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Onengut-Gumuscu S, Concannon P, Akolkar B, Erlich HA, Julier C, Morahan G, Nierras CR, Pociot F, Todd JA, Rich SS. Type 1 Diabetes Genetics Consortium. J Clin Endocrinol Metab 2025; 110:1505-1513. [PMID: 40117445 DOI: 10.1210/clinem/dgaf181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Revised: 03/08/2025] [Accepted: 03/20/2025] [Indexed: 03/23/2025]
Abstract
Type 1 diabetes (T1D) results from the autoimmune destruction of the insulin-producing β cells. Genetic factors account for approximately 50% of the risk for T1D but, by the late 1990s, the genetic basis was limited. The Type 1 Diabetes Genetics Consortium (T1DGC) was formed in 2002 to accelerate discovery of genes contributing to T1D risk through a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to assemble existing data and samples from affected sib-pair families and to establish new collections. In recognition of the 75th anniversary of the NIDDK, this manuscript highlights the contributions made by the T1DGC to understanding the genetic basis of T1D using both family (for linkage) and case-control (for genome-wide association) designs. The T1DGC conducted large-scale genetic research and used fine mapping to define risk regions. The T1DGC data, results, and samples have been made available to the scientific community, leading to the discovery of more than 100 loci associated with T1D risk, many with small effects and relevant to autoimmune pathways. The T1DGC not only expanded the list of genes contributing to disease risk but also identified noncoding genetic variation in disease-relevant cell types that contribute to the etiology of T1D. The success of the T1DGC and the NIDDK investment in the global consortium is highlighted in its continuing effect on mapping genetic variants to their function and identifying pathways that provide new targets for the prediction, prevention, and treatment of T1D.
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Affiliation(s)
- Suna Onengut-Gumuscu
- Department of Genome Sciences, The University of Virginia, Charlottesville, VA 22908, USA
| | - Patrick Concannon
- Department of Pathology, Immunology and Laboratory Medicine, Genetics Institute, University of Florida, Gainesville, FL 32610, USA
| | - Beena Akolkar
- Division of Diabetes, Endocrinology, & Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Henry A Erlich
- Department of Genetics and Genomics, UCSF Benioff Children's Hospital Oakland Research Institute, Oakland, CA 94609, USA
| | - Cécile Julier
- Université Paris Cité, Institut Cochin, Paris 75014, France
| | - Grant Morahan
- Centre for Diabetes Research, Harry Perkins Institute of Medical Research, Perth, WA 6009, Australia
| | - Concepcion R Nierras
- Juvenile Diabetes Research Foundation (now Breakthrough T1D), New York, NY 10281, USA
| | - Flemming Pociot
- Translational Type 1 Diabetes Research, Department of Clinical Research, Steno Diabetes Center Copenhagen, Herlev 2730, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - John A Todd
- Diabetes and Inflammation Laboratory, Centre for Human Genetics, Nuffield Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 7BN, UK
| | - Stephen S Rich
- Department of Genome Sciences, The University of Virginia, Charlottesville, VA 22908, USA
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Świeca A, Franaszczyk M, Maryniak A, Lipiński P, Płoski R, Szczałuba K. Maternal Uniparental Isodisomy of Chromosome 6: A Novel Case of Teratoma and Autism Spectrum Disorder with a Diagnostic and Management Framework. Genes (Basel) 2025; 16:434. [PMID: 40282394 PMCID: PMC12026494 DOI: 10.3390/genes16040434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2025] [Revised: 03/28/2025] [Accepted: 04/04/2025] [Indexed: 04/29/2025] Open
Abstract
BACKGROUND Uniparental disomy (UPD) is the inheritance of both copies of a chromosome from a single parent, leading to distinct genetic conditions. Maternal UPD of chromosome 6 (UPD(6)mat) is extremely rare, with few molecularly confirmed cases reported. METHODS We report a prematurely born female with isodisomic UPD(6)mat, presenting with intrauterine growth restriction (IUGR), developmental delay, autism spectrum disorder, dysmorphic features, and a sacrococcygeal teratoma. In addition, we reviewed 24 confirmed UPD(6)mat cases to assess clinical patterns in prenatal findings, birth outcomes, and postnatal features. RESULTS Trio whole-exome sequencing revealed complete isodisomy of chromosome 6 and a de novo heterozygous DIAPH2 variant of uncertain significance. In the literature review, IUGR was present in 87% of cases, with most individuals born small for gestational age and preterm. Failure to thrive and neurodevelopmental issues were also frequent. While the exact molecular basis remains unknown, imprinting disturbances-similar to those in UPD(6)pat-and cryptic trisomy 6 mosaicism, particularly in heterodisomy, are the most likely mechanisms. No specific gene or consistent epigenetic abnormality has been identified. CONCLUSIONS This study aims to enhance the understanding of the genetic and phenotypic spectrum of UPD(6)mat, improving diagnostic and management approaches for this ultra-rare genetic disorder. We propose a detailed list of clinical assessments and tests to be performed following the detection of maternal uniparental disomy of chromosome 6.
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Affiliation(s)
- Aleksandra Świeca
- Department of Medical Genetics, Medical University of Warsaw, 02-106 Warsaw, Poland
| | - Maria Franaszczyk
- Department of Medical Genetics, Medical University of Warsaw, 02-106 Warsaw, Poland
| | | | - Patryk Lipiński
- Institute of Clinical Sciences, Maria Skłodowska-Curie Medical Academy, 00-136 Warsaw, Poland
- Department of Pediatrics, Bielański Hospital, 01-809 Warsaw, Poland
| | - Rafał Płoski
- Department of Medical Genetics, Medical University of Warsaw, 02-106 Warsaw, Poland
| | - Krzysztof Szczałuba
- Department of Medical Genetics, Medical University of Warsaw, 02-106 Warsaw, Poland
- Center of Excellence for Rare and Undiagnosed Disorders, Medical University of Warsaw, 02-106 Warsaw, Poland
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Liu T, Sankareswaran A, Paterson G, Fraser DP, Hodgson S, Huang QQ, Heng TH, Ladwa M, Thomas N, van Heel DA, Weedon MN, Yajnik CS, Oram RA, Chandak GR, Martin HC, Finer S. Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores. Sci Rep 2025; 15:1168. [PMID: 39805939 PMCID: PMC11729895 DOI: 10.1038/s41598-024-80348-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 11/18/2024] [Indexed: 01/30/2025] Open
Abstract
Correct classification of type 1 (T1D) and type 2 diabetes (T2D) is challenging due to overlapping clinical features and the increasingly early onset of T2D, particularly in South Asians. Polygenic risk scores (PRSs) for T1D and T2D have been shown to work relatively well in South Asians, despite being derived from largely European-ancestry samples. Here we used PRSs to investigate the rate of potential misclassification of diabetes amongst British Bangladeshis and Pakistanis. Using linked health records from the Genes & Health cohort (n = 38,344) we defined two reference groups meeting stringent diagnostic criteria: 31 T1D cases, 1842 T2D cases, and after excluding these, two further groups: 839 insulin-treated diabetic individuals with ambiguous features and 5174 non-diabetic controls. Combining these with 307 confirmed T1D cases and 307 controls from India, we calculated ancestry-corrected PRSs for T1D and T2D, with which we estimated the proportion of T1D cases within the ambiguous group at ~ 6%, dropping to ~ 4.5% within the subset who had T2D codes in their health records (and are thus most likely to have been misclassified). We saw no significant association between the T1D or T2D PRS and BMI at diagnosis, time to insulin, or the presence of T1D or T2D diagnostic codes amongst the T2D or ambiguous cases, suggesting that these clinical features are not particularly helpful for aiding diagnosis in ambiguous cases. Our results emphasise that robust identification of T1D cases and appropriate clinical care may require routine measurement of diabetes autoantibodies and C-peptide.
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Affiliation(s)
- Timing Liu
- Wellcome Trust Sanger Institute, Saffron Walden, UK
| | - Alagu Sankareswaran
- Genomic Research on Complex diseases Group (GRC-Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Gordon Paterson
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
- Barts Health NHS Trust, London, UK
| | | | - Sam Hodgson
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | | | - Meera Ladwa
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
- Blizard Institute, Queen Mary University of London, London, UK
| | | | | | | | | | | | - Giriraj R Chandak
- Genomic Research on Complex diseases Group (GRC-Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | | | - Sarah Finer
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
- Barts Health NHS Trust, London, UK.
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Khaiz Y, Al Idrissi N, Bakkali M, Ahid S. Association of the Immunity Genes with Type 1 Diabetes Mellitus. Curr Diabetes Rev 2025; 21:38-46. [PMID: 38310481 DOI: 10.2174/0115733998275617231218101116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/27/2023] [Accepted: 12/05/2023] [Indexed: 02/05/2024]
Abstract
Type 1 diabetes mellitus (T1D) is a complicated illness marked by the death of insulin- producing pancreatic beta cells, which ultimately leads to insulin insufficiency and hyperglycemia. T lymphocytes are considered to destroy pancreatic beta cells in the etiology of T1D as a result of hereditary and environmental factors. Although the latter factors are very important causes of T1D development, this disease is very genetically predisposed, so there is a significant genetic component to T1D susceptibility. Among the T1D-associated gene mutations, those that affect genes that encode the traditional Human Leukocyte Antigens (HLA) entail the highest risk of T1D development. Accordingly, the results of decades of genetic linkage and association studies clearly demonstrate that mutations in the HLA genes are the most associated mutations with T1D. They can, therefore, be used as biomarkers for prediction strategies and may even prove to be of value for personalized treatments. Other immunity-associated genetic loci are also associated with higher T1D risk. Indeed, T1D is considered an autoimmune disease. Its prevalence is rising globally, especially among children and young people. Given the global rise of, and thus interest in, autoimmune diseases, here we present a short overview of the link between immunity, especially HLA, genes and T1D.
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Affiliation(s)
- Youssef Khaiz
- Laboratory of Genomics, Bioinformatics and Digital Health, School of Medicine, Mohammed VI University of Science and Health, Casablanca, Morocco
| | - Najib Al Idrissi
- Laboratory of Genomics, Bioinformatics and Digital Health, School of Medicine, Mohammed VI University of Science and Health, Casablanca, Morocco
| | - Mohammed Bakkali
- Departamento de Genética, Facultad de Ciencias, Universidad de Granada, Fuentenueva S/N, 18071, Granada, Spain
| | - Samir Ahid
- Laboratory of Genomics, Bioinformatics and Digital Health, School of Medicine, Mohammed VI University of Science and Health, Casablanca, Morocco
- Pharmaco-Epidemiology and Pharmaco-Economics Research Team, Faculty of Medicine and Pharmacy, Mohammed V University of Rabat, Rabat, Morocco
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Zhao Y, Zhou R, Mu Z, Carbonetto P, Zhong X, Xie B, Luo K, Cham CM, Koval J, He X, Dahl AW, Liu X, Chang EB, Basu A, Pott S. Cell-type-resolved chromatin accessibility in the human intestine identifies complex regulatory programs and clarifies genetic associations in Crohn's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.12.10.24318718. [PMID: 39711713 PMCID: PMC11661348 DOI: 10.1101/2024.12.10.24318718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2024]
Abstract
Crohn's disease (CD) is a complex inflammatory bowel disease resulting from an interplay of genetic, microbial, and environmental factors. Cell-type-specific contributions to CD etiology and genetic risk are incompletely understood. Here we built a comprehensive atlas of cell-type- resolved chromatin accessibility comprising 557,310 candidate cis-regulatory elements (cCREs) in terminal ileum and ascending colon from patients with active and inactive CD and healthy controls. Using this atlas, we identified cell-type-, anatomic location-, and context-specific cCREs and characterized the regulatory programs underlying inflammatory responses in the intestinal mucosa of CD patients. Genetic variants that disrupt binding motifs of cell-type-specific transcription factors significantly affected chromatin accessibility in specific mucosal cell types. We found that CD heritability is primarily enriched in immune cell types. However, using fine- mapped non-coding CD variants we identified 29 variants located within cCREs several of which were accessible in epithelial and stromal cells implicating cell types from additional lineages in mediating CD risk in some loci. Our atlas provides a comprehensive resource to study gene regulatory effects in CD and health, and highlights the cellular complexity underlying CD risk.
