how patterns are established during early development, these authors have created an artificial system to mimic aspects of the early Drosophila embryo
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Introduction
Engineering a system to emulate a particular behaviour can be an extremely informative approach to systems biology [1,2,3,4,5]. Even if the natural biochemical interactions are well characterized, it remains a considerable challenge to reconstruct a physical system with the appropriate behaviour. Step-by-step reconstruction allows theoretical assumptions and models to be refined. Not only is complexity reduced by removing the context of the whole organism, but by reconstitution of pattern formation from purified substances (in this case, RNA polymerases, ribonucleotides, and a translation extract) the sufficiency of a proposed mechanism of pattern formation can be demonstrated.
In this work, our primary aim was the development of an in vitro system that allows the careful buildup of complex networks under controlled conditions. To demonstrate the usefulness of such a system, we decided to reconstruct a developmental pattern-formation program based on the formation of a gradient of a transcription activator—a “morphogen”—and to link it to a network of transcription repressors. In a sense, we set out to design a patterning system similar to chemical reaction-diffusion systems (see below). However, the use of components such as transcription activators and repressors in an in vitro transcription-translation system made the system significantly closer to a biological system, albeit highly simplified when compared to the complexity of eukaryotic transcription [6].
Patterning systems can be thought of as belonging to one of two principal types: First, there are systems with homogeneous initial conditions that self-organise after early random symmetry-breaking events ([7,8]; for a biological example, Fucus, see [9]). Second, there are systems with initial localisation of the components, which can form concentration gradients of activities from their respective sources [10,11].
The first class of patterning system, involving reaction-diffusion from initially homogeneous conditions, was first proposed by Turing in 1952 [7] and was further developed by Meinhardt and Gierer in the 1970s, in their model of patterning with short-range autoactivator and long-range lateral inhibitor [8]. Although there are many likely biological candidates for such activator-inhibitor systems (reviewed in [12]), none has been reengineered from first principles. In contrast, significant progress has been made in reconstituting purely chemical reactions that self-organise, such as the Belousov-Zhabotinski reactions [13,14,15] and Turing-type, Meinhardt-Gierer (M-G), and oscillatory reactions [16,17,18,19,20]. In a more biological context, similar spatiotemporal patterns, consisting of propagating concentration waves, have been modelled for a glycolytic enzyme oscillator in yeast [21].
In the second class of patterning system, where the initial conditions are nonhomogeneous, patterning begins with an asymmetry: a localised source of morphogen (shape-defining molecules that form a concentration gradient and function in a concentration-dependent manner so as to determine positional information in a patterning field [22]; reviewed in [23,24,25]).
The question of how morphogen gradients are formed and maintained is still a matter of keen debate and study [26,27,28], with many proposed mechanisms (reviewed in [29,30]). In the simplest case, a stable gradient could be formed by passive diffusion [31] and uniform degradation, although it has been suggested that enhanced morphogen degradation near the source leads to increased robustness against morphogen fluctuations during patterning [32]. Also, “ligand trapping” by the receptor for a morphogen can have significant effects on the shape of a morphogen gradient [33], as in the case of Torso diffusing in the extracellular space surrounding the Drosophila oocyte [34].
In most cases in metazoa, morphogens define patterns over fields of many cells (reviewed in [30,35]), but there is one special case in embryonic development that has been particularly well studied, in which a morphogen gradient operates in a single-celled, multinuclear syncytium: the early Drosophila embryo [10,11]. In this system, early patterning is mediated by maternal morphogen factors, which are thought to diffuse and form gradients to guide patterning within a large multinuclear cell (reviewed in [36,37,38]). After egg deposition, an embryo forms a segmentation pattern within 3 h, under the influence of a hierarchical sequence of gene expression interactions involving gap genes, pair-rule genes, and segment polarity genes [39]. Maternal elements—in particular, the morphogen Bicoid—guide this process, setting distinct initial conditions.
Work by Driever and Nüsslein-Volhard [10,11] demonstrated that Bicoid protein possessed three characteristics of a classic morphogen: (i) a localised source of cytoplasmic activity (through bicoid RNA transport to the anterior pole of the egg, involving microtubules and maternal genes [40,41,42,43,44]); (ii) formation of a concentration gradient from the source; and (iii) concentration-dependent activity that determines positional information within the gradient (reviewed in [38]). Bicoid has at least two functions that contribute to its function as a morphogen: transcription activation and translation inhibition. Acting as a transcription factor, Bicoid can activate a number of downstream gap genes, including hunchback, knirps, giant, and Krüppel, whose products cross-react in a complex and mainly repressive interaction network to modulate each other's expression (reviewed in [37]; modelled in [45,46]). However, Bicoid does not simply function independently as a morphogen at the top of the gap gene hierarchy. Although Bicoid is responsible for anterior expression of zygotic Hunchback [47], it actually requires maternally expressed Hunchback as a cofactor to function anteriorly [48]. Meanwhile, in the posterior, the maternal hunchback mRNA is initially translationally repressed by the posterior determinant Nanos [49]. Moreover, the terminal gap genes tailless and huckebein are activated independently but serve to repress zygotic gap gene expression at the poles of the embryo, thus influencing patterning [50,51,52]. In its other role, as a translation inhibitor of caudal mRNA, Bicoid initially inhibits the uniformly expressed mRNA to form a concentration gradient of the protein Caudal [53,54]. Thus, the Caudal gradient [55] is essentially the inverse of the Bicoid gradient, and Caudal functions as a transcription activator in the posterior of the egg, further influencing expression of the gap gene network. Some of the important interactions in the gap gene network are shown in Figure 1A, although it should be noted that this overview is an oversimplification and does not consider differences in maternal and zygotic factor expression over time, nor does it consider all of the factors involved.
Figure 1 Gene Circuits and Chambers
(A) Principal interactions in the Drosophila gap gene network, modelled after [37]. Relative levels and distributions of Hunchback (Hb), Giant (Gt), Krüppel (Kr), Knirps (Kni), Bicoid (Bcd), and Caudal (Cad) shown from anterior (left) to posterior (right). Green arrows indicate activation, red T-bars repression.
(B) Artificial gene network design, with transcription activators T7 and SP6 polymerases, and zinc finger repressors A, B, and C. Genes are immobilised on paramagnetic beads, and T7 forms a directional concentration gradient.
(C) Principal interactions in a simple designed network.
(D) Transcription-translation chamber. Genes for repressor A are localised at the “poles,” whereas B and C are ubiquitous. Gel slabs 4–6 have been excised, exposing the magnets below, illustrating gel dissection for Western blot analysis.
(E) Normalised Western data for four replicate chambers, showing mean levels of A, B, and C after 20 min (± One standard deviation).
(F) Sample Western blot from the four-replicate experiment.
From the outset, we chose to model our system around elements of the Drosophila gap gene network (Figure 1A), because we could study many elements of morphogenesis, such as gradient formation and the sufficiency of cross-repression for setting pattern boundaries, without the need for considering multiple cells, membrane-bound receptors, and cell-to-cell interactions. Using an in vitro model, we wanted to address the following questions about patterning. First, how easy is it to generate an expression pattern in a gradient, using a diffusing activator from a localised source? Second, one of the outstanding issues in the field is to what extent correct positioning of the gap protein domain boundaries is specified by maternal morphogen gradients and by cross-repression between gap genes; a recent model suggests that repression is crucial for patterning and that threshold-dependent interpretation of the maternal morphogen concentration is not sufficient [56]. We therefore wanted to test the effect of transcription repression on pattern formation directly, by progressively adding more repression interactions in a designed gene network. Third, uniform degradation of a diffusing morphogen is often assumed to account for steady-state gradient formation, so we set out to test the effects of adding controlled degradation to an in vitro patterning system. Finally, we wanted to use our model to see how the scale and pattern of the system are affected by the relative rates of diffusion of individual components, and whether nonuniform diffusion of activators and inhibitors are required to form a pattern.
Results
Design of the Network and Development of the In Vitro Experimental Platform
We began by designing a simplified gene network to emulate elements of the Drosophila gap gene system (Figure 1). The aim was to develop a fully synthetic approach in which protein analogues completely unrelated to Drosophila would emulate some of the transcription activation and repression interactions thought to be important for patterning in the gap gene system. As activators, two sequence-specific polymerases were employed, T7 and SP6, that have been used successfully by others to engineer gene networks [57]. These two polymerases bind to their respective consensus DNA recognition sites to initiate transcription, and thus represent an extremely simplified mode of transcription when compared to the multifactor complexes required for eukaryotic transcription (reviewed in [6]).
T7 polymerase was chosen to be the “master activator” of the system and, by crude analogy, was expected to carry out some of the functions of Bicoid, namely transcription activation of downstream members in the gap gene hierarchy, in a concentration-dependent manner, from a localised source [10,11]. In Drosophila, the Bicoid morphogen gradient initially controls the shape of the Caudal protein gradient through translational repression of maternal mRNA [53,54], although later Caudal expression is under zygotic transcriptional control. To simplify this level of complexity, we decided to model Caudal transcription activation by a second gradient of T7 polymerase, from the opposite pole to our primary “Bicoid” gradient (compare Figure 1A and 1C). Residual activation between other members of the gap gene members (e.g., Figure 1A, Hunchback activating Krüppel or Krüppel activating knirps) was modelled nonexplicitly by having a homogeneous distribution of a second sequence-specific transcription activator, SP6 polymerase (Figure 1A and 1C).
Repression interactions between gap gene members were modelled by constructing three site-specific repressors to represent the repressor activities of Hunchback, Giant, and Krüppel (Figure 1B, repressors A, B, and C, respectively). The repressors were derived from artificial zinc finger DNA-binding domains that were engineered by phage display [58]. Variable gene-repression networks were therefore constructed by placing binding sites for the repressors in the appropriate gene expression constructs (see for comparison Figure 2). Repressor sites were either overlapping with the polymerase initiation sites (demonstrated to be effective using triplex-forming oligonucleotides [59]), or immediately downstream of the initiation sites (Figure 2). Therefore, by changing the identity of the repressor sites, the connectivity of the network could readily be modified to add or remove cross-repressive interactions.
Figure 2 Map of the Constructs Used in This Study
The repressor binding sites overlap with T7 or SP6 promoters and vary between constructs. In this way, it is possible to alter the connectivity of the repressive interactions by the products of genes A, B, and C. Repressive interactions are denoted by T-bars. The start codon of each gene is in Kozak context and is denoted by “GCC ATG G.”
Key to the strategy was the development of an experimental platform in which to model the volume of the Drosophila embryo and to carry out artificial gene network reactions. Plastic chambers were therefore developed, constructed over printed templates on petri dishes (Figure 1D; see also Materials and Methods and Protocol S1). The chambers were filled with a customised transcription-translation mixture, allowing gene network reactions to be carried out in situ. Additionally, small bar magnets were fixed under the chamber to create a spatially defined array, over which paramagnetic beads could be dispensed. By coating such streptavidin-linked beads with biotinylated PCR products, specific gene network constructs were tethered and sublocalised on the array.
Furthermore, ultra-low melting point agarose was added to the transcription-translation mixture, both to increase viscosity and to allow the reaction to be “fixed” in a gelling step at 4 °C. Through fixing, gel slices could be excised and assayed by Western blotting against FLAG-epitope tags on the expressed proteins. This design therefore enabled quantification of each output species present in the network (genes A, B, and C) for any given chamber position and time point.
The chambers were constructed such that the system components could be pipetted wherever desired, either homogeneously mixed with the transcription-translation mix or pipetted at defined loci, such as at the edges or “poles” of the chamber. As described above, these system components included the two soluble, purified transcription activators (T7 and SP6 polymerases) and three bead-tethered zinc finger transcription-repressor constructs, A, B, and C, which were themselves activated by the polymerases and could cross-repress each other (see Figure 1).
Positional information was therefore introduced into the artificial system in two ways. First, by injecting purified T7 polymerase at either pole of the chamber, the Bicoid activator distribution could be transiently modelled. Second, beads coated with different gene network constructs (genes A, B, and C), could be fixed at different positions on the magnetic array (see Figure 1D).
For example, repressor A genes were placed solely at the chamber edges (“poles”) to model, loosely, the distribution of embryonic Hunchback activity. This part of the model is a significant oversimplification: Although Hunchback is eventually expressed in two domains, one anterior and one posterior [48], it is only expressed anteriorly in the early embryo. Furthermore, while maternal hunchback mRNA is evenly distributed, the anterior domain of Hunchback protein forms through zygotic translation and transcription activation (under the control of Bicoid), while maternal RNA is translationally repressed posteriorly, under the influence of Nanos [49,60,61]. In the later phase of hunchback regulation, the posterior Hunchback domain forms through a combination of factors, including activation by Tailless [62,63] and hunchback autoactivation [64].
To complete the model, the genes for repressors B and C were distributed uniformly throughout the chamber on magnetic beads, to represent the ubiquitous distribution of genes in nuclei, throughout the embryo. Therefore, the spatial expression of genes B and C, who represent the downstream gap gene members giant and Krüppel (see Figure 1A and 1C), was dependent on differential activation by the T7 polymerase gradient and crossregulation between gene network members. However, it should be noted that the initial expression of these genes in Drosophila may not be achieved by crossregulation, because localised mRNA is seen before any protein is detectable (Krüppel and Giant are only detected unambiguously in early cycle 13 [46]).
Pattern Generation In Vitro from a Transcription Network
In our first experiments, we constructed a simple, minimal network with sequential transcription activation and repression (see Figure 1B and 1C). Although this basic system is far less complex than the Drosophila gap gene system, it was indeed sufficient to generate a crude target behaviour (see Figure 1E and 1F). Qualitatively, the pattern can be explained as follows, Gene A is activated by T7 polymerase from its source at either end of the chamber, and so is expressed most highly at these poles. Gene B is similarly activated, and so it is also less expressed in the middle of the cell. However, since gene B is repressed by protein A, its levels are also reduced at either pole. Finally, Gene C is activated by a ubiquitous SP6 polymerase, but is repressed by proteins A and B, and is consequently centrally distributed.
Progressing from the minimal network, we explored systems with a variety of connectivities (Figure 3), including a control network without repression interactions (Figure 3A), and one with extensive mutual or feedback interactions (Figure 3C). These were compared with the original network (Figure 3B) in a series of time-course experiments. Generally, we observed that the more repression interactions in a system, the lower the overall protein production but the “sharper” the pattern.
Figure 3 Alternative Gene Networks
At five set time points (15, 25, 35, 60, and 90 min), transcription-translation chambers were dissected into nine slabs for Western blot analysis.
(A) Control network with no repression sites between genes A, B, and C.
(B) Minimally repressed network (compare Figure 1).
(C) Mutual repression network with extensive negative interactions between species. Adding protease (“+ Degradation”) creates weak but time-stable patterns for both the “Repressed” and “Mutual” networks (35 versus 90 min). Quantitated graphs for the above data are available in Protocol S1.
All patterns degenerated to a significant degree by 60 min, indicating the transience of the system (Figure 3, 60 min). However, by adding Factor Xa protease, we were able approximately to match levels of production and degradation. Thus, the outputs became sharper, weaker, and more dynamically stable, hardly varying between 35 and 90 min (Figure 3B and 3C, “+ Degradation”).
Computer Exploration of Parameter Space
To study parameter sensitivity in our system more comprehensively, we constructed a computer model of the chamber and networks (Protocol S1). A series of coupled differential equations were simulated, yielding expression levels of gene products A, B, and C, for the three different levels of network connectivity coded by our gene network designs (Figure 4). The modelling was carried out at two scales, that of our experimental system (18 mm long) and that of a Drosophila embryo (500 μm long). As in the experimental system (Figures 3 and 4A), the simulations revealed a large difference of pattern between the unrepressed and repressed systems. The patterns are more similar, however, between the simple and mutually repressed networks (Figure 4B) but, as in our in vitro experiments (e.g., see Figure 3B and 3C, 15 min), adding feedback repression makes the peaks better resolved.
Figure 4 Comparison of Experimental Data and Computer Simulations
Data are shown for the three gene networks described in Figure 3, showing outputs for proteins A (cyan), B (magenta) and C (dark blue).
(A) Quantitated Western blot data from Figure 3, after 25 min.
(B) Simulation data plotted as percentage of total output protein against chamber length, at the chamber (18-mm) or Drosophila (0.5-mm) scale. The model is described in full in Protocol S1.
Next, we explored the sensitivity of the simple repression network to diffusion parameters (Figure 5). Generally, we found that the A- and C-peaks were least sensitive to parameter variation, as there are no antagonistic forces against their formation (Figure 5A and 5C). By contrast, gene B is more sensitive: Twin-peak formation correlates with the relative diffusion ratios of activator (T7) and other mRNA/protein components (Figure 5B). To generate “target behaviour,” the activator must diffuse more rapidly than other species, within certain limits (approximately 5- to 50-fold faster for Figure 5B). However, the absolute values (and ratios) for diffusion merely alter the timing of the transient B-peak formation in a system of a given scale. For simplicity, only the 0.5-mm system is illustrated in Figure 5; similar conclusions were drawn from the 18-mm scale model.
Figure 5 Varying Diffusion and Degradation Parameters
Computer model of gene network, scaled to Drosophila length (0.5 mm). Diffusion parameters are varied for mRNA (Dm), protein (Dp), and T7 activator (DX). Data are plotted as percentage of total output protein (y-axes) against chamber position (x-axes), for 10-min simulations.
(A) Outputs for protein A.
(B) Output for protein B. Graphs with “target behaviour” are shaded grey, and the four asterisks mark the parameter sets used to generate outputs for proteins A and C.
(C) Outputs for protein C.
(D) Effect of adding protease degradation to B-output, shown at 15-min intervals, over a 2.5-h time course (parameters: DX = 0.43 μm2s−1; Dm = Dp = 0.02 μm2s−1; t1/2 = 770 s).
As in our chamber experiments, the computer model output became more “time-stable” by adding a degradation element (Figure 5D). Drosophila may exploit such mechanisms to some extent, since Bicoid protein degrades in vivo (t1/2 ≤ 1800 s [11]), although bicoid mRNA is unusually stable [65].
Discussion
To develop a fully synthetic approach that will emulate elements of gap gene expression domain pattern formation, we created an in vitro transcription-translation system that allows flexible spatial gene network construction. The system is widely applicable, allowing control over factors such as localisation or diffusion, and the ability to add or remove components at will.
Repressive Interactions and Pattern Formation
A basic aim of our system was to see whether we could engineer a gradient of protein expression, using a diffusing activator from a localised source. We found this task straightforward in the transcription-translation chambers, using injected T7 polymerase, and this led us to try more complex expression-repression interactions. We constructed three types of gene network—unrepressed, simple repressed, and mutually repressed (see Figure 3)—representing different levels of network connectivity. Generally, in both our in vitro and computer models, we found that adding more connections resulted in better-resolved patterning, although the absolute levels of gene expression were reduced. Our in vitro results are essentially qualitative at this stage, but appear to agree with the observations of others—that crossrepression is crucial for the control of patterning boundaries [56]. It will be interesting to learn whether more sophisticated elements can be engineered into the system to begin to emulate the more complex features of gap gene expression domain patterning. For example, dynamic anterior shifts are seen in domain expression over time because of asymmetric gap-gap crossrepression [56]. Asymmetric repression and other circuits could, in the future, be engineered into our chambers by altering the repressor binding sites in the appropriate constructs. Such a system would require component turnover to achieve steady-state patterning. We have begun to tackle this project through our experiments with controlled protease degradation, but a further requirement would be to have autocatalytic production of T7 polymerase from a localised source, rather than the injected pulse of purified polymerase in our current model.
Interestingly, our experimental data showed a reproducible degree of patterning even in the unrepressed system (Figure 3A, 15 min, C output). Because gene C is activated by a separate polymerase (SP6), this patterning cannot result from competition for activator. Therefore, competition for other resources (such as ribosomes, nucleotides, and tRNAs) may allow A and B to “inhibit” C. Indeed, supplying extra components (particularly wheat germ extract and SP6 polymerase together) increases protein production under these conditions, including that of C (unpublished data). If competition can generate patterns, albeit less well defined ones than repression-connected networks, this could perhaps represent an evolutionary “network precursor” state: Weak patterns could be generated by localisation and competition between factors, and these could later be consolidated by evolution of a “true” negative network connection. However, this hypothesis may not be relevant to the situation inside an insect egg, as this has probably evolved to deliver nutrients very efficiently to the embryo, even at a very primitive evolutionary stage. It therefore remains to be seen whether competition effects would be as significant in vivo.
Since our models use minimal components to achieve spatial pattern formation, they demonstrate the ease with which very simple networks might evolve. Patterning may be achieved with only localisation, diffusion, and some kind of functional network connection, such as transcription activation, competition, or repression. The addition of extra layers of network properties, such as controlled degradation, could then fix and stabilize such patterns. In fact, since sublocalisation—followed by stepwise addition of network components—is sufficient to generate crude patterns, it might provide a plausible mechanism for early spatial network evolution inside a single cell. However, it should be noted that gap gene expression domain patterning probably evolved by a different mechanism, from an earlier multicellular state, where segments were added sequentially through polar growth. In fact, bicoid is absent in most other insects, and it has been proposed that Drosophila evolved bicoid by duplication of the homeodomain-encoding gene zerknüllt, found in lower Diptera [66].
