1
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Jing X, Liu Z, Li W, Ma K, Zhang J, Yan Z, Zhang S, Lin J, Zhao J, Ong KK, Perry JRB, Zhao Y. Protein-truncating variants in UQCRC1 are associated with Parkinson's disease: evidence from half-million people. NPJ Parkinsons Dis 2025; 11:120. [PMID: 40346065 PMCID: PMC12064775 DOI: 10.1038/s41531-025-00987-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2025] [Accepted: 04/30/2025] [Indexed: 05/11/2025] Open
Abstract
Recent studies have suggested a potential but inconsistent link between UQCRC1 and Parkinson's disease (PD). For the first time, we systematically investigated the association between non-synonymous variants in UQCRC1 and PD risk using data from the UK Biobank with half-million participants, which provide evidence supporting the role of UQCRC1 Protein-truncating variants (PTVs) in PD (P = 1.20 × 10-6, OR = 6.59) and highlight the importance of large-scale population studies in identifying rare genetic risk factors.
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Affiliation(s)
- Xiaoxi Jing
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China
| | - Zongzhi Liu
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China
| | - Wenwen Li
- Innovation Center for Neurological Disorders and Department of Neurology, National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Kaiyan Ma
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China
| | - Jiaxiang Zhang
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China
| | - Zeqi Yan
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China
| | - Shuo Zhang
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China
| | - Jiecong Lin
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China
| | - Junpeng Zhao
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China
| | - Ken K Ong
- Innovation Center for Neurological Disorders and Department of Neurology, National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - John R B Perry
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Yajie Zhao
- Changping Laboratory, Yard-28, Science Park Road, Changping District, Beijing, P. R. China.
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2
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Ajadee A, Mahmud S, Ali MA, Mollah MMH, Ahmmed R, Mollah MNH. In-silico discovery of type-2 diabetes-causing host key genes that are associated with the complexity of monkeypox and repurposing common drugs. Brief Bioinform 2025; 26:bbaf215. [PMID: 40370100 PMCID: PMC12078936 DOI: 10.1093/bib/bbaf215] [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: 10/02/2024] [Revised: 04/11/2025] [Accepted: 04/21/2025] [Indexed: 05/16/2025] Open
Abstract
Monkeypox (Mpox) is a major global human health threat after COVID-19. Its treatment becomes complicated with type-2 diabetes (T2D). It may happen due to the influence of both disease-causing common host key genes (cHKGs). Therefore, it is necessary to explore both disease-causing cHKGs to reveal their shared pathogenetic mechanisms and candidate drugs as their common treatments without adverse side effect. This study aimed to address these issues. At first, 3 transcriptomics datasets for each of Mpox and 6 T2D datasets were analyzed and found 52 common host differentially expressed genes (cHDEGs) that can separate both T2D and Mpox patients from the control samples. Then top-ranked six cHDEGs (HSP90AA1, B2M, IGF1R, ALD1HA1, ASS1, and HADHA) were detected as the T2D-causing cHKGs that are associated with the complexity of Mpox through the protein-protein interaction network analysis. Then common pathogenetic processes between T2D and Mpox were disclosed by cHKG-set enrichment analysis with biological processes, molecular functions, cellular components and Kyoto Encyclopedia of Genes and Genomes pathways, and regulatory network analysis with transcription factors and microRNAs. Finally, cHKG-guided top-ranked three drug molecules (tecovirimat, vindoline, and brincidofovir) were recommended as the repurposable common therapeutic agents for both Mpox and T2D by molecular docking. The absorption, distribution, metabolism, excretion, and toxicity and drug-likeness analysis of these drug molecules indicated their good pharmacokinetics properties. The 100-ns molecular dynamics simulation results (root mean square deviation, root mean square fluctuation, and molecular mechanics generalized born surface area) with the top-ranked three complexes ASS1-tecovirimat, ALDH1A1-vindoline, and B2M-brincidofovir exhibited good pharmacodynamics properties. Therefore, the results provided in this article might be important resources for diagnosis and therapies of Mpox patients who are also suffering from T2D.
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Affiliation(s)
- Alvira Ajadee
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Sabkat Mahmud
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Md Ahad Ali
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh
- Department of Chemistry, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Md Manir Hossain Mollah
- Department of Physical Sciences, Independent University Bangladesh, Bashundhara Residential Area, Dhaka 1245, Bangladesh
| | - Reaz Ahmmed
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh
- Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh
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3
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Mörseburg A, Zhao Y, Kentistou KA, Perry JRB, Ong KK, Day FR. Genetic determinants of proteomic aging. NPJ AGING 2025; 11:30. [PMID: 40287427 PMCID: PMC12033249 DOI: 10.1038/s41514-025-00205-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Accepted: 02/21/2025] [Indexed: 04/29/2025]
Abstract
Changes in the proteome and its dysregulation have long been known to be a hallmark of aging. We derived a proteomic aging trait using data on 1459 plasma proteins from 44,435 UK Biobank individuals measured using an antibody-based assay. This metric is strongly associated with four age-related disease outcomes, even after adjusting for chronological age. Survival analysis showed that one-year older proteomic age, relative to chronological age, increases all-cause mortality hazard by 13 percent. We performed a genome-wide association analysis of proteomic age acceleration (proteomic aging trait minus chronological age) to identify its biological determinants. Proteomic age acceleration showed modest genetic correlations with four epigenetic clocks (Rg = 0.17 to 0.19) and telomere length (Rg = -0.2). Once we removed associations that were explained by a single pQTL, we were left with three signals mapping to BRCA1, POLR2A and TET2 with apparent widespread effects on plasma proteomic aging. Genetic variation at these three loci has been shown to affect other omics-related aging measures. Mendelian randomisation analyses showed causal effects of higher BMI and type 2 diabetes on faster proteomic age acceleration. This supports the idea that obesity and other features of metabolic syndrome have an adverse effect on the processes of human aging.
