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Christiansen C, Potier L, Martin TC, Villicaña S, Castillo-Fernandez JE, Mangino M, Menni C, Tsai PC, Campbell PJ, Mullin S, Ordoñana JR, Monteagudo O, Sachdev PS, Mather KA, Trollor JN, Pietilainen KH, Ollikainen M, Dalgård C, Kyvik K, Christensen K, van Dongen J, Willemsen G, Boomsma DI, Magnusson PKE, Pedersen NL, Wilson SG, Grundberg E, Spector TD, Bell JT. Enhanced resolution profiling in twins reveals differential methylation signatures of type 2 diabetes with links to its complications. EBioMedicine 2024; 103:105096. [PMID: 38574408 PMCID: PMC11004697 DOI: 10.1016/j.ebiom.2024.105096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) susceptibility is influenced by genetic and environmental factors. Previous findings suggest DNA methylation as a potential mechanism in T2D pathogenesis and progression. METHODS We profiled DNA methylation in 248 blood samples from participants of European ancestry from 7 twin cohorts using a methylation sequencing platform targeting regulatory genomic regions encompassing 2,048,698 CpG sites. FINDINGS We find and replicate 3 previously unreported T2D differentially methylated CpG positions (T2D-DMPs) at FDR 5% in RGL3, NGB and OTX2, and 20 signals at FDR 25%, of which 14 replicated. Integrating genetic variation and T2D-discordant monozygotic twin analyses, we identify both genetic-based and genetic-independent T2D-DMPs. The signals annotate to genes with established GWAS and EWAS links to T2D and its complications, including blood pressure (RGL3) and eye disease (OTX2). INTERPRETATION The results help to improve our understanding of T2D disease pathogenesis and progression and may provide biomarkers for its complications. FUNDING Funding acknowledgements for each cohort can be found in the Supplementary Note.
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Affiliation(s)
| | - Louis Potier
- APHP, Paris Cité University, INSERM, Paris, France
| | | | | | | | | | | | - Pei-Chien Tsai
- King's College London, UK; Department of Biomedical Sciences, Chang Gung University, Taoyuan City, Taiwan; Molecular Infectious Disease Research Center, Chang Gung Memorial Hospital, Taoyuan City, Taiwan
| | - Purdey J Campbell
- Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Shelby Mullin
- Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia; School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
| | | | | | | | | | | | - Kirsi H Pietilainen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Finland; HealthyWeightHub, Abdominal Center, Helsinki University Hospital and University of Helsinki, Finland
| | - Miina Ollikainen
- Minerva Foundation Institute for Medical Research, Helsinki, Finland; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Finland
| | | | | | | | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, the Netherlands
| | | | | | - Scott G Wilson
- King's College London, UK; Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia; School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
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Ganekal P, Vastrad B, Kavatagimath S, Vastrad C, Kotrashetti S. Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59020309. [PMID: 36837510 PMCID: PMC9967176 DOI: 10.3390/medicina59020309] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/19/2023] [Accepted: 01/29/2023] [Indexed: 02/10/2023]
Abstract
Background and Objectives: A subject with diabetes and obesity is a class of the metabolic disorder. The current investigation aimed to elucidate the potential biomarker and prognostic targets in subjects with diabetes and obesity. Materials and Methods: The next-generation sequencing (NGS) data of GSE132831 was downloaded from Gene Expression Omnibus (GEO) database. Functional enrichment analysis of DEGs was conducted with ToppGene. The protein-protein interactions network, module analysis, target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed. Furthermore, hub genes were validated by receiver operating characteristic (ROC) analysis. A total of 872 DEGs, including 439 up-regulated genes and 433 down-regulated genes were observed. Results: Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for subjects with diabetes and obesity. The hub genes were validated in subjects with diabetes and obesity. Conclusion: This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for subjects with diabetes and obesity.
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Affiliation(s)
- Prashanth Ganekal
- Department of General Medicine, Basaveshwara Medical College, Chitradurga 577501, Karnataka, India
| | - Basavaraj Vastrad
- Department of Pharmaceutical Chemistry, K.L.E. College of Pharmacy, Gadag 582101, Karnataka, India
| | - Satish Kavatagimath
- Department of Pharmacognosy, K.L.E. College of Pharmacy, Belagavi 590010, Karnataka, India
| | - Chanabasayya Vastrad
- Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India
- Correspondence: ; Tel.: +91-9480073398
| | - Shivakumar Kotrashetti
- Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India
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Sex Differences in Coronary Artery Disease and Diabetes Revealed by scRNA-Seq and CITE-Seq of Human CD4+ T Cells. Int J Mol Sci 2022; 23:ijms23179875. [PMID: 36077273 PMCID: PMC9456056 DOI: 10.3390/ijms23179875] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 11/20/2022] Open
Abstract
Despite the decades-old knowledge that males and people with diabetes mellitus (DM) are at increased risk for coronary artery disease (CAD), the reasons for this association are only partially understood. Among the immune cells involved, recent evidence supports a critical role of T cells as drivers and modifiers of CAD. CD4+ T cells are commonly found in atherosclerotic plaques. We aimed to understand the relationship of CAD with sex and DM by single-cell RNA (scRNA-Seq) and antibody sequencing (CITE-Seq) of CD4+ T cells. Peripheral blood mononuclear cells (PBMCs) of 61 men and women who underwent cardiac catheterization were interrogated by scRNA-Seq combined with 49 surface markers (CITE-Seq). CAD severity was quantified using Gensini scores, with scores above 30 considered CAD+ and below 6 considered CAD-. Four pairs of groups were matched for clinical and demographic parameters. To test how sex and DM changed cell proportions and gene expression, we compared matched groups of men and women, as well as diabetic and non-diabetic subjects. We analyzed 41,782 single CD4+ T cell transcriptomes for sex differences in 16 women and 45 men with and without coronary artery disease and with and without DM. We identified 16 clusters in CD4+ T cells. The proportion of cells in CD4+ effector memory cluster 8 (CD4T8, CCR2+ Em) was significantly decreased in CAD+, especially among DM+ participants. This same cluster, CD4T8, was significantly decreased in female participants, along with two other CD4+ T cell clusters. In CD4+ T cells, 31 genes showed significant and coordinated upregulation in both CAD and DM. The DM gene signature was partially additive to the CAD gene signature. We conclude that (1) CAD and DM are clearly reflected in PBMC transcriptomes, and (2) significant differences exist between women and men and (3) between subjects with DM and non-DM.