Diffusion Rates and Patterning
We were intrigued by our observations, both in vitro and in silico, that patterning required the activator molecule to diffuse or propagate more rapidly than the inhibitors. This is interesting because it is the opposite of the M-G system (described in the Introduction and reviewed in [12]). Long-range activation is not unknown in chemical patterning systems [67], although many biological models appear to require the M-G criterion for long-range inhibition (e.g., [68]). The other obvious differences between our system and the M-G model are the initial localisation of components and the lack of autocatalysis of the activator. It will be interesting to determine whether such M-G patterning systems can be recreated in our chambers, once further factors are considered, such as the avoidance of “autocatalytic explosions” or “global inhibitions.”
The computer model that we developed allowed us to test a broad range of parameters, such as diffusion and degradation rates (Figure 5), revealing differences between the requirements for patterning among the different species in the gene network. First, the more-connected member of the network (gene B) was much more sensitive to parameter variation than the less-connected members (genes A and C). This is perhaps to be expected, since protein B has two separate boundaries of expression (defined as a function of T7 distribution and both A and C expression), whereas proteins A and C have only single “edges” to be defined.
Another important feature of the system emerged when scaling the parameters for the model patterns to Drosophila scale (Figure 5, 0.5 mm “embryo,” 2.5 h). We found that, assuming simple diffusion, B-peak formation was compatible only with unphysiologically slow diffusion values (diffusion constants for mRNA [Dm] and protein [Dp] = 0.02 μm2s−1; for T7 activator [DX] = 0.43 μm2s−1). Since cellular proteins are expected to diffuse more rapidly (approximately 1–100 μm2s−1), this could be an artefact, reflecting the simplicity of our model. Nonetheless, simple diffusion still appears too rapid to account for Drosophila-scale patterning. It should be noted that a potential barrier to free diffusion is the active nuclear import of Bicoid and Hunchback [69]. In a separate example, diffusion of pair-rule transcripts is overridden by microtubule transport [70]. Controlled sublocalisation may therefore be crucial to limit apparent diffusion in vivo, allowing more precise patterning.
Perspectives
The understanding of how precision of patterning is achieved in Drosophila is still far from complete. In a recent study, it was shown that the Bicoid profile is far more variable between embryos than that of Hunchback, but the mechanism by which this noise is filtered remains unknown [71]. As more and more detailed experimental data are collected [72], and new mechanisms are proposed to account for patterning, it will be important to test the sufficiency of these mechanisms through experimental reconstitution. For such purposes, the chambers described here may be easily adapted to test different hypotheses. In vitro systems are a useful first step towards testing the sufficiency of a network—which might then be reengineered in the original target organism.
Combining simple reconstruction with theoretical modelling is a useful tool to discover and test general design principles in gene networks [73,74,75,76]. Until now, however, the spatial component essential in many biological processes has been ignored in these approaches. We anticipate that other networks, such as signalling cascades or metabolic networks, might also be studied using our system and that the spatial element, introduced through the beads, might provide new insights into complex systems.
Materials and Methods
Magnetic chamber construction
A detailed, step-by-step description of the construction of the chamber can be found in Protocol S1. Briefly, nine stirring-bar magnets (1.5 mm × 8 mm; VWR International, Vienna, Austria; #4429025) were inserted vertically into a plasticine-filled standard petri dish, creating a magnetic array (see Figure 1D). Construction was guided with a grid template, laser-printed on a transparent acetate sheet, and fixed over the magnets and plasticine. The template was a 3 mm × 18 rectangle with nine subdivisions (“slabs”). A sterile cell culture dish (Nalge Nunc, Rochester, New York, United States; #150350) was fixed immediately above the magnetic array. Chamber borders (1 mm deep) were constructed on the base of this second dish, following the template, using strips cut from adhesive Hybriwell chambers (Sigma-Aldrich, St. Louis, Missouri, United States; #H1159–100EA).
Gene network constructs
Maps of the constructs are illustrated in Figure 2. Repressors A, B, and C were derived from previously engineered zinc fingers [58]. Repressor A contained six zinc fingers, recognising the sequence 5′-
AGGGAGGCGGACTGGGGA-3′, fused to the residues 11–55 of the Kox-1 repression domain [77] and a six-repeat FLAG epitope tag [78]. Repressor B contained six zinc fingers, recognising the sequence 5′-
AGGGAGGCGGGAGCTTTC-3′ and fused to a three-repeat FLAG-tag. Repressor C contained three zinc fingers, recognising the sequence 5′-
GGAGCTTTC-3′, fused to the Kox domain and a three-repeat FLAG-tag. The following polymerase consensus promoter regions were used: T7, 5′-
TAATACGACTCACTATAG
GGAG-3′; SP6, 5′-
ATTTAGGTGACACTATAG
AAGGG-3′. The gene network promoters were linked with neutral or repressor sites to the polymerase promoters.
In the following nucleotide sequences, zinc finger binding sites are indicated in lowercase, initiation nucleotides in bold, and promoter overlaps underlined. Unrepressed T7, 5′-
TAATACGACTCACTATAG
GGAGAAACACCATAG-3′ (see Figure 3A, constructs A and B, and Figure 3B, construct A). Unrepressed SP6, 5′-
ATTTAGGTGACACTATAG
AAGGGAAACACCATAG-3′ (see Figure 3A, construct C). T7 repressed by A (and weakly by B), 5′-
TAATACGACTCACTATagggaggcggactgggga -3′ (see Figure 3B, construct B). SP6 repressed by A (and weakly by B), 5′-
ATTTAGGTGACACTATAGAagggaggcggactgggga-3′ (see Figure 3B and 3C, construct C). T7 repressed by A and C (and weakly by B), 5′-
TAATACGACTCACTATagggaggcggactggggaTggagctttc-3′ (see Figure 3C, construct B). T7 repressed by C (and weakly by B), 5′-
TAATACGACTCACTATAGggagctttc-3′ (see Figure 3C, construct A). Constructs were cloned in pCaSpeR4, sequenced, and used to generate PCR DNA for in vitro transcription-translation.
Gene network reactions
Paramagnetic beads were coated with PCR DNA (with one primer biotinylated) using a Dynabeads Kilobase Binder Kit (Dynal, Oslo, Norway; #601.01). Typically, gene A was used at 800 fmol per 10 μl of beads, resuspended in 8 μl of water; 200 fmol of gene B and 140 fmol of gene C were combined with 20 μl of beads, and resuspended in 20 μl of water.
Transcription-translation mixture was prepared that included 2.5 μl of water; 28 μl of ultra-low melting point agarose (Sigma; #A2576) solution (prepared as 1.5% [w/v] in boiling water and cooled to 30 °C); and TNT Coupled Wheat Germ Extract System (Promega, Madison, Wisconsin, United States; #L4130 and #L4140), which comprised 20 μl of TNT wheat germ extract, 1.2 μl of TNT reaction buffer, 0.6 μl of amino acid mixture (1 mM), 1.2 μl of RNasin (not included in TNT kit), and 0.5 μl of SP6 polymerase. 54 μl of this mixture was dispensed per chamber.
For degradation experiments, 2.25 units of Factor Xa (Amersham Biosciences, Little Chalfont, United Kingdom) were added per chamber. Coated Dynabeads were injected at appropriate positions over the magnetic array: typically, 100 fmol of gene A (1 μl), 5 fmol of gene B, and 3.5 fmol of gene C (0.5 μl). T7 polymerase (0.5 μl; from Promega TNT kit) was immediately injected at the chamber edges. After timed incubations at 25 °C, chambers were transferred to 4 °C for 35 min, to form a gel. Gel slices were cut with a razor blade (guided by the printed template) and aspirated with a P10 Gilson pipette. Samples were mixed with 10 μl of SDS-loading buffer and analysed by SDS-PAGE, Western blotting, and ECL, with anti-M2 FLAG antibody (Sigma; #F3165). Further details on this step can be found in Protocol S1.
Computer modelling
A Perl script was written to simulate the diffusion-coupled expression of genes A, B, and C, by T7 and SP6 phage polymerases, in a translation extract. The program parameters and script are fully described in Protocols S1–S3. 18 mm-scale chamber model: Parameters included separate diffusion (and degradation) rates for RNA and protein; a separate apparent diffusion for injected T7, modelled from experimental observations (rapid initial diffusion with exponential decay; Section 5 of Protocol S1); estimated binding constants for all interacting species (zinc finger dissociation constants were estimated from previous work on related three- and six-finger constructs [58,79,80]); and estimated transcription-translation rates. For adapting the model to the 0.5-mm Drosophila scale, chamber size was scaled down, and only simple diffusion was allowed for all components; for simplicity, transcription-translation rates were not varied (Section 3 of Protocol S1).
Supporting Information
Protocol S1 Detailed Description of Model
(1.2 MB PDF).
Click here for additional data file.
Protocol S2 Parameter File for Simulations
This file contains the default parameters for the computer model in a format that can be read by the Perl script.
(5 KB DOC).
Click here for additional data file.
Protocol S3 Computer Program Script for Simulations
This text file is a Perl script to run the computer simulations described in the manuscript.
(22 KB DOC).
Click here for additional data file.
Accession Numbers
The Locuslink (http://www.ncbi.nlm.nih.gov/LocusLink/, or GeneID (www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene, accession numbers of the genes and proteins discussed in this paper are Bicoid (40830), caudal (35341), giant (31227), huckebein (40549), hunchback (41032), knirps (40287), Krüppel (38012), Nanos (42297), tailless (43656), Torso (35717), and zerknüllt (40828).
We would like to thank K. Michalodimitrakis, C. Gonzalez, and B. Schönwetter for helpful discussions. MI is supported by an International Research Fellowship from the Wellcome Trust, United Kingdom.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. MI and LS conceived and designed the experiments. MI performed the experiments. CL analyzed the data and contributed reagents/materials/analysis tools. MI, CL, and LS wrote the paper.
Citation: Isalan M, Lemerle C, Serrano L (2005) Engineering gene networks to emulate Drosophila embryonic pattern formation. PLoS Biol 3(3): e64.
Abbreviations
Dmdiffusion constant for mRNA
Dpdiffusion constant for protein
DXdiffusion constant for T7 activator
M-GMeinhardt-Gierer
==== Refs
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| 15736977 | PMC1044831 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 22; 3(3):e64 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030064 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573697810.1371/journal.pbio.0030071Research ArticleEcologyEvolutionGenetics/Genomics/Gene TherapyHomo (Human)Recent Origin and Cultural Reversion of a Hunter–Gatherer Group Cultural Reversion in the MlabriOota Hiroki
1
¤Pakendorf Brigitte
1
Weiss Gunter
1
2
von Haeseler Arndt
2
3
Pookajorn Surin
4
‡Settheetham-Ishida Wannapa
5
Tiwawech Danai
6
Ishida Takafumi
7
Stoneking Mark [email protected]
1
1Max Planck Institute for Evolutionary AnthropologyLeipzigGermany2WE Informatik, Heinrich-Heine-UniversitätDüsseldorfGermany3Neumann-Institute for Computing, Forschungszentrum JülichDüsseldorfGermany4Faculty of Archaeology, Silpakorn UniversityBangkokThailand5Faculty of Medicine, Khon Kaen UniversityKhon KaenThailand6National Cancer InstituteBangkokThailand7Department of Biological Sciences, School of ScienceUniversity of TokyoJapanPenny David Academic EditorMassey UniversityNew Zealand3 2005 22 2 2005 22 2 2005 3 3 e716 7 2004 21 12 2004 Copyright: © 2005 Oota et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
An Evolutionary Road Less Traveled: From Farming to Hunting and Gathering
Contemporary hunter–gatherer groups are often thought to serve as models of an ancient lifestyle that was typical of human populations prior to the development of agriculture. Patterns of genetic variation in hunter–gatherer groups such as the !Kung and African Pygmies are consistent with this view, as they exhibit low genetic diversity coupled with high frequencies of divergent mtDNA types not found in surrounding agricultural groups, suggesting long-term isolation and small population sizes. We report here genetic evidence concerning the origins of the Mlabri, an enigmatic hunter–gatherer group from northern Thailand. The Mlabri have no mtDNA diversity, and the genetic diversity at Y-chromosome and autosomal loci are also extraordinarily reduced in the Mlabri. Genetic, linguistic, and cultural data all suggest that the Mlabri were recently founded, 500–800 y ago, from a very small number of individuals. Moreover, the Mlabri appear to have originated from an agricultural group and then adopted a hunting–gathering subsistence mode. This example of cultural reversion from agriculture to a hunting–gathering lifestyle indicates that contemporary hunter–gatherer groups do not necessarily reflect a pre-agricultural lifestyle.
Genes, language and culture reveal that the Mlabri reverted from an agricultural to a hunter-gatherer lifestyle, suggesting that hunter-gatherer groups might not always represent the pre- agricultural lifestyle of humans
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Introduction
The Mlabri are an enigmatic group of about 300 people who nowadays range across the Nan, Phrae, and Phayao provinces of north and northeastern Thailand and the Sayaburi province of western Laos [1,2]. Their traditional lifestyle is to move frequently through the dense forests of the high mountains, building temporary structures of bamboo sticks thatched with banana leaves, which they occupy for a few days, until the leaves turn yellow (thus accounting for their traditional Thai name, Phi Tong Luang, which means “spirit of the yellow leaves”). First contacted by Europeans in 1936 [3], they are unique among the hill tribes of northern Thailand in that, until recently, they subsisted by hunting and gathering combined with occasional barter trade with villagers.
The origins of the Mlabri are controversial. Some investigators have assumed that there is a direct connection between the Mlabri and the ancient Hoabinhian hunting–gathering culture of Southeast Asia [1]. However, a limited investigation of blood group variation [4] raised the possibility that the Mlabri originated via a founder event from an agricultural group, and preliminary linguistic analyses support this idea. The Mlabri language seems lexically most closely related to Khmu and Tin, two languages of the Khmuic branch of the Mon-Khmer sub-family of Austro-Asiatic languages, both of which are spoken in agricultural highland villages [5]. The cluster of dialects jointly referred to as Tin, or Mal/Prai, [6] is spoken in the Thailand–Laos border region that the Mlabri also occupy, whereas Khmu is spoken over a much wider area [7]. The grammar of Mlabri additionally has features that deviate markedly from typical Mon-Khmer, suggesting that Mlabri developed as a result of contact between speakers of a Khmuic language and speakers of a quite different language of unknown affiliation [2,8].
We report here the results of an investigation of genetic diversity in the Mlabri, to see whether patterns of genetic variation might provide further insights into the question of an agricultural versus hunting–gathering origin for the Mlabri. The rationale for using genetic analyses to investigate this question is that previous work has shown that hunter–gatherer groups typically differ from their agricultural neighbors in having reduced genetic diversity and high frequencies of unique mtDNA types [9,10,11,12,13,14], so we might expect a similar pattern if the Mlabri have always been hunter–gatherers. The genetic results, combined with linguistic and cultural evidence, suggest that the most probable explanation for the origin of the Mlabri is an extreme founder event from an agricultural group, followed by adoption of a hunting–gathering lifestyle.
Results/Discussion
Genetic Analyses: mtDNA Diversity
We analyzed 360 bp of the first hypervariable segment (HV1) of the mtDNA control region in 58 Mlabri; surprisingly, all of the sequences were identical, with the following differences from the reference sequence [15]: 16140C, 16189C, and 16266A, as well as the common Asian 9-bp deletion in the intergenic region between the cytochrome oxidase subunit II and lysine tRNA genes [16]. No other human population has been found to lack mtDNA HV1 variation, and mtDNA HV1 variation in six other hill tribes (all agricultural groups) from the same region of Thailand was significantly higher (Figure 1; Table 1).
Figure 1 Genetic Diversity in the Mlabri and Other Hill Tribes
Genetic diversity based on mtDNA HV1 sequences, Y-STR haplotypes, and autosomal STR (A-STR) genotypes in the Mlabri, compared to the average genetic diversity for six other hill tribes. The haplotype diversity is indicated for the mtDNA and Y-STR data, while the average heterozygosity is indicated for the autosomal STR loci.
Table 1 Genetic Diversity Parameters Based on mtDNA HV1 Sequences, Y-STR Haplotypes, and Autosomal STR Genotypes for the Mlabri and the Six Other Hill Tribes
Diversity in the Mlabri is significantly lower than the average for the other groups for all three genetic systems, based on t-tests (not shown)
a Probability of the observed heterozygosity excess under the stepwise mutation model, Wilcoxon one-tailed test
Y-Chromosome Diversity
We analyzed nine short tandem repeat (STR) loci on the Y chromosome in 54 Mlabri, and again found significantly reduced variation in the Mlabri compared to the other six hill tribes (Figure 1; Table 1). The Mlabri had just four Y-chromosome STR (Y-STR) haplotypes, two of which differed by a single repeat at a single locus from one each of the other two haplotypes (Table 2). The Y-STR haplotype diversity in the Mlabri is again lower than that reported for any other human population [17,18]; the Akha, one of the six other hill tribes, also exhibited very low Y-STR diversity (Table 1). The average variance in the allele size distribution at the nine Y-STR loci shows an even greater contrast between the Mlabri and the other hill tribes: the average variance was 0.11 for the Mlabri, versus an average of 1.45 for the other six hill tribes.
Table 2 Y-STR Haplotypes in the Mlabri
The number of repeats for the allele at each locus in the four haplotypes is given
Autosomal DNA Diversity
We analyzed nine autosomal STR loci in the Mlabri and the other six hill tribes, and again found significantly reduced variation in the Mlabri (Figure 1; Table 1). The genotype frequencies did not deviate significantly from Hardy–Weinberg expectations for any locus in the Mlabri; however, even though these nine STR loci are on different chromosomes and hence unlinked, eight pairs of loci exhibited significant linkage disequilibrium (LD) (p < 0.05; Figure 2), as measured by a likelihood ratio test [19]. This is significantly more (p < 0.01) than the 1.8 pairs expected by chance (out of 36 pairwise comparisons) to exhibit this level of LD. For each of the six agricultural hill tribes, the number of pairs of loci exhibiting significant LD was within expectations (Figure 2). Moreover, the p-value of the likelihood ratio test is a measure of the strength of the association between two loci [19]; the average p-value was 0.20 for the Mlabri, versus 0.31–0.55 for the other six hill tribes, indicating that overall associations between these unlinked loci were stronger in the Mlabri than in the other hill tribes. However, the sample size for the Mlabri for the autosomal STR analyses was larger than the sample size for the other hill tribes (n = 35 for the Mlabri, versus n = 29–30 for the others), so it is possible that the lower average p-value for the Mlabri reflects more statistical power due to a larger sample size and not more LD. To test this, we sampled 30 Mlabri at random and redid the LD analysis; the conclusions did not change, indicating that the lower average p-value for the Mlabri does reflect more LD in the Mlabri.
Figure 2 Associations amongst Unlinked Autosomal STR Loci in the Mlabri and the Other Hill Tribes
Probability values of the likelihood ratio test of association versus no association for nine unlinked autosomal STR loci in the Mlabri and six other hill tribes (average probability over the 36 pairs of loci in parentheses).
One explanation for the reduced diversity at mtDNA, Y-STR loci, and autosomal STR loci, and the significant number of pairs of unlinked autosomal STR loci in LD, is a severe reduction in population size in the Mlabri. Following such an event, the number of alleles is reduced more than the heterozygosity, leading to an excess of observed heterozygosity compared to that expected for the observed number of alleles under mutation–drift equilibrium [20]. We therefore compared the observed and expected heterozygosity (at mutation–drift equilibrium, conditioned on the observed number of alleles) for the autosomal STR loci in the Mlabri and the six other hill tribes, under a stepwise mutation model. Only the Mlabri exhibited a significant excess of observed heterozygosity (Table 1). Although more complicated scenarios are possible, the simplest explanation is that the Mlabri (but not the other hill tribes) have undergone a severe reduction in population size, as also indicated by the mtDNA and Y-STR haplotype data, and as also suggested by a previous study of blood group variation [4].
Population Size Reduction in the Mlabri
Assuming that there was a reduction in population size in the Mlabri that set the mtDNA and Y-chromosome diversity near or equal to zero, the coalescence times for the Mlabri mtDNA and Y-STR haplotypes provide an upper estimate as to when the population reduction occurred. We therefore applied Bayesian-based coalescence analysis [21] to the mtDNA sequences and the Y-STR haplotypes from the Mlabri and the other six hill tribes. For the six agricultural hill tribes, the resulting estimates of coalescence time are broadly distributed (Figure 3), indicating little information in the data (except for the Akha, who do show a pronounced peak in the posterior probability distribution for the Y-STR data, in accordance with their lower Y-STR haplotype diversity). By contrast, the estimates of coalescence time for the Mlabri show a sharp peak (Figure 3), with a median time of 770 y (approximate 95% credible interval 250–4,270 y) for the mtDNA sequences and 490 y (approximate 95% credible interval 170–1,290 y) for the Y-STR haplotypes.
Figure 3 Time to the Most Recent Common Ancestor for mtDNA and Y-STR Types for the Mlabri and the Other Hill Tribes
Posterior probability distribution of the time back to the most recent common ancestor for the mtDNA (A) and Y-STR haplotype (B) data for the Mlabri and six other hill tribes.