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Affiliation(s)
- Alexander Mörseburg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Felix R Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
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4
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Wallis NJ, McClellan A, Mörseburg A, Kentistou KA, Jamaluddin A, Dowsett GKC, Schofield E, Morros-Nuevo A, Saeed S, Lam BYH, Sumanasekera NT, Chan J, Kumar SS, Zhang RM, Wainwright JF, Dittmann M, Lakatos G, Rainbow K, Withers D, Bounds R, Ma M, German AJ, Ladlow J, Sargan D, Froguel P, Farooqi IS, Ong KK, Yeo GSH, Tadross JA, Perry JRB, Gorvin CM, Raffan E. Canine genome-wide association study identifies DENND1B as an obesity gene in dogs and humans. Science 2025; 387:eads2145. [PMID: 40048553 DOI: 10.1126/science.ads2145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Accepted: 01/10/2025] [Indexed: 03/29/2025]
Abstract
Obesity is a heritable disease, but its genetic basis is incompletely understood. Canine population history facilitates trait mapping. We performed a canine genome-wide association study for body condition score-a measure of obesity-in 241 Labrador retrievers. Using a cross-species approach, we showed that canine obesity genes are also associated with rare and common forms of obesity in humans. The lead canine association was within the gene DENN domain containing 1B (DENND1B). Each copy of the alternate allele was associated with ~7.5% greater body fat. We demonstrate a role for this gene in regulating signaling and trafficking of melanocortin 4 receptor, a critical controller of energy homeostasis. Thus, canine genetics identified obesity genes and mechanisms relevant to both dogs and humans.
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Affiliation(s)
- Natalie J Wallis
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Alyce McClellan
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Alexander Mörseburg
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Aqfan Jamaluddin
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, UK
| | - Georgina K C Dowsett
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ellen Schofield
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Anna Morros-Nuevo
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Sadia Saeed
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Brian Y H Lam
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Natasha T Sumanasekera
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Justine Chan
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Sambhavi S Kumar
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Rey M Zhang
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Jodie F Wainwright
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Marie Dittmann
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Gabriella Lakatos
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Kara Rainbow
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - David Withers
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Rebecca Bounds
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre, Cambridge, UK
| | - Marcella Ma
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Alexander J German
- Institute of Life Course and Medical Sciences and School of Veterinary Science, University of Liverpool, Neston, UK
| | - Jane Ladlow
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - David Sargan
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Philippe Froguel
- INSERM UMR 1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - I Sadaf Farooqi
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giles S H Yeo
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- MRC Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - John A Tadross
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Histopathology and Cambridge Genomics Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - John R B Perry
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Caroline M Gorvin
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, Birmingham, UK
| | - Eleanor Raffan
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
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5
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He W, Loganathan N, Belsham DD. IGF1 Signaling Regulates Neuropeptide Expression in Hypothalamic Neurons Under Physiological and Pathological Conditions. Endocrinology 2025; 166:bqaf051. [PMID: 40105689 PMCID: PMC11949690 DOI: 10.1210/endocr/bqaf051] [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: 01/29/2025] [Revised: 02/28/2025] [Accepted: 03/17/2025] [Indexed: 03/20/2025]
Abstract
Insulin-like growth factor 1 (IGF1) plays a critical role in metabolism and aging, but its role in the brain remains unclear. This study examined whether hypothalamic neurons respond to IGF1 and how its actions are modulated. RT-qPCR and single-cell RNA sequencing indicated that Igf1r mRNA is expressed in neuropeptide Y/Agouti-related peptide (NPY/AgRP) neurons but has higher expression in pro-opiomelanocortin (POMC) neurons. IGF1 binding proteins Igfbp3 and Igfbp5 were significantly expressed, whereby Igfbp5 levels were modulated by fasting, nutrient availability, and circadian rhythms, implying that IGF1 signaling can be controlled by multiple mechanisms. In mouse and human models, IGF1 regulated Agrp, Npy, Pomc, Cartpt, Spx, Gal, and Fam237b expression, producing an overall anorexigenic profile. Hyperinsulinemia induced IGF1 resistance, accompanied by reduced IGF1R protein, as well as Igf1r and Irs2 mRNA expression via over-activation of phosphoinositide 3-kinase/forkhead box O1 (PI3K-FOXO1) signaling. Thus, hypothalamic neurons respond to IGF1 under physiological conditions, and hyperinsulinemia is a novel mechanism that drives cellular IGF1 resistance.
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Affiliation(s)
- Wenyuan He
- Department of Physiology, University of Toronto, Toronto, ON, Canada M5S 1A8
| | - Neruja Loganathan
- Department of Physiology, University of Toronto, Toronto, ON, Canada M5S 1A8
| | - Denise D Belsham
- Department of Physiology, University of Toronto, Toronto, ON, Canada M5S 1A8
- Department of Medicine, University of Toronto, Toronto, ON, Canada M5S 1A8
- Department of Obstetrics and Gynaecology, University of Toronto, Toronto, ON, Canada M5S 1A8
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6
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Tadross JA, Steuernagel L, Dowsett GKC, Kentistou KA, Lundh S, Porniece M, Klemm P, Rainbow K, Hvid H, Kania K, Polex-Wolf J, Knudsen LB, Pyke C, Perry JRB, Lam BYH, Brüning JC, Yeo GSH. A comprehensive spatio-cellular map of the human hypothalamus. Nature 2025; 639:708-716. [PMID: 39910307 PMCID: PMC11922758 DOI: 10.1038/s41586-024-08504-8] [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: 09/20/2023] [Accepted: 12/09/2024] [Indexed: 02/07/2025]
Abstract
The hypothalamus is a brain region that plays a key role in coordinating fundamental biological functions1. However, our understanding of the underlying cellular components and neurocircuitries have, until recently, emerged primarily from rodent studies2,3. Here we combine single-nucleus sequencing of 433,369 human hypothalamic cells with spatial transcriptomics, generating a comprehensive spatio-cellular transcriptional map of the hypothalamus, the 'HYPOMAP'. Although conservation of neuronal cell types between humans and mice, as based on transcriptomic identity, is generally high, there are notable exceptions. Specifically, there are significant disparities in the identity of pro-opiomelanocortin neurons and in the expression levels of G-protein-coupled receptors between the two species that carry direct implications for currently approved obesity treatments. Out of the 452 hypothalamic cell types, we find that 291 neuronal clusters are significantly enriched for expression of body mass index (BMI) genome-wide association study genes. This enrichment is driven by 426 'effector' genes. Rare deleterious variants in six of these (MC4R, PCSK1, POMC, CALCR, BSN and CORO1A) associate with BMI at population level, and CORO1A has not been linked previously to BMI. Thus, HYPOMAP provides a detailed atlas of the human hypothalamus in a spatial context and serves as an important resource to identify new druggable targets for treating a wide range of conditions, including reproductive, circadian and metabolic disorders.