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Yee J, Park T, Park M. Identification of the associations between genes and quantitative traits using entropy-based kernel density estimation. Genomics Inform 2022; 20:e17. [PMID: 35794697 PMCID: PMC9299569 DOI: 10.5808/gi.22033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/15/2022] [Indexed: 11/20/2022] Open
Abstract
Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.
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Affiliation(s)
- Jaeyong Yee
- Department of Physiology and Biophysics, Eulji University, Daejeon 34824, Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea
| | - Mira Park
- Department of Preventive Medicine, Eulji University, Daejeon 34824, Korea
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Kawai VK, Shi M, Liu G, Feng Q, Wei W, Chung CP, Walunas TL, Gordon AS, Linneman JG, Hebbring SJ, Harley JB, Cox NJ, Roden DM, Stein CM, Mosley JD. Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis. Lupus 2021; 30:1264-1272. [PMID: 33977795 PMCID: PMC8205989 DOI: 10.1177/09612033211014952] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
OBJECTIVES To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. METHODS Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. RESULTS The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10-5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. CONCLUSION A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.
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Affiliation(s)
- Vivian K. Kawai
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ge Liu
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - WeiQi Wei
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Cecilia P. Chung
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Rheumatology, Department of Medicine Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System - Nashville Campus
| | - Theresa L. Walunas
- Center for Health Information Partnerships, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Adam S. Gordon
- Center for Genetic Medicine, Northwestern University, Chicago, IL
| | - James G. Linneman
- Office of Research, Computing, and Analytics, Marshfield Clinic Research Institute, Marshfield, WI
| | - Scott J. Hebbring
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin USA
| | - John B. Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Cincinnati VA Medical Center, Cincinnati, OH, USA
| | - Nancy J. Cox
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Dan M. Roden
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - C. Michael Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan D. Mosley
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
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Yang BH, Wang K, Wan S, Liang Y, Yuan X, Dong Y, Cho S, Xu W, Jepsen K, Feng GS, Lu LF, Xue HH, Fu W. TCF1 and LEF1 Control Treg Competitive Survival and Tfr Development to Prevent Autoimmune Diseases. Cell Rep 2020; 27:3629-3645.e6. [PMID: 31216480 PMCID: PMC6701704 DOI: 10.1016/j.celrep.2019.05.061] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 04/26/2019] [Accepted: 05/17/2019] [Indexed: 12/22/2022] Open
Abstract
CD4+ Foxp3+ T regulatory (Treg) cells are key players in preventing lethal autoimmunity. Tregs undertake differentiation processes and acquire diverse functional properties. However, how Treg’s differentiation and functional specification are regulated remains incompletely understood. Here, we report that gradient expression of TCF1 and LEF1 distinguishes Tregs into three distinct subpopulations, particularly highlighting a subset of activated Treg (aTreg) cells. Treg-specific ablation of TCF1 and LEF1 renders the mice susceptible to systemic autoimmunity. TCF1 and LEF1 are dispensable for Treg’s suppressive capacity but essential for maintaining a normal aTreg pool and promoting Treg’s competitive survival. As a consequence, the development of T follicular regulatory (Tfr) cells, which are a subset of aTreg, is abolished in TCF1/LEF1-conditional knockout mice, leading to unrestrained T follicular helper (Tfh) and germinal center B cell responses. Thus, TCF1 and LEF1 act redundantly to control the maintenance and functional specification of Treg subsets to prevent autoimmunity. Transcriptional regulation of Treg differentiation and function remains incompletely understood. Yang et al. report that two TCF family transcription factors regulate the survival and functional specification of a subset of Treg cells to prevent autoimmunity.
