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Karimzadeh K, Uju C, Zahmatkesh A, Unniappan S. Fat mass and obesity associated gene and homeobox transcription factor iriquois-3 mRNA profiles in the metabolic tissues of zebrafish are modulated by feeding and food deprivation. Gen Comp Endocrinol 2024:114621. [PMID: 39414090 DOI: 10.1016/j.ygcen.2024.114621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 09/26/2024] [Accepted: 10/07/2024] [Indexed: 10/18/2024]
Abstract
Fat mass and obesity associated gene (FTO) has been strongly associated with obesity, and it is functionally linked to the homeobox transcription factor iriquois-3 (IRX3). In mammals, FTO and IRX3 are involved in the regulation of food intake and metabolism. This study aimed to determine whether FTO and IRX3are affected by feeding and food unavailability. FTO and IRX3 mRNA and protein were found widely distributed in all tissues examined, including the brain, muscle, gut, and liver. Postprandial increase in the abundance of FTO and IRX3 mRNAs was observed in metabolic tissues of both male and female zebrafish at 1 h post-feeding. Meanwhile, their expression in the brain and gut decreased at 3 h post-feeding, reaching preprandial levels. Additionally, FTO and IRX3 mRNA abundance in examined tissues increased after 7 days of food deprivation, but substantially decreased after refeeding for 24 h. In summary, we report that both FTO and IRX3 are meal-sensitive genes in zebrafish. The fasting-induced increase suggests a possible appetite regulatory role for FTO and IRX3 in zebrafish. These findings highlight the importance of FTO and IRX3 in appetite and metabolic regulation in zebrafish.
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Affiliation(s)
- Katayoon Karimzadeh
- Laboratory of Integrative Neuroendocrinology, Department of Veterinary Biomedical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B4, Canada; Marine Biology Department, Islamic Azad University, Lahijan Branch, Lahijan, Iran
| | - Chinelo Uju
- Laboratory of Integrative Neuroendocrinology, Department of Veterinary Biomedical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B4, Canada
| | - Asgar Zahmatkesh
- Aquaculture Department, Gilan Agricultural and Natural Resources Research and Education Center, AREEO, Gilan, Iran
| | - Suraj Unniappan
- Laboratory of Integrative Neuroendocrinology, Department of Veterinary Biomedical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B4, Canada.
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Clemons HJ, Hogan DJ, Brown PO. Depot-specific mRNA expression programs in human adipocytes suggest physiological specialization via distinct developmental programs. PLoS One 2024; 19:e0311751. [PMID: 39401200 PMCID: PMC11472956 DOI: 10.1371/journal.pone.0311751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 09/24/2024] [Indexed: 10/17/2024] Open
Abstract
Adipose tissue is distributed in diverse locations throughout the human body. Not much is known about the extent to which anatomically distinct adipose depots are functionally distinct, specialized organs, nor whether depot-specific characteristics result from intrinsic developmental programs, as opposed to reversible physiological responses to differences in tissue microenvironment. We used DNA microarrays to compare mRNA expression patterns of isolated human adipocytes and cultured adipose stem cells, before and after ex vivo adipocyte differentiation, from seven anatomically diverse adipose tissue depots. Adipocytes from different depots display distinct gene expression programs, which are most closely shared with anatomically related depots. mRNAs whose expression differs between anatomically diverse groups of depots (e.g., subcutaneous vs. internal) suggest important functional specializations. These depot-specific differences in gene expression were recapitulated when adipocyte progenitor cells from each site were differentiated ex vivo, suggesting that progenitor cells from specific anatomic sites are deterministically programmed to differentiate into depot-specific adipocytes. Many developmental transcription factors show striking depot-specific patterns of expression, suggesting that adipocytes in each anatomic depot are programmed during early development in concert with anatomically related tissues and organs. Our results support the hypothesis that adipocytes from different depots are functionally distinct and that their depot-specific specialization reflects distinct developmental programs.
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Affiliation(s)
- Heather J. Clemons
- Department of Biochemistry, Stanford University School of Medicine, Palo Alto, California, United States of America
- Howard Hughes Medical Institute, Stanford University School of Medicine, Palo Alto, California, United States of America
| | - Daniel J. Hogan
- Department of Biochemistry, Stanford University School of Medicine, Palo Alto, California, United States of America
- Howard Hughes Medical Institute, Stanford University School of Medicine, Palo Alto, California, United States of America
| | - Patrick O. Brown
- Department of Biochemistry, Stanford University School of Medicine, Palo Alto, California, United States of America
- Howard Hughes Medical Institute, Stanford University School of Medicine, Palo Alto, California, United States of America
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Chen X, Dou Z, Son JE, Duan M, Yang F, Zhu S, Hui CC. A novel genetic mouse model of osteoporosis with double heterozygosity of Irx3 and Irx5 characterizes sex-dependent phenotypes in bone homeostasis. Bone 2024; 190:117282. [PMID: 39401533 DOI: 10.1016/j.bone.2024.117282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 09/06/2024] [Accepted: 10/11/2024] [Indexed: 10/19/2024]
Abstract
Iroquois homeobox gene 3 (Irx3) and Irx5 encode transcription factors that play crucial roles in limb development and bone formation. Previous studies using knockout mice have revealed a role of Irx3 and Irx5 in osteogenesis in young adult mice. However, whether these genes are also essential for bone homeostasis in adulthood and contribute to bone diseases remain poorly understood. Osteoporosis is a disease characterized by lower bone mineral density and disrupted bone microarchitecture, typically occurs in postmenopausal women. Here, we demonstrate that Irx3/5dHet mice with a half-reduction of Irx3 and Irx5 dosage serve as a novel model of osteoporosis. By micro-computed tomography, we found that Irx3/5dHet mice exhibited sex-dependent bone loss patterns. While male Irx3/5dHet mice progressively lost trabecular microstructures with aging, female mutants exhibited lower bone mineral density (BMD) and bone volume fraction (BV/TV) at early adulthood (9-15 weeks old) but without further loss later at 1 year of age. Bone marrow adipocytes are known to be elevated at the expenses of lower osteogenesis in osteoporotic bone marrow. Surprisingly, we found sex-dependent changes in adipogenesis at the age of skeletal maturity that bone marrow adipocytes were reduced in female Irx3/5dHet mice along with deteriorated osteogenesis, while male mice exhibited elevated adipogenesis. In summary, we reported a novel genetic model for osteoporosis-like phenotypes, highlighting sex-dependent bone mineral density and bone marrow adipocyte characteristics.
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Affiliation(s)
- Xinyu Chen
- Chronic Disease Research Institute, The Children's Hospital, National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China; Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Zhengchao Dou
- Department of Molecular Genetics, University of Toronto, Program in Developmental & Stem Cell Biology, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada
| | - Joe Eun Son
- Department of Molecular Genetics, University of Toronto, Program in Developmental & Stem Cell Biology, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada; School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Korea
| | - Meng Duan
- Chronic Disease Research Institute, The Children's Hospital, National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China; Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Fei Yang
- Chronic Disease Research Institute, The Children's Hospital, National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China; Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shankuan Zhu
- Chronic Disease Research Institute, The Children's Hospital, National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China; Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
| | - Chi-Chung Hui
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Program in Developmental & Stem Cell Biology, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada.