Both the mtDNA and the Y-STR data therefore indicate that the Mlabri underwent a substantial reduction in population size about 500–800 y ago (and not more than about 1,300 y ago, if the mtDNA and Y-chromosome data reflect the same event). There are two possible scenarios: (1) a bottleneck, in which the Mlabri were reduced from a formerly large population to a much smaller population size, which then increased to the current level of about 300 individuals; or (2) a founder event, in which the Mlabri began as a very small number of individuals, became isolated, and then increased over time to their present size. Similar reductions in genetic diversity are predicted under either scenario, so the genetic data cannot distinguish between these. But some information can be obtained by considering the magnitude of the reduction in population size needed to completely eliminate mtDNA diversity in the Mlabri.
The amount of population size reduction needed to completely eliminate mtDNA diversity in the Mlabri depends on how much mtDNA diversity was present prior to the size reduction. We assumed that the ancestral Mlabri population would have the same mtDNA diversity as one of the other hill tribes and then estimated the amount of population size reduction needed to completely eliminate mtDNA diversity by resampling with replacement various numbers of mtDNA types from the ancestral (pre-bottleneck) population. For example, we started with an ancestral population with the same distribution of mtDNA types as the Akha. We then sampled two mtDNA types (with replacement) from this ancestral population, repeated this procedure 1,000 times, and found that 243 out of the 1,000 resamples of size two had no mtDNA diversity; thus, the probability is 0.243 that a reduction to just two individuals would eliminate mtDNA diversity in an ancestral population that started with the same mtDNA diversity as the Akha. We then repeated this procedure, sampling three mtDNA types (with replacement), and obtained a probability of 0.007 that there would be no mtDNA diversity following a reduction to three individuals. Therefore, if the Mlabri were derived from a population with the same mtDNA diversity as the Akha, the population had to be reduced to not more than two unrelated females, in order to completely eliminate mtDNA diversity.
This resampling analysis was carried out six times, with the putative ancestral mtDNA diversity corresponding to each of the six hill tribes. The results of this analysis were that for five of the ancestral populations, resampling three (or more) individuals gave a probability of no mtDNA diversity of less than 0.05; for the remaining ancestral population (which had the same starting mtDNA diversity as the Red Karen), resampling four (or more) individuals gave a probability of no mtDNA diversity of less than 0.05.
We also carried out a similar analysis for the Y-STR types in the Mlabri. Here we again assumed an ancestral population with the same Y-STR haplotype diversity as one of the other hill tribes, then determined the maximum number of individuals that could be sampled at random that would have not more than two Y-STR types (since the four Y-STR types in the Mlabri consist of two pairs that differ by a single-step mutation at a single locus). The results of this analysis were that at most 3–6 individuals (depending on which hill tribe the ancestral population resembled most in terms of Y-STR diversity) could have been present after the size reduction, otherwise, with greater than 95% probability, more than two Y-STR types would have been retained.
A critical assumption is the amount of genetic diversity present in the ancestral Mlabri population prior to the size reduction. The estimates used in the above analysis are based on agricultural populations, which in general have more mtDNA diversity than hunter–gatherer populations. We therefore also constructed putative ancestral populations with frequency distributions of mtDNA types identical to those found in the !Kung and in African Pygmies [22]; the results of the resampling analysis were the same.
Another assumption of this analysis is that the event that led to the population size reduction completely eliminated the mtDNA diversity. Alternatively, some mtDNA diversity may have been present after the population size reduction, but was subsequently lost because of drift. Loss of mtDNA diversity due to subsequent drift is not likely if there was a single event reducing the Mlabri population size that was followed by population growth, since mtDNA diversity is retained in growing populations [23]. However, if the reduction in size occurred over several generations, then it may not have been as dramatic a bottleneck as the resampling analysis implies.
To investigate this further, we employed a Bayesian approach, following the procedure previously used to estimate the number of founders for the Maoris [24] but allowing for new mutations, to estimate the number of founders for the Mlabri, assuming various time periods since the founding event. The results (Figure 4) indicate that the most probable number of founders is one over all time periods; however, for longer time periods since the founding event, there is decreasing information on the number of founders from the observation of no mtDNA diversity in the Mlabri. As expected, the longer the time since the founding event (i.e., the slower the population growth rate), the greater the influence of drift in eliminating diversity that might have been present in the founding population. Nevertheless, given that the coalescent analyses indicate an upper date for the origin of the Mlabri of about 1,000 y ago, the lack of mtDNA diversity in the Mlabri is most consistent with a very small founding population size, perhaps even only one female lineage.
Figure 4 Number of Founding Individuals in the Mlabri, Given No mtDNA Diversity
Posterior probability distribution for the number of founding individuals (k), conditioned on the observation of no diversity in a sample of 58 mtDNA sequences and the time since the founding event. The prior probability is indicated by the dashed black line.
Origin of the Mlabri
The group that gave rise to the founder event that established the Mlabri could have been either a hunter–gatherer group, in which case the Mlabri maintained their hunting–gathering lifestyle from before, or an agricultural group, in which case the Mlabri subsequently adopted their current hunting–gathering lifestyle. While the genetic data cannot unequivocally distinguish between these two possibilities, they do suggest the latter. Other hunter–gatherer groups typically share few, if any, mtDNA types with neighboring agricultural groups, consistent with long-term isolation of the hunter–gatherer groups. For example, !Kung, African Pygmies, Andamanese Islanders, and south Indian hunter–gatherer groups can readily be distinguished from nearby agricultural groups on the basis of their mtDNA sequences [9,13,14,25]. By contrast, the Mlabri mtDNA sequence has been reported in other, agricultural hill tribes [26,27], and identical or closely related sequences have also been reported from Southeast Asia and China [9,28,29]. Similarly, the Mlabri Y-STR haplotypes are identical or closely related (differing by a single-step mutation at one locus) to Y-STR haplotypes found in Southeast Asia and Oceania [30,31]. Also, the Mlabri do not exhibit any alleles at the nine autosomal STR loci that are not found in the agricultural hill tribes.
The widespread sharing of mtDNA, Y-STR, and autosomal STR alleles between the Mlabri and agricultural groups in Southeast Asia are not expected if the Mlabri have always been hunter–gatherers. Instead, the genetic data suggest that the Mlabri are derived from an agricultural group. Moreover, the Mlabri vocabulary and folklore also give some evidence of ancient familiarity with agriculture coexisting with hunting and gathering (J. Rischel, personal communication). While preliminary in nature, the available linguistic evidence suggests that the present-day Mlabri language arose after some speakers of a Khmuic language, most likely Tin, became isolated and subsequently experienced intensive contact with speakers of some other, presently unknown language [2,8]. Just how long ago the Mlabri and Tin languages diverged cannot be determined, but it has been suggested that Tin branched from Khmu about 600 y ago, and that Tin then branched into two varieties (Mal and Prai) some 200–300 y ago [6,32]. These time estimates are based on a calibration of the chronology of sound changes in Tin against reasonably secure datings of sound changes in neighboring languages; the actual time depth may be underestimated, but most likely by not more than a few centuries. Thus, the linguistic evidence would date the origin of the Mlabri at less than 1,000 y ago, in excellent agreement with the genetic evidence.
Other data that may shed light on the origins of the Mlabri, such as historical information, are scarce, since the Mlabri do not have a written language and the first recorded contact was only in 1936. However, the Tin Prai have an oral tradition concerning the origin of the Mlabri (J. Rischel, personal communication), in which several hundred years ago, Tin Prai villagers expelled two children and sent them downriver on a raft. They survived and escaped into the forest, turning to a foraging lifestyle and thus becoming the Mlabri. Although it is difficult to know how to evaluate such oral traditions, this story nevertheless intriguingly parallels the genetic and linguistic evidence concerning the origins of the Mlabri.
In sum, genetic, linguistic, and cultural data all suggest a founding event in the Mlabri, involving a single maternal lineage and 1–4 paternal lineages some 500–1,000 y ago, from an ancestral agricultural population. The Mlabri then subsequently adopted their present hunting and gathering lifestyle, possibly because the group size at the time of founding was too small to support an agricultural lifestyle. Other examples of such cultural reversion are rare; probably the best known involves Polynesian hunter–gatherers on the Chatham Islands and the South Island of New Zealand [33], who abandoned agriculture and adopted a maritime-based foraging subsistence because of the rich marine resources and the inability of these islands to support cultivation of tropical crops. Other hypothesized examples of cultural reversion, such as the Punan of Borneo [34], the Guajá and other lowland Amazonian groups [35], and the Sirionó of Bolivia [36], are controversial, as it is not clear whether these groups are descended directly from earlier hunter–gatherer groups or whether they indeed have undergone cultural reversion. Detailed genetic analyses, as carried out here for the Mlabri, may shed further light in these cases.
In any event, our conclusion that the Mlabri, a present-day group of hunters and gatherers, was founded recently and in all probability from an agricultural group further supports the contention that contemporary hunter–gatherer groups cannot be automatically assumed to represent the pre-agricultural lifestyle of human populations, descended unchanged from the Stone Age [37]. Indeed, even if they have not reverted from an agricultural lifestyle, most (if not all) contemporary hunter–gatherer groups interact with, and have evolved and changed along with, agricultural groups [38]. The Mlabri provide a unique opportunity to investigate the circumstances and consequences of a reversion from an agricultural to a hunting–gathering lifestyle that apparently was not dictated by purely ecological reasons (as in the case of Polynesian hunter–gatherers).
Materials and Methods
Samples
There are three linguistically distinct subgroups of Mlabri [39], designated A, B, and C. Subgroup A (also known erroneously as “Mrabri”) is the only group that has been studied in detail [1]; subgroup B (minor Mlabri) is practically extinct [2], and subgroup C (formerly “Yumbri”) comprises less than 30 people [39]. Blood samples and genealogies of 91 Mlabri (all from subgroup A) were obtained with informed consent in 1999, and cell lines were prepared and DNA was extracted from the cell lines. The genealogical data were used to identify and exclude known relatives from the genetic analyses. Data on mtDNA and Y-STR variation from six agricultural hill tribes in the same geographic region (Akha, Lahu, White Karen, Red Karen, CR Lisu [from near Chiang Rai], and MHS Lisu [from near Mae Hong Son]), all of whom speak Sino-Tibetan languages, were published previously [26].
Genetic analyses
The first hypervariable segment (HV1) of the mtDNA control region (nucleotide positions 16,024–16,385) was amplified and sequenced directly, as described previously [29], from 58 Mlabri. PCR analysis of the intergenic region between the cytochrome oxidase subunit II and lysine tRNA genes, which harbors an informative 9-bp deletion, was carried out as described previously [28]. Nine Y-STR loci (DYS385a, DYS385b, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, and DYS394) were amplified and genotypes determined, using previously described methods [30], for 54 Mlabri. Nine autosomal STR loci (D3S1358, vWA, FGA, D8S1179, D21S11, D18S51, D5S818, D13S317, and D7S820) plus the amelogenin locus were amplified with the AmpFLSTR Profiler Plus PCR Amplification Kit (Applied Biosystems, Foster City, California, United States), using 2–4 ng of DNA in a 15-μl reaction volume. Genotypes were determined by fragment analysis on an ABI377 (Applied Biosystems) for 35 Mlabri, 29 Lahu, and 30 individuals from each of the other hill tribes.
Statistical analyses
Genetic diversity, heterozygosity, and tests for goodness of fit to Hardy–Weinberg expectations were calculated with Arlequin 2.000 [40]. LD was estimated as the probability of the likelihood of the data assuming linkage equilibrium versus the likelihood of the data assuming association [19]; Arlequin 2.000 was used to obtain maximum-likelihood estimates of the haplotype frequencies for each pair of loci with the EM algorithm [41], and the null distribution of the p-value of the likelihood ratio test was generated by 10,000 random permutations. The program Bottleneck (http://www.ensam.inra.fr/URLB/bottleneck/bottleneck.html) was used to compare the observed heterozygosity at each autosomal STR locus to that expected at mutation–drift equilibrium for the observed number of alleles, assuming a stepwise mutation model. Bayesian-based coalescence analyses of Y-STR haplotypes [42] were performed using the software Batwing (http://www.maths.abdn.ac.uk/~ijw/downloads/batwing/batguide/node6.html) and previously described prior distributions for the initial effective population size, population growth rate, and Y-STR mutation rates [30]. The coalescence time for mtDNA HV1 sequences was also estimated by a Bayesian procedure [21] as described previously for Xq13.3 sequences [43], using the same initial effective population size and population growth rate priors as for the Y-STR analysis, and a γ-distribution with parameters α = 14.74 and β = 0.0005 (corresponding mean = 0.00737) as a prior for the mutation rate [44]. Resampling of mtDNA and Y-STR types, in order to estimate the magnitude of population size reduction needed to eliminate mtDNA and reduce Y-STR diversity, was performed with the software Resample (http://www.pbs.port.ac.uk/~woodm/resample.htm). Bayesian analysis of the number of founders for the Mlabri was performed by pooling the mtDNA types in the other hill tribes to obtain a starting population, from which a certain number of founding mtDNA types were selected at random, assuming a uniform prior distribution between one and 20 founders. The sample was then allowed to grow from the number of founders to size 300 (the current size of the Mlabri population) over various time intervals, such that the shorter the time interval, the faster the growth rate. Simulations were performed both under the assumption of no new mutations, and with a mutation rate of one mutation/sequence/10,000 y. For each combination of parameters, 1,000,000 simulations were carried out. The simulation results were converted via Bayes's theorem into a posterior probability for the number of founding individuals, conditioned on the observation of no diversity in a random sample of size 58 (the sample size in this study). In practice, the posterior probability distributions were independent of the mutation rate (analyses not shown).
We thank S. Brauer for technical assistance, M. Wood for assistance with the resampling analyses, and R. Cordaux, D. Gil, and M. Kayser for useful discussion. We are especially grateful to J. Rischel for permission to cite his unpublished observations, and for discussions of the linguistic and cultural evidence concerning the Mlabri. HO was supported by a fellowship from the Japan Society for the Promotion of Science (JSPS). Sample collection was supported by funds from the JSPS and the Ministry of Education, Science, and Culture, Japan; genetic research was supported by the Max Planck Society, Germany. We dedicate this paper to the memory of Surin Pookajorn.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. MS and HO conceived and designed the experiments. HO and BP performed the experiments. MS, HO, BP, GW, and AvH analyzed the data. HO, SP, WSI, DT, and TI contributed reagents/materials/analysis tools. MS, HO, and BP wrote the paper.
¤Current address: Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America
‡Deceased 13 July 2004.
Citation: Oota H, Pakendorf B, Weiss G, von Haeseler A, Pookajorn S, et al. (2005) Recent origin and cultural reversion of a hunter–gatherer group. PLoS Biol 3(3): e71.
Abbreviations
LDlinkage disequilibrium
STRshort tandem repeat
Y-STRY-chromosome short tandem repeat
==== Refs
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| 15736978 | PMC1044832 | CC BY | 2021-01-05 08:28:12 | no | PLoS Biol. 2005 Mar 22; 3(3):e71 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030071 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573697910.1371/journal.pbio.0030077Research ArticleEcologyEvolutionMicrobiologyZoologyAnimalsArchaeaModeling the Mutualistic Interactions between Tubeworms and Microbial Consortia Tubeworm ModelCordes Erik E [email protected]
1
Arthur Michael A
2
Shea Katriona
1
Arvidson Rolf S
3
Fisher Charles R
1
1Biology Department, Pennsylvania State UniversityUniversity Park, PennsylvaniaUnited States of America2Geosciences Department, Pennsylvania State UniversityUniversity Park, PennsylvaniaUnited States of America3Department of Earth Science, Rice UniversityHouston, TexasUnited States of AmericaVrijenhoek Robert C. Academic EditorMonterey Bay Aquarium Research InstituteUnited States of America3 2005 22 2 2005 22 2 2005 3 3 e775 7 2004 23 12 2004 Copyright: © 2005 Cordes et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Microfauna-Macrofauna Interaction in the Seafloor: Lessons from the Tubeworm
Tubeworm May Live Longer by Cycling Its Sulfur Downward
The deep-sea vestimentiferan tubeworm Lamellibrachia luymesi forms large aggregations at hydrocarbon seeps in the Gulf of Mexico that may persist for over 250 y. Here, we present the results of a diagenetic model in which tubeworm aggregation persistence is achieved through augmentation of the supply of sulfate to hydrocarbon seep sediments. In the model, L. luymesi releases the sulfate generated by its internal, chemoautotrophic, sulfide-oxidizing symbionts through posterior root-like extensions of its body. The sulfate fuels sulfate reduction, commonly coupled to anaerobic methane oxidation and hydrocarbon degradation by bacterial–archaeal consortia. If sulfate is released by the tubeworms, sulfide generation mainly by hydrocarbon degradation is sufficient to support moderate-sized aggregations of L. luymesi for hundreds of years. The results of this model expand our concept of the potential benefits derived from complex interspecific relationships, in this case involving members of all three domains of life.
Modeling the interactions between deep-sea tubeworms and bacteria/archaea at hydrocarbon seeps provides a solution to their long term energy source and could help to explain the tubeworm's extreme longevity
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Introduction
Complex positive species interactions have been shown to expand the ecological niche and increase the persistence of the organisms involved in a variety of systems. In terrestrial systems, increased diversity of mycorrhizal symbionts is correlated with increased biodiversity of plant communities, resulting in greater stability and longer persistence at the community level [1]. In marine ecosystems, the coral Oculina arbuscula harbors a majid crab, Mithrax forceps, that prevents overgrowth of macroalgae and shading of the corals [2]. This allows O. arbuscula to maintain its facultative mutualism with photosynthetic zooxanthellae in well-lit habitats off the Atlantic coast of North Carolina, increasing the amount of energy available to the coral for growth and reproduction. At cold seeps in the Cascadia [3,4] and Aleutian [5] subduction zones, bioirrigation through burrow formation and bioturbation by clams (Calyptogena spp.) has been shown to significantly affect the distribution of microbial anaerobic methane oxidation.
Lamellibrachia luymesi inhabits areas associated with advection of hydrocarbons and other reduced chemicals to the seafloor (hydrocarbon or brine seeps) on the upper Louisiana slope (ULS) of the Gulf of Mexico from 400 to 1,000 m depth. L. luymesi does not posses a digestive system; rather, it acquires energy via internal sulfide-oxidizing bacterial symbionts [6]. L. luymesi differs from other vestimentiferan tubeworms by its ability to use a posterior extension of its body, the “root,” to acquire sulfide from interstitial pools in sediments [7,8]. Near the anterior plumes of tubeworms, sulfide concentrations typically decline below 0.1 μM as the tubeworms approach 1 m in length [9]. By using its roots, L. luymesi is able to delve into deeper sediment layers, providing access to more persistent sulfide sources. In the apparent absence of lethal predation [10,11], the most significant hazard that this vestimentiferan tubeworm faces is sulfide limitation. Its high uptake rate of sulfide from hydrocarbon seep sediments, estimated at over 30 μmol · h−1 for a moderate-sized individual [12], suggests that sulfide flux may be limiting in L. luymesi's habitat.
A diverse chemosynthetic community relies on the sulfide generated as a by-product of anaerobic degradative processes in the Gulf of Mexico [10,11]. Reduction of seawater sulfate utilizing methane or other hydrocarbons as electron donors produces the majority of sulfide available at ULS seeps [13,14]. Anaerobic methane oxidation is most commonly carried out by microbial consortia consisting of sulfate-reducing bacteria along with methanogenic archaea executing reverse methanogenesis [15,16]. Methane oxidation linked to sulfate reduction and subsequent authigenic carbonate precipitation constrain ocean–atmosphere carbon fluxes [3,4], accounting for up to 20% of the global methane flux to the atmosphere [17]. Oxidation of other hydrocarbons and organic material, carried out by sulfate-reducing bacteria in monoculture and in consortia with other microbes [18], may account for a larger proportion of sulfate depletion in ULS sediments [14]. These processes can result in a decoupling of sulfate reduction and methane oxidation rates [14], and form carbonates consisting mainly of non-methane-derived carbon [19]. L. luymesi may influence these anaerobic processes by utilizing its roots to release the sulfate generated by its symbionts during sulfide oxidation [7,8,12]. This hypothetical mechanism would provide sulfate for anaerobic methane oxidation and hydrocarbon degradation at sediment depths normally devoid of energetically favorable oxidants, thereby augmenting exogenous sulfide production.
In this study, we address the question of whether known biogeochemical processes could supply sulfide at rates sufficient to match the requirements of long-lived L. luymesi aggregations. In the diagenetic model presented here, the hypothesized release of sulfate in sediments with sufficient electron donors results in sulfide generation at rates matching the sulfide uptake rate of L. luymesi aggregations for over 250 y. We speculate that the mutual benefits derived from the syntrophy among symbiotic tubeworms and microbial consortia implicit in the model would expand our current concept for the potential complexity of positive interspecific interactions and the benefits they confer.