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Affiliation(s)
- John A Tadross
- Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, UK
- Cambridge Genomics Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Lukas Steuernagel
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Georgina K C Dowsett
- Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, UK
| | - Katherine A Kentistou
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Sofia Lundh
- Research & Early Development, Novo Nordisk A/S, Måløv, Denmark
| | - Marta Porniece
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Paul Klemm
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Kara Rainbow
- Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, UK
| | - Henning Hvid
- Research & Early Development, Novo Nordisk A/S, Måløv, Denmark
| | - Katarzyna Kania
- Genomics Core, Cancer Research UK Cambridge Institute, Cambridge, UK
| | | | | | - Charles Pyke
- Research & Early Development, Novo Nordisk A/S, Måløv, Denmark
| | - John R B Perry
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Brian Y H Lam
- Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, UK
| | - Jens C Brüning
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany.
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.
- Center for Endocrinology, Diabetes and Preventive Medicine (CEDP), University Hospital Cologne, Cologne, Germany.
- National Center for Diabetes Research (DZD), Neuherberg, Germany.
| | - Giles S H Yeo
- Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, UK.
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7
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Xi X, Li J, Jia J, Meng Q, Li C, Wang X, Wei L, Zhang X. A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions. Nat Commun 2025; 16:1284. [PMID: 39900922 PMCID: PMC11790924 DOI: 10.1038/s41467-025-56475-9] [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: 06/02/2024] [Accepted: 01/15/2025] [Indexed: 02/05/2025] Open
Abstract
Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory mechanisms, limiting the decoding of regulations behind key cellular events, such as cell state transitions. Here, we present regX, a deep neural network incorporating both gene-level regulation and gene-gene interaction mechanisms, which enables prioritizing potential driver regulators of cell state transitions and providing mechanistic interpretations. Applied to single-cell multi-omics data on type 2 diabetes and hair follicle development, regX reliably prioritizes key transcription factors and candidate cis-regulatory elements that drive cell state transitions. Some regulators reveal potential new therapeutic targets, drug repurposing possibilities, and putative causal single nucleotide polymorphisms. This method to analyze single-cell multi-omics data demonstrates how the interpretable design of neural networks can better decode biological systems.
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Affiliation(s)
- Xi Xi
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China
| | - Jiaqi Li
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China
| | - Jinmeng Jia
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China
| | - Qiuchen Meng
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China
| | - Chen Li
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China
| | - Xiaowo Wang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China
| | - Lei Wei
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China.
- School of Life Sciences, Tsinghua University, Beijing, China.
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8
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Hodgson S, Williamson A, Bigossi M, Stow D, Jacobs BM, Samuel M, Gafton J, Zöllner J, Spreckley M, Langenberg C, van Heel DA, Mathur R, Siddiqui MK, Finer S. Genetic basis of early onset and progression of type 2 diabetes in South Asians. Nat Med 2025; 31:323-331. [PMID: 39592779 PMCID: PMC11750703 DOI: 10.1038/s41591-024-03317-8] [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: 03/07/2024] [Accepted: 09/16/2024] [Indexed: 11/28/2024]
Abstract
South Asians develop type 2 diabetes (T2D) early in life and often with normal body mass index (BMI). However, reasons for this are poorly understood because genetic research is largely focused on European ancestry groups. We used recently derived multi-ancestry partitioned polygenic scores (pPSs) to elucidate underlying etiological pathways British Pakistani and British Bangladeshi individuals with T2D (n = 11,678) and gestational diabetes mellitus (GDM) (n = 1,965) in the Genes & Health study (n = 50,556). Beta cell 2 (insulin deficiency) and Lipodystrophy 1 (unfavorable fat distribution) pPSs were most strongly associated with T2D, GDM and younger age at T2D diagnosis. Individuals at high genetic risk of both insulin deficiency and lipodystrophy were diagnosed with T2D 8.2 years earlier with BMI 3 kg m-2 lower compared to those at low genetic risk. The insulin deficiency pPS was associated with poorer HbA1c response to SGLT2 inhibitors. Insulin deficiency and lipodystrophy pPSs were associated with faster progression to insulin dependence and microvascular complications. South Asians had a greater genetic burden from both of these pPSs than white Europeans in the UK Biobank. In conclusion, genetic predisposition to insulin deficiency and lipodystrophy in British Pakistani and British Bangladeshi individuals is associated with earlier onset of T2D, faster progression to complications, insulin dependence and poorer response to medication.
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Grants
- Wellcome Trust
- Wellcome Trust (Wellcome)
- SH is funded by a Wellcome HARP Doctoral Fellowship 227532/Z/23/Z. RM and MKS are funded by Barts Charity (MGU0504). DS is funded by the Tackling Multimorbidity at Scale Strategic Priorities Fund programme [grant number MR/W014416/1] delivered by the Medical Research Council and the National Institute for Health Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council. Genes & Health is/has recently been core-funded by Wellcome (WT102627, WT210561), the Medical Research Council (UK) (M009017, MR/X009777/1, MR/X009920/1), Higher Education Funding Council for England Catalyst, Barts Charity (845/1796), Health Data Research UK (for London substantive site), and research delivery support from the NHS National Institute for Health Research Clinical Research Network (North Thames). Genes & Health is/has recently been funded by Alnylam Pharmaceuticals, Genomics PLC; and a Life Sciences Industry Consortium of Astra Zeneca PLC, Bristol-Myers Squibb Company, GlaxoSmithKline Research and Development Limited, Maze Therapeutics Inc, Merck Sharp & Dohme LLC, Novo Nordisk A/S, Pfizer Inc, Takeda Development Centre Americas Inc. We thank Social Action for Health, Centre of The Cell, members of our Community Advisory Group, and staff who have recruited and collected data from volunteers. We thank the NIHR National Biosample Centre (UK Biocentre), the Social Genetic & Developmental Psychiatry Centre (King's College London), Wellcome Sanger Institute, and Broad Institute for sample processing, genotyping, sequencing and variant annotation.