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Affiliation(s)
- Bi-Huei Yang
- Pediatric Diabetes Research Center, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Ke Wang
- Pediatric Diabetes Research Center, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Shuo Wan
- Pediatric Diabetes Research Center, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Parasitology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Yan Liang
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA; PhD Program, Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Xiaomei Yuan
- Pediatric Diabetes Research Center, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Yi Dong
- Pediatric Diabetes Research Center, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Sunglim Cho
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Wanqing Xu
- Pediatric Diabetes Research Center, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Kristen Jepsen
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Gen-Sheng Feng
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA; Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Li-Fan Lu
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Hai-Hui Xue
- Department of Microbiology and Immunology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA; Iowa City Veterans Affairs Health Care System, Iowa City, IA 52246, USA.
| | - Wenxian Fu
- Pediatric Diabetes Research Center, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
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miR-22-3p is involved in gluconeogenic pathway modulated by 3,5-diiodo-L-thyronine (T2). Sci Rep 2019; 9:16645. [PMID: 31719576 PMCID: PMC6851083 DOI: 10.1038/s41598-019-53019-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 10/21/2019] [Indexed: 12/17/2022] Open
Abstract
The 3,5-diiodo-L-thyronine (T2) has emerged as an active iodothyronine and its beneficial effects on glucose metabolism including glucose tolerance and insulin resistance is well established. However, little is known about its molecular mechanisms. Given the emerging importance of microRNAs in various metabolic diseases, in this study a possible link between the effects of T2 on glucose metabolism and miRNA expression was investigated by using an in vivo model in which T2 was administered in rats receiving a high fat diet, a condition known to impair glucose homeostasis. The results showed that T2-treated rats had a better tolerance to glucose load and a better performance at the insulin tolerance test in comparison to high fat diet animals. Interestingly, in the serum of the animals treated with T2 there was a general decrease of miRNAs with miR-22a-3p, miR-34c-5p and miR-33a-3p significantly downregulated. Furthermore, miR-22a-3p had the largest variation pointing toward its preeminent role in T2 metabolic effect. In fact, in liver there was an up-regulation of its target (Transcription Factor 7) Tcf7, which had an important impact on gluconeogenesis. This study provide, for the first time, evidences that miRNAs are involved in the effects exerted by T2 on glucose homeostasis.
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Prabhu YD, Sekar N, Abilash VG. Screening of Polymorphisms of Transcription Factor 7-like 2 Gene in Polycystic Ovary Syndrome using Polymerase Chain Reaction-restriction Fragment Length Polymorphism Analysis. J Hum Reprod Sci 2018; 11:137-141. [PMID: 30158809 PMCID: PMC6094535 DOI: 10.4103/jhrs.jhrs_123_15] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder occurring in premenopausal women, with a prevalence rate of 5%–7%. It has been observed in multiple number of studies the coexistence between diabetes mellitus 2 and obesity with this endocrinopathic disorder. Transcription factor 7-like 2 (TCF7L2) gene is shown to be associated with insulin secretion. Aim: To screen whether the gene variant of TCF7L2 (formerly TCF4) gene is significantly associated and has susceptibilities with type 2 diabetes in PCOS. This study is essential to uncover diabetogenic association of the TCF7L2 gene variants with PCOS. Design: This was a hospital-based study. Methods: In this work, blood samples from 43 PCOS patients with age and sex similar to 43 control samples were collected, followed by isolation of DNA. Further genotyping of the TCF7L2 gene was carried out by performing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Statistical Analysis: Genotype frequencies of the TCF7L2 rs7903146 gene were checked by Hardy–Weinberg equilibrium of genotype in both PCOS and the control group, and also, the frequencies of the genotype were performed accordingly. Results: There was no significant allelic variation observed among the patient and the control samples. From the patient details, it was observed that women between the age group of 21 and 25 years are susceptible to PCOS. Conclusion: From the PCR-RFLP analysis, it can be stated that there are no expected gene polymorphisms seen in this study, unlike the study carried out on the Chinese population where they observed genotype variations CC, CT, and TT. From this study, we can conclude that TCF7L2 rs7903146 gene cannot be considered as the candidate gene for the occurrence of PCOS.
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Affiliation(s)
- Yogamaya D Prabhu
- Department of Biomedical Sciences, School of Bio Sciences and Technology, VIT, Vellore, Tamil Nadu, India
| | - Nishu Sekar
- Department of Biomedical Sciences, School of Bio Sciences and Technology, VIT, Vellore, Tamil Nadu, India.,Department of Biotechnology, FASH, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
| | - V G Abilash
- Department of Biomedical Sciences, School of Bio Sciences and Technology, VIT, Vellore, Tamil Nadu, India
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Campbell JE, Ussher JR, Mulvihill EE, Kolic J, Baggio LL, Cao X, Liu Y, Lamont BJ, Morii T, Streutker CJ, Tamarina N, Philipson LH, Wrana JL, MacDonald PE, Drucker DJ. TCF1 links GIPR signaling to the control of beta cell function and survival. Nat Med 2015; 22:84-90. [DOI: 10.1038/nm.3997] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 10/26/2015] [Indexed: 12/18/2022]
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10
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Kaur K, Vig S, Srivastava R, Mishra A, Singh VP, Srivastava AK, Datta M. Elevated Hepatic miR-22-3p Expression Impairs Gluconeogenesis by Silencing the Wnt-Responsive Transcription Factor Tcf7. Diabetes 2015; 64:3659-69. [PMID: 26193896 DOI: 10.2337/db14-1924] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Accepted: 06/29/2015] [Indexed: 11/13/2022]
Abstract
Levels of miR-22-3p, a highly abundant hepatic microRNA, are abnormally increased in mouse models of insulin resistance and type 2 diabetes, yet its contribution to deregulated hepatic metabolism under diseased states is not well understood. Here, we unravel a novel link between elevated hepatic miR-22-3p expression and impaired gluconeogenesis in diabetic db/db mice via the regulation of Tcf7 (transcription factor 7). Our data demonstrate that miR-22-3p binds to the 3' untranslated region of TCF7 and downregulates it, and this microRNA-mediated regulation of TCF7 increases the expression of enzymes of the gluconeogenic pathway in HepG2 cells. Small interfering RNA-mediated knockdown of TCF7 in HepG2 cells also causes similar upregulation of gluconeogenic genes. Furthermore, in vivo silencing of miR-22-3p by antagomiR administration lowered random as well as fasting glucose levels in diabetic mice. miR-22-3p antagonism improved glucose tolerance and insulin sensitivity. Importantly, the hepatic Tcf7 levels were restored along with reduced hepatic glucose output, which was also reflected by the decreased expression of gluconeogenic genes. Our results support a critical role for miR-22-3p and its target, Tcf7, in the pathogenesis of diabetes by upregulating gluconeogenesis. Moreover, targeting the miR-22/Tcf7/Wnt axis might hold therapeutic potential for the treatment of altered hepatic physiology during insulin resistance and type 2 diabetes.