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Yazdanpanah E, Pazoki A, Dadfar S, Nemati MH, Sajad Siadati SM, Tarahomi M, Orooji N, Haghmorad D, Oksenych V. Interleukin-27 and Autoimmune Disorders: A Compressive Review of Immunological Functions. Biomolecules 2024; 14:1489. [PMID: 39766196 PMCID: PMC11672993 DOI: 10.3390/biom14121489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Revised: 11/19/2024] [Accepted: 11/20/2024] [Indexed: 01/11/2025] Open
Abstract
Autoimmune disorders (ADs) pose significant health and economic burdens globally, characterized by the body's immune system mistakenly attacking its own tissues. While the precise mechanisms driving their development remain elusive, a combination of genetic predisposition(s) and environmental triggers is implicated. Interleukin-27 (IL-27), among numerous cytokines involved, has emerged as a key regulator, exhibiting dual roles in immune modulation. This review delves into the molecular structure and signaling mechanisms of IL-27, highlighting its diverse effects on various immune cells. Additionally, it explores the involvement of IL-27 in autoimmune diseases, such as multiple sclerosis (MS) and rheumatoid arthritis (RA), offering insights into its potential therapeutic implications. Moreover, its involvement in autoimmune diseases like type 1 diabetes (T1D), inflammatory bowel disease (IBD), myasthenia gravis (MG), Sjögren's syndrome (SS), and Guillain-Barré syndrome (GBS) is multifaceted, with potential diagnostic and therapeutic implications across these conditions. Further research is essential to fully understand IL-27's mechanisms of action and therapeutic potential in autoimmune diseases.
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Affiliation(s)
- Esmaeil Yazdanpanah
- Student Research Committee, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
- Department of Immunology, School of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
| | - Alireza Pazoki
- Student Research Committee, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
- Department of Immunology, School of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
| | - Sepehr Dadfar
- Department of Immunology, School of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
| | - Mohammad Hosein Nemati
- Department of Immunology, School of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
| | | | - Mahdieh Tarahomi
- Department of Immunology, School of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
| | - Niloufar Orooji
- Department of Immunology, School of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
| | - Dariush Haghmorad
- Department of Immunology, School of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran
| | - Valentyn Oksenych
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7028 Trondheim, Norway
- Department of Biosciences and Nutrition, Karolinska Institutet, 14183 Huddinge, Sweden
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Goode EC, Fachal L, Panousis N, Moutsianas L, McIntyre RE, Bai BYH, Kawasaki N, Wittmann A, Raine T, Rushbrook SM, Anderson CA. Fine-mapping and molecular characterisation of primary sclerosing cholangitis genetic risk loci. Nat Commun 2024; 15:9594. [PMID: 39505854 PMCID: PMC11541731 DOI: 10.1038/s41467-024-53602-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/17/2024] [Indexed: 11/08/2024] Open
Abstract
Genome-wide association studies of primary sclerosing cholangitis have identified 23 susceptibility loci. The majority of these loci reside in non-coding regions of the genome and are thought to exert their effect by perturbing the regulation of nearby genes. Here, we aim to identify these genes to improve the biological understanding of primary sclerosing cholangitis, and nominate potential drug targets. We first build an eQTL map for six primary sclerosing cholangitis-relevant T-cell subsets obtained from the peripheral blood of primary sclerosing cholangitis and ulcerative colitis patients. These maps identify 10,459 unique eGenes, 87% of which are shared across all six primary sclerosing cholangitis T-cell types. We then search for colocalisations between primary sclerosing cholangitis loci and eQTLs and undertake Bayesian fine-mapping to identify disease-causing variants. In this work, colocalisation analyses nominate likely primary sclerosing cholangitis effector genes and biological mechanisms at five non-coding (UBASH3A, PRKD2, ETS2 and AP003774.1/CCDC88B) and one coding (SH2B3) primary sclerosing cholangitis loci. Through fine-mapping we identify likely causal variants for a third of all primary sclerosing cholangitis-associated loci, including two to single variant resolution.
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Affiliation(s)
- Elizabeth C Goode
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- University of Cambridge, Cambridge, UK
- Norfolk and Norwich University Hospital, Norwich, UK
| | - Laura Fachal
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | | | | | | | - Benjamin Yu Hang Bai
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- University of Cambridge, Cambridge, UK
| | | | | | - Tim Raine
- University of Cambridge, Cambridge, UK
| | - Simon M Rushbrook
- Norfolk and Norwich University Hospital, Norwich, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
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Bougnères P, Le Fur S, Kamatani Y, Mai TN, Belot MP, Perge K, Shao X, Lathrop M, Valleron AJ. Genomic variants associated with age at diagnosis of childhood-onset type 1 diabetes. J Hum Genet 2024; 69:585-590. [PMID: 38982180 DOI: 10.1038/s10038-024-01272-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 06/22/2024] [Accepted: 06/25/2024] [Indexed: 07/11/2024]
Abstract
Age at diagnosis (AAD) of Type 1 diabetes (T1D) is determined by the age at onset of the autoimmune attack and by the rate of beta cell destruction that follows. Twin studies found that T1D AAD is strongly influenced by genetics, notably in young children. In young UK, Finnish, Sardinian patients AAD-associated genomic variants were previously identified, which may vary across populations and with time. In 1956 children of European ancestry born in mainland France in 1980-2008 who declared T1D before 15 years, we tested 94 T1D-associated SNPs for their association with AAD using nonparametric Kruskal-Wallis test. While high-risk HLA genotypes were not found to be associated with AAD, fourteen SNPs located in 12 non-HLA loci showed a strong association (2.9 × 10-12 < P < 1.4 × 10-3 after FDR correction). Four of these loci have been associated with AAD in previous cohorts (GSDMB, IL2, TNFAIP3, IL1), supporting a partially shared genetic influence on AAD of T1D in the studied European populations. In contrast, the association of 8 new loci CLEC16A, TYK2, ERBB3, CCR7, FCRL3, DNAH2, FGF3/4, and HPSE2 with AAD is novel. The 12 protein-coding genes located within these loci are involved in major immune pathways or in predisposition to other autoimmune diseases, which suggests a prominent role for these genes in the early immune mechanisms of beta cell destruction.
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Affiliation(s)
- Pierre Bougnères
- Inserm U1169, now at MIRCEN, Commissariat à l'Énergie Atomique, Fontenay-aux-Roses, France.
- GETDOC Association, Paris, France.
| | - Sophie Le Fur
- Inserm U1169, now at MIRCEN, Commissariat à l'Énergie Atomique, Fontenay-aux-Roses, France
- GETDOC Association, Paris, France
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN Center now at the Graduate School of Frontier Sciences, Tokyo University, Tokyo, Japan
| | - Thanh-Nga Mai
- Inserm U1169, now at MIRCEN, Commissariat à l'Énergie Atomique, Fontenay-aux-Roses, France
| | - Marie-Pierre Belot
- Inserm U1169, now at MIRCEN, Commissariat à l'Énergie Atomique, Fontenay-aux-Roses, France
- GETDOC Association, Paris, France
| | - Kevin Perge
- Service d'Endocrinologie Diabétologie Pédiatrique, Hôpital Mère-Enfant, Lyon, France
| | - XiaoJian Shao
- Digital Technologies Research Center, National Research Council Canada, Ottawa, ON, K1A 0R6, Canada
| | - Mark Lathrop
- Genome Québec Innovation Centre, Quantitative Life Sciences, McGill University, Montréal, QC, Canada
| | - Alain-Jacques Valleron
- Inserm U1169, now at MIRCEN, Commissariat à l'Énergie Atomique, Fontenay-aux-Roses, France
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Wu W, Zhang JW, Li Y, Huang K, Chen RM, Maimaiti M, Luo JS, Chen SK, Wu D, Zhu M, Wang CL, Su Z, Liang Y, Yao H, Wei HY, Zheng RX, Du HW, Luo FH, Li P, Wang E, Polychronakos C, Fu JF. Population-based prevalence of self-reported pediatric diabetes and screening for undiagnosed type 2 diabetes in Chinese children in years 2017-2019, a cross-sectional study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 52:101206. [PMID: 39324120 PMCID: PMC11422556 DOI: 10.1016/j.lanwpc.2024.101206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 08/22/2024] [Accepted: 09/04/2024] [Indexed: 09/27/2024]
Abstract
Background The worldwide geographical and temporal variation in the prevalence of diabetes represents a challenge, but also an opportunity for gaining etiological insights. Encompassing the bulk of East Asians, a large and distinct proportion of the world population, China can be a source of valuable epidemiological insights for diabetes, especially in early life, when pathophysiology begins. We carried out a nationwide, epidemiological survey of Prevalence and Risk of Obesity and Diabetes in Youth (PRODY) in China, from 2017 to 2019, to estimate the population-based prevalence of diagnosed pediatric diabetes and screen for undiagnosed pediatric type 2 diabetes (T2D). Methods PRODY was a nation-wide, school population-based, cross-sectional, multicenter survey by questionnaire, fasting urine glucose test and simple oral glucose tolerance test (s-OGTT), among a total number of 193,801 general-population children and adolescents (covered a pediatric population of more than 96.8 million), aged 3-18, from twelve provinces across China. The prevalence of the self-reported pediatric diabetes, the proportion of subtypes, the crude prevalence of undiagnosed T2D and prediabetes in general juvenile population and the main risk factors of type 1 (T1D) and type 2 (T2D) diabetes had been analyzed in the study. Findings The prevalence of all self-reported pediatric diabetes was estimated at 0.62/1000 (95% CI: 0.51-0.74), with T1D at 0.44/1000 (95% CI: 0.35-0.54) and T2D at 0.18/1000 (95% CI: 0.13-0.25). For undiagnosed T2D, the crude prevalence was almost ten-fold higher, at 1.59/1000, with an estimated extra 28.45/1000 of undiagnosed impaired glucose tolerance (IGT) and 53.74/1000 of undiagnosed impaired fasting glucose (IFG) by s-OGTT screening. Maternal diabetes history is the major risk factors for all subtypes of pediatric diabetes in China. Interpretation The PRODY study provides the first population-based estimate of the prevalence of pediatric diabetes China and reveals a magnitude of the problem of undiagnosed pediatric T2D. We propose a practical screening strategy by s-OGTT to address this serious gap. Funding The National Key Research and Development Programme of China, Key R&D Program of Zhejiang, the National Natural Science Foundation of China and the Zhejiang Provincial Key Disciplines of Medicine, Key R&D Program Projects in Zhejiang Province.
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Affiliation(s)
- Wei Wu
- Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Regional Center for Children's Health, 3333 Binsheng Road, 310051, Hangzhou, Zhejiang, China
| | - Jian-Wei Zhang
- Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Regional Center for Children's Health, 3333 Binsheng Road, 310051, Hangzhou, Zhejiang, China
- Shaoxing Women and Children Hospital, 321000, Shaoxing, Zhejiang, China
| | - Yangxi Li
- Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Regional Center for Children's Health, 3333 Binsheng Road, 310051, Hangzhou, Zhejiang, China
- Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC, Canada
| | - Ke Huang
- Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Regional Center for Children's Health, 3333 Binsheng Road, 310051, Hangzhou, Zhejiang, China
| | - Rui-Min Chen
- Fuzhou Children's Hospital of Fujian Province, 350005, Fuzhou, Fujian, China
| | - Mireguli Maimaiti
- The First Affiliated Hospital of Xinjiang Medical University, 830011, Wulumuqi, Xinjiang Uygur Autonomous Region, China
| | - Jing-Si Luo
- The Maternity Hospital of Guangxi Zhuang Autonomous Region, 537406, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Shao-Ke Chen
- The Second Affiliated Hospital of Guangxi Medical University, 537406, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Di Wu
- Beijing Children's Hospital, Capital Medical University, 100045, Beijing, China
| | - Min Zhu
- The Children's Hospital of Chongqing Medical University, 400014, Chongqing, China
| | - Chun-Lin Wang
- The First Affiliated Hospital, Zhejiang University School of Medicine, 310053, Hangzhou, Zhejiang, China
| | - Zhe Su
- Shenzhen Children's Hospital, 518034, Shenzhen, Guangdong, China
| | - Yan Liang
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China
| | - Hui Yao
- Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China
| | - Hai-Yan Wei
- Children's Hospital Affiliated Zhengzhou University, 450066, Zhengzhou, Henan, China
| | - Rong-Xiu Zheng
- Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Hong-Wei Du
- The First Bethune Hospital of Jilin University, 130061, Changchun, Jilin, China
| | - Fei-Hong Luo
- Children's Hospital of Fudan University, 200433, Shanghai, China
| | - Pin Li
- Shanghai Children's Hospital, Shanghai Jiao Tong University, 200433, Shanghai, China
| | - Ergang Wang
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, 27708, USA
| | - Constantin Polychronakos
- Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC, Canada
| | - Jun-Fen Fu
- Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Regional Center for Children's Health, 3333 Binsheng Road, 310051, Hangzhou, Zhejiang, China
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10
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McGrail C, Sears TJ, Kudtarkar P, Carter H, Gaulton K. Genetic association and machine learning improves discovery and prediction of type 1 diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.31.24311310. [PMID: 39132494 PMCID: PMC11312647 DOI: 10.1101/2024.07.31.24311310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can lead to novel biological and therapeutic discovery and improved risk prediction. In this study, we performed genetic association and fine-mapping analyses in 817,718 European ancestry samples genome-wide and 29,746 samples at the MHC locus, which identified 165 independent risk signals for T1D of which 19 were novel. We used risk variants to train a machine learning model (named T1GRS) to predict T1D, which highly differentiated T1D from non-disease and type 2 diabetes (T2D) in Europeans as well as African Americans at or beyond the level of current standards. We identified extensive non-linear interactions between risk loci in T1GRS, for example between HLA-DQB1*57 and INS, coding and non-coding HLA alleles, and DEXI, INS and other beta cell loci, that provided mechanistic insight and improved risk prediction. T1D individuals formed distinct clusters based on genetic features from T1GRS which had significant differences in age of onset, HbA1c, and renal disease severity. Finally, we provided T1GRS in formats to enhance accessibility of risk prediction to any user and computing environment. Overall, the improved genetic discovery and prediction of T1D will have wide clinical, therapeutic, and research applications.