Results/Discussion
L. luymesi Sulfate Release Allows Persistence of Aggregations
The model predicts that inputs from known sources, including diffusion and advection of deep sulfide along with reduced seawater sulfate, will support a moderately-sized aggregation of 1,000 individuals for an average of 39 y (range, 22 to 78 y) (Figure 1). A smaller aggregation of 200 individuals could be maintained with these sources for an average of 64.1 y (standard deviation, 10.6 y). In this model configuration, the duration of adequate sulfide flux is not congruent with the known sizes of aggregations and existing age estimates of L. luymesi individuals and aggregations. Adding sulfate release by tubeworm roots to the model results in sulfide generation and flux at rates that match the demands of large aggregations, allowing the tubeworms to survive for over 250 y (Figure 1). This additional source of sulfate results in a two-orders-of-magnitude increase in sulfate flux in older (>100 y) aggregations, accounting for over 90% of sulfate available after only 24 y. The sulfate released by the tubeworms would be used for anaerobic methane oxidation and hydrocarbon degradation. The nature of the relationship between symbiotic tubeworms and microbial consortia that we are proposing is a coupling of the sulfur cycle only, and not carbon. Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons is apparently not taken up by tubeworms as the carbon stable isotope signatures of L. luymesi are heavier than would be expected from a methane-derived DIC source [20,21]. In addition, the well-studied hydrothermal vent tubeworm, Riftia pachyptila, obtains carbon in the form of CO2 across its plume [22]. However, this does not necessarily exclude the passive diffusion of DIC across the root surface, which could account for some of the variability observed in L. luymesi carbon stable isotope signatures [20,21]. By augmenting the sulfate supply to microbial consortia for sulfate reduction, large aggregations of tubeworms may survive for hundreds of years in the model, mirroring the population sizes and individual lengths regularly observed and collected at seeps on the ULS [23].
Figure 1 Ratio of Sulfide Supply to Sulfide Uptake Rate of L. luymesi Aggregations
Equilibrium line (1:1 ratio) and average, maximum, and minimum values for 1,000 iterations presented. Supply rate based on known sources without sulfate release by tubeworm roots shown in blue. Sulfide supply declines below demand after approximately 40 y. Supply rate including sulfate release from tubeworm roots shown in red, with sulfate release constrained by tubeworm symbionts' sulfide oxidation rate. Sulfide supply exceeds demand for the duration of the model.
Model Results Are Robust to Parameter Variation
An alternate hypothesis to explain the discordance between estimated sulfide supply and uptake rates is the presence of locally elevated seepage rates. Sensitivity analyses were carried out to determine the potential effects of uncertainty in seepage rate on supply estimated for aggregations without root sulfate release. A 10% increase in seepage rate resulted in a 5.6% increase in sulfide supply to aggregations 200 y old and older. This corresponds to only 16.4% of the sulfide required, which does not serve to extend aggregation survivorship (average, 39 y; range, 21 to 79 y) beyond that determined for lower flow rates. To supply the sulfide flux required by older aggregations, seepage rate would have to be at least 363 mm · y−1. This is over ten times greater than the rate used in the model (32 mm · y−1), which is the highest region-wide estimate for the Gulf of Mexico [24]. A rate of over 300 mm · y−1 approaches rates reported for active venting of fluids (Table 1). Active venting would result in the visual manifestation of seepage in the form of methane bubbles and oil droplets, which are generally restricted to mussel (Bathymodiolus childressi) beds at these sites [25]. In addition, larger, and therefore older [26], aggregations have lower epibenthic sulfide concentrations [8,9,25] suggesting that seepage becomes less vigorous over time and is not in the form of active venting in larger tubeworm aggregations. While difficult to obtain, in situ measures of advection rate of fluids at Gulf of Mexico seeps could be used to test these assumptions and may lend insight into the relationship between variability in tubeworm growth rate and sulfide availability.
Table 1 Reported Seepage Rates for Hydrocarbon and Methane Seeps
The high degree of variability in growth rate and recruitment rate could also affect the ratio of supply and demand in the model. In an aggregation exhibiting anomalously low recruitment, the size of the rhizosphere would increase more rapidly than the biomass of the aggregation. This would lead to high rates of sulfide delivery and generation and low rates of sulfide uptake by tubeworm roots. When initial recruitment rate (a in equations 1 and 2) is decreased by 10%, the length of time that supply exceeds demand increases by 3.7%. This effect appears to be linear, with a 20% decrease in initial recruitment rate resulting in a 7.4% increase in persistence. If growth rate is increased, thereby increasing the rate of rhizosphere growth in terms of surface area for diffusion and advection, there appears to be little effect of the ratio of supply to demand (20% increase in growth—0% change in persistence time). In fact, increasing growth to the upper limits of the error term (equation 5) lowers the amount of time that the aggregation can be supported since biomass and sulfide demand increase more rapidly than increases in supply resulting from additional surface area. By decreasing growth rate, aggregations may be supported for longer periods of time, with a 20% decrease leading to a 6.3% increase in persistence time and a decrease of 88% leading to persistence for over 250 y. While an 88% lower growth rate lies outside of the range of existing growth data, this could be accomplished by ceasing growth for extended periods of time in a quiescent stage. This possibility remains to be investigated in L. luymesi. By utilizing a variable recruitment rate in the model, both between realized aggregations and between years within a model run, along with a growth error term encompassing the full range of observed growth data, the model is capable of generating aggregations within the range of the 10%–20% variability tested in this analysis. Even these outlying aggregations (presented as maxima and minima in Figure 1) support the qualitative conclusions drawn from model results.
While the model was based on empirical data to the greatest degree possible, estimates of many of the parameters necessary to resolve the model were not available or are extremely difficult to measure in deep water with existing technology. Uptake rates were measured in the laboratory [8] for relatively small individuals (<50 cm). While we attempted to approximate metabolic scaling by covarying uptake and growth rates, it is possible that large individuals require even lower sulfide flux. Model predictions are not overly sensitive to variability in this parameter. A reduction by 10% of the overall sulfide uptake rate results in a 5.2% increase in persistence time. To maintain an aggregation for over 250 y, mass-specific uptake rate would have to be reduced 6-fold. While this could also be accomplished by entering a period of quiescence as mentioned before, there is no existing evidence for this ability in vestimentiferans.
The second version of the model is based on the assumption that L. luymesi is capable of releasing sulfate through its roots. It should be noted that in the model, sulfate release is constrained by the rate of sulfate generation by the tubeworm's sulfide-oxidizing symbionts, resulting in the near 1:1 ratio of supply and demand in Figure 1. Though modeled sulfate flux across the roots into the rhizosphere may exceed 20 mmol · h−1 in older aggregations, the roots provide an ample respiratory surface such that rates of sulfate flux per unit root surface area do not exceed 0.4 μmol · h−1· cm−2 in the model. It remains possible that a proportion of the sulfate could be released through the plume of the tubeworms, though the energy required to pump sulfate against a concentration gradient (seawater [SO4] = 29 mM) [13] suggests that it would be more energetically favorable for the sulfate to passively diffuse out of the roots. It is also possible that sulfate flux could be increased by active bioirrigation delivering seawater to deeper sediment layers through the tubeworm tubes. This could allow the sulfide-oxidizing symbionts to store some of the oxidized sulfide as elemental sulfur rather than releasing it as sulfate, while maintaining sufficient sulfate flux to deeper sediment layers for sulfide generation. These mechanisms remain hypothetical and require further experimental investigations to evaluate their potential role in this system.
Tubeworms Impact Seep Biogeochemistry
Tubeworm sulfate release, in conjunction with high sulfide uptake rates, could contribute to the observation of declining advection rate in older aggregations. By increasing sulfate flux to deeper sediments, L. luymesi increases integrated rates of anaerobic methane oxidation and hydrocarbon degradation, which would enhance authigenic calcium carbonate precipitation within the rhizosphere. Under the conditions of root sulfate release in the model, calcium carbonate precipitation is rapid (0.109 to 0.316 μmol · l−1 · sec−1) in the first 53 y, with rates declining exponentially thereafter. By creating a barrier to fluid advection [4], this could result in the observed decrease in epibenthic sulfide concentration in older aggregations [8,9] and the predicted cessation of tubeworm recruitment around this time [12,23].
In order to prevent the precipitation of carbonate directly on the root surface, L. luymesi individuals may release hydrogen ions as well as sulfate through their roots. While hydrogen ion flux through the roots has not yet been empirically demonstrated, none of the nearly 5,000 tubeworms examined as part of this study were observed to have carbonate formed directly on their roots, suggesting that this form of precipitation is inhibited in some manner. In the model, diffusion of hydrogen ions across the root surface (the only form of release explicitly modeled) accounts for less than 40% of ion generation when carbonate precipitation is most vigorous. We speculate that L. luymesi may utilize the excess hydrogen ions generated by their sulfide-oxidizing symbionts to periodically raise the rate of hydrogen ion flux from their roots. This would not only supply additional hydrogen ions to sulfate-reducing bacteria, but could inhibit carbonate precipitation on the tubes and subsequent reduction of the root area utilizable as a respiratory surface. Further pH reduction could dissolve existing carbonate in sediments beneath the rhizosphere, thereby opening seepage pathways and allowing further root growth. This possibility is corroborated by the observation of young tubeworms that had apparently bored through bivalve shells in an experimental system (R. Carney, personal communication). Empirical measurements of hydrogen ion flux across the root tissue of L. luymesi are required to test these hypothetical mechanisms.
The release of sulfate by tubeworm roots potentially explains the frequent observation of highly degraded hydrocarbons in the vicinity of large tubeworm aggregations [27]. The majority of sulfate supplied by tubeworm roots is utilized for microbial hydrocarbon degradation in the model (Figure2). This process alone accounts for over 60% of the sulfide available to aggregations after approximately 80 y. In the absence of liquid and solid phase hydrocarbons, methane flux would have to be approximately four times the rate in the model in order to fuel sufficient sulfate reduction to support an aggregation for over 200 y. This could occur in sediments overlying rapidly sublimating gas hydrates, and hydrate abundance has been previously suggested as a potential factor influencing the distribution of chemosynthetic communities in the Gulf of Mexico [10]. However, model results indicate that large chain hydrocarbons are the most significant energy source for sulfate reduction in tubeworm-dominated sediments. Increased integrated rates of hydrocarbon degradation would lead to highly biologically altered hydrocarbon pools among the roots of tubeworm aggregations. Hydrocarbon oxidation has been implicated as one of the dominant processes in the carbon cycle at ULS seeps, accounting for over 90% of the carbon in carbonates collected in the vicinity of tubeworm aggregations [19]. Model analysis indicates that the minimum amount of organic carbon (including hydrocarbons as well as buried organic material) in sediments required to supply sulfide at rates matching aggregation demand (1:1 supply:uptake ratio) is 1.03% by weight, remarkably close to the lowest value found in any of the seep sediment core samples (1.2%) [13,28], and greater than that found in ULS sediments away from seeps (0.71%) [29]. Determination of organic carbon concentration in sediments beneath tubeworm aggregations is necessary to test the prediction that elevated carbon content at seeps, primarily resulting from oil seepage, provides the energy source required to generate sufficient sulfide for tubeworm aggregations.
Figure 2 Sources of Sulfide Available to Tubeworm Aggregations over Time in the Model
Sources of sulfide include advection and diffusion of sulfide from deep sources (yellow) or sulfate reduction using methane (blue), buried organic carbon (green), or C6+ hydrocarbons (dark grey) as electron donors. Sulfate is provided by diffusion from sediments surrounding the rhizosphere, diffusion at the sediment–water interface, and release from tubeworm roots.
Additional sulfate flux from tubeworm roots could also explain the high apparent sulfate diffusion coefficients determined for tubeworm-impacted sediments [13]. Anomalous sulfate fluxes have been proposed to be a result of bioturbation and bioirrigation by macrofauna [3,5], and recycling by microbial mats [13]. The results of the model presented here provide evidence for macrofaunal sulfur recycling, an additional component to be considered in future investigations of cold seep biogeochemistry. The hypothesized release of sulfate by tubeworm roots potentially explains numerous, apparently disparate observations, hinting at the great impact that L. luymesi aggregations may have on their abiotic environment.
While the proposed interactions between symbiotic tubeworms and sulfate-reducing bacteria are essential for the persistence of L. luymesi aggregations in the model, we suggest that there are significant effects on the microbial community as well. This syntrophy will increase the abundance of sulfate-reducing bacteria and therefore increase the rates of anaerobic methane oxidation and hydrocarbon degradation carried out by microbial consortia that rely on sulfate as an oxidant. Tubeworm-generated sulfate supplies a more energetically favorable electron acceptor below the normal depth of sulfate penetration at seeps, relaxing the limitation on anaerobic oxidative processes at these sediment depths. Deeper sediment layers then become habitable to sulfate reducers, significantly altering the microbial community structure within the rhizosphere. Model configurations neglect the potential role of bioirrigation of seawater sulfate through L. luymesi tubes, which could further increase sulfate supply to deeper sediment layers. The possible role of tubeworm roots as substrata for the growth of microbial consortia, analogous to the habitat afforded mycorrhizal symbionts of higher plants, remains another possible benefit for the microbes. These predictions may be tested by determination of the relative abundance of microbial consortia at different depths of sediments both impacted by and isolated from tubeworms. Localization of the microbes on the root surface would provide evidence for a more intricate relationship. It is our hope that the results of this model may provide the impetus for future rigorous experimental tests of these ideas.
Summary
The model results presented here are consistent with the hypothesis that L. luymesi releases sulfate into hydrocarbon-rich sediments to fuel sulfide generation, allowing for the persistence of the longest-lived animal known. The importance of this process to sulfide generation in the modeled rhizosphere implies a complex relationship between an animal with bacterial endosymbionts and external sulfate-reducing bacteria, often in consortia with methane-oxidizing or hydrocarbon-degrading microbes. This positive interspecific relationship, including members of all three domains, would benefit both the tubeworms and the microbial consortia involved. This expands our existing concept of the potential for complexity in mutualisms and the benefits they may confer. Further complex relationships are likely to be discovered through continued research into the role of positive species interactions at the individual and community levels.
Materials and Methods
This study couples an individual-based population growth and sulfide uptake model [12] to a diagenetic diffusion/advection model to compare the relative magnitude of sulfide supply and uptake for long-lived tubeworm aggregations. A series of 1,000 iterations of the model under three different initial conditions (known sources of sulfate, known sources plus root sulfate supply, and known sources with elevated seepage rates) were carried out. The rhizosphere (volume of sediment encompassed by the root system of an aggregation) is modeled as an inverted dome beneath the sediment with a radius equal to the average root length of the population (Figure 3). The rhizosphere was approximated by a series of two-dimensional discs at 2-cm intervals in order to reduce the complexity of a three-dimensional solution for a sphere of changing size. Sulfate (SO4
2−), methane (CH4), sulfide (HS−), bicarbonate (HCO3
−), and hydrogen ion (H+) fluxes across the rhizosphere boundary are determined. Sulfate reduction rates using methane, larger chain hydrocarbons, and buried organic matter as electron donors are modeled in order to estimate the sulfide available to tubeworm aggregations as they change in size over the course of 250 y.
Figure 3 Model Construction
Population model includes individual size-specific growth and mortality rates, and population size-specific recruitment rate. Growth rate was determined by in situ staining of tubeworm aggregations using a blue chitin stain (in situ photograph of stained aggregation demonstrating annual growth shown here) and collection after 12–14 mo. Diagenetic model included advection and diffusion of sulfate, sulfide, methane, bicarbonate, and hydrogen ions as well as organic carbon content of sediments. Fluxes across the rhizosphere (root system) boundary were compared to sulfide uptake rates for simulated aggregations to determine whether sulfide supply could match the required uptake rates of aggregations (for specific methodology see methods). HC, C6+ hydrocarbons; orgC, organic carbon; ox, oxidation reaction; red, reduction reaction.
Population growth model
The population growth model follows the methodology presented in [12] and includes population growth, mortality rate, individual growth rate, and sulfide uptake rate. The parameters underlying the population growth model were refined using growth data from an additional 615 individuals and population data from an additional 11 aggregations comprising 4,908 individuals. The model presented here includes data from a total of 23 tubeworm aggregations from three nearby sites (Green Canyon oil lease blocks 184, 232, and 234) collected over a period of 7 y on the ULS to arrive at generalized population growth parameters.
L. luymesi individuals are dioecious, with males releasing sperm into the water column. Fertilization is believed to be external [30], though sperm has been found within the oviducts of females of the hydrothermal vent tubeworm R. pachyptila [31]. Eggs and embryos are positively buoyant and develop into a swimming trochophore-like larval stage within 3 d of fertilization [32]. Larvae are lecithotrophic and may remain in the water column for several weeks [32]. They require hard substrata for settlement, and acquire symbionts from their environment after metamorphosis [33,34]. Settlement is initially rapid, and continues until the available substrate is occupied [12,23,35]. Population sizes of aggregations collected with existing sampling devices typically vary between 100 and 1,500 individuals ([12,23]; this study), though far larger aggregations covering tens to hundreds of square meters are common at the sites sampled. Previous studies have shown that L. luymesi has an average longevity of 135 y [12], and requires an average of 210 y to reach 2 m in length [26], a size not uncommon among collected animals. Mortality events are exceedingly rare, dropping below 1% annual mortality probability for animals over 30 cm [12]. The expanded datasets of growth and mortality rates included here extend the longevity estimate for L. luymesi to an average of 176 y and the estimated age of a 2-m-long animal to 216 y.
At the beginning of each iteration, population growth parameters are chosen for the following population growth model:
where N is population size, t is time (in years), K is carrying capacity (set to 1,000 individuals for all simulations presented here), a describes the initial slope of the line, b defines the degree of density dependence, and c is a shape parameter. The first parameter (a) was generated using the following function:
where ɛ[N(0,1)] is a normally distributed random deviate with an average of zero and a standard deviation of one. This allows the initial recruitment rate to vary within the range of all recruitment trajectories that have been observed [12]. The other parameters were not normally distributed; therefore, the log-transformed distributions were used to define the distribution of the random numbers generated. As the three parameters in the model were significantly correlated (ln(a) and ln(b), r = −0.853, p < 0.001; ln(a) and ln (c), r = −0.461, p = 0.036), values of b and c were chosen from their relationship with a:
The value of a was allowed to vary each year according to the pooled standard error associated with the estimates of a from the empirical data (standard error, 0.105). Once population size equaled or exceeded carrying capacity, recruitment was ceased, representing the lack of additional substrate or sulfide available in the water column.
Once recruitment was determined for that year, the individual-based portion of the model began. Each individual was traced through time with respect to its length, root length, mass, mortality probability, mass-specific sulfide uptake rate, sulfate excretion rate, and hydrogen ion elimination rate. Growth rates of tubeworms were determined by staining tubes in situ (Figure 3) and collection 12 to 14 mo later ([26]; this study). Individual growth rate was determined from the following function (Figure 4):
Figure 4
L. luymesi Growth Rate
Size-specific growth of L. luymesi determined from stained tubeworms. Different colors indicate growth data from different aggregations. Blue points labeled “2000” are all from Bergquist et al. [26]. Other colored points refer to submersible dive numbers from 2003 when stained aggregations were collected.
(A) Growth function and 95% confidence interval for size-specific growth.
(B) Error function fitted to the residuals of the model.
Length (l) is defined here as the distance from the anterior end of the tube to an outer tube diameter of 2 mm following the methodology of [26]. All growth rates were standardized to 365 d. The error term is an additional function fitted to the residuals of the first regression function (Figure 4B), resulting in a variable growth rate. This error term was used rather than varying growth within the 95% confidence interval of the regression of length and growth rate because of the high degree of variability in growth among individuals. It should also be noted that there is a certain degree of variability in growth rate between aggregations (Figure 4). This may be attributable to spatial or temporal variability in seepage rate or sulfide concentration between aggregations. Aggregations may be subject to persistently differing conditions on a small (meter) scale, or may encounter periodic fluctuations in habitat characteristics. Because we are uncertain whether this variation is persistent on the temporal scales that we are simulating, between-aggregation variability is not explicitly modeled, though by chance certain realized aggregations deviated from mean growth rate.
The ratio of root length to tube length was determined from individual length using the following function:
Annual mortality rate was approximated as the size-specific frequency of empty tubes in collected aggregations [12] with an overall annual mortality rate of 0.569%. This approximation is conservative and likely overestimates yearly mortality, as available data indicate that empty tubes should persist longer than 1 y [12,36]. Mortality probability was determined for each 10-cm size class using the following function:
where m is mortality probability and l is length. Individuals were considered dead if their probability of mortality exceeded a uniform random number between zero and one.
By using generalized population growth parameters in the model presented here, we attempt to encompass the range of empirical data from sampled aggregations in our examination of sulfide uptake and supply rates. Taken together, the population growth model including recruitment, growth, and mortality provides a good qualitative if not quantitative fit for any individual aggregation, reflecting the size frequency of tubeworms within sampled aggregations [12]. It should be noted that the modeling of specific aggregations was not the aim of this study; rather, an attempt has been made to encompass the variability observed in the various populations of tubeworms that have been sampled. To examine the effect of uncertainty in the population growth parameters, sensitivity analyses were carried out. The initial slope of the recruitment rate (a in equation 1) was varied while individual size-specific growth rate was held constant (no error term in equation 5). Growth rate was then varied while holding the initial rate of population growth constant (no error term in equation 2). The effect of a 10% change in each parameter was determined, and then changes of greater magnitude were examined to determine the fastest rate of population or individual growth that could be supported by the sulfide available to the aggregation in the absence of sulfate release.