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Affiliation(s)
- Kirandeep Kaur
- Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Saurabh Vig
- Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, New Delhi, India Academy of Scientific and Innovative Research, Training and Development Complex, Council of Scientific and Industrial Research Campus, Taramani, Chennai, India
| | - Rohit Srivastava
- Council of Scientific and Industrial Research-Central Drug Research Institute, Jankipuram Extension, Lucknow, India
| | - Akansha Mishra
- Council of Scientific and Industrial Research-Central Drug Research Institute, Jankipuram Extension, Lucknow, India
| | - Vijay Pal Singh
- Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Arvind K Srivastava
- Council of Scientific and Industrial Research-Central Drug Research Institute, Jankipuram Extension, Lucknow, India
| | - Malabika Datta
- Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, New Delhi, India Academy of Scientific and Innovative Research, Training and Development Complex, Council of Scientific and Industrial Research Campus, Taramani, Chennai, India
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Esplin ED, Oei L, Snyder MP. Personalized sequencing and the future of medicine: discovery, diagnosis and defeat of disease. Pharmacogenomics 2014; 15:1771-1790. [PMID: 25493570 DOI: 10.2217/pgs.14.117] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The potential for personalized sequencing to individually optimize medical treatment in diseases such as cancer and for pharmacogenomic application is just beginning to be realized, and the utility of sequencing healthy individuals for managing health is also being explored. The data produced requires additional advancements in interpretation of variants of unknown significance to maximize clinical benefit. Nevertheless, personalized sequencing, only recently applied to clinical medicine, has already been broadly applied to the discovery and study of disease. It is poised to enable the earlier and more accurate diagnosis of disease risk and occurrence, guide prevention and individualized intervention as well as facilitate monitoring of healthy and treated patients, and play a role in the prevention and recurrence of future disease. This article documents the advancing capacity of personalized sequencing, reviews its impact on disease-oriented scientific discovery and anticipates its role in the future of medicine.
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Affiliation(s)
- Edward D Esplin
- 300 Pasteur Drive, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
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12
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Fichna M, Żurawek M, Bratland E, Husebye ES, Kasperlik-Załuska A, Czarnocka B, Januszkiewicz-Lewandowska D, Nowak J. Interleukin-2 and subunit alpha of its soluble receptor in autoimmune Addison's disease--an association study and expression analysis. Autoimmunity 2014; 48:100-7. [PMID: 25347332 DOI: 10.3109/08916934.2014.976628] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Autoimmune Addison's disease (AAD) results from T cell-mediated destruction of the adrenal cortex, commonly accompanied by autoantibodies to 21-hydroxylase (21OH). In order to gain insight into the obscure aetiology of this disease, we investigated the roles of the IL2 and IL2RA genes, encoding interleukin-2 and subunit alpha of its receptor (IL2Ra), respectively. The association of AAD with IL2 and IL2RA polymorphisms (rs6822844, rs2069762, rs3136534, rs11594656, rs3118470 and rs2104286) was tested in 223 patients and 672 healthy controls. Functional studies consisted of gene expression analysis in cultured PBMCs exposed to 21OH and evaluation of serum interleukin by ELISA assays. The frequency of the minor C allele of rs3136534 was significantly decreased in AAD subjects compared to controls (OR 0.71; 95%CI 0.561-0.887; p = 0.003). Only AAD cells responded to 21OH with an elevated IL2 and IL2RA mRNA synthesis (p = 0.004 and p = 0.009 versus controls, respectively), paralleled by increased supernatant levels of both cytokines (p = 0.031 and p = 0.001 versus controls). IL2 mRNA level in 21OH-stimulated AAD PBMCs correlated negatively with age (p = 0.036) and positively with serum antibodies to 21OH (p = 0.006). Carriers of the rs2104286 AA genotype demonstrated higher IL2RA mRNA (p = 0.022) and soluble IL2Ra secretion (p = 0.029) upon 21OH stimulation. Serum interleukin-2 in AAD subjects was significantly higher compared to controls (4.61 ± 4.3 versus 1.71 ± 3.2 pg/mL, p < 0.001), whereas sIL2Ra levels remained similar in both groups (p = 0.885). In conclusion, the study reveals an association between AAD and IL2 locus. It confirms specific 21OH-directed reactivity of the peripheral AAD lymphocytes, which display increased synthesis of interleukin-2 and sIL2Ra.