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Pudjihartono M, Pudjihartono N, O'Sullivan JM, Schierding W. Melanoma-specific mutation hotspots in distal, non-coding, promoter-interacting regions implicate novel candidate driver genes. Br J Cancer 2024:10.1038/s41416-024-02870-w. [PMID: 39367275 DOI: 10.1038/s41416-024-02870-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 09/23/2024] [Accepted: 09/26/2024] [Indexed: 10/06/2024] Open
Abstract
BACKGROUND To develop targeted treatments, it is crucial to identify the full spectrum of genetic drivers in melanoma, including those in non-coding regions. However, recent efforts to explore non-coding regions have primarily focused on gene-adjacent elements such as promoters and non-coding RNAs, leaving intergenic distal regulatory elements largely unexplored. METHODS We used Hi-C chromatin contact data from melanoma cells to map distal, non-coding, promoter-interacting regulatory elements genome-wide in melanoma. Using this "promoter-interaction network", alongside whole-genome sequence and gene expression data from the Pan Cancer Analysis of Whole Genomes, we developed multivariate linear regression models to identify distal somatic mutation hotspots that affect promoter activity. RESULTS We identified eight recurrently mutated hotspots that are novel, melanoma-specific, located in promoter-interacting distal regulatory elements, alter transcription factor binding motifs, and affect the expression of genes (e.g., HSPB7, CLDN1, ADCY9 and FDXR) previously implicated as tumour suppressors/oncogenes in various cancers. CONCLUSIONS Our study suggests additional non-coding drivers beyond the well-characterised TERT promoter in melanoma, offering new insights into the disruption of complex regulatory networks by non-coding mutations that may contribute to melanoma development. Furthermore, our study provides a framework for integrating multiple levels of biological data to uncover cancer-specific non-coding drivers.
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Affiliation(s)
- Michael Pudjihartono
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | | | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand.
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand.
- Australian Parkinson's Mission, Garvan Institute of Medical Research, Sydney, NSW, Australia.
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand.
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand.
- Department of Ophthalmology, University of Auckland, Auckland, New Zealand.
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Bruner WS, Grant SFA. Translation of genome-wide association study: from genomic signals to biological insights. Front Genet 2024; 15:1375481. [PMID: 39421299 PMCID: PMC11484060 DOI: 10.3389/fgene.2024.1375481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 09/24/2024] [Indexed: 10/19/2024] Open
Abstract
Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes.
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Affiliation(s)
- Winter S. Bruner
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Struan F. A. Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Division of Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
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Akamatsu K, Golzari S, Amariuta T. Powerful mapping of cis-genetic effects on gene expression across diverse populations reveals novel disease-critical genes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.25.24314410. [PMID: 39399015 PMCID: PMC11469471 DOI: 10.1101/2024.09.25.24314410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/15/2024]
Abstract
While disease-associated variants identified by genome-wide association studies (GWAS) most likely regulate gene expression levels, linking variants to target genes is critical to determining the functional mechanisms of these variants. Genetic effects on gene expression have been extensively characterized by expression quantitative trait loci (eQTL) studies, yet data from non-European populations is limited. This restricts our understanding of disease to genes whose regulatory variants are common in European populations. While previous work has leveraged data from multiple populations to improve GWAS power and polygenic risk score (PRS) accuracy, multi-ancestry data has not yet been used to better estimate cis-genetic effects on gene expression. Here, we present a new method, Multi-Ancestry Gene Expression Prediction Regularized Optimization (MAGEPRO), which constructs robust genetic models of gene expression in understudied populations or cell types by fitting a regularized linear combination of eQTL summary data across diverse cohorts. In simulations, our tool generates more accurate models of gene expression than widely-used LASSO and the state-of-the-art multi-ancestry PRS method, PRS-CSx, adapted to gene expression prediction. We attribute this improvement to MAGEPRO's ability to more accurately estimate causal eQTL effect sizes (p < 3.98 × 10-4, two-sided paired t-test). With real data, we applied MAGEPRO to 8 eQTL cohorts representing 3 ancestries (average n = 355) and consistently outperformed each of 6 competing methods in gene expression prediction tasks. Integration with GWAS summary statistics across 66 complex traits (representing 22 phenotypes and 3 ancestries) resulted in 2,331 new gene-trait associations, many of which replicate across multiple ancestries, including PHTF1 linked to white blood cell count, a gene which is overexpressed in leukemia patients. MAGEPRO also identified biologically plausible novel findings, such as PIGB, an essential component of GPI biosynthesis, associated with heart failure, which has been previously evidenced by clinical outcome data. Overall, MAGEPRO is a powerful tool to enhance inference of gene regulatory effects in underpowered datasets and has improved our understanding of population-specific and shared genetic effects on complex traits.
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Affiliation(s)
- Kai Akamatsu
- School of Biological Sciences, UC San Diego, La Jolla, CA, USA
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
| | - Stephen Golzari
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
- Shu Chien-Gene Lay Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | - Tiffany Amariuta
- Department of Medicine, Division of Biomedical Informatics, UC San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA
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Mace K, Zimmerman A, Chesi A, Doldur-Balli F, Kim H, Almeraya Del Valle E, Pack AI, Grant SFA, Kayser MS. Cross-species evidence for a developmental origin of adult hypersomnia with loss of synaptic adhesion molecules beat-Ia/CADM2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.25.615048. [PMID: 39386457 PMCID: PMC11463363 DOI: 10.1101/2024.09.25.615048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Idiopathic hypersomnia (IH) is a poorly-understood sleep disorder characterized by excessive daytime sleepiness despite normal nighttime sleep. Combining human genomics with behavioral and mechanistic studies in fish and flies, we uncover a role for beat-Ia/CADM2 , synaptic adhesion molecules of the immunoglobulin superfamily, in excessive sleepiness. Neuronal knockdown of Drosophila beat-Ia results in sleepy flies and loss of the vertebrate ortholog of beat-Ia , CADM2 , results in sleepy fish. We delineate a developmental function for beat-Ia in synaptic elaboration of neuropeptide F (NPF) neurites projecting to the suboesophageal zone (SEZ) of the fly brain. Brain connectome and experimental evidence demonstrate these NPF outputs synapse onto a subpopulation of SEZ GABAergic neurons to stabilize arousal. NPF is the Drosophila homolog of vertebrate neuropeptide Y (NPY), and an NPY receptor agonist restores sleep to normal levels in zebrafish lacking CADM2 . These findings point towards NPY modulation as a treatment target for human hypersomnia.
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Davletgildeeva AT, Kuznetsov NA. Dealkylation of Macromolecules by Eukaryotic α-Ketoglutarate-Dependent Dioxygenases from the AlkB-like Family. Curr Issues Mol Biol 2024; 46:10462-10491. [PMID: 39329974 PMCID: PMC11431407 DOI: 10.3390/cimb46090622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 09/17/2024] [Accepted: 09/18/2024] [Indexed: 09/28/2024] Open
Abstract
Alkylating modifications induced by either exogenous chemical agents or endogenous metabolites are some of the main types of damage to DNA, RNA, and proteins in the cell. Although research in recent decades has been almost entirely devoted to the repair of alkyl and in particular methyl DNA damage, more and more data lately suggest that the methylation of RNA bases plays an equally important role in normal functioning and in the development of diseases. Among the most prominent participants in the repair of methylation-induced DNA and RNA damage are human homologs of Escherichia coli AlkB, nonheme Fe(II)/α-ketoglutarate-dependent dioxygenases ABH1-8, and FTO. Moreover, some of these enzymes have been found to act on several protein targets. In this review, we present up-to-date data on specific features of protein structure, substrate specificity, known roles in the organism, and consequences of disfunction of each of the nine human homologs of AlkB. Special attention is given to reports about the effects of natural single-nucleotide polymorphisms on the activity of these enzymes and to potential consequences for carriers of such natural variants.