Individual sulfide uptake was allowed to vary within the range of laboratory-determined sulfide uptake rates according to that individual's growth rate for that year:
where u is uptake rate (in micromoles per gram per hour), m is mass (in grams), and g is growth rate (in centimeters per year). Growth rate was divided by the maximum growth rate (10 cm · y−1) such that highest growth rates resulted in highest uptake rates. By scaling uptake rate with growth, we approximate metabolic scaling, resulting in a decline in uptake rate by a factor of 3.7 over the range of tubeworm sizes in this study [12]. The amount of sulfate that could be excreted by each individual was determined from the amount generated by sulfide oxidation carried out by the internal chemoautotrophic symbionts assuming constant internal sulfate concentration, thereby accounting for changes in body volume. We do not account for the binding of sulfur by free amino acids, as this is believed to relatively minor compared the flux rates of sulfate and sulfide, and is reversible [37]. Hydrogen ions are also generated in the oxidation of sulfide by the tubeworm symbionts. Hydrogen ion elimination rate was determined in the model in the same fashion as sulfide uptake, with growth rate determining the variability in this metabolic flux according to laboratory-measured ion fluxes (mean, 10.96 μmol · g−1 · h−1; standard deviation, 1.88 μmol · g−1 · h−1) [38]. Simple diffusion of hydrogen ions across the root surface was included in the model, though the exact mode of proton flux has not yet been determined experimentally for L. luymesi [38]. As diffusion across the roots accounts for a relatively small proportion of total proton flux (less than 10% in large individuals), additional pathways are likely and require further investigation.
Geochemical setting
Known sources of sulfide available to L. luymesi aggregations are sulfide transported with seeping fluids [10] and sulfide generated via reduction of seawater sulfate [39,40]. The majority of the sulfide present at ULS sites is believed to be related to sulfate reduction coupled to anaerobic hydrocarbon oxidation [14,39]. Other potential sources of sulfide associated with seepage include anaerobic oxidation of deeply buried organic material [10], “sour” hydrocarbons containing a proportion of sulfur [41], and hydrocarbon interactions with sulfur-bearing minerals such as gypsum and anhydrite found in the salt dome cap rocks of the ULS [8,42,43].
Concentrations of all chemical species in the sediments surrounding the rhizosphere were derived from the dataset included in Arvidson et al. [13] and Morse et al. [28]. Only those sediment cores taken around the “drip line” of tubeworm aggregations that contained detectable sulfide concentrations were used. Due to the vagaries of sampling with a submersible in sediments heavily impacted by carbonate and roots, those cores with detectable sulfide are believed to more accurately represent conditions around the periphery of the rhizosphere.
Dissolved organic carbon (DOC) concentration was used as an estimate of methane concentration. While other forms of DOC make up this total concentration, methane accounts for 90%–95% of the hydrocarbon gasses dissolved in pore waters [28]. In seep sediments, the majority of DOC is likely to be in the form of hydrocarbon gasses. Because estimates of organic acid concentrations were not available, they could not be explicitly modeled. This would not affect the overall concentration of electron donors in the model, but could affect the sulfate reduction rate. Since sulfate reduction rate estimates for methane seeps in the Gulf of Mexico are among the highest recorded [14,39], any differences in DOC composition (e.g., higher relative concentrations of dissolved organic acids) would serve to lower the overall sulfate reduction rate and sulfide availability. Sulfide supply estimates presented are likely overestimated most by the model without root sulfate release owing to the greater reliance on anaerobic methane oxidation in this form of the model. Simulations including sulfate release by tubeworms are affected to a lesser extent as the concentration of electron donors is not limiting in this model configuration.
Solid and liquid phase organic carbon was separated into hydrocarbons and buried organic material according to their relative concentrations in hydrocarbon seep and surrounding Gulf of Mexico sediments. Background sediments on the ULS contain 0.71% organic carbon by weight [29]. At hydrocarbon seeps on the ULS, organic carbon accounts for 4.47% of total weight. This was assumed to be the sum of background organic input plus carbon in the form of C6+ hydrocarbons. It is possible that the higher biomass located at ULS seeps in the form of non-living macrofaunal and microbial materials may also contribute to the increased organic carbon concentration, but without empirical estimates, this could not be accounted for in the model. Hydrocarbons may consist of between 50% and 95% labile materials [44,45,46]. Based on existing data on degradation rates and residual hydrocarbons subjected to degradation [42,47], a value of 50% labile material was used here. These assumptions of hydrocarbon concentration and degradation potential are therefore believed to be conservative.
The following functions were fitted to the sulfide, sulfate, and methane concentration profiles (Figure 5) to determine the boundary conditions at any given depth:
Figure 5 Concentration Profiles of Sulfate, Sulfide, and DOC
Points represent average concentration at a given depth from 13 sediment cores taken around the periphery of tubeworm aggregations (see Materials and Methods and original data in [13,28]). Best-fitted line based on least squares fit of equation 9.
where C
0 is initial concentration, C∞ is concentration at infinite distance, and Ci is concentration at depth d. As there were no existing data for sediments below 30 cm, concentrations at infinite depth (C∞) were used (SO4
2− = 0 mmol · l−1, HS− = 12 mmol · l−1, DOC = 11 mmol · l−1, DIC = 20 mmol · l−1, pH = 7.78). The first derivatives of the sulfide and methane profiles were used for the calculation of advective flux from depth. The first derivative of the sulfate profile was used for diffusive flux across the water–sediment interface of the rhizosphere, with advection rate subtracted from diffusive flux of sulfate across this surface. Advection (seepage) rate varied with time according to the following function:
where t is simulation time in years and sed is sedimentation rate (6 cm · 1,000 y−1) [29]. Early seepage rate approximated the highest flux rates measured or estimated for methane seeps and declined with time in the model to the highest estimates for persistent, region-wide seepage in the Gulf of Mexico (Table 1). This follows a pattern of hydrocarbon seep development, with the highest seepage rates early in the evolution of the local seepage source followed by occlusion of fluid migration pathways by carbonate precipitation, hydrate formation, and possibly tubeworm root growth. By using the highest rate estimated (32 mm · y−1 = 0.000365 cm · h−1 in equation 10) as the basal seepage rate, we are testing the possibility that tubeworm aggregations could survive under the most favorable conditions possible in the absence of tubeworm sulfate supply.
For sediments encompassed by the rhizosphere, sulfide, sulfate, methane, DOC, and hydrogen ion concentration profiles were determined iteratively prior to model implementation using a central difference scheme:
where C
i(t) is concentration in cell i at time t, D is the diffusion coefficient, k is the maximum reaction rate, and Ks is the half-saturation constant for the reaction (Table 2). Reactions included anaerobic methane oxidation (equation 17), tubeworm sulfide uptake rate (equation 8), and carbonate precipitation rate (equation 22). The concentration in each 2 × 2 cm cell was calculated at 1 h time steps. At the end of each year, diffusion distance increased. The number of cells (total diffusion distance) was determined by the average root length of L. luymesi populations as realized in independent runs of the population growth model described above, and included here as model input only. A separate function was fitted to each of the concentration profiles:
Table 2 Parameters Involved in Diagenetic Model
aDiffusion coefficients all corrected for temperature, pressure, and salinity according to Stumm and Morgan [51] and Pilson [52]
bAll disassociation constants corrected for temperature, salinity, and pressure according to Stumm and Morgan [51] and Pilson [52] except: CaOH, no correction; CaHCO3, CaSO4, CaSO4H2O, MgHCO3, temperature only; H2CO3, temperature and salinity only; and HSO4, temperature and pressure only
where d is radial distance. The relationship between the parameter a and distance was used to generate concentration profiles for each disc comprising the rhizosphere. Because of the tight linear relationship between diffusion distance and the shape of the curve, concentration profiles could be generated for a disc of any size using the following function:
where α is 1.7344 and β is 1.0104 for HS−, α is 0.2111 and β is 0.3363 for SO4
2−, and α is 0.1626 and β is 0.2518 for CH4. Diffusional fluxes of sulfide, sulfate, and methane were calculated according to the first and second derivatives of the concentration profiles as determined by the diameter of each disc.
Model implementation
The model estimates sulfide availability to the aggregation as a whole by summing the fluxes separately determined for each 2-cm disc composing the rhizosphere. Depth-dependant boundary conditions were set for each disc separately based on the sediment profiles (Figure 5). Diffusional fluxes into each disc were calculated from the shape of the concentration profiles according to the following function [48]:
where C is concentration, r is disc radius, and Ds is the diffusion coefficient corrected for porosity by:
where Do is the diffusion coefficient corrected for temperature and pressure, n is the chemical species-specific constant, and φ is porosity. The value of n was set to 2.75 as this was found to be a reasonable fit for all chemical species examined [49]. The ionic states of each species at the average pH value of tubeworm-dominated sediments (7.78) were used for the determination of diffusion coefficients. Porosity was determined from the following function:
where φz is porosity at depth z, φ0 is porosity at the sediment–water interface, and φ∞ is porosity at infinite depth; φ0 was set at 0.841, φ∞ at 0.765, and a at 0.210, as determined from the best fit with the porosity data (Figure 6) from Morse et al. [28].
Figure 6 Sediment Porosity Values
Points represent average porosity at a given depth from 13 sediment cores taken around the periphery of tubeworm aggregations (see Materials and Methods and original data in [13,28]). Best-fitted line based on least squares fit of equation 9.
Diffusion across the sediment–water interface of the rhizosphere was also considered as an additional input of sulfate and hydrogen ions. This was included as one-dimensional diffusion across a circular surface (subtracting the area encompassed by the tubeworm tubes) with diffusion distance equal to rhizosphere diameter, and concentration differential from seawater concentration to the average concentration within the rhizosphere. Sulfate and hydrogen ion diffusion across the root surface was then added (if included in the set of model realizations) as simple Fickian diffusion. Concentration differential was the difference between internal concentration and average concentration for each disc of the rhizosphere assuming roots were evenly proportioned according to the volume encompassed by each disc. Internal sulfate concentration and pH (Table 2) represented an average of the values determined for R. pachyptila [22], a hydrothermal vent tubeworm. Internal sulfate concentrations and pH of L. luymesi have not been reported, but these values are generally consistent within taxa [50]. Uptake of sulfide and release of sulfate were summed across the entire tubeworm population, again assuming an even distribution of roots within the rhizosphere. The paucity of empirical data on the location of any individual tubeworm's roots within an aggregation precluded modeling space explicitly; therefore, it is assumed that each individual has equal access to the resources available within the rhizosphere.
Within the rhizosphere, sulfide generation may be limited by sulfate supply, electron donor availability, or sulfate reduction rate. Sulfate supply was determined as the sum of flux across the series of discs approximating the rhizosphere dome, across the sediment–water interface, and from root sulfate (if available). Available sulfate is utilized for anaerobic methane oxidation first (the more energetically favorable process), then hydrocarbon and organic matter degradation. Electron donors included methane, complex hydrocarbons, and buried organic material. Solid and liquid phase hydrocarbons and organic material were assumed to be homogenous within the rhizosphere. Methane supply was determined as the sum of flux across each rhizosphere disc boundary. Hydrocarbon and organic material concentrations were determined as the amounts encompassed within the rhizosphere volume minus that oxidized in previous years. Sulfate reduction rate was determined from the relative amounts of the various electron donors with higher rates (0.71 μmol · ml−1 · h−1) for methane oxidation and lower rates (0.083 μmol · ml−1 · h−1) for organic matter or hydrocarbon degradation [39]. Microbes carrying out these processes are assumed to be evenly distributed within the rhizosphere.
Total hydrogen sulfide availability to the aggregation was determined as the sum of sulfide diffusion and advection across each rhizosphere disc and sulfide generated within the rhizosphere from sulfate reduction according to the following reactions:
SO42− + CH4 → HS− + HCO3− + H2O
SO42− + 2CH2O → HS− + 2HCO3− + H2O
SO42− + 1.47CnH2n+2 → HS− + 1.47HCO3− + H2O
Bicarbonate (HCO3
−) is generated at a 1:1 stoichiometry during anaerobic methane oxidation and a 2:1 stoichiometry in the degradation of organic material. As hydrocarbons are degraded forming smaller chain hydrocarbons and organic acids, bicarbonate is generated at different stoichiometries. Because different-sized hydrocarbons and organic acids were not accounted for in the model, a rough average of these stoichiometries (1.47:1) based on toluene, ethylbenzene, xylene, and hexadecane degradation [18] was used to determine the amount of bicarbonate generated per mole of carbon. Hydrogen ions are also used up in a 1:1 stoichiometry with sulfate in the sulfate reduction half reaction as included in reaction 17.
In order to account for carbonate precipitation, the model traced DIC concentration, calcium concentration, hydrogen ion concentration, buffer capacity, carbonate saturation, and carbonate precipitation rate. The buffer state of the rhizosphere was calculated to determine changes in pH resulting from hydrogen ion flux. Buffer capacity (β) was calculated using the following function [51]:
where A and B represent the concentrations of the various acids and bases in the buffer system. In addition to hydrogen and hydroxyl ions, the buffer system included carbonate (CO2, H2CO3, HCO3
−, and CO3
2−), sulfide (H2S and HS−), sulfate (HSO4
− and SO4
2−), and borate (B[OH]4
− and B[OH]3) speciation. Current pH was used to determine the ionic state of each species according to temperature-, pressure-, and salinity-corrected disassociation constants when available [51,52] (Table 2). Change in pH was determined from hydrogen ion flux and buffer capacity as follows:
Saturation state is highly dependent on the degree to which calcium and bicarbonate form complexes with other ions. The “free” calcium was determined as the proportion of calcium that is not associated with complexed bicarbonate (HCO3
−), carbonate (CO3
2−), hydroxyl (OH−), or sulfate (SO4
2−) ions. Free carbonate was determined as the amount not forming complexes with calcium (Ca+) or magnesium (Mg+) ions in solution. Saturation state was then calculated from the product of the concentrations of free calcium and carbonate divided by the solubility product constant. If the saturation state was above one, then carbonate precipitation occurred at a rate determined by:
where k
1 is 0.00597 l · mol−1 · sec−1 and k3 = 0.456 l · mol−1 · sec−1 [51]. Because there is no empirical relationship between weight percent of carbonate and sediment porosity in tubeworm-dominated sediments [28], precipitation did not directly affect porosity. Precipitation was accounted for in the model by subtracting the volume of carbonate precipitate from the total volume encompassed by the rhizosphere.
At the end of each annual time step, model output included average length of individuals, population size, sulfide uptake rate, sulfide supply rate, root sulfate flux (if included), root hydrogen ion flux, amount of sulfide supply accounted for by each process (sulfide seepage, anaerobic methane oxidation, organic matter degradation, and hydrocarbon degradation), number of individuals that could be supported by sulfide supply, carbonate precipitation rate, volume of carbonate precipitate, and pH.
We would like to acknowledge K. Montooth, P. Hudson, and five anonymous reviewers for providing helpful comments on drafts of the manuscript. We are indebted to J. Freytag, S. Dattagupta, D. Bergquist, R. Carney, and R. Sassen for the many discussions and advice provided. EEC acknowledges funding from the Center for Environmental Chemistry and Geochemistry at Pennsylvania State University and the Nancy Foster Scholarship Program at the National Oceanographic and Atmospheric Administration (NOAA). This work was supported by the U.S. Minerals Management Service, the NOAA National Undersea Research Program, and the National Science Foundation.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. EEC, MAA, and CRF conceived and designed the experiments. EEC performed the experiments and analyzed the data. KS and RSA contributed reagents/materials/analysis tools. EEC, MAA, KS, and CRF wrote the paper.
Citation: Cordes EE, Arthur MA, Shea K, Arvidson RS, Fisher CR (2005) Modeling the mutualistic interactions between tubeworms and microbial consortia. PLoS Biol 3(3): e77.
Abbreviations
DICdissolved inorganic carbon
DOCdissolved organic carbon ULS
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| 15736979 | PMC1044833 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 22; 3(3):e77 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030077 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573698010.1371/journal.pbio.0030078Research ArticleNeuroscienceCatLocation Coding by Opponent Neural Populations in the Auditory Cortex Opponent-Channel Code for Auditory SpaceStecker G. Christopher [email protected]
1
¤Harrington Ian A
1
Middlebrooks John C
1
1Kresge Hearing Research InstituteUniversity of Michigan, Ann Arbor, MichiganUnited States of AmericaSemple Malcolm Academic EditorNew York UniversityUnited States of America3 2005 22 2 2005 22 2 2005 3 3 e783 11 2004 20 12 2004 Copyright: © 2005 Stecker et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
How the Brain Signals a Sound Source
Although the auditory cortex plays a necessary role in sound localization, physiological investigations in the cortex reveal inhomogeneous sampling of auditory space that is difficult to reconcile with localization behavior under the assumption of local spatial coding. Most neurons respond maximally to sounds located far to the left or right side, with few neurons tuned to the frontal midline. Paradoxically, psychophysical studies show optimal spatial acuity across the frontal midline. In this paper, we revisit the problem of inhomogeneous spatial sampling in three fields of cat auditory cortex. In each field, we confirm that neural responses tend to be greatest for lateral positions, but show the greatest modulation for near-midline source locations. Moreover, identification of source locations based on cortical responses shows sharp discrimination of left from right but relatively inaccurate discrimination of locations within each half of space. Motivated by these findings, we explore an opponent-process theory in which sound-source locations are represented by differences in the activity of two broadly tuned channels formed by contra- and ipsilaterally preferring neurons. Finally, we demonstrate a simple model, based on spike-count differences across cortical populations, that provides bias-free, level-invariant localization—and thus also a solution to the “binding problem” of associating spatial information with other nonspatial attributes of sounds.
A model relying on properties of auditory cortical neurons recorded in the cat can account for the accurate localization of sounds
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Introduction
Topographic representation is a hallmark of cortical organization: primary somatosensory cortex contains a somatotopic map of the body surface, primary visual cortex contains a retinotopic map of visual (retinal) space, and primary auditory cortex contains a cochleotopic map of sound frequency. The necessity of auditory cortex for normal sound localization (which is disrupted by cortical lesions [1,2,3]) strongly implies a cortical representation of auditory space. That representation has been reasonably expected to consist of a spatiotopic map, based on the existence of such maps in other sensory systems and on the view, proposed by Jeffress [4], that spatial processing in the auditory brainstem and midbrain might involve a “local code” consisting of topographic maps of interaural spatial cues. A local code, or “place code,” is one in which particular locations in space, or the spatial cues that correspond to those locations, are represented by neural activity at restricted locations in the brain. Evidence for local coding of auditory space has been demonstrated in mammalian superior colliculus [5,6] and in avian inferior colliculus (IC) [7,8] and optic tectum (homologous to mammalian superior colliculus) [9]. Nevertheless, local spatial coding has not thus far been demonstrated in the mammalian ascending auditory pathway.
If the Jeffress model is correct and a local code for spatial cues exists subcortically, one might anticipate local coding to be maintained in the cortex, where the various cues might finally be integrated into a coherent map of auditory space. Numerous studies, however, have failed to provide evidence for such a map. The spatial tuning of neurons is often characterized using rate–azimuth functions (RAFs), which specify the average response rate (spikes per trial or per second) as a function of stimulus location in the horizontal dimension. Throughout the auditory cortex, such functions typically exhibit broad peaks (up to 180° wide) that cover the contralateral hemifield and broaden further with increasing sound level [10,11,12,13,14]. Similar functions have been reported for cortical sensitivity to interaural cues [15,16], and for spatial and interaural sensitivity in the auditory brainstem and midbrain [17,18,19,20,21,22,23,24], thus questioning Jeffress's view of binaural processing in mammals. The emerging alternative view replaces the local code with a “distributed code,” in which sound-source locations are represented by patterns of activity across populations of broadly tuned neurons [12,24,25].
In the past, we argued for a distributed spatial code in the auditory cortex in part because the broad spatial tuning of cortical neurons would seem to preclude the existence of a local code and also because individual neurons are able to transmit spatial information throughout much, if not all, of auditory space [25,26]. At least implicitly, we have advocated a uniform distributed code, assuming that uniform sampling of space by RAF peaks is required for maximally accurate spatial coding. Spatial centroids of neurons in the posterior auditory field (PAF), for example, sample space more uniformly than neurons in the primary auditory field (A1), and we have suggested that this feature partially underlies the increased ability of ensembles of PAF neurons to accurately signal sound-source locations [14].
A number of observations demonstrate, however, that the auditory cortex samples space nonuniformly. RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory cortex in Figure 1, to illustrate a common observation of location-sensitive auditory cortical neurons: the majority favor contralateral stimulation, and typically exhibit either “hemifield” or “axial” tuning [11], responding to stimuli located throughout contralateral space or near the acoustic axis of the contralateral pinna, respectively. A smaller number of ipsilaterally tuned units are also observed, the majority of which exhibit hemifield or axial tuning characteristics similar to those of contralateral units. In A1 and DZ, ipsilateral- and/or midline-tuned neurons may be arranged in bands—parallel to the tonotopic axis—that interdigitate with bands of contralaterally tuned cells [25,27,28,29]. The overall preponderance of contralateral tuning among cortical units seems to justify the view that each hemisphere represents the contralateral spatial hemifield, a view that is also supported by the contralateral sound-localization deficits that follow auditory cortical lesions [2,30,31]. Even within a single hemifield, however, no strong evidence for a topographic representation has been reported, and the observation that many units share similar hemifield RAFs demonstrates a profound inhomogeneity in the way cortical populations sample auditory space.
