abstract: A report from the Keystone Symposium on Molecular and Cellular Biology, 'Deregulation of transcription in cancer: controlling cell fate decisions', Killarney, Ireland, 21-26 July 2009.
author: Aisling M Redmond; Jason S Carroll
date: 2009
institute: 1Endocrine Oncology Research Group, Department of Surgery, Royal College of Surgeons in Ireland, St Stephens Green, Dublin 2, Ireland; 2Cancer Research UK, Cambridge Research Institute, Robinson Way, Cambridge CB2 0RE, UK
title: Defining and targeting transcription factors in cancer

This Keystone meeting focused on mechanisms of transcriptional regulation, the effects of transcriptional deregulation and the consequences on cancer, and the possibilities for inhibiting transcription factors as potential therapeutic targets. Greg Verdine (Harvard University, Cambridge, USA) suggested that, until now, only 20% of the genome was targetable with drugs (druggable). Transcription factors have traditionally been considered too difficult to target, but with increased understanding of transcription factor biology and technological advances, targeting them is becoming a realistic option.

The role of polycomb complexes in regulating transcriptional activity was an important theme to the meeting. Kristian Helin (University of Copenhagen, Denmark) summarized the role of polycomb repressor complexes (involving EZH2) in controlling senescence, in particular their role in the regulation of the p16^INK4A^ and p14^ARF^ tumor suppressor proteins. Competitive binding between polycomb repressor complexes and the histone demethylase JMJD3 determines transcriptional activity, suggesting that JMJD3 can act as a tumor suppressor. Maarten Van Lohuizen (Netherlands Cancer Institute, Amsterdam, The Netherlands) presented work on BMI1, a member of the polycomb repressor complex PRC1, which is crucial in the maintenance of adult stem cells. Loss of BMI1 in mouse models causes a dramatic reduction in proliferation in the mammary gland. Levels of BMI1 and EZH2 and interactions between them influence tumor progression. Qiang Yu (Genome Institute of Singapore, Singapore) focused on the possibility of targeting EZH2 in cancer. Their compound, 3-deazaneplanocin A (DZNep), an *S*-adenosylhomocysteine hydrolase inhibitor, depletes EZH2 in breast cancer cells. In colorectal cancer, DZNep reactivates the tumor suppressor microRNA *miR449*, leading to cell cycle arrest. He suggested that DZNep, in combination with 5-azacytidine, may be an effective cancer treatment.

Several talks used contemporary genomics technologies to define transcription factor binding sites, regulatory regions and changes in chromatin structure, all of which can contribute to altered cellular growth. Manel Esteller (Catalan Institute of Oncology, Barcelona, Spain) has assessed global DNA methylation in normal and cancer cells and found significant changes in DNA methylation at promoters. He discussed the DNA epigenome project, an ambitious project that will study the methylation state of 10,000 promoters in tumors from more than 1,000 patients. Bing Ren (University of California, San Diego, USA) discussed global DNA methylation and histone modification data generated by chromatin immunoprecipitation microarrays (ChIP-chip) or ChIP-sequencing (ChIP-seq). In human embryonic stem cells that were induced to differentiate, he showed that 30,000 putative enhancers exist in pre- and post-differentiation states, but only 8,000 of these were common between the two states. Within the regions shown to have altered chromatin structure following differentiation, he could show enrichments of motifs for various transcription factors. Both positive and inverse correlations were found when the histone maps were combined with DNA methylation data. Importantly, he could show that in specific cell types, non-CG methylation could occur and this was usually depleted from promoters of actively transcribed genes.

Two talks from members of the Genome Institute of Singapore highlighted new data on estrogen receptor (ER) transcription and chromatin dynamics. Yijun Ruan presented data on a novel technique called 'whole genome chromatin interaction analysis using paired-end ditagging' (ChIA-PET), which is a global method for identifying chromatin loops that form during transcription. By applying this to estrogen-induced gene transcription in breast cancer cells, his group found many hundreds of estrogen-induced intrachromosomal chromatin loops that form over distances as great at 1 Mb, representing *cis*-regulatory components that physically interact. As a follow-up to this presentation, Ed Liu presented data from recent genome-wide mapping of ER binding sites. He showed that only small subsets of predicted motifs are actual binding sites *in vivo* and that the ER binding sites occur in gene-rich areas. Within the list of ER binding sites, the strongest (that is, those most enriched by ChIP-seq) were more likely to contain responsive motifs and to be adjacent to genes that are differentially regulated by estrogen. This suggests that there is a hierarchy of ER binding sites, in which the strongest sites are more likely to be functional, possibly as a result of superior ER-DNA interactions.

Maintaining the theme of nuclear receptor transcriptional activity, Ralf Kittler (University of Chicago, USA) has engineered bacterial artificial chromosomes of more than 20 different nuclear receptor (NR) genes to include an enhanced green fluorescent protein tag, and these have subsequently been used for genome-wide mapping using ChIP-chip. By correlating the binding profiles of all NRs, they found profile similarities and common binding sites between ER and retinoic acid receptor α; they hypothesized that this was an antagonistic interaction.

Arul Chinnaiyan (University of Michigan, Ann Arbor, USA) presented data characterizing gene fusions in prostate cancer, including his group's original discovery of fusions between the *TMPRSS2* gene (which encodes a transmembrane serine protease) and the *ERG* ETS-family oncogene in prostate cancers. He showed the results from large-scale genomic screens, which have resulted in more than 100 validated gene fusions. Interestingly, all of the fusions contain an ETS factor as the transcriptionally active partner and the other half of the fusion is usually an androgen-regulated gene target. He showed that fusions of various kinds are found in more than half of all prostate cancers and that their specificity to cancer and not normal tissue has led to a urine-based test for detecting fusion genes that may function as a prognostic indicator of prostate cancer. One of the major issues that researchers in the field face is defining which fusions are driving factors and which ones are passenger events.

One of the key themes of the meeting was the ability to target transcription factors using drugs. Traditionally, transcription factors were generally considered too difficult to target, and kinase pathways or cell surface proteins have instead been popular therapeutic targets. However, transcription factors are the downstream effectors of many pathways and this, coupled with technological advances, has made them attractive and realistic drug targets. Greg Verdine presented data on 'stapled peptides'. Normally, peptides used to block or inhibit transcription factors are easily degraded or have poor solubility. Verdine's approach provides a stabilizing backbone (or 'staple') to the short peptides or proteins, thereby generating proteins that are stable and maintain correct conformation. His group has successfully developed stapled peptides that target Bcl-2, Bax and BID (proapoptotic proteins containing only the BH-3 motif). One of their inhibitors is about to enter clinical trials, and the approach provides a realistic option for targeting a specific transcription factor in cancer.

John Rossi (Beckman Research Institute of the City of Hope, Duarte, USA) discussed his group's RNA interference method as a potential therapeutic approach. He suggested that the delivery of small interfering RNAs (siRNAs) to target cells has improved considerably, but one of the major problems is producing sufficient quantities of any specific siRNA. He also showed that they can generate siRNAs that target two different transcripts simultaneously, allowing increased effectiveness from a single siRNA. Lyubomir Vassilev (Roche Pharmaceuticals, Nutley, USA) showed data on Nutlin, an inhibitor of the interaction between the p53 tumor suppressor and its regulator Mdm2. This inhibitor binds selectively to the pocket of Mdm2, resulting in increased p53 levels. Nutlin was an effective inhibitor in cell lines and in mice with various types of cancer and was bioavailable when administered orally. These data confirm that protein-protein interactions between transcription factors and regulatory proteins can be blocked successfully with chemical inhibitors. Sandra Dunn (University of British Columbia, Vancouver, Canada) provided compelling evidence for an important role for the transcription factor YB-1, which is expressed in 40% of breast cancers but not normal breast tissue. YB-1 was shown to correlate with poor prognosis in patients and in mouse models, and YB-1 transgenic mice readily generated tumors. YB-1 activity is dependent on phosphorylation, and Dunn's group has shown that the ribosomal S6 kinase (Rsk) complex is involved. They are currently testing the effectiveness of peptides that target YB-1.

René Bernards (Netherlands Cancer Institute) stressed the need to be able to stratify breast cancer patients, firstly in order to restrict chemotherapeutic drug administration only to those who will benefit from it, and secondly to allow informed decisions regarding the choice of drug for each patient. As stated by Joe Nevins (Duke University Medical Center, Durham, USA), by finding and focusing on the specific subset of patients that will probably benefit from an individual therapy, a potential failed drug can become a blockbuster drug. Nevins presented gene expression profiles resulting from exposure to the major cancer drugs; from these, his group could generate signatures that represent a likely response or lack of response to individual therapies. They could use this approach to predict patients who would benefit from particular drug regimes. They are currently using these genomic screening tools in trials. Similarly, Bernards discussed gene expression signatures that predict outcome in women with breast cancer. He also showed data from a 159 gene signature of activated phosphatidylinositol 3-kinase, which was used to predict outcome in colon cancers. Bernards also showed data from a short hairpin RNA (shRNA) library screen to find genes involved in resistance to trastuzumab (known as Herceptin) in a BT474 breast cancer cell line model. By simultaneously screening 24,000 human shRNAs against 8,000 genes, his group could identify genes required for trastuzumab effectiveness. They identified and validated the phosphatase pTEN as an essential component in the trastuzumab response.

Clearly, the single gene or single protein approach is rapidly becoming redundant. The use of screens allows researchers to simultaneously assess all genes, identify thousands of regulatory sites, test a multitude of compounds and combine these different screens in multifactorial ways. By distilling this information we can progress more rapidly towards personalized treatments.


date: 2010-04-16
title: Findings of research misconduct.

