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Bao K, Cui Z, Wang H, Xiao H, Li T, Kong X, Liu T. Pseudotime Ordering Single-Cell Transcriptomic of β Cells Pancreatic Islets in Health and Type 2 Diabetes. PHENOMICS (CHAM, SWITZERLAND) 2021; 1:199-210. [PMID: 36939754 PMCID: PMC9590480 DOI: 10.1007/s43657-021-00024-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/21/2021] [Accepted: 08/27/2021] [Indexed: 12/17/2022]
Abstract
β cells are defined by the ability to produce and secret insulin. Recent studies have evaluated that human pancreatic β cells are heterogeneous and demonstrated the transcript alterations of β cell subpopulation in diabetes. Single-cell RNA sequence (scRNA-seq) analysis helps us to refine the cell types signatures and understand the role of the β cells during metabolic challenges and diseases. Here, we construct the pseudotime trajectory of β cells from publicly available scRNA-seq data in health and type 2 diabetes (T2D) based on highly dispersed and highly expressed genes using Monocle2. We identified three major states including 1) Normal branch, 2) Obesity-like branch and 3) T2D-like branch based on biomarker genes and genes that give rise to bifurcation in the trajectory. β cell function-maintain-related genes, insulin expression-related genes, and T2D-related genes enriched in three branches, respectively. Continuous pseudotime spectrum might suggest that β cells transition among different states. The application of pseudotime analysis is conducted to clarify the different cell states, providing novel insights into the pathology of β cells in T2D. Supplementary Information The online version contains supplementary material is available at 10.1007/s43657-021-00024-z.
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Affiliation(s)
- Kaixuan Bao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai, 201203 China
| | - Zhicheng Cui
- State Key Laboratory of Genetic Engineering, School of Life Sciences, and Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, 200438 China
| | - Hui Wang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200433 China
| | - Hui Xiao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai, 201203 China
| | - Ting Li
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai, 201203 China
| | - Xingxing Kong
- State Key Laboratory of Genetic Engineering, School of Life Sciences, and Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, 200438 China
| | - Tiemin Liu
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai, 201203 China
- State Key Laboratory of Genetic Engineering, School of Life Sciences, and Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, 200438 China
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200433 China
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032 China
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52
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Supruniuk E, Żebrowska E, Chabowski A. Branched chain amino acids-friend or foe in the control of energy substrate turnover and insulin sensitivity? Crit Rev Food Sci Nutr 2021; 63:2559-2597. [PMID: 34542351 DOI: 10.1080/10408398.2021.1977910] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Branched chain amino acids (BCAA) and their derivatives are bioactive molecules with pleiotropic functions in the human body. Elevated fasting blood BCAA concentrations are considered as a metabolic hallmark of obesity, insulin resistance, dyslipidaemia, nonalcoholic fatty liver disease, type 2 diabetes and cardiovascular disease. However, since increased BCAA amount is observed both in metabolically healthy and obese subjects, a question whether BCAA are mechanistic drivers of insulin resistance and its morbidities or only markers of metabolic dysregulation, still remains open. The beneficial effects of BCAA on body weight and composition, aerobic capacity, insulin secretion and sensitivity demand high catabolic potential toward amino acids and/or adequate BCAA intake. On the opposite, BCAA-related inhibition of lipogenesis and lipolysis enhancement may preclude impairment in insulin sensitivity. Thereby, the following review addresses various strategies pertaining to the modulation of BCAA catabolism and the possible roles of BCAA in energy homeostasis. We also aim to elucidate mechanisms behind the heterogeneity of ramifications associated with BCAA modulation.
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Affiliation(s)
- Elżbieta Supruniuk
- Department of Physiology, Medical University of Bialystok, Bialystok, Poland
| | - Ewa Żebrowska
- Department of Physiology, Medical University of Bialystok, Bialystok, Poland
| | - Adrian Chabowski
- Department of Physiology, Medical University of Bialystok, Bialystok, Poland
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Lu L, Zhuang L, Shen X, Yang L. Glucotoxicity Activation of IL6 and IL11 and Subsequent Induction of Fibrosis May Be Involved in the Pathogenesis of Islet Dysfunction. Front Mol Biosci 2021; 8:708127. [PMID: 34497830 PMCID: PMC8419433 DOI: 10.3389/fmolb.2021.708127] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/02/2021] [Indexed: 12/26/2022] Open
Abstract
Background: Islet dysfunction is the main pathological process of type 2 diabetes mellitus (T2DM). Fibrosis causes islet dysfunction, but the current mechanism is still unclear. Here, bioinformatics analysis identified gene clusters closely related to T2DM and differentially expressed genes related to fibrosis, and animal models verified the roles of these genes. Methods: Human islet transcriptomic datasets were obtained from the Gene Expression Omnibus (GEO), and weighted gene coexpression network analysis (WGCNA) was applied to screen the key gene modules related to T2DM and analyze the correlations between the modules and clinical characteristics. Enrichment analysis was performed to identify the functions and pathways of the key module genes. WGCNA, protein-protein interaction (PPI) analysis and receiver operating characteristic (ROC) curve analysis were used to screen the hub genes. The hub genes were verified in another GEO dataset, the islets of high-fat diet (HFD)-fed Sprague-Dawley rats were observed by H&E and Masson’s trichrome staining, the fibrotic proteins were verified by immunofluorescence, and the hub genes were tested by immunohistochemistry. Results: The top 5,000 genes were selected according to the median absolute deviation, and 18 modules were analyzed. The yellow module was highly associated with T2DM, and its positive correlation with glycated hemoglobin (HbA1c) was significantly stronger than that with body mass index (BMI). Enrichment analysis revealed that extracellular matrix organization, the collagen-containing extracellular matrix and cytokine−cytokine receptor interaction might influence T2DM progression. The top three hub genes, interleukin 6 (IL6), IL11 and prostaglandin-endoperoxide synthase 2 (PTGS2), showed upregulated expression in T2DM. In the validation dataset, IL6, IL11, and PTGS2 levels were upregulated in T2DM, and IL6 and PTGS2 expression was positively correlated with HbA1c and BMI; however, IL11 was positively correlated only with HbA1c. In HFD-fed Sprague-Dawley rats, the positive of IL6 and IL11 in islets was stronger, but PTGS2 expression was not significantly altered. The extent of fibrosis, irregular cellular arrangement and positive actin alpha 2 (ACTA2) staining in islets was significantly greater in HFD-fed rats than in normal diet-fed rats. Conclusion: Glucotoxicity is a major factor leading to increased IL6 and IL11 expression, and IL6-and IL11-induced fibrosis might be involved in islet dysfunction.
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Affiliation(s)
- Liqin Lu
- Endocrinology Department, The First Affifiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Lili Zhuang
- Endocrinology Department, The First Affifiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ximei Shen
- Endocrinology Department, The First Affifiliated Hospital of Fujian Medical University, Fuzhou, China.,Diabetes Research Institute of Fujian Province, Fuzhou, China
| | - Liyong Yang
- Endocrinology Department, The First Affifiliated Hospital of Fujian Medical University, Fuzhou, China.,Diabetes Research Institute of Fujian Province, Fuzhou, China
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54
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Brown MR, Holmes H, Rakshit K, Javeed N, Her TK, Stiller AA, Sen S, Shull GE, Prakash YS, Romero MF, Matveyenko AV. Electrogenic sodium bicarbonate cotransporter NBCe1 regulates pancreatic β cell function in type 2 diabetes. J Clin Invest 2021; 131:142365. [PMID: 34623331 DOI: 10.1172/jci142365] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 07/22/2021] [Indexed: 12/13/2022] Open
Abstract
Pancreatic β cell failure in type 2 diabetes mellitus (T2DM) is attributed to perturbations of the β cell's transcriptional landscape resulting in impaired glucose-stimulated insulin secretion. Recent studies identified SLC4A4 (a gene encoding an electrogenic Na+-coupled HCO3- cotransporter and intracellular pH regulator, NBCe1) as one of the misexpressed genes in β cells of patients with T2DM. Thus, in the current study, we set out to test the hypothesis that misexpression of SLC4A4/NBCe1 in T2DM β cells contributes to β cell dysfunction and impaired glucose homeostasis. To address this hypothesis, we first confirmed induction of SLC4A4/NBCe1 expression in β cells of patients with T2DM and demonstrated that its expression was associated with loss of β cell transcriptional identity, intracellular alkalinization, and β cell dysfunction. In addition, we generated a β cell-selective Slc4a4/NBCe1-KO mouse model and found that these mice were protected from diet-induced metabolic stress and β cell dysfunction. Importantly, improved glucose tolerance and enhanced β cell function in Slc4a4/NBCe1-deficient mice were due to augmented mitochondrial function and increased expression of genes regulating β cell identity and function. These results suggest that increased β cell expression of SLC4A4/NBCe1 in T2DM plays a contributory role in promotion of β cell failure and should be considered as a potential therapeutic target.
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Affiliation(s)
- Matthew R Brown
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Heather Holmes
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Kuntol Rakshit
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Naureen Javeed
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Tracy K Her
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Alison A Stiller
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Satish Sen
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Gary E Shull
- Department of Molecular Genetics, Biochemistry, and Microbiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Y S Prakash
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA.,Department of Anesthesiology
| | - Michael F Romero
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA.,Division of Nephrology and Hypertension and
| | - Aleksey V Matveyenko
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA.,Division of Endocrinology, Metabolism, Diabetes, and Nutrition, Department of Medicine, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
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55
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Jeong J, Jang S, Park S, Kwon W, Kim SY, Jang S, Ko J, Park SJ, Lim SG, Yoon D, Yi J, Lee S, Kim MO, Choi SK, Ryoo ZY. JAZF1 heterozygous knockout mice show altered adipose development and metabolism. Cell Biosci 2021; 11:161. [PMID: 34407873 PMCID: PMC8375039 DOI: 10.1186/s13578-021-00625-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 06/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Juxtaposed with another zinc finger protein 1 (JAZF1) is associated with metabolic disorders, including type 2 diabetes mellitus (T2DM). Several studies showed that JAZF1 and body fat mass are closely related. We attempted to elucidate the JAZF1 functions on adipose development and related metabolism using in vitro and in vivo models. RESULTS The JAZF1 expression was precisely regulated during adipocyte differentiation of 3T3-L1 preadipocyte and mouse embryonic fibroblasts (MEFs). Homozygous JAZF1 deletion (JAZF1-KO) resulted in impaired adipocyte differentiation in MEF. The JAZF1 role in adipocyte differentiation was demonstrated by the regulation of PPARγ-a key regulator of adipocyte differentiation. Heterozygous JAZF1 deletion (JAZF1-Het) mice fed a normal diet (ND) or a high-fat diet (HFD) had less adipose tissue mass and impaired glucose homeostasis than the control (JAZF1-Cont) mice. However, other metabolic organs, such as brown adipose tissue and liver, were negligible effect on JAZF1 deficiency. CONCLUSION Our findings emphasized the JAZF1 role in adipocyte differentiation and related metabolism through the heterozygous knockout mice. This study provides new insights into the JAZF1 function in adipose development and metabolism, informing strategies for treating obesity and related metabolic disorders.
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Affiliation(s)
- Jain Jeong
- Digestive Diseases Section, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Soyoung Jang
- School of Life Sciences, BK21 FOUR KNU Creative BioResearch, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Song Park
- Core Protein Resources Center, DGIST, Daegu, 42988, Republic of Korea.,Department of Brain and Cognitive Sciences, DGIST, Daegu, Republic of Korea
| | - Wookbong Kwon
- Division of Biotechnology, DGIST, Daegu, Republic of Korea
| | - Si-Yong Kim
- School of Life Sciences, BK21 FOUR KNU Creative BioResearch, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Soyoen Jang
- School of Life Sciences, BK21 FOUR KNU Creative BioResearch, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Jiwon Ko
- School of Life Sciences, BK21 FOUR KNU Creative BioResearch, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Si Jun Park
- School of Life Sciences, BK21 FOUR KNU Creative BioResearch, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Su-Geun Lim
- School of Life Sciences, BK21 FOUR KNU Creative BioResearch, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Duhak Yoon
- Department of Animal Science, Kyungpook National University, Daegu, 37224, Republic of Korea
| | - Junkoo Yi
- Gyeongsangbukdo Livestock Research Institute, Yeongju, Republic of Korea
| | - Sanggyu Lee
- School of Life Sciences, BK21 FOUR KNU Creative BioResearch, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Myoung Ok Kim
- School of Animal Science and Biotechnology, Kyungpook National University, Daegu, Korea
| | - Seong-Kyoon Choi
- Core Protein Resources Center, DGIST, Daegu, 42988, Republic of Korea. .,Division of Biotechnology, DGIST, Daegu, Republic of Korea.
| | - Zae Young Ryoo
- School of Life Sciences, BK21 FOUR KNU Creative BioResearch, Kyungpook National University, Daegu, 41566, Republic of Korea.
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56
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Ustianowski P, Malinowski D, Kopytko P, Czerewaty M, Tarnowski M, Dziedziejko V, Safranow K, Pawlik A. ADCY5, CAPN10 and JAZF1 Gene Polymorphisms and Placental Expression in Women with Gestational Diabetes. Life (Basel) 2021; 11:life11080806. [PMID: 34440550 PMCID: PMC8399092 DOI: 10.3390/life11080806] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/06/2021] [Accepted: 08/07/2021] [Indexed: 12/12/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is carbohydrate intolerance that occurs during pregnancy. This disease may lead to various maternal and neonatal complications; therefore, early diagnosis is very important. Because of the similarity in pathogenesis of type 2 diabetes and GDM, the genetic variants associated with type 2 diabetes are commonly investigated in GDM. The aim of the present study was to examine the associations between the polymorphisms in the ADCY5 (rs11708067, rs2877716), CAPN10 (rs2975760, rs3792267), and JAZF1 (rs864745) genes and GDM as well as to determine the expression of these genes in the placenta. This study included 272 pregnant women with GDM and 348 pregnant women with normal glucose tolerance. The diagnosis of GDM was based on a 75 g oral glucose tolerance test (OGTT) at 24–28 weeks gestation, according to International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. There were no statistically significant differences in the distribution of the ADCY5 gene (rs11708067, rs2877716) and CAPN10 gene (rs2975760, rs3792267) polymorphisms between pregnant women with normal carbohydrate tolerance and pregnant women with GDM. We have shown a lower frequency of JAZF1 gene rs864745 C allele carriers among women with GDM CC + CT vs. TT (OR = 0.60, 95% CI = 0.41–0.87, p = 0.006), and C vs. T (OR = 0.75, 95% CI = 0.60–0.95, p = 0.014). In addition, ADCY5 and JAZF1 gene expression was statistically significantly increased in the placentas of women with GDM compared with that of healthy women. The expression of the CAPN10 gene did not differ significantly between women with and without GDM. Our results indicate increased expression of JAZF1 and ADCY5 genes in the placentas of women with GDM as well as a protective effect of the C allele of the JAZF1 rs864745 gene polymorphism on the development of GDM in pregnant women.