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Affiliation(s)
- Marta Fichna
- Department of Endocrinology and Metabolism, Poznan University of Medical Sciences , Poznan , Poland
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Evidence of stage- and age-related heterogeneity of non-HLA SNPs and risk of islet autoimmunity and type 1 diabetes: the diabetes autoimmunity study in the young. Clin Dev Immunol 2013; 2013:417657. [PMID: 24367383 PMCID: PMC3866813 DOI: 10.1155/2013/417657] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Revised: 09/10/2013] [Accepted: 09/23/2013] [Indexed: 11/24/2022]
Abstract
Previously, we examined 20 non-HLA SNPs for association with islet autoimmunity (IA) and/or progression to type 1 diabetes (T1D). Our objective was to investigate fourteen additional non-HLA T1D candidate SNPs for stage- and age-related heterogeneity in the etiology of T1D. Of 1634 non-Hispanic white DAISY children genotyped, 132 developed IA (positive for GAD, insulin, or IA-2 autoantibodies at two or more consecutive visits); 50 IA positive children progressed to T1D. Cox regression was used to analyze risk of IA and progression to T1D in IA positive children. Restricted cubic splines were used to model SNPs when there was evidence that risk was not constant with age. C1QTNF6 (rs229541) predicted increased IA risk (HR: 1.57, CI: 1.20–2.05) but not progression to T1D (HR: 1.13, CI: 0.75–1.71). SNP (rs10517086) appears to exhibit an age-related effect on risk of IA, with increased risk before age 2 years (age 2 HR: 1.67, CI: 1.08–2.56) but not older ages (age 4 HR: 0.84, CI: 0.43–1.62). C1QTNF6 (rs229541), SNP (rs10517086), and UBASH3A (rs3788013) were associated with development of T1D. This prospective investigation of non-HLA T1D candidate loci shows that some SNPs may exhibit stage- and age-related heterogeneity in the etiology of T1D.
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14
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Short AD, Boag A, Catchpole B, Kennedy LJ, Massey J, Rothwell S, Husebye E, Ollier B. A candidate gene analysis of canine hypoadrenocorticism in 3 dog breeds. J Hered 2013; 104:807-20. [PMID: 23997205 DOI: 10.1093/jhered/est051] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Canine hypoadrenocorticism is believed to be an immune-related condition. It is rare in the overall dog population but shows a breed-related predisposition with Standard poodles and Portuguese water dogs having a greater prevalence of the condition. It shares many similarities with human primary adrenal insufficiency and is believed to be a naturally occurring, spontaneous model for the human condition. Short haplotype blocks and low levels of linkage disequilibrium in the human genome mean that the identification of genetic contributors to the condition requires large sample numbers. Pedigree dogs have high linkage disequilibrium and long haplotypes within a breed, increasing the potential of identifying novel genes that contribute to canine genetic disease. We investigated 222 SNPs from 42 genes that have been associated or may be implicated in human Addison's disease. We conducted case-control analyses in 3 pedigree dog breeds (Labrador retriever: affected n = 30, unaffected = 76; Cocker Spaniel: affected n = 19, unaffected = 53; Springer spaniel: affected n = 26, unaffected = 46) and identified 8 associated alleles in genes COL4A4, OSBPL9, CTLA4, PTPN22, and STXBP5 in 3 pedigree breeds. Association with immune response genes PTPN22 and CTLA4 in certain breeds suggests an underlying immunopathogenesis of the disease. These results suggest that canine hypoadrenocorticism could be a useful model for studying comparative genetics relevant to human Addison's disease.
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Affiliation(s)
- Andrea D Short
- the Centre for Integrated Genomic Medical Research, The University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, UK
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15
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Thessen Hedreul M, Möller S, Stridh P, Gupta Y, Gillett A, Daniel Beyeen A, Öckinger J, Flytzani S, Diez M, Olsson T, Jagodic M. Combining genetic mapping with genome-wide expression in experimental autoimmune encephalomyelitis highlights a gene network enriched for T cell functions and candidate genes regulating autoimmunity. Hum Mol Genet 2013; 22:4952-66. [PMID: 23900079 PMCID: PMC3836475 DOI: 10.1093/hmg/ddt343] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The experimental autoimmune encephalomyelitis (EAE) is an autoimmune disease of the central nervous system commonly used to study multiple sclerosis (MS). We combined clinical EAE phenotypes with genome-wide expression profiling in spleens from 150 backcross rats between susceptible DA and resistant PVG rat strains during the chronic EAE phase. This enabled correlation of transcripts with genotypes, other transcripts and clinical EAE phenotypes and implicated potential genetic causes and pathways in EAE. We detected 2285 expression quantitative trait loci (eQTLs). Sixty out of 599 cis-eQTLs overlapped well-known EAE QTLs and constitute positional candidate genes, including Ifit1 (Eae7), Atg7 (Eae20-22), Klrc3 (eEae22) and Mfsd4 (Eae17). A trans-eQTL that overlaps Eae23a regulated a large number of small RNAs and implicates a master regulator of transcription. We defined several disease-correlated networks enriched for pathways involved in cell-mediated immunity. They include C-type lectins, G protein coupled receptors, mitogen-activated protein kinases, transmembrane proteins, suppressors of transcription (Jundp2 and Nr1d1) and STAT transcription factors (Stat4) involved in interferon signaling. The most significant network was enriched for T cell functions, similar to genetic findings in MS, and revealed both established and novel gene interactions. Transcripts in the network have been associated with T cell proliferation and differentiation, the TCR signaling and regulation of regulatory T cells. A number of network genes and their family members have been associated with MS and/or other autoimmune diseases. Combining disease and genome-wide expression phenotypes provides a link between disease risk genes and distinct molecular pathways that are dysregulated during chronic autoimmune inflammation.