# Findings of Research Misconduct

Notice Number: NOT-OD-10-085

Key Dates**
** Release Date: April 16, 2010

Issued by**
** Department of Health and Human Services

Notice is hereby given that the Office of Research Integrity (ORI) and the Assistant Secretary for Health have taken final action in the following case:

Emily M. Horvath, Indiana University: Based on the Respondent's own admissions in sworn testimony and as set forth below, Indiana University (IU) and the U.S. Public Health Service (PHS) found that Ms. Emily M. Horvath, former graduate student, IU, engaged in research misconduct in research supported by National Center for Complementary and Alternative Medicine (NCCAM), National Institutes of Health (NIH), grant R01 AT001846 and Predoctoral Fellowship Award F31 AT003977-01, and National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIH, grant R01 DK082773-01.

Specifically, the Respondent admitted to falsifying the original research data when entering values into computer programs for statistical analysis with the goal of reducing the magnitude of errors within groups, thereby gaining greater statistical power. The Respondent, IU, and ORI agree that the figures identified below in specific grant applications and published papers are false and that these falsifications rise to the level of research misconduct:

Respondent admitted to falsifying Figures 6B, 18, 22, 23B, and 24 in NCCAM, NIH, grant application R01 AT001846-06, \`\`Chromium Enhanced Insulin & GLUT4 Action via Lipid Rafts,' Jeffery S. Elmendorf, P.I. (07/01/04-05/31/20) (application was withdrawn in May 2009).

Respondent admitted to falsifying Figures 6B, 8, 9D, 16D, and 21 in NIDDK, NIH, grant application R01 DK082773-01, \`\`Mechanisms of Membrane-Based Insulin Resistance & Therapeutic Reversal Strategies,' Jeffrey S. Elmendork, P.I. (3/15/09-01/31/13).

Respondent admitted to falsifying Figures 2C, 5, 6D, and 11 in the publication: Horvath, E.M., Tacket, L., McCarthy, A.M., Raman, P., Brozinick, J.T., & Elmendorf, J.S. \`\`Antidiabetogenic Effects of Chromium Mitigate Hyperinsulinemia-induced Cellular Insulin Resistance via Correction of Plasma Membrane Cholesterol Imbalance.' Molecular Endocrinology 22:937-950, 2008.

Respondent admitted to falsifying Figure 2C in the publication: Bhonagiri, P., Patter, G.R., Horvath, E.M., Habegger, K.M., McCarthy, A.M., Elmendorf, J.S. \`\`Hexosamine biosysthesis pathway flux contributes to insulin resistance via altering membrane PIP 2 and cortical F-actin.' Endocrinology 150(4):1636-1645, 2009.

Respondent also admitted to falsifying Figures 2C, 5, 6D, 11, 13C, 15A, 16A, 17A, 18, 19C, and 20A, which are included in her thesis, \`\`Cholesterol-dependent mechanism(s) of insulin-sensitizing therapeutics.' The Ph.D. was awarded to the Respondent on December 31, 2008. Respondent was supported by a Predoctoral Fellowship Award F31 AT003977 from 09/30/2006 to 09/29/2009.

Ms. Horvath has entered into a Voluntary Settlement Agreement in which she has voluntarily agreed, for a period of three (3) years, beginning on March 22, 2010:

\(1\) To exclude herself from serving in any advisory capacity to PHS, including but not limited to service on any PHS advisory committee, board, and/or peer review committee, or as a consultant;

\(2\) That any institution that submits an application for PHS support for a research project on which the Respondent's participation is proposed or that uses her in any capacity on PHS-supported research, or that submits a report of PHS-funded research in which she is involved, must concurrently submit a plan for supervision of her duties to the funding agency for approval; the supervisory plan must be designed to ensure the scientific integrity of her research contribution; respondent agreed that she will not participate in any PHS-supported research until such a supervisory plan is submitted to ORI;

\(3\) That any institution employing her submits, in conjunction with each application for PHS funds or report, manuscript, or abstract of PHS-funded research in which the Respondent is involved, a certification that the data provided by the Respondent are based on actual experiments or are otherwise legitimately derived and that the data, procedures, analyses, and methodology are accurately reported in the application, report, manuscript, or abstract; the Respondent must ensure that the institution sends a copy of the certification to ORI; and

\(4\) That she will write letters, approved by ORI, to relevant journal editors of the published papers cited above to state what she falsified/fabricated and to provide corrections if she has not already done so. These letters should state that her falsifications/fabrications were the underlying reason for the retraction/corrections.

Inquiries

FOR FURTHER INFORMATION CONTACT:**

** Director**
** Division of Investigative Oversight**
** Office of Research Integrity**
** 1101 Wootton Parkway, Suite 750**
** Rockville, MD 20852**
** (240) 453-8800


abstract: # Background
  .
  Many clinicians depend solely on journal abstracts to guide clinical decisions.
  .
  # Objectives
  .
  This study aims to determine if there are differences in the accuracy of responses to simulated cases between resident physicians provided with an abstract only and those with full-text articles. It also attempts to describe their information-seeking behaviour.
  .
  # Methods
  .
  Seventy-seven resident physicians from four specialty departments of a tertiary care hospital completed a paper-based questionnaire with clinical simulation cases, then randomly assigned to two intervention groups—access to abstracts-only and access to both abstracts and full-text. While having access to medical literature, they completed an online version of the same questionnaire.
  .
  # Findings
  .
  The average improvement across departments was not significantly different between the abstracts-only group and the full-text group (p=0.44), but when accounting for an interaction between intervention and department, the effect was significant (p=0.049) with improvement greater with full-text in the surgery department. Overall, the accuracy of responses was greater after the provision of either abstracts-only or full-text (p\<0.0001). Although some residents indicated that 'accumulated knowledge' was sufficient to respond to the patient management questions, in most instances (83% of cases) they still sought medical literature.
  .
  # Conclusions
  .
  Our findings support studies that doctors will use evidence when convenient and current evidence improved clinical decisions. The accuracy of decisions improved after the provision of evidence. Clinical decisions guided by full-text articles were more accurate than those guided by abstracts alone, but the results seem to be driven by a significant difference in one department.
author: Alvin Marcelo; Alex Gavino; Iris Thiele Isip-Tan; Leilanie Apostol-Nicodemus; Faith Joan Mesa-Gaerlan; Paul Nimrod Firaza; John Francis Faustorilla; Fiona M Callaghan; Paul FonteloCorrespondence to: **Dr Alex Gavino**, Office of High Performance Computing and Communications, Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA; <email@example.com>
date: 2013-04
institute: 1National Telehealth Center, University of the Philippines, Manila, Philippines; 2Department of Surgery, Philippine General Hospital, Manila, Philippines; 3National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA; 4Department of Medicine, Philippine General Hospital, Manila, Philippines; 5Department of Family and Community Medicine, Philippine General Hospital, Manila, Philippines; 6Department of Emergency Medicine, Philippine General Hospital, Manila, Philippines
references:
title: A comparison of the accuracy of clinical decisions based on full-text articles and on journal abstracts alone: a study among residents in a tertiary care hospital

# Introduction

## Background

Broad estimates show that a specialist would need almost two million pieces of data to practice good medicine.1 To keep updated and apply evidence-based medicine (EBM) in practice, a physician must critically appraise full-text articles to guide their clinical decision making.2 However, owing to limited access to full-text articles, inadequate critical appraisal skills or lack of time to read the entire article, many clinicians depend solely on journal abstracts to answer clinical questions.3–8 Journal abstracts may have become the de facto resource for health professionals wanting to practice EBM because they are easy to read and are easily accessible anywhere.2 5 7 9

Although abstracts are commonly utilised for clinical decisions, caution should be made in using them because they may not completely reflect the entire article.7 Studies by Pitkin *et al*10 11 and Peacock *et al*12 identified abstracts that contained data which were different or missing in the full-text. High-impact factor journals had abstracts that failed to include harm despite being mentioned in the main article.13 A study by Berwanger *et al*8 found that the abstracts of randomised controlled trials from major journals were reported with suboptimal quality. Moreover, abstracts are also subject to authors' biases which may mislead the readers.14

Efforts have been made to improve the quality and accuracy of journal abstracts since they are often the most commonly read part of an article—if not the only part read.10 11 15 In 1987, the Ad Hoc Working Group for Critical Appraisal of the Medical Literature introduced a seven-heading format (Objectives, Design, Setting, Patients, Interventions, Measurements and Conclusion) for structured abstracts.16 Variations in structured abstracts include the eight-heading format proposed by Haynes *et al*,17 IMRAD18–20 (Introduction, Methods, Results and Discussion), and more recently, *BMJ*'s pico format21 (Patient, Intervention, Comparison and Outcome). Structured abstracts tend to be longer than traditional ones but they also tend to have better content, readability, recall and retrieval.14 18 22–25 Aside from structuring, 'quality criteria' and guidelines have been developed to assist authors in preparing abstracts.26 27

Most of the research on journal abstracts focus on their quality compared to the full-text,10 11 13 22 23 or based on their structure.14 20 24 Given the tendency of physicians to use abstracts for evidence, there is a need to evaluate their reliability in clinical decision making. A study by Barry *et al*5 looked at the effect of abstract format on physicians' management decisions. However, we are unable to find studies that compare clinical decisions between those with access to abstract-only or full-text.

The primary objective of this study was to determine whether there is a significant difference in the accuracy of the clinical decisions made on simulated cases by residents with access to full-text articles and those with access to abstract-only. The specific objectives were: (1) to compare the effect of access to abstracts-only or full-text articles on the clinical decision-making of residents; (2) to determine whether providing either the abstract or full-text article increased the accuracy of clinical decisions and (3) to characterise the information-seeking behaviour and use of information resources by residents of four departments in a tertiary care hospital.