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Affiliation(s)
- Anastasiia T. Davletgildeeva
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, Novosibirsk 630090, Russia;
| | - Nikita A. Kuznetsov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, Novosibirsk 630090, Russia;
- Department of Natural Sciences, Novosibirsk State University, Novosibirsk 630090, Russia
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Goldberg LR, Baskin BM, Adla Y, Beierle JA, Kelliher JC, Yao EJ, Kirkpatrick SL, Reed ER, Jenkins DF, Cox J, Luong AM, Luttik KP, Scotellaro JA, Drescher TA, Crotts SB, Yazdani N, Ferris MT, Johnson WE, Mulligan MK, Bryant CD. Atp1a2 and Kcnj9 are candidate genes underlying sensitivity to oxycodone-induced locomotor activation and withdrawal-induced anxiety-like behaviors in C57BL/6 substrains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589731. [PMID: 38798314 PMCID: PMC11123399 DOI: 10.1101/2024.04.16.589731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Opioid use disorder is heritable, yet its genetic etiology is largely unknown. C57BL/6J and C57BL/6NJ mouse substrains exhibit phenotypic diversity in the context of limited genetic diversity which together can facilitate genetic discovery. Here, we found C57BL/6NJ mice were less sensitive to oxycodone (OXY)-induced locomotor activation versus C57BL/6J mice in a conditioned place preference paradigm. Narrow-sense heritability was estimated at 0.22-0.31, implicating suitability for genetic analysis. Quantitative trait locus (QTL) mapping in an F2 cross identified a chromosome 1 QTL explaining 7-12% of the variance in OXY locomotion and anxiety-like withdrawal in the elevated plus maze. A second QTL for EPM withdrawal behavior on chromosome 5 near Gabra2 (alpha-2 subunit of GABA-A receptor) explained 9% of the variance. To narrow the chromosome 1 locus, we generated recombinant lines spanning 163-181 Mb, captured the QTL for OXY locomotor traits and withdrawal, and fine-mapped a 2.45-Mb region (170.16-172.61 Mb). Transcriptome analysis identified five, localized striatal cis-eQTL transcripts and two were confirmed at the protein level (KCNJ9, ATP1A2). Kcnj9 codes for a potassium channel (GIRK3) that is a major effector of mu opioid receptor signaling. Atp1a2 codes for a subunit of a Na+/K+ ATPase enzyme that regulates neuronal excitability and shows functional adaptations following chronic opioid administration. To summarize, we identified two candidate genes underlying the physiological and behavioral properties of opioids, with direct preclinical relevance to investigators employing these widely used substrains and clinical relevance to human genetic studies of opioid use disorder.
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Affiliation(s)
- Lisa R. Goldberg
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
- Graduate Program in Biomolecular Pharmacology, Department of Pharmacology, Physiology & Biophysics, Boston University Chobanian and Avedisian School of Medicine, Boston, MA USA
| | - Britahny M. Baskin
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
- T32 Training Program on Development of Medications for Substance Use Disorder, Center for Drug Discovery, Northeastern University
| | - Yahia Adla
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
| | - Jacob A. Beierle
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
- Graduate Program in Biomolecular Pharmacology, Department of Pharmacology, Physiology & Biophysics, Boston University Chobanian and Avedisian School of Medicine, Boston, MA USA
- Transformative Training Program in Addiction Science, Boston University
| | - Julia C. Kelliher
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
| | - Emily J. Yao
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
| | - Stacey L. Kirkpatrick
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
| | - Eric R. Reed
- Graduate Program in Bioinformatics, Boston University, Boston, MA USA
| | - David F. Jenkins
- Graduate Program in Bioinformatics, Boston University, Boston, MA USA
| | - Jiayi Cox
- Genetics and Graduate Program in Genetics and Genomics, Program in Biomedical Sciences, Boston University Chobanian & Avedisian School of Medicine
| | - Alexander M. Luong
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
| | - Kimberly P. Luttik
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
| | - Julia A. Scotellaro
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
- Undergraduate Research Opportunity Program (UROP), Boston University
| | - Timothy A. Drescher
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
| | - Sydney B. Crotts
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
| | - Neema Yazdani
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
- Graduate Program in Biomolecular Pharmacology, Department of Pharmacology, Physiology & Biophysics, Boston University Chobanian and Avedisian School of Medicine, Boston, MA USA
- Transformative Training Program in Addiction Science, Boston University
| | - Martin T. Ferris
- Department of Genetics, University of North Carolina, Chapel Hill, NC USA
| | - W. Evan Johnson
- Division of Infectious Disease, Department of Medicine, Center for Data Science, Rutgers University, New Jersey, USA
| | - Megan K. Mulligan
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN USA
| | - Camron D. Bryant
- Laboratory of Addiction Genetics, Department of Pharmaceutical Sciences and Center for Drug Discovery, Northeastern University, Boston, MA USA
- T32 Training Program on Development of Medications for Substance Use Disorder, Center for Drug Discovery, Northeastern University
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10
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Wenz BM, He Y, Chen NC, Pickrell JK, Li JH, Dudek MF, Li T, Keener R, Voight BF, Brown CD, Battle A. Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.04.610850. [PMID: 39282458 PMCID: PMC11398312 DOI: 10.1101/2024.09.04.610850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
Background Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories. Results We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes. Conclusions Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.
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Affiliation(s)
- Brandon M. Wenz
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Biomedical Graduate Studies, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA 19104
| | - Yuan He
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University; Baltimore, MD, 21218
| | | | | | - Max F. Dudek
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA 19104
| | - Taibo Li
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Benjamin F. Voight
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA, 19104
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania – Perelman School of Medicine, Philadelphia, PA, 19104