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Affiliation(s)
- Melanie Thessen Hedreul
- Department of Clinical Neuroscience, Neuroimmunology Unit, Center for Molecular Medicine L8:04, Karolinska Institutet, L8:04, 17176 Stockholm, Sweden
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16
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Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HYK, Chen R, Miriami E, Karczewski KJ, Hariharan M, Dewey FE, Cheng Y, Clark MJ, Im H, Habegger L, Balasubramanian S, O'Huallachain M, Dudley JT, Hillenmeyer S, Haraksingh R, Sharon D, Euskirchen G, Lacroute P, Bettinger K, Boyle AP, Kasowski M, Grubert F, Seki S, Garcia M, Whirl-Carrillo M, Gallardo M, Blasco MA, Greenberg PL, Snyder P, Klein TE, Altman RB, Butte AJ, Ashley EA, Gerstein M, Nadeau KC, Tang H, Snyder M. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012; 148:1293-307. [PMID: 22424236 DOI: 10.1016/j.cell.2012.02.009] [Citation(s) in RCA: 863] [Impact Index Per Article: 71.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2011] [Revised: 01/27/2012] [Accepted: 02/04/2012] [Indexed: 12/18/2022]
Abstract
Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.
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Affiliation(s)
- Rui Chen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
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17
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Chen R, Mias G, Li-Pook-Than J, Jiang L, Lam H, Chen R, Miriami E, Karczewski K, Hariharan M, Dewey F, Cheng Y, Clark M, Im H, Habegger L, Balasubramanian S, O'Huallachain M, Dudley J, Hillenmeyer S, Haraksingh R, Sharon D, Euskirchen G, Lacroute P, Bettinger K, Boyle A, Kasowski M, Grubert F, Seki S, Garcia M, Whirl-Carrillo M, Gallardo M, Blasco M, Greenberg P, Snyder P, Klein T, Altman R, Butte AJ, Ashley E, Gerstein M, Nadeau K, Tang H, Snyder M. Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes. Cell 2012. [DOI: 78495111110.1016/j.cell.2012.02.009' target='_blank'>'"<>78495111110.1016/j.cell.2012.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [78495111110.1016/j.cell.2012.02.009','', '10.1038/gene.2009.92')">Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
78495111110.1016/j.cell.2012.02.009" />
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18
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Zhang X, Ye L, Hu J, Tang W, Liu R, Yang M, Hong J, Wang W, Ning G, Gu W. Acute response of peripheral blood cell to autologous hematopoietic stem cell transplantation in type 1 diabetic patient. PLoS One 2012; 7:e31887. [PMID: 22384093 PMCID: PMC3285188 DOI: 10.1371/journal.pone.0031887] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2011] [Accepted: 01/17/2012] [Indexed: 01/28/2023] Open
Abstract
Objective Autologous nonmyeloablative hematopoietic stem cell transplantation (AHST) was the first therapeutic approach that can improve β cell function in type 1 diabetic (T1D) patients. This study was designed to investigate the potential mechanisms involved. Design and methods We applied AHST to nine T1D patients diagnosed within six months and analyzed the acute responses in peripheral blood for lymphocyte subpopulation as well as for genomic expression profiling at the six-month follow-up. Results We found six patients obtained insulin free (IF group) and three remained insulin dependent (ID group); C-peptide production was significantly higher in IF group compared to ID group. The acute responses in lymphocytes at six-month follow-up include declined CD3+CD4+, CD3+CD8+ T cell population and recovered B cell, NK cell population in both groups but with no significant differences between the two groups; most immune-related genes and pathways were up-regulated in peripheral blood mononuclear cell (PBMC) of both groups while none of transcription factors for immune regulatory component were significantly changed; the IF group demonstrated more AHST-modified genetic events than the ID group and distinct pattern of top pathways, co-expression network as well as ‘hub’ genes (eg, TCF7 and GZMA) were associated with each group. Conclusions AHST could improve the islet function in newly diagnosed T1D patients and elimination of the islet specific autoreactive T cells might be one of the mechanisms involved; T1D patients responded differently to AHST possibly due to the distinct transcriptional events occurring in PBMC. Trial Registration ClinicalTrials.gov NCT00807651
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Affiliation(s)
- Xiaofang Zhang
- Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai E-institute for Endocrinology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
| | - Lei Ye
- Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai E-institute for Endocrinology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
| | - Jiong Hu
- Department of Hematology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
| | - Wei Tang
- Department of Hematology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