- As above
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Affiliation(s)
- Sam Hodgson
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Alice Williamson
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Margherita Bigossi
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Daniel Stow
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Benjamin M Jacobs
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Miriam Samuel
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Joseph Gafton
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Marie Spreckley
- Blizard Institute, Queen Mary University of London, London, UK
| | - Claudia Langenberg
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | | | - Rohini Mathur
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Moneeza K Siddiqui
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
| | - Sarah Finer
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
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9
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Kouri C, Jia RY, Kentistou KA, Gardner EJ, Perry JRB, Flück CE, Ong KK. Population-Based Study of Rare Coding Variants in NR5A1/SF-1. J Endocr Soc 2024; 8:bvae178. [PMID: 39479520 PMCID: PMC11521259 DOI: 10.1210/jendso/bvae178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Indexed: 11/02/2024] Open
Abstract
Background Steroidogenic Factor 1/Nuclear Receptor Subfamily 5 Group A Member 1 (SF-1/NR5A1) is critical for the development and function of sex organs, influencing steroidogenesis and reproduction. While rare deleterious NR5A1/SF-1 variants have been identified in individuals with various differences of sex development (DSD), primary ovarian insufficiency, and infertility, their impact on the general population remains unclear. Methods We analyzed health records and exome sequencing data from up to 420 162 individuals (227 858 women) from the UK Biobank study to assess the impact of rare (frequency < 0.1%) predicted deleterious NR5A1/SF-1 variants on age at menopause and 26 other traits. Results No carriers of rare protein truncating variants in NR5A1/SF-1 were identified. We found that the previously reported association of rare deleterious missense NR5A1/SF-1 variants with earlier age at menopause is driven by variants in the DNA binding domain (DBD) and ligand binding domain (LBD) (combined test: beta = -2.36 years/allele, [95% CI: 3.21, -1.51], N = 107 carriers, P = 4.6 × 10-8). Carriers also had a higher risk of adult obesity (OR = 1.061, [95% CI: 1.003, 1.104], N = 344, P = .015), particularly among women (OR = 1.095 [95% CI: 1.034, 1.163, P = 3.87 × 10-3], N = 176), but not men (OR = 1.019, [95% CI: 0.955, 1.088], P = .57, N = 168). Conclusion Deleterious missense variants in the DBD and LBD likely disrupt NR5A1/SF-1 function. This study broadens the relevance of deleterious NR5A1/SF-1 variants beyond rare DSDs, suggesting the need for extended phenotyping and monitoring of affected individuals.
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Affiliation(s)
- Chrysanthi Kouri
- Department of Pediatrics, Pediatric Endocrinology, Diabetology and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008 Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland
| | - Raina Y Jia
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Christa E Flück
- Department of Pediatrics, Pediatric Endocrinology, Diabetology and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008 Bern, Switzerland
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
- Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
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10
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Lockhart SM, Muso M, Zvetkova I, Lam BYH, Ferrari A, Schoenmakers E, Duckett K, Leslie J, Collins A, Romartínez-Alonso B, Tadross JA, Jia R, Gardner EJ, Kentistou K, Zhao Y, Day F, Mörseburg A, Rainbow K, Rimmington D, Mastantuoni M, Harrison J, Nus M, Guma'a K, Sherratt-Mayhew S, Jiang X, Smith KR, Paul DS, Jenkins B, Koulman A, Pietzner M, Langenberg C, Wareham N, Yeo GS, Chatterjee K, Schwabe J, Oakley F, Mann DA, Tontonoz P, Coll AP, Ong K, Perry JRB, O'Rahilly S. Damaging mutations in liver X receptor-α are hepatotoxic and implicate cholesterol sensing in liver health. Nat Metab 2024; 6:1922-1938. [PMID: 39322746 PMCID: PMC11496107 DOI: 10.1038/s42255-024-01126-4] [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: 05/17/2024] [Accepted: 08/05/2024] [Indexed: 09/27/2024]
Abstract
Liver X receptor-α (LXRα) regulates cellular cholesterol abundance and potently activates hepatic lipogenesis. Here we show that at least 1 in 450 people in the UK Biobank carry functionally impaired mutations in LXRα, which is associated with biochemical evidence of hepatic dysfunction. On a western diet, male and female mice homozygous for a dominant negative mutation in LXRα have elevated liver cholesterol, diffuse cholesterol crystal accumulation and develop severe hepatitis and fibrosis, despite reduced liver triglyceride and no steatosis. This phenotype does not occur on low-cholesterol diets and can be prevented by hepatocyte-specific overexpression of LXRα. LXRα knockout mice exhibit a milder phenotype with regional variation in cholesterol crystal deposition and inflammation inversely correlating with steatosis. In summary, LXRα is necessary for the maintenance of hepatocyte health, likely due to regulation of cellular cholesterol content. The inverse association between steatosis and both inflammation and cholesterol crystallization may represent a protective action of hepatic lipogenesis in the context of excess hepatic cholesterol.
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Affiliation(s)
- Sam M Lockhart
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Milan Muso
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Ilona Zvetkova
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Brian Y H Lam
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Alessandra Ferrari
- Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA
| | - Erik Schoenmakers
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Katie Duckett
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Jack Leslie
- Newcastle Fibrosis Research Group, Bioscience Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Amy Collins
- Newcastle Fibrosis Research Group, Bioscience Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Beatriz Romartínez-Alonso
- Institute of Structural and Chemical Biology, Department of Molecular and Cell Biology, University of Leicester, Leicester, UK
| | - John A Tadross
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Histopathology and Cambridge Genomics Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Raina Jia
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Eugene J Gardner
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Katherine Kentistou
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Yajie Zhao
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Felix Day
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Alexander Mörseburg
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Kara Rainbow
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Debra Rimmington
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Matteo Mastantuoni
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - James Harrison
- VPD Heart and Lung Research Institute, Dept. Medicine, University of Cambridge, Cambridge, UK
| | - Meritxell Nus
- VPD Heart and Lung Research Institute, Dept. Medicine, University of Cambridge, Cambridge, UK
| | - Khalid Guma'a
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Sam Sherratt-Mayhew
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Xiao Jiang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Benjamin Jenkins
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- NIHR BRC Core Metabolomics and Lipidomics Laboratory, Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Albert Koulman
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- NIHR BRC Core Metabolomics and Lipidomics Laboratory, Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Maik Pietzner
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Claudia Langenberg
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Nicholas Wareham
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giles S Yeo
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Krishna Chatterjee
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - John Schwabe
- Department of Histopathology and Cambridge Genomics Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Fiona Oakley
- Newcastle Fibrosis Research Group, Bioscience Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Derek A Mann
- Newcastle Fibrosis Research Group, Bioscience Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Peter Tontonoz
- Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA
| | - Anthony P Coll
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken Ong
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - John R B Perry
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Stephen O'Rahilly
- Medical Research Council (MRC) Metabolic Diseases Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK.