# Methods

## Ethics review

The research protocol was submitted for technical review to the Research Grants Administration Office of the University of the Philippines Manila and for ethical evaluation to the Institutional Review Board, both of which approved the study.

## Prestudy clinical case development

A physician consultant from each of four clinical departments (Surgery, Internal Medicine, Emergency Medicine and Family and Community Medicine) prepared five simulated clinical cases of varying complexity and the corresponding clinical questions to assess the residents' management decisions. They searched PubMed for at least three recent (from 2007 onwards) journal articles that were deemed relevant for each case. 'Gold standard' answers to the clinical questions were based on the journal articles and other relevant information (applicability and appropriateness to local conditions, available resources and practice environments). A paper-based questionnaire was used for the preintervention assessment while an online version was used during the intervention phase to allow access to journal abstracts or full-text articles.

## Study participants and setting

Seventy-seven resident physicians from the four clinical departments (above) at the Philippine General Hospital participated in the study. The Philippine General Hospital is a 1500-bed tertiary care, state-owned, referral center and teaching hospital of the University of the Philippines College of Medicine, College of Nursing, College of Dentistry and allied colleges. It is the largest government hospital with a yearly patient load of 600 000, mostly indigent patients. Fourteen clinical departments offer residency and fellowship training.

## Study design

During the prestudy briefing, the residents were informed that they were to answer questions related to the case simulations and that they could access reference articles if needed during the online phase of the study. Written consent was obtained and paper-based case simulations were given to each resident to replicate the hospital scenario of paper patient records. After reading the case simulations, they were asked to respond to five clinical questions and indicate whether they considered a literature search was needed to answer the questions or accumulated knowledge28 was adequate. Accumulated knowledge was defined in this study as the residents' personal knowledge base accumulated through years of formal education, training, research of the medical literature and clinical experience. Immediately after the preintervention phase, the residents were randomly assigned to one of two groups—access to 'full-text' or 'abstracts-only,' stratified by department. The same clinical cases and questions in the preintervention phase were presented to the residents using the online version of the questionnaire to simulate real-time access to medical literature. A 20-min time limit was allotted for each question both for the paper-based and online questionnaire. The journal material provided, whether abstracts-only or full-text, was dependent on their assigned group. If the resident assigned to the abstracts-only group clicked on the link to the full-text, a prompt saying, 'Sorry, full-text is unavailable' appeared. Although access to either journal abstracts or full-text articles on the online version was available to all residents, they had the option of not using any resource at all. The residents' actions regarding the use or non-use of medical literature were recorded. Mouse clicks related to the residents' request for the articles' abstracts or full-text were logged in the server. The accuracy of response was a measure of correctness of residents' answer when compared with the answers ('gold standard') provided by the consultants. The same consultants who prepared the clinical cases and questions evaluated the accuracy of the residents' answers. A correct response was scored as '1' and an incorrect response scored '0'. Incomplete responses were rated as inaccurate and scored '0'. Resident responses were anonymised in both the paper and online versions.

## Data analysis

In order to account for the repeated measures nature of the data (physicians answered multiple questions), we fit mixed effects logistic regression models with department type, intervention, and the interaction between department and intervention as independent variables, and accuracy of the response as the dependent variable. Unless otherwise stated, all results were based on this model. Resident year level was also considered as a predictor in the model but was not found to be significant and was dropped. We also fit a model with an interaction between intervention and department. For univariate analysis, we used the nonparametric Wilcoxon two-sample tests and Fisher exact tests, as appropriate.29 All analyses were performed using R Statistical Software.30

# Results

## Participant profile

Seventy-seven residents from the departments of Surgery (n=20), Internal Medicine (n=20), Emergency Medicine (n=20) and Family and Community Medicine (n=17) participated in this study. Table 1 shows the description of the study participants by department.

Characteristics of participating resident trainees by department

| Characteristics | Surgery, n=20 | Internal medicine, n=20 | Emergency medicine, n=20 | Family and community medicine, n=17 |
|:---|----|----|----|----|
| Mean age, years (SD\*) | 29.2 (2.7) | 28.1 (1.6) | 30.8 (3.3) | 30.5 (2.4) |
| Gender, n (%) |  |  |  |  |
|  Female | 5 (25) | 3 (15) | 6 (30) | 13 (76) |
|  Male | 15 (75) | 17 (85) | 14 (70) | 4 (24) |
| Years in residency training, n (%) |  |  |  |  |
|  1 | 6 (30) | 7 (35) | 7 (35) | 11 (65) |
|  2 | 2 (10) | 6 (30) | 7 (35) | 1 (6) |
|  \>3 | 12 (60) | 7 (35) | 6 (30) | 5 (29) |

\*Standard deviation.

## Comparing the effect of abstract-only and full-text access on the accuracy of clinical decision making

The first objective was to answer the question: Is there a significant difference in the accuracy of responses of residents in the abstract-only group and full-text group? Overall, there was no significant difference between the interventions (p* *= 0.44). Post-hoc power of the experiment to detect an overall difference between the interventions was low and varied from approximately 44–58%,31 depending on the level of correlation between the answers within each department. In a model fit to include an interaction between intervention and department, the interaction was significant suggesting that intervention effects differed by department (p* *= 0.03). In that model, access to full-text was significantly better than access to abstracts-only (p=0.049).

We then compared the effect of the interventions within departments in order to investigate which departments seemed to respond differently to the others with respect to the effect of the interventions. We found no significant difference between the interventions for the Internal Medicine, Emergency Medicine and Family Medicine departments (p=0.73, 0.13 and 0.37, respectively), but there was a difference between the interventions for the Surgery department (p=0.02). The OR for each department is given in table 2. The full-text group had 3.6 times the probability of getting a correct answer on a case simulation compared to the abstract-only group. Note that the CI for Surgery does not include 1.0, which indicates a significant difference. There were no differences found between the interventions for any other department. Power to detect a difference between the interventions in a specific department was low (approx. 17%) because of the reduced sample size in each group (n=10). We also investigated whether resident year was a significant predictor of clinical decision accuracy, but it was not significant in any model.

Estimates of the odds of getting an accurate response after full-text intervention compared to abstract-only intervention

| Department         | OR (95% CI)       |
|:-------------------|:------------------|
| Surgery            | 3.6 (1.3 to 10.3) |
| Internal medicine  | 0.8 (0.3 to 2.2)  |
| Emergency medicine | 0.5 (0.2 to 1.3)  |
| Family medicine    | 1.6 (0.6 to 4.7)  |

## Accuracy of clinical decisions before and after access to literature search

We calculated the mean percentage of accurate responses to the simulated clinical questions before and after each intervention. Overall, mean accuracy increased from 42% to 68% for the abstract-only intervention, and 48–75% for the full-text intervention. The differences between the scores before and after the two interventions were significant (p\<0.0001). Table 3 shows the comparison of these percentages by department and the tests of significance.

Comparison of the average percentage of accurate of responses before and after interventions and tests of significance

|  | N | Average % of accurate responses before intervention (SD) | Average % of accurate responses after intervention (SD) | Difference in % before and after (95% CI)\* | Test of significance for each department and overall (p value)† | Does the amount of improvement differ by department? (p value)† |
|:---|----|:---|:---|----|----|:---|
| Abstract-only (intervention 1) |  |  |  |  |  |  |
|  Department |  |  |  |  |  |  |
|   Surgery | 10 | 48 (27) | 62 (15) | 14 (0 to 40) | 0.16 |  |
|   Internal medicine | 10 | 36 (18) | 66 (16) | 30 (20 to 60) | 0.003 | 0.06 |
|   Emergency medicine | 10 | 54 (21) | 72 (14) | 18 (0 to 40) | 0.06 |  |
|   Family medicine | 8 | 25 (14) | 75 (14) | 50 (40 to 60) | \<0.0001 |  |
|   Overall | 38 | 42 (23) | 68 (15) | 27 (20 to 40) | \<0.0001 |  |
| Full-text (intervention 2) |  |  |  |  |  |  |
|  Department |  |  |  |  |  |  |
|   Surgery | 10 | 60 (13) | 86 (0.16) | 26 (20 to 40) | 0.003 |  |
|   Internal medicine | 10 | 46 (16) | 68 (0.1) | 22 (0 to 40) | 0.03 | 0.0001 |
|   Emergency medicine | 10 | 64 (25) | 64 (0.23) | 0 (−20 to 20) | 1.0 |  |
|   Family medicine | 9 | 20 (14) | 82 (0.21) | 62 (40 to 80) | \<0.0001 |  |
|   Overall | 39 | 48 (24) | 75 (0.2) | 27 (20 to 40) | \<0.0001 |  |

\*95% CI based on Wilcoxon test.

†Based on values from the logistic regression mixed model.

When given full-text articles, the departments of Surgery, Internal Medicine and Family Medicine showed significant improvements (p=0.003, 0.03 and \<0.0001, respectively), while there was no change for the department of Emergency Medicine (p* *= 1.0). The differences among the departments were significant (p* *\< 0.0001) for full-text intervention group. This suggests that full-text was more effective for Surgery, Internal Medicine and Family Medicine, but not in the Emergency Medicine department. However, the sample size was small (n=10 or less) at this level. The effect of the abstract-only intervention seems to have been in a similar direction for all the departments, and no significant difference in effects across departments was detected.

## Information-seeking trends of the residents

The majority of the residents (86%) indicated that the articles provided in the online version were adequate to answer their questions and 77% indicated that they had actually read the articles. When asked whether they used abstracts-only or full-text articles to answer clinical questions in actual practice, 53 of the 77 residents (69%) indicated that they relied on abstracts most of the time, while only 24 (31%) said they would read the full-text article.