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Affiliation(s)
- Przemysław Ustianowski
- Department of Obstetrics and Gynecology, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Damian Malinowski
- Department of Experimental and Clinical Pharmacology, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Patrycja Kopytko
- Department of Physiology, Pomeranian Medical University, 70-111 Szczecin, Poland; (P.K.); (M.C.); (M.T.)
| | - Michał Czerewaty
- Department of Physiology, Pomeranian Medical University, 70-111 Szczecin, Poland; (P.K.); (M.C.); (M.T.)
| | - Maciej Tarnowski
- Department of Physiology, Pomeranian Medical University, 70-111 Szczecin, Poland; (P.K.); (M.C.); (M.T.)
| | - Violetta Dziedziejko
- Department of Biochemistry and Medical Chemistry, Pomeranian Medical University, 70-111 Szczecin, Poland; (V.D.); (K.S.)
| | - Krzysztof Safranow
- Department of Biochemistry and Medical Chemistry, Pomeranian Medical University, 70-111 Szczecin, Poland; (V.D.); (K.S.)
| | - Andrzej Pawlik
- Department of Physiology, Pomeranian Medical University, 70-111 Szczecin, Poland; (P.K.); (M.C.); (M.T.)
- Correspondence:
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Abstract
Free fatty acids (FFAs) are implicated in the pathogenesis of metabolic diseases that includes obesity, type 2 diabetes mellitus, and cardiovascular disease (CVD). FFAs serve as ligands for free fatty acid receptors (FFARs) that belong to the family of rhodopsin-like G protein-coupled receptors (GPCRs) and are expressed throughout the body to maintain energy homeostasis under changing nutritional conditions. Free fatty acid receptor 4 (FFAR4), also known as G protein-coupled receptor 120, is a long-chain fatty acid receptor highly expressed in adipocytes, endothelial cells, and macrophages. Activation of FFAR4 helps maintain metabolic homeostasis by regulating adipogenesis, insulin sensitivity, and inflammation. Furthermore, dysfunction of FFAR4 is associated with insulin resistance, obesity, and eccentric remodeling in both humans and mice, making FFAR4 an attractive therapeutic target for treating or preventing metabolic diseases. While much of the previous literature on FFAR4 has focused on its role in obesity and diabetes, recent studies have demonstrated that FFAR4 may also play an important role in the development of atherosclerosis and CVD. Most notably, FFAR4 activation reduces monocyte-endothelial cell interaction, enhances cholesterol efflux from macrophages, reduces lesion size in atherogenic mouse models, and stimulates oxylipin production in myocytes that functions in a feed-forward cardioprotective mechanism. This review will focus on the role of FFAR4 in metabolic diseases and highlights an underappreciated role of FFAR4 in the development of atherosclerosis and CVD.
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Affiliation(s)
- Gage M Stuttgen
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Daisy Sahoo
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Medicine, Division of Endocrinology & Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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58
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Chung Y, Lee H. Correlation between Alzheimer's disease and type 2 diabetes using non-negative matrix factorization. Sci Rep 2021; 11:15265. [PMID: 34315930 PMCID: PMC8316581 DOI: 10.1038/s41598-021-94048-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/24/2021] [Indexed: 02/07/2023] Open
Abstract
Alzheimer's disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal-Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments.
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Affiliation(s)
- Yeonwoo Chung
- grid.61221.360000 0001 1033 9831School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Korea.
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Muhammad SA, Qousain Naqvi ST, Nguyen T, Wu X, Munir F, Jamshed MB, Zhang Q. Cisplatin's potential for type 2 diabetes repositioning by inhibiting CDKN1A, FAS, and SESN1. Comput Biol Med 2021; 135:104640. [PMID: 34261004 DOI: 10.1016/j.compbiomed.2021.104640] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 12/16/2022]
Abstract
Cisplatin is a DNA-damaging chemotherapeutic agent used for treating cancer. Based on cDNA dataset analysis, we investigated how cisplatin modified gene expression and observed cisplatin-induced dysregulation and system-level variations relating to insulin resistance and type 2 diabetes mellitus (T2DM). T2DM is a multifactorial disease affecting 462 million people in the world, and drug-induced T2DM is a serious issue. To understand this etiology, we designed an integrative, system-level study to identify associations between cisplatin-induced differentially expressed genes (DEGs) and T2DM. From a list of differential expressed genes, cisplatin downregulated the cyclin-dependent kinase inhibitor 1 (CDKN1A), tumor necrosis factor (FAS), and sestrin-1 (SESN1) genes responsible for modifying signaling pathways, including the p53, JAK-STAT, FOXO, MAPK, mTOR, P13-AKT, Toll-like receptor (TLR), adipocytokine, and insulin signaling pathways. These enriched pathways were expressively associated with the disease. We observed significant gene signatures, including SMAD3, IRS, PDK1, PRKAA1, AKT, SOS, RAS, GRB2, MEK1/2, and ERK, interacting with source genes. This study revealed the value of system genetics for identifying the cisplatin-induced genetic variants responsible for the progression of T2DM. Also, by cross-validating gene expression data for T2DM islets, we found that downregulating IRS and PRK families is critical in insulin and T2DM signaling pathways. Cisplatin, by inhibiting CDKN1A, FAS, and SESN1, promotes IRS and PRK activity in a similar way to rosiglitazone (a popular drug used for T2DM treatment). Our integrative, network-based approach can help in understanding the drug-induced pathophysiological mechanisms of diabetes.
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Affiliation(s)
- Syed Aun Muhammad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan.
| | | | - Thanh Nguyen
- Informatics Institute, School of Medicine, The University of Alabama, Birmingham, AL, USA
| | - Xiaogang Wu
- The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fahad Munir
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Muhammad Babar Jamshed
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - QiYu Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
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MicroRNA Sequences Modulated by Beta Cell Lipid Metabolism: Implications for Type 2 Diabetes Mellitus. BIOLOGY 2021; 10:biology10060534. [PMID: 34203703 PMCID: PMC8232095 DOI: 10.3390/biology10060534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 12/23/2022]
Abstract
Alterations in lipid metabolism within beta cells and islets contributes to dysfunction and apoptosis of beta cells, leading to loss of insulin secretion and the onset of type 2 diabetes. Over the last decade, there has been an explosion of interest in understanding the landscape of gene expression which influences beta cell function, including the importance of small non-coding microRNA sequences in this context. This review sought to identify the microRNA sequences regulated by metabolic challenges in beta cells and islets, their targets, highlight their function and assess their possible relevance as biomarkers of disease progression in diabetic individuals. Predictive analysis was used to explore networks of genes targeted by these microRNA sequences, which may offer new therapeutic strategies to protect beta cell function and delay the onset of type 2 diabetes.
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61
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Rian K, Hidalgo MR, Çubuk C, Falco MM, Loucera C, Esteban-Medina M, Alamo-Alvarez I, Peña-Chilet M, Dopazo J. Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data. Comput Struct Biotechnol J 2021; 19:2968-2978. [PMID: 34136096 PMCID: PMC8170118 DOI: 10.1016/j.csbj.2021.05.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/21/2021] [Accepted: 05/11/2021] [Indexed: 12/13/2022] Open
Abstract
Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios.
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Affiliation(s)
- Kinza Rian
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Laboratory of Innovative Technologies (LTI), National School of Applied Sciences in Tangier, UAE, Morocco
| | - Marta R. Hidalgo
- Bioinformatics and Biostatistics Unit, Centro de Investigación Príncipe Felipe (CIPF), 46012 Valencia, Spain
| | - Cankut Çubuk
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Matias M. Falco
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla 41013, Spain
| | - Carlos Loucera
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
| | - Marina Esteban-Medina
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
| | - Inmaculada Alamo-Alvarez
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
| | - María Peña-Chilet
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
| | - Joaquín Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
- Functional Genomics Node (INB-ELIXIR-es), Sevilla, Spain
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Kobiita A, Godbersen S, Araldi E, Ghoshdastider U, Schmid MW, Spinas G, Moch H, Stoffel M. The Diabetes Gene JAZF1 Is Essential for the Homeostatic Control of Ribosome Biogenesis and Function in Metabolic Stress. Cell Rep 2021; 32:107846. [PMID: 32640216 DOI: 10.1016/j.celrep.2020.107846] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/23/2020] [Accepted: 06/11/2020] [Indexed: 02/07/2023] Open
Abstract
The ability of pancreatic β-cells to respond to increased demands for insulin during metabolic stress critically depends on proper ribosome homeostasis and function. Excessive and long-lasting stimulation of insulin secretion can elicit endoplasmic reticulum (ER) stress, unfolded protein response, and β-cell apoptosis. Here we show that the diabetes susceptibility gene JAZF1 is a key transcriptional regulator of ribosome biogenesis, global protein, and insulin translation. JAZF1 is excluded from the nucleus, and its expression levels are reduced upon metabolic stress and in diabetes. Genetic deletion of Jazf1 results in global impairment of protein synthesis that is mediated by defects in ribosomal protein synthesis, ribosomal RNA processing, and aminoacyl-synthetase expression, thereby inducing ER stress and increasing β-cell susceptibility to apoptosis. Importantly, JAZF1 function and its pleiotropic actions are impaired in islets of murine T2D and in human islets exposed to metabolic stress. Our study identifies JAZF1 as a central mediator of metabolic stress in β-cells.
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Affiliation(s)
- Ahmad Kobiita
- Institute of Molecular Health Sciences, ETH Zurich, Otto-Stern-Weg 7, HPL H36, 8093 Zürich, Switzerland
| | - Svenja Godbersen
- Institute of Molecular Health Sciences, ETH Zurich, Otto-Stern-Weg 7, HPL H36, 8093 Zürich, Switzerland
| | - Elisa Araldi
- Institute of Molecular Health Sciences, ETH Zurich, Otto-Stern-Weg 7, HPL H36, 8093 Zürich, Switzerland
| | - Umesh Ghoshdastider
- Institute of Molecular Health Sciences, ETH Zurich, Otto-Stern-Weg 7, HPL H36, 8093 Zürich, Switzerland
| | - Marc W Schmid
- MWSchmid GmbH, Möhrlistrasse 25, 8006 Zurich, Switzerland
| | - Giatgen Spinas
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitäts-Spital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University and University Hospital Zürich, Schmelzbergstrasse 12, 8091 Zürich, Switzerland
| | - Markus Stoffel
- Institute of Molecular Health Sciences, ETH Zurich, Otto-Stern-Weg 7, HPL H36, 8093 Zürich, Switzerland; Medical Faculty, University of Zurich, Zurich, Switzerland.
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63
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Benáková Š, Holendová B, Plecitá-Hlavatá L. Redox Homeostasis in Pancreatic β-Cells: From Development to Failure. Antioxidants (Basel) 2021; 10:antiox10040526. [PMID: 33801681 PMCID: PMC8065646 DOI: 10.3390/antiox10040526] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/16/2022] Open
Abstract
Redox status is a key determinant in the fate of β-cell. These cells are not primarily detoxifying and thus do not possess extensive antioxidant defense machinery. However, they show a wide range of redox regulating proteins, such as peroxiredoxins, thioredoxins or thioredoxin reductases, etc., being functionally compartmentalized within the cells. They keep fragile redox homeostasis and serve as messengers and amplifiers of redox signaling. β-cells require proper redox signaling already in cell ontogenesis during the development of mature β-cells from their progenitors. We bring details about redox-regulated signaling pathways and transcription factors being essential for proper differentiation and maturation of functional β-cells and their proliferation and insulin expression/maturation. We briefly highlight the targets of redox signaling in the insulin secretory pathway and focus more on possible targets of extracellular redox signaling through secreted thioredoxin1 and thioredoxin reductase1. Tuned redox homeostasis can switch upon chronic pathological insults towards the dysfunction of β-cells and to glucose intolerance. These are characteristics of type 2 diabetes, which is often linked to chronic nutritional overload being nowadays a pandemic feature of lifestyle. Overcharged β-cell metabolism causes pressure on proteostasis in the endoplasmic reticulum, mainly due to increased demand on insulin synthesis, which establishes unfolded protein response and insulin misfolding along with excessive hydrogen peroxide production. This together with redox dysbalance in cytoplasm and mitochondria due to enhanced nutritional pressure impact β-cell redox homeostasis and establish prooxidative metabolism. This can further affect β-cell communication in pancreatic islets through gap junctions. In parallel, peripheral tissues losing insulin sensitivity and overall impairment of glucose tolerance and gut microbiota establish local proinflammatory signaling and later systemic metainflammation, i.e., low chronic inflammation prooxidative properties, which target β-cells leading to their dedifferentiation, dysfunction and eventually cell death.
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Affiliation(s)
- Štěpánka Benáková
- Department of Mitochondrial Physiology, Institute of Physiology, Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; (Š.B.); (B.H.)
- First Faculty of Medicine, Charles University, Katerinska 1660/32, 121 08 Prague, Czech Republic
| | - Blanka Holendová
- Department of Mitochondrial Physiology, Institute of Physiology, Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; (Š.B.); (B.H.)
| | - Lydie Plecitá-Hlavatá
- Department of Mitochondrial Physiology, Institute of Physiology, Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; (Š.B.); (B.H.)
- Department of Mitochondrial Physiology, Czech Academy of Sciences, Videnska 1083, 142 20 Prague 4, Czech Republic
- Correspondence: ; Tel.: +420-296-442-285
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64
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Ghislain J, Poitout V. Targeting lipid GPCRs to treat type 2 diabetes mellitus - progress and challenges. Nat Rev Endocrinol 2021; 17:162-175. [PMID: 33495605 DOI: 10.1038/s41574-020-00459-w] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/04/2020] [Indexed: 02/07/2023]
Abstract
Therapeutic approaches to the treatment of type 2 diabetes mellitus that are designed to increase insulin secretion either directly target β-cells or indirectly target gastrointestinal enteroendocrine cells (EECs), which release hormones that modulate insulin secretion (for example, incretins). Given that β-cells and EECs both express a large array of G protein-coupled receptors (GPCRs) that modulate insulin secretion, considerable research and development efforts have been undertaken to design therapeutic drugs targeting these GPCRs. Among them are GPCRs specific for free fatty acid ligands (lipid GPCRs), including free fatty acid receptor 1 (FFA1, otherwise known as GPR40), FFA2 (GPR43), FFA3 (GPR41) and FFA4 (GPR120), as well as the lipid metabolite binding glucose-dependent insulinotropic receptor (GPR119). These lipid GPCRs have demonstrated important roles in the control of islet and gut hormone secretion. Advances in lipid GPCR pharmacology have led to the identification of a number of synthetic agonists that exert beneficial effects on glucose homeostasis in preclinical studies. Yet, translation of these promising results to the clinic has so far been disappointing. In this Review, we present the physiological roles, pharmacology and clinical studies of these lipid receptors and discuss the challenges associated with their clinical development for the treatment of type 2 diabetes mellitus.
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Affiliation(s)
- Julien Ghislain
- Montreal Diabetes Research Center, Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Vincent Poitout
- Montreal Diabetes Research Center, Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada.
- Department of Medicine, Université de Montréal, Montréal, QC, Canada.
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65
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Croze ML, Guillaume A, Ethier M, Fergusson G, Tremblay C, Campbell SA, Maachi H, Ghislain J, Poitout V. Combined Deletion of Free Fatty-Acid Receptors 1 and 4 Minimally Impacts Glucose Homeostasis in Mice. Endocrinology 2021; 162:6128704. [PMID: 33543237 DOI: 10.1210/endocr/bqab002] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Indexed: 12/16/2022]
Abstract
The free fatty-acid receptors FFAR1 (GPR40) and FFAR4 (GPR120) are implicated in the regulation of insulin secretion and insulin sensitivity, respectively. Although GPR120 and GPR40 share similar ligands, few studies have addressed possible interactions between these 2 receptors in the control of glucose homeostasis. Here we generated mice deficient in gpr120 (Gpr120KO) or gpr40 (Gpr40KO), alone or in combination (Gpr120/40KO), and metabolically phenotyped male and female mice fed a normal chow or high-fat diet. We assessed insulin secretion in isolated mouse islets exposed to selective GPR120 and GPR40 agonists singly or in combination. Following normal chow feeding, body weight and energy intake were unaffected by deletion of either receptor, although fat mass increased in Gpr120KO females. Fasting blood glucose levels were mildly increased in Gpr120/40KO mice and in a sex-dependent manner in Gpr120KO and Gpr40KO animals. Oral glucose tolerance was slightly reduced in male Gpr120/40KO mice and in Gpr120KO females, whereas insulin secretion and insulin sensitivity were unaffected. In hyperglycemic clamps, the glucose infusion rate was lower in male Gpr120/40KO mice, but insulin and c-peptide levels were unaffected. No changes in glucose tolerance were observed in either single or double knock-out animals under high-fat feeding. In isolated islets from wild-type mice, the combination of selective GPR120 and GPR40 agonists additively increased insulin secretion. We conclude that while simultaneous activation of GPR120 and GPR40 enhances insulin secretion ex vivo, combined deletion of these 2 receptors only minimally affects glucose homeostasis in vivo in mice.