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Affiliation(s)
- Carolyn McGrail
- Biomedical sciences graduate program, University of California San Diego, La Jolla CA
| | - Timothy J. Sears
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla CA
| | - Hannah Carter
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA
- Moore’s Cancer Center, University of California San Diego, La Jolla CA
- Department of Medicine, University of California San Diego, La Jolla CA
| | - Kyle Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla CA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA
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11
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Veronese-Paniagua DA, Hernandez-Rincon DC, Taylor JP, Tse HM, Millman JR. Coxsackievirus B infection invokes unique cell-type specific responses in primary human pancreatic islets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.23.604861. [PMID: 39211206 PMCID: PMC11361082 DOI: 10.1101/2024.07.23.604861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Coxsackievirus B (CVB) infection has long been considered an environmental factor precipitating Type 1 diabetes (T1D), an autoimmune disease marked by loss of insulin-producing β cells within pancreatic islets. Previous studies have shown CVB infection negatively impacts islet function and viability but do not report on how virus infection individually affects the multiple cell types present in human primary islets. Therefore, we hypothesized that the various islet cell populations have unique transcriptional responses to CVB infection. Here, we performed single-cell RNA sequencing on human cadaveric islets treated with either CVB or poly(I:C), a viral mimic, for 24 and 48 hours. Our global analysis reveals CVB differentially induces dynamic transcriptional changes associated with multiple cell processes and functions over time whereas poly(I:C) promotes an immune response that progressively increases with treatment duration. At the single-cell resolution, we find CVB infects all islet cell types at similar rates yet induces unique cell-type specific transcriptional responses with β, α, and ductal cells having the strongest response. Sequencing and functional data suggest that CVB negatively impacts mitochondrial respiration and morphology in distinct ways in β and α cells, while also promoting the generation of reactive oxygen species. We also observe an increase in the expression of the long-noncoding RNA MIR7-3HG in β cells with high viral titers and reveal its knockdown reduces gene expression of viral proteins as well as apoptosis in stem cell-derived islets. Together, these findings demonstrate a cell-specific transcriptional, temporal, and functional response to CVB infection and provide new insights into the relationship between CVB infection and T1D.
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12
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Wang X, Liu H, Wang Y, Wang P, Yi Y, Lin Y, Li X. Novel protein C6ORF120 promotes liver fibrosis by activating hepatic stellate cells through the PI3K/Akt/mTOR pathway. J Gastroenterol Hepatol 2024; 39:1422-1430. [PMID: 38523410 DOI: 10.1111/jgh.16538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/14/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND AND AIM The role of C6ORF120 in promoting CCL4-induced hepatic fibrosis and its possible mechanisms were explored in C6orf120 knockout rats (C6orf120-/-) and LX-2 cells (a type of human hepatic stellate cell line). METHODS In vivo experiments, wild-type and C6orf120-/- rats were used to investigate the function of C6ORF120. In the in vitro experiments, C6ORF120 recombinant protein (rC6ORF120) at a concentration of 200 ng/mL was used to stimulate LX-2 cells. Sirius Red staining, Masson staining, western blotting, polymerase chain reaction, immunohistochemistry, and immunofluorescence were used to explore fibrosis-associated factors. RESULTS C6orf120-/- rats showed mild fibrosis and liver injury in the CCL4-induced liver fibrosis model. Furthermore, RNA-seq revealed that C6orf120-/- rats had less extracellular matrix deposition and activated stellate cells. Consistent with the in vivo, the rC6ORF120 induced LX-2 cell activation. Moreover, mechanistic studies revealed that the p-PI3K/PI3K, p-Akt/Akt, and p-mTOR/mTOR levels were significantly elevated and LY294002 (a PI3K/Akt/mTOR typical pathway inhibitor) reversed the function of C6ORF120 in activating LX-2 cells. CONCLUSION C6ORF120 could activate hepatic stellate cells and promote hepatic fibrosis via the PI3K/Akt/mTOR signaling pathway.
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Affiliation(s)
- Xin Wang
- Department of Center of Integrated Traditional Chinese and Western Medicine, Peking University Ditan Teaching Hospital, Beijing, China
| | - Hui Liu
- Department of Center of Infectious Disease, Beijing Ditan Hospital; Capital Medical University, Beijing, China
| | - Yuqi Wang
- Department of Center of Integrated Traditional Chinese and Western Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Peng Wang
- Department of Center of Integrated Traditional Chinese and Western Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Yi
- Department of Center of Integrated Traditional Chinese and Western Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Yingying Lin
- Department of Center of Integrated Traditional Chinese and Western Medicine, Peking University Ditan Teaching Hospital, Beijing, China
| | - Xin Li
- Department of Center of Integrated Traditional Chinese and Western Medicine, Peking University Ditan Teaching Hospital, Beijing, China
- Department of Center of Integrated Traditional Chinese and Western Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, China
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13
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Patil AR, Schug J, Liu C, Lahori D, Descamps HC, Naji A, Kaestner KH, Faryabi RB, Vahedi G. Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets. Cell Rep Med 2024; 5:101535. [PMID: 38677282 PMCID: PMC11148720 DOI: 10.1016/j.xcrm.2024.101535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/22/2024] [Accepted: 04/07/2024] [Indexed: 04/29/2024]
Abstract
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.
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Affiliation(s)
- Abhijeet R Patil
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jonathan Schug
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Chengyang Liu
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Deeksha Lahori
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Hélène C Descamps
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ali Naji
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Klaus H Kaestner
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Robert B Faryabi
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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14
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Mine K, Nagafuchi S, Akazawa S, Abiru N, Mori H, Kurisaki H, Shimoda K, Yoshikai Y, Takahashi H, Anzai K. TYK2 signaling promotes the development of autoreactive CD8 + cytotoxic T lymphocytes and type 1 diabetes. Nat Commun 2024; 15:1337. [PMID: 38351043 PMCID: PMC10864272 DOI: 10.1038/s41467-024-45573-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 01/25/2024] [Indexed: 02/16/2024] Open
Abstract
Tyrosine kinase 2 (TYK2), a member of the JAK family, has attracted attention as a potential therapeutic target for autoimmune diseases. However, the role of TYK2 in CD8+ T cells and autoimmune type 1 diabetes (T1D) is poorly understood. In this study, we generate Tyk2 gene knockout non-obese diabetes (NOD) mice and demonstrate that the loss of Tyk2 inhibits the development of autoreactive CD8+ T-BET+ cytotoxic T lymphocytes (CTLs) by impairing IL-12 signaling in CD8+ T cells and the CD8+ resident dendritic cell-driven cross-priming of CTLs in the pancreatic lymph node (PLN). Tyk2-deficient CTLs display reduced cytotoxicity. Increased inflammatory responses in β-cells with aging are dampened by Tyk2 deficiency. Furthermore, treatment with BMS-986165, a selective TYK2 inhibitor, inhibits the expansion of T-BET+ CTLs, inflammation in β-cells and the onset of autoimmune T1D in NOD mice. Thus, our study reveals the diverse roles of TYK2 in driving the pathogenesis of T1D.
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Affiliation(s)
- Keiichiro Mine
- Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.
- Division of Host Defense, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
| | - Seiho Nagafuchi
- Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan
| | - Satoru Akazawa
- Department of Endocrinology and Metabolism, Unit of Translational Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Norio Abiru
- Department of Endocrinology and Metabolism, Unit of Translational Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
- Midori Clinic, Nagasaki, Japan
| | - Hitoe Mori
- Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan
| | - Hironori Kurisaki
- Department of Medical Science and Technology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kazuya Shimoda
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Yasunobu Yoshikai
- Division of Host Defense, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Hirokazu Takahashi
- Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan
- Liver Center, Saga University Hospital, Saga University, Saga, Japan
| | - Keizo Anzai
- Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan
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15
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Wit M, Belykh A, Sumara G. Protein kinase D (PKD) on the crossroad of lipid absorption, synthesis and utilization. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2024; 1871:119653. [PMID: 38104800 DOI: 10.1016/j.bbamcr.2023.119653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 10/19/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023]
Abstract
Inappropriate lipid levels in the blood, as well as its content and composition in different organs, underlie multiple metabolic disorders including obesity, non-alcoholic fatty liver disease, type 2 diabetes, and atherosclerosis. Multiple processes contribute to the complex metabolism of triglycerides (TGs), fatty acids (FAs), and other lipid species. These consist of digestion and absorption of dietary lipids, de novo FAs synthesis (lipogenesis), uptake of TGs and FAs by peripheral tissues, TGs storage in the intracellular depots as well as lipid utilization for β-oxidation and their conversion to lipid-derivatives. A majority of the enzymatic reactions linked to lipogenesis, TGs synthesis, lipid absorption, and transport are happening at the endoplasmic reticulum, while β-oxidation takes place in mitochondria and peroxisomes. The Golgi apparatus is a central sorting, protein- and lipid-modifying organelle and hence is involved in lipid metabolism as well. However, the impact of the processes taking part in the Golgi apparatus are often overseen. The protein kinase D (PKD) family (composed of three members, PKD1, 2, and 3) is the master regulator of Golgi dynamics. PKDs are also a sensor of different lipid species in distinct cellular compartments. In this review, we discuss the roles of PKD family members in the regulation of lipid metabolism including the processes executed by PKDs at the Golgi apparatus. We also discuss the role of PKDs-dependent signaling in different cellular compartments and organs in the context of the development of metabolic disorders.
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Affiliation(s)
- Magdalena Wit
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warszawa, Poland
| | - Andrei Belykh
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warszawa, Poland
| | - Grzegorz Sumara
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warszawa, Poland.
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16
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Mittal R, Camick N, Lemos JRN, Hirani K. Gene-environment interaction in the pathophysiology of type 1 diabetes. Front Endocrinol (Lausanne) 2024; 15:1335435. [PMID: 38344660 PMCID: PMC10858453 DOI: 10.3389/fendo.2024.1335435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/11/2024] [Indexed: 02/15/2024] Open
Abstract
Type 1 diabetes (T1D) is a complex metabolic autoimmune disorder that affects millions of individuals worldwide and often leads to significant comorbidities. However, the precise trigger of autoimmunity and disease onset remain incompletely elucidated. This integrative perspective article synthesizes the cumulative role of gene-environment interaction in the pathophysiology of T1D. Genetics plays a significant role in T1D susceptibility, particularly at the major histocompatibility complex (MHC) locus and cathepsin H (CTSH) locus. In addition to genetics, environmental factors such as viral infections, pesticide exposure, and changes in the gut microbiome have been associated with the development of T1D. Alterations in the gut microbiome impact mucosal integrity and immune tolerance, increasing gut permeability through molecular mimicry and modulation of the gut immune system, thereby increasing the risk of T1D potentially through the induction of autoimmunity. HLA class II haplotypes with known effects on T1D incidence may directly correlate to changes in the gut microbiome, but precisely how the genes influence changes in the gut microbiome, and how these changes provoke T1D, requires further investigations. These gene-environment interactions are hypothesized to increase susceptibility to T1D through epigenetic changes such as DNA methylation and histone modification, which in turn modify gene expression. There is a need to determine the efficacy of new interventions that target these epigenetic modifications such as "epidrugs", which will provide novel avenues for the effective management of T1D leading to improved quality of life of affected individuals and their families/caregivers.
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Affiliation(s)
- Rahul Mittal
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Nathanael Camick
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, United States
| | - Joana R. N. Lemos
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Khemraj Hirani
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
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17
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Bawatneh A, Darwish A, Eideh H, Darwish HM. Identification of gene mutations associated with type 1 diabetes by next-generation sequencing in affected Palestinian families. Front Genet 2024; 14:1292073. [PMID: 38274107 PMCID: PMC10808782 DOI: 10.3389/fgene.2023.1292073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/04/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction: Diabetes Mellitus is a group of metabolic disorders characterized by hyperglycemia secondary to insulin resistance or deficiency. It is considered a major health problem worldwide. T1DM is a result of a combination of genetics, epigenetics, and environmental factors. Several genes have been associated with T1DM, including HLA, INS, CTLA4, and PTPN22. However, none of these findings have been based on linkage analysis because it is rare to find families with several diabetic individuals. Two Palestinian families with several afflicted members with variations in the mode of inheritance were identified and selected for this study. This study aimed to identify the putative causative gene(s) responsible for T1DM development in these families to improve our understanding of the molecular genetics of the disease. Methods: One afflicted member from each family was selected for Whole-Exome Sequencing. Data were mapped to the reference of the human genome, and the resulting VCF file data were filtered. The variants with the highest phenotype correlation score were checked by Sanger sequencing for all family members. The confirmed variants were analyzed in silico by bioinformatics tools. Results: In one family, the IGF1R p.V579F variant, which follows autosomal dominant inheritance, was confirmed and segregated in the family. In another family, the NEUROD1 p.P197H variant, which follows autosomal recessive inheritance, was positively confirmed and segregated. Conclusion: IGF1R p.V579F and NEUROD1 p.P197H variants were associated with T1DM development in the two inflicted families. Further analysis and functional assays will be performed, including the generation of mutant model cell systems, to unravel their specific molecular mechanism in the disease development.