Residents were asked whether or not they felt they needed extra information in order to answer the question correctly. We recorded whether they clicked on the links for the abstract or the full-text. We wanted to answer the question: does a perceived need for more information correlate with how often the physicians actually accessed the links for abstract-only or full-text? For the 157 cases where the resident indicated that they did not require additional information, there were 131 (83%) instances where literature was actually accessed (95% CI 77% to 88%). In contrast, out of the 228 cases where residents indicated that they needed additional information, there were only 12 (5%) cases where they did not actually access literature (95% CI 3% to 9%). Table 4 shows a summary of whether the resident requested additional information and whether they actually accessed literature.

Residents' perceived need for additional information and actual access of literature

|  |  | Was literature actually accessed? |  |
|:---|:---|:---|:---|
|  |  | No | Yes |
| Perceived need for more information? | No | 26 (17%) | 131 (83%) |
|  | Yes | 12 (5%) | 216 (95%) |

Row percentages are displayed.

# Discussion

The main question we wanted to address in this study was whether there is a significant difference in the clinical decisions between residents who have access to abstracts-only and those with access to full-text articles. Overall, our results seem to demonstrate no difference in the accuracy of responses between residents provided with full-text articles and those with abstracts-only (p=0.4415). When we consider the clustering of physicians by department, we found a difference between the two interventions (p=0.0494) but further analysis showed that this difference was observed only in the department of Surgery (p=0.016). The effects of abstracts-only and full-text were not significantly different for the Internal Medicine, Emergency Medicine and Family Medicine departments. However, the study had low power to detect differences between the interventions within a department.

Our study provides preliminary but useful information related to the use of journal abstracts in evidence-based practice. We believe this to be the first report involving physicians, that attempted to evaluate how abstracts measure up to full-text articles in guiding clinical decisions. This finding offers support for using 'consensus abstracts' (concurring and corroborating abstracts from independently conducted randomised clinical studies and systematic research from meta-analysis and systematic reviews that form the basis of clinical evidence) as a possible alternative when access to full-text is limited or in other circumstances when it is not feasible.2 However, clinicians who want to practice EBM will also find online many summaries, reviews and preappraised free resources (TRIP Database, ACP Journal Club, Cochrane Library, etc) or by subscription (UpToDate, 5-Minute Clinical Consult, etc). EBM websites will have links to these. Many of these resources will have applications for mobile devices like the iPhone or Android devices. Our observations set the stage for further research on the role of using abstracts in evidence-based practice. Future studies may include randomised controlled trials with real-time clinical decision-making encountered at the bedside.

EBM encourages the use of timely and relevant information to complement the clinical acumen of clinicians.32 We found that the average improvement in the accuracy of responses across all the departments when either abstracts or full-text articles were provided was significant (p\<0.0001 for both interventions). This finding supports previous research regarding the role of medical literature in improving clinical decisions.33–36 However, when individual departments were considered, there seems to be a significant difference between the departments in the full-text intervention group (p=0.0001). This difference in the effect of full-text between the departments appears to be due to the fact that there was no change in the accuracy of responses of Emergency Medicine residents compared to the increase in scores for the other residents when full-text was provided. This may mean that full-text articles were beneficial to Surgery, Family Medicine and Internal Medicine residents but did not benefit Emergency Medicine residents. A possible explanation for this is that the Emergency Medicine department is fast paced and residents may not have the time to read the full-text article. This hypothesis was further supported by data for the abstract-only group where we found no significant difference between the departments on how the intervention improved the accuracy of the responses by residents.

Our study also demonstrated some trends in information-seeking and utilisation of evidence by residents when presented with clinical questions. We observed that although residents indicated that accumulated knowledge was sufficient to answer the questions, in most instances (83.4%), they still accessed the medical literature provided. This observation supports earlier studies that health professionals will use evidence from the literature when they are easily accessible at the time the question arises.37

More than a third of the residents (68.8%) who participated in this study claimed that they commonly used abstracts in seeking answers to their clinical dilemma. Other studies have reported similar observations. A study by Haynes *et al*3 found that two-thirds of clinical decisions were influenced by literature even if the full-text was not read. Moreover, internists reported that in 63% of the articles they come across, only the abstracts were read.4 These findings may even be higher among physicians in low- and middle-income countries because of even more limited availability of full-text articles.

## Limitations

The small sample of residents from a tertiary government hospital in the Philippines limits the generalisability of the study to the larger medical community. Simulated clinical cases were used as surrogate to actual clinical encounters that a resident may be presented with. The clinical questions were specific within the realm of the disciplines and are not necessarily comparable to each other. The residents only answered five questions which reduced the variation in the study. A 'learning effect' was considered to explain the higher score during the intervention phase but was deemed unlikely because of the short interval period—the residents took the online version questions immediately after the preintervention session. Furthermore, this study does not address whether access to full-text would have more impact than access to the abstract in a complex case, a case in which the details of a treatment or outcome or magnitude or significance might affect practice. It also does not address the impact on standard or routine or long-term practice. Finally, although there was reasonable power to detect a difference between the interventions overall, there was low power to detect differences within a department. It is possible that there were differences between the interventions for each department but our study did not have enough sample size to investigate the effect of the intervention at the department level.

# Conclusion

In this study, we demonstrated that clinical decisions made by residents improved when evidence, either abstracts or full-text articles were provided. However, this study also indicates that some clinical questions may be simple enough; answered quickly using accumulated knowledge, but accumulated knowledge was enhanced by the use of appropriate medical information. The residents, in spite of initially stating that accumulated knowledge was adequate to answer clinical questions, accessed evidence anyway. This confirms previous findings that easy availability of evidence encourages the practice of evidence-based medicine. When clustered by department, clinical decisions guided by full-text articles were more accurate than those guided by abstracts alone, but this difference can be largely attributed to a significant difference in Surgery. It may be less or not at all in the other three departments but the analysis is not conclusive because of the limited power of this study. Without departmental clustering, the findings seem to show that they may not be significantly different.

# References


author: Alexandre B. Hardy; Emma M. Allister; Michael B. WheelerCorresponding author: Michael B. Wheeler, <firstname.lastname@example.com>.
date: 2013-08
references:
title: Response to Comment on: Allister et al. UCP2 Regulates the Glucagon Response to Fasting and Starvation. Diabetes 2013;62:1623–1633

We thank Dr. Gylfe (1) for his interest in our work showing a role for uncoupling protein 2 (UCP2) in regulating α-cell glucagon secretion and suggesting that this is an interesting and potentially important finding. We agree that the role and mechanism of glucose sensing in α-cells is still highly controversial and that two opposing models are promoted in the literature. Our data suggests that the presence of UCP2 in α-cells may play a role in glucose sensing because the absence of UCP2 impaired the release of glucagon, and this was accompanied by reduced mitochondrial membrane potential hyperpolarization, increased reactive oxygen species production, more depolarized plasma membrane potential, and lower intracellular calcium levels. So although the objective of this study was not to define the mechanisms of glucose-regulated glucagon secretion in control cells, but rather to investigate the impact of UCP2 on α-cell function, we have attempted to fit our data into the published models. The first model was suggested by Dr. Gylfe and is based on the role of glucose as an activator of Ca^2+^ sequestration in the endoplasmic reticulum, which inhibits glucagon secretion (2,3). This model also suggests a depolarizing effect of low glucose concentration on α-cell plasma membrane potential (2,4,5). The second model (by Rorsman and colleagues \[6\]) describes regulation of glucagon secretion by an ATP-sensitive potassium channel–dependent pathway. This model predicts that glucose metabolism increases intracellular ATP, closing ATP-sensitive potassium channels. The channel closure depolarizes α-cell membrane potential to a level that inactivates Na^+^ and Ca^2+^ ion channels, thereby reducing glucagon secretion (7). In our study, we present data that fits with both models. The glucose-induced changes in membrane potential recorded in isolated dispersed α-cells fit with the first model; we show that low glucose concentration caused depolarization and increased intracellular calcium levels along with enhanced secretion. However, the α-cell–specific UCP2 knockout mouse α-cells were more depolarized under both high and low glucose concentrations and secreted less glucagon, which fits with the model by Rorsman and colleagues. In addition, our data show that low-dose diazoxide (1µmol/L), which should hyperpolarize the membrane, increased glucagon secretion under high glucose conditions in control α-cells and could enhance glucagon secretion to control levels in the absence of UCP2. Again these data are in line with the second model of glucagon secretion and perhaps point to the α-cell being secretory within a narrow range of plasma membrane potentials. There may be differences in the data based on the use of dispersed versus whole islets, which were used for the electrophysiological and secretion experiments, respectively. Complex factors such as release of paracrine molecules can regulate glucagon secretion and may play a role in the whole islets studies. In addition, it cannot be ignored that the elevated reactive oxygen species levels (even under low glucose conditions) in the α-cell–specific UCP2 knockout mouse islets could potentially be affecting secretion via a channel-independent mechanism, and this area deserves more investigation. However, as pointed out by Dr. Gylfe, in the context of normal α-cells, low glucose in our hands caused depolarization.

## ACKNOWLEDGMENTS

No potential conflicts of interest relevant to this article were reported.

# REFERENCES


abstract: Meta-analysis is a popular way to try to get the most out of different studies linking genes and disease. But there are cons as well as pros.
author: Gregory A Petsko
date: 2008
institute: 1Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454-9110, USA
title: Meta-morphosis

There's an old chemistry joke, based on the *ortho, meta* and *para* nomenclature for substituents attached to a benzene ring, that goes like this:

OK, I know it's not very funny, but how many funny chemistry jokes are there? Anyway, someone reminded me of this joke the other day and while I was busy not laughing I was also thinking about something else. Because these days, for most people in the life sciences, and I bet nearly everybody in genome biology, the prefix *meta* doesn't conjure up images of di-substituted benzene. It calls to mind the meta-analysis of data.