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Affiliation(s)
- Marine L Croze
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
| | | | - Mélanie Ethier
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
| | - Grace Fergusson
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
| | | | | | - Hasna Maachi
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
| | - Julien Ghislain
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
| | - Vincent Poitout
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
- Department of Medicine, Université de Montréal, Montréal, QC, Canada
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66
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Croze ML, Flisher MF, Guillaume A, Tremblay C, Noguchi GM, Granziera S, Vivot K, Castillo VC, Campbell SA, Ghislain J, Huising MO, Poitout V. Free fatty acid receptor 4 inhibitory signaling in delta cells regulates islet hormone secretion in mice. Mol Metab 2021; 45:101166. [PMID: 33484949 PMCID: PMC7873385 DOI: 10.1016/j.molmet.2021.101166] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE Maintenance of glucose homeostasis requires the precise regulation of hormone secretion from the endocrine pancreas. Free fatty acid receptor 4 (FFAR4/GPR120) is a G protein-coupled receptor whose activation in islets of Langerhans promotes insulin and glucagon secretion and inhibits somatostatin secretion. However, the contribution of individual islet cell types (α, β, and δ cells) to the insulinotropic and glucagonotropic effects of GPR120 remains unclear. As gpr120 mRNA is enriched in somatostatin-secreting δ cells, we hypothesized that GPR120 activation stimulates insulin and glucagon secretion via inhibition of somatostatin release. METHODS Glucose tolerance tests were performed in mice after administration of selective GPR120 agonist Compound A. Insulin, glucagon, and somatostatin secretion were measured in static incubations of isolated mouse islets in response to endogenous (ω-3 polyunsaturated fatty acids) and/or pharmacological (Compound A and AZ-13581837) GPR120 agonists. The effect of Compound A on hormone secretion was tested further in islets isolated from mice with global or somatostatin cell-specific knock-out of gpr120. Gpr120 expression was assessed in pancreatic sections by RNA in situ hybridization. Cyclic AMP (cAMP) and calcium dynamics in response to pharmacological GPR120 agonists were measured specifically in α, β, and δ cells in intact islets using cAMPER and GCaMP6 reporter mice, respectively. RESULTS Acute exposure to Compound A increased glucose tolerance, circulating insulin, and glucagon levels in vivo. Endogenous and/or pharmacological GPR120 agonists reduced somatostatin secretion in isolated islets and concomitantly demonstrated dose-dependent potentiation of glucose-stimulated insulin secretion and arginine-stimulated glucagon secretion. Gpr120 was enriched in δ cells. Pharmacological GPR120 agonists reduced cAMP and calcium levels in δ cells but increased these signals in α and β cells. Compound A-mediated inhibition of somatostatin secretion was insensitive to pertussis toxin. The effect of Compound A on hormone secretion was completely absent in islets from mice with either global or somatostatin cell-specific deletion of gpr120 and partially reduced upon blockade of somatostatin receptor signaling by cyclosomatostatin. CONCLUSIONS Inhibitory GPR120 signaling in δ cells contributes to both insulin and glucagon secretion in part by mitigating somatostatin release.
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Affiliation(s)
- Marine L Croze
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
| | - Marcus F Flisher
- Department of Neurobiology, Physiology, and Behavior, College of Biological Sciences, University of California Davis, Davis, CA, USA
| | | | | | - Glyn M Noguchi
- Department of Neurobiology, Physiology, and Behavior, College of Biological Sciences, University of California Davis, Davis, CA, USA
| | | | - Kevin Vivot
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
| | - Vincent C Castillo
- Department of Neurobiology, Physiology, and Behavior, College of Biological Sciences, University of California Davis, Davis, CA, USA
| | | | - Julien Ghislain
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada
| | - Mark O Huising
- Department of Neurobiology, Physiology, and Behavior, College of Biological Sciences, University of California Davis, Davis, CA, USA; Department of Physiology and Membrane Biology, School of Medicine, University of California Davis, Davis, CA, USA
| | - Vincent Poitout
- Montreal Diabetes Research Center, CRCHUM, Montréal, QC, Canada; Department of Medicine, Université de Montréal, Montréal, QC, Canada.
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Identification of MEDAG as a Hub Candidate Gene in the Onset and Progression of Type 2 Diabetes Mellitus by Comprehensive Bioinformatics Analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:3947350. [PMID: 33728329 PMCID: PMC7938259 DOI: 10.1155/2021/3947350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 11/27/2020] [Accepted: 02/14/2021] [Indexed: 01/09/2023]
Abstract
Objectives We conducted the present study to identify novel hub candidate genes in the pathogenesis of type 2 diabetes mellitus (T2DM) and provide potential biomarkers or therapeutic targets for dealing with the disease. Methods We conducted weighted gene coexpression network analysis on a series of the expression profiles of the pancreas islet of T2DM patients obtained from the Gene Expression Omnibus database to construct a weighted coexpression network. After dividing genes into separated coexpression modules, we identified a T2DM-related module using Pearson's correlation analysis. Then, hub genes were identified from the T2DM-related module using the Maximal Clique Centrality method and validated by correlation analysis with clinical traits, differentially expressed gene analysis, validation in other datasets, and single-gene gene set enrichment analysis (GSEA). Results Genes were divided into 16 coexpression modules, and one module was identified as a T2DM-related module. Four hub candidate genes were identified, and MEDAG was a novel hub candidate gene. The expression level of MEDAG was positively correlated with hemoglobin A1c (HbA1c) and was evidently overexpressed in the pancreas islet tissue of T2DM patients compared with normal control. Analyses on two other datasets supported the results. GSEA verified that MEDAG plays essential roles in T2DM. Conclusions MEDAG is a novel hub candidate of T2DM, and its irregular expression in the pancreas islet plays vital roles in the pathogenesis of T2DM. MEDAG is a potential target of intervention in the future for the treatment of T2DM.
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68
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Conventional and Unconventional Mechanisms by which Exocytosis Proteins Oversee β-cell Function and Protection. Int J Mol Sci 2021; 22:ijms22041833. [PMID: 33673206 PMCID: PMC7918544 DOI: 10.3390/ijms22041833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/02/2021] [Accepted: 02/08/2021] [Indexed: 02/06/2023] Open
Abstract
Type 2 diabetes (T2D) is one of the prominent causes of morbidity and mortality in the United States and beyond, reaching global pandemic proportions. One hallmark of T2D is dysfunctional glucose-stimulated insulin secretion from the pancreatic β-cell. Insulin is secreted via the recruitment of insulin secretory granules to the plasma membrane, where the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) and SNARE regulators work together to dock the secretory granules and release insulin into the circulation. SNARE proteins and their regulators include the Syntaxins, SNAPs, Sec1/Munc18, VAMPs, and double C2-domain proteins. Recent studies using genomics, proteomics, and biochemical approaches have linked deficiencies of exocytosis proteins with the onset and progression of T2D. Promising results are also emerging wherein restoration or enhancement of certain exocytosis proteins to β-cells improves whole-body glucose homeostasis, enhances β-cell function, and surprisingly, protection of β-cell mass. Intriguingly, overexpression and knockout studies have revealed novel functions of certain exocytosis proteins, like Syntaxin 4, suggesting that exocytosis proteins can impact a variety of pathways, including inflammatory signaling and aging. In this review, we present the conventional and unconventional functions of β-cell exocytosis proteins in normal physiology and T2D and describe how these insights might improve clinical care for T2D.
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69
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A novel GPR120-selective agonist promotes insulin secretion and improves chronic inflammation. Life Sci 2021; 269:119029. [PMID: 33450256 DOI: 10.1016/j.lfs.2021.119029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/29/2020] [Accepted: 01/05/2021] [Indexed: 11/22/2022]
Abstract
AIMS The present study aimed to disclose a potent and selective GPR120 agonist LXT34 and its anti-diabetic effects. MAIN METHODS Calcium mobilization assay was used to measure the agonistic potency and selectivity of LXT34 in GPR120 or GPR40-overexpression Chinese hamster ovary (CHO) cells. Glucagon-like peptide-1 (GLP-1) release and glucose-stimulated insulin secretion (GSIS) were evaluated in human colonic epithelial cell line NCI-H716 and mouse insulinoma cell line MIN6 by enzyme-linked immunosorbent assay (ELISA), respectively. The anti-inflammatory effect was determined in lipopolysaccharide (LPS)-induced murine macrophage cell line RAW264.7. Oral glucose tolerance test (OGTT) and insulin tolerance test (ITT) were performed to assess the anti-diabetic effects of LXT34 in db/db mice, and chronic inflammation in liver and adipose tissues were investigated using histomorphology, immunoblot and gene expression analysis. KEY FINDINGS LXT34 was a potent GPR120 agonist with negligible activity toward human and mouse GPR40. LXT34 could potentiate GSIS and suppress LPS-induced inflammation in macrophages. LXT34 not only markedly improved glucose tolerance and insulin resistance, but also distinctly reduced macrophages infiltration, pro-inflammatory cytokines expression and JNK phosphorylation of both liver and adipose tissues in db/db mice. SIGNIFICANCE LXT34, a novel and potent GPR120-selective agonist, showed beneficial effects on improving glucose homeostasis in obesity-related type 2 diabetes.
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70
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Lagou V, Mägi R, Hottenga JJ, Grallert H, Perry JRB, Bouatia-Naji N, Marullo L, Rybin D, Jansen R, Min JL, Dimas AS, Ulrich A, Zudina L, Gådin JR, Jiang L, Faggian A, Bonnefond A, Fadista J, Stathopoulou MG, Isaacs A, Willems SM, Navarro P, Tanaka T, Jackson AU, Montasser ME, O'Connell JR, Bielak LF, Webster RJ, Saxena R, Stafford JM, Pourcain BS, Timpson NJ, Salo P, Shin SY, Amin N, Smith AV, Li G, Verweij N, Goel A, Ford I, Johnson PCD, Johnson T, Kapur K, Thorleifsson G, Strawbridge RJ, Rasmussen-Torvik LJ, Esko T, Mihailov E, Fall T, Fraser RM, Mahajan A, Kanoni S, Giedraitis V, Kleber ME, Silbernagel G, Meyer J, Müller-Nurasyid M, Ganna A, Sarin AP, Yengo L, Shungin D, Luan J, Horikoshi M, An P, Sanna S, Boettcher Y, Rayner NW, Nolte IM, Zemunik T, Iperen EV, Kovacs P, Hastie ND, Wild SH, McLachlan S, Campbell S, Polasek O, Carlson O, Egan J, Kiess W, Willemsen G, Kuusisto J, Laakso M, Dimitriou M, Hicks AA, Rauramaa R, Bandinelli S, Thorand B, Liu Y, Miljkovic I, Lind L, Doney A, Perola M, Hingorani A, Kivimaki M, Kumari M, Bennett AJ, Groves CJ, Herder C, Koistinen HA, Kinnunen L, Faire UD, Bakker SJL, Uusitupa M, Palmer CNA, Jukema JW, Sattar N, Pouta A, Snieder H, Boerwinkle E, Pankow JS, Magnusson PK, Krus U, Scapoli C, de Geus EJCN, Blüher M, Wolffenbuttel BHR, Province MA, Abecasis GR, Meigs JB, Hovingh GK, Lindström J, Wilson JF, Wright AF, Dedoussis GV, Bornstein SR, Schwarz PEH, Tönjes A, Winkelmann BR, Boehm BO, März W, Metspalu A, Price JF, Deloukas P, Körner A, Lakka TA, Keinanen-Kiukaanniemi SM, Saaristo TE, Bergman RN, Tuomilehto J, Wareham NJ, Langenberg C, Männistö S, Franks PW, Hayward C, Vitart V, Kaprio J, Visvikis-Siest S, Balkau B, Altshuler D, Rudan I, Stumvoll M, Campbell H, van Duijn CM, Gieger C, Illig T, Ferrucci L, Pedersen NL, Pramstaller PP, Boehnke M, Frayling TM, Shuldiner AR, Peyser PA, Kardia SLR, Palmer LJ, Penninx BW, Meneton P, Harris TB, Navis G, Harst PVD, Smith GD, Forouhi NG, Loos RJF, Salomaa V, Soranzo N, Boomsma DI, Groop L, Tuomi T, Hofman A, Munroe PB, Gudnason V, Siscovick DS, Watkins H, Lecoeur C, Vollenweider P, Franco-Cereceda A, Eriksson P, Jarvelin MR, Stefansson K, Hamsten A, Nicholson G, Karpe F, Dermitzakis ET, Lindgren CM, McCarthy MI, Froguel P, Kaakinen MA, Lyssenko V, Watanabe RM, Ingelsson E, Florez JC, Dupuis J, Barroso I, Morris AP, Prokopenko I. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability. Nat Commun 2021; 12:24. [PMID: 33402679 PMCID: PMC7785747 DOI: 10.1038/s41467-020-19366-9] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 09/22/2020] [Indexed: 12/20/2022] Open
Abstract
Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
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Affiliation(s)
- Vasiliki Lagou
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Department of Microbiology and Immunology, Laboratory of Adaptive Immunity, KU Leuven, Leuven, Belgium
- VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jouke- Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, VU University medical center, Amsterdam, the Netherlands
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Nabila Bouatia-Naji
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- INSERM U970, Paris Cardiovascular Research Center PARCC, 75006, Paris, France
| | - Letizia Marullo
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Denis Rybin
- Boston University Data Coordinating Center, Boston, MA, USA
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Josine L Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center Al. Fleming, Vari, Greece
| | - Anna Ulrich
- Department of Medicine, Imperial College London, London, UK
| | | | - Jesper R Gådin
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Longda Jiang
- Department of Medicine, Imperial College London, London, UK
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | | | - Amélie Bonnefond
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Department of Medicine, Imperial College London, London, UK
| | - Joao Fadista
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | | | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- CARIM School for Cardiovascular Diseases and Maastricht Centre for Systems Biology (MaCSBio, Maastricht University, Maastricht, the Netherlands
- Department of Physiology, Maastricht University, Maastricht, the Netherlands
| | - Sara M Willems
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Pau Navarro
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Toshiko Tanaka
- Translational Gerontology Branch, Longitudinal Study Section, National Institute on Aging, Baltimore, MD, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Jeff R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rebecca J Webster
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Richa Saxena
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Departmentartment of Anesthesia, Critical Care and Pain Medicine, MGH, Boston, MA, USA
| | - Jeanette M Stafford
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Perttu Salo
- Public Health Genomics Unit, Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - So-Youn Shin
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Najaf Amin
- Department of Epidemiology Erasmus MC, Rotterdam, the Netherlands
| | - Albert V Smith
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Guo Li
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Paul C D Johnson
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
- Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Toby Johnson
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Karen Kapur
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
| | | | - Rona J Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Evelin Mihailov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ross M Fraser
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Synpromics Ltd, Roslin Innovation Centre, Easter Bush Campus, Edinburgh, EH25 9RG, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Genentech, 340 Point San Bruno Boulevard, South San Francisco, CA, 94080, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala Universitet, Uppsala, Sweden
| | - Marcus E Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Günther Silbernagel
- Division of Angiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Julia Meyer
- Institute of Genetic Epidemiology,Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology,Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology and Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-University, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI, University Medical Center, Johannes Gutenberg University, 55101, Mainz, Germany
| | - Andrea Ganna
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Antti-Pekka Sarin
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Public Health Genomics Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Loic Yengo
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Dmitry Shungin
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Odontology, Umeå University, Umeå, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Momoko Horikoshi
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- RIKEN, Center for Integrative Medical Sciences, Laboratory for Endocrinology, Metabolism and Kidney Disease, Yokohama, Japan
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Serena Sanna
- Istituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Italy
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Yvonne Boettcher
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | - N William Rayner
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Wellcome Trust Sanger Institute, Hinxton, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Erik van Iperen
- Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Peter Kovacs
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | - Nicholas D Hastie
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Sarah H Wild
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Susan Campbell
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Olga Carlson
- Laboratory of Clinical Investigation, National Institute of Aging, Baltimore, MD, USA
| | - Josephine Egan
- Laboratory of Clinical Investigation, National Institute of Aging, Baltimore, MD, USA
| | - Wieland Kiess
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