| | - Christopher D. Brown
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
- Department of Computer Science, Johns Hopkins University; Baltimore, MD, 21218
- Department of Genetic Medicine, Johns Hopkins University; Baltimore, MD, 21218
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218
- Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, 21218
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11
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Engreitz JM, Lawson HA, Singh H, Starita LM, Hon GC, Carter H, Sahni N, Reddy TE, Lin X, Li Y, Munshi NV, Chahrour MH, Boyle AP, Hitz BC, Mortazavi A, Craven M, Mohlke KL, Pinello L, Wang T, Kundaje A, Yue F, Cody S, Farrell NP, Love MI, Muffley LA, Pazin MJ, Reese F, Van Buren E, Dey KK, Kircher M, Ma J, Radivojac P, Balliu B, Williams BA, Huangfu D, Park CY, Quertermous T, Das J, Calderwood MA, Fowler DM, Vidal M, Ferreira L, Mooney SD, Pejaver V, Zhao J, Gazal S, Koch E, Reilly SK, Sunyaev S, Carpenter AE, Buenrostro JD, Leslie CS, Savage RE, Giric S, Luo C, Plath K, Barrera A, Schubach M, Gschwind AR, Moore JE, Ahituv N, Yi SS, Hallgrimsdottir I, Gaulton KJ, Sakaue S, Booeshaghi S, Mattei E, Nair S, Pachter L, Wang AT, Shendure J, Agarwal V, Blair A, Chalkiadakis T, Chardon FM, Dash PM, Deng C, Hamazaki N, Keukeleire P, Kubo C, Lalanne JB, Maass T, Martin B, McDiarmid TA, Nobuhara M, Page NF, Regalado S, Sims J, Ushiki A, Best SM, Boyle G, Camp N, Casadei S, Da EY, Dawood M, Dawson SC, Fayer S, Hamm A, James RG, Jarvik GP, McEwen AE, Moore N, Pendyala S, Popp NA, Post M, Rubin AF, Smith NT, Stone J, Tejura M, Wang ZR, Wheelock MK, Woo I, Zapp BD, Amgalan D, Aradhana A, Arana SM, Bassik MC, Bauman JR, Bhattacharya A, Cai XS, Chen Z, Conley S, Deshpande S, Doughty BR, Du PP, Galante JA, Gifford C, Greenleaf WJ, Guo K, Gupta R, Isobe S, Jagoda E, Jain N, Jones H, Kang HY, Kim SH, Kim Y, Klemm S, Kundu R, Kundu S, Lago-Docampo M, Lee-Yow YC, Levin-Konigsberg R, Li DY, Lindenhofer D, Ma XR, Marinov GK, Martyn GE, McCreery CV, Metzl-Raz E, Monteiro JP, Montgomery MT, Mualim KS, Munger C, Munson G, Nguyen TC, Nguyen T, Palmisano BT, Pampari A, Rabinovitch M, Ramste M, Ray J, Roy KR, Rubio OM, Schaepe JM, Schnitzler G, Schreiber J, Sharma D, Sheth MU, Shi H, Singh V, Sinha R, Steinmetz LM, Tan J, Tan A, Tycko J, Valbuena RC, Amiri VVP, van Kooten MJFM, Vaughan-Jackson A, Venida A, Weldy CS, Worssam MD, Xia F, Yao D, Zeng T, Zhao Q, Zhou R, Chen ZS, Cimini BA, Coppin G, Coté AG, Haghighi M, Hao T, Hill DE, Lacoste J, Laval F, Reno C, Roth FP, Singh S, Spirohn-Fitzgerald K, Taipale M, Teelucksingh T, Tixhon M, Yadav A, Yang Z, Kraus WL, Armendariz DA, Dederich AE, Gogate A, El Hayek L, Goetsch SC, Kaur K, Kim HB, McCoy MK, Nzima MZ, Pinzón-Arteaga CA, Posner BA, Schmitz DA, Sivakumar S, Sundarrajan A, Wang L, Wang Y, Wu J, Xu L, Xu J, Yu L, Zhang Y, Zhao H, Zhou Q, Won H, Bell JL, Broadaway KA, Degner KN, Etheridge AS, Koller BH, Mah W, Mu W, Ritola KD, Rosen JD, Schoenrock SA, Sharp RA, Bauer D, Lettre G, Sherwood R, Becerra B, Blaine LJ, Che E, Francoeur MJ, Gibbs EN, Kim N, King EM, Kleinstiver BP, Lecluze E, Li Z, Patel ZM, Phan QV, Ryu J, Starr ML, Wu T, Gersbach CA, Crawford GE, Allen AS, Majoros WH, Iglesias N, Rai R, Venukuttan R, Li B, Anglen T, Bounds LR, Hamilton MC, Liu S, McCutcheon SR, McRoberts Amador CD, Reisman SJ, ter Weele MA, Bodle JC, Streff HL, Siklenka K, Strouse K, Bernstein BE, Babu J, Corona GB, Dong K, Duarte FM, Durand NC, Epstein CB, Fan K, Gaskell E, Hall AW, Ham AM, Knudson MK, Shoresh N, Wekhande S, White CM, Xi W, Satpathy AT, Corces MR, Chang SH, Chin IM, Gardner JM, Gardell ZA, Gutierrez JC, Johnson AW, Kampman L, Kasowski M, Lareau CA, Liu V, Ludwig LS, McGinnis CS, Menon S, Qualls A, Sandor K, Turner AW, Ye CJ, Yin Y, Zhang W, Wold BJ, Carilli M, Cheong D, Filibam G, Green K, Kawauchi S, Kim C, Liang H, Loving R, Luebbert L, MacGregor G, Merchan AG, Rebboah E, Rezaie N, Sakr J, Sullivan DK, Swarna N, Trout D, Upchurch S, Weber R, Castro CP, Chou E, Feng F, Guerra A, Huang Y, Jiang L, Liu J, Mills RE, Qian W, Qin T, Sartor MA, Sherpa RN, Wang J, Wang Y, Welch JD, Zhang Z, Zhao N, Mukherjee S, Page CD, Clarke S, Doty RW, Duan Y, Gordan R, Ko KY, Li S, Li B, Thomson A, Raychaudhuri S, Price A, Ali TA, Dey KK, Durvasula A, Kellis M, Iakoucheva LM, Kakati T, Chen Y, Benazouz M, Jain S, Zeiberg D, De Paolis Kaluza MC, Velyunskiy M, Gasch A, Huang K, Jin Y, Lu Q, Miao J, Ohtake M, Scopel E, Steiner RD, Sverchkov Y, Weng Z, Garber M, Fu Y, Haas N, Li X, Phalke N, Shan SC, Shedd N, Yu T, Zhang Y, Zhou H, Battle A, Jerby L, Kotler E, Kundu S, Marderstein AR, Montgomery SB, Nigam A, Padhi EM, Patel A, Pritchard J, Raine I, Ramalingam V, Rodrigues KB, Schreiber JM, Singhal A, Sinha R, Wang AT, Abundis M, Bisht D, Chakraborty T, Fan J, Hall DR, Rarani ZH, Jain AK, Kaundal B, Keshari S, McGrail D, Pease NA, Yi VF, Wu H, Kannan S, Song H, Cai J, Gao Z, Kurzion R, Leu JI, Li F, Liang D, Ming GL, Musunuru K, Qiu Q, Shi J, Su Y, Tishkoff S, Xie N, Yang Q, Yang W, Zhang H, Zhang Z, Beer MA, Hadjantonakis AK, Adeniyi S, Cho H, Cutler R, Glenn RA, Godovich D, Hu N, Jovanic S, Luo R, Oh JW, Razavi-Mohseni M, Shigaki D, Sidoli S, Vierbuchen T, Wang X, Williams B, Yan J, Yang D, Yang Y, Sander M, Gaulton KJ, Ren B, Bartosik W, Indralingam HS, Klie A, Mummey H, Okino ML, Wang G, Zemke NR, Zhang K, Zhu H, Zaitlen N, Ernst J, Langerman J, Li T, Sun Y, Rudensky AY, Periyakoil PK, Gao VR, Smith MH, Thomas NM, Donlin LT, Lakhanpal A, Southard KM, Ardy RC, Cherry JM, Gerstein MB, Andreeva K, Assis PR, Borsari B, Douglass E, Dong S, Gabdank I, Graham K, Jolanki O, Jou J, Kagda MS, Lee JW, Li M, Lin K, Miyasato SR, Rozowsky J, Small C, Spragins E, Tanaka FY, Whaling IM, Youngworth IA, Sloan CA, Belter E, Chen X, Chisholm RL, Dickson P, Fan C, Fulton L, Li D, Lindsay T, Luan Y, Luo Y, Lyu H, Ma X, Macias-Velasco J, Miga KH, Quaid K, Stitziel N, Stranger BE, Tomlinson C, Wang J, Zhang W, Zhang B, Zhao G, Zhuo X, Brennand K, Ciccia A, Hayward SB, Huang JW, Leuzzi G, Taglialatela A, Thakar T, Vaitsiankova A, Dey KK, Ali TA, Kim A, Grimes HL, Salomonis N, Gupta R, Fang S, Lee-Kim V, Heinig M, Losert C, Jones TR, Donnard E, Murphy M, Roberts E, Song S, Mostafavi S, Sasse A, Spiro A, Pennacchio LA, Kato M, Kosicki M, Mannion B, Slaven N, Visel A, Pollard KS, Drusinsky S, Whalen S, Ray J, Harten IA, Ho CH, Sanjana NE, Caragine C, Morris JA, Seruggia D, Kutschat AP, Wittibschlager S, Xu H, Fu R, He W, Zhang L, Osorio D, Bly Z, Calluori S, Gilchrist DA, Hutter CM, Morris SA, Samer EK. Deciphering the impact of genomic variation on function. Nature 2024; 633:47-57. [PMID: 39232149 DOI: 10.1038/s41586-024-07510-0] [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: 04/11/2023] [Accepted: 05/02/2024] [Indexed: 09/06/2024]
Abstract
Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.