Meta-analysis - sometimes contracted to metanalysis - is one of those Sudoku-like fads that seem to pop up overnight and sweep the entire country in about as little time. 'Metanalysis' doesn't mean the same thing to scientists as it does to other academics. In linguistics, metanalysis is the act of breaking down a word or phrase into segments or meanings not original to it. The term was coined by the linguist Otto Jespersen, and comes from the Greek for 'a change of breakdown'. Here's an example, courtesy of Wikipedia: in the phrase "God rest ye merry gentlemen", originally merry was a complement with rest (as in "God rest ye merry, gentlemen" - note the position of the comma - that is, "\[may God\] give you gentlemen a pleasant repose"). But now, by a process of metanalysis, merry is frequently construed as an ordinary adjective modifying gentlemen ("God rest ye, merry gentlemen") - and in all probability it can be relexicalized with the current sense of merry, that is, cheerful, jolly, though that is harder to be certain of. (The expression "to rest merry" and the like was once generally current, by the way.) Incidentally, I love this sort of thing, but it isn't what 'meta-analysis' means in biomedical research.

Meta-analysis for us is a technique whereby all data from all available studies of something are combined, often regardless of the relative quality of the data. The method is used by researchers to get a maximum amount of statistical information from a set of studies that might not have large enough individual sample sizes, or whose results may be of marginal statistical significance.

In a typical meta-analysis, the results of, say, four different clinical trials of a drug, or the data from five independent studies of the association of a genetic variation with a particular disease, are merged into a single statistical sample. The sample is then analyzed for the same correlation, or lack thereof.

I don't know where it the practice came from but it's of relatively recent origin - the late 1970s or so. Certainly when I was a student there wasn't an entire subfield devoted to pooling studies and reinterpreting the results. But the medical literature, and the literature of human genetics, is awash with it now.

Imagine the excitement of the person who thought this up. "Omigod! I can take other people's results, throw them together, and come up with a conclusion that in some cases will horrify them, and I can get it published in good journals -sometimes with quite high visibility if the data concern an important human disease or a dietary substance that's popular - and I don't have to do any of the really hard work! I don't have to actually do the studies. I don't have to find the patients, or collect the questionnaires, or analyze the genomes, or any of that difficult, real science. They'll do it for me, and then I can use their data for my own benefit. This is like making money with other people's money! This is how the first banker must have felt!!"

I am not a professional statistician, but I have taught statistics, and as a protein crystallographer I have to be familiar with most of the rudimentary forms of statistical analysis of data. And everything I know about the subject suggests to me that meta-analysis could lead to all kinds of trouble. One of the things I've learned is that you don't improve bad data by combining it with good data. In fact, exactly the opposite occurs: the bad data degrade the better. "When in doubt, leave it out" is what I usually tell my students - occasionally modified to "when in doubt, weight it down". But most meta-analyses don't apply relative weights to the studies they combine (it's pretty hard to see where they would get the weights anyway), so studies of all sorts of varying quality are sometimes pooled as though they were equally reliable.

And even if we allow that the people who do this may get better statistical precision this way, owing to an enlargement of the sample size, I don't think statistical precision is the issue here. Adding more data may reduce the random errors, but I believe that in most biomedical and genomics studies - especially the latter - the real importance may lie in the systematic errors, or, more precisely (pun intended), the systematic differences.

Meta-analysts justify combining data from different studies in part because doing the same experiment in different ways has long been a way to avoid problems caused by non-random or systematic errors. And it is true that using multiple studies that involve different questions or different experimental techniques, with different patient populations and other variations, may allow global trends to emerge from underneath spikes of systematic differences. But there is a danger there. The first meta-analysis I ever encountered, some years ago, concerned a gene I was interested in. It had been reported that a particular polymorphism in this gene was associated with a reduction in risk for certain diseases in the Han Chinese population. Several other studies, including some with much smaller sample sizes, had looked for a disease association in other ethnic groups but had failed to find any. The meta-analysis pooled all of these studies and concluded, rather definitively, that the particular variant did not confer a change in risk for any of the diseases in question. And their overall statistics certainly bore that out.

There was just one problem: I had read the original Han Chinese study rather carefully, and the work was well done, and the statistical correlation with disease risk was absolutely significant. But if you only read the meta-analysis -and I've seen that referred to repeatedly since and even quoted from at meetings - you wouldn't know that.

Because in the age of genomics, it's not about the general population anymore. Personalized medicine is coming, and our studies of haplotypes and other natural variations make it clear that, even if we can't quite get down to the level of the individual yet in all studies, the genetic (and environmental) background of the population being evaluated can make a huge difference. Ethnic and geographical differences aren't not systematic errors to be averaged out; they're essential components of the ways genes (or drugs, or nutrients) interact with the human body. The right question to be asking in the case of the study I referred to is not whether the polymorphism alters disease risk in the general population, but rather why it does so in the Han Chinese population. What are the particular combinations of other genetic and environmental factors that cause this variant to become associated with a set of diseases? That's where the really interesting science, and medicine, lies. Average different studies together willy-nilly and you run the risk that sometimes you may average out precisely the those variations that provide the clues we are looking for as to how human health really works.

Now you may think that this column is all about trashing meta-analysis, and, to be honest, it started out that way. But then I had a discussion with David Altshuler, a human geneticist at Harvard Medical School and the Broad Institute at MIT, that made me rethink what was about to become a blanket condemnation. He pointed out to me another goal of meta-analysis in current human genetic studies. The key issues, as he put it, are first, the different statistical thresholds needed in discovery science with a low prior (as compared with hypothesis testing in a well-established field), and second, in human genetics the ability to use the rest of the genome as independent tests to assess the matching of cases and controls in human genetics.

Here's how he explained the first issue: in genetic mapping with the goal of initially determining which genes might actually play a role in human disease, there is a very low *a priori* probability of any variant being associated with any risk (on the order of one in a million), and - that is, with an initial goal of determining which genes might actually play a role in human disease risk - one can't say that a *p* value of 0.01 or even 0.0001 is 'absolutely significant'. He's right: most findings of that magnitude turn out to be entirely irreproducible, and are likely to be false positives. But finding consistent results by meta-analysis increases evidence that the null hypothesis is wrong - and confidence that there is a relationship between the variant and risk. Without that confidence, he asserts, the field was awash with wishful thinking about lots of candidate genes that reductionist biologists had found in cells and in animal models, and wanted to believe were 'genetically validated in people' - but that turned out to be noise.

If you look into it, you will find that there is a long history in genetic mapping of setting the right threshold based on the number of tests. In linkage mapping, the LOD score that indicated linkage between a gene variant and a trait was 3.0 -not *p* \< 0.05 (LOD stands for logarithm to the base 10 of odds; a LOD score of 3.0 means the likelihood of observing the given pedigree if two loci are not linked is less than 1 in 1000). By contrast, in genome-wide human genetic studies, a *p* of less than or equal to 0.0000001 is typically required for proof of association of a gene with a trait. And while biologists often like to say that for 'candidate genes' like the one I mentioned above it is more like *p* \< 0.05, that threshold has a history of not always supporting reproducible discoveries. It is in gathering enough data to really get the confidence level to where it needs to be that meta-analysis has proven proved valuable in many cases.

The second issue is the quality of the data that goes into meta-analysis, and the ability to compare and align it. In most studies, Altshuler agrees with me that you can't know if you are washing out good data with bad. But he makes a strong case, with which I am forced to agree, that in the current wave of genome-wide association studies, the studies often use the same phenotype definition, or the same microarray protocols, and so the data may well be comparable (clearly, it is important to see whether that is the case, if you want to evaluate the meta-analysis critically). When done properly, information from the rest of the genome can be used to assess the properties of the data, matching of cases and controls, and so on, which, as he puts it, "can result in valid combination of lots of good data, rather than sloppy mashing up of lots of bad data". Finally, sometimes you actually may want to test the hypothesis across ethnicity or age or other variables. In those instances, meta-analysis allows you to put the data together in a valid way - a way that is probably better than just reading the papers and trying to compare them.

Dr Altshuler went on to make a very valuable point, which I think belongs in this column: he feels there that is currently a big and unfortunate divide between cell and molecular biologists and people using human genetics to find disease genes. The tone of the first part of my column just reinforced his concern, I'm sorry to say. For disease research, it's certainly true that knowing up front that a gene influences the disease in humans is invaluable, and I think he's right that meta-analysis can be a powerful tool for getting that correct. (As he asked me: "Would you want to do functional work on a gene that turns out to be a false positive?")

A final point: meta-analysis is currently being used in two ways in human genetics, and I may have given a false impression that there is only one. One method is when there is a pre-existing hypothesis that needs to be tested. A good example comes from Altshuler's own work (Altshuler D, *et al.: Nat Genet* 2000, **26:**76-80). The data the authors had collected gave a positive result, but others had claimed the opposite. The studies were comparable enough that meta-analysis was able to show that the data were actually consistent and reinforced one another. In such cases meta-analysis serves as a reality check, and helps avoid possible bias in the selection of data that might support one hypothesis or another.

But careful meta-analysis can also in some cases be hypothesis generating, because enough studies might, as I mentioned above, allow previously undetected signals to rise above the noise. These can then be tested experimentally, and of course, need to be.