- Pediatric Research Center, Department of Women's & Child Health, University of Leipzig, Leipzig, Germany
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Maria Dimitriou
- Department of Dietetics-Nutrition, Harokopio University, Athens, Greece
| | - Andrew A Hicks
- Center for Biomedicine, European Academy Bozen/Bolzano (EURAC) (Affiliated Institute of the University of LübeckLübeckGermany), Bolzano, Italy
| | - Rainer Rauramaa
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | | | - Barbara Thorand
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Iva Miljkovic
- Department of Epidemiology, Center for Aging and Population Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Akademiska sjukhuset, Uppsala, Sweden
| | - Alex Doney
- Pat McPherson Centre for Pharmacogenetics and Pharmacogenomics, Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Markus Perola
- Public Health Genomics Unit, Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Aroon Hingorani
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, London, UK
- University of Essex, Wivenhoe Park, Colchester, Essex, UK
| | - Amanda J Bennett
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Christian Herder
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Heikki A Koistinen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, P.O. Box 340, Haartmaninkatu 4, Helsinki, FI-00029, Finland
- Minerva Foundation Institute for Medical Research, Biomedicum 2U, Tukholmankatu 8, Helsinki, FI-00290, Finland
| | - Leena Kinnunen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
| | - Ulf de Faire
- Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Stephan J L Bakker
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Colin N A Palmer
- Pat McPherson Centre for Pharmacogenetics and Pharmacogenomics, Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - J Wouter Jukema
- Dept of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Anneli Pouta
- Department of Government Services, Finnish Institute for Health and Welfare, Helsinki, Finland
- PEDEGO Research Unit, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Eric Boerwinkle
- IMM Center for Human Genetics, University of Texas Health Science Center at Houston, Houston, TX, USA
- Division of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MiI, USA
| | - Patrik K Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ulrika Krus
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
| | - Chiara Scapoli
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Eco J C N de Geus
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, VU University medical center, Amsterdam, the Netherlands
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Bruce H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Goncalo R Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- General Medicine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - G Kees Hovingh
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, the Netherlands
- Novo Nordisk A/S, Copenhagen, Denmark
| | - Jaana Lindström
- Finnish Institute for Health and Welfare, Diabetes Prevention Unit, Helsinki, Finland
| | - James F Wilson
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Alan F Wright
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | | | - Stefan R Bornstein
- Department of Medicine, Division for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Peter E H Schwarz
- Department for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of the Helmholtz Center Munich at University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | | | - Bernhard O Boehm
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore and Imperial College London, Singapore, Singapore
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Antje Körner
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Timo A Lakka
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio Campus, Kuopio, Finland
| | - Sirkka M Keinanen-Kiukaanniemi
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Unit of General Practice, Oulu University Hospital, Oulu, Finland
| | - Timo E Saaristo
- Finnish Diabetes Association, Tampere, Finland
- Pirkanmaa Hospital District, Tampere, Finland
| | - Richard N Bergman
- Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jaakko Tuomilehto
- Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Centre for Vascular Prevention, Danube-University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Satu Männistö
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Public Health & Clinical Medicine, Units of Medicine and Nutritional Research, Umeå University, Umeå, Sweden
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Veronique Vitart
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | | | - Beverley Balkau
- Inserm, CESP Center for Research in Epidemiology and Public Health, U1018, Villejuif, France
- Univ Paris-Saclay, Univ Paris Sud, UVSQ, UMRS 1018, UMRS 1018, Villejuif, France
| | - David Altshuler
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Igor Rudan
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | | | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Centre for Medical Systems Biology, Leiden, the Netherlands
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter P Pramstaller
- Center for Biomedicine, European Academy Bozen/Bolzano (EURAC) (Affiliated Institute of the University of LübeckLübeckGermany), Bolzano, Italy
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Timothy M Frayling
- Genetics of Complex Traits, Peninsula Medical School, University of Exeter, Exeter, UK
| | - Alan R Shuldiner
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
- The Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lyle J Palmer
- School of Public Health, University of Adelaide, Adelaide, Australia
| | - Brenda W Penninx
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Pierre Meneton
- U872 Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, 75006, Paris, France
| | - Tamara B Harris
- Geriatric Epidemiology Section, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA
| | - Gerjan Navis
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Ruth J F Loos
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Nicole Soranzo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Leif Groop
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
- Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
| | - Albert Hofman
- Department of Epidemiology Erasmus MC, Rotterdam, the Netherlands
- Netherlands Consortium for healthy ageing, the Hague, the Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine University of Iceland, Reykjavik, Iceland
| | - David S Siscovick
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Cecile Lecoeur
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
| | - Peter Vollenweider
- Department of Medicine, University Hospital Lausanne, Lausanne, Switzerland
| | - Anders Franco-Cereceda
- Cardiothoracic Surgery Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Per Eriksson
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics and HPA-MRC Center, School of Public Health, Imperial College London, London, UK
- Institue of Health Sciences, University of Oulu, Oulu, Finland
| | - Kari Stefansson
- deCODE Genetics, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden
- Department of Cardiology, Karolinska University Hospital Solna, Stockholm, Sweden
| | | | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK
- Genentech, 340 Point San Bruno Boulevard, South San Francisco, CA, 94080, USA
| | - Philippe Froguel
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Department of Medicine, Imperial College London, London, UK
| | - Marika A Kaakinen
- Department of Medicine, Imperial College London, London, UK
- School of Biosciences and Medicine, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Richard M Watanabe
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
- Department of Physiology & Neuroscience, Keck School of Medicine of USC, Los Angeles, CA, USA
- USC Diabetes and Obesity Research Institute, Los Angeles, CA, USA
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Jose C Florez
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Inês Barroso
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, UK
- Exeter Centre of ExcEllence in Diabetes (ExCEED), University of Exeter Medical School, Exeter, UK
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Inga Prokopenko
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
- Department of Medicine, Imperial College London, London, UK.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
- School of Biosciences and Medicine, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK.
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre Russian Academy of Sciences, Ufa, Russian Federation.
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Mencucci MV, Flores LE, Gagliardino JJ, Abba MC, Maiztegui B. Integrative transcriptomic analysis of pancreatic islets from patients with prediabetes/type 2 diabetes. Diabetes Metab Res Rev 2021; 37:e3359. [PMID: 32500584 DOI: 10.1002/dmrr.3359] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 05/21/2020] [Accepted: 05/25/2020] [Indexed: 12/20/2022]
Abstract
AIM To identify new transcriptomic alterations in pancreatic islets associated with metabolic dysfunctions in people with prediabetes (PD)/type 2 diabetes (T2D). MATERIALS AND METHODS We collected information from public data repositories T2D related microarray datasets from pancreatic islets. We identified Differential Expressed Genes (DEGs) in non-diabetic (ND) vs people with T2D in each study. To identify relevant DEGs in T2D, we selected those that varied consistently in the different studies for further meta-analysis and functional enrichment analysis. DEGs were also evaluated at the PD stage. RESULTS A total of seven microarray datasets were collected and analysed to find the DEGs in each study and meta-analysis was performed with 245 ND and 96 T2D cases. We identified 55 transcriptional alterations potentially associated with specific metabolic dysfunctions in T2D. Meta-analysis showed that 87% of transcripts identified as DEGs (48 out of 55) were confirmed as having statistically significant up- or down-modulation in T2D compared to ND. Notably, nine of these DEGs have not been previously reported as dysregulated in pancreatic islets from people with T2D. Consistently, the most significantly enriched pathways were related to the metabolism and/or development/maintenance of β-cells. Eighteen of the 48 selected DEGs (38%) showed an altered expression in islets from people with PD. CONCLUSIONS These results provide new evidence to interpret the pathogenesis of T2D and the transition from PD to T2D. Further studies are necessary to validate its potential use for the development/implementation of efficient new strategies for the prevention, diagnosis/prognosis and treatment of T2D.
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Affiliation(s)
- María V Mencucci
- CENEXA. Centro de Endocrinología Experimental y Aplicada (UNLP-CONICET-CEAS CICPBA), Facultad de Ciencias Médicas UNLP, La Plata, Argentina
| | - Luis E Flores
- CENEXA. Centro de Endocrinología Experimental y Aplicada (UNLP-CONICET-CEAS CICPBA), Facultad de Ciencias Médicas UNLP, La Plata, Argentina
| | - Juan J Gagliardino
- CENEXA. Centro de Endocrinología Experimental y Aplicada (UNLP-CONICET-CEAS CICPBA), Facultad de Ciencias Médicas UNLP, La Plata, Argentina
| | - Martín C Abba
- CINIBA. Centro de Investigaciones Inmunológicas Básicas y Aplicadas (UNLP-CICPBA), Facultad de Ciencias Médicas UNLP, La Plata, Argentina
| | - Bárbara Maiztegui
- CENEXA. Centro de Endocrinología Experimental y Aplicada (UNLP-CONICET-CEAS CICPBA), Facultad de Ciencias Médicas UNLP, La Plata, Argentina
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72
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Marselli L, Piron A, Suleiman M, Colli ML, Yi X, Khamis A, Carrat GR, Rutter GA, Bugliani M, Giusti L, Ronci M, Ibberson M, Turatsinze JV, Boggi U, De Simone P, De Tata V, Lopes M, Nasteska D, De Luca C, Tesi M, Bosi E, Singh P, Campani D, Schulte AM, Solimena M, Hecht P, Rady B, Bakaj I, Pocai A, Norquay L, Thorens B, Canouil M, Froguel P, Eizirik DL, Cnop M, Marchetti P. Persistent or Transient Human β Cell Dysfunction Induced by Metabolic Stress: Specific Signatures and Shared Gene Expression with Type 2 Diabetes. Cell Rep 2020; 33:108466. [PMID: 33264613 DOI: 10.1016/j.celrep.2020.108466] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/06/2020] [Accepted: 11/10/2020] [Indexed: 12/16/2022] Open
Abstract
Pancreatic β cell failure is key to type 2 diabetes (T2D) onset and progression. Here, we assess whether human β cell dysfunction induced by metabolic stress is reversible, evaluate the molecular pathways underlying persistent or transient damage, and explore the relationships with T2D islet traits. Twenty-six islet preparations are exposed to several lipotoxic/glucotoxic conditions, some of which impair insulin release, depending on stressor type, concentration, and combination. The reversal of dysfunction occurs after washout for some, although not all, of the lipoglucotoxic insults. Islet transcriptomes assessed by RNA sequencing and expression quantitative trait loci (eQTL) analysis identify specific pathways underlying β cell failure and recovery. Comparison of a large number of human T2D islet transcriptomes with those of persistent or reversible β cell lipoglucotoxicity show shared gene expression signatures. The identification of mechanisms associated with human β cell dysfunction and recovery and their overlap with T2D islet traits provide insights into T2D pathogenesis, fostering the development of improved β cell-targeted therapeutic strategies.
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Affiliation(s)
- Lorella Marselli
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy.
| | - Anthony Piron
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels 1070, Belgium
| | - Mara Suleiman
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Maikel L Colli
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels 1070, Belgium
| | - Xiaoyan Yi
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels 1070, Belgium
| | - Amna Khamis
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille University Hospital, Lille 59000, France
| | - Gaelle R Carrat
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology, and Metabolism, Imperial College, London, UK
| | - Guy A Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology, and Metabolism, Imperial College, London, UK; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Marco Bugliani
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Laura Giusti
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy; School of Pharmacy, University of Camerino, Camerino, Italy
| | - Maurizio Ronci
- Department of Pharmacy, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; Centre for Advanced Studies and Technologies (CAST), University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Mark Ibberson
- Vital-IT Group, Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | | | - Ugo Boggi
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa 56126, Italy; Division of General and Transplant Surgery, Cisanello University Hospital, Pisa 56124, Italy
| | - Paolo De Simone
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa 56126, Italy; Division of Liver Surgery and Transplantation, Cisanello University Hospital, Pisa 56124, Italy
| | - Vincenzo De Tata
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa 56126, Italy
| | - Miguel Lopes
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels 1070, Belgium
| | - Daniela Nasteska
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels 1070, Belgium
| | - Carmela De Luca
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Marta Tesi
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Emanuele Bosi
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Pratibha Singh
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels 1070, Belgium
| | - Daniela Campani
- Department of Surgical, Medical and Molecular Pathology and the Critical Areas, University of Pisa, Pisa 56126, Italy
| | - Anke M Schulte
- Sanofi-Aventis Deutschland GmbH, Diabetes Research, Frankfurt, Germany
| | - Michele Solimena
- Paul Langerhans Institute Dresden of the Helmholtz Center Munich at University Hospital Carl Gustav Carus and Faculty of Medicine, TU Dresden, Dresden 01307, Germany; German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Peter Hecht
- Sanofi-Aventis Deutschland GmbH, Diabetes Research, Frankfurt, Germany
| | | | | | | | | | - Bernard Thorens
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Mickaël Canouil
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille University Hospital, Lille 59000, France
| | - Philippe Froguel
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
| | - Decio L Eizirik
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels 1070, Belgium; WELBIO, Université Libre de Bruxelles, Brussels, Belgium; Indiana Biosciences Research Institute, Indianapolis, IN, USA; Division of Endocrinology, ULB Erasmus Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Miriam Cnop
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels 1070, Belgium; Division of Endocrinology, ULB Erasmus Hospital, Université Libre de Bruxelles, Brussels, Belgium.
| | - Piero Marchetti
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy.
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Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study. Genome Med 2020; 12:109. [PMID: 33261667 PMCID: PMC7708171 DOI: 10.1186/s13073-020-00806-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/11/2020] [Indexed: 01/04/2023] Open
Abstract
Background The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Conclusions Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.
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Bosi E, Marselli L, De Luca C, Suleiman M, Tesi M, Ibberson M, Eizirik DL, Cnop M, Marchetti P. Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes. NAR Genom Bioinform 2020; 2:lqaa097. [PMID: 33575641 PMCID: PMC7679065 DOI: 10.1093/nargab/lqaa097] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/26/2020] [Accepted: 10/30/2020] [Indexed: 02/06/2023] Open
Abstract
Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (n = 801), we found 210 upregulated and 16 downregulated genes, identifying key pathways for T2D pathogenesis, including defective insulin secretion, SREBP signaling and oxidative stress. We also compared these results with previous data of human T2D β-cells from laser capture microdissection and diabetic rat islets, revealing shared β-cell genes. Overall, the present study encourages the pursuit of single β-cell RNA-seq analysis, preventing presently identified sources of variability, to identify transcriptomic changes associated with human T2D and underscores specific traits of dysfunctional β-cells across different models and techniques.