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11
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Norazman SI, Mohd Zaffarin AS, Shuid AN, Hassan H, Soleiman IN, Kuan WS, Alias E. A Review of Animal Models for Studying Bone Health in Type-2 Diabetes Mellitus (T2DM) and Obesity. Int J Mol Sci 2024; 25:9399. [PMID: 39273348 PMCID: PMC11394783 DOI: 10.3390/ijms25179399] [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: 07/09/2024] [Revised: 08/24/2024] [Accepted: 08/27/2024] [Indexed: 09/15/2024] Open
Abstract
Preclinical research on diabetes and obesity has been carried out in various animal models over the years. These animal models are developed from genetic manipulation that affects their body metabolism, chemical-induced procedures, diet alteration/modifications, or combinations of the aforementioned approaches. The diabetic and obesity animal models have allowed researchers to not only study the pathological aspect of the diseases but also enable them to screen and explore potential therapeutic compounds. Besides several widely known complications such as macrovascular diseases, diabetic neuropathy, nephropathy and retinopathy, type 2 diabetes mellitus is also known to affect bone health. There is also evidence to suggest obesity affects bone health. Therefore, continuous research needs to be conducted to find a remedy or solution to this matter. Previous literature reported evidence of bone loss in animal models of diabetes and obesity. These findings, as highlighted in this review, further augment the suggestion of an inter-relationship between diabetes, obesity and bone loss.
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Affiliation(s)
- Saiful Iqbal Norazman
- The Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia
| | - Anis Syauqina Mohd Zaffarin
- The Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia
| | - Ahmad Nazrun Shuid
- Department of Pharmacology, Faculty of Medicine, Universiti Teknologi MARA, Sg Buloh 47000, Malaysia
| | - Haniza Hassan
- Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Ima Nirwana Soleiman
- The Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia
| | - Wong Sok Kuan
- The Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia
| | - Ekram Alias
- The Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur 56000, Malaysia
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12
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Liu A, Genovese G, Zhao Y, Pirinen M, Zekavat SM, Kentistou KA, Yang Z, Yu K, Vlasschaert C, Liu X, Brown DW, Hudjashov G, Gorman BR, Dennis J, Zhou W, Momozawa Y, Pyarajan S, Tuzov V, Pajuste FD, Aavikko M, Sipilä TP, Ghazal A, Huang WY, Freedman ND, Song L, Gardner EJ, Sankaran VG, Palotie A, Ollila HM, Tukiainen T, Chanock SJ, Mägi R, Natarajan P, Daly MJ, Bick A, McCarroll SA, Terao C, Loh PR, Ganna A, Perry JRB, Machiela MJ. Genetic drivers and cellular selection of female mosaic X chromosome loss. Nature 2024; 631:134-141. [PMID: 38867047 DOI: 10.1038/s41586-024-07533-7] [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: 01/20/2023] [Accepted: 05/07/2024] [Indexed: 06/14/2024]
Abstract
Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.
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Affiliation(s)
- Aoxing Liu
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Giulio Genovese
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Seyedeh M Zekavat
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | | | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Derek W Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bryan R Gorman
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Booz Allen Hamilton, McLean, VA, USA
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Weiyin Zhou
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Valdislav Tuzov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Mervi Aavikko
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Timo P Sipilä
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Awaisa Ghazal
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexander Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
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13
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Lin J, Zhan L, Chen Z, Lin X, Zhu R. The effect of SGLT2i on the GH/IGF1 axis in newly diagnosed male T2D patients - a prospective, randomized case-control study. Endocrine 2024; 84:203-212. [PMID: 38168834 DOI: 10.1007/s12020-023-03652-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE To investigate the effect of SGLT2i on the GH/IGF1 axis in male patients with newly diagnosed type 2 diabetes (T2D). METHODS Sixty male patients with newly diagnosed T2D were recruited, and randomly assigned to Metformin+SGLT2i group or Metformin group after baseline assessment. All patients received standard lifestyle interventions, and blood indices were obtained before and after 12 weeks of treatment. RESULTS After 12 weeks of treatment with Metformin+SGLT2i, there were noteworthy improvements in patients' FPG (Fasting plasma glucose), HBA1c, HOMA-IR, HOMA-β, TyG (Triglyceride-glucose) index and UACR (P < 0.05). Both IGF1 (P = 0.01) and the IGF1/IGFBP3 ratio (P < 0.01) considerably increased, while GH and IGFBP3 did not show significant changes. When comparing Metformin+SGLT2i group to Metformin group, SGLT2i significantly improved HOMA-IR [P = 0.04], and elevated IGF1/IGFBP3 ratio [P = 0.04], SGLT2i showed a tendency of increasing IGF1 (P = 0.10), but this was not statistically meaningful. There was no effect on GH and IGFBP3. Correlation analysis showed that blood IGF1 was negatively correlated with FPG, HBA1c, HOMA-IR, TyG index and positively correlated with IGFBP3. Regression analysis indicated that FPG and testosterone had a negative effect on blood IGF1 level, while HOMA-IR had no obvious effect. CONCLUSION In male patients with newly diagnosed T2D, SGLT2i can increase IGF1/IGFBP3 ratio, alleviate insulin resistance, but has no significant effect on GH and IGF1 levels. Additionally, our study showed that Metformin+SGLT2i treatment resulted in an increase in blood IGF1 levels and improved insulin resistance, suggesting a potentially beneficial role of IGF1 in newly diagnosed T2D.
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Affiliation(s)
- Jing Lin
- Department of Endocrinology, The 95th Hospital of Putian, Putian, Fujian, 351100, P.R. China
| | - Liqin Zhan
- Department of Endocrinology, The 95th Hospital of Putian, Putian, Fujian, 351100, P.R. China
| | - Zheng Chen
- Department of Endocrinology, The 95th Hospital of Putian, Putian, Fujian, 351100, P.R. China
| | - Xiaying Lin
- Department of Endocrinology, The 95th Hospital of Putian, Putian, Fujian, 351100, P.R. China
| | - Rongfeng Zhu
- Department of Endocrinology, The 95th Hospital of Putian, Putian, Fujian, 351100, P.R. China.
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14
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Zhao Y, Chukanova M, Kentistou KA, Fairhurst-Hunter Z, Siegert AM, Jia RY, Dowsett GKC, Gardner EJ, Lawler K, Day FR, Kaisinger LR, Tung YCL, Lam BYH, Chen HJC, Wang Q, Berumen-Campos J, Kuri-Morales P, Tapia-Conyer R, Alegre-Diaz J, Barroso I, Emberson J, Torres JM, Collins R, Saleheen D, Smith KR, Paul DS, Merkle F, Farooqi IS, Wareham NJ, Petrovski S, O'Rahilly S, Ong KK, Yeo GSH, Perry JRB. Protein-truncating variants in BSN are associated with severe adult-onset obesity, type 2 diabetes and fatty liver disease. Nat Genet 2024; 56:579-584. [PMID: 38575728 PMCID: PMC11018524 DOI: 10.1038/s41588-024-01694-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024]
Abstract
Obesity is a major risk factor for many common diseases and has a substantial heritable component. To identify new genetic determinants, we performed exome-sequence analyses for adult body mass index (BMI) in up to 587,027 individuals. We identified rare loss-of-function variants in two genes (BSN and APBA1) with effects substantially larger than those of well-established obesity genes such as MC4R. In contrast to most other obesity-related genes, rare variants in BSN and APBA1 were not associated with normal variation in childhood adiposity. Furthermore, BSN protein-truncating variants (PTVs) magnified the influence of common genetic variants associated with BMI, with a common variant polygenic score exhibiting an effect twice as large in BSN PTV carriers than in noncarriers. Finally, we explored the plasma proteomic signatures of BSN PTV carriers as well as the functional consequences of BSN deletion in human induced pluripotent stem cell-derived hypothalamic neurons. Collectively, our findings implicate degenerative processes in synaptic function in the etiology of adult-onset obesity.