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12
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M JN, Bharadwaj D. The complex web of obesity: from genetics to precision medicine. Expert Rev Endocrinol Metab 2024; 19:403-418. [PMID: 38869356 DOI: 10.1080/17446651.2024.2365785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/05/2024] [Indexed: 06/14/2024]
Abstract
INTRODUCTION Obesity is a growing public health concern affecting both children and adults. Since it involves both genetic and environmental components, the management of obesity requires both, an understanding of the underlying genetics and changes in lifestyle. The knowledge of obesity genetics will enable the possibility of precision medicine in anti-obesity medications. AREAS COVERED Here, we explore health complications and the prevalence of obesity. We discuss disruptions in energy balance as a symptom of obesity, examining evolutionary theories, its multi-factorial origins, and heritability. Additionally, we discuss monogenic and polygenic obesity, the converging biological pathways, potential pharmacogenomics applications, and existing anti-obesity medications - specifically focussing on the leptin-melanocortin and incretin pathways. Comparisons between childhood and adult obesity genetics are made, along with insights into structural variants, epigenetic changes, and environmental influences on epigenetic signatures. EXPERT OPINION With recent advancements in anti-obesity drugs, genetic studies pinpoint new targets and allow for repurposing existing drugs. This creates opportunities for genotype-informed treatment options. Also, lifestyle interventions can help in the prevention and treatment of obesity by altering the epigenetic signatures. The comparison of genetic architecture in adults and children revealed a significant overlap. However, more robust studies with diverse ethnic representation is required in childhood obesity.
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Affiliation(s)
- Janaki Nair M
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
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13
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Burton EA, Argenziano M, Cook K, Ridler M, Lu S, Su C, Manduchi E, Littleton SH, Leonard ME, Hodge KM, Wang LS, Schellenberg GD, Johnson ME, Pahl MC, Pippin JA, Wells AD, Anderson SA, Brown CD, Grant SF, Chesi A. Variant-to-function mapping of late-onset Alzheimer's disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.22.609230. [PMID: 39229212 PMCID: PMC11370593 DOI: 10.1101/2024.08.22.609230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Late-onset Alzheimer's disease (LOAD) research has principally focused on neurons over the years due to their known role in the production of amyloid beta plaques and neurofibrillary tangles. In contrast, recent genomic studies of LOAD have implicated microglia as culprits of the prolonged inflammation exacerbating the neurodegeneration observed in patient brains. Indeed, recent LOAD genome-wide association studies (GWAS) have reported multiple loci near genes related to microglial function, including TREM2, ABI3, and CR1. However, GWAS alone cannot pinpoint underlying causal variants or effector genes at such loci, as most signals reside in non-coding regions of the genome and could presumably confer their influence frequently via long-range regulatory interactions. We elected to carry out a combination of ATAC-seq and high-resolution promoter-focused Capture-C in two human microglial cell models (iPSC-derived microglia and HMC3) in order to physically map interactions between LOAD GWAS-implicated candidate causal variants and their corresponding putative effector genes. Notably, we observed consistent evidence that rs6024870 at the GWAS CASS4 locus contacted the promoter of nearby gene, RTFDC1. We subsequently observed a directionallly consistent decrease in RTFDC1 expression with the the protective minor A allele of rs6024870 via both luciferase assays in HMC3 cells and expression studies in primary human microglia. Through CRISPR-Cas9-mediated deletion of the putative regulatory region harboring rs6024870 in HMC3 cells, we observed increased pro-inflammatory cytokine secretion and decreased DNA double strand break repair related, at least in part, to RTFDC1 expression levels. Our variant-to-function approach therefore reveals that the rs6024870-harboring regulatory element at the LOAD 'CASS4' GWAS locus influences both microglial inflammatory capacity and DNA damage resolution, along with cumulative evidence implicating RTFDC1 as a novel candidate effector gene.
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Affiliation(s)
- Elizabeth A. Burton
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- CAMB Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mariana Argenziano
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kieona Cook
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Molly Ridler
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Chun Su
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Elisabetta Manduchi
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sheridan H. Littleton
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- CAMB Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michelle E. Leonard
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Li-San Wang
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gerard D. Schellenberg
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew E. Johnson
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Matthew C. Pahl
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - James A. Pippin
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew D. Wells
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stewart A. Anderson
- Department of Child and Adolescent Psychiatry and Behavioral Services, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D. Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Struan F.A. Grant
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division Endocrinology and Diabetes, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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14
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Lian Z, Chen R, Xian M, Huang P, Xu J, Xiao X, Ning X, Zhao J, Xie J, Duan J, Li B, Wang W, Shi X, Wang X, Jia N, Chen X, Li J, Yang Z. Targeted inhibition of m6A demethylase FTO by FB23 attenuates allergic inflammation in the airway epithelium. FASEB J 2024; 38:e23846. [PMID: 39093041 DOI: 10.1096/fj.202400545r] [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: 03/10/2024] [Revised: 07/02/2024] [Accepted: 07/21/2024] [Indexed: 08/04/2024]
Abstract
Epithelial cells play a crucial role in asthma, contributing to chronic inflammation and airway hyperresponsiveness. m6A modification, which involves key proteins such as the demethylase fat mass and obesity-associated protein (FTO), is crucial in the regulation of various diseases, including asthma. However, the role of FTO in epithelial cells and the development of asthma remains unclear. In this study, we investigated the demethylase activity of FTO using a small-molecule inhibitor FB23 in epithelial cells and allergic inflammation in vivo and in vitro. We examined the FTO-regulated transcriptome-wide m6A profiling by methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-seq under FB23 treatment and allergic inflammation conditions. Immunofluorescence staining was performed to assess the tissue-specific expression of FTO in asthmatic bronchial mucosa. We demonstrated that FB23 alleviated allergic inflammation in IL-4/IL-13-treated epithelial cells and house dust mite (HDM)-induced allergic airway inflammation mouse model. The demethylase activity of FTO contributed to the regulation of TNF-α signaling via NF-κB and epithelial-mesenchymal transition-related pathways under allergic inflammation conditions in epithelial cells. FTO was expressed in epithelial, submucosal gland, and smooth muscle cells in human bronchial mucosa. In conclusion, FB23-induced inhibition of FTO alleviates allergic inflammation in epithelial cells and HDM-induced mice, potentially through diverse cellular processes and epithelial-mesenchymal transition signaling pathways, suggesting that FTO is a potential therapeutic target in asthma management.
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Affiliation(s)
- Zexuan Lian
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Ruchong Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Mo Xian
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Peiying Huang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Jiahan Xu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Xiaojun Xiao
- State Key Laboratory of Respiratory Disease Allergy Division at Shenzhen University, Shenzhen Key Laboratory of Allergy and Immunology, Shenzhen University, Shenzhen, Guangdong, P.R. China
| | - Xiaoping Ning
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Jin Zhao
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Jianlei Xie
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Jielin Duan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Bizhou Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Wanjun Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Xu Shi
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Xinru Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Nan Jia
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Xuepeng Chen
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, P.R. China
| | - Jing Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
| | - Zhaowei Yang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, P.R. China
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15
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Trang KB, Pahl MC, Pippin JA, Su C, Littleton SH, Sharma P, Kulkarni NN, Ghanem LR, Terry NA, O’Brien JM, Wagley Y, Hankenson KD, Jermusyk A, Hoskins JW, Amundadottir LT, Xu M, Brown KM, Anderson SA, Yang W, Titchenell PM, Seale P, Cook L, Levings MK, Zemel BS, Chesi A, Wells AD, Grant SF. 3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.30.23294092. [PMID: 37693606 PMCID: PMC10491377 DOI: 10.1101/2023.08.30.23294092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.