Figure 1 Example RAFs
Plotted are normalized mean spike counts (y-axis) elicited by broadband stimuli (20 dB above unit threshold) varying in azimuth (x-axis). Lines represent units recorded in cortical area DZ. Left: contralaterally responsive units. Right: ipsilaterally responsive units.
Additional evidence that the cortical representation of auditory space is inhomogeneous comes from studies of the ability of cortical responses to classify stimulus locations. Stecker et al. [14] found that the responses of most spatially sensitive units in cat cortical areas A1 and PAF could accurately discriminate the lateral hemifield (left versus right) of a stimulus, but often confused locations within the hemifield. This is shown for six PAF neurons represented by confusion matrices in Figure 2. Similarly, Middlebrooks et al. [12] measured median localization errors—based on neural-network analyses of responses in the second auditory field (A2) and the field of the anterior ectosylvian sulcus (AES)—between 37.5° ± 8.9° and 43.7°± 10.2°, just under the theoretical limit of 45° attainable through perfect left/right discrimination and within-hemifield confusion. Taken together, these results suggest that auditory space is represented within the cortex by a population of broadly tuned neurons, each of which is able to indicate the lateral hemifield from which a sound originated, but generally little more.
Figure 2 Classification Performance of Accurate PAF Units from [14]
Neural spike patterns were classified according to the stimulus location most likely to have elicited them. In each panel, a confusion matrix plots the relative proportion of classifications of each target azimuth (x-axis) to each possible response azimuth (y-axis). Proportions are indicated by the area of a circle located at the intersection of target and response locations. Example units were selected from among those transmitting the most spatial information in their responses. In each case, discrimination of contralateral azimuths (negative values) from ipsilateral azimuths (positive values) is apparent, accompanied by significant within-hemifield confusion. As such, neural responses are sufficient for left/right discrimination only, and the spatial information transmitted by the most accurate units tends not to be much greater than one bit per stimulus.
Results/Discussion
Preferred Locations Oversample Lateral Regions of Contralateral Space—Steepest RAF Slopes Straddle the Midline
The idea that sound locations are signaled by the peaks of RAFs, which tend to be centered deep within the lateral hemifields, is at odds with localization behavior, which shows greatest resolution near the interaural midline [32,33]. An alternative view, however, has emerged for the processing of interaural time and level differences by cortical and subcortical neurons. In that view, locations are coded by the slopes, rather than the peaks, of rate–interaural-time-difference or rate–interaural-level-difference functions [22,24,34]. Moreover, these slopes appear aligned with the interaural midline and provide maximum spatial information in that region [20]. If a similar arrangement can explain the inhomogeneity of spatial sampling in the auditory cortex, then we would expect to find cortical RAF slopes to be steepest near the interaural midline as well.
In this report, we compare the responses of neurons in primary auditory cortex (A1) and two higher-order auditory cortical fields (PAF and DZ) in the cat. Compared to A1, areas PAF and DZ exhibit spectrotemporally complex responses that are significantly more sensitive to variations in sound-source location [14,25]. Therefore, these areas are the most likely candidate regions of cat auditory cortex for spatial specialization. PAF, in particular, appears necessary for sound localization by behaving cats [30].
Figure 3 depicts the distribution of preferred locations (“azimuth centroids”; see [14]) along with locations of peak RAF slopes in all three fields. As we have reported previously, centroid distributions in Figure 3 reveal a preponderance of contralateral sensitivity regardless of cortical area or stimulus level [14,25]. Distributions of peak-slope location, however, are tightly clustered around the frontal midline (median± standard error [see Materials and Methods] in A1, +15° ± 2.5°; in DZ, +5° ± 2.1°; in PAF, −5° ± 2.6°). Values in A1 fall significantly farther into the ipsilateral field than do those in DZ (p < 0.0004) or PAF (p < 0.0002), consistent with both the broader spatial tuning and less extreme azimuth centroids of A1 compared to PAF or DZ units [14,25]. Overall, the positioning of RAF slopes near the interaural midline suggests that auditory space is sampled inhomogeneously by the cortical population; the midline represents a transition region between locations eliciting responses from populations of contralateral- and ipsilateral-preferring units.
Figure 3 RAF Slopes Are Steepest near the Interaural Midline
Plotted are summaries of preferred locations (centroids) and points of maximum RAF slope for 254 units recorded in A1 (left), 411 in PAF (middle), and 298 in DZ (right) for levels 20 and 40 dB above threshold (thr) (bottom and top rows, respectively). In each panel, units are sorted by centroid (blue crosses) on the y-axis. Thin red lines denote the region of azimuth (x-axis) containing the centroid and bounded by the points of steepest slope. For units with centroids lateralized more than 10° from the midline, we marked either the steepest positive slope (for ipsilaterally tuned units) or negative slope (for contralateral units) with a black circle. These points represent the location of most rapid response change that occurs toward the front of the animal (relative to the centroid; for units that respond throughout the frontal hemifield, this point can occur toward the rear). Distributions of centroid (blue line) and peak slope (black line), calculated using kernel density estimation with 20° rectangular bins, are plotted below each panel. These indicate that while preferred locations (centroids) are strongly biased toward contralateral azimuths, peak slopes are tightly packed about the interaural midline, consistent with the opponent-channel hypothesis.
Neural Response Patterns Discriminate Best across Midline
Modulation of spike count is generally the most salient location-sensitive feature of neural responses, especially when data are averaged over many trials. However, temporal features of the neural response—such as first-spike latency, the temporal dispersion of spikes, and specific temporal features such as prototyped bursts of spikes or periods of inhibition—could also play an important role in stimulus coding by cortical neurons, and we have studied this role using pattern-recognition analyses applied to spike patterns [12,14,35]. Here, we assess the ability of neural spike patterns to subserve pairwise discrimination of stimulus locations by adapting the pattern-recognition approach of Stecker et al. [14] to a discrimination paradigm. This approach is similar to the receiver-operating-characteristic analysis used to estimate interaural and/or spatial thresholds from neural spike counts [36,37,38], with the addition of spike-timing information. Given a spike pattern—a smoothed, bootstrap-averaged peristimulus time histogram (2-ms bins) that approximates the instantaneous probability of spike firing over the course of 200 ms following stimulus onset—elicited by stimulation from an unknown location in space, the algorithm estimates the relative likelihood that the pattern was evoked by a sound from each of the 18 tested locations. From these relative likelihoods, we compute the index of discriminability, d′ [39], for each pair of stimulus locations. In Figure 4 (right), pairwise d′ is plotted as a function of the midpoint and separation between paired stimuli for a single PAF unit; the contour d′ = 1 (dashed line) indicates the spatial discrimination threshold. Note that in this example suprathreshold discrimination is possible at much narrower stimulus separations when the stimuli span the interaural midline (left/right discrimination) than in cases of front/back discrimination spanning +/− 90°, about which point many features of the neural response (e.g., spike rate and latency) are symmetrical. As a result, the minimum discriminable angle (MDA, defined as the minimum separation along the d′ = 1 contour) of 25° is found at a best azimuth (BA, the midpoint location of the most discriminable pair) of −5°, near the frontal midline.
Figure 4 Discrimination Analysis Based on Responses of One PAF Unit
Left: raster plot of spike times (x-axis) recorded in response to broadband noise stimuli varying in azimuth (y-axis). Note the strong modulation of spike count, response latency, and temporal features of the response between contralateral and ipsilateral locations.
Right: pairwise spatial discrimination. Colors indicate d′ values for pairs of stimulus locations varying in separation (y-axis) and overall azimuth (x-axis, midpoint of two azimuths). The dashed line indicates threshold discrimination (d′ = 1), and the red circle marks the unit's MDA (y-axis) and BA (x-axis).
In Figure 5, MDA is plotted as a function of BA for the entire population of A1, PAF, and DZ units in which discrimination thresholds could be calculated. Overall, two main features of the results should be noted. First, despite the broad azimuth tuning of cortical neurons, the majority can discriminate between stimuli separated by less than 40°. A number of neurons successfully discriminate even smaller separations—especially in DZ, in which the median MDA (30.5° ± 2.5°) is significantly smaller than in A1 (40° ± 2.6°; p < 0.007) or PAF (43° ± 3.8°; p < 0.0002). Note that MDAs of even the most sensitive units exceed behavioral estimates of 5°–6° minimum audible angles in cats [40], but likely underestimate the true neuronal performance because loudspeaker separations were tested in minimum steps of 20°, and thus discrimination at smaller separations can only be assessed through extrapolation. Second, the distribution of BAs is tightly clustered around the interaural midline, with 50% of BA values falling within 18.5° (PAF), 25° (A1), or 26° (DZ) of the 0° or 180° azimuth. Note that this does not mean that units cannot discriminate off-midline locations. It does indicate, however, that the majority of units capable of discriminating between stimulus azimuths do so best for location pairs near the interaural midline. Very few units exhibit BAs located far within either lateral hemifield, although A1 units exhibit significantly more ipsilateral BA values (median, +14.5° ± 3.9°) than those in PAF (0° ± 2.9°; p < 0.004) or DZ (5° ± 4.3°; p < 0.05). As with the analysis of RAF slopes, the pairwise discrimination data reveal an inhomogeneous arrangement of spatial sampling by neurons in the cortical population. Accurate discrimination is found where RAF slopes are steepest (the midline), rather than where units respond most strongly (the lateral poles).
Figure 5 MDA by BA
MDA (y-axis) is plotted against BA (x-axis) for each unit exhibiting suprathreshold spatial discrimination (see Materials and Methods). Symbols indicate the cortical area of each unit. Left and lower panels plot distributions of MDA and BA (in numbers of units per rectangular 20° bin), respectively.
An Opponent-Channel Code for Auditory Space?
We have demonstrated quantitatively that the representation of auditory space in the cortex is inhomogeneous, consisting mainly of broadly tuned neurons whose responses change abruptly across the interaural midline. The population of auditory cortical neurons, then, appears to contain at least two subpopulations broadly responsive to contralateral and ipsilateral space. Neurons within each population exhibit similar spatial tuning and thus appear redundant with respect to spatial coding. The similarity of spatial tuning of units in these populations stands in contrast to their more profound differences in frequency tuning, for example. Each subpopulation, or “spatial channel,” is capable of representing locations on the slopes of their response areas (i.e., across the interaural midline) by graded changes in response—a “rate code” for azimuth, generalized to incorporate spatially informative temporal features of the cortical response [14,35,41]. In other words, each spatial channel encodes space more or less panoramically, as we have argued previously for individual cortical neurons [12], although it now seems clear that some regions of space are represented with greater precision than others. In the past, we have argued that auditory space is encoded by patterns of activation across populations of such panoramic neurons. Here, we amend that view—which remains tenable—to reflect the observed inhomogeneity of spatial sampling in the cortex and account for differences in coding accuracy of midline and other locations. Following the proposals of von Békésy [42] and van Bergeijk [43] regarding interaural coding in the brainstem, we propose that auditory space is encoded specifically by differences in the activity of two broad spatial channels corresponding to subpopulations of contralateral and ipsilateral units within each hemisphere (i.e., by a left/right opponent process). We will refer to this proposal as the opponent-channel theory of spatial coding in the auditory cortex.
An important consequence of the opponent-channel theory is that spatial coding may be robust in the face of changes in stimulus level. As is evident from past work, an important constraint on spatial coding in the cortex is the level dependence of many neurons' tuning widths, such that sharp tuning is seen predominantly for low-level stimulation. For example, a number of narrowly tuned units in Figure 3 exhibit locations of peak slope that closely track their centroids at 20 dB above threshold, and one could argue that such units form the basis of a local (e.g., topographic) spatial code when stimulus levels are low. Such a code, however, would be significantly impaired by increases in stimulus level—predicting that sound localization should be most accurate at low levels. That prediction is not borne out in psychophysical tests [44,45], and we have argued that spatial coding in the auditory cortex must employ relatively level-invariant features of the neural response [12]. Rather than relying on such features as they naturally occur, the opponent-channel mechanism constructs level-invariant features by comparing the activity of neurons that respond similarly to changes in level but differentially to changes in location, similarly to the coding of color by opponent-process cells in the visual system [46].
To illustrate the level invariance achieved by opponent-process coding, we analyzed the ability of cortical population responses to signal sound-source locations in the frontal hemifield under different stimulus-level conditions. The analysis (see Materials and Methods) is simplified in a number of ways—for example, it utilizes a simple linear decision rule that weights contralateral and ipsilateral input equally, sums across multiple neurons within each subpopulation (ignoring any complexity of neural circuitry), combines data across different cortical areas known to exhibit different spatial sensitivities, and reduces each neural response pattern to a single overall spike count—but serves as a “proof of concept” that differences between the responses of neural subpopulations with quasi-independent spatial tuning can be used to estimate sound-source locations in an unbiased manner when stimulus levels vary, whereas the individual population responses cannot.
Population responses (means of normalized spike rate across neurons in a population) to stimuli varying in location and level were computed separately for subpopulations composed of contralateral units or ipsilateral units in our sample of recordings in A1, PAF, and DZ. These subpopulations correspond to hypothetical “left” and “right” channels of a spatial coding mechanism. Classification of stimulus locations was based on either one of the subpopulation responses or the difference between the two, and involved linear matching to templates computed from a separate training set [14]. Population and difference RAFs are plotted in Figure 6 (left), along with confusion matrices (similar to Figure 2) for sound-source classification based on each (right). Relatively accurate classification is exhibited by both subpopulation responses and by their difference when test and training sets reflect the same stimulus level. When training and test sets differ, however, responses are systematically biased. After training with 20-dB stimuli, localization of 40-dB stimuli by the contralateral subpopulation is biased toward the contralateral hemifield, because 40-dB ipsilateral stimuli and 20-dB contralateral stimuli elicit similar responses. Similarly, when trained with 40-dB stimuli, localization of 20-dB stimuli is biased toward the ipsilateral hemifield. This pattern is clear in the responses of both the contralateral-preferring and ipsilateral-preferring subpopulations. Classification based on their difference, however, is relatively unbiased. Significant undershoot (central responses for peripheral stimulus locations) results from compression of population RAFs by intense sound. While we know of no behavioral data relating to the effects of stimulus level on sound localization by cats, undershoot has been reported in numerous studies of their localization behavior [47,48,49]. Such undershoots, however, need not be assumed to reflect a limitation of the underlying neural representation of auditory space.
Figure 6 Difference between Channel Responses Is Less Sensitive to Changes in Level Than Are Channel Responses Themselves
Left: population responses (y-axis; see Materials and Methods) are plotted as a function of azimuth (x-axis) for stimuli presented 20 dB (red) and 40 dB (blue) above unit thresholds. Population responses were computed separately for subpopulations composed of contralateral units (top) or ipsilateral units (middle) corresponding to hypothetical “left” and “right” channels of an opponent-channel spatial coding mechanism. The difference (bottom) between responses of the two subpopulations is more consistent across stimulus level than is either subpopulation response alone. Error bars indicate the standard deviation of responses across 120 simulated trials.
Right: stimulus–response matrices (confusion matrices; see Figure 2) showing the proportion (area of black circle) of responses to a given (unknown) stimulus azimuth (x-axis) classified at each response azimuth (y-axis). Classification assigned each neural population response in the “test” set to the stimulus azimuth whose mean population response in an independently selected set of “training” trials was most similar. In some conditions, test and training trials were drawn from the same set of (matching level) trials: 20 dB (first column) or 40 dB (far right column). In others, test and training trials reflected different-level stimuli: 40-dB test stimuli classified based on a 20-dB training set (second column), or 20-dB test stimuli classified based on a 40-dB training set (third column). The contralateral and ipsilateral subpopulation responses (top and middle rows) accurately localize fixed-level stimuli, but are strongly biased when tested at non-trained stimulus levels. In contrast, the difference between responses (bottom row) remains relatively unbiased in all conditions, although responses to stimuli at untrained levels do exhibit compressed range and increased variability of classification.
Based on its manner of level-invariant spatial coding, it seems clear that an opponent-channel mechanism should behave similarly in the presence of any stimulus change (e.g., in frequency, modulation, or bandwidth) that acts to increase or decrease the response of both channels. This suggests an efficient means for combining spatial information with information about other stimulus dimensions. This general principle of opponent-process coding should hold in any case where both channels exhibit similar sensitivity to the nuisance dimension (level, frequency, etc.) but dissimilar sensitivity to space, and illustrates one strength of the opponent-channel coding strategy: the ability to recover spatial information from the responses of neurons that are strongly modulated by other stimulus dimensions. As long as some of the cortical neurons involved in coding a particular acoustic feature are contralaterally driven and others are ipsilaterally driven, the spatial location of that feature can be computed without imposing additional distortion of its neural representation.
Note that the opponent-channel theory as presented here involves contralateral and ipsilateral channels within each hemisphere. This feature is based on the observation of both types of neurons in a single hemisphere, and on the results of unilateral cortical lesions, which produce localization deficits mainly in contralesional space [2,30,31,50]. The lesion data prevent us from considering opponent-channel mechanisms that place each channel in a separate hemisphere (e.g., left-hemisphere contralateral units versus right-hemisphere contralateral units) because in that case unilateral lesions should abolish localization throughout the entire acoustic field. As proposed here, however, the opponent-channel mechanism in either hemisphere should be capable of coding locations throughout space, not just in the contralateral hemifield. This would suggest that only bilateral lesions could produce localization deficits, which is also not the case. At this point, we can merely speculate that auditory cortical structures in each hemisphere provide input only to those multimodal spatial or sensorimotor structures that subserve localization behavior in contralateral space and, furthermore, that these inputs cannot be modified in adulthood following cortical lesions.
General Discussion
In summary, the available data suggest that space is sampled nonuniformly in all fields of auditory cortex, with the majority of neurons responding broadly within one hemifield and modulating their responses abruptly across the interaural midline. Consistent with this view, we found cortical responses to be most sensitive to changes in stimulus azimuth at midline locations. Cortical neurons' RAFs tend to be steepest near the midline even though their preferred locations are found distributed throughout the contralateral hemifield. Spatial discrimination by neural responses is also best at or near the interaural midline. Results of both analyses are compatible with the existence of a limited number of spatial channels in the cortex, and incompatible with either a uniform distributed representation or a local representation (e.g., a topographic map). The relative paucity of units with sharp tuning peaking near the midline strongly suggests that behavioral sound-localization acuity is mediated by the slopes and not the peaks of spatial receptive fields.
In this report, we consider a model of spatial coding based on differences in the response rates of two broad spatial channels in the auditory cortex. It is similar to the mechanism proposed by Boehnke and Phillips [51] to account for differences in human gap detection when gaps are bounded by auditory stimuli occurring in the same or opposite hemifields. In each proposal, neural response rates are compared across channels, but each is also consistent with information encoded in the relative response timing of cortical neurons [25,52]. Although the psychophysical and physiological data seem to agree on a two-channel mechanism, it is important to note that in this study, we treat units that respond more strongly to forward than rearward locations (“axial” units) as equivalent to units that respond equally to both quadrants (“hemifield” units). Similarly, we do not specifically examine the small number of units that respond best to midline locations. Distributed coding of interaural intensity by neural populations differing in binaural facilitation has been suggested previously [24]; similarly, populations of midline and/or axial units could be treated separately in a three-, four-, or five-channel opponent model of spatial coding. Such a model would follow the general principles of opponent-channel coding described here, but might differ in its ability to accurately code locations over wide regions of azimuth (see [24]).
That the representation of space appears inhomogeneous in both primary and higher-order auditory cortical fields argues against the existence of a topographic “space map” within sensory cortex, pushing the emergence of any such map further into central structures than previously expected. The processing of interaural cues begins at the level of the superior olivary complex, but the integration of such cues into a complete topographic map of auditory space is presumed to begin with processing at the level of the IC or cortex. The suggestion that interaural cues are represented by a limited number of binaural channels in the IC [22] seems to imply that the space map must emerge at the level of auditory cortex or beyond, and the results of this study, along with others [15,16], suggest that a “limited channel” code is maintained throughout primary and non-primary fields of the auditory cortex as well. PAF, in particular, appears to sit at the top of the auditory cortical processing hierarchy [53] but is similar to primary auditory cortex (A1) in this regard.
We should note that spatial coding must subserve at least two distinct behavioral tasks, namely, the discrimination of sound-source locations and the localization of individual sources (e.g., orientation, or pointing). Much of the current discussion has focused on aspects of spatial coding relevant to discrimination, and on the observation that the RAF slopes of cortical neurons are better suited to the discrimination of nearby locations than are their broad RAF peaks. Nevertheless, we are interested in general mechanisms of spatial representation, and argue that cortical neurons' broad spatial tuning suggests that neither aspect of sound localization is likely mediated by RAF peaks in cat cortex. This stands in contrast to the neural mechanism for sound localization in the IC of the owl, where sharp circumscribed spatial receptive fields form a place code for localization [7]. Owls' behavioral discrimination of spatial locations, however, is sharper than these neural receptive fields, and appears—as in mammals—to be mediated by receptive-field slopes [38]. Thus, the owl makes use of place and rate codes for different behavioral tasks. The cat's auditory cortex, on the other hand, lacks the sharp spatial tuning necessary for map-based localization, so one coding strategy underlies both types of behaviors.