Dr Altshuler closed our discussion with a nice comment: "Meta-analysis," he pointed out, "is just a method. Garbage in, garbage out. But in genetic mapping of common complex diseases, where it is clear there are many different variants contributing, and where studies are expensive, combining data to learn the most is well justified and valid."

It is true that it's often the original studies that matter, that data need to be examined carefully, and that a study ideally must be accepted or rejected on its own merits. People are fond of saying that the devil is in the details. But God is in the details too. In the age of genomics, the details are ultimately what matter: the complex interplay of individual gene and genetic background, diet, environment, perhaps even state of mind, is what determines whether we are prone to this disease or that, react poorly or well to this drug or that, age well or badly. Meta-analysis can hide those details, but used properly, it can also be revealing. I guess you could say that writing this column has led to a meta-morphosis in the way I think about this subject, and that I have to back away from my meta-phor about making money with other people's money. In the right hands, meta-analysis can be a valuable tool, which is a pity, because it's so much more fun to write a column that completely trashes something. Meta-physically speaking, of course.


author: T Pap; A Cinski; A Drynda; A Wille; A Baier; S Gay; I Meinecke
date: 2005
institute: 1Division of Molecular Medicine of Musculoskeletal Tissue, University Hospital, Munster, Germany; 2Division of Experimental Rheumatology, University Hospital Magdeburg, Germany; 3Center of Experimental Rheumatology, Department of Rheumatology, University Hospital, Zurich, Switzerland; 4Department of Traumatology, Hand and Reconstructive Surgery, University Hospital, Munster, Germany
title: Regulating apoptosis in fibroblasts

Apoptosis constitutes a highly selective way of eliminating aged and injured cells and is a key mechanism for the balanced growth and regeneration of tissues. In addition to genotoxic stress and the withdrawal of growth factors, apoptosis can be induced by death receptors. Such receptors trigger apoptosis through an approximately 90 amino acid death domain. Prominent and best-characterized members of the death domain receptor family are Fas (CD-95/Apo-1) and the p55 TNF receptor (TNFRI). Alterations in receptor-mediated apoptosis may result in changes of tissue homeostasis and have been found in a variety of malignancies as well as in the inflamed and hyperplastic synovium in rheumatoid arthritis. Accumulating evidence suggests that the activation of rheumatoid arthritis synovial fibroblasts (RASF) is associated with alterations in apoptosis, especially at sites of invasion into cartilage and bone. Specifically, it has been demonstrated that RASF are less susceptible to Fas-induced cell death than osteoarthritis synovial fibroblasts but show higher expression levels of Fas.

Recent studies have shown that both cytokine-dependent and cytokine-independent mechanisms contribute to the resistance of RASF against Fas-induced apoptosis. Thus, tumor necrosis factor alpha (TNF-α) – a major inflammatory cytokine in the rheumatoid synovium – fails to induce apoptosis in RASF, but reduces the susceptibility of these cells to Fas-mediated cell death through the induction of the transcription factor NF-κB. Providing a link between altered apoptosis and cartilage destruction, we have shown that overexpression of TIMP-3 through gene transfer not only reduces the invasiveness of RASF but also modulates the apoptosis-inhibiting effects of TNF-α. RASF overexpressing TIMP-3 are sensitized strongly to Fas/CD95-mediated cell death by TNF-α, and gene transfer of TIMP-3 inhibits the TNF-α-induced activation of NF-κB. While these effects of TNF-α on apoptosis can be found in different fibroblasts, several anti-apoptotic molecules have been demonstrated to be upregulated specifically in RASF.

In this context, we have investigated the role of the small ubiquitin-like modifier SUMO-1 in the regulation of apoptosis. We found that increased levels of SUMO-1 in RASF but not in osteoarthritis synovial fibroblasts were associated with a reduced susceptibility of RASF to Fas-induced apoptosis. Using small interfering (si)RNA to knock-down the expression of SUMO-1 as well as retroviral gene transfer of SUMO-1, we could establish a functional relationship between the expression of SUMO-1 and the resistance of RASF against apoptosis. Moreover, it was demonstrated by gene transfer of the nuclear SUMO-protease SENP1 that, rather than by interacting directly with the Fas-associated death domain, SUMO-1 inhibits apoptosis through recruiting pro-apoptotic molecules such as DAXX into nuclear PML bodies, where they cannot exert their pro-apoptotic effects.

Taken together, there is growing evidence that in activated fibroblasts, such as in RASF, there is a close association between anti-apoptotic and destructive pathways. In addition to cytokine-dependent inflammatory mechanisms, the intrinsic upregulation of anti-apoptotic molecules contributes to the resistance of RASF against apoptosis. Post-translational modification of nuclear proteins through SUMO-1 appears to constitute an important mechanism of apoptosis regulation. Therefore, the inhibition of molecules that confer the resistance of RASF to apoptosis constitutes a most interesting therapeutic target.


abstract: The primary associations of the HLA class II genes, *HLA-DRB1* and *HLA-DQB1*, and the class I genes, *HLA-A* and *HLA-B*, with type 1 diabetes (T1D) are well established. However, the role of polymorphism at the *HLA-DRB3, HLA-DRB4*, and *HLA-DRB5* loci remains unclear. In two separate studies, one of 500 subjects and 500 control subjects and one of 366 DRB1\*03:01–positive samples from selected multiplex T1D families, we used Roche 454 sequencing with Conexio Genomics ASSIGN ATF 454 HLA genotyping software analysis to analyze sequence variation at these three *HLA-DRB* loci. Association analyses were performed on the two HLA-DRB loci haplotypes (*DRB1*-*DRB3*, -*DRB4*, or -*DRB5*). Three common *HLA-DRB3* alleles (\*01:01, \*02:02, \*03:01) were observed. DRB1\*03:01 haplotypes carrying DRB3\*02:02 conferred a higher T1D risk than did DRB1\*03:01 haplotypes carrying DRB3\*01:01 in DRB1\*03:01/\*03:01 homozygotes with two DRB3\*01:01 alleles (odds ratio \[OR\] 3.4 \[95% CI 1.46–8.09\]), compared with those carrying one or two DRB3\*02:02 alleles (OR 25.5 \[3.43–189.2\]) (*P* = 0.033). For DRB1\*03:01/\*04:01 heterozygotes, however, the *HLA-DRB3* allele did not significantly modify the T1D risk of the DRB1\*03:01 haplotype (OR 7.7 for \*02:02; 6.8 for \*01:01). These observations were confirmed by sequence analysis of *HLA-DRB3* exon 2 in a targeted replication study of 281 informative T1D family members and 86 affected family-based association control (AFBAC) haplotypes. The frequency of DRB3\*02:02 was 42.9% in the DRB1\*03:01/\*03:01 patients and 27.6% in the DRB1\*03:01/\*04 (*P* = 0.005) compared with 22.6% in AFBAC DRB1\*03:01 chromosomes (*P* = 0.001). Analysis of T1D-associated alleles at other HLA loci (*HLA-A, HLA-B*, and *HLA-DPB1*) on DRB1\*03:01 haplotypes suggests that DRB3\*02:02 on the DRB1\*03:01 haplotype can contribute to T1D risk.
author: Henry A. Erlich; Ana Maria Valdes; Shana L. McDevitt; Birgitte B. Simen; Lisbeth A. Blake; Kim R. McGowan; John A. Todd; Stephen S. Rich; Janelle A. Noble; Corresponding author: Henry A. Erlich, <email@example.com>.
date: 2013-07
references:
title: Next Generation Sequencing Reveals the Association of DRB3\*02:02 With Type 1 Diabetes

The Type 1 Diabetes Genetics Consortium (T1DGC) (1) is an international collaboration that has collected thousands of multiplex family samples, as well as case and control samples, and has carried out linkage and association analysis for genome-wide single nucleotide polymorphisms (SNPs), candidate gene SNPs, and major histocompatibility complex (MHC) SNPs, as well as for alleles and haplotypes at the highly polymorphic HLA class I and class II genes (2–7). More than 40 different loci have been associated with T1D (3); however, linkage and association analyses have identified the HLA region as the major genetic determinant of T1D risk. The most strongly T1D-associated MHC markers are defined by the HLA-DRB1, -DQA1, and -DQB1 haplotypes (4), although alleles at other HLA loci, notably *HLA-A* and *HLA-B*, as well as *HLA DPB1* (5–11) and other non-HLA loci across the genome, also contribute to T1D risk (3).

Although the association of *HLA-DRB1* alleles with T1D is well established, the role of polymorphism at the *HLA-DRB3*, *HLA-DRB4*, and *HLA-DRB5* loci has been studied less frequently, partly due to technical issues in genotyping. Most high-resolution genotyping strategies for *HLA-DRB1* depend on DRB1-specific PCR primers to minimize confounding signals from coamplified secondary DRB loci. All copies of chromosome 6 have a DRB1 locus, and most, but not all, have a functional second DRB locus. This second DRB locus is DRB3 for DRB1\*03, \*11, \*12, \*13, and \*14 haplotypes, DRB4 for DRB1\*04, \*07, and \*09 haplotypes, and DRB5 for DRB1\*15 and \*16 haplotypes. DRB1\*01, DRB1\*08, and DRB1\*10 haplotypes do not have a second DRB locus.

The clonal sequencing property of next generation sequencing systems, such as the Roche 454 GS FLX and GS Junior Systems, allows the use of generic DRB primers to coamplify and sequence exon 2 from DRB1, DRB3, DRB4, and DRB5 loci (12–14). We have used the Roche 454 amplicon sequencing system with "fusion primers" containing the 454 adaptor (A and B) sequences and 10 base multiplex ID tags (MIDs) to amplify and sequence exon 2 from different individuals (13,14). The genotyping software consolidates these clonal sequence readings, sorts them to individual DRB loci and to individual samples, and compares them to the IMGT/HLA Sequence Database to assign specific genotypes at the *HLA-DRB1*, *HLA-DRB3*, *HLA-DRB4*, and *HLA-DRB5* loci.