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Affiliation(s)
- Emanuele Bosi
- Department of Experimental and Clinical Medicine, Pancreatic Islets Laboratory, University of Pisa, Pisa, I-56124, Italy
| | - Lorella Marselli
- Department of Experimental and Clinical Medicine, Pancreatic Islets Laboratory, University of Pisa, Pisa, I-56124, Italy
| | - Carmela De Luca
- Department of Experimental and Clinical Medicine, Pancreatic Islets Laboratory, University of Pisa, Pisa, I-56124, Italy
| | - Mara Suleiman
- Department of Experimental and Clinical Medicine, Pancreatic Islets Laboratory, University of Pisa, Pisa, I-56124, Italy
| | - Marta Tesi
- Department of Experimental and Clinical Medicine, Pancreatic Islets Laboratory, University of Pisa, Pisa, I-56124, Italy
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier Sorge, CH-1015 Lausanne, Switzerland
| | - Decio L Eizirik
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, B-1070, Belgium
| | - Miriam Cnop
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, B-1070, Belgium
| | - Piero Marchetti
- Department of Experimental and Clinical Medicine, Pancreatic Islets Laboratory, University of Pisa, Pisa, I-56124, Italy
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75
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Schuitemaker JHN, Beernink RHJ, Franx A, Cremers TIFH, Koster MPH. First trimester secreted Frizzled-Related Protein 4 and other adipokine serum concentrations in women developing gestational diabetes mellitus. PLoS One 2020; 15:e0242423. [PMID: 33206702 PMCID: PMC7673552 DOI: 10.1371/journal.pone.0242423] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/02/2020] [Indexed: 01/03/2023] Open
Abstract
Background The aim of this study was to evaluate whether soluble frizzled-related protein 4 (sFRP4) concentration in the first trimester of pregnancy is individually, or in combination with Leptin, Chemerin and/or Adiponectin, associated with the development of gestational diabetes (GDM). Methods In a nested case-control study, 50 women with GDM who spontaneously conceived and delivered a live-born infant were matched with a total of 100 uncomplicated singleton control pregnancies based on body mass index (± 2 kg/m2), gestational age at sampling (exact day) and maternal age (± 2 years). In serum samples, obtained between 70–90 days gestational age, sFRP4, Chemerin, Leptin and Adiponectin concentrations were determined by ELISA. Statistical comparisons were performed using univariate and multi-variate logistic regression analysis after logarithmic transformation of the concentrations. Discrimination of the models was assessed by the area under the curve (AUC). Results First trimester sFRP4 concentrations were significantly increased in GDM cases (2.04 vs 1.93 ng/ml; p<0.05), just as Chemerin (3.19 vs 3.15 ng/ml; p<0.05) and Leptin (1.44 vs 1.32 ng/ml; p<0.01). Adiponectin concentrations were significantly decreased (2.83 vs 2.94 ng/ml; p<0.01) in GDM cases. Further analysis only showed a weak, though significant, correlation of sFRP4 with Chemerin (R2 = 0.124; p<0.001) and Leptin (R2 = 0.145; p<0.001), and Chemerin with Leptin (R2 = 0.282; p<0.001) in the control group. In a multivariate logistic regression model of these four markers, only Adiponectin showed to be significantly associated with GDM (odds ratio 0.12, 95%CI 0.02–0.68). The AUC of this model was 0.699 (95%CI 0.605–0.793). Conclusion In the first trimester of pregnancy, a multi-marker model with sFRP4, Leptin, Chemerin and Adiponectin is associated with the development of GDM. Therefore, this panel seems to be an interesting candidate to further evaluate for prediction of GDM in a prospective study.
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Affiliation(s)
- Joost H. N. Schuitemaker
- Division of Medical Biology, Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Research & Development, IQ Products BV, Groningen, The Netherlands
| | - Rik H. J. Beernink
- Research & Development, IQ Products BV, Groningen, The Netherlands
- Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
- * E-mail:
| | - Arie Franx
- Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Thomas I. F. H. Cremers
- Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
| | - Maria P. H. Koster
- Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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Asif S, Morrow NM, Mulvihill EE, Kim KH. Understanding Dietary Intervention-Mediated Epigenetic Modifications in Metabolic Diseases. Front Genet 2020; 11:590369. [PMID: 33193730 PMCID: PMC7593700 DOI: 10.3389/fgene.2020.590369] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 09/21/2020] [Indexed: 12/12/2022] Open
Abstract
The global prevalence of metabolic disorders, such as obesity, diabetes and fatty liver disease, is dramatically increasing. Both genetic and environmental factors are well-known contributors to the development of these diseases and therefore, the study of epigenetics can provide additional mechanistic insight. Dietary interventions, including caloric restriction, intermittent fasting or time-restricted feeding, have shown promising improvements in patients' overall metabolic profiles (i.e., reduced body weight, improved glucose homeostasis), and an increasing number of studies have associated these beneficial effects with epigenetic alterations. In this article, we review epigenetic changes involved in both metabolic diseases and dietary interventions in primary metabolic tissues (i.e., adipose, liver, and pancreas) in hopes of elucidating potential biomarkers and therapeutic targets for disease prevention and treatment.
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Affiliation(s)
- Shaza Asif
- University of Ottawa Heart Institute, Ottawa, ON, Canada
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Nadya M. Morrow
- University of Ottawa Heart Institute, Ottawa, ON, Canada
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Erin E. Mulvihill
- University of Ottawa Heart Institute, Ottawa, ON, Canada
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Kyoung-Han Kim
- University of Ottawa Heart Institute, Ottawa, ON, Canada
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
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77
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Zhao YF, Li XC, Liang XY, Zhao YY, Xie R, Zhang LJ, Zhang XC, Chen C. GPR120 Regulates Pancreatic Polypeptide Secretion From Male Mouse Islets via PLC-Mediated Calcium Mobilization. Endocrinology 2020; 161:5900686. [PMID: 32877513 DOI: 10.1210/endocr/bqaa157] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 08/29/2020] [Indexed: 02/07/2023]
Abstract
The free fatty acid receptor G protein-coupled receptor 120 (GPR120) is expressed in pancreatic islets, but its specific cell distribution and function have not been fully established. In this study, a GPR120-IRES-EGFP knockin (KI) mouse was generated to identify GPR120-expressing cells with enhanced green fluorescence proteins (EGFP). EGFP-positive cells collected from KI mouse islets by flow cytometry had a significantly higher expression of pancreatic polypeptide (PP) evidenced by reverse transcriptase (RT)-quantitative polymerase chain reaction (qPCR). Single-cell RT-PCR and immunocytochemical double staining also demonstrated the coexpression of GPR120 with PP in mouse islets. The GPR120-specific agonist TUG-891 significantly increased plasma PP levels in mice. TUG-891 significantly increased PP levels in islet medium in vitro, which was markedly attenuated by GPR120 small interfering RNA treatment. TUG-891-stimulated PP secretion in islets was fully blocked by pretreatment with YM-254890 (a Gq protein inhibitor), U73122 (a phospholipase C inhibitor), or thapsigargin (an inducer of endoplasmic reticulum Ca2+ depletion), respectively. TUG-891 triggered the increase in intracellular free Ca2+ concentrations ([Ca2+]i) in PP cells, which was also eliminated by YM-254890, U73122, or thapsigargin. GPR120 gene expression was significantly reduced in islets of high-fat diet (HFD)-induced obese mice. TUG-891-stimulated PP secretion was also significantly diminished in vivo and in vitro in HFD-induced obese mice compared with that in normal-chow diet control mice. In summary, this study demonstrated that GPR120 is expressed in mouse islet PP cells and GPR120 activation stimulated PP secretion via the Gq/PLC-Ca2+ signaling pathway in normal-chow diet mice but with diminished effects in HFD-induced obese mice.
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Affiliation(s)
- Yu-Feng Zhao
- Institute of Basic Medical Sciences, Xi'an Medical University, Xi'an, China
| | - Xiao-Cheng Li
- Institute of Basic Medical Sciences, Xi'an Medical University, Xi'an, China
| | - Xiang-Yan Liang
- Institute of Basic Medical Sciences, Xi'an Medical University, Xi'an, China
| | - Yan-Yan Zhao
- Institute of Basic Medical Sciences, Xi'an Medical University, Xi'an, China
| | - Rong Xie
- Institute of Basic Medical Sciences, Xi'an Medical University, Xi'an, China
| | - Li-Jun Zhang
- Institute of Basic Medical Sciences, Xi'an Medical University, Xi'an, China
| | - Xiao-Chun Zhang
- Institute of Basic Medical Sciences, Xi'an Medical University, Xi'an, China
| | - Chen Chen
- School of Biomedical Sciences, University of Queensland, Brisbane, Australia
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Viñuela A, Varshney A, van de Bunt M, Prasad RB, Asplund O, Bennett A, Boehnke M, Brown AA, Erdos MR, Fadista J, Hansson O, Hatem G, Howald C, Iyengar AK, Johnson P, Krus U, MacDonald PE, Mahajan A, Manning Fox JE, Narisu N, Nylander V, Orchard P, Oskolkov N, Panousis NI, Payne A, Stitzel ML, Vadlamudi S, Welch R, Collins FS, Mohlke KL, Gloyn AL, Scott LJ, Dermitzakis ET, Groop L, Parker SCJ, McCarthy MI. Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D. Nat Commun 2020; 11:4912. [PMID: 32999275 PMCID: PMC7528108 DOI: 10.1038/s41467-020-18581-8] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 08/12/2020] [Indexed: 02/08/2023] Open
Abstract
Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.
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Affiliation(s)
- Ana Viñuela
- grid.8591.50000 0001 2322 4988Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland ,grid.8591.50000 0001 2322 4988Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland ,grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland ,grid.1006.70000 0001 0462 7212Biosciences Institute, Faculty of Medical Sciences, Newcastle University, NE1 4EP Newcastle, UK
| | - Arushi Varshney
- grid.214458.e0000000086837370Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Martijn van de Bunt
- grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN UK ,grid.4991.50000 0004 1936 8948Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE UK ,grid.410556.30000 0001 0440 1440Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, OX3 7LE UK
| | - Rashmi B. Prasad
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Olof Asplund
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Amanda Bennett
- grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN UK
| | - Michael Boehnke
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Andrew A. Brown
- grid.8591.50000 0001 2322 4988Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland ,grid.8591.50000 0001 2322 4988Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland ,grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland ,grid.8241.f0000 0004 0397 2876Population Health and Genomics, University of Dundee, Dundee, Scotland, DD1 9SY UK
| | - Michael R. Erdos
- grid.280128.10000 0001 2233 9230Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - João Fadista
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden ,grid.6203.70000 0004 0417 4147Department of Epidemiology Research, Statens Serum Institut, Copenhagen, DK 2300 Denmark ,grid.7737.40000 0004 0410 2071Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
| | - Ola Hansson
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden ,grid.7737.40000 0004 0410 2071Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
| | - Gad Hatem
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Cédric Howald
- grid.8591.50000 0001 2322 4988Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland ,grid.8591.50000 0001 2322 4988Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland ,grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Apoorva K. Iyengar
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Paul Johnson
- grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN UK
| | - Ulrika Krus
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Patrick E. MacDonald
- grid.17089.37Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta Canada
| | - Anubha Mahajan
- grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN UK ,grid.418158.10000 0004 0534 4718Present Address: Human Genetics, Genentech, 1 DNA Way, South San Francisco, CA 94080 USA
| | - Jocelyn E. Manning Fox
- grid.17089.37Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta Canada
| | - Narisu Narisu
- grid.280128.10000 0001 2233 9230Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Vibe Nylander
- grid.4991.50000 0004 1936 8948Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE UK
| | - Peter Orchard
- grid.214458.e0000000086837370Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Nikolay Oskolkov
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Nikolaos I. Panousis
- grid.8591.50000 0001 2322 4988Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland ,grid.8591.50000 0001 2322 4988Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland ,grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Anthony Payne
- grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN UK
| | - Michael L. Stitzel
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA ,grid.63054.340000 0001 0860 4915Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032 USA
| | - Swarooparani Vadlamudi
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Ryan Welch
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Francis S. Collins
- grid.280128.10000 0001 2233 9230Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Karen L. Mohlke
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Anna L. Gloyn
- grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN UK ,grid.4991.50000 0004 1936 8948Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE UK ,grid.410556.30000 0001 0440 1440Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, OX3 7LE UK ,grid.168010.e0000000419368956Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA USA
| | - Laura J. Scott
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Emmanouil T. Dermitzakis
- grid.8591.50000 0001 2322 4988Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland ,grid.8591.50000 0001 2322 4988Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland ,grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Leif Groop
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden ,grid.7737.40000 0004 0410 2071Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
| | - Stephen C. J. Parker
- grid.214458.e0000000086837370Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Mark I. McCarthy
- grid.4991.50000 0004 1936 8948Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN UK ,grid.4991.50000 0004 1936 8948Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE UK ,grid.410556.30000 0001 0440 1440Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, OX3 7LE UK ,grid.418158.10000 0004 0534 4718Present Address: Human Genetics, Genentech, 1 DNA Way, South San Francisco, CA 94080 USA
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Luo Q, Fu Q, Zhang X, Zhang H, Qin T. Application of Single-Cell RNA Sequencing in Pancreatic Cancer and the Endocrine Pancreas. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1255:143-152. [PMID: 32949397 DOI: 10.1007/978-981-15-4494-1_12] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The pancreas is a complex organ composed of an endocrine (pancreatic islets) and an exocrine portion. This mixed cell population has resulted in an implacable barrier to exploring the detailed mechanism and function of each cell type in previous investigative approaches. In recent years, single-cell RNA sequencing (scRNA-seq) technologies have provided in-depth analysis of cell heterogeneity in the pancreas and in pancreatic cancer. It is especially effective in cell-type-specific molecule identification and detection of interactions between cancer cells and the stromal microenvironment. To date, numerous reports have described the application of scRNA-seq in studies of pancreatic islets and pancreatic cancer. The aim of this paper is to review recent advances of pancreatic transcriptomics and pancreatic cancer using scRNA-seq strategies.
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Affiliation(s)
- Qiankun Luo
- Department of Hepato-Biliary-Pancreatic Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qiang Fu
- Department of Hepato-Biliary-Pancreatic Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xu Zhang
- Department of Hepato-Biliary-Pancreatic Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hongwei Zhang
- Department of Hepato-Biliary-Pancreatic Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Tao Qin
- Department of Hepato-Biliary-Pancreatic Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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80
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Expression Profile of SARS-CoV-2 Host Receptors in Human Pancreatic Islets Revealed Upregulation of ACE2 in Diabetic Donors. BIOLOGY 2020; 9:biology9080215. [PMID: 32784802 PMCID: PMC7465557 DOI: 10.3390/biology9080215] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/03/2020] [Accepted: 08/03/2020] [Indexed: 01/08/2023]
Abstract
Cellular entry of SARS-CoV-2 is thought to occur through the binding of viral spike S1 protein to ACE2. The entry process involves priming of the S protein by TMPRSS2 and ADAM17, which collectively mediate the binding and promote ACE2 shedding. In this study, microarray and RNA-sequencing (RNA-seq) expression data were utilized to profile the expression pattern of ACE2, ADAM17, and TMPRSS2 in type 2 diabetic (T2D) and non-diabetic human pancreatic islets. Our data show that pancreatic islets express all three receptors irrespective of diabetes status. The expression of ACE2 was significantly increased in diabetic/hyperglycemic islets compared to non-diabetic/normoglycemic. Islets from female donors showed higher ACE2 expression compared to males; the expression of ADAM17 and TMPRSS2 was not affected by gender. The expression of the three receptors was statistically similar in young (≤40 years old) versus old (≥60 years old) donors. Obese (BMI > 30) donors have significantly higher expression levels of ADAM17 and TMPRSS2 relative to those from non-obese donors (BMI < 25). TMPRSS2 expression correlated positively with HbA1c and negatively with age, while ADAM17 and TMPRSS2 correlated positively with BMI. The expression of the three receptors was statistically similar in muscle and subcutaneous adipose tissues obtained from diabetic and nondiabetic donors. Lastly, ACE2 expression was higher in sorted pancreatic β-cell relative to other endocrine cells. In conclusion, ACE2 expression is increased in diabetic human islets. More studies are required to investigate whether variations of ACE2 expression could explain the severity of COVID-19 infection-related symptoms between diabetics and non-diabetic patients.