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Affiliation(s)
- Yajie Zhao
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Maria Chukanova
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Katherine A Kentistou
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Zammy Fairhurst-Hunter
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Anna Maria Siegert
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Raina Y Jia
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Georgina K C Dowsett
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Katherine Lawler
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Felix R Day
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Lena R Kaisinger
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Yi-Chun Loraine Tung
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Brian Yee Hong Lam
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Hsiao-Jou Cortina Chen
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Jaime Berumen-Campos
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Pablo Kuri-Morales
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
- Instituto Tecnológico de Estudios Superiores de Monterrey, Tecnológico, Monterrey, Mexico
| | - Roberto Tapia-Conyer
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Jesus Alegre-Diaz
- Experimental Medicine Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Copilco Universidad, Mexico City, Mexico
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Jonathan Emberson
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jason M Torres
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Florian Merkle
- Institute of Metabolic Science and Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - I Sadaf Farooqi
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Nick J Wareham
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Stephen O'Rahilly
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Giles S H Yeo
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
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15
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Wang W, Zhang N, Chen L, Zhao X, Shan Y, Yang F, Wang B, Gao H, Xu M, Tang P, Qin S, Gu S. Whole-genome sequencing and RNA sequencing analysis reveals novel risk genes and differential expression patterns in hepatoblastoma. Gene 2024; 897:147991. [PMID: 37972697 DOI: 10.1016/j.gene.2023.147991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
Hepatoblastoma (HB) is an uncommon malignant liver cancer primarily affecting infants and children, characterized by the presence of tissue that resembling fetal hepatocytes, mature liver cells or bile duct cells. The primary symptom in affected children is abdominal lumps. HB constitutes approximately 28% of all liver tumors and two-thirds of liver malignancies in the pediatric and adolescent population. Despite its high prevalence, the underlying mechanism of HB pathogenesis remain largely unknown. To reveal the genetic alternations associated with HB, we conducted a comprehensive genomic study using whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) techniques on five HB patients. We aimed to use WGS to identify somatic variant loci associated with HB, including single nucleotide polymorphisms (SNPs), insertions and deletions (Indels), and copy number variations (CNVs). Notably, we found deleterious mutation in CTNNB1, AXIN2 and PARP1, previously implicated in HB. In addition, we discovered multiple novel genes potentially associated with HB, including BRCA2 and GPC3 which require further functional validation to reveal their contributions to HB development. Furthermore, the American College of Medical Genetics and Genomics (ACMG) analysis identified the ABCC2 gene was the pathogenic gene as a potential risk gene linked with HB. To study the gene expression patterns in HB, we performed RNA-seq analysis and qPCR validation to reveal differential expression of four candidate genes (IGF1R, METTL1, AXIN2 and TP53) in tumors compared to nonneoplastic liver tissue in HB patients (P-Val < 0.01). These findings shed lights on the molecular mechanisms underlying HB development and facilitate to advance future personalized diagnosis and therapeutic interventions of HB.
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Affiliation(s)
- Wuqian Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China; Jiaxing Maternity and Children Health Care Hospital, Affiliated Women and Children Hospital Jiaxing University, Jiaxing, Zhejiang, China
| | - Na Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Luan Chen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xianglong Zhao
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yuhua Shan
- Department of General Surgery, Shanghai Children's Medical Center, (National Children's Medical Center), School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Fan Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China; Research Center for Lin He Academician New Medicine, Institutes for Shanghai Pudong Decoding Life, Shanghai, China
| | - Bo Wang
- The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 511436, China
| | - Hongxiang Gao
- Department of General Surgery, Shanghai Children's Medical Center, (National Children's Medical Center), School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Min Xu
- Department of General Surgery, Shanghai Children's Medical Center, (National Children's Medical Center), School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ping Tang
- Jiaxing Maternity and Children Health Care Hospital, Affiliated Women and Children Hospital Jiaxing University, Jiaxing, Zhejiang, China.
| | - Shengying Qin
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Song Gu
- Department of General Surgery, Shanghai Children's Medical Center, (National Children's Medical Center), School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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16
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Duckett K, Williamson A, Kincaid JWR, Rainbow K, Corbin LJ, Martin HC, Eberhardt RY, Huang QQ, Hurles ME, He W, Brauner R, Delaney A, Dunkel L, Grinspon RP, Hall JE, Hirschhorn JN, Howard SR, Latronico AC, Jorge AAL, McElreavey K, Mericq V, Merino PM, Palmert MR, Plummer L, Rey RA, Rezende RC, Seminara SB, Salnikov K, Banerjee I, Lam BYH, Perry JRB, Timpson NJ, Clayton P, Chan YM, Ong KK, O’Rahilly S. Prevalence of Deleterious Variants in MC3R in Patients With Constitutional Delay of Growth and Puberty. J Clin Endocrinol Metab 2023; 108:e1580-e1587. [PMID: 37339320 PMCID: PMC10655545 DOI: 10.1210/clinem/dgad373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/30/2023] [Accepted: 06/16/2023] [Indexed: 06/22/2023]
Abstract
CONTEXT The melanocortin 3 receptor (MC3R) has recently emerged as a critical regulator of pubertal timing, linear growth, and the acquisition of lean mass in humans and mice. In population-based studies, heterozygous carriers of deleterious variants in MC3R report a later onset of puberty than noncarriers. However, the frequency of such variants in patients who present with clinical disorders of pubertal development is currently unknown. OBJECTIVE This work aimed to determine whether deleterious MC3R variants are more frequently found in patients clinically presenting with constitutional delay of growth and puberty (CDGP) or normosmic idiopathic hypogonadotropic hypogonadism (nIHH). METHODS We examined the sequence of MC3R in 362 adolescents with a clinical diagnosis of CDGP and 657 patients with nIHH, experimentally characterized the signaling properties of all nonsynonymous variants found and compared their frequency to that in 5774 controls from a population-based cohort. Additionally, we established the relative frequency of predicted deleterious variants in individuals with self-reported delayed vs normally timed menarche/voice-breaking in the UK Biobank cohort. RESULTS MC3R loss-of-function variants were infrequent but overrepresented in patients with CDGP (8/362 [2.2%]; OR = 4.17; P = .001). There was no strong evidence of overrepresentation in patients with nIHH (4/657 [0.6%]; OR = 1.15; P = .779). In 246 328 women from the UK Biobank, predicted deleterious variants were more frequently found in those self-reporting delayed (aged ≥16 years) vs normal age at menarche (OR = 1.66; P = 3.90E-07). CONCLUSION We have found evidence that functionally damaging variants in MC3R are overrepresented in individuals with CDGP but are not a common cause of this phenotype.