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Affiliation(s)
- Abrar Bawatneh
- Molecular Genetics and Genetics Toxicology Program, Faculty of Graduate Studies, Arab American University, Jenin, Palestine
| | - Alaa Darwish
- Faculty of Health Professions, AlQuds University, Jerusalem, Palestine
| | | | - Hisham M. Darwish
- Molecular Genetics and Genetics Toxicology Program, Faculty of Graduate Studies, Arab American University, Jenin, Palestine
- Faculty of Allied Medical Sciences, Arab American University, Jenin, Palestine
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18
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BORA J, MALIK S, KISHORE S, RUSTAGI S, AHMAD F, FAGOONEE S, PELLICANO R, HAQUE S. Therapeutic applications of CRISPR-Cas9 in diabetes mellitus: a perspective review. MINERVA BIOTECHNOLOGY AND BIOMOLECULAR RESEARCH 2024; 35. [DOI: 10.23736/s2724-542x.23.02996-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2025]
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19
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McGrail C, Chiou J, Elgamal R, Luckett AM, Oram RA, Benaglio P, Gaulton KJ. Genetic discovery and risk prediction for type 1 diabetes in individuals without high-risk HLA-DR3/DR4 haplotypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.11.23298405. [PMID: 37986756 PMCID: PMC10659516 DOI: 10.1101/2023.11.11.23298405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Over 10% of type 1 diabetes (T1D) cases do not have high-risk HLA-DR3 or DR4 haplotypes with distinct clinical features such as later onset and reduced insulin dependence. To identify genetic drivers of T1D in the absence of DR3/DR4, we performed association and fine-mapping analyses in 12,316 non-DR3/DR4 samples. Risk variants at the MHC and other loci genome-wide had heterogeneity in effects on T1D dependent on DR3/DR4, and non-DR3/DR4 T1D had evidence for a greater polygenic burden. T1D-assocated variants in non-DR3/DR4 were more enriched for loci, regulatory elements, and pathways for antigen presentation, innate immunity, and beta cells, and depleted in T cells, compared to DR3/DR4. Non-DR3/DR4 T1D cases were poorly classified based on an existing genetic risk score GRS2, and we created a new GRS which highly discriminated non-DR3/DR4 T1D from both non-diabetes and T2D. In total we identified heterogeneity in T1D genetic risk and disease mechanisms dependent on high-risk HLA haplotype and which enabled accurate classification of T1D across HLA background.
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Affiliation(s)
- Carolyn McGrail
- Biomedical Sciences Graduate Program, UC San Diego, La Jolla, CA
| | - Joshua Chiou
- Biomedical Sciences Graduate Program, UC San Diego, La Jolla, CA
| | - Ruth Elgamal
- Biomedical Sciences Graduate Program, UC San Diego, La Jolla, CA
| | - Amber M Luckett
- University of Exeter College of Medicine and Health, Exeter, UK
| | - Richard A Oram
- University of Exeter College of Medicine and Health, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
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20
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Bora J, Dey A, Lyngdoh AR, Dhasmana A, Ranjan A, Kishore S, Rustagi S, Tuli HS, Chauhan A, Rath P, Malik S. A critical review on therapeutic approaches of CRISPR-Cas9 in diabetes mellitus. NAUNYN-SCHMIEDEBERG'S ARCHIVES OF PHARMACOLOGY 2023; 396:3459-3481. [PMID: 37522916 DOI: 10.1007/s00210-023-02631-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 07/14/2023] [Indexed: 08/01/2023]
Abstract
Diabetes mellitus (D.M.) is a common metabolic disorder caused mainly by combining two primary factors, which are (1) defects in insulin production by the pancreatic β-cells and (2) responsiveness of insulin-sensitive tissues towards insulin. Despite the rapid advancement in medicine to suppress elevated blood glucose levels (hyperglycemia) and insulin resistance associated with this hazard, a demand has undoubtedly emerged to find more effective and curative dimensions in therapeutic approaches against D.M. The administration of diabetes treatment that emphasizes insulin production and sensitivity may result in unfavorable side effects, reduced adherence, and potential treatment ineffectiveness. Recent progressions in genome editing technologies, for instance, in zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeat (CRISPR-Cas)-associated nucleases, have greatly influenced the gene editing technology from concepts to clinical practices. Improvements in genome editing technologies have also opened up the possibility to target and modify specific genome sequences in a cell directly. CRISPR/Cas9 has proven effective in utilizing ex vivo gene editing in embryonic stem cells and stem cells derived from patients. This application has facilitated the exploration of pancreatic beta-cell development and function. Furthermore, CRISPR/Cas9 enables the creation of innovative animal models for diabetes and assesses the effectiveness of different therapeutic strategies in treating the condition. We, therefore, present a critical review of the therapeutic approaches of the genome editing tool CRISPR-Cas9 in treating D.M., discussing the challenges and limitations of implementing this technology.
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Affiliation(s)
- Jutishna Bora
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, 834001, India
| | - Ankita Dey
- Department of Biochemistry, North Eastern Hill University, Shillong, Meghalaya, 793022, India
| | - Antonia R Lyngdoh
- Department of Biochemistry, North Eastern Hill University, Shillong, Meghalaya, 793022, India
| | - Archna Dhasmana
- Himalayan School of Biosciences, Swami Rama Himalayan University, Jolly Grant, Dehradun, Uttarakhand, India
| | - Anuj Ranjan
- Academy of Biology and Biotechnology, Southern Federal University, Stachki 194/1, Rostov-On-Don, 344090, Russia
| | - Shristi Kishore
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, 834001, India
| | - Sarvesh Rustagi
- School of Applied and Life Sciences, Uttaranchal University, 22 Dehradun, Uttarakhand, India
| | - Hardeep Singh Tuli
- Department of Biotechnology, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to Be University), Mullana-Ambala, 133207, India
| | - Abhishek Chauhan
- Amity Institute of Environmental Toxicology Safety and Management, Amity University, Sector 125, Noida, Uttar Pradesh, India
| | - Prangya Rath
- Amity Institute of Environmental Sciences, Amity University, Noida, Uttar Pradesh, 201303, India
| | - Sumira Malik
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, 834001, India.
- School of Applied and Life Sciences, Uttaranchal University, 22 Dehradun, Uttarakhand, India.
- Guru Nanak College of Pharmaceutical Sciences, Dehradun, Uttarakhand, India.
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21
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Kondegowda NG, Filipowska J, Do JS, Leon-Rivera N, Li R, Hampton R, Ogyaadu S, Levister C, Penninger JM, Reijonen H, Levy CJ, Vasavada RC. RANKL/RANK is required for cytokine-induced beta cell death; osteoprotegerin, a RANKL inhibitor, reverses rodent type 1 diabetes. SCIENCE ADVANCES 2023; 9:eadf5238. [PMID: 37910614 PMCID: PMC10619938 DOI: 10.1126/sciadv.adf5238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 09/29/2023] [Indexed: 11/03/2023]
Abstract
Treatment for type 1 diabetes (T1D) requires stimulation of functional β cell regeneration and survival under stress. Previously, we showed that inhibition of the RANKL/RANK [receptor activator of nuclear factor kappa Β (NF-κB) ligand] pathway, by osteoprotegerin and the anti-osteoporotic drug denosumab, induces rodent and human β cell proliferation. We demonstrate that the RANK pathway mediates cytokine-induced rodent and human β cell death through RANK-TRAF6 interaction and induction of NF-κB activation. Osteoprotegerin and denosumab protected β cells against this cytotoxicity. In human immune cells, osteoprotegerin and denosumab reduce proinflammatory cytokines in activated T-cells by inhibiting RANKL-induced activation of monocytes. In vivo, osteoprotegerin reversed recent-onset T1D in nonobese diabetic/Ltj mice, reduced insulitis, improved glucose homeostasis, and increased plasma insulin, β cell proliferation, and mass in these mice. Serum from T1D subjects induced human β cell death and dysfunction, but not α cell death. Osteoprotegerin and denosumab reduced T1D serum-induced β cell cytotoxicity and dysfunction. Inhibiting RANKL/RANK could have therapeutic potential.
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Affiliation(s)
- Nagesha Guthalu Kondegowda
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA 91010, USA
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joanna Filipowska
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA 91010, USA
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jeong-su Do
- Department of Immunology and Theranostics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Nancy Leon-Rivera
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Rosemary Li
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Rollie Hampton
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Selassie Ogyaadu
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Division of Endocrinology and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Camilla Levister
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Division of Endocrinology and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Josef M. Penninger
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna 1030, Austria
- Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, Canada
| | - Helena Reijonen
- Department of Immunology and Theranostics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Carol J. Levy
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Division of Endocrinology and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Rupangi C. Vasavada
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA 91010, USA
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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22
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Nethander M, Movérare-Skrtic S, Kämpe A, Coward E, Reimann E, Grahnemo L, Borbély É, Helyes Z, Funck-Brentano T, Cohen-Solal M, Tuukkanen J, Koskela A, Wu J, Li L, Lu T, Gabrielsen ME, Mägi R, Hoff M, Lerner UH, Henning P, Ullum H, Erikstrup C, Brunak S, Langhammer A, Tuomi T, Oddsson A, Stefansson K, Pettersson-Kymmer U, Ostrowski SR, Pedersen OBV, Styrkarsdottir U, Mäkitie O, Hveem K, Richards JB, Ohlsson C. An atlas of genetic determinants of forearm fracture. Nat Genet 2023; 55:1820-1830. [PMID: 37919453 PMCID: PMC10632131 DOI: 10.1038/s41588-023-01527-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 09/13/2023] [Indexed: 11/04/2023]
Abstract
Osteoporotic fracture is among the most common and costly of diseases. While reasonably heritable, its genetic determinants have remained elusive. Forearm fractures are the most common clinically recognized osteoporotic fractures with a relatively high heritability. To establish an atlas of the genetic determinants of forearm fractures, we performed genome-wide association analyses including 100,026 forearm fracture cases. We identified 43 loci, including 26 new fracture loci. Although most fracture loci associated with bone mineral density, we also identified loci that primarily regulate bone quality parameters. Functional studies of one such locus, at TAC4, revealed that Tac4-/- mice have reduced mechanical bone strength. The strongest forearm fracture signal, at WNT16, displayed remarkable bone-site-specificity with no association with hip fractures. Tall stature and low body mass index were identified as new causal risk factors for fractures. The insights from this atlas may improve fracture prediction and enable therapeutic development to prevent fractures.
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Grants
- Wellcome Trust
- IngaBritt och Arne Lundbergs Forskningsstiftelse (Ingabritt and Arne Lundberg Research Foundation)
- Novo Nordisk Fonden (Novo Nordisk Foundation)
- Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation)
- the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (ALFGBG-720331 and ALFGBG-965235)
- the Hungarian Brain research Program 3.0, Hungarian National Research, Development and Innovation Office (OTKA K- 138046, OTKA FK-137951, TKP2021-EGA-16), New National Excellence Program of the Ministry for Innovation and Technology (ÚNKP-22-5-PTE-1447), János Bolyai János Scholarship (BO/00496/21/5) of the Hungarian Academy of Sciences, Eotvos Lorad Research Network, National Laboratory for Drug Research and Development.
- Vetenskapsrådet (Swedish Research Council)
- Svenska Läkaresällskapet (Swedish Society of Medicine)
- Kempestiftelserna (Kempe Foundations)
- the Swedish Sports Research Council (87/06) the Medical Faculty of Umeå University (ALFVLL:968:22-2005, ALFVLL: 937-2006, ALFVLL:223:11-2007, ALFVLL:78151-2009) the county council of Västerbotten (Spjutspetsanslag VLL:159:33-2007)
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Affiliation(s)
- Maria Nethander
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sofia Movérare-Skrtic
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anders Kämpe
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Eivind Coward
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ene Reimann
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Louise Grahnemo
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Éva Borbély
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Pécs, Hungary
- National Laboratory for Drug Research and Development, Budapest, Hungary
| | - Zsuzsanna Helyes
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Pécs, Hungary
- National Laboratory for Drug Research and Development, Budapest, Hungary
- Eotvos Lorand Research Network, Chronic Pain Research Group, University of Pécs, Pécs, Hungary
| | - Thomas Funck-Brentano
- BIOSCAR UMRS 1132, Université Paris Diderot, Sorbonne Paris Cité, INSERM, Paris, France
| | - Martine Cohen-Solal
- BIOSCAR UMRS 1132, Université Paris Diderot, Sorbonne Paris Cité, INSERM, Paris, France
| | - Juha Tuukkanen
- Department of Anatomy and Cell Biology, Faculty of Medicine, Institute of Cancer Research and Translational Medicine, University of Oulu, Oulu, Finland
| | - Antti Koskela
- Department of Anatomy and Cell Biology, Faculty of Medicine, Institute of Cancer Research and Translational Medicine, University of Oulu, Oulu, Finland
| | - Jianyao Wu
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Lei Li
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Maiken E Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mari Hoff
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Rheumatology, St Olavs Hospital, Trondheim, Norway
| | - Ulf H Lerner
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Petra Henning
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Endocrinology, Abdominal Center, Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | | | - Kari Stefansson
- deCODE genetics, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Sisse Rye Ostrowski
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Copenhagen Hospital Biobank Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Ole Birger Vesterager Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Koege, Denmark
| | | | - Outi Mäkitie
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Folkhälsan Institute of Genetics, Helsinki, Finland
- Children's Hospital and Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, and Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Drug Treatment, Gothenburg, Sweden.