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81
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Lindqvist A, Shcherbina L, Prasad RB, Miskelly MG, Abels M, Martínez-Lopéz JA, Fred RG, Nergård BJ, Hedenbro J, Groop L, Hjerling-Leffler J, Wierup N. Ghrelin suppresses insulin secretion in human islets and type 2 diabetes patients have diminished islet ghrelin cell number and lower plasma ghrelin levels. Mol Cell Endocrinol 2020; 511:110835. [PMID: 32371087 DOI: 10.1016/j.mce.2020.110835] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/07/2020] [Accepted: 04/21/2020] [Indexed: 01/22/2023]
Abstract
It is not known how ghrelin affects insulin secretion in human islets from patients with type 2 diabetes (T2D) or whether islet ghrelin expression or circulating ghrelin levels are altered in T2D. Here we sought out to identify the effect of ghrelin on insulin secretion in human islets and the impact of T2D on circulating ghrelin levels and on islet ghrelin cells. The effect of ghrelin on insulin secretion was assessed in human T2D and non-T2D islets. Ghrelin expression was assessed with RNA-sequencing (n = 191) and immunohistochemistry (n = 21). Plasma ghrelin was measured with ELISA in 40 T2D and 40 non-T2D subjects. Ghrelin exerted a glucose-dependent insulin-suppressing effect in islets from both T2D and non-T2D donors. Compared with non-T2D donors, T2D donors had reduced ghrelin mRNA expression and 75% less islet ghrelin cells, and ghrelin mRNA expression correlated negatively with HbA1c. T2D subjects had 25% lower fasting plasma ghrelin levels than matched controls. Thus, ghrelin has direct insulin-suppressing effects in human islets and T2D patients have lower fasting ghrelin levels, likely as a result of reduced number of islet ghrelin cells. These findings support inhibition of ghrelin signaling as a potential therapeutic avenue for stimulation of insulin secretion in T2D patients.
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Affiliation(s)
- A Lindqvist
- Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - L Shcherbina
- Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - R B Prasad
- Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - M G Miskelly
- Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - M Abels
- Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - J A Martínez-Lopéz
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - R G Fred
- Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | | | - J Hedenbro
- Lund University Diabetes Centre, Lund University, Malmö, Sweden; Aleris Obesitas, Lund, Sweden
| | - L Groop
- Lund University Diabetes Centre, Lund University, Malmö, Sweden; Finnish Institute of Molecular Medicine, Helsinki, Finland
| | - J Hjerling-Leffler
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - N Wierup
- Lund University Diabetes Centre, Lund University, Malmö, Sweden.
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82
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Lu Y, Li Y, Li G, Lu H. Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis. Mol Med Rep 2020; 22:1868-1882. [PMID: 32705173 PMCID: PMC7411335 DOI: 10.3892/mmr.2020.11281] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 01/20/2020] [Indexed: 12/15/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β-cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein-protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in ‘inflammatory response’ and ‘lipid metabolic process’, and 37 downregulated candidate genes were involved in ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’. These candidate genes were also involved in different signaling pathways associated with ‘PI3K/Akt signaling pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ and ‘MAPK signaling pathway’, which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)-target gene regulatory network and a transcription factor-target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa-miR-192-5p, hsa-miR-124-5p and hsa-miR-335-5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C-endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C-C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic-based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.
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Affiliation(s)
- Yana Lu
- Key Laboratory of Dai and Southern Medicine of Xishuangbanna Dai Autonomous Prefecture, Yunnan Branch, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Jinghong, Yunnan 666100, P.R. China
| | - Yihang Li
- Key Laboratory of Dai and Southern Medicine of Xishuangbanna Dai Autonomous Prefecture, Yunnan Branch, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Jinghong, Yunnan 666100, P.R. China
| | - Guang Li
- Key Laboratory of Dai and Southern Medicine of Xishuangbanna Dai Autonomous Prefecture, Yunnan Branch, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Jinghong, Yunnan 666100, P.R. China
| | - Haitao Lu
- Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
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Abstract
Background Pancreatic Islets of Langerhans are heterogeneous tissues consisting of multiple endocrine cell types that carry out distinct yet coordinated roles to regulate blood glucose homeostasis. Islet dysfunction and specifically failure of the beta cells to secrete adequate insulin are known precursors to type 2 diabetes (T2D) onset. However, the exact genetic, (epi)genomic, and environmental mechanisms that contribute to islet failure, and ultimately to T2D pathogenesis, require further elucidation. Scope of review This review summarizes efforts and advances in dissection of the complex genetic underpinnings of islet function and resilience in T2D pathogenesis. In this review, we will highlight results of the latest T2D genome-wide association study (GWAS) and discuss how these data are being combined with clinical measures in patients to uncover putative T2D subtypes and with functional (epi)genomic studies in islets to understand the genetic programming of islet cell identity, function, and adaptation. Finally, we discuss new and important opportunities to address major knowledge gaps in our understanding of islet (dys)function in T2D risk and progression. Major conclusions Genetic variation exerts clear effects on the islet epigenome, regulatory element usage, and gene expression. Future (epi)genomic comparative analyses between T2D and normal islets should incorporate genetics to distinguish patient-specific from disease-specific differences. Incorporating genotype information into future analyses and studies will also enable more precise insights into the molecular genetics of islet deficiency and failure in T2D risk, and should ultimately contribute to a stratified view of T2D and more precise treatment strategies. Islet cellular heterogeneity continues to remain a challenge for understanding the associations between islet failure and T2D development. Further efforts to obtain purified islet cell type populations and determine the specific genetic and environmental effects on each will help address this. Beyond observation of islets at steady state conditions, more research of islet stress and stimulation responses are needed to understand the transition of these tissues from a healthy to diseased state. Together, focusing on these objectives will provide more opportunities to prevent, treat, and manage T2D.
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CHL1 promotes insulin secretion and negatively regulates the proliferation of pancreatic β cells. Biochem Biophys Res Commun 2020; 525:1095-1102. [PMID: 32184019 DOI: 10.1016/j.bbrc.2020.03.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 03/09/2020] [Indexed: 12/28/2022]
Abstract
Cell adhesion molecule L1-like protein (CHL1) is a member of neural recognition molecules of immunoglobulin superfamily primarily expressing in the nervous system. CHL1 regulates neuronal migration, axonal growth, and dendritic projection. Downregulation of CHL1 has been reported in β cells of patients with type 2 diabetes (T2DM). However, the detailed role of CHL1 in β cells has not been characterized. In this study, Real-Time PCR and Western blot were applied to investigate the tissue/cell distribution and expression of CHL1. Gain- or loss-of function studies were conducted in MIN6 cells to determine the effects of CHL1 on cell proliferation, apoptosis, cell cycle, and insulin secretion. Following silencing of CHL1 in MIN6 cells (si-CHL1), insulin secretion and the number of insulin secretary granules <50 nm from the cell membrane decreased in response to 20 mM glucose. Besides, silencing of CHL1 induced cell proliferation, reduced apoptosis, and prolonged S phase and shortened G1 phase of the cell cycle, contrary to overexpressing of CHL1. The inhibitor of ERK1/2MAPK eliminated the effect of CHL1 deficiency on the proliferation of MIN6 cells. In addition, high-fat diet could result in increased islet volume and β cell proliferation, decreased CHL1 expression and activation of ERK pathway in mice islets. Consequently, CHL1 expression was decreased in islets of high-fat induced mice, which resulted in cell proliferation via ERK pathway and regulation of the cell cycle through p53 pathway. These mechanisms may contribute to pancreatic β cell compensatory hyperplasia in obesity-induced pre-diabetes.
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85
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Marchetti P, Suleiman M, De Luca C, Baronti W, Bosi E, Tesi M, Marselli L. A direct look at the dysfunction and pathology of the β cells in human type 2 diabetes. Semin Cell Dev Biol 2020; 103:83-93. [PMID: 32417220 DOI: 10.1016/j.semcdb.2020.04.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 03/27/2020] [Accepted: 04/09/2020] [Indexed: 12/25/2022]
Abstract
β cells uniquely produce and secrete insulin under the control of several, integrated signals, to maintain blood glucose concentrations within a narrow physiological interval. β cell failure is key to the onset and progression of type 2 diabetes, due to impaired function and reduced mass. In this review we focus on several features of human β cell dysfunction and pathology in type 2 diabetes, as revealed by direct assessment of isolated islet traits and examination of pancreatic tissue from organ donors, surgical samples or autoptic specimens. Insulin secretion defects and pathology findings are discussed in relation to some of the major underlying mechanisms, to also provide clues for conceiving better prevention and treatment of type 2 diabetes by targeting the pancreatic β cells.
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Affiliation(s)
- Piero Marchetti
- Department of Clinical and Experimental Medicine - University of Pisa, Via Savi 10, Pisa, Italy.
| | - Mara Suleiman
- Department of Clinical and Experimental Medicine - University of Pisa, Via Savi 10, Pisa, Italy
| | - Carmela De Luca
- Department of Clinical and Experimental Medicine - University of Pisa, Via Savi 10, Pisa, Italy
| | - Walter Baronti
- Department of Clinical and Experimental Medicine - University of Pisa, Via Savi 10, Pisa, Italy
| | - Emanuele Bosi
- Department of Clinical and Experimental Medicine - University of Pisa, Via Savi 10, Pisa, Italy
| | - Marta Tesi
- Department of Clinical and Experimental Medicine - University of Pisa, Via Savi 10, Pisa, Italy
| | - Lorella Marselli
- Department of Clinical and Experimental Medicine - University of Pisa, Via Savi 10, Pisa, Italy
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86
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Carrat GR, Haythorne E, Tomas A, Haataja L, Müller A, Arvan P, Piunti A, Cheng K, Huang M, Pullen TJ, Georgiadou E, Stylianides T, Amirruddin NS, Salem V, Distaso W, Cakebread A, Heesom KJ, Lewis PA, Hodson DJ, Briant LJ, Fung AC, Sessions RB, Alpy F, Kong AP, Benke PI, Torta F, Teo AKK, Leclerc I, Solimena M, Wigley DB, Rutter GA. The type 2 diabetes gene product STARD10 is a phosphoinositide-binding protein that controls insulin secretory granule biogenesis. Mol Metab 2020; 40:101015. [PMID: 32416313 PMCID: PMC7322359 DOI: 10.1016/j.molmet.2020.101015] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/24/2020] [Accepted: 05/05/2020] [Indexed: 02/09/2023] Open
Abstract
OBJECTIVE Risk alleles for type 2 diabetes at the STARD10 locus are associated with lowered STARD10 expression in the β-cell, impaired glucose-induced insulin secretion, and decreased circulating proinsulin:insulin ratios. Although likely to serve as a mediator of intracellular lipid transfer, the identity of the transported lipids and thus the pathways through which STARD10 regulates β-cell function are not understood. The aim of this study was to identify the lipids transported and affected by STARD10 in the β-cell and the role of the protein in controlling proinsulin processing and insulin granule biogenesis and maturation. METHODS We used isolated islets from mice deleted selectively in the β-cell for Stard10 (βStard10KO) and performed electron microscopy, pulse-chase, RNA sequencing, and lipidomic analyses. Proteomic analysis of STARD10 binding partners was executed in the INS1 (832/13) cell line. X-ray crystallography followed by molecular docking and lipid overlay assay was performed on purified STARD10 protein. RESULTS βStard10KO islets had a sharply altered dense core granule appearance, with a dramatic increase in the number of "rod-like" dense cores. Correspondingly, basal secretion of proinsulin was increased versus wild-type islets. The solution of the crystal structure of STARD10 to 2.3 Å resolution revealed a binding pocket capable of accommodating polyphosphoinositides, and STARD10 was shown to bind to inositides phosphorylated at the 3' position. Lipidomic analysis of βStard10KO islets demonstrated changes in phosphatidylinositol levels, and the inositol lipid kinase PIP4K2C was identified as a STARD10 binding partner. Also consistent with roles for STARD10 in phosphoinositide signalling, the phosphoinositide-binding proteins Pirt and Synaptotagmin 1 were amongst the differentially expressed genes in βStard10KO islets. CONCLUSION Our data indicate that STARD10 binds to, and may transport, phosphatidylinositides, influencing membrane lipid composition, insulin granule biosynthesis, and insulin processing.
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Affiliation(s)
- Gaelle R. Carrat
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK
| | - Elizabeth Haythorne
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK
| | - Alejandra Tomas
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK
| | - Leena Haataja
- Division of Metabolism, Endocrinology & Diabetes, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Andreas Müller
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany,Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich, University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany,German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany,Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Dresden, Germany
| | - Peter Arvan
- Division of Metabolism, Endocrinology & Diabetes, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alexandra Piunti
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK,Lille 1 University-Science and Technology, Cité Scientifique, 59655, Villeneuve d'Ascq Cedex, France
| | - Kaiying Cheng
- Section of Structural Biology, Department of Medicine, Imperial College London, London, UK
| | - Mutian Huang
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK
| | - Timothy J. Pullen
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK,Department of Diabetes, Faculty of Life Science and Medicine, King's College London, London, UK
| | - Eleni Georgiadou
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK
| | - Theodoros Stylianides
- Loughborough University, Centre of Innovative and Collaborative Construction Engineering, Leicestershire, LE11 3TU, UK
| | - Nur Shabrina Amirruddin
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), A∗STAR, Proteos, Singapore, 138673, Singapore,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Victoria Salem
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK,Section of Investigative Medicine, Department of Medicine, Imperial College London, du Cane Road, London, W12 0NN, UK
| | - Walter Distaso
- Imperial College Business School, Imperial College London, Exhibition Road, London, SW7 2AZ, UK
| | - Andrew Cakebread
- London Metallomics Facility, King's College London, Strand, London, WC2R 2LS, UK
| | | | | | - David J. Hodson
- Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK,Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, UK,Centre of Membrane Proteins and Receptors, University of Birmingham and University of Nottingham, Midlands, UK
| | - Linford J. Briant
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford, OX3 7LE, UK
| | - Annie C.H. Fung
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Richard B. Sessions
- School of Biochemistry, Faculty of Life Sciences, University of Bristol, Bristol, BS8 1TD, UK
| | - Fabien Alpy
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U1258, Centre National de la Recherche Scientifique (CNRS), UMR 7104, Université de Strasbourg, 1 rue Laurent Fries, 67404 Illkirch, France
| | - Alice P.S. Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Peter I. Benke
- Singapore Lipidomics Incubator, Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Mdical Drive, Singapore, 117596, Singapore
| | - Federico Torta
- Singapore Lipidomics Incubator, Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Mdical Drive, Singapore, 117596, Singapore
| | - Adrian Kee Keong Teo
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), A∗STAR, Proteos, Singapore, 138673, Singapore,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117596, Singapore
| | - Isabelle Leclerc
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK
| | - Michele Solimena
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany,Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich, University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany,German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany,Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Dresden, Germany
| | - Dale B. Wigley
- Section of Structural Biology, Department of Medicine, Imperial College London, London, UK
| | - Guy A. Rutter
- Section of Cell Biology and Functional Genomics, Imperial College London, du Cane Road, London, W12 0NN, UK,Corresponding author. +44 (0)20 7594 3340.