To assess the potential role of these secondary HLA-DRB loci in T1D, association analyses were carried out at the two DRB locus haplotype levels, focusing on the DRB1\*03:01 haplotypes bearing DRB3\*01:01 or DRB3\*02:02. The role of *HLA-DRB3* polymorphism in T1D risk was initially evaluated in a case/control study and then examined in a targeted replication study of informative patients from multiplex HLA-genotyped families, allowing analysis of HLA haplotypes and the potential role of T1D-associated alleles at other HLA loci.

# RESEARCH DESIGN AND METHODS

## Subjects.

The initial set of DNA samples from 500 case and 500 control subjects was provided by the JDRF/Wellcome Trust Diabetes and Inflammation Laboratory (case subjects) and the British 1958 Birth Cohort (control subjects) from a study described previously (15).

To test the hypothesis generated in the case/control study, DNA samples from the T1DGC and the Human Biological Data Interchange (HBDI) multiplex family collections were used. Samples from 280 T1D-affected family members were selected if they had a DRB1\*03 haplotype. Samples were selected based on the known T1D risk DRB1 genotypes, DRB1\*03:01/DRB1\*03:01 (*n* = 54 T1D subjects; 105 independent DRB1\*03:01 haplotypes), DRB1\*03:01/DRB1\*04:01 (*n* = 154 DRB1\*03:01 haplotypes), and DRB1\*03:01/DRB1\*04:04 (*n* = 73 DRB1\*03:01 haplotypes), with the aim of testing the hypothesis that DRB3\*02:02 and \*01:01 alleles may confer differential T1D risk. Parent samples with a DRB1\*03:01 allele known not to be transmitted to an affected T1D patient were also selected as control subjects from the T1DGC and the HBDI collections (affected family-based association controls \[AFBACs\], *n* = 115 haplotypes) (16). These AFBAC control haplotypes were used to determine population frequencies of DRB3\*02:02 and DRB3\*01:01 alleles on DRB1\*03:01 haplotypes.

## HLA genotyping using next generation sequencing (Roche 454).

HLA sequence data were generated on the Roche 454 GS FLX and GS Junior Systems and analyzed using Conexio Genomics HLA ASSIGN ATF genotyping software to interpret the sequence files as HLA genotypes (13,14). Amplicons were generated from genomic DNA using DRB generic exon 2 454 fusion primers. The 454 fusion primers consist of a locus-specific sequence on the 3′ end, a 10-bp MID tag, and an "A" or "B" 454-specific primer sequence on the 5′ end. The MID tag serves as a sample barcode recognized by Conexio ASSIGN ATF genotyping software.

Amplicons were purified with AMPure beads (Becton Dickinson, Franklin Lakes, NJ), quantified using the Quant-iT PicoGreen dsDNA reagent (Life Technologies, Foster City, CA), and mixed with capture beads after dilution. Individual DRB exon 2 amplicon molecules captured by these beads were amplified in an emulsion PCR amplification and DNA-containing beads subsequently analyzed by pyrosequencing to obtain sequence readings originating from a single molecule (12–14). Sequencing on the GS FLX and GS Junior System was performed, as described (13,14). HLA genotypes were assigned to samples using Conexio ASSIGN ATF genotyping software, as described (13,14).

## Statistical analysis.

DRB3 allele frequencies on DR3 haplotypes in DR3 homozygotes, DR3/DR4 heterozygotes, and AFBACs were compared using a Fisher exact test where the overall sample size was less than 50 or a Pearson χ^2^ statistic.

# RESULTS

## Case/control study.

A total of 500 case and 500 control DNA samples from the Wellcome Trust/JDRF Diabetes Inflammation Laboratory collection were amplified with 454 DRB fusion primers containing 32 MID tags and sequenced in two GS FLX System runs using PicoTiterPlates fitted with 16-region gaskets. The long read lengths of \>400 bp spanned the amplicon in both directions and allowed setting phase (haplotyping) for the polymorphisms within exon 2. This provided, in most cases, unambiguous genotype assignments for *HLA-DRB1*, *HLA-DRB3*, and *HLA-DRB5*. For *HLA-DRB4*, however, several different genotypes were consistent with the observed sequence reads (ambiguity string). (The *HLA-DRB4* exon 2 sequence reads were identical for the DRB4\*01:01, DRB4\*01:03, and DRB4\*01:06 alleles.) This DRB4 exon 2 sequence was present on all DRB1\*04, \*07, and \*08 haplotypes; thus, the role of DRB4 alleles for association with T1D or for defining specific linkage disequilibrium (LD) patterns could not be evaluated.

For *HLA-DRB3* and *HLA-DRB5*, the allele assignments were unambiguous and, as expected, a pattern of very strong LD between *HLA-DRB1* and the secondary DRB locus was observed. The T1D association analyses were performed on the two-locus haplotypes (Table 1). In these data, DRB1\*15:01 was linked exclusively to DRB5\*01:01, and although the numbers were small (*n* = 6), DRB1\*16:01 was linked exclusively to DRB5\*02:02. Three common DRB3 alleles (\*01:01, \*02:02, \*03:01) were observed. Of those haplotypes carrying a DRB3 locus, most DRB1 alleles were linked to a unique DRB3 allele in both case and control subjects (Table 1). The DRB1\*03:01 and the DRB1\*13:01 haplotypes, however, were linked to one of two different DRB3 alleles (\*01:01 or \*02:02).

DRB1-DRB3 and DRB1-DRB5 haplotype frequencies in T1D case and control subjects

![](2618tbl1)

Thus, the role of DRB3\*01:01 versus DRB3\*02:02 on DRB1\*03:01 haplotypes could be evaluated. DRB1\*03:01 haplotypes carrying DRB3\*02:02 had a nominally higher risk for T1D; this difference in T1D risk was observed in the DRB1\*03:01/\*03:01 homozygotes with two DRB3\*01:01 alleles (odds ratio \[OR\] 3.4 \[95% CI 1.46–8.09\]) compared with those with one or two DRB3\*02:02 alleles (25.5 \[3.43–189.2\]; *P* = 0.033; Table 2). For DRB1\*03:01/\*04:01 heterozygotes, however, there was no difference in the T1D risk between DRB1\*03:01 haplotypes distinguished by the DRB3 allele (OR 7.7 vs. 6.8; *P* = 0.29; Table 2). That the apparent difference in risk for DRB1\*03:01 haplotypes bearing DRB3\*02:02 versus DRB3\*01:01 is not evident in DRB1\*03:01/DRB1\*04 heterozygotes may reflect the very high risk associated with this genotype and attributed, in part, to the *trans*-complementing DQ-α (\*05:01) or DQ-β (\*03:02) heterodimer (4).

Association of DRB1\*03:01/03:01 and DRB1\*03:01/04:01 genotypes with T1D stratified by DRB3 genotype

![](2618tbl2)

Although many samples were sequenced in this case/control study, the number of informative haplotypes (DRB1\*03:01) and genotypes was limited; therefore, the statistical power of the association with DRB3:02:02 was very modest. Access to the T1DGC collection of HLA-genotyped families allowed selective targeting of informative genotypes to directly address the hypothesis of an effect of DRB3\*02:02 in DRB1\*03:01/\*03:01 homozygotes and replicate the results of the case/control study.

## Targeted replication study of informative samples from the T1DGC family collection.

To further evaluate the putative role of *HLA-DRB3* polymorphism on DRB1\*03:01 haplotype risk, a panel of DRB1\*03:01/\*03:01 homozygotes (*n* = 54 T1D family members, 105 nonshared chromosomes) and DRB1\*03:01/DRB1\*04-DQB1\*03:02 (*n* = 227) heterozygous T1D members were selected from the T1DGC families. Of the DRB1\*03:01/DRB1\*04 subjects, 154 were \*04:01 and 73 were \*04:04. The distribution of DRB3 alleles in these T1D subjects is reported in Table 3. The frequency of the DRB3\*02:02 allele in the DRB1\*03:01/\*03:01 T1D members was significantly greater than in those with DRB1\*03:01/\*04-DQB1\*03:02 (42.9 vs. 27.6%, *P* \< 0.005), consistent with the observations in the previous case/control study (Table 2). The distribution of DRB3 alleles on control DRB1\*03:01 chromosomes was evaluated by examining the nontransmitted AFBAC (16) DRB1\*03:01 chromosomes from heterozygous parents. The frequency of DRB3\*02:02 in those chromosomes not transmitted to a T1D-affected child was 22.6%, significantly less than the 42.9% (*P* = 0.001) observed in the DRB1\*03:01/DRB1\*03:01 homozygous patients but not significantly different from the frequency in DRB1\*03:01/DRB1\*04:01 heterozygous T1D patients (27.3%).