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Affiliation(s)
- Khanh B. Trang
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Matthew C. Pahl
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - James A. Pippin
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Chun Su
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sheridan H. Littleton
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prabhat Sharma
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nikhil N. Kulkarni
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Louis R. Ghanem
- Division of Gastroenterology, Hepatology, and Nutrition, Children’s Hospital of Philadelphia, PA, USA
| | - Natalie A. Terry
- Division of Gastroenterology, Hepatology, and Nutrition, Children’s Hospital of Philadelphia, PA, USA
| | - Joan M. O’Brien
- Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, PA, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Disease
| | - Yadav Wagley
- Department of Orthopedic Surgery University of Michigan Medical School Ann Arbor, MI, USA
| | - Kurt D. Hankenson
- Department of Orthopedic Surgery University of Michigan Medical School Ann Arbor, MI, USA
| | - Ashley Jermusyk
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jason W. Hoskins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Laufey T. Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Mai Xu
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Kevin M Brown
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Stewart A. Anderson
- Department of Child and Adolescent Psychiatry, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wenli Yang
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M. Titchenell
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Patrick Seale
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura Cook
- Department of Microbiology and Immunology, University of Melbourne, Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Department of Critical Care, Melbourne Medical School, University of Melbourne, Melbourne, VIC, Australia
- Division of Infectious Diseases, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Megan K. Levings
- Department of Surgery, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Babette S. Zemel
- Division of Gastroenterology, Hepatology, and Nutrition, Children’s Hospital of Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew D. Wells
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Struan F.A. Grant
- Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division Endocrinology and Diabetes, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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16
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Zha X, Gao Z, Li M, Xia X, Mao Z, Wang S. Insight into the regulatory mechanism of m 6A modification: From MAFLD to hepatocellular carcinoma. Biomed Pharmacother 2024; 177:116966. [PMID: 38906018 DOI: 10.1016/j.biopha.2024.116966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/05/2024] [Accepted: 06/15/2024] [Indexed: 06/23/2024] Open
Abstract
In recent years, there has been a significant increase in the incidence of metabolic-associated fatty liver disease (MAFLD), which has been attributed to the increasing prevalence of type 2 diabetes mellitus (T2DM) and obesity. MAFLD affects more than one-third of adults worldwide, making it the most prevalent liver disease globally. Moreover, MAFLD is considered a significant risk factor for hepatocellular carcinoma (HCC), with MAFLD-related HCC cases increasing. Approximately 1 in 6 HCC patients are believed to have MAFLD, and nearly 40 % of these HCC patients do not progress to cirrhosis, indicating direct transformation from MAFLD to HCC. N6-methyladenosine (m6A) is commonly distributed in eukaryotic mRNA and plays a crucial role in normal development and disease progression, particularly in tumors. Numerous studies have highlighted the close association between abnormal m6A modification and cellular metabolic alterations, underscoring its importance in the onset and progression of MAFLD. However, the specific impact of m6A modification on the progression of MAFLD to HCC remains unclear. Can targeting m6A effectively halt the progression of MAFLD-related HCC? In this review, we investigated the pivotal role of abnormal m6A modification in the transition from MAFLD to HCC, explored the potential of m6A modification as a therapeutic target for MAFLD-related HCC, and proposed possible directions for future investigations.
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Affiliation(s)
- Xuan Zha
- Department of Laboratory Medicine, the Affiliated Hospital of Jiangsu University, Zhenjiang, China; Department of Immunology, Jiangsu Key Laboratory of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China
| | - Zewei Gao
- Department of Immunology, Jiangsu Key Laboratory of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China
| | - Min Li
- Department of Immunology, Jiangsu Key Laboratory of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China
| | - Xueli Xia
- Department of Immunology, Jiangsu Key Laboratory of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China
| | - Zhenwei Mao
- Department of Laboratory Medicine, Affiliated People's Hospital, Jiangsu University, Zhenjiang, China.
| | - Shengjun Wang
- Department of Laboratory Medicine, the Affiliated Hospital of Jiangsu University, Zhenjiang, China; Department of Immunology, Jiangsu Key Laboratory of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, China.
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17
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Oh JW, Beer MA. Gapped-kmer sequence modeling robustly identifies regulatory vocabularies and distal enhancers conserved between evolutionarily distant mammals. Nat Commun 2024; 15:6464. [PMID: 39085231 PMCID: PMC11291912 DOI: 10.1038/s41467-024-50708-z] [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: 10/27/2023] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
Gene regulatory elements drive complex biological phenomena and their mutations are associated with common human diseases. The impacts of human regulatory variants are often tested using model organisms such as mice. However, mapping human enhancers to conserved elements in mice remains a challenge, due to both rapid enhancer evolution and limitations of current computational methods. We analyze distal enhancers across 45 matched human/mouse cell/tissue pairs from a comprehensive dataset of DNase-seq experiments, and show that while cell-specific regulatory vocabulary is conserved, enhancers evolve more rapidly than promoters and CTCF binding sites. Enhancer conservation rates vary across cell types, in part explainable by tissue specific transposable element activity. We present an improved genome alignment algorithm using gapped-kmer features, called gkm-align, and make genome wide predictions for 1,401,803 orthologous regulatory elements. We show that gkm-align discovers 23,660 novel human/mouse conserved enhancers missed by previous algorithms, with strong evidence of conserved functional activity.
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Affiliation(s)
- Jin Woo Oh
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michael A Beer
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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18
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Qiu T, Zeng L, Chen Y, Yang Y. Nucleic acid demethylase MpAlkB1 regulates the growth, development, and secondary metabolite biosynthesis in Monascus purpureus. World J Microbiol Biotechnol 2024; 40:282. [PMID: 39060812 DOI: 10.1007/s11274-024-04094-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 07/23/2024] [Indexed: 07/28/2024]
Abstract
Nucleic acid demethylases of α-ketoglutarate-dependent dioxygenase (AlkB) family can reversibly erase methyl adducts from nucleobases, thus dynamically regulating the methylation status of DNA/RNA and playing critical roles in multiple cellular processes. But little is known about AlkB demethylases in filamentous fungi so far. The present study reports that Monascus purpureus genomes contain a total of five MpAlkB genes. The MpAlkB1 gene was disrupted and complemented through homologous recombination strategy to analyze its biological functions in M. purpureus. MpAlkB1 knockout significantly accelerated the growth of strain, increased biomass, promoted sporulation and cleistothecia development, reduced the content of Monascus pigments (Mps), and strongly inhibited citrinin biosynthesis. The downregulated expression of the global regulator gene LaeA, and genes of Mps biosynthesis gene cluster (BGC) or citrinin BGC in MpAlkB1 disruption strain supported the pleiotropic trait changes caused by MpAlkB1 deletion. These results indicate that MpAlkB1-mediated demethylation of nucleic acid plays important roles in regulating the growth and development, and secondary metabolism in Monascus spp.
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Affiliation(s)
- Tiaoshuang Qiu
- Bioengineering College, Chongqing University, Chongqing, 400044, China
| | - Lingqing Zeng
- Bioengineering College, Chongqing University, Chongqing, 400044, China
| | - Yuling Chen
- Bioengineering College, Chongqing University, Chongqing, 400044, China
| | - Yingwu Yang
- Bioengineering College, Chongqing University, Chongqing, 400044, China.
