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Reed JN, Huang J, Li Y, Ma L, Banka D, Wabitsch M, Wang T, Ding W, Björkegren JL, Civelek M. Systems genetics analysis of human body fat distribution genes identifies adipocyte processes. Life Sci Alliance 2024; 7:e202402603. [PMID: 38702075 PMCID: PMC11068934 DOI: 10.26508/lsa.202402603] [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: 01/18/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024] Open
Abstract
Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.
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Affiliation(s)
- Jordan N Reed
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jiansheng Huang
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Yong Li
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dhanush Banka
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Martin Wabitsch
- Division of Paediatric Endocrinology and Diabetes, Department of Paediatrics and Adolescent Medicine, Ulm University Medical Centre, Ulm, Germany
| | - Tianfang Wang
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Wen Ding
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Johan Lm Björkegren
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Karolinska Institutet, Huddinge, Stockholm, Sweden
| | - Mete Civelek
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
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Reed JN, Huang J, Li Y, Ma L, Banka D, Wabitsch M, Wang T, Ding W, Björkegren JLM, Civelek M. Systems genetics analysis of human body fat distribution genes identifies Wnt signaling and mitochondrial activity in adipocytes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.556534. [PMID: 37732278 PMCID: PMC10508754 DOI: 10.1101/2023.09.06.556534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
BACKGROUND Excess fat in the abdomen is a sexually dimorphic risk factor for cardio-metabolic disease. The relative storage between abdominal and lower-body subcutaneous adipose tissue depots is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Genome-wide association studies (GWAS) identified 346 loci near 495 genes associated with WHRadjBMI. Most of these genes have unknown roles in fat distribution, but many are expressed and putatively act in adipose tissue. We aimed to identify novel sex- and depot-specific drivers of WHRadjBMI using a systems genetics approach. METHODS We used two independent cohorts of adipose tissue gene expression with 362 - 444 males and 147 - 219 females, primarily of European ancestry. We constructed sex- and depot- specific Bayesian networks to model the gene-gene interactions from 8,492 adipose tissue genes. Key driver analysis identified genes that, in silico and putatively in vitro, regulate many others, including the 495 WHRadjBMI GWAS genes. Key driver gene function was determined by perturbing their expression in human subcutaneous pre-adipocytes using lenti-virus or siRNA. RESULTS 51 - 119 key drivers in each network were replicated in both cohorts. We used single-cell expression data to select replicated key drivers expressed in adipocyte precursors and mature adipocytes, prioritized genes which have not been previously studied in adipose tissue, and used public human and mouse data to nominate 53 novel key driver genes (10 - 21 from each network) that may regulate fat distribution by altering adipocyte function. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We selected seven genes whose expression is highly correlated with WHRadjBMI to further study their effects on adipogenesis/Wnt signaling (ANAPC2, PSME3, RSPO1, TYRO3) or mitochondrial function (C1QTNF3, MIGA1, PSME3, UBR1).Adipogenesis was inhibited in cells overexpressing ANAPC2 and RSPO1 compared to controls. RSPO1 results are consistent with a positive correlation between gene expression in the subcutaneous depot and WHRadjBMI, therefore lower relative storage in the subcutaneous depot. RSPO1 inhibited adipogenesis by increasing β-catenin activation and Wnt-related transcription, thus repressing PPARG and CEBPA. PSME3 overexpression led to more adipogenesis than controls. In differentiated adipocytes, MIGA1 and UBR1 downregulation led to mitochondrial dysfunction, with lower oxygen consumption than controls; MIGA1 knockdown also lowered UCP1 expression. SUMMARY ANAPC2, MIGA1, PSME3, RSPO1, and UBR1 affect adipocyte function and may drive body fat distribution.
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Merchant JP, Zhu K, Henrion MYR, Zaidi SSA, Lau B, Moein S, Alamprese ML, Pearse RV, Bennett DA, Ertekin-Taner N, Young-Pearse TL, Chang R. Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease. Commun Biol 2023; 6:503. [PMID: 37188718 PMCID: PMC10185548 DOI: 10.1038/s42003-023-04791-5] [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] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/31/2023] [Indexed: 05/17/2023] Open
Abstract
Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.
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Affiliation(s)
- Julie P Merchant
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Neuroscience Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kuixi Zhu
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Marc Y R Henrion
- Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, Pembroke Place, L3 5QA, UK
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, PO Box 30096, Blantyre, Malawi
| | - Syed S A Zaidi
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Branden Lau
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
- Arizona Research Labs, Genetics Core, University of Arizona, Tucson, AZ, USA
| | - Sara Moein
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Melissa L Alamprese
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Tracy L Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Harvard Stem Cell Institute, Harvard University, Boston, MA, USA.
| | - Rui Chang
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA.