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23
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Liao WL, Huang YN, Chang YW, Liu TY, Lu HF, Tiao ZY, Su PH, Wang CH, Tsai FJ. Combining polygenic risk scores and human leukocyte antigen variants for personalized risk assessment of type 1 diabetes in the Taiwanese population. Diabetes Obes Metab 2023; 25:2928-2936. [PMID: 37455666 DOI: 10.1111/dom.15187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 07/18/2023]
Abstract
AIMS To analyse the genome-wide association study (GWAS) data of patients with type 1 diabetes mellitus (T1D) in order to develop a risk score for the genetic effects on T1D risk and age at diagnosis in the Taiwanese population. MATERIALS AND METHODS We selected 610 patients with T1D and 2511 healthy individuals from an electronic medical record database of more than 300 000 individuals with genetic information, analysed their GWAS data, and developed a polygenic risk score (PRS). RESULTS The PRS, based on 149 selected single-nucleotide polymorphisms, could effectively predict T1D risk. A PRS increase was associated with increased T1D risk (odds ratio [OR] 2.09, 95% confidence interval [CI] 1.72-2.55). Moreover, a 1-unit increase in standardized T1D PRS decreased the age at diagnosis by 0.74 years. Combined PRS and human leukocyte antigen (HLA) DQA1*03:02-DQA1*05:01 genotypes could accurately predict T1D risk. In multivariable models, HLA variants and PRS were independent risk factors for T1D risk (OR 3.76 [95% CI 1.54-9.16] and 1.71 [95% CI 1.37-2.13] for HLA DQA1*03:02-DQA1*05:01 and PRS, respectively). In a limited study population of those aged ≤18 years, PRS remained significantly associated with T1D risk. The association between T1D PRS and age at diagnosis was more obvious among males and patients aged ≤18 years. CONCLUSIONS Polygenic risk score and HLA variations enable personalized risk estimates, enhance newborn screening efficiency for ketoacidosis prevention, and addresses the gap in data on T1D prediction in isolated Asian populations.
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Affiliation(s)
- Wen-Ling Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Yu-Nan Huang
- Division of Genetics and Metabolism, Children's Hospital of China Medical University, Taichung, Taiwan
- Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Ya-Wen Chang
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, Taiwan
- Department of Medical Research, Genetic Center, China Medical University Hospital, Taichung, Taiwan
| | - Ting-Yuan Liu
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Hsing-Fang Lu
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Zih-Yu Tiao
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Pen-Hua Su
- Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chung-Hsing Wang
- Division of Genetics and Metabolism, Children's Hospital of China Medical University, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, Genetic Center, China Medical University Hospital, Taichung, Taiwan
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
- Division of Medical Genetics, China Medical University Children's Hospital, Taichung, Taiwan
- Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan
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24
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Deng Q, Gupta A, Jeon H, Nam JH, Yilmaz AS, Chang W, Pietrzak M, Li L, Kim HJ, Chung D. graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data. Front Genet 2023; 14:1079198. [PMID: 37501720 PMCID: PMC10370274 DOI: 10.3389/fgene.2023.1079198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 06/21/2023] [Indexed: 07/29/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.
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Affiliation(s)
- Qiaolan Deng
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Arkobrato Gupta
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Hyeongseon Jeon
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| | - Jin Hyun Nam
- Division of Big Data Science, Korea University Sejong Campus, Sejong, Republic of Korea
| | - Ayse Selen Yilmaz
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Hang J. Kim
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
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25
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Sharma S, Tan X, Boyer J, Clarke D, Costanzo A, Abe B, Kain L, Holt M, Armstrong A, Rihanek M, Su A, Speake C, Gottlieb P, Gottschalk M, Pettus J, Teyton L. Measuring anti-islet autoimmunity in mouse and human by profiling peripheral blood antigen-specific CD4 T cells. Sci Transl Med 2023; 15:eade3614. [PMID: 37406136 PMCID: PMC10495123 DOI: 10.1126/scitranslmed.ade3614] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 06/16/2023] [Indexed: 07/07/2023]
Abstract
The endocrine pancreas is one of the most inaccessible organs of the human body. Its autoimmune attack leads to type 1 diabetes (T1D) in a genetically susceptible population and a lifelong need for exogenous insulin replacement. Monitoring disease progression by sampling peripheral blood would provide key insights into T1D immune-mediated mechanisms and potentially change preclinical diagnosis and the evaluation of therapeutic interventions. This effort has been limited to the measurement of circulating anti-islet antibodies, which despite a recognized diagnostic value, remain poorly predictive at the individual level for a fundamentally CD4 T cell-dependent disease. Here, peptide-major histocompatibility complex tetramers were used to profile blood anti-insulin CD4 T cells in mice and humans. While percentages of these were not directly informative, the state of activation of anti-insulin T cells measured by RNA and protein profiling was able to distinguish the absence of autoimmunity versus disease progression. Activated anti-insulin CD4 T cell were detected not only at time of diagnosis but also in patients with established disease and in some at-risk individuals. These results support the concept that antigen-specific CD4 T cells might be used to monitor autoimmunity in real time. This advance can inform our approach to T1D diagnosis and therapeutic interventions in the preclinical phase of anti-islet autoimmunity.
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Affiliation(s)
- Siddhartha Sharma
- Department of Immunology and Microbial Science, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Xuqian Tan
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, CA 92037, USA
- School of Biological Science, University of California San Diego, La Jolla, CA 92093, USA
| | - Josh Boyer
- Department of Immunology and Microbial Science, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Don Clarke
- Department of Immunology and Microbial Science, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Anne Costanzo
- Department of Immunology and Microbial Science, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Brian Abe
- Department of Immunology and Microbial Science, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Lisa Kain
- Department of Immunology and Microbial Science, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Marie Holt
- Department of Immunology and Microbial Science, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Adrienne Armstrong
- Division of Endocrinology, University of California San Diego, San Diego, CA 92123, USA
| | - Marynette Rihanek
- Barbara Davis Center, University of Colorado, Boulder, CO 80045, USA
| | - Andrew Su
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, CA 92037, USA
| | - Cate Speake
- Diabetes Clinical Research Program, Benaroya Research Institute at Virginia Mason, Seattle, WA,98101, USA
- Center for Interventional Immunology, Diabetes Clinical Research Program, Benaroya Research Institute at Virginia Mason, Seattle, WA 98101, USA
| | - Peter Gottlieb
- Barbara Davis Center, University of Colorado, Boulder, CO 80045, USA
| | - Michael Gottschalk
- Division of Pediatric Endocrinology, University of California San Diego, School of Medicine, Rady Children's Hospital, San Diego, CA 92123, USA
| | - Jeremy Pettus
- Division of Endocrinology, University of California San Diego, San Diego, CA 92123, USA
| | - Luc Teyton
- Department of Immunology and Microbial Science, Scripps Research Institute, La Jolla, CA 92037, USA
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26
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Gray PE, David C. Inborn Errors of Immunity and Autoimmune Disease. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2023; 11:1602-1622. [PMID: 37119983 DOI: 10.1016/j.jaip.2023.04.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 04/01/2023] [Accepted: 04/21/2023] [Indexed: 05/01/2023]
Abstract
Autoimmunity may be a manifestation of inborn errors of immunity, specifically as part of the subgroup of primary immunodeficiency known as primary immune regulatory disorders. However, although making a single gene diagnosis can have important implications for prognosis and management, picking patients to screen can be difficult, against a background of a high prevalence of autoimmune disease in the population. This review compares the genetics of common polygenic and rare monogenic autoimmunity, and explores the molecular mechanisms, phenotypes, and inheritance of autoimmunity associated with primary immune regulatory disorders, highlighting the emerging importance of gain-of-function and non-germline somatic mutations. A novel framework for identifying rare monogenic cases of common diseases in children is presented, highlighting important clinical and immunologic features that favor single gene disease and guides clinicians in selecting appropriate patients for genomic screening. In addition, there will be a review of autoimmunity in non-genetically defined primary immunodeficiency such as common variable immunodeficiency, and of instances where primary autoimmunity can result in clinical phenocopies of inborn errors of immunity.
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Affiliation(s)
- Paul Edgar Gray
- Sydney Children's Hospital, Randwick, NSW, Australia; Western Sydney University, Penrith, NSW, Australia.
| | - Clementine David
- Sydney Children's Hospital, Randwick, NSW, Australia; The School of Women's & Children's Health, University of New South Wales, Randwick, NSW, Australia
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27
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Wang Y, Zhao J, Gu Y, Wang H, Jiang M, Zhao S, Qing H, Ni J. Cathepsin H: molecular characteristics and clues to function and mechanism. Biochem Pharmacol 2023; 212:115585. [PMID: 37148981 DOI: 10.1016/j.bcp.2023.115585] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/26/2023] [Accepted: 04/28/2023] [Indexed: 05/08/2023]
Abstract
Cathepsin H (CatH) is a lysosomal cysteine protease with a unique aminopeptidase activity that is extensively expressed in the lung, pancreas, thymus, kidney, liver, skin, and brain. Owing to its specific enzymatic activity, CatH has critical effects on the regulation of biological behaviours of cancer cells and pathological processes in brain diseases. Moreover, a neutral pH level is optimal for CatH activity, so it is expected to be active in the extra-lysosomal and extracellular space. In the present review, we describe the expression, maturation, and enzymatic properties of CatH, and summarize the available experimental evidence that mechanistically links CatH to various physiological and pathological processes. Finally, we discuss the challenges and potentials of CatH inhibitors in CatH-induced disease therapy.
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Affiliation(s)
- Yanfeng Wang
- Key Laboratory of Molecular Medicine and Biotherapy, Department of Biology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Juan Zhao
- Key Laboratory of Molecular Medicine and Biotherapy, Department of Biology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China; Aerospace Medical Center, Aerospace Center Hospital, Beijing, 100081, China
| | - Yebo Gu
- Department of Stomatology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China
| | - Haiping Wang
- School of Pharmaceutical Science, Nanjing Tech University, Nanjing, China
| | - Muzhou Jiang
- Department of Periodontics, Liaoning Provincial Key Laboratory of Oral Diseases, School and Hospital of Stomatology, China Medical University, Shenyang, 110002, China
| | - Shuxuan Zhao
- Key Laboratory of Molecular Medicine and Biotherapy, Department of Biology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Hong Qing
- Key Laboratory of Molecular Medicine and Biotherapy, Department of Biology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
| | - Junjun Ni
- Key Laboratory of Molecular Medicine and Biotherapy, Department of Biology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
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Alvarez KLF, Aguilar-Pineda JA, Ortiz-Manrique MM, Paredes-Calderon MF, Cardenas-Quispe BC, Vera-Lopez KJ, Goyzueta-Mamani LD, Chavez-Fumagalli MA, Davila-Del-Carpio G, Peralta-Mestas A, Musolino PL, Lino Cardenas CL. Co-occurring pathogenic variants in 6q27 associated with dementia spectrum disorders in a Peruvian family. Front Mol Neurosci 2023; 16:1104585. [PMID: 36873109 PMCID: PMC9978490 DOI: 10.3389/fnmol.2023.1104585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/18/2023] [Indexed: 02/18/2023] Open
Abstract
Evidence suggests that there may be racial differences in risk factors associated with the development of Alzheimer's disease and related dementia (ADRD). We used whole-genome sequencing analysis and identified a novel combination of three pathogenic variants in the heterozygous state (UNC93A: rs7739897 and WDR27: rs61740334; rs3800544) in a Peruvian family with a strong clinical history of ADRD. Notably, the combination of these variants was present in two generations of affected individuals but absent in healthy members of the family. In silico and in vitro studies have provided insights into the pathogenicity of these variants. These studies predict that the loss of function of the mutant UNC93A and WDR27 proteins induced dramatic changes in the global transcriptomic signature of brain cells, including neurons, astrocytes, and especially pericytes and vascular smooth muscle cells, indicating that the combination of these three variants may affect the neurovascular unit. In addition, known key molecular pathways associated with dementia spectrum disorders were enriched in brain cells with low levels of UNC93A and WDR27. Our findings have thus identified a genetic risk factor for familial dementia in a Peruvian family with an Amerindian ancestral background.