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19
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Leite JMRS, Pereira JL, Alves de Souza C, Pavan Soler JM, Mingroni-Netto RC, Fisberg RM, Rogero MM, Sarti FM. Novel loci linked to serum lipid traits are identified in a genome-wide association study of a highly admixed Brazilian population - the 2015 ISA Nutrition. Lipids Health Dis 2024; 23:229. [PMID: 39060932 PMCID: PMC11282745 DOI: 10.1186/s12944-024-02085-1] [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: 11/08/2023] [Accepted: 03/20/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND Cardiovascular diseases (CVDs) comprise major causes of death worldwide, leading to extensive burden on populations and societies. Alterations in normal lipid profiles, i.e., dyslipidemia, comprise important risk factors for CVDs. However, there is lack of comprehensive evidence on the genetic contribution to dyslipidemia in highly admixed populations. The identification of single nucleotide polymorphisms (SNPs) linked to blood lipid traits in the Brazilian population was based on genome-wide associations using data from the São Paulo Health Survey with Focus on Nutrition (ISA-Nutrition). METHODS A total of 667 unrelated individuals had genetic information on 330,656 SNPs available, and were genotyped with Axiom™ 2.0 Precision Medicine Research Array. Genetic associations were tested at the 10- 5 significance level for the following phenotypes: low-density lipoprotein cholesterol (LDL-c), very low-density lipoprotein cholesterol (VLDL-c), high-density lipoprotein cholesterol (HDL-c), HDL-c/LDL-c ratio, triglycerides (TGL), total cholesterol, and non-HDL-c. RESULTS There were 19 significantly different SNPs associated with lipid traits, the majority of which corresponding to intron variants, especially in the genes FAM81A, ZFHX3, PTPRD, and POMC. Three variants (rs1562012, rs16972039, and rs73401081) and two variants (rs8025871 and rs2161683) were associated with two and three phenotypes, respectively. Among the subtypes, non-HDL-c had the highest proportion of associated variants. CONCLUSIONS The results of the present genome-wide association study offer new insights into the genetic structure underlying lipid traits in underrepresented populations with high ancestry admixture. The associations were robust across multiple lipid phenotypes, and some of the phenotypes were associated with two or three variants. In addition, some variants were present in genes that encode ncRNAs, raising important questions regarding their role in lipid metabolism.
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Affiliation(s)
| | | | | | - Júlia M Pavan Soler
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | | | - Regina M Fisberg
- School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Marcelo M Rogero
- School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Flavia M Sarti
- School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, Brazil.
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20
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Hartl C, Zhuang J, Tyler A, Zhou B, Wong E, Merberg D, Farrell B, DeBoever C, Bryant J, Diogo D. CREdb: A comprehensive database of Cis-Regulatory Elements and their activity in human cells and tissues. Epigenetics Chromatin 2024; 17:21. [PMID: 39014503 PMCID: PMC11253421 DOI: 10.1186/s13072-024-00545-7] [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: 02/09/2024] [Accepted: 06/08/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND Cis-regulatory elements (CREs) play a pivotal role in gene expression regulation, allowing cells to serve diverse functions and respond to external stimuli. Understanding CREs is essential for personalized medicine and disease research, as an increasing number of genetic variants associated with phenotypes and diseases overlap with CREs. However, existing databases often focus on subsets of regulatory elements and present each identified instance of element individually, confounding the effort to obtain a comprehensive view. To address this gap, we have created CREdb, a comprehensive database with over 10 million human regulatory elements across 1,058 cell types and 315 tissues harmonized from different data sources. We curated and aligned the cell types and tissues to standard ontologies for efficient data query. RESULTS Data from 11 sources were curated and mapped to standard ontological terms. 11,223,434 combined elements are present in the final database, and these were merged into 5,666,240 consensus elements representing the combined ranges of the individual elements informed by their overlap. Each consensus element contains curated metadata including the number of elements supporting it and a hash linking to the source databases. The inferred activity of each consensus element in various cell-type and tissue context is also provided. Examples presented here show the potential utility of CREdb in annotating non-coding genetic variants and informing chromatin accessibility profiling analysis. CONCLUSIONS We developed CREdb, a comprehensive database of CREs, to simplify the analysis of CREs by providing a unified framework for researchers. CREdb compiles consensus ranges for each element by integrating the information from all instances identified across various source databases. This unified database facilitates the functional annotation of non-coding genetic variants and complements chromatin accessibility profiling analysis. CREdb will serve as an important resource in expanding our knowledge of the epigenome and its role in human diseases.
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Affiliation(s)
- Chris Hartl
- Rancho BioSciences LLC, San Diego, California, USA
| | - Jiali Zhuang
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, San Diego, CA, 92121, USA
| | - Aaron Tyler
- Rancho BioSciences LLC, San Diego, California, USA
| | - Bing Zhou
- Rancho BioSciences LLC, San Diego, California, USA
| | - Emily Wong
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, San Diego, CA, 92121, USA
- Data Science and Operations, Vir Biotechnology Inc, San Francisco, CA, 94158, USA
| | - David Merberg
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, Cambridge, MA, 02139, USA
| | - Brad Farrell
- Rancho BioSciences LLC, San Diego, California, USA
| | - Chris DeBoever
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, San Diego, CA, 92121, USA
| | - Julie Bryant
- Rancho BioSciences LLC, San Diego, California, USA
| | - Dorothée Diogo
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, Cambridge, MA, 02139, USA.
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21
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Manipur I, Reales G, Sul JH, Shin MK, Longerich S, Cortes A, Wallace C. CoPheScan: phenome-wide association studies accounting for linkage disequilibrium. Nat Commun 2024; 15:5862. [PMID: 38997278 PMCID: PMC11245513 DOI: 10.1038/s41467-024-49990-8] [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: 07/05/2023] [Accepted: 06/25/2024] [Indexed: 07/14/2024] Open
Abstract
Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS is restricted by an inability to distinguish confounding due to linkage disequilibrium (LD) from true pleiotropy. Here we describe CoPheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systematic exploration of causal associations while simultaneously addressing LD confounding. We demonstrate its performance through simulation, showing considerably better control of false positive rates than a conventional approach not accounting for LD. We used CoPheScan to perform PheWAS of protein-truncating variants and fine-mapped variants from disease and pQTL studies, in 2275 disease phenotypes from the UK Biobank. Our results identify the complexity of known pleiotropic genes such as APOE, and suggest a new causal role for TGM3 in skin cancer.
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Affiliation(s)
- Ichcha Manipur
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK.
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK.
| | - Guillermo Reales
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
| | | | | | | | - Adrian Cortes
- Human Genetics and Genomics, GSK, Heidelberg, 69117, Germany
| | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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22
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Sonti S, Littleton SH, Pahl MC, Zimmerman AJ, Chesi A, Palermo J, Lasconi C, Brown EB, Pippin JA, Wells AD, Doldur-Balli F, Pack AI, Gehrman PR, Keene AC, Grant SFA. Perturbation of the insomnia WDR90 genome-wide association studies locus pinpoints rs3752495 as a causal variant influencing distal expression of neighboring gene, PIG-Q. Sleep 2024; 47:zsae085. [PMID: 38571402 PMCID: PMC11236950 DOI: 10.1093/sleep/zsae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 01/28/2024] [Indexed: 04/05/2024] Open
Abstract
Although genome-wide association studies (GWAS) have identified loci for sleep-related traits, they do not directly uncover the underlying causal variants and corresponding effector genes. The majority of such variants reside in non-coding regions and are therefore presumed to impact cis-regulatory elements. Our previously reported 'variant-to-gene mapping' effort in human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs), combined with validation in both Drosophila and zebrafish, implicated phosphatidyl inositol glycan (PIG)-Q as a functionally relevant gene at the insomnia "WDR90" GWAS locus. However, importantly that effort did not characterize the corresponding underlying causal variant. Specifically, our previous 3D genomic datasets nominated a shortlist of three neighboring single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium within an intronic enhancer region of WDR90 that contacted the open PIG-Q promoter. We sought to investigate the influence of these SNPs collectively and then individually on PIG-Q modulation to pinpoint the causal "regulatory" variant. Starting with gross level perturbation, deletion of the entire region in NPCs via CRISPR-Cas9 editing and subsequent RNA sequencing revealed expression changes in specific PIG-Q transcripts. Results from individual luciferase reporter assays for each SNP in iPSCs revealed that the region with the rs3752495 risk allele (RA) induced a ~2.5-fold increase in luciferase expression. Importantly, rs3752495 also exhibited an allele-specific effect, with the RA increasing the luciferase expression by ~2-fold versus the non-RA. In conclusion, our variant-to-function approach and in vitro validation implicate rs3752495 as a causal insomnia variant embedded within WDR90 while modulating the expression of the distally located PIG-Q.