- Department of Neurology, University of Arizona, Tucson, AZ, USA.
- INTelico Therapeutics LLC, Tucson, AZ, USA.
- PATH Biotech LLC, Tucson, AZ, USA.
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Chang R, Trushina E, Zhu K, Zaidi SSA, Lau BM, Kueider-Paisley A, Moein S, He Q, Alamprese ML, Vagnerova B, Tang A, Vijayan R, Liu Y, Saykin AJ, Brinton RD, Kaddurah-Daouk R, Alzheimer’s Disease Neuroimaging Initiative Alzheimer’s Disease Metabolomics Consortium. Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease. Alzheimers Dement 2023; 19:518-531. [PMID: 35481667 PMCID: PMC10402890 DOI: 10.1002/alz.12675] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/20/2022] [Accepted: 02/17/2022] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine. METHODS Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort. RESULTS Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients. DISCUSSION These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.
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Affiliation(s)
- Rui Chang
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Eugenia Trushina
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Kuixi Zhu
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Syed Shujaat Ali Zaidi
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Branden M. Lau
- Arizona Research Labs, Genetics Core, University of Arizona, Tucson, AZ, USA
| | | | - Sara Moein
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Qianying He
- Department of Biosystems Engineering, University of Arizona, Tucson, AZ, USA
| | - Melissa L. Alamprese
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Barbora Vagnerova
- Department of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Andrew Tang
- Department of Neuroscience, University of Arizona, Tucson, AZ, USA
| | | | - Yanyun Liu
- Department of Neurology, University of Arizona, Tucson, AZ, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Roberta D. Brinton
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
- Department of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
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Sheng CY, Son YH, Jang J, Park SJ. In vitro skeletal muscle models for type 2 diabetes. BIOPHYSICS REVIEWS 2022; 3:031306. [PMID: 36124295 PMCID: PMC9478902 DOI: 10.1063/5.0096420] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
Type 2 diabetes mellitus, a metabolic disorder characterized by abnormally elevated blood sugar, poses a growing social, economic, and medical burden worldwide. The skeletal muscle is the largest metabolic organ responsible for glucose homeostasis in the body, and its inability to properly uptake sugar often precedes type 2 diabetes. Although exercise is known to have preventative and therapeutic effects on type 2 diabetes, the underlying mechanism of these beneficial effects is largely unknown. Animal studies have been conducted to better understand the pathophysiology of type 2 diabetes and the positive effects of exercise on type 2 diabetes. However, the complexity of in vivo systems and the inability of animal models to fully capture human type 2 diabetes genetics and pathophysiology are two major limitations in these animal studies. Fortunately, in vitro models capable of recapitulating human genetics and physiology provide promising avenues to overcome these obstacles. This review summarizes current in vitro type 2 diabetes models with focuses on the skeletal muscle, interorgan crosstalk, and exercise. We discuss diabetes, its pathophysiology, common in vitro type 2 diabetes skeletal muscle models, interorgan crosstalk type 2 diabetes models, exercise benefits on type 2 diabetes, and in vitro type 2 diabetes models with exercise.
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Affiliation(s)
- Christina Y. Sheng
- Biohybrid Systems Group, Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, Georgia 30322, USA
| | - Young Hoon Son
- Biohybrid Systems Group, Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, Georgia 30322, USA
| | | | - Sung-Jin Park
- Biohybrid Systems Group, Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, Georgia 30322, USA
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Memon B, Elsayed AK, Bettahi I, Suleiman N, Younis I, Wehedy E, Abou-Samra AB, Abdelalim EM. iPSCs derived from insulin resistant offspring of type 2 diabetic patients show increased oxidative stress and lactate secretion. Stem Cell Res Ther 2022; 13:428. [PMID: 35987697 PMCID: PMC9392338 DOI: 10.1186/s13287-022-03123-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 08/05/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The genetic factors associated with insulin resistance (IR) are not well understood. Clinical studies on first-degree relatives of type 2 diabetic (T2D) patients, which have the highest genetic predisposition to T2D, have given insights into the role of IR in T2D pathogenesis. Induced pluripotent stem cells (iPSCs) are excellent tools for disease modeling as they can retain the genetic imprint of the disease. Therefore, in this study, we aimed to investigate the genetic perturbations associated with insulin resistance (IR) in the offspring of T2D parents using patient-specific iPSCs.