It seems clear that these different coding strategies in owls and cats necessitate different mechanisms for generating motor responses and orienting to sound sources. The owl's space map exhibits a straightforward correspondence between restricted neural activity and locations in space, which might be ideal for computing audiovisual correspondence but requires further translation into motor coordinate systems before action can take place. It is possible that the opponent-channel code is transformed into a similar auditory space map within multisensory or sensorimotor areas, that is, not within auditory cortex itself. Alternatively, opponent-channel population codes in the auditory domain might be directly transformed into population codes in the motor domain without an intervening map-like representation. In either case, we could argue that the fundamental mode of spatial coding within the auditory system per se is non-topographic. In fact, it might be that auditory spatial topography is an emergent property of widespread neural populations and is evident only in perception and behavior, not in the physiology of single neurons.
In considering the relative advantages of opponent-channel spatial coding within the cortex, one might wonder whether the formation of a spatiotopic map would be necessary or desirable. As described above, the opponent-channel mechanism could subserve behavior without an intervening map, and it provides an efficient means of combining information about space with information about other stimulus features. In this regard, at least, the opponent-channel mechanism solves—or simply avoids—the so-called binding problem [54] of how multiple stimulus features can be associated to create a unified neural representation. It does so without recourse to specialized mechanisms for binding [55] and without an explosion in the number of neurons necessary for a complete combinatorial code [56]. So long as feature maps (e.g., of frequency) contain neurons of each class (i.e., contralateral and ipsilateral), the spatial position of any particular feature can be reconstructed without the difficulty of binding activity in one feature map (frequency) with that in another (location).
Finally, the three cortical fields studied in this report exhibited similar evidence for an opponent-channel mechanism, despite previously reported differences in their spatial sensitivity [14]. Although such differences appear modest when assessed physiologically, studies indicate that some fields are more critical for localization behavior than others [30]. An intriguing question for future research involves cortical fields—such as the anterior auditory field—that are not necessary for accurate localization. Are spatial channels maintained in such fields, or are they combined to produce space-invariant representations of other stimulus features?
Materials and Methods
Data analyzed for this report were collected from extracellular recordings of 254, 411, and 298 units in areas A1, PAF, and DZ (respectively) of the cortex of chloralose-anesthetized cats [14,25]. Methods of animal preparation, stimulus delivery, unit recording, and basic analysis have been described previously [14], and were approved by the University of Michigan Committee on Use and Care of Animals. Stimuli were delivered from loudspeakers placed in the free field, and consisted of 80-ms broadband noise bursts presented at levels 20–40 dB above unit threshold. Stimulus locations spanned 360° of azimuth in 20° steps, and are identified by angular distance from the frontal midline (0°). Positive azimuths increase to to the right (ipsilateral to the recording site), whereas negative values correspond to contralateral locations on the cat's left side. Unit activity was recorded extracellularly from the right cerebral hemisphere using 16-channel electrode arrays (“Michigan probes”), and spikes were sorted off-line based on principal-components analysis of their waveshapes.
Locations of peak slope and centroids
Each unit's preferred location was characterized by the azimuth centroid of response (dark blue crosses in Figure 3; see [14]); this is the spike-count-weighted average of contiguous stimulus locations eliciting a normalized response at or above 75% of maximum spike count per stimulus presentation. We additionally determined the locations of peak slope for each unit by smoothing its RAF (circular convolution with a 40° boxcar) and calculating the first spatial derivative of the result. Maximum and minimum values of the derivative indicate two peak-slope azimuths for each unit (black circles and endpoints of red horizontal lines in Figure 3).
Spatial discrimination by neural response patterns
Analyses of pairwise spatial discrimination (see Figures 4 and 5) employed a statistical pattern-recognition algorithm [14] to estimate the relative likelihood of each stimulus location, given the temporal pattern of neural response to a single (unknown) stimulus. We computed, for each pair of locations θ1 and θ2 in the loudspeaker array, the index of pairwise discriminability d′ [39] based on the estimated relative likelihoods:
where z(P) represents scaling to the standard normal distribution and the probability P of responding “1” is given by the (estimated) relative likelihood l of location θ1 (versus θ2), conditional on the actual stimulus location θi.
The analysis produces a map of d′ between each pair of stimulus locations, plotted in coordinates of stimulus separation and overall location in Figure 4. The map was interpolated to find a contour of d′ = 1, which we define as threshold discrimination. The smallest stimulus separation along the threshold contour defines the MDA, and the overall location of that stimulus pair defines the unit's BA. Symbols in Figures 4 and 5 indicate values of MDA and BA for individual units.
Evaluation of a simple population code for space
To assess the level invariance of opponent-channel coding, we analyzed a simplified model of population spatial coding in the cortex. For each neural unit in a channel (e.g., a subpopulation of contralateral-preferring units), we accumulated a list of responses (spike counts normalized to the maximum response across all trials) on each trial with a given combination of stimulus azimuth and level. Azimuths were confined to the frontal hemifield (−80° to +80°) to avoid front–back confusions, which obscure but do not alter the appearance of bias in classification responses, and levels were either 20 or 40 dB above individual unit thresholds. We then computed population responses by randomly selecting one trial (with matching stimulus azimuth and level) from each unit and computing the mean of individual responses. We repeated the selection process 120 times for each combination of azimuth and level to simulate a set of 120 population “trials.” The mean of these population responses for each stimulus is plotted on the left in Figure 6. Separate “training” and “test” sets of population responses were computed by this method and used to assess the ability of subpopulations to classify stimulus locations. Individual population responses in the test set were classified to the azimuth with the most-similar mean population response across the training set. Confusion matrices in Figure 6 plot the proportion of test-set responses assigned to each stimulus azimuth. In some conditions, test and training sets were drawn from the same trials (matching level); in other conditions, training and test sets differed in stimulus level.
We tested classification based on responses of a contralateral subpopulation, an ipsilateral subpopulation, and on the difference between subpopulation responses. Contralateral and ipsilateral subpopulations were composed of all units with centroids falling farther than 30° into the corresponding hemifield in our sample of A1, PAF, and DZ units. Differences were computed from the two subpopulation responses on a trial-by-trial basis, and classification was tested in the same manner as for the population responses themselves.
Statistical procedures
Tests of statistical significance in this study were conducted using a 5,000-permutation bootstrap test (see [14] for details), reported to one significant digit. Standard error of the median, where reported, was obtained using a 2,000-permutation bootstrap, drawing N (the total number of data points) samples from the data with replacement on each permutation and recomputing the median. Distributions in Figures 3 and 5 were computed by kernel density estimation (convolution) with a 20° rectangular window to obtain a continuous function of units per 20° bin.
We thank Ewan Macpherson for assistance with data collection, Zekiye Onsan for technical and administrative support, and three anonymous reviewers for insightful comments. Funding was provided by the National Science Foundation (grant DBI-0107567) and the National Institute on Deafness and Other Communication Disorders (grants R01 DC00420, P30 DC05188, F32 DC006113, and T32 DC00011). Recording probes were provided by the University of Michigan Center for Neural Communication Technology (CNCT, NIBIB P41 EB002030).
Competing interests. The authors have declared that no competing interests exist.
Author contributions. GCS, IAH, and JCM conceived and designed the experiments. GCS and IAH performed the experiments and analyzed the data. GCS contributed reagents/materials/analysis tools. GCS wrote the paper.
¤Current address: Human Cognitive Neurophysiology Lab, Department of Veterans Affairs Research Service, VA Northern California Health Care System, Martinez, California, United States of America
Citation: Stecker GC, Harrington IA, Middlebrooks JC (2005) Location coding by opponent neural populations in the auditory cortex. PLoS Biol 3(3): e78.
Abbreviations
A1primary auditory field
BAbest azimuth
DZdorsal zone
ICinferior colliculus
MDAminimum discriminable angle
PAFposterior auditory field
RAFrate–azimuth function
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| 15736980 | PMC1044834 | CC BY | 2021-01-05 08:28:12 | no | PLoS Biol. 2005 Mar 22; 3(3):e78 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030078 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573698110.1371/journal.pbio.0030079Research ArticleNeuroscienceHomo (Human)Grasping the Intentions of Others with One's Own Mirror Neuron System Grasping Intentions with Mirror NeuronsIacoboni Marco [email protected]
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Molnar-Szakacs Istvan
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Gallese Vittorio
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Buccino Giovanni
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Mazziotta John C
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Rizzolatti Giacomo
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1Ahmanson-Lovelace Brain Mapping Center, Neuropsychiatric InstituteDavid Geffen School of Medicine, University of California, Los Angeles, CaliforniaUnited States of America2Department of Psychiatry and Biobehavioral Sciences, David Geffen School of MedicineUniversity of California, Los Angeles, CaliforniaUnited States of America3Brain Research Institute, David Geffen School of MedicineUniversity of California, Los Angeles, CaliforniaUnited States of America4Center for Culture, Brainand Development, University of California, Los Angeles, CaliforniaUnited States of America5Department of NeurosciencesUniversity of ParmaItaly6Department of Neurology, David Geffen School of MedicineUniversity of California, Los Angeles, CaliforniaUnited States of America7Department of Pharmacology, David Geffen School of MedicineUniversity of California, Los Angeles, CaliforniaUnited States of America8Department of Radiological Sciences, David Geffen School of MedicineUniversity of California, Los Angeles, CaliforniaUnited States of AmericaAshe James Academic EditorUniversity of MinnesotaUnited States of America3 2005 22 2 2005 22 2 2005 3 3 e7925 7 2004 27 12 2004 Copyright: © 2005 Iacoboni et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Predicting the Future: Mirror Neurons Reflect the Intentions of Others
Understanding the intentions of others while watching their actions is a fundamental building block of social behavior. The neural and functional mechanisms underlying this ability are still poorly understood. To investigate these mechanisms we used functional magnetic resonance imaging. Twenty-three subjects watched three kinds of stimuli: grasping hand actions without a context, context only (scenes containing objects), and grasping hand actions performed in two different contexts. In the latter condition the context suggested the intention associated with the grasping action (either drinking or cleaning). Actions embedded in contexts, compared with the other two conditions, yielded a significant signal increase in the posterior part of the inferior frontal gyrus and the adjacent sector of the ventral premotor cortex where hand actions are represented. Thus, premotor mirror neuron areas—areas active during the execution and the observation of an action—previously thought to be involved only in action recognition are actually also involved in understanding the intentions of others. To ascribe an intention is to infer a forthcoming new goal, and this is an operation that the motor system does automatically.
Functional magnetic resonance imaging is used to explore the responses of premotor cortical areas to observing the actions of others
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Introduction
The ability to understand the intentions associated with the actions of others is a fundamental component of social behavior, and its deficit is typically associated with socially isolating mental diseases such as autism [1,2]. The neural mechanisms underlying this ability are poorly understood. Recently, the discovery of a special class of neurons in the primate premotor cortex has provided some clues with respect to such mechanisms. Mirror neurons are premotor neurons that fire when the monkey performs object-directed actions such as grasping, tearing, manipulating, holding, but also when the animal observes somebody else, either a conspecific or a human experimenter, performing the same class of actions [3,4,5]. In fact, even the sound of an action in the dark activates these neurons [6,7]. In the macaque, two major areas containing mirror neurons have been identified so far, area F5 in the inferior frontal cortex and area PF/PFG in the inferior parietal cortex [8]. Inferior frontal and posterior parietal human areas with mirror properties have also been described with different techniques in several labs [9,10,11,12,13,14,15,16,17,18,19,20,21].
It was proposed early on that mirror neurons may provide a neural mechanism for understanding the intentions of other people [22]. The basic properties of mirror neurons, however, could be interpreted more parsimoniously, such as that mirror neurons provide a mechanism for recognizing the observed motor acts (e.g., grasping, holding, bringing to the mouth). The mirror neuron mechanism is, in fact, reminiscent of categorical perception [23,24]. For example, some mirror neurons do not discriminate between stimuli of the same category (i.e., the sight of different kinds of grasping actions can activate the same neuron), but discriminate well between actions belonging to different categories, even when the observed actions share several visual features. These properties seem to indicate an action recognition mechanism (“that's a grasp”) rather than an intention-coding mechanism.
Action recognition, however, has a special status with respect to recognition, for instance, of objects or sounds. Action implies a goal and an agent. Consequently, action recognition implies the recognition of a goal, and, from another perspective, the understanding of the agent's intentions. John sees Mary grasping an apple. By seeing her hand moving toward the apple, he recognizes what she is doing (“that's a grasp”), but also that she wants to grasp the apple, that is, her immediate, stimulus-linked “intention,” or goal.
More complex and interesting, however, is the problem of whether the mirror neuron system also plays a role in coding the global intention of the actor performing a given motor act. Mary is grasping an apple. Why is she grasping it? Does she want to eat it, or give it to her brother, or maybe throw it away? The aim of the present study is to investigate the neural basis of intention understanding in this sense and, more specifically, the role played by the human mirror neuron system in this type of intention understanding. The term “intention” will be always used in this specific sense, to indicate the “why” of an action.
An important clue for clarifying the intentions behind the actions of others is given by the context in which these actions are performed. The same action done in two different contexts acquires different meanings and may reflect two different intentions. Thus, what we aimed to investigate was whether the observation of the same grasping action, either embedded in contexts that cued the intention associated with the action or in the absence of a context cueing the observer, elicited the same or differential activity in mirror neuron areas for grasping in the human brain. If the mirror neuron system simply codes the type of observed action and its immediate goal, then the activity in mirror neuron areas should not be influenced by the presence or the absence of context. If, in contrast, the mirror neuron system codes the global intention associated with the observed action, then the presence of a context that cues the observer should modulate activity in mirror neuron areas. To test these competing hypotheses, we studied normal volunteers using functional magnetic resonance imaging, which allows in vivo monitoring of brain activity. We found that observing grasping actions embedded in contexts yielded greater activity in mirror neuron areas in the inferior frontal cortex than observing grasping actions in the absence of contexts or while observing contexts only. This suggests that the human mirror neuron system does not simply provide an action recognition mechanism, but also constitutes a neural system for coding the intentions of others.
Results
Subjects watched three different types of movie clips (see Figure 1): Context, Action, and Intention, interspersed with periods of blank screen (rest condition). The Context condition consisted of two scenes with three-dimensional objects (a teapot, a mug, cookies, a jar, etc). The objects were arranged either as just before having tea (the “drinking” context) or as just after having tea (the “cleaning” context). The Action condition consisted of a hand grasping a cup in the absence of a context on an objectless background. Two types of grasping actions were shown in the same block an equal number of times: a precision grip (the fingers grasping the cup handle) and a whole-hand prehension (the hand grasping the cup body). In the Intention condition, the grasping actions (also precision grip and whole-hand prehension shown for an equal number of times) were embedded in the two scenes used in the Context condition, the “drinking” context and the “cleaning” context (Figure 1). Here, the context cued the intention behind the action. The “drinking” context suggested that the hand was grasping the cup to drink. The “cleaning” context suggested that the hand was grasping the cup to clean up. Thus, the Intention condition contained information that allowed the understanding of intention, whereas the Action and Context conditions did not (i.e., the Action condition was ambiguous, and the Context condition did not contain any action).
Figure 1 Six Images Taken from the Context, Action, and Intention Clips
The images are organized in three columns and two rows. Each column corresponds to one of the experimental conditions. From left to right: Context, Action, and Intention. In the Context condition there were two types of clips, a “before tea” context (upper row) and an “after tea” context (lower row). In the Action condition two types of grips were displayed an equal number of times, a whole-hand prehension (upper row) and a precision grip (lower row). In the Intention condition there were two types of contexts surrounding a grasping action. The “before tea” context suggested the intention of drinking (upper row), and the “after tea” context suggested the intention of cleaning (lower row). Whole-hand prehension (displayed in the upper row of the Intention column) and precision grip (displayed in the lower row of the Intention column) were presented an equal number of times in the “drinking” Intention clip and the “cleaning” Intention clip.
Figure 2 displays the brain areas showing significant signal increase, indexing increased neural activity, for Action, Context, and Intention, compared to rest. As expected, given the complexity of the stimuli, large increases in neural activity were observed in occipital, posterior temporal, parietal, and frontal areas (especially robust in the premotor cortex) for observation of the Action and Intention conditions.
Figure 2 Areas of Increased Signal for the Three Experimental Conditions
Threshold of Z = 2.3 at voxel level and a cluster level corrected for the whole brain at p < 0.05.
Notably, the observation of the Intention and of the Action clips compared to rest yielded significant signal increase in the parieto-frontal cortical circuit for grasping. This circuit is known to be active during the observation, imitation, and execution of finger movements (“mirror neuron system”) [10,11,12,13,14,15,16,17,18,19,20,21,25,26,27]. The observation of the Context clip compared to rest yielded signal increases in largely similar cortical areas, with the notable exceptions of the superior temporal sulcus (STS) region and inferior parietal lobule. The STS region is known to respond to biological motion [28,29], and the absence of the grasping action in the Context condition explains the lack of increased signal in the STS. The lack of increased signal in the inferior parietal lobule is also explained by the absence of an action in the Context condition. Note that, in monkeys, inferior parietal area PF/PFG contains mirror neurons for grasping [8]. Thus, it is likely that the human homologue of PF/PFG is activated by the sight of the grasping action in the Action and Intention conditions, but not in the Context condition, where the action is not presented. The Context condition activates the inferior frontal areas for grasping, even though only graspable objects—but no grasping actions—are shown. In the monkey brain, ventral premotor area F5 contains, in addition to mirror neurons, a population of cells called canonical neurons [4]. These neurons fire during the execution of grasping actions as well as during the passive observation of graspable objects, but not during the observation of an action directed at the graspable object. Neurons with these properties mediate the visuo-motor transformations required by object-directed actions [30,31] and are likely activated by the sight of the Context clips.
The critical question for this study was whether there are significant differences between the Intention condition and the Action and Context conditions in areas known to have mirror properties in the human brain. Figure 3 displays these differences. The Intention condition yielded significant signal increases—compared to the Action condition—in visual areas and in the right inferior frontal cortex, in the dorsal part of the pars opercularis of the inferior frontal gyrus (Figure 3, upper row). The increased activity in visual areas is expected, given the presence of objects in the Intention condition, but not in the Action condition. The increased right inferior frontal activity is located in a frontal area known to have mirror neuron properties, thus suggesting that this cortical area does not simply provide an action recognition mechanism (“that's a grasp”) but rather it is critical for understanding the intentions behind others' actions.
Figure 3 Signal Increases for Intention minus Action and Intention minus Context
Threshold of Z = 2.3 at voxel level and a cluster level corrected for the whole brain at p < 0.05. The black arrow indicates the only area showing signal increase in both comparisons. The area is located in the dorsal sector of pars opercularis, where mirror activity has been repeatedly observed [10,11,12,13,14,15,16,17,18,19,20,27]. See Tables S1 and S2 for coordinates of local maxima.
To further test the functional properties of the signal increase in inferior frontal cortex, we looked at signal changes in the Intention condition minus the Context condition (Figure 3, lower row). These signal increases were most likely due to grasping neurons located in the inferior parietal lobule, to neurons responding to biological motion in the posterior part of the STS region, and to motion-responsive neurons present in the MT/V5 complex. Most importantly, signal increase was also found in right frontal areas, including the same voxels—as confirmed by masking procedures—in inferior frontal cortex previously seen activated in the comparison of the Intention condition versus Action condition. Thus, the differential activation in inferior frontal cortex observed in the Intention condition versus Action condition, cannot be simply due to the presence of objects in the Intention clips, given that the Context clips also contain objects.
From the contrasts Intention–Action and Intention–Context it is clear that the strongest activity in right inferior frontal cortex is present in the Intention condition. This could be due to two factors, not mutually exclusive: (1) a summation of canonical and mirror neurons activity, and (2) additional activation of mirror neurons of the inferior frontal cortex that code the action the agent will most likely make next. Because in the Intention clips the same action was shown in two contexts (“drinking” and “cleaning”), one can test the intention-coding hypothesis by analyzing the signal increase during observation of the Intention clips. A differential signal increase for the “drinking” Intention clip compared to the “cleaning” Intention clip would indicate neural activity specifically coding the intention of the agent. This logic would hold only if there is no differential signal increase in the “drinking” and “cleaning” Context conditions, when no action is displayed.
To test this hypothesis, we compared the signal change in the inferior frontal area in the two Intention clips and the two Context clips. The “drinking” Intention clip yielded a much stronger response than the “cleaning” Intention clip (p < 0.003; Figure 4). In contrast, no reliable difference was observed between the “drinking” Context clip and the “cleaning” Context clip (p > 0.19). These findings clearly show that coding intention activates a specific set of inferior frontal cortex neurons and that this activation cannot be attributed either to the grasping action (identical in both “drinking” and “cleaning” Intention clips) or to the surrounding objects, given that these objects produced identical signal increase in the “drinking” and “cleaning” Context clips, when no action was displayed.
Figure 4 Time Series of the Inferior Frontal Area Showing Increased Signal in the Comparisons Intention minus Action and Intention minus Context
The drinking Intention condition yielded a much stronger response than the cleaning Intention condition (p < 0.003), whereas no reliable difference was observed between the drinking and cleaning Context conditions (p > 0.19). The time series represents the average activity for all subjects in all voxels reaching statistical threshold in the right inferior frontal cortex.