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Affiliation(s)
- Katie Duckett
- Wellcome-MRC Institute of Metabolic Science, Box 289, Level 4, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Alice Williamson
- Wellcome-MRC Institute of Metabolic Science, Box 289, Level 4, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - John W R Kincaid
- Wellcome-MRC Institute of Metabolic Science, Box 289, Level 4, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Kara Rainbow
- Wellcome-MRC Institute of Metabolic Science, Box 289, Level 4, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Laura J Corbin
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Hilary C Martin
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Ruth Y Eberhardt
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Qin Qin Huang
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Matthew E Hurles
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Wen He
- Division of Endocrinology, Department of Pediatrics, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Raja Brauner
- Pediatric Endocrinology Unit, Hôpital Fondation Adolphe de Rothschild and Université Paris Cité, 25 rue Manin, 75019 Paris, France
| | - Angela Delaney
- Division of Endocrinology, Department of Pediatric Medicine, St. Jude Children’s Research Hospital, 262 Danny Thomas Place MS 737, Memphis, TN 38105, USA
| | - Leo Dunkel
- Centre for Endocrinology, William Harvey Research Institute, Barts & the London Medical School, Charterhouse Square, London EC1M 6BQ, UK
| | - Romina P Grinspon
- Centro de Investigaciones Endocrinolègicas “Dr. César Bergadá” (CEDIE), CONICET–FEI–Divisièn de Endocrinología, Hospital de Niños Ricardo Gutiérrez, Gallo 1330, C1425EFD Buenos Aires, Argentina
| | - Janet E Hall
- Clinical Research Branch, Division of Intramural Research, National Institute of Environmental Science, National Institute of Health, 111 TW Alexander Dr, Bldg 101 – A222, Research Triangle Park, NC 27709, USA
| | - Joel N Hirschhorn
- Division of Endocrinology, Department of Pediatrics, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Sasha R Howard
- Centre for Endocrinology, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Ana C Latronico
- Departamento de Clínica Médica, Av. Dr. Arnaldo, 455 - Cerqueira César, 01246903 São Paulo - SP, Brazil
| | - Alexander A L Jorge
- Departamento de Clínica Médica, Av. Dr. Arnaldo, 455 - Cerqueira César, 01246903 São Paulo - SP, Brazil
| | - Ken McElreavey
- Institut Pasteur, Université de Paris, CNRS UMR3738, Human Developmental Genetics, F-75015 Paris, France
| | - Verónica Mericq
- Institute of Maternal and Child Research, Faculty of Medicine, University of Chile, Santa Rosa 1234, 2° piso, Santiago 8320000, Chile
| | - Paulina M Merino
- Institute of Maternal and Child Research, Faculty of Medicine, University of Chile, Santa Rosa 1234, 2° piso, Santiago 8320000, Chile
| | - Mark R Palmert
- Division of Endocrinology, The Hospital for Sick Children and Departments of Pediatrics and Physiology, University of Toronto, Toronto, ON M5G 1X8, Canada
| | - Lacey Plummer
- Massachusetts General Hospital Harvard Center for Reproductive Medicine and Reproductive Endocrine Unit, Massachusetts General Hospital, Bartlett Hall Extension, 5th Floor, 55 Fruit Street, Boston, MA 02114, USA
| | - Rodolfo A Rey
- Centro de Investigaciones Endocrinolègicas “Dr. César Bergadá” (CEDIE), CONICET–FEI–Divisièn de Endocrinología, Hospital de Niños Ricardo Gutiérrez, Gallo 1330, C1425EFD Buenos Aires, Argentina
| | - Raíssa C Rezende
- Departamento de Clínica Médica, Av. Dr. Arnaldo, 455 - Cerqueira César, 01246903 São Paulo - SP, Brazil
| | - Stephanie B Seminara
- Massachusetts General Hospital Harvard Center for Reproductive Medicine and Reproductive Endocrine Unit, Massachusetts General Hospital, Bartlett Hall Extension, 5th Floor, 55 Fruit Street, Boston, MA 02114, USA
| | - Kathryn Salnikov
- Massachusetts General Hospital Harvard Center for Reproductive Medicine and Reproductive Endocrine Unit, Massachusetts General Hospital, Bartlett Hall Extension, 5th Floor, 55 Fruit Street, Boston, MA 02114, USA
| | - Indraneel Banerjee
- Department of Paediatric Endocrinology, Royal Manchester Children’s Hospital, Manchester M13 9WL, UK
| | - Brian Y H Lam
- Wellcome-MRC Institute of Metabolic Science, Box 289, Level 4, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - John R B Perry
- Wellcome-MRC Institute of Metabolic Science, Box 289, Level 4, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Peter Clayton
- Paediatric Endocrinology, Royal Manchester Children’s Hospital, Oxford Road, Manchester M13 9WL, UK
| | - Yee-Ming Chan
- Division of Endocrinology, Department of Pediatrics, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus Box 285, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Stephen O’Rahilly
- Wellcome-MRC Institute of Metabolic Science, Box 289, Level 4, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
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17
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Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Mandla R, Schroeder PH, Westerman KE, Szczerbinski L, Majarian TD, Kaur V, Williamson A, Claussnitzer M, Florez JC, Manning AK, Mercader JM, Gaulton KJ, Udler MS. Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity. RESEARCH SQUARE 2023:rs.3.rs-3399145. [PMID: 37886436 PMCID: PMC10602111 DOI: 10.21203/rs.3.rs-3399145/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.