Comparison of distribution of DRB3 alleles on DR3 haplotypes in control subjects (AFBAC), DRB1\*03:01 homozygous patients, and DRB1\*03:01/DRB1\*04 heterozygous patients

![](2618tbl3)

## Does DRB3\*02:02 allele modify risk or is it simply a marker for a high-risk DRB1\*03:01 haplotype?

On the basis of these two studies, the DRB3\*02:02 allele appears to mark a higher-risk DRB1\*03:01 haplotype than the DRB1\*03:01-DRB3\*01:01 haplotype, and the effect of this high-risk haplotype can be discerned primarily in the DRB1\*03:01/\*03:01 homozygotes. T1D risk heterogeneity in DRB1\*03:01 haplotypes has been previously reported with HLA-A\*30:02, HLA-B\*18:01, and DPB1\*02:02 alleles distinguishing the higher-risk from the lower-risk DRB1\*03:01 haplotypes (17). To investigate whether the DRB3\*02:02 allele might contribute to T1D risk or simply mark a high-risk DRB1\*03:01 haplotype in which HLA class I (or other alleles) was a modifying risk, we examined the distribution of these and other T1D-associated alleles at other HLA loci on these two (DRB3\*01:01 and DRB3\*02:02) DRB1\*03:01 haplotypes. The increase of DRB3\*02:02 on DRB1\*03:01 haplotypes in DRB1\*03:01/\*03:01 case subjects versus DRB1\*03:01/DRB1\*04 case subjects and AFBAC control subjects could reflect LD with alleles at other high-risk loci. The analysis of the case subjects selected from T1DGC families with informative HLA-DRB1 genotypes included parents; thus, haplotypes across the HLA region could be determined.

Eight locus haplotypes were phased and assigned based on familial inheritance. In the absence of parental genotyping data for the DRB3 locus, only a fraction of these could be assigned phase unambiguously with regard to the remaining eight loci. In total, 327 DRB1\*03:01 haplotypes were used for this analysis of extended HLA haplotypes. The DRB1\*03:01 haplotype counts in the three groups (AFBAC, DRB1\*03:01/DRB1\*04, DRB1\*03:01/DRB1\*03:01) for selected DPB1, HLA-B, -C, and -A alleles are reported in Table 4 and [Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1387/-/DC1). These alleles were selected based on previously published reports of T1D association. Table 4 and [Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1387/-/DC1) compare the distribution of DRB3\*01:01 and \*02:02 alleles on DRB1\*03:01 haplotypes bearing various high-risk alleles at other HLA loci in DRB1\*03:01/\*03:01 versus DRB1\*03:01/\*04 patients.

Distribution of DRB3\*02:02 and \*01:01 alleles on the extended DRB1\*03:01-DPB1\*03:01 haplotype

![](2618tbl4)

We note that the alleles A\*01:01, B\*08:01, C\*07:02, and DPB1\*01:01 are in very strong LD with DRB3\*01:01. These alleles on this extended haplotype mark a "lower-risk" DRB1\*03:01 haplotype. In addition, the LD between alleles on the extended DRB1\*03:01 haplotype, A\*30:02-B\*18:01-DRB1\*03:01-DRB3\*02:02-DPB1\*02:02 is sufficiently strong that it does not permit evaluation of the independent contribution of DRB3\*02:02. One haplotype, the DRB1\*03:01-DPB1\*03:01, however, is informative; DRB1\*03:01 haplotypes carrying both DPB1\*03:01 and DRB3\*02:02 are significantly increased (*P* = 0.0006) in DRB1\*03:01/\*03:01 versus DRB1\*03:01/\*04 case subjects compared with those who carry DRB3\*01:01 (Table 4). This effect cannot be explained by the presence of A\*30:02 or B\*18:01, which are also markers of high-risk DRB1\*03:01 haplotypes on these 16 DPB1\*03:01-DRB3\*02:02 haplotypes. Only 4 of these 16 haplotypes carry A\*30:02, and of the 9 DPB1\*03:01-DRB3\*02:02 haplotypes that also carry B\*18:01, 5 are found in DR3/DR4 and 4 in DR3/DR3 case subjects. These observations, taken together, suggest that the DRB3\*02:02 allele may contribute to T1D risk rather than simply marking a high-risk DRB1\*03:01 haplotype.

DRB1\*13:01 is the only other DRB1 haplotype that can carry DRB3\*01:01 or DRB3\*02:02 (Table 1). We note that the OR of the DRB1\*13:01-DRB3\*02:02 haplotype is nominally higher than the DRB1\*13:01-DRB3\*01:01 haplotype (0.29 vs. 0.15; Table 1). However, the CIs overlap, indicating that much larger sample sizes will be necessary to evaluate the risk of these two haplotypes.

# DISCUSSION

Long-read next generation clonal sequencing allows genotyping the secondary HLA-DRB loci (*HLA-DRB3*, *HLA-DRB4*, and *HLA-DRB5*) as well as *HLA-DRB1* by using DRB generic 454 fusion primers for exon 2. Using the Roche 454 GS FLX and GS Junior Systems, we have used this capability to examine the role of the secondary HLA-DRB loci in T1D susceptibility. Most *HLA-DRB1* alleles are linked exclusively to a specific allele at the secondary DRB locus (Table 1); however, DRB1\*03:01 and DRB1\*13:01 can carry DRB3\*01:01 or \*02:02. DRB1\*03:01 haplotypes carrying DRB3\*02:02 appear to confer higher risk than those carrying DRB3\*01:01. The difference in T1D risk is observed in DRB1\*03:01/\*03:01 homozygotes but not in DRB1\*03:01/DRB1\*04-DQB1\*03:02 heterozygotes. The very high risk for T1D associated with this heterozygous genotype has been attributed to the *trans*-complementing DQ heterodimer formed by the DQ-α chain encoded by the DQA1\*05:01 allele on the DRB1\*03:01 haplotype and the DQ-β chain encoded by the DQB1\*03:02 on the DRB1\*04-DQB1\*03:02 haplotype (4). One possible explanation of the different effect of *HLA-DRB3* in this heterozygote and in the DRB1\*03:01/DRB1\*03:01 homozygote is that the putative risk conferred by the *trans-*complementing DQ heterodimers in the heterozygote is sufficiently large so that the risk difference between the "higher-risk" and "lower-risk" DRB1\*03:01 haplotypes has a minimal effect on the overall T1D risk for the heterozygote. A recent study on the effect of other MHC markers (*HLA-B*, *HLA-A*, *HLA-DPB1*, and *TNF-α*) on DRB1\*03:01 haplotype risk reported a similar observation—these markers were associated with differential risk in DRB1\*03:01/DRB1\*03:01 homozygotes but not in the DRB1\*03:01/DRB1\*04-DQB1\*03:02 heterozygotes (17).

Does DRB3\*02:02 play a role in T1D risk or does it only *mark* the higher-risk DRB1\*03:01 haplotype? To address this question, eight locus DRB1\*03:01 haplotypes were phased and assigned based on familial inheritance, and the distribution of the DRB3\*01:01 and \*02:02 alleles on extended haplotypes bearing high-risk alleles at other HLA loci was compared (Table 4 and [Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1387/-/DC1)). The linkage disequilibrium between A\*30:02, B\*18:01, DPB1\*02:02, and DRB3\*02:02 was so great (virtually 100%) that the effect of these alleles on this high-risk DRB1\*03:01 haplotype could not be distinguished. Some DRB1\*03:01-DRB3\*02:02 haplotypes, however, *do not* carry these alleles, associated with the high-risk DRB1\*03:01 haplotype, but still demonstrate a significant increase in DRB1\*03:01/\*03:01 case subjects versus DRB1\*03:01/\*04. In particular, DRB1\*03:01 haplotypes bearing DPB1\*03:01 can carry DRB3\*01:01 or DRB3\*02:02; those that carry DRB3\*02:02 are dramatically increased among the DRB1\*03:01/\*03:01 case subjects (*P* = 0.0006). These observations are consistent with the hypothesis that DRB3\*02:02 is not simply a marker of high-risk haplotypes but, in fact, increases the risk of DRB1\*03:01 haplotypes, particularly in DRB1\*03:01/\*03:01 homozygotes.

The amino acid sequence differences between DRB3\*01:01 and DRB3\*02:02 are substantial (10 differences). Consequently, DRB3\*02:02 is likely to have a different repertoire of peptide binding and presentation and may thus confer greater T1D risk than DRB3\*01:01 in certain genotype contexts (i.e., DRB1\*03:01/\*03:01 homozygotes) through altered peptide binding and T-cell repertoire. The crystallographic structure of the DRB3\*01:01 (DR52a) and the DRB3\*03:01 (DR52c) molecules have been reported recently (18,19). The P9 pocket of the DRB3\*02:02 (DR52b) differs substantially from that of DRB3\*01:01 (DR52a); in particular, it contains Tyr-37, Ala-38, Asp-57, and Tyr-60, making the pocket more accommodating to smaller, polar, or charged peptide residues. A structural model with bound peptide of the DRB3\*02:02 molecule with the differences highlighted is shown in [Supplementary Fig. 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1387/-/DC1).

The results presented here support the conclusion that the T1D risk of a given HLA haplotype is determined by specific combinations of alleles at a variety of HLA loci with genotype-dependent effects and support a role for the DRB3 locus in T1D susceptibility conferred by the DRB1\*03:01 haplotype.

# Supplementary Material

###### Supplementary Data

## ACKNOWLEDGMENTS

This research used resources provided by the T1DGC, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development, and JDRF and was supported by U01 DK-062418. This work also was supported in part by National Institutes of Health Grant DK-61722 (J.A.N.).

No potential conflicts of interest relevant to this article were reported.

H.A.E. designed the initial study, designed the second study with the support of the T1DGC Steering Committee, and drafted the manuscript. A.M.V. provided statistical analyses and edited the manuscript. S.L.M. contributed to the manuscript and with L.A.B. and K.R.M., they all contributed to generating sequence data. B.B.S. contributed to generating sequence data and edited the manuscript. J.A.T. provided samples for the first study and edited the manuscript. S.S.R. edited the manuscript. J.A.N. designed the second study with the support of the T1DGC Steering Committee and edited the manuscript. H.A.E. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

The authors are grateful to the Wellcome Trust and JDRF support for J.A.T., and also to Neil Walker and Helen Stevens, of the JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, for providing DNA samples and HLA typing information.

# REFERENCES