| | - Ruixin Liu
- Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai E-institute for Endocrinology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
| | - Minglan Yang
- Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai E-institute for Endocrinology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
| | - Jie Hong
- Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai E-institute for Endocrinology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
| | - Weiqing Wang
- Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai E-institute for Endocrinology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
| | - Guang Ning
- Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai E-institute for Endocrinology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
- Laboratory for Endocrine and Metabolic Diseases, Institute of Health Science, Shanghai Institutes for Biological Sciences, School of Medicine, Shanghai JiaoTong University, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Weiqiong Gu
- Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai E-institute for Endocrinology, School of Medicine, Shanghai Jiaotong University, Ruijin Hospital, Shanghai, People's Republic of China
- * E-mail:
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Abstract
The canonical Wnt signaling pathway is evolutionarily conserved and plays key roles during development of many organ systems. This pathway utilizes TCF/LEF transcription factors, β-catenin coactivator, and TLE/GRG corepressors to achieve balanced regulation of its downstream gene expression. It is well established that several Wnt ligands and their effector proteins are crucial for normal T cell development. Recent studies have also revealed critical requirements for TCF-1 in generation and persistence of functional memory CD8(+) T cells, and in promoting Th2-differentiation and suppressing Th17-differentiation of activated CD4(+) T cells. Activation of β-catenin facilitated CD8(+) memory T cell formation, with enhanced protective capacity and extended survival of CD4(+) CD25(+) regulatory T cells. Upregulation of Wnt ligands was observed in Drosophila in response to Toll signaling as well as in mammalian dendritic cells and macrophages upon microbial stimulation. These new findings suggest that modulating the activity of Wnt pathway may be a powerful approach to enhance protective immunity and treat autoimmune diseases.
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Affiliation(s)
- Hai-Hui Xue
- Department of Microbiology, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.
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20
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Oestreich KJ, Huang AC, Weinmann AS. The lineage-defining factors T-bet and Bcl-6 collaborate to regulate Th1 gene expression patterns. ACTA ACUST UNITED AC 2011; 208:1001-13. [PMID: 21518797 PMCID: PMC3092354 DOI: 10.1084/jem.20102144] [Citation(s) in RCA: 125] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
T-bet acts as a functional repressor in association with Bcl-6 to antagonize SOCS1, SOCS3, TCF-1, and late-stage IFN-γ to regulate Th1 development. The T-box transcription factor T-bet is important for the differentiation of naive CD4+ T helper cells (Th cells) into the Th1 phenotype. Much is known about T-bet’s role as a transcriptional activator, but less is known about the mechanisms by which T-bet functionally represses alternative Th cell genetic programs. In this study, we first identify Socs1, Socs3, and Tcf7 (TCF-1) as gene targets that are negatively regulated by T-bet. Significantly, T-bet’s role in the repression of these genes is through a direct interaction with their promoters. Consistent with this, we identified two T-bet DNA-binding elements in the Socs1 promoter that are functionally used to down-regulate transcription in primary Th1 cells. Importantly, T-bet’s novel role in transcriptional repression is because of its ability to physically associate with, and functionally recruit, the transcriptional repressor Bcl-6 to a subset of promoters. Furthermore, T-bet functionally recruits Bcl-6 to the Ifng locus in late stages of Th1 differentiation to repress its activity, possibly to prevent the overproduction of IFN-γ, which could result in autoimmunity. Collectively, these data establish a novel mechanism for T-bet–mediated gene repression in which two lineage-defining transcription factors, one a classical activator and one a repressor, collaborate to promote and properly regulate Th1 development.
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Pociot F, Akolkar B, Concannon P, Erlich HA, Julier C, Morahan G, Nierras CR, Todd JA, Rich SS, Nerup J. Genetics of type 1 diabetes: what's next? Diabetes 2010; 59:1561-71. [PMID: 20587799 PMCID: PMC2889752 DOI: 10.2337/db10-0076] [Citation(s) in RCA: 229] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Flemming Pociot
- Department of Genome Biology, Hagedorn Research Institute, Gentofte, Denmark.