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87
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Yi J, Chen B, Bao Y, Wang J. Simultaneous determination of TUG-891 and its metabolites in rat plasma using LC-HRMS with application to preclinical pharmacokinetic study. Biomed Chromatogr 2020; 34:e4870. [PMID: 32346871 DOI: 10.1002/bmc.4870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/15/2020] [Accepted: 04/24/2020] [Indexed: 11/09/2022]
Abstract
In this study, a simple and reliable LC-MS/MS method was first proposed for the simultaneous determination of TUG-891 and its metabolites TUG-891-alcohol, TUG-891-aldehyde, and TUG-891-acid in rat plasma. The analytes and fasiglifam (internal standard) were extracted from plasma samples with acetonitrile and separated using an Acquity BEH C18 column (1.7 μm, 2.1 × 50 mm) with water containing 0.05% ammonium hydroxide and acetonitrile containing 0.05% ammonium hydroxide as the mobile phase. A Q-Exactive Orbitrap mass spectrometer in full-scan mode was used for mass detection, and the data analysis was obtained using a mass extraction window of 5 ppm. The calibration curves exhibited excellent linearity (correlation coefficient > 0.9981) in the concentration range of 0.5-1000 ng/mL. The lower limit of quantification was 0.5 ng/mL for all analytes. The intra- and inter-day precision was less than 11.31%, and the accuracy ranged from -11.50 to 9.50%. The extraction recovery of the analytes from rat plasma was greater than 82.31%, and no obvious matrix effect was found. The established method was further applied to the pharmacokinetic study of TUG-891, TUG-891-alcohol, TUG-891-aldehyde, and TUG-891-acid in rat after a single dose of 5-mg/kg treatment of TUG-891. The results demonstrated that TUG-891 was rapidly metabolized into its metabolites and the systemic exposures of the metabolites were much higher than those of TUG-891.
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Affiliation(s)
- Jiping Yi
- Department of Neurology, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, Hunan Province, China
| | - Bin Chen
- Department of Spine Surgery, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, Hunan Province, China
| | - Yongzheng Bao
- Department of Spine Surgery, Yuebei People's Hospital, Shantou University, Shaoguan, Guangdong Province, China
| | - Jingyu Wang
- Department of Clinical Pharmacy, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, Hunan Province, China
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88
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Hachim MY, Aljaibeji H, Hamoudi RA, Hachim IY, Elemam NM, Mohammed AK, Salehi A, Taneera J, Sulaiman N. An Integrative Phenotype-Genotype Approach Using Phenotypic Characteristics from the UAE National Diabetes Study Identifies HSD17B12 as a Candidate Gene for Obesity and Type 2 Diabetes. Genes (Basel) 2020; 11:genes11040461. [PMID: 32340285 PMCID: PMC7230604 DOI: 10.3390/genes11040461] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 04/15/2020] [Accepted: 04/21/2020] [Indexed: 12/14/2022] Open
Abstract
The United Arab Emirates National Diabetes and Lifestyle Study (UAEDIAB) has identified obesity, hypertension, obstructive sleep apnea, and dyslipidemia as common phenotypic characteristics correlated with diabetes mellitus status. As these phenotypes are usually linked with genetic variants, we hypothesized that these phenotypes share single nucleotide polymorphism (SNP)-clusters that can be used to identify causal genes for diabetes. We explored the National Human Genome Research Institute-European Bioinformatics Institute Catalog of Published Genome-Wide Association Studies (NHGRI-EBI GWAS) to list SNPs with documented association with the UAEDIAB-phenotypes as well as diabetes. The shared chromosomal regions affected by SNPs were identified, intersected, and searched for Enriched Ontology Clustering. The potential SNP-clusters were validated using targeted DNA next-generation sequencing (NGS) in two Emirati diabetic patients. RNA sequencing from human pancreatic islets was used to study the expression of identified genes in diabetic and non-diabetic donors. Eight chromosomal regions containing 46 SNPs were identified in at least four out of the five UAEDIAB-phenotypes. A list of 34 genes was shown to be affected by those SNPs. Targeted NGS from two Emirati patients confirmed that the identified genes have similar SNP-clusters. ASAH1, LRP4, FES, and HSD17B12 genes showed the highest SNPs rate among the identified genes. RNA-seq analysis revealed high expression levels of HSD17B12 in human islets and to be upregulated in type 2 diabetes (T2D) donors. Our integrative phenotype-genotype approach is a novel, simple, and powerful tool to identify clinically relevant potential biomarkers in diabetes. HSD17B12 is a novel candidate gene for pancreatic β-cell function.
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Affiliation(s)
- Mahmood Y. Hachim
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah 27273, UAE; (M.Y.H.); (H.A.); (R.A.H.); (N.M.E.); (A.K.M.)
| | - Hayat Aljaibeji
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah 27273, UAE; (M.Y.H.); (H.A.); (R.A.H.); (N.M.E.); (A.K.M.)
| | - Rifat A. Hamoudi
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah 27273, UAE; (M.Y.H.); (H.A.); (R.A.H.); (N.M.E.); (A.K.M.)
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, UAE;
| | - Ibrahim Y. Hachim
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, UAE;
| | - Noha M. Elemam
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah 27273, UAE; (M.Y.H.); (H.A.); (R.A.H.); (N.M.E.); (A.K.M.)
| | - Abdul Khader Mohammed
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah 27273, UAE; (M.Y.H.); (H.A.); (R.A.H.); (N.M.E.); (A.K.M.)
| | - Albert Salehi
- Department of Clinical Sciences, Division of Islets Cell Pathology, Lund University, SE-205 02 Malmö, Sweden;
| | - Jalal Taneera
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah 27273, UAE; (M.Y.H.); (H.A.); (R.A.H.); (N.M.E.); (A.K.M.)
- Department of Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, UAE
- Correspondence: (J.T.); (N.S.)
| | - Nabil Sulaiman
- Department of Family and Community Medicine and Behavioral Sciences, College of Medicine, University of Sharjah, Sharjah 27272, UAE
- Baker Heart and Diabetes Institute, Melbourne 3004, Australia
- Correspondence: (J.T.); (N.S.)
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89
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A combination of genetics and microbiota influences the severity of the obesity phenotype in diet-induced obesity. Sci Rep 2020; 10:6118. [PMID: 32273571 PMCID: PMC7145845 DOI: 10.1038/s41598-020-63340-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 03/30/2020] [Indexed: 12/03/2022] Open
Abstract
Obesity has emerged as a major global health problem and is associated with various diseases, such as metabolic syndrome, type 2 diabetes mellitus, and cardiovascular diseases. The inbred C57BL/6 mouse strain is often used for various experimental investigations, such as metabolic research. However, over time, genetically distinguishable C57BL/6 substrains have evolved. The manifestation of genetic alterations has resulted in behavioral and metabolic differences. In this study, a comparison of diet-induced obesity in C57BL/6JHanZtm, C57BL/6NCrl and C57BL/6 J mice revealed several metabolic and immunological differences such as blood glucose level and cytokine expression, respectively, among these C57BL/6 substrains. For example, C57BL/6NCrl mice developed the most pronounced adiposity, whereas C57BL/6 J mice showed the highest impairment in glucose tolerance. Moreover, our results indicated that the immunological phenotype depends on the intestinal microbiota, as the cell subset composition of the colon was similar in obese ex-GF B6NRjB6JHanZtm and obese B6JHanZtm mice. Phenotypic differences between C57BL/6 substrains are caused by a complex combination of genetic and microbial alterations. Therefore, in performing metabolic research, considering substrain-specific characteristics, which can influence the course of study, is important. Moreover, for unbiased comparison of data, the entire strain name should be shared with the scientific community.
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90
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Vitamin D metabolites influence expression of genes concerning cellular viability and function in insulin producing β-cells (INS1E). Gene 2020; 746:144649. [PMID: 32251702 DOI: 10.1016/j.gene.2020.144649] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 03/31/2020] [Accepted: 04/03/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND Studies have shown that vitamin D can enhance glucose-stimulated insulin secretion (GSIS) and change the expression of genes in pancreatic β-cells. Still the mechanisms linking vitamin D and GSIS are unknown. MATERIAL AND METHODS We used an established β-cell line, INS1E. INS1E cells were pre-treated with 10 nM 1,25(OH)2vitamin D or 10 nM 25(OH)vitamin D for 72 h and stimulated with 22 mM glucose for 60 min. RNA was extracted for gene expression analysis. RESULTS Expression of genes affecting viability, apoptosis and GSIS changed after pre-treatment with both 1,25(OH)2vitamin D and 25(OH)vitamin D in INS1E cells. Stimulation with glucose after pre-treatment of INS1E cells with 1,25(OH)2vitamin D resulted in 181 differentially expressed genes, whereas 526 genes were differentially expressed after pre-treatment with 25(OH)vitamin D. CONCLUSION Vitamin D metabolites may affect pancreatic β-cells and GSIS through changed gene expression for genes involved in β-cell function and viability.
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91
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Zhyzhneuskaya SV, Al-Mrabeh A, Peters C, Barnes A, Aribisala B, Hollingsworth KG, McConnachie A, Sattar N, Lean MEJ, Taylor R. Time Course of Normalization of Functional β-Cell Capacity in the Diabetes Remission Clinical Trial After Weight Loss in Type 2 Diabetes. Diabetes Care 2020; 43:813-820. [PMID: 32060017 DOI: 10.2337/dc19-0371] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 12/29/2019] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To assess functional β-cell capacity in type 2 diabetes during 2 years of remission induced by dietary weight loss. RESEARCH DESIGN AND METHODS A Stepped Insulin Secretion Test with Arginine was used to quantify functional β-cell capacity by hyperglycemia and arginine stimulation. Thirty-nine of 57 participants initially achieved remission (HbA1c <6.5% [<48 mmol/mol] and fasting plasma glucose <7 mmol/L on no antidiabetic drug therapy) with a 16.4 ± 7.7 kg weight loss and were followed up with supportive advice on avoidance of weight regain. At 2 years, 20 participants remained in remission in the study. A nondiabetic control (NDC) group, matched for age, sex, and weight after weight loss with the intervention group, was studied once. RESULTS During remission, median (interquartile range) maximal rate of insulin secretion increased from 581 (480-811) pmol/min/m2 at baseline to 736 (542-998) pmol/min/m2 at 5 months, 942 (565-1,240) pmol/min/m2 at 12 months (P = 0.028 from baseline), and 936 (635-1,435) pmol/min/m2 at 24 months (P = 0.023 from baseline; n = 20 of 39 of those initially in remission). This was comparable to the NDC group (1,016 [857-1,507] pmol/min/m2) by 12 (P = 0.064) and 24 (P = 0.244) months. Median first-phase insulin response increased from baseline to 5 months (42 [4-67] to 107 [59-163] pmol/min/m2; P < 0.0001) and then remained stable at 12 and 24 months (110 [59-201] and 125 [65-166] pmol/min/m2, respectively; P < 0.0001 vs. baseline) but lower than that of the NDC group (250 [226-429] pmol/min/m2; P < 0.0001). CONCLUSIONS A gradual increase in assessed functional β-cell capacity occurred after weight loss, becoming similar to that of NDC group participants by 12 months. This result was unchanged at 2 years with continuing remission of type 2 diabetes.
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Affiliation(s)
- Sviatlana V Zhyzhneuskaya
- Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, U.K
| | - Ahmad Al-Mrabeh
- Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, U.K
| | - Carl Peters
- Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, U.K
| | - Alison Barnes
- Human Nutrition Research Centre, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, U.K
| | | | - Kieren G Hollingsworth
- Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, U.K
| | - Alex McConnachie
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, U.K
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, U.K
| | - Michael E J Lean
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, U.K
| | - Roy Taylor
- Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, U.K.
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92
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Fan J, Du W, Kim-Muller JY, Son J, Kuo T, Larrea D, Garcia C, Kitamoto T, Kraakman MJ, Owusu-Ansah E, Cirulli V, Accili D. Cyb5r3 links FoxO1-dependent mitochondrial dysfunction with β-cell failure. Mol Metab 2020; 34:97-111. [PMID: 32180563 PMCID: PMC7031142 DOI: 10.1016/j.molmet.2019.12.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 12/03/2019] [Accepted: 12/12/2019] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE Diabetes is characterized by pancreatic β-cell dedifferentiation. Dedifferentiating β cells inappropriately metabolize lipids over carbohydrates and exhibit impaired mitochondrial oxidative phosphorylation. However, the mechanism linking the β-cell's response to an adverse metabolic environment with impaired mitochondrial function remains unclear. METHODS Here we report that the oxidoreductase cytochrome b5 reductase 3 (Cyb5r3) links FoxO1 signaling to β-cell stimulus/secretion coupling by regulating mitochondrial function, reactive oxygen species generation, and nicotinamide actin dysfunction (NAD)/reduced nicotinamide actin dysfunction (NADH) ratios. RESULTS The expression of Cyb5r3 is decreased in FoxO1-deficient β cells. Mice with β-cell-specific deletion of Cyb5r3 have impaired insulin secretion, resulting in glucose intolerance and diet-induced hyperglycemia. Cyb5r3-deficient β cells have a blunted respiratory response to glucose and display extensive mitochondrial and secretory granule abnormalities, consistent with altered differentiation. Moreover, FoxO1 is unable to maintain expression of key differentiation markers in Cyb5r3-deficient β cells, suggesting that Cyb5r3 is required for FoxO1-dependent lineage stability. CONCLUSIONS The findings highlight a pathway linking FoxO1 to mitochondrial dysfunction that can mediate β-cell failure.
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Affiliation(s)
- Jason Fan
- Naomi Berrie Diabetes Center and Departments of Medicine, Columbia University, New York, NY 10032, USA
| | - Wen Du
- Naomi Berrie Diabetes Center and Departments of Medicine, Columbia University, New York, NY 10032, USA
| | - Ja Young Kim-Muller
- Naomi Berrie Diabetes Center and Departments of Medicine, Columbia University, New York, NY 10032, USA
| | - Jinsook Son
- Naomi Berrie Diabetes Center and Departments of Medicine, Columbia University, New York, NY 10032, USA
| | - Taiyi Kuo
- Naomi Berrie Diabetes Center and Departments of Medicine, Columbia University, New York, NY 10032, USA
| | - Delfina Larrea
- Department of Neurology, Columbia University, New York, NY 10032, USA
| | - Christian Garcia
- Physiology and Cellular Biophysics, Columbia University, New York, NY 10032, USA
| | - Takumi Kitamoto
- Naomi Berrie Diabetes Center and Departments of Medicine, Columbia University, New York, NY 10032, USA
| | - Michael J Kraakman
- Naomi Berrie Diabetes Center and Departments of Medicine, Columbia University, New York, NY 10032, USA
| | - Edward Owusu-Ansah
- Physiology and Cellular Biophysics, Columbia University, New York, NY 10032, USA
| | - Vincenzo Cirulli
- Department of Medicine, UW-Diabetes Institute, Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Domenico Accili
- Naomi Berrie Diabetes Center and Departments of Medicine, Columbia University, New York, NY 10032, USA.