Methods
We generated iPSCs from IR individuals (IR-iPSCs) that were offspring of T2D parents as well as from insulin-sensitive (IS-iPSCs) individuals. We then performed transcriptomics to identify key dysregulated gene networks in the IR-iPSCs in comparison to IS-iPSCs and functionally validated them.
Results
Transcriptomics on IR-iPSCs revealed dysregulated gene networks and biological processes indicating that they carry the genetic defects associated with IR that may lead to T2D. The IR-iPSCs had increased lactate secretion and a higher phosphorylation of AKT upon stimulation with insulin. IR-iPSCs have increased cellular oxidative stress indicated by a high production of reactive oxygen species and higher susceptibility to H2O2 -induced apoptosis.
Conclusions
IR-iPSCs generated from offspring of diabetic patients confirm that oxidative stress and increased lactate secretion, associated with IR, are inherited in this population, and may place them at a high risk of T2D. Overall, our IR-iPSC model can be employed for T2D modeling and drug screening studies that target genetic perturbations associated with IR in individuals with a high risk for T2D.
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Balliu B, Carcamo-Orive I, Gloudemans MJ, Nachun DC, Durrant MG, Gazal S, Park CY, Knowles DA, Wabitsch M, Quertermous T, Knowles JW, Montgomery SB. An integrated approach to identify environmental modulators of genetic risk factors for complex traits. Am J Hum Genet 2021; 108:1866-1879. [PMID: 34582792 PMCID: PMC8546041 DOI: 10.1016/j.ajhg.2021.08.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 08/27/2021] [Indexed: 12/16/2022] Open
Abstract
Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. We propose a framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits. We perturbed human skeletal-muscle-, fat-, and liver-relevant cell lines with 21 perturbations affecting insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWASs from 31 distinct polygenic traits, we show that the heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS Catalog. Overall, we demonstrate the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.
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Affiliation(s)
- Brunilda Balliu
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Ivan Carcamo-Orive
- Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute and Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael J Gloudemans
- Biomedical Informatics Training Program and Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Daniel C Nachun
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew G Durrant
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Chong Y Park
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David A Knowles
- New York Genome Center, New York, NY 10013, USA; Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Martin Wabitsch
- Department of Pediatrics and Adolescent Medicine, Division of Pediatric Endocrinology, Ulm University, Ulm 89075, Germany
| | - Thomas Quertermous
- Department of Medicine, Division of Cardiology and Cardiovascular Institute, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Joshua W Knowles
- Department of Medicine, Division of Cardiology and Cardiovascular Institute, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA.
| | - Stephen B Montgomery
- Department of Pathology and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
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Elsayed AK, Vimalraj S, Nandakumar M, Abdelalim EM. Insulin resistance in diabetes: The promise of using induced pluripotent stem cell technology. World J Stem Cells 2021; 13:221-235. [PMID: 33815671 PMCID: PMC8006014 DOI: 10.4252/wjsc.v13.i3.221] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 02/07/2021] [Accepted: 03/11/2021] [Indexed: 02/06/2023] Open
Abstract
Insulin resistance (IR) is associated with several metabolic disorders, including type 2 diabetes (T2D). The development of IR in insulin target tissues involves genetic and acquired factors. Persons at genetic risk for T2D tend to develop IR several years before glucose intolerance. Several rodent models for both IR and T2D are being used to study the disease pathogenesis; however, these models cannot recapitulate all the aspects of this complex disorder as seen in each individual. Human pluripotent stem cells (hPSCs) can overcome the hurdles faced with the classical mouse models for studying IR. Human induced pluripotent stem cells (hiPSCs) can be generated from the somatic cells of the patients without the need to destroy a human embryo. Therefore, patient-specific hiPSCs can generate cells genetically identical to IR individuals, which can help in distinguishing between genetic and acquired defects in insulin sensitivity. Combining the technologies of genome editing and hiPSCs may provide important information about the genetic factors underlying the development of different forms of IR. Further studies are required to fill the gaps in understanding the pathogenesis of IR and diabetes. In this review, we summarize the factors involved in the development of IR in the insulin-target tissues leading to diabetes. Also, we highlight the use of hPSCs to understand the mechanisms underlying the development of IR.
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Affiliation(s)
- Ahmed K Elsayed
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
| | | | - Manjula Nandakumar
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
| | - Essam M Abdelalim
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
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