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Affiliation(s)
- Karla Lucia F. Alvarez
- Laboratory of Genomics and Neurovascular Diseases, Universidad Católica de Santa María, Arequipa, Peru
| | | | | | | | - Bryan C. Cardenas-Quispe
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Karin Jannet Vera-Lopez
- Laboratory of Genomics and Neurovascular Diseases, Universidad Católica de Santa María, Arequipa, Peru
| | - Luis D. Goyzueta-Mamani
- Laboratory of Genomics and Neurovascular Diseases, Universidad Católica de Santa María, Arequipa, Peru
| | | | | | - Antero Peralta-Mestas
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Patricia L. Musolino
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
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Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections. SCIENCE CHINA. LIFE SCIENCES 2023:10.1007/s11427-021-2187-3. [PMID: 36738430 PMCID: PMC9898710 DOI: 10.1007/s11427-021-2187-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/10/2022] [Indexed: 02/05/2023]
Abstract
With the exception of an extremely small number of cases caused by single gene mutations, most autoimmune diseases result from the complex interplay between environmental and genetic factors. In a nutshell, etiology of the common autoimmune disorders is unknown in spite of progress elucidating certain effector cells and molecules responsible for pathologies associated with inflammatory and tissue damage. In recent years, population genetics approaches have greatly enriched our knowledge regarding genetic susceptibility of autoimmunity, providing us with a window of opportunities to comprehensively re-examine autoimmunity-associated genes and possible pathways. In this review, we aim to discuss etiology and pathogenesis of common autoimmune disorders from the perspective of human genetics. An overview of the genetic basis of autoimmunity is followed by 3 chapters detailing susceptibility genes involved in innate immunity, adaptive immunity and inflammatory cell death processes respectively. With such attempts, we hope to expand the scope of thinking and bring attention to lesser appreciated molecules and pathways as important contributors of autoimmunity beyond the 'usual suspects' of a limited subset of validated therapeutic targets.
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Gingerich MA, Liu X, Chai B, Pearson GL, Vincent MP, Stromer T, Zhu J, Sidarala V, Renberg A, Sahu D, Klionsky DJ, Schnell S, Soleimanpour SA. An intrinsically disordered protein region encoded by the human disease gene CLEC16A regulates mitophagy. Autophagy 2023; 19:525-543. [PMID: 35604110 PMCID: PMC9851259 DOI: 10.1080/15548627.2022.2080383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
CLEC16A regulates mitochondrial health through mitophagy and is associated with over 20 human diseases. However, the key structural and functional regions of CLEC16A, and their relevance for human disease, remain unknown. Here, we report that a disease-associated CLEC16A variant lacks a C-terminal intrinsically disordered protein region (IDPR) that is critical for mitochondrial quality control. IDPRs comprise nearly half of the human proteome, yet their mechanistic roles in human disease are poorly understood. Using carbon detect NMR, we find that the CLEC16A C terminus lacks secondary structure, validating the presence of an IDPR. Loss of the CLEC16A C-terminal IDPR in vivo impairs mitophagy, mitochondrial function, and glucose-stimulated insulin secretion, ultimately causing glucose intolerance. Deletion of the CLEC16A C-terminal IDPR increases CLEC16A ubiquitination and degradation, thus impairing assembly of the mitophagy regulatory machinery. Importantly, CLEC16A stability is dependent on proline bias within the C-terminal IDPR, but not amino acid sequence order or charge. Together, we elucidate how an IDPR in CLEC16A regulates mitophagy and implicate pathogenic human gene variants that disrupt IDPRs as novel contributors to diabetes and other CLEC16A-associated diseases.Abbreviations : CAS: carbon-detect amino-acid specific; IDPR: intrinsically disordered protein region; MEFs: mouse embryonic fibroblasts; NMR: nuclear magnetic resonance.
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Affiliation(s)
- Morgan A. Gingerich
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA,Program in Cellular and Molecular Biology, University of Michigan, Ann Arbor, MI, USA
| | - Xueying Liu
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA,Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Biaoxin Chai
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Gemma L. Pearson
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Michael P. Vincent
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Tracy Stromer
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Jie Zhu
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Vaibhav Sidarala
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Aaron Renberg
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA
| | - Debashish Sahu
- BioNMR Core Facility, Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA
| | - Daniel J. Klionsky
- Life Sciences Institute and Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Santiago Schnell
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Scott A. Soleimanpour
- Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Ann Arbor, MI, USA,Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA,Medicine Service, Endocrinology and Metabolism Section, VA Ann Arbor Health Care System, Ann Arbor, MI, USA,CONTACT Scott A. Soleimanpour Department of Internal Medicine and Division of Metabolism, Endocrinology & Diabetes, University of Michigan, Wall Street, Brehm Tower Room, Ann Arbor, MI, USA
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Liu X, Miao Y, Liu C, Lu W, Feng Q, Zhang Q. Identification of multiple novel susceptibility genes associated with autoimmune thyroid disease. Front Immunol 2023; 14:1161311. [PMID: 37197658 PMCID: PMC10183592 DOI: 10.3389/fimmu.2023.1161311] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/20/2023] [Indexed: 05/19/2023] Open
Abstract
Background Autoimmune thyroid disease (AITD) is induced by various factors, including inheritability, which regulates gene expression. Multiple loci correlated with AITD have been discovered utilizing genome-wide association studies (GWASs). Nevertheless, demonstrating the biological relevance and function of these genetic loci is difficult. Methods The FUSION software was utilized to define genes that were expressed differentially in AITD using a transcriptome-wide association study (TWAS) method in accordance with GWAS summary statistics from the largest genome-wide association study of 755,406 AITD individuals (30,234 cases and 725,172 controls) and levels of gene expression from two tissue datasets (blood and thyroid). Further analyses were performed such as colocalization, conditional, and fine-mapping analyses to extensively characterize the identified associations, using functional mapping and annotation (FUMA) to conduct functional annotation of the summary statistics of 23329 significant risk SNPs (P < 5 × 10-8) recognized by GWAS, together with summary-data-based mendelian randomization (SMR) for identifying functionally related genes at the loci in GWAS. Results There were 330 genes with transcriptome-wide significant differences between cases and controls, and the majority of these genes were new. 9 of the 94 unique significant genes had strong, colocalized, and potentially causal correlations with AITD. Such strong associations included CD247, TPO, KIAA1524, PDE8B, BACH2, FYN, FOXK1, NKX2-3, and SPATA13. Subsequently, applying the FUMA approach, novel putative AITD susceptibility genes and involved gene sets were detected. Furthermore, we detected 95 probes that showed strong pleiotropic association with AITD through SMR analysis, such as CYP21A2, TPO, BRD7, and FCRL3. Lastly, we selected 26 genes by integrating the result of TWAS, FUMA, and SMR analysis. A phenome-wide association study (pheWAS) was then carried out to determine the risk of other related or co-morbid phenotypes for AITD-related genes. Conclusions The current work provides further insight into widespread changes in AITD at the transcriptomic level, as well as characterized the genetic component of gene expression in AITD by validating identified genes, establishing new correlations, and uncovering novel susceptibility genes. Our findings indicate that the genetic component of gene expression plays a significant part in AITD.
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Monos DS, Rajalingam R. The Major Histocompatibility Complex. Clin Immunol 2023. [DOI: 10.1016/b978-0-7020-8165-1.00005-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2023]
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Cardinale CJ, Chang X, Wei Z, Qu HQ, Bradfield JP, Polychronakos C, Hakonarson H. Genome-wide association study of the age of onset of type 1 diabetes reveals HTATIP2 as a novel T cell regulator. Front Immunol 2023; 14:1101488. [PMID: 36817429 PMCID: PMC9930890 DOI: 10.3389/fimmu.2023.1101488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Type 1 diabetes, a disorder caused by autoimmune destruction of pancreatic insulin-producing cells, is more difficult to manage when it presents at a younger age. We sought to identify genetic correlates of the age of onset by conducting the first genome-wide association study (GWAS) treating the age of first diagnosis as a quantitative trait. Methods We performed GWAS with a discovery cohort of 4,014 cases and a replication cohort of 493 independent cases. Genome-wide significant SNPs were mapped to a causal variant by Bayesian conditional analysis and gel shift assay. The causal protein-coding gene was identified and characterized by RNA interference treatment of primary human pan-CD4+ T cells with RNA-seq of the transcriptome. The candidate gene was evaluated functionally in primary cells by CD69 staining and proliferation assays. Results Our GWAS replicated the known association of the age of diagnosis with the human leukocyte antigen complex (HLA-DQB1). The second signal identified was in an intron of the NELL1 gene on chromosome 11 and fine-mapped to variant rs10833518 (P < 1.54 × 10-9). Homozygosity for the risk allele leads to average age of onset one year earlier. Knock-down of HIV TAT-interacting protein 2 (HTATIP2), but not other genes in the locus, resulted in alterations to gene expression in signal transduction pathways including MAP kinases and PI3-kinase. Higher levels of HTATIP2 expression are associated with increased viability, proliferation, and activation of T cells in the presence of signals from antigen and cytokine receptors. Discussion This study implicates HTATIP2 as a new type 1 diabetes gene acting via T cell regulation. Larger population sample sizes are expected to reveal additional loci.
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Affiliation(s)
- Christopher J Cardinale
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Xiao Chang
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,College of Artificial Intelligence and Big Data For Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong, China
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, United States
| | - Hui-Qi Qu
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | | | | | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
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Gou Z, Zhou Y, Jia H, Yang Z, Zhang Q, Yan X. Prenatal diagnosis and mRNA profiles of fetal tetralogy of Fallot. BMC Pregnancy Childbirth 2022; 22:853. [PMID: 36402964 PMCID: PMC9675103 DOI: 10.1186/s12884-022-05190-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/07/2022] [Indexed: 11/21/2022] Open
Abstract
Tetralogy of fallot (TOF) in the fetus is a typical congential heart disease that occurs during the early embryonic period, being characterized by the abnormal development of conus arteriosus. The early diagnosis and prevention of fetal TOF is very important and there is a great need for exploring the pathogenesis of it in clinic. In this study, there were three cases being detected with TOF by fetal echocardiogram and confirmed by autopsy. We characterize the difference of expression of lncRNAs and mRNAs through sequencing analysis of 3 pairs of myocardial tissues of fetal TOF and those of age-matched controls. Compared with normal group, there were 94 differentially expressed lncRNAs and 83 mRNA transcripts in TOF (P < 0.05). Correlation analysis between lncRNA and mRNA further showed that differentially expressed lncRNA can be linked to mRNAs, suggesting the potential regulator role of lncRNA in mRNA expression. Our data serve as a fundamental resource for understanding the disease etiology of TOF.
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Affiliation(s)
- Zhongshan Gou
- grid.89957.3a0000 0000 9255 8984Cardiovascular Disease Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Jiangsu 215008 Suzhou, P.R. China
| | - Yan Zhou
- grid.452799.4Department of Ultrasonography, The Fourth Affiliated Hospital of Anhui Medical University, 23000 Hefei, Anhui P.R. China
| | - Hongjing Jia
- grid.89957.3a0000 0000 9255 8984Department of Ultrasonography, The Affiliated Suzhou Hospital of Nanjing Medical University, 215008 Suzhou, Jiangsu P.R. China
| | - Zhong Yang
- grid.89957.3a0000 0000 9255 8984Department of Ultrasonography, The Affiliated Suzhou Hospital of Nanjing Medical University, 215008 Suzhou, Jiangsu P.R. China
| | - Qian Zhang
- grid.89957.3a0000 0000 9255 8984Department of Pharmacology, The Affiliated Suzhou Hospital of Nanjing Medical University, Jiangsu 215008 Suzhou, P.R. China
| | - Xinxin Yan
- grid.89957.3a0000 0000 9255 8984Department of Pharmacology, The Affiliated Suzhou Hospital of Nanjing Medical University, Jiangsu 215008 Suzhou, P.R. China
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Yeung CHC, Au Yeung SL, Schooling CM. Association of autoimmune diseases with Alzheimer's disease: A mendelian randomization study. J Psychiatr Res 2022; 155:550-558. [PMID: 36198219 DOI: 10.1016/j.jpsychires.2022.09.052] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/18/2022] [Accepted: 09/24/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Alzheimer's disease may have an autoimmune component, but the association is unclear. OBJECTIVE The objective of this Mendelian randomization (MR) study was to evaluate the association of liability to autoimmune diseases with Alzheimer's disease. METHODS A systematic search was done using PubMed to identify autoimmune diseases that have been suggested as associated with Alzheimer's disease. Genetic predictors of these autoimmune diseases were obtained from the largest and most recent genome-wide association studies (GWAS). Genetic associations with clinically-diagnosed Alzheimer's disease were obtained from the International Genomics of Alzheimer's Project GWAS (21982 cases; 41944 controls); and with parental and sibling history of Alzheimer's disease from the UK Biobank GWAS (27696 maternal, 14338 paternal and 2171 sibling cases). MR estimates were obtained using inverse variance weighting, MR-Egger and weighted median. To address possible selection bias due to inevitably recruiting only survivors, the analysis was repeated in younger people, i.e., UK Biobank siblings and adjusting for competing risk of Alzheimer's disease. RESULTS Of the 7 autoimmune diseases considered, liability to psoriasis and sarcoidosis were not associated with Alzheimer's disease. Some evidence was found for liability to multiple sclerosis being associated with higher risk and liability to Sjogren's syndrome with lower risk of Alzheimer's disease. Associations found for liability to giant cell arteritis, type 1 diabetes and rheumatoid arthritis were inconsistent in sensitivity analyses. CONCLUSION Liability to multiple sclerosis and Sjogren's syndrome could be associated with Alzheimer's disease. The underlying mechanisms, such as the role of myelin and neuroinflammation, should be further investigated.