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Affiliation(s)
- Kirk Smith
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron J. Deutsch
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carolyn McGrail
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Hyunkyung Kim
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Sarah Hsu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ravi Mandla
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Philip H. Schroeder
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth E. Westerman
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Lukasz Szczerbinski
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Timothy D. Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Varinderpal Kaur
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alice Williamson
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Melina Claussnitzer
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jose C. Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alisa K. Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M. Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Miriam S. Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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18
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Shekari S, Stankovic S, Gardner EJ, Hawkes G, Kentistou KA, Beaumont RN, Mörseburg A, Wood AR, Prague JK, Mishra GD, Day FR, Baptista J, Wright CF, Weedon MN, Hoffmann ER, Ruth KS, Ong KK, Perry JRB, Murray A. Penetrance of pathogenic genetic variants associated with premature ovarian insufficiency. Nat Med 2023; 29:1692-1699. [PMID: 37349538 DOI: 10.1038/s41591-023-02405-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/17/2023] [Indexed: 06/24/2023]
Abstract
Premature ovarian insufficiency (POI) affects 1% of women and is a leading cause of infertility. It is often considered to be a monogenic disorder, with pathogenic variants in ~100 genes described in the literature. We sought to systematically evaluate the penetrance of variants in these genes using exome sequence data in 104,733 women from the UK Biobank, 2,231 (1.14%) of whom reported at natural menopause under the age of 40 years. We found limited evidence to support any previously reported autosomal dominant effect. For nearly all heterozygous effects on previously reported POI genes, we ruled out even modest penetrance, with 99.9% (13,699 out of 13,708) of all protein-truncating variants found in reproductively healthy women. We found evidence of haploinsufficiency effects in several genes, including TWNK (1.54 years earlier menopause, P = 1.59 × 10-6) and SOHLH2 (3.48 years earlier menopause, P = 1.03 × 10-4). Collectively, our results suggest that, for the vast majority of women, POI is not caused by autosomal dominant variants either in genes previously reported or currently evaluated in clinical diagnostic panels. Our findings, plus previous studies, suggest that most POI cases are likely oligogenic or polygenic in nature, which has important implications for future clinical genetic studies, and genetic counseling for families affected by POI.
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Affiliation(s)
- Saleh Shekari
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Stasa Stankovic
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Gareth Hawkes
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Alexander Mörseburg
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Julia K Prague
- Exeter Centre of Excellence for Diabetes Research, University of Exeter, Exeter, UK
- Macleod Diabetes and Endocrinology Centre, Royal Devon and Exeter National Health Service Foundation Trust, Exeter, UK
| | - Gita D Mishra
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Felix R Day
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Julia Baptista
- Peninsula Medical School, University of Plymouth, Plymouth, UK
| | - Caroline F Wright
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Michael N Weedon
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Eva R Hoffmann
- Department of Cellular and Molecular Medicine, DNRF Center for Chromosome Stability, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Katherine S Ruth
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Ken K Ong
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Anna Murray
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
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Andrews A, Cottrell E, Maharaj A, Ladha T, Williams J, Schilbach K, Kaisinger LR, Perry JRB, Metherell LA, McCormick PJ, Storr HL. Characterization of dominant-negative growth hormone receptor variants reveals a potential therapeutic target for short stature. Eur J Endocrinol 2023; 188:353-365. [PMID: 36943306 DOI: 10.1093/ejendo/lvad039] [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: 12/24/2022] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 03/23/2023]
Abstract
OBJECTIVE Growth hormone insensitivity (GHI) encompasses growth restriction, normal/elevated growth hormone (GH), and low insulin-like growth factor I (IGF1). "Nonclassical" GHI is poorly characterized and is rarely caused by heterozygous dominant-negative (DN) variants located in the intracellular or transmembrane domains of the GH receptor (GHR). We sought to determine the molecular mechanisms underpinning the growth restriction in 2 GHI cases. METHODS AND DESIGN A custom-made genetic investigative pipeline was exploited to identify the genetic cause of growth restriction in patients with GHI. Nanoluc binary technology (NanoBiT), in vitro splicing assays, western blotting, and flow cytometry, characterized the novel GHR variants. RESULTS Novel heterozygous GHR variants were identified in 2 unrelated patients with GHI. In vitro splicing assays indicated both variants activated the same alternative splice acceptor site resulting in aberrant splicing and exclusion of 26 base pairs of GHR exon 9. The GHR variants produced truncated receptors and impaired GH-induced GHR signaling. NanoBiT complementation and flow cytometry showed increased cell surface expression of variant GHR homo/heterodimers compared to wild-type (WT) homodimers and increased recombinant human GH binding to variant GHR homo/heterodimers and GH binding protein (GHBP) cleaved from the variant GHRs. The findings demonstrated increased variant GHR dimers and GHBP with resultant GH sequestration. CONCLUSION We identified and characterized 2 novel, naturally occurring truncated GHR gene variants. Intriguingly, these DN GHR variants act via the same cryptic splice acceptor site, highlighting impairing GH binding to excess GHBP as a potential therapeutic approach.
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Affiliation(s)
- Afiya Andrews
- Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University London, London, United Kingdom
| | - Emily Cottrell
- Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University London, London, United Kingdom
| | - Avinaash Maharaj
- Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University London, London, United Kingdom
| | - Tasneem Ladha
- Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University London, London, United Kingdom
| | - Jack Williams
- Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University London, London, United Kingdom
| | - Katharina Schilbach
- Endocrine Laboratory, Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, Munich, Germany
| | - Lena R Kaisinger
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, United Kingdom
| | - John R B Perry
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, United Kingdom
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, United Kingdom
| | - Louise A Metherell
- Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University London, London, United Kingdom
| | - Peter J McCormick
- Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University London, London, United Kingdom
| | - Helen L Storr
- Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University London, London, United Kingdom
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20
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Starling S. Genomics suggest role for IGF1 resistance in T2DM. Nat Rev Endocrinol 2023; 19:64. [PMID: 36414677 DOI: 10.1038/s41574-022-00782-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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21
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Florez JC. Genomic discoveries unveil mechanistic insights in diabetes. CELL GENOMICS 2022; 2:100230. [PMID: 36778053 PMCID: PMC9903746 DOI: 10.1016/j.xgen.2022.100230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Two diabetes-related papers are featured in this issue of Cell Genomics. Gardner et al.1 focus on type 2 diabetes through exome sequencing, and Benaglio et al.2 employ a functional genomics approach to advance understanding in type 1 diabetes. In this preview, Jose Florez highlights their contribution toward clinical translation of genomics discoveries.
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Affiliation(s)
- Jose C. Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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