Automaticity of the Human Mirror Neuron System
We also tested whether a top-down modulation of cognitive strategy may affect the neural systems critical to intention understanding. The 23 volunteers recruited for the experiment received two different kinds of instructions. Eleven participants were told to simply watch the movie clips (Implicit task). Twelve participants were told to attend to the displayed objects while watching the Context clips and to attend to the type of grip while watching the Action clips. These participants were also told to infer the intention of the grasping action according to the context in which the action occurred in the Intention clips (Explicit task). After the imaging experiment, participants were debriefed. All participants had clearly attended to the stimuli and could answer appropriately to questions regarding the movie clips. In particular, all participants associated the intention of drinking to the grasping action in the “during tea” Intention clip, and the intention of cleaning up to the grasping action in the “after tea” Intention clip, regardless of the type of instruction received.
The two groups of participants that received the two types of instructions had similar patterns of increased signal versus rest for Action, Context, and Intention (see Figures S1 and S2). The effect of task instructions is displayed in Figure 5. In all conditions, participants that received the Explicit instructions had signal increases in the left frontal lobe, and, in particular, in the mesial frontal and cingulate areas. This signal increase is likely due to the greater effort required by the Explicit instructions [32,33], rather than to understanding the intentions behind the observed actions. In fact, participants receiving either type of instructions understood the intentions associated with the grasping action equally well. Critically, the right inferior frontal cortex—the grasping mirror neuron area that showed increased signal for Intention compared to Action and Context—showed no differences between participants receiving Explicit instructions and those receiving Implicit instructions. This suggests that top-down influences are unlikely to modulate the activity of mirror neuron areas. This lack of top-down influences is a feature typical of automatic processing.
Figure 5 Significant Signal Changes in Subjects Receiving Explicit Instructions Compared to Subjects Receiving Implicit Instructions in the Three Tasks Versus Rest
Threshold of Z = 2.3 at voxel level and a cluster level corrected for the whole brain at p < 0.05. The two black arrows indicate two foci of activity in dorsal premotor cortex that are located deep in the sulci and thus not easily visible on the three-dimensional surface rendering. See Tables S3–S5 for coordinates of local maxima.
Discussion
The data of the present study suggest that the role of the mirror neuron system in coding actions is more complex than previously shown and extends from action recognition to the coding of intentions. Experiments in monkeys demonstrated that frontal and parietal mirror neurons code the “what” of the observed action (e.g., “the hand grasps the cup”) [4,6,8,34]. They did not address, however, the issue of whether these neurons, or a subset of them, also code the “why” of an action (e.g., “the hand grasps the cup in order to drink”).
The findings of the present study showing increased activity of the right inferior frontal cortex for the Intention condition strongly suggest that this mirror neuron area actively participates in understanding the intentions behind the observed actions. If this area were only involved in action understanding (the “what” of an action), a similar response should have been observed in the inferior frontal cortex while observing grasping actions, regardless of whether a context surrounding the observed grasping action was present or not.
Before accepting this conclusion, however, there are some points that must be clarified. First, one might argue that the signal increase observed in the inferior frontal cortex was simply due to detecting an action in any context. That is, it is the complexity of observing an action embedded in a scene, and not the coding of the intention behind actions, that determined the signal increase. A second issue, closely related to the first one, is the issue of canonical neurons. These neurons fire at the sight of graspable objects. Because they are also located in the inferior frontal cortex, one might be led to conclude that the increased activity we observed in the Intention clips was due to the presence of objects. Note, however, that canonical neurons do not fire at the sight of an action directed to a graspable object, even though the object is visible [35].
A strong argument against both these objections is that the activity in inferior frontal cortex is reliably different between “drinking” Intention clips and “cleaning” Intention clips, even though graspable objects were present in both conditions. In contrast, no differences in activity in the inferior frontal region were observed when “drinking” and “cleaning” clips of the Context condition were compared. Thus, the simple presence of an action embedded in a scene is not sufficient to explain the findings. Similarly, the sum of canonical and mirror neurons cannot account for the observed signal increase in the Intention condition, because this increase should be identical for both “drinking” and “cleaning.” Because “drinking” and “cleaning” contexts determined different activations in the Intention condition, it appears that there are sets of neurons in human inferior frontal cortex that specifically code the “why” of the action and respond differently to different intentions.
An important issue to consider in interpreting these data is the relationship between the present results and the activity of single neurons in the activated area. On the basis of our current knowledge of physiological properties of the inferior frontal cortex, the most parsimonious explanation of the findings reported here is that mirror neurons are the likely neurons driving the signal changes in our study. This proposal needs, however, a clarification.
The characteristic property of most mirror neurons is the congruence between their visual and motor properties. A neuron discharging during the execution of grasping also fires during observation of grasping done by another individual. This property cannot account for the present findings, specifically, the differences in response observed between the drinking and cleaning Intention clips. Our results suggest that a subset of mirror neurons in the inferior frontal cortex discharge in response to the motor acts that are most likely to follow the observed one. In other words, in the Intention condition, there is activation of classical mirror neurons, plus activation of another set of neurons coding other potential actions sequentially related to the observed one.
This interpretation of our findings implies that, in addition to the classically described mirror neurons that fire during the execution and observation of the same motor act (e.g., observed and executed grasping), there are neurons that are visually triggered by a given motor act (e.g., grasping observation), but discharge during the execution not of the same motor act, but of another act, functionally related to the observed act (e.g., bringing to the mouth). Neurons of this type have indeed been previously reported in F5 and referred to as “logically related” neurons [34]. In that previous study, however, the role of these “logically related” mirror neurons was never theoretically discussed and their functions remained unclear. The present findings not only allow one to attribute a functional role to these “logically related” mirror neurons, but also suggest that they may be part of a chain of neurons coding the intentions of other people's actions.
What are the possible factors that selectively trigger these “logically related” mirror neurons? The most straightforward interpretation of our results is that the selection of these neurons is due to the observation of an action, also coded by classical mirror neurons, in a context in which that action is typically followed by a subsequent specific motor act. In other words, observing an action carried out in a specific context recalls the chain of motor acts that typically is carried out in that context to actively achieve a goal.
Another possible explanation of how mirror neurons are triggered can be related not only to the context, but also to the way in which the action is performed. It is more common to grasp the handle of the cup with a precision grip while drinking, and to use a whole-hand prehension while cleaning up. Thus, the grasp itself may convey information about the intention behind the grasping action. Although this consideration is very plausible, in general, there are reasons to believe that it is unlikely that this mechanism played a role in our study. First, in all presented grasping actions, when the handle was on the same side of the approaching hand, the grasp was always a precision grip, but when the handle was on the opposite side of the approaching hand, the grasp was always a whole-hand prehension. Thus, the hand always adopted the type of grasp afforded by the orientation of the cup, minimizing the impression that the type of grip would reflect the intentional state of the agent. Second, this hypothesis cannot explain the empirical data. In fact, in both drinking and cleaning Intention clips there was always the same number of precision grips and whole-hand prehensions. However, as Figure 4 shows, the drinking Intention entailed a much larger signal increase than the cleaning Intention. Thus, the differential brain responses in the two Intention clips cannot be explained by a possible meaning conveyed by the grasp type, and cannot even be explained by a possible “compatibility effect” between grasp type and context type (for instance, a whole-hand prehension in a context suggesting cleaning).
The stronger activation of the inferior frontal cortex in the “drinking” as compared to the “cleaning” Intention condition is consistent with our interpretation that a specific chain of neurons coding a probable sequence of motor acts underlies the coding of intention. There is no doubt that, of these two actions, drinking is not only more common and practiced, but also belongs to a more basic motor repertoire, while cleaning is culturally acquired. It is not surprising, therefore, that the chain of neurons coding the intention of drinking is more easily recruited and more widely represented in the inferior frontal cortex than the chain of neurons coding the intention of cleaning.
The conventional view on intention understanding is that the description of an action and the interpretation of the reason why that action is executed rely on largely different mechanisms. In contrast, the present data show that the intentions behind the actions of others can be recognized by the motor system using a mirror mechanism. Mirror neurons are thought to recognize the actions of others, by matching the observed action onto its motor counterpart coded by the same neurons. The present findings strongly suggest that coding the intention associated with the actions of others is based on the activation of a neuronal chain formed by mirror neurons coding the observed motor act and by “logically related” mirror neurons coding the motor acts that are most likely to follow the observed one, in a given context. To ascribe an intention is to infer a forthcoming new goal, and this is an operation that the motor system does automatically.
Materials and Methods
Participants
Through newspaper advertisements we recruited 23 right-handed participants, with a mean age of 26.3 ± 6.3. Eleven participants (six females) received Implicit instructions while 12 participants (nine females) received Explicit instructions. Participants gave informed consent following the guidelines of the UCLA Institutional Review Board. Handedness was determined by a questionnaire adapted from the Edinburgh Handedness Inventory [36]. All participants were screened to rule out medication use, a history of neurological or psychiatric disorders, head trauma, substance abuse, and other serious medical conditions.
Image acquisition
Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar imaging (EPI) (Advanced NMR Systems, Woburn, Massachusetts, United States). A two-dimensional spin-echo image (TR = 4,000 ms, TE = 40 ms, 256 by 256, 4-mm thick, 1-mm spacing) was acquired in the sagittal plane to allow prescription of the slices to be obtained in the remaining sequences. This sequence also ensured the absence of structural abnormalities in the brain of the enrolled participants. For each participant, a high-resolution structural T2-weighted EPI volume (spin-echo, TR = 4,000 ms, TE 54 ms, 128 by 128, 26 slices, 4-mm thick, 1-mm spacing) was acquired coplanar with the functional scans. Four functional EPI scans (gradient-echo, TR = 4,000 ms, TE = 25 ms, flip angle = 90, 64 by 64, 26 slices, 4-mm thick, 1-mm spacing) were acquired, each for a duration of 4 min and 36 s. Each functional scan covered the whole brain and was composed of 69 brain volumes. The first three volumes were not included in the analyses owing to expected initial signal instability in the functional scans. The remaining 66 volumes corresponded to six 24-s rest periods (blank screen) and five 24-s task periods (video clips). In each scan there were two Context clips (during tea; after tea), one Action clip, and two Intention clips (drinking; cleaning) (see next section). The order of presentation of the clips was counterbalanced across scans and participants.
Stimuli and instructions
There were three different types of 24-s video clips (Context, Action, and Intention). There were two types of Context video clips. They both showed a scene with a series of three-dimensional objects (a teapot, a mug, cookies, a jar, etc). The objects were displayed either as just before having tea (“drinking” context) or as just after having had tea (“cleaning” context). In the Action video clip, a hand was shown grasping a cup in absence of a context on an objectless background. The grasping action was either a precision grip (the hand grasping the cup handle) or a whole-hand prehension (the hand grasping the cup body). The two grips were intermixed in the Action clip. There were two types of Intention video clips. They presented the grasping action in the two Context conditions, the “drinking” and the “cleaning” contexts. Precision grip and whole-hand prehension were intermixed in both “drinking” and “cleaning” Intention clips. A total of eight grasping actions were shown during each Action clip and each Intention clip.
The participants receiving Implicit instructions were simply instructed to watch the clips. The participants receiving Explicit instructions were told to pay attention to the various objects displayed in the Context clips, to pay attention to the type of grip in the Action clip, and to try to figure out the intention motivating the grasping action in the Context clips. All participants were debriefed after the imaging session.
Data processing
GE image files were converted in Analyze files and processed with FSL (http://www.fmrib.ox.ac.uk/fsl). Brain volumes within each fMRI run were motion corrected with Motion Correction using the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) Linear Image Registration Tool (MCFLIRT) [37]. Spatial smoothing was applied using a Gaussian-weighted kernel of 5 mm at full-width half-maximum, and data were high-pass filtered with sigma = 15.0 s and intensity normalized. Functional images were first registered to the co-planar high-resolution structural T2-weighted EPI volume after non-brain structures had been removed with FMRIB's Brain Extraction Tool (BET) from the co-planar high-resolution T2-weighted EPI volume [38]. The co-planar high-resolution structural T2-weighted EPI volume was subsequently registered to the Montreal Neurological Institute Talairach-compatible MR atlas averaging 152 normal subjects using FMRIB's Linear Image Registration Tool (FLIRT) [37].
Statistical analyses
Data analyses were performed by modeling the three conditions (Context, Action, and Intention) as stimulus functions, applying the general linear model as implemented in FSL (http://www.fmrib.ox.ac.uk/fsl). Statistical analyses were carried out at three levels: an individual-run level; a higher-order, multiple-runs individual-subject level; and a further higher-order intra- and inter-group comparison level. Time-series statistical analyses were carried out using FMRIB's Improved Linear model (FILM) with local autocorrelation correction [39]. Higher-level intra- and inter-group statistics were carried out using mixed effect (random effects) implemented in FLAME (FMRIB's Local Analysis of Mixed Effects) [40]. Z image statistics were performed with a threshold of Z = 2.3 at voxel level and a cluster level corrected for the whole brain at p < 0.05 [41,42]. The signal change displayed in Figure 4 was statistically analyzed with repeated measures ANOVA and subsequent planned contrasts.
Supporting Information
Figure S1 Significant Signal Changes in Subjects Receiving Implicit Instructions for Each Task Versus Rest
With a threshold of Z = 2.3 at voxel level and a cluster level corrected for the whole brain at p < 0.05.
(1 MB JPG).
Click here for additional data file.
Figure S2 Significant Signal Changes in Subjects Receiving Explicit Instructions for Each Task Versus Rest
With a threshold of Z = 2.3 at voxel level and a cluster level corrected for the whole brain at p < 0.05.
(1.1 MB JPG).
Click here for additional data file.
Table S1 Intention minus Action
Local maxima in Talairach coordinates. Only the six local maxima with highest Z score in each cluster are provided in the table. Cluster size is in voxels. When the cluster encompasses more than one anatomical location, the localization given corresponds to the local maxima with the highest Z score.
(82 KB PDF).
Click here for additional data file.
Table S2 Intention minus Context
Local maxima in Talairach coordinates. Only the six local maxima with highest Z score in each cluster are provided in the table. Cluster size is in voxels. When the cluster encompasses more than one anatomical location, the localization given corresponds to the local maxima with the highest Z score. LH, left hemisphere; RH, right hemisphere; TPO, temporo-parieto-occipital.
(82 KB PDF).
Click here for additional data file.
Table S3 Effect of Task Instructions: Explicit minus Implicit Instruction, Action Versus Rest
Local maxima in Talairach coordinates. Only the six local maxima with highest Z score in each cluster are provided in the table. Cluster size is in voxels. When the cluster encompasses more than one anatomical location, the localization given corresponds to the local maxima with the highest Z score. SMA, supplementary motor area.
(82 KB PDF).
Click here for additional data file.
Table S4 Effect of Task Instructions: Explicit minus Implicit Instruction, Context Versus Rest
Local maxima in Talairach coordinates. Only the six local maxima with highest Z score in each cluster are provided in the table. Cluster size is in voxels. When the cluster encompasses more than one anatomical location, the localization given corresponds to the local maxima with the highest Z score.
(82 KB PDF).
Click here for additional data file.
Table S5 Effect of Task Instructions: Explicit minus Implicit Instruction, Intention Versus Rest
Local maxima in Talairach coordinates. Only the six local maxima with highest Z score in each cluster are provided in the table. Cluster size is in voxels. When the cluster encompasses more than one anatomical location, the localization given corresponds to the local maxima with the highest Z score. ACC, anterior cingulate cortex; VLPFC, ventrolateral prefrontal cortex.
(82 KB PDF).
Click here for additional data file.
We thank Stephen Wilson for comments. Supported in part by Brain Mapping Medical Research Organization, Brain Mapping Support Foundation, Pierson-Lovelace Foundation, The Ahmanson Foundation, Tamkin Foundation, Jennifer Jones-Simon Foundation, Capital Group Companies Charitable Foundation, Robson Family, William M. and Linda R. Dietel Philanthropic Fund at the Northern Piedmont Community Foundation, Northstar Fund, and grants from National Center for Research Resources (RR12169, RR13642 and RR08655), National Science Foundation (REC-0107077), and National Institute of Mental Health (MH63680). GR, GB, and VG were supported by European Union Contract QLG3-CT-2002–00746 (Mirror), by grants from the Origin of Man, Language and Languages project of the European Science Foundation, Fondo per gli Investimenti della Ricerca di Base (grant RBNE018ET9), and Ministero dell'Istruzione, dell'Università e della Ricerca.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. MI, VG, and GR conceived and designed the experiments. MI and IMS performed the experiments and analyzed the data. VG, GB, JCM, and GR contributed reagents/materials/analysis tools. MI and GR wrote the paper.
Citation: Iacoboni M, Molnar-Szakacs I, Gallese V, Buccino G, Mazziotta JC, et al. (2005) Grasping the intentions of others with one's own mirror neuron system. PLoS Biol 3(3): e79.
Abbreviations
EPIecho-planar imaging
FMRIBOxford Centre for Functional Magnetic Resonance Imaging of the Brain
STSsuperior temporal sulcus
==== Refs
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| 15736981 | PMC1044835 | CC BY | 2021-01-05 08:28:12 | no | PLoS Biol. 2005 Mar 22; 3(3):e79 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030079 | oa_comm |
==== Front
PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573698210.1371/journal.pbio.0030099SynopsisEcologyEvolutionGenetics/Genomics/Gene TherapyPlantsAnimalsSelection on Sex Cells Favors a Recombination Gender
Gap Synopsis3 2005 22 2 2005 22 2 2005 3 3 e99Copyright: © 2005 Public Library of
Science.2005This is an open-access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium, provided the original author
and source are properly credited.
Recombination Difference between Sexes: A Role for Haploid
Selection
==== Body
Males and females of the same species can be strikingly different. Peacocks strut around
with flashy feathers to attract mates, while peahens blend into their surroundings with
more subdued colors. But differences are not always as obvious or easily explainable as
in this classic example. Even the amount of genetic reshuffling that goes on during egg
and sperm production differs between males and females in most species. An evolutionary
reason for this has eluded researchers since the phenomenon was originally discovered in
fruitflies, Chinese silk worms, and amphipods almost 100 years ago.
Genetic diversity among organisms is promoted when genetic information is rearranged
during meiosis, the cell division process that yields sperm and eggs (generically called
gametes). During this genetic reshuffling, chromosome pairs overlap, forming structures
called chiasmata (“crosses” in Greek), and physically recombine. This
process does not just create diversity, it is also an example of
diversity—recombination rates vary across chromosomes, sexes, and species.
Male and female recombination rates differ
An early 20th century hypothesis to explain the sex difference in recombination proposed
that recombination is restrained within a pair of unlike sex chromosomes (X and Y, for
example) and that the suppression spills over to the rest of the chromosomes. Under this
idea, the sex with dissimilar sex chromosomes (XY instead of XX, for example) should be
the one with the least amount of recombination in all chromosomes. But that is not
always the case. Some hermaphroditic species of flatworms, for example, lack sex
chromosomes altogether but still display marked differences in male and female
recombination rates. In one salamander genus, more reshuffling unexpectedly occurs in
the sex with two different sex chromosomes.
In a new study analyzing an updated dataset of 107 plants and animals, Thomas Lenormand
and Julien Dutheil bolster the argument against the recombination suppression hypothesis
by showing that in species with sex chromosomes, the sex with two dissimilar sex
chromosomes doesn't necessarily have a reduced recombination rate. Additionally, they
found that, as a trait, the sex difference in recombination rate is not a lot more
similar between two species in the same genus than between two species in different
genera, suggesting that the difference evolves quickly.
An alternative hypothesis suggests that sexual selection might play a role in
recombination differences. Reproductive success among males is often highly influenced
by selection, so mixing up successful genetic combinations in males could be
evolutionarily counterproductive. But in past studies, sexual selection was not related
to variation in recombination rates.
Putting a new twist on this hypothesis, Lenormand and Dutheil realized that selection was
not necessarily limited to the adult stage and that differences in selection among eggs
or sperm might help account for recombination differences between the sexes. The authors
reasoned that more opportunity for selection on sperm than egg should correspond to less
recombination during sperm than egg production (and vice versa), consistent with the
idea that genetic combinations surviving selection should remain more intact in the sex
experiencing the strongest selection at the gametic stage.
Though male gametes might be expected to be under stronger selection in many species, in
true pines it seems to be the female gametes. The ovules compete with each other for
resources over an entire year before being fertilized, and, indeed, from the dataset
analysis, ovule production involves low recombination rates compared with male pollen in
this group. In males, the opportunity for pollen competition was indirectly estimated
using self-fertilization rates. The authors assumed that pollen grains competing for
ovules of a self-fertilizing plant would be genetically similar and therefore experience
less selection. Again, in the analysis, low selection correlated with less recombination
in female gamete production, as predicted.
Is selection among eggs and sperm the evolutionary force generating sex-based variation
in genetic shuffling? By demonstrating that differences may be influenced by gamete
selection in plants, this work has added clarity to otherwise contradictory
observations.
| 15736982 | PMC1044836 | CC BY | 2021-01-05 08:36:13 | no | PLoS Biol. 2005 Mar 22; 3(3):e99 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030099 | oa_comm |
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