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Affiliation(s)
- Chris Ho Ching Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Graduate School of Public Health and Health Policy, City University of New York, New York, USA
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Celen C, Chuang JC, Shen S, Li L, Maggiore G, Jia Y, Luo X, Moore A, Wang Y, Otto JE, Collings CK, Wang Z, Sun X, Nassour I, Park J, Ghaben A, Wang T, Wang SC, Scherer PE, Kadoch C, Zhu H. Arid1a loss potentiates pancreatic β-cell regeneration through activation of EGF signaling. Cell Rep 2022; 41:111581. [DOI: 10.1016/j.celrep.2022.111581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/18/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
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Nethander M, Coward E, Reimann E, Grahnemo L, Gabrielsen ME, Wibom C, Mägi R, Funck-Brentano T, Hoff M, Langhammer A, Pettersson-Kymmer U, Hveem K, Ohlsson C. Assessment of the genetic and clinical determinants of hip fracture risk: Genome-wide association and Mendelian randomization study. Cell Rep Med 2022; 3:100776. [PMID: 36260985 PMCID: PMC9589021 DOI: 10.1016/j.xcrm.2022.100776] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/07/2022] [Accepted: 09/19/2022] [Indexed: 11/05/2022]
Abstract
Hip fracture is the clinically most important fracture, but the genetic architecture of hip fracture is unclear. Here, we perform a large-scale hip fracture genome-wide association study meta-analysis and Mendelian randomization study using five cohorts from European biobanks. The results show that five genetic signals associate with hip fractures. Among these, one signal associates with falls, but not with bone mineral density (BMD), while four signals are in loci known to be involved in bone biology. Mendelian randomization analyses demonstrate a strong causal effect of decreased femoral neck BMD and moderate causal effects of Alzheimer's disease and having ever smoked regularly on risk of hip fractures. The substantial causal effect of decreased femoral neck BMD on hip fractures in both young and old subjects and in both men and women supports the use of change in femoral neck BMD as a surrogate outcome for hip fractures in clinical trials.
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Affiliation(s)
- Maria Nethander
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Vita Stråket 11, 41345 Gothenburg, Sweden; Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eivind Coward
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Ene Reimann
- Estonian Genome Center, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
| | - Louise Grahnemo
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Vita Stråket 11, 41345 Gothenburg, Sweden
| | - Maiken E Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Carl Wibom
- Department of Radiation Sciences, Oncology, Umea University, Umea, Sweden
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
| | - Thomas Funck-Brentano
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Vita Stråket 11, 41345 Gothenburg, Sweden; Department of Rheumatology, Lariboisière Hospital, INSERM U1132, Université de Paris, Paris, France
| | - Mari Hoff
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway; Department of Rheumatology, St Olavs Hospital, Trondheim, Norway
| | - Arnulf Langhammer
- HUNT Research Centre, Forskningsveien 2, 7600 Levanger, Norway"; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | | | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7491 Trondheim, Norway; HUNT Research Centre, Forskningsveien 2, 7600 Levanger, Norway"; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Vita Stråket 11, 41345 Gothenburg, Sweden; Region Västra Götaland, Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden.
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Jia P, Hu R, Yan F, Dai Y, Zhao Z. scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies. Genome Biol 2022; 23:220. [PMID: 36253801 PMCID: PMC9575201 DOI: 10.1186/s13059-022-02785-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 10/05/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes. RESULTS scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants. CONCLUSIONS We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.
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Affiliation(s)
- Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Ruifeng Hu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
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Genetic association and Mendelian randomization for hypothyroidism highlight immune molecular mechanisms. iScience 2022; 25:104992. [PMID: 36093044 PMCID: PMC9460554 DOI: 10.1016/j.isci.2022.104992] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 05/30/2022] [Accepted: 08/17/2022] [Indexed: 11/22/2022] Open
Abstract
We carried out a genome-wide association analysis including 51,194 cases of hypothyroidism and 443,383 controls. In total, 139 risk loci were associated to hypothyroidism with genes involved in lymphocyte function. Candidate genes associated with hypothyroidism were identified by using molecular quantitative trait loci, colocalization, and enhancer-promoter chromatin looping. Mendelian randomization (MR) identified 42 blood expressed genes and circulating proteins as candidate causal molecules in hypothyroidism. Drug-gene interaction analysis provided evidence that immune checkpoint and tyrosine kinase inhibitors used in cancer therapy increase the risk of hypothyroidism. Hence, integrative mapping and MR support that expression of genes and proteins enriched in lymphocyte function are associated with the risk of hypothyroidism and provide genetic evidence for drug-induced hypothyroidism and identify actionable potential drug targets.
GWAS for hypothyroidism identified 139 risk loci including 76 novel associations GWAS was enriched in pathways related to lymphocyte function In total, 28 potentially deleterious missense variants were identified Mendelian randomization and colocalization identified 61 blood causal candidate genes
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40
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Liu N, Sadlon T, Wong YY, Pederson S, Breen J, Barry SC. 3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk. Epigenetics Chromatin 2022; 15:24. [PMID: 35773720 PMCID: PMC9244893 DOI: 10.1186/s13072-022-00456-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 06/06/2022] [Indexed: 11/26/2022] Open
Abstract
Background Genome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D. Results Using 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research. Conclusions We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility. Supplementary Information The online version contains supplementary material available at 10.1186/s13072-022-00456-5.
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Affiliation(s)
- Ning Liu
- South Australian Health and Medical Research Institute, Adelaide, Australia.,Robinson Research Institute, University of Adelaide, Adelaide, Australia.,Bioinformatics Hub, School of Biological Sciences, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia
| | - Timothy Sadlon
- Robinson Research Institute, University of Adelaide, Adelaide, Australia.,Women's and Children's Health Network, Women's and Children's Hospital, Adelaide, Australia
| | - Ying Y Wong
- Robinson Research Institute, University of Adelaide, Adelaide, Australia.,Women's and Children's Health Network, Women's and Children's Hospital, Adelaide, Australia
| | - Stephen Pederson
- Bioinformatics Hub, School of Biological Sciences, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia
| | - James Breen
- South Australian Health and Medical Research Institute, Adelaide, Australia. .,Robinson Research Institute, University of Adelaide, Adelaide, Australia. .,Bioinformatics Hub, School of Biological Sciences, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia. .,Black Ochre Data Labs, Indigenous Genomics, Telethon Kids Institute, Adelaide, Australia. .,John Curtin School of Medical Research, Australian National University, Canberra, Australia.
| | - Simon C Barry
- Robinson Research Institute, University of Adelaide, Adelaide, Australia.,Women's and Children's Health Network, Women's and Children's Hospital, Adelaide, Australia
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41
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Khunsriraksakul C, Markus H, Olsen NJ, Carrel L, Jiang B, Liu DJ. Construction and Application of Polygenic Risk Scores in Autoimmune Diseases. Front Immunol 2022; 13:889296. [PMID: 35833142 PMCID: PMC9271862 DOI: 10.3389/fimmu.2022.889296] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.
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Affiliation(s)
- Chachrit Khunsriraksakul
- Graduate Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Havell Markus
- Graduate Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Nancy J. Olsen
- Department of Medicine, Division of Rheumatology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Bibo Jiang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Dajiang J. Liu
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, United States
- *Correspondence: Dajiang J. Liu,
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Guo MH, Sama P, LaBarre BA, Lokhande H, Balibalos J, Chu C, Du X, Kheradpour P, Kim CC, Oniskey T, Snyder T, Soghoian DZ, Weiner HL, Chitnis T, Patsopoulos NA. Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets. Genome Biol 2022; 23:127. [PMID: 35672799 PMCID: PMC9175345 DOI: 10.1186/s13059-022-02694-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 05/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background Multiple sclerosis (MS) is an autoimmune condition of the central nervous system with a well-characterized genetic background. Prior analyses of MS genetics have identified broad enrichments across peripheral immune cells, yet the driver immune subsets are unclear. Results We utilize chromatin accessibility data across hematopoietic cells to identify cell type-specific enrichments of MS genetic signals. We find that CD4 T and B cells are independently enriched for MS genetics and further refine the driver subsets to Th17 and memory B cells, respectively. We replicate our findings in data from untreated and treated MS patients and find that immunomodulatory treatments suppress chromatin accessibility at driver cell types. Integration of statistical fine-mapping and chromatin interactions nominate numerous putative causal genes, illustrating complex interplay between shared and cell-specific genes. Conclusions Overall, our study finds that open chromatin regions in CD4 T cells and B cells independently drive MS genetic signals. Our study highlights how careful integration of genetics and epigenetics can provide fine-scale insights into causal cell types and nominate new genes and pathways for disease. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02694-y.
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43
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Chu X, Janssen AWM, Koenen H, Chang L, He X, Joosten I, Stienstra R, Kuijpers Y, Wijmenga C, Xu CJ, Netea MG, Tack CJ, Li Y. A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes. eLife 2022; 11:73709. [PMID: 35638288 PMCID: PMC9205632 DOI: 10.7554/elife.73709] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Background: The large inter-individual variability in immune-cell composition and function determines immune responses in general and susceptibility o immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown. Methods: Blood samples were collected from 243 patients with T1D of Dutch descent. We applied genetic association analysis on >200 immune-cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls. Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTLs) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation. Conclusions: This study provides new insights into the genetic factors that affect immunological responses in T1D. Funding: This work was supported by an ERC starting grant (no. 948207) and a Radboud University Medical Centre Hypatia grant (2018) to YL and an ERC advanced grant (no. 833247) and a Spinoza grant of the Netherlands Association for Scientific Research to MGN CT received funding from the Perspectief Biomarker Development Center Research Programme, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). AJ was funded by a grant from the European Foundation for the Study of Diabetes (EFSD/AZ Macrovascular Programme 2015). XC was supported by the China Scholarship Council (201706040081). Every year around the world, over 100,000 people are diagnosed with type 1 diabetes. This disease develops when the immune system mistakenly destroys the cells that produce a hormone called insulin, leaving affected individuals unable to regulate their blood sugar levels. Type 1 diabetes patients must rely on regular injections of manufactured insulin to survive. The composition and activity of the human immune system is under genetic control, and people with certain changes in their genes are more susceptible than others to develop type 1 diabetes. Previous studies have identified around 60 locations in the human DNA (known as loci) associated with the condition, but it remains unclear how these loci influence the immune system and whether diabetes will emerge. Chu, Janssen, Koenen et al. explored how variations in genetic information can influence the composition of the immune system, and the type of molecules it releases to perform its role. To do so, blood samples from 243 individuals of Dutch descent with type 1 diabetes were collected, and genetic associations were investigated. The results revealed that a major type of immune actors known as T cells are under the control of genetic factors associated with type 1 diabetes susceptibility. For instance, a specific type of T cells showed shared genetic control with type 1 diabetes. In addition, 15 loci were identified that influenced immune responses in the patients. Among those, 12 have never been reported to be involved in immune responses in healthy people, implying that these regions might only regulate the immune system of individuals with type 1 diabetes and other similar disorders. Finally, Chu, Janssen, Koenen et al. propose 11 genes within the identified loci as potential targets for new diabetes medication. These results represent an important resource for researchers exploring the genetic and immune basis of type 1 diabetes, and they could open new avenues for drug development.