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22
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Julier C, Akolkar B, Concannon P, Morahan G, Nierras C, Pugliese A. The Type I Diabetes Genetics Consortium 'Rapid Response' family-based candidate gene study: strategy, genes selection, and main outcome. Genes Immun 2010; 10 Suppl 1:S121-7. [PMID: 19956109 DOI: 10.1038/gene.2009.99] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Candidate gene studies have long been the principal method for identification of susceptibility genes for type I diabetes (T1D), resulting in the discovery of HLA, INS, PTPN22, CTLA4, and IL2RA. However, many of the initial studies that relied on this strategy were largely underpowered, because of the limitations in genomic information and genotyping technology, as well as the limited size of available cohorts. The Type I Diabetes Genetic Consortium (T1DGC) has established resources to re-evaluate earlier reported genes associated with T1D, using its collection of 2298 Caucasian affected sib-pair families (with 11 159 individuals). A total of 382 single-nucleotide polymorphisms (SNPs) located in 21 T1D candidate genes were selected for this study and genotyped in duplicate on two platforms, Illumina and Sequenom. The genes were chosen based on published literature as having been either 'confirmed' (replicated) or not (candidates). This study showed several important features of genetic association studies. First, it showed the major impact of small rates of genotyping errors on association statistics. Second, it confirmed associations at INS, PTPN22, IL2RA, IFIH1 (earlier confirmed genes), and CTLA4 (earlier confirmed, with distinct SNPs) loci. Third, it did not find evidence for an association with T1D at SUMO4, despite confirmed association in Asian populations, suggesting the potential for population-specific gene effects. Fourth, at PTPN22, there was evidence for a novel contribution to T1D risk, independent of the replicated effect of the R620W variant. Fifth, among the candidate genes selected for replication, the association of TCF7-P19T with T1D was newly replicated in this study. In summary, this study was able to replicate some genetic effects, reject others, and provide suggestions of association with several of the other candidate genes in stratified analyses (age at onset, HLA status, population of origin). These results have generated additional interesting functional hypotheses that will require further replication in independent cohorts.
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Affiliation(s)
- C Julier
- INSERM U958, Centre National de Génotypage, 2 rue Gaston Crémieux, Evry, France.
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23
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Rich SS, Akolkar B, Concannon P, Erlich H, Hilner JE, Julier C, Morahan G, Nerup J, Nierras C, Pociot F, Todd JA. Overview of the Type I Diabetes Genetics Consortium. Genes Immun 2009; 10 Suppl 1:S1-4. [PMID: 19956093 PMCID: PMC2805448 DOI: 10.1038/gene.2009.84] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The Type I Diabetes Genetics Consortium (T1DGC) is an international, multicenter research program with two primary goals. The first goal is to identify genomic regions and candidate genes whose variants modify an individual's risk of type I diabetes (T1D) and help explain the clustering of the disease in families. The second goal is to make research data available to the research community and to establish resources that can be used by, and that are fully accessible to, the research community. To facilitate the access to these resources, the T1DGC has developed a Consortium Agreement (http://www.t1dgc.org) that specifies the rights and responsibilities of investigators who participate in Consortium activities. The T1DGC has assembled a resource of affected sib-pair families, parent-child trios, and case-control collections with banks of DNA, serum, plasma, and EBV-transformed cell lines. In addition, both candidate gene and genome-wide (linkage and association) studies have been performed and displayed in T1DBase (http://www.t1dbase.org) for all researchers to use in their own investigations. In this supplement, a subset of the T1DGC collection has been used to investigate earlier published candidate genes for T1D, to confirm the results from a genome-wide association scan for T1D, and to determine associations with candidate genes for other autoimmune diseases or with type II diabetes that may be involved with beta-cell function.
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Affiliation(s)
- S S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
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Howson JMM, Walker NM, Smyth DJ, Todd JA. Analysis of 19 genes for association with type I diabetes in the Type I Diabetes Genetics Consortium families. Genes Immun 2009; 10 Suppl 1:S74-84. [PMID: 19956106 PMCID: PMC2810493 DOI: 10.1038/gene.2009.96] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In recent years the pace of discovery of genetic associations with type I diabetes (T1D) has accelerated, with the total number of confirmed loci, including the major histocompatibility complex (MHC) region, reaching 43. However, much of the deciphering of the associations at these, and the established T1D loci, has yet to be performed in sufficient numbers of samples or with sufficient markers. Here, 257 single-nucleotide polymorphisms (SNPs) have been genotyped in 19 candidate genes (INS, PTPN22, IL2RA, CTLA4, IFIH1, SUMO4, VDR, PAX4, OAS1, IRS1, IL4, IL4R, IL13, IL12B, CEACAM21, CAPSL, Q7Z4c4(5Q), FOXP3, EFHB) in 2300 affected sib-pair families and tested for association with T1D as part of the Type I Diabetes Genetics Consortium's candidate gene study. The study had approximately 80% power at alpha=0.002 and a minor allele frequency of 0.2 to detect an effect with a relative risk (RR) of 1.20, which drops to just 40% power for a RR of 1.15. At the INS gene, rs689 (-23 HphI) was the most associated SNP (P=3.8 x 10(-31)), with the estimated RR=0.57 (95% confidence interval, 0.52-0.63). In addition, rs689 was associated with age-at-diagnosis of T1D (P=0.001), with homozygosity for the T1D protective T allele, delaying the onset of T1D by approximately 2 years in these families. At PTPN22, rs2476601 (R620W), in agreement with previous reports, was the most significantly associated SNP (P=6.9 x 10(-17)), with RR=1.55 (1.40-1.72). Evidence for association with T1D was observed for the IFIH1 SNP, rs1990760 (P=7.0 x 10(-4)), with RR=0.88 (0.82-0.95) and the CTLA4 SNP rs1427676 (P=0.0005), with RR=1.14 (1.06-1.23). In contrast, no convincing evidence of association was obtained for SUMO4, VDR, PAX4, OAS1, IRS1, IL4, IL4R, IL13, IL12B, CEACAM21 or CAPSL gene regions (http://www.T1DBase.org).
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Affiliation(s)
- J M M Howson
- Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, UK.
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