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93
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Cardiac miRNA Expression and their mRNA Targets in a Rat Model of Prediabetes. Int J Mol Sci 2020; 21:ijms21062128. [PMID: 32244869 PMCID: PMC7139428 DOI: 10.3390/ijms21062128] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 03/12/2020] [Accepted: 03/16/2020] [Indexed: 02/08/2023] Open
Abstract
Little is known about the mechanism of prediabetes-induced cardiac dysfunction. Therefore, we aimed to explore key molecular changes with transcriptomic and bioinformatics approaches in a prediabetes model showing heart failure with preserved ejection fraction phenotype. To induce prediabetes, Long-Evans rats were fed a high-fat diet for 21 weeks and treated with a single low-dose streptozotocin at week 4. Small RNA-sequencing, in silico microRNA (miRNA)-mRNA target prediction, Gene Ontology analysis, and target validation with qRT-PCR were performed in left ventricle samples. From the miRBase-annotated 752 mature miRNA sequences expression of 356 miRNAs was detectable. We identified two upregulated and three downregulated miRNAs in the prediabetic group. We predicted 445 mRNA targets of the five differentially expressed miRNAs and selected 11 mRNAs targeted by three differentially expressed miRNAs, out of which five mRNAs were selected for validation. Out of these five targets, downregulation of three mRNAs i.e., Juxtaposed with another zinc finger protein 1 (Jazf1); RAP2C, member of RAS oncogene family (Rap2c); and Zinc finger with KRAB and SCAN domains 1 (Zkscan1) were validated. This is the first demonstration that prediabetes alters cardiac miRNA expression profile. Predicted targets of differentially expressed miRNAs include Jazf1, Zkscan1, and Rap2c mRNAs. These transcriptomic changes may contribute to the diastolic dysfunction and may serve as drug targets.
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94
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Taddeo EP, Alsabeeh N, Baghdasarian S, Wikstrom JD, Ritou E, Sereda S, Erion K, Li J, Stiles L, Abdulla M, Swanson Z, Wilhelm JJ, Bellin MD, Kibbey RG, Liesa M, Shirihai OS. Mitochondrial Proton Leak Regulated by Cyclophilin D Elevates Insulin Secretion in Islets at Nonstimulatory Glucose Levels. Diabetes 2020; 69:131-145. [PMID: 31740442 PMCID: PMC6971491 DOI: 10.2337/db19-0379] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 11/11/2019] [Indexed: 12/13/2022]
Abstract
Fasting hyperinsulinemia precedes the development of type 2 diabetes. However, it is unclear whether fasting insulin hypersecretion is a primary driver of insulin resistance or a consequence of the progressive increase in fasting glycemia induced by insulin resistance in the prediabetic state. Herein, we have discovered a mechanism that specifically regulates non-glucose-stimulated insulin secretion (NGSIS) in pancreatic islets that is activated by nonesterified free fatty acids, the major fuel used by β-cells during fasting. We show that the mitochondrial permeability transition pore regulator cyclophilin D (CypD) promotes NGSIS, but not glucose-stimulated insulin secretion, by increasing mitochondrial proton leak. Islets from prediabetic obese mice show significantly higher CypD-dependent proton leak and NGSIS compared with lean mice. Proton leak-mediated NGSIS is conserved in human islets and is stimulated by exposure to nonesterified free fatty acids at concentrations observed in obese subjects. Mechanistically, proton leak activates islet NGSIS independently of mitochondrial ATP synthesis but ultimately requires closure of the KATP channel. In summary, we have described a novel nonesterified free fatty acid-stimulated pathway that selectively drives pancreatic islet NGSIS, which may be therapeutically exploited as an alternative way to halt fasting hyperinsulinemia and the progression of type 2 diabetes.
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Affiliation(s)
- Evan P Taddeo
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Nour Alsabeeh
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
- Department of Physiology, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
| | - Siyouneh Baghdasarian
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Jakob D Wikstrom
- Dermatology and Venereology Unit, Department of Medicine, Karolinska Institutet, and Department of Dermato-Venereology, Karolinska University Hospital, Stockholm, Sweden
| | - Eleni Ritou
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Samuel Sereda
- Endocrinology, Diabetes, Nutrition and Weight Management Section, Department of Medicine, Boston University School of Medicine, Boston, MA
| | - Karel Erion
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Jin Li
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Linsey Stiles
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Muhamad Abdulla
- Department of Surgery and Schulze Diabetes Institute, University of Minnesota School of Medicine, Minneapolis, MN
| | - Zachary Swanson
- Department of Surgery and Schulze Diabetes Institute, University of Minnesota School of Medicine, Minneapolis, MN
| | - Joshua J Wilhelm
- Department of Surgery and Schulze Diabetes Institute, University of Minnesota School of Medicine, Minneapolis, MN
| | - Melena D Bellin
- Department of Surgery and Schulze Diabetes Institute, University of Minnesota School of Medicine, Minneapolis, MN
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN
| | - Richard G Kibbey
- Departments of Internal Medicine (Endocrinology) and Cellular & Molecular Physiology, Yale University, New Haven, CT
| | - Marc Liesa
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA
| | - Orian S Shirihai
- Division of Endocrinology, Diabetes and Hypertension, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
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Lin Y, Li J, Wu D, Wang F, Fang Z, Shen G. Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis. Diabetes Metab Syndr Obes 2020; 13:1793-1801. [PMID: 32547141 PMCID: PMC7250707 DOI: 10.2147/dmso.s245165] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/23/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in the world with complicated pathogenesis. This study aimed to identify differentially expressed genes (DEGs) and molecular pathways in T2DM using bioinformatics analysis. MATERIALS AND METHODS To explore potential therapeutic targets for T2DM, we analyzed three microarray datasets (GSE50397, GSE38642, and GSE44035) acquired from the Gene Expression Omnibus (GEO) database. DEGs between T2DM islet and normal islet were picked out by the GEO2R tool and Venn diagram software. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify the pathways and functional annotation of DEGs. Then, protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). RESULTS In total, we identified 36 DEGs in the three datasets, including 32 up-regulated genes and four down-regulated genes. The improved functions and pathways of the DEGs enriched in cytokine-cytokine receptor interaction, pathways in cancer, PI3K-Akt signaling pathway, and Rheumatoid arthritis. Among them, ten hub genes with a high degree of connectivity were selected. Furthermore, via the re-analysis of DAVID, four genes (IL6, MMP3, MMP1, and IL11) were significantly enriched in the Rheumatoid arthritis pathway. CONCLUSION Our study, based on the GEO database, identified four significant up-regulated DEGs and provided novel targets for diagnosis and treatment of T2DM.
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Affiliation(s)
- YiXuan Lin
- Graduate School of Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China
| | - Jinju Li
- Graduate School of Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China
| | - Di Wu
- Graduate School of Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China
| | - FanJing Wang
- Graduate School of Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China
| | - ZhaoHui Fang
- Department of Endocrinology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui, People’s Republic of China
- Anhui Academic of Traditional Chinese Medicine Diabetes Research Institute, Hefei, Anhui, People’s Republic of China
- Correspondence: ZhaoHui Fang; GuoMing Shen Tel +86-13085513100 Email ;
| | - GuoMing Shen
- Graduate School of Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China
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96
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Peña-Chilet M, Esteban-Medina M, Falco MM, Rian K, Hidalgo MR, Loucera C, Dopazo J. Using mechanistic models for the clinical interpretation of complex genomic variation. Sci Rep 2019; 9:18937. [PMID: 31831811 PMCID: PMC6908734 DOI: 10.1038/s41598-019-55454-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/28/2019] [Indexed: 02/07/2023] Open
Abstract
The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.
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Affiliation(s)
- María Peña-Chilet
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocío, 41013, Sevilla, Spain
- Bioinformatics in RareDiseases (BiER). Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013, Sevilla, Spain
| | - Marina Esteban-Medina
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocío, 41013, Sevilla, Spain
| | - Matias M Falco
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocío, 41013, Sevilla, Spain
- Bioinformatics in RareDiseases (BiER). Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013, Sevilla, Spain
| | - Kinza Rian
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocío, 41013, Sevilla, Spain
| | - Marta R Hidalgo
- Bioinformatics and Biostatistics Unit, Centro de Investigación Príncipe Felipe (CIPF), 46012, Valencia, Spain
| | - Carlos Loucera
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocío, 41013, Sevilla, Spain
| | - Joaquín Dopazo
- Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocío, 41013, Sevilla, Spain.
- Bioinformatics in RareDiseases (BiER). Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013, Sevilla, Spain.
- INB-ELIXIR-es, FPS, Hospital Virgen del Rocío, Sevilla, 42013, Spain.
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97
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Henquin JC. The challenge of correctly reporting hormones content and secretion in isolated human islets. Mol Metab 2019; 30:230-239. [PMID: 31767174 PMCID: PMC6829677 DOI: 10.1016/j.molmet.2019.10.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 09/28/2019] [Accepted: 10/07/2019] [Indexed: 12/30/2022] Open
Abstract
Background An increased access of research laboratories to isolated human islets has improved our understanding of the biology of the endocrine pancreas and hence the mechanisms causing diabetes. However, in vitro studies of human islets remain technically challenging, and optimal use of such precious material requires a minimum of rigor and coordination to optimize the reliability and share of the information. A detailed report of the demographics of pancreas donors and of the procedures of islet handling after isolation is important but insufficient. Correct characterization of islet basic functions (a token of quality) at the time of experimentation is also crucial. Scope of review I have analyzed the literature reporting measurements of insulin and glucagon in the human pancreas or isolated human islets. The published information is often fragmentary. Elementary features such as islet size, insulin content, or rate of hormone secretion are either unreported or incorrectly reported in many papers. Although internal comparisons between control and test groups may remain valid, comparisons with data from other laboratories are problematic. The drawbacks, pitfalls and errors of common ways of expressing hormone content or secretion rates are discussed and alternatives to harmonize data presentation are proposed. Major Conclusions Greater coherence and rigor in the report of in vitro studies using human islets are necessary to ensure optimal progress in our understanding of the pathogenesis of diabetes.
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Affiliation(s)
- Jean-Claude Henquin
- Unit of Endocrinology and Metabolism, Faculty of Medicine, University of Louvain, Brussels, Belgium.
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98
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Taneera J, Dhaiban S, Hachim M, Mohammed AK, Mukhopadhyay D, Bajbouj K, Hamoudi R, Salehi A, Hamad M. Reduced Expression of Chl1 gene Impairs Insulin Secretion by Down-Regulating the Expression of Key Molecules of β-cell Function. Exp Clin Endocrinol Diabetes 2019; 129:864-872. [PMID: 31614370 DOI: 10.1055/a-1014-2544] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Silencing of Chl1 gene expression has been previously reported to reduce insulin secretion. Nevertheless, the mechanism underlying this effect remains unclear. In this study, we performed a serial of studies to investigate how Chl1 affects insulin secretion in INS-1 cells. RNA-sequencing was used to investigate the expression of CHL1 in human adipose, liver, muscle, and human islets. Silencing of Chl1 in INS-1 cells was done to assess its impact on the insulin secretion, content, cell viability, and apoptosis. In addition, gene set enrichment analysis (GSEA) was performed to identify possible molecular signatures that associate with Chl1 expression silencing.RNA sequencing data revealed a high expression of CHL1 in pancreatic islets and adipose tissues compared to liver and muscles tissues. Diabetic islets exhibited a lower expression of CHL1 as compared to non-diabetic islets. CHL1 expression was found to correlate positively with insulin secretory index, GLP1R but inversely with HbA1c and BMI. Silencing of Chl1 in INS-1 cells markedly reduced insulin content and secretion. The expression of key molecules of β-cell function including Insulin, Pdx1, Gck, Glut2, and Insrβ was down-regulated in Chl1-silenced cells at transcriptional and translational levels. Cell viability, apoptosis, and proliferation rate were not affected. GSEA showed that the insulin-signaling pathway was influenced in Chl1-silenced cells. Silencing of Chl1 impairs β-cell function by disrupting the activity of key signaling pathways of importance for insulin biosynthesis and secretion.
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Affiliation(s)
- Jalal Taneera
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Sarah Dhaiban
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Mahmood Hachim
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Abdul Khader Mohammed
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Debasmita Mukhopadhyay
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Khuloud Bajbouj
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Rifat Hamoudi
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Division of Surgery and Interventional Science, University College London, London, UK
| | - Albert Salehi
- Department of Clinical Sciences, Division of Islets Cell Pathophysiology, Lund University, Malmoe, Sweden
| | - Mawieh Hamad
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
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99
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Hörbelt T, Knebel B, Fahlbusch P, Barbosa D, de Wiza DH, Van de Velde F, Van Nieuwenhove Y, Lapauw B, Thoresen GH, Al-Hasani H, Müller-Wieland D, Ouwens DM, Kotzka J. The adipokine sFRP4 induces insulin resistance and lipogenesis in the liver. Biochim Biophys Acta Mol Basis Dis 2019; 1865:2671-2684. [DOI: 10.1016/j.bbadis.2019.07.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 06/19/2019] [Accepted: 07/18/2019] [Indexed: 01/04/2023]
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100
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Drag MH, Kogelman LJA, Maribo H, Meinert L, Thomsen PD, Kadarmideen HN. Characterization of eQTLs associated with androstenone by RNA sequencing in porcine testis. Physiol Genomics 2019; 51:488-499. [PMID: 31373884 DOI: 10.1152/physiolgenomics.00125.2018] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Characterization of genetic variants affecting genome-wide gene expression levels (expression quantitative trait loci or eQTLs) in pig testes may improve our understanding of genetic architecture of boar taint (an animal welfare trait) and helps in genome-assisted or genomic selection programs. The aims of this study were to identify eQTLs associated with androstenone, to find candidate eQTLs for low androstenone, and to validate the top eQTL by reverse transcriptase quantitative PCR (RT-qPCR). Gene expression profiles were obtained by RNA sequencing in testis from Danish cross-bred pigs and genotype data by 80K single nucleotide polymorphism panel. A total of 262 eQTLs [false discovery rate (FDR) < 0.05] were identified by using two software packages: Matrix eQTL and Krux eQTL. Of these, 149 cis-acting eQTLs were significantly associated with androstenone concentrations and gene expression (FDR < 0.05). The eQTLs were associated with several genes of boar taint relevance including CYP1A2, CYB5D1, and SPHK2. One eQTL gene, AMPH, was differentially expressed (FDR < 0.05) and affected by chicory. Five candidate eQTLs associated with low androstenone concentrations were discovered, including the top eQTL associated with CYP1A2. RT-qPCR confirmed target gene expression to be significantly (P < 0.05) different based on eQTL genotypes. Furthermore, eQTLs were enriched as QTLs for 15 boar taint related traits from the PigQTLdb. This is the first study to report eQTLs in testes of commercial crossbred pigs used in pork production and to reveal genetic architecture of boar taint. Potential applications include development of a DNA test and in advanced genomic selection models for boar taint.
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Affiliation(s)
- Markus H Drag
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lisette J A Kogelman
- Department of Neurology, Danish Headache Center, Rigshospitalet Glostrup, Faculty of Health and Medical Sciences, University of Copenhagen, Glostrup, Denmark
| | - Hanne Maribo
- SEGES, Danish Pig Research Center, Copenhagen, Denmark
| | - Lene Meinert
- Danish Meat Research Institute (DMRI), Danish Technological Institute, Taastrup, Denmark
| | - Preben D Thomsen
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Haja N Kadarmideen
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
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