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Affiliation(s)
- Xiaojing Chu
- Department of Genetics, University Medical Center Groningen, Groningen, Netherlands
| | - Anna W M Janssen
- Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Hans Koenen
- Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Linzhung Chang
- Department of Genetics, University Medical Center Groningen, Groningen, Netherlands
| | - Xuehui He
- Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Irma Joosten
- Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Rinke Stienstra
- Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Yunus Kuijpers
- Department of Computational Biology for Individualised Infection Medicine, Helmholtz Centre for Infection Research, Hannover, Germany
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, Groningen, Netherlands
| | - Cheng-Jian Xu
- Department of Computational Biology for Individualised Infection Medicine, Helmholtz Centre for Infection Research, Hannover, Germany
| | - Mihai G Netea
- Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Cees J Tack
- Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Yang Li
- Department of Computational Biology for Individualised Infection Medicine, Helmholtz Centre for Infection Research, Hannover, Germany
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44
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Gootjes C, Zwaginga JJ, Roep BO, Nikolic T. Functional Impact of Risk Gene Variants on the Autoimmune Responses in Type 1 Diabetes. Front Immunol 2022; 13:886736. [PMID: 35603161 PMCID: PMC9114814 DOI: 10.3389/fimmu.2022.886736] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/08/2022] [Indexed: 11/17/2022] Open
Abstract
Type 1 diabetes (T1D) is an autoimmune disease that develops in the interplay between genetic and environmental factors. A majority of individuals who develop T1D have a HLA make up, that accounts for 50% of the genetic risk of disease. Besides these HLA haplotypes and the insulin region that importantly contribute to the heritable component, genome-wide association studies have identified many polymorphisms in over 60 non-HLA gene regions that also contribute to T1D susceptibility. Combining the risk genes in a score (T1D-GRS), significantly improved the prediction of disease progression in autoantibody positive individuals. Many of these minor-risk SNPs are associated with immune genes but how they influence the gene and protein expression and whether they cause functional changes on a cellular level remains a subject of investigation. A positive correlation between the genetic risk and the intensity of the peripheral autoimmune response was demonstrated both for HLA and non-HLA genetic risk variants. We also observed epigenetic and genetic modulation of several of these T1D susceptibility genes in dendritic cells (DCs) treated with vitamin D3 and dexamethasone to acquire tolerogenic properties as compared to immune activating DCs (mDC) illustrating the interaction between genes and environment that collectively determines risk for T1D. A notion that targeting such genes for therapeutic modulation could be compatible with correction of the impaired immune response, inspired us to review the current knowledge on the immune-related minor risk genes, their expression and function in immune cells, and how they may contribute to activation of autoreactive T cells, Treg function or β-cell apoptosis, thus contributing to development of the autoimmune disease.
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Affiliation(s)
- Chelsea Gootjes
- Laboratory of Immunomodulation and Regenerative Cell Therapy, Department of Internal Medicine, Leiden University Medical Center, Leiden, Netherlands
| | - Jaap Jan Zwaginga
- Laboratory of Immunomodulation and Regenerative Cell Therapy, Department of Internal Medicine, Leiden University Medical Center, Leiden, Netherlands
| | - Bart O Roep
- Laboratory of Immunomodulation and Regenerative Cell Therapy, Department of Internal Medicine, Leiden University Medical Center, Leiden, Netherlands
| | - Tatjana Nikolic
- Laboratory of Immunomodulation and Regenerative Cell Therapy, Department of Internal Medicine, Leiden University Medical Center, Leiden, Netherlands
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45
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Bouland GA, Beulens JWJ, Nap J, van der Slik AR, Zaldumbide A, 't Hart LM, Slieker RC. Diabetes risk loci-associated pathways are shared across metabolic tissues. BMC Genomics 2022; 23:368. [PMID: 35568807 PMCID: PMC9107144 DOI: 10.1186/s12864-022-08587-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 03/23/2022] [Indexed: 11/28/2022] Open
Abstract
Aims/hypothesis Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants. Methods In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms. Results One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated. Conclusion Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08587-5.
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Affiliation(s)
- Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam UMC, Location VUMC, Amsterdam Public Health Institute, Amsterdam, the Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joey Nap
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands
| | - Arno R van der Slik
- Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands
| | - Arnaud Zaldumbide
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands
| | - Leen M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands.,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUMC, Amsterdam Public Health Institute, Amsterdam, the Netherlands.,Molecular Epidemiology Section, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands. .,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUMC, Amsterdam Public Health Institute, Amsterdam, the Netherlands.
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46
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Kissler S. Genetic Modifiers of Thymic Selection and Central Tolerance in Type 1 Diabetes. Front Immunol 2022; 13:889856. [PMID: 35464420 PMCID: PMC9021641 DOI: 10.3389/fimmu.2022.889856] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 03/21/2022] [Indexed: 02/02/2023] Open
Abstract
Type 1 diabetes (T1D) is caused by the T cell-driven autoimmune destruction of insulin-producing cells in the pancreas. T1D served as the prototypical autoimmune disease for genome wide association studies (GWAS) after having already been the subject of many linkage and association studies prior to the development of GWAS technology. Of the many T1D-associated gene variants, a minority appear disease-specific, while most are shared with one or more other autoimmune condition. Shared disease variants suggest defects in fundamental aspects of immune tolerance. The first layer of protective tolerance induction is known as central tolerance and takes place during the thymic selection of T cells. In this article, we will review candidate genes for type 1 diabetes whose function implicates them in central tolerance. We will describe examples of gene variants that modify the function of T cells intrinsically and others that indirectly affect thymic selection. Overall, these insights will show that a significant component of the genetic risk for T1D - and autoimmunity in general - pertains to the earliest stages of tolerance induction, at a time when protective intervention may not be feasible.
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Affiliation(s)
- Stephan Kissler
- Section for Immunobiology, Joslin Diabetes Center, Boston, MA, United States,Department of Medicine, Harvard Medical School, Boston, MA, United States,*Correspondence: Stephan Kissler,
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47
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Convergence of case-specific epigenetic alterations identify a confluence of genetic vulnerabilities tied to opioid overdose. Mol Psychiatry 2022; 27:2158-2170. [PMID: 35301427 PMCID: PMC9133127 DOI: 10.1038/s41380-022-01477-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 01/19/2022] [Accepted: 02/08/2022] [Indexed: 11/08/2022]
Abstract
Opioid use disorder is a highly heterogeneous disease driven by a variety of genetic and environmental risk factors which have yet to be fully elucidated. Opioid overdose, the most severe outcome of opioid use disorder, remains the leading cause of accidental death in the United States. We interrogated the effects of opioid overdose on the brain using ChIP-seq to quantify patterns of H3K27 acetylation in dorsolateral prefrontal cortical neurons isolated from 51 opioid-overdose cases and 51 accidental death controls. Among opioid cases, we observed global hypoacetylation and identified 388 putative enhancers consistently depleted for H3K27ac. Machine learning on H3K27ac patterns predicted case-control status with high accuracy. We focused on case-specific regulatory alterations, revealing 81,399 hypoacetylation events, uncovering vast inter-patient heterogeneity. We developed a strategy to decode this heterogeneity based on convergence analysis, which leveraged promoter-capture Hi-C to identify five genes over-burdened by alterations in their regulatory network or "plexus": ASTN2, KCNMA1, DUSP4, GABBR2, ENOX1. These convergent loci are enriched for opioid use disorder risk genes and heritability for generalized anxiety, number of sexual partners, and years of education. Overall, our multi-pronged approach uncovers neurobiological aspects of opioid use disorder and captures genetic and environmental factors perpetuating the opioid epidemic.
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48
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Ashrafizadeh M, Kumar AP, Aref AR, Zarrabi A, Mostafavi E. Exosomes as Promising Nanostructures in Diabetes Mellitus: From Insulin Sensitivity to Ameliorating Diabetic Complications. Int J Nanomedicine 2022; 17:1229-1253. [PMID: 35340823 PMCID: PMC8943613 DOI: 10.2147/ijn.s350250] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/07/2022] [Indexed: 12/11/2022] Open
Abstract
Diabetes mellitus (DM) is among the chronic metabolic disorders that its incidence rate has shown an increase in developed and wealthy countries due to lifestyle and obesity. The treatment of DM has always been of interest, and significant effort has been made in this field. Exosomes belong to extracellular vesicles with nanosized features (30-150 nm) that are involved in cell-to-cell communication and preserving homeostasis. The function of exosomes is different based on their cargo, and they may contain lipids, proteins, and nucleic acids. The present review focuses on the application of exosomes in the treatment of DM; both glucose and lipid levels are significantly affected by exosomes, and these nanostructures enhance lipid metabolism and decrease its deposition. Furthermore, exosomes promote glucose metabolism and affect the level of glycolytic enzymes and glucose transporters in DM. Type I DM results from the destruction of β cells in the pancreas, and exosomes can be employed to ameliorate apoptosis and endoplasmic reticulum (ER) stress in these cells. The exosomes have dual functions in mediating insulin resistance/sensitivity, and M1 macrophage-derived exosomes inhibit insulin secretion. The exosomes may contain miRNAs, and by transferring among cells, they can regulate various molecular pathways such as AMPK, PI3K/Akt, and β-catenin to affect DM progression. Noteworthy, exosomes are present in different body fluids such as blood circulation, and they can be employed as biomarkers for the diagnosis of diabetic patients. Future studies should focus on engineering exosomes derived from sources such as mesenchymal stem cells to treat DM as a novel strategy.
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Affiliation(s)
- Milad Ashrafizadeh
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, 34956, Istanbul, Turkey
| | - Alan Prem Kumar
- NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore
- Cancer Science Institute of Singapore and Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
| | - Amir Reza Aref
- Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Translational Sciences, Xsphera Biosciences Inc., Boston, MA, 02210, USA
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, 34396, Turkey
| | - Ebrahim Mostafavi
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
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Farid MMM, Abdel-Mageed AI, El-sherbini A, Mohamed NR, Mohsen M. Study of the association between GLIS3 rs10758593 and type 2 diabetes mellitus in Egyptian population. EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2022. [DOI: 10.1186/s43042-022-00254-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
GLIS3 (Gli-similar 3), a transcription factor, is involved in the maturation of pancreatic beta cells in fetal life, maintenance of cell mass as well as the control of insulin gene expression in adults. As a result, GLIS3 was reported as a susceptibility gene for type 1 diabetes, type 2 diabetes, and neonatal diabetes. Therefore, the goal of this study was to look into the association between the rs10758593 single nucleotide polymorphism (SNP) in the GLIS3 gene and T2DM in the Egyptian population.
Methods
Frequencies of the rs10758593 (A/G) SNPs were determined in 100 T2DM patients (cases) and in 100 non-diabetic healthy subjects (controls) using real-time PCR.
Results
The prevalence of the mutant genotypes, AA and AG, differed significantly between patients and controls. The AA genotype was more prevalent in the patients' group. The (AA) was found in 39% of the patients and 18% of the controls. While AG (heterozygous) genotype was found in 61% of the patients and 81% of the controls (p = 0.003). The AA genotype was significantly associated with T2DM. Moreover, The GLIS3 rs 10758593 mutation was found to be associated with the presence of diabetic retinopathy and nephropathy. In diabetic patients, a significant correlation between HbA1c with fasting glucose, fasting insulin, and HOMA-IR was found.
Conclusion
The rs10758593 polymorphism of the GLIS3 gene was found to be significantly associated with T2DM in an Egyptian population sample. Additionally, significant association between GLIS3 rs 10758593 mutation and the glycemic control was found.
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Emont MP, Jacobs C, Essene AL, Pant D, Tenen D, Colleluori G, Di Vincenzo A, Jørgensen AM, Dashti H, Stefek A, McGonagle E, Strobel S, Laber S, Agrawal S, Westcott GP, Kar A, Veregge ML, Gulko A, Srinivasan H, Kramer Z, De Filippis E, Merkel E, Ducie J, Boyd CG, Gourash W, Courcoulas A, Lin SJ, Lee BT, Morris D, Tobias A, Khera AV, Claussnitzer M, Pers TH, Giordano A, Ashenberg O, Regev A, Tsai LT, Rosen ED. A single-cell atlas of human and mouse white adipose tissue. Nature 2022; 603:926-933. [PMID: 35296864 PMCID: PMC9504827 DOI: 10.1038/s41586-022-04518-2] [Citation(s) in RCA: 412] [Impact Index Per Article: 137.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 02/04/2022] [Indexed: 12/13/2022]
Abstract
White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defence1. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes2. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.
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Affiliation(s)
- Margo P Emont
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher Jacobs
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adam L Essene
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Deepti Pant
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Danielle Tenen
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Georgia Colleluori
- Department of Experimental and Clinical Medicine, Center of Obesity, Marche Polytechnic University, Ancona, Italy
| | - Angelica Di Vincenzo
- Department of Experimental and Clinical Medicine, Center of Obesity, Marche Polytechnic University, Ancona, Italy
| | - Anja M Jørgensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Hesam Dashti
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adam Stefek
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Saaket Agrawal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gregory P Westcott
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Amrita Kar
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Molly L Veregge
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Anton Gulko
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Harini Srinivasan
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zachary Kramer
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Eleanna De Filippis
- Division of Endocrinology, Diabetes and Metabolism, Mayo Clinic Scottsdale, AZ, USA
| | - Erin Merkel
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jennifer Ducie
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Christopher G Boyd
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - William Gourash
- Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anita Courcoulas
- Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Samuel J Lin
- Division of Plastic Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Bernard T Lee
- Division of Plastic Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Donald Morris
- Division of Plastic Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Adam Tobias
- Division of Plastic Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Amit V Khera
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of Plastic Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Tune H Pers
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Antonio Giordano
- Department of Experimental and Clinical Medicine, Center of Obesity, Marche Polytechnic University, Ancona, Italy
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Linus T Tsai
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Evan D Rosen
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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