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Affiliation(s)
- Shilpa Sonti
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sheridan H Littleton
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Amber J Zimmerman
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory, Medicine University of Pennsylvania Perelman School of Medicine, Philadelphia PA, USA
| | - Justin Palermo
- Department of Biology, Texas A&M University, College Station, TX, USA
| | - Chiara Lasconi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Elizabeth B Brown
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Fusun Doldur-Balli
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allan I Pack
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Phillip R Gehrman
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alex C Keene
- Department of Biology, Texas A&M University, College Station, TX, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Divisions of Human Genetics and Endocrinology & Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
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23
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Song S, Wang L, Hou L, Liu JS. Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations. Nat Commun 2024; 15:5769. [PMID: 38982044 PMCID: PMC11233643 DOI: 10.1038/s41467-024-49924-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 06/25/2024] [Indexed: 07/11/2024] Open
Abstract
TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.
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Affiliation(s)
- Shuang Song
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Lijun Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Lin Hou
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China.
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.
| | - Jun S Liu
- Department of Statistics, Harvard University, Cambridge, MA, USA.
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24
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Renganaath K, Albert FW. Trans-eQTL hotspots shape complex traits by modulating cellular states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.14.567054. [PMID: 38014174 PMCID: PMC10680915 DOI: 10.1101/2023.11.14.567054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.
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Affiliation(s)
- Kaushik Renganaath
- Department of Genetics, Cell Biology, & Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Frank W Albert
- Department of Genetics, Cell Biology, & Development, University of Minnesota, Minneapolis, MN 55455, USA
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Lu Z, Ding L, Jiang X, Zhang S, Yan M, Yang G, Tian X, Wang Q. Single-nucleus RNA transcriptome profiling reveals murine adipose tissue endothelial cell proliferation gene networks involved in obesity development. Arch Biochem Biophys 2024; 757:110029. [PMID: 38729594 DOI: 10.1016/j.abb.2024.110029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/18/2024] [Accepted: 05/07/2024] [Indexed: 05/12/2024]
Abstract
Endothelial cells play an important role in the metabolism of adipose tissue (AT). This study aimed to analyze the changes that adipose tissue in AT endothelial cells undergo during the development of obesity, using single-nucleus RNA sequence (snRNA-seq). Mouse paraepididymal AT cells were subjected to snRNA-seq with the 10X Genomics platform. The cell types were then clustered using t-distributed stochastic neighbor embedding and unbiased computational informatics analyses. Protein-protein interactions network was established using the STRING database and visualized using Cytoscape. The dataset was subjected to differential gene enrichment analysis. In total, 21,333 cells acquired from 24 mouse paraepididymal AT samples were analyzed using snRNA-seq. This study identified 18 distinct clusters and annotated macrophages, fibroblasts, epithelial cells, T cells, endothelial cells, stem cells, neutrophil cells, and neutrophil cell types based on representative markers. Cluster 12 was defined as endothelial cells. The proportion of endothelial cells decreased with the development of obesity. Inflammatory factors, such as Vegfa and Prdm16 were upregulated in the medium obesity group but downregulated in the obesity group. Genes, such as Prox1, Erg, Flt4, Kdr, Flt1, and Pecam1 promoted the proliferation of AT endothelial cells and maintained the internal environment of AT. This study established a reference model and general framework for studying the mechanisms, biomarkers, and therapeutic targets of endothelial cell dysfunction-related diseases at the single-cell level.
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Affiliation(s)
- Zhimin Lu
- College of Sport and Health, Shandong Sport University, 250102, Jinan, China
| | - Ling Ding
- College of Sport and Health, Shandong Sport University, 250102, Jinan, China
| | - Xing Jiang
- College of Sport and Health, Shandong Sport University, 250102, Jinan, China
| | - Sen Zhang
- College of Sport and Health, Shandong Sport University, 250102, Jinan, China
| | - Min Yan
- College of Sport and Health, Shandong Sport University, 250102, Jinan, China
| | - Guangxin Yang
- College of Sport and Health, Shandong Sport University, 250102, Jinan, China
| | - Xuewen Tian
- College of Sport and Health, Shandong Sport University, 250102, Jinan, China.
| | - Qinglu Wang
- College of Sport and Health, Shandong Sport University, 250102, Jinan, China.
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26
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Kang J, Castro VM, Ripperger M, Venkatesh S, Burstein D, Linnér RK, Rocha DB, Hu Y, Wilimitis D, Morley T, Han L, Kim RY, Feng YCA, Ge T, Heckers S, Voloudakis G, Chabris C, Roussos P, McCoy TH, Walsh CG, Perlis RH, Ruderfer DM. Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits. Am J Psychiatry 2024; 181:608-619. [PMID: 38745458 DOI: 10.1176/appi.ajp.20230247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
OBJECTIVE Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD. METHODS Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks. RESULTS Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. CONCLUSIONS This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.
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Affiliation(s)
- JooEun Kang
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Victor M Castro
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Michael Ripperger
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Sanan Venkatesh
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - David Burstein
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Richard Karlsson Linnér
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Daniel B Rocha
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Yirui Hu
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Drew Wilimitis
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Theodore Morley
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Lide Han
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Rachel Youngjung Kim
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Yen-Chen Anne Feng
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Tian Ge
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Stephan Heckers
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Georgios Voloudakis
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Christopher Chabris
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Panos Roussos
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Thomas H McCoy
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Colin G Walsh
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Roy H Perlis
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute (Kang, Morley, Han, Ruderfer), Department of Psychiatry (Castro, Kim, Ge, McCoy, Perlis) and Center for Quantitative Health (Castro, Kim, McCoy, Perlis), Massachusetts General Hospital, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); Department of Psychiatry, Center for Disease Neurogenomics, Friedman Brain Institute, Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, and Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York (Venkatesh, Burstein, Voloudakis, Roussos); Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, N.Y. (Venkatesh, Burstein, Voloudakis, Roussos); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Linnér, Chabris); Department of Economics, Leiden University, Leiden, the Netherlands (Linnér); Phenomic Analytics and Clinical Data Core (Rocha) and Population Health Sciences (Hu), Geisinger, Danville, Pa.; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei (Feng)
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Eghbali M, Mottaghi A, Taghizadeh S, Cheraghi S. Genetic Variants in the Fat Mass and Obesity-Associated Gene and Risk of Obesity/Overweight in Children and Adolescents: A Systematic Review and Meta-Analysis. Endocrinol Diabetes Metab 2024; 7:e00510. [PMID: 38973101 PMCID: PMC11227992 DOI: 10.1002/edm2.510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/16/2024] [Accepted: 06/23/2024] [Indexed: 07/09/2024] Open
Abstract
OBJECTIVE The variations in the single-nucleotide polymorphisms (SNPs) of the fat mass and obesity (FTO)-associated gene have been linked to being overweight or obese in children. In this research a thorough examination was performed to elucidate the connection between various FTO gene SNPs and overweight or obesity in children and adolescents. METHOD We searched PubMed, Google scholar, Web of Science and Scopus until January 2024 to find studies that investigate the association between different SNPs of FTO gene and the risk of overweight/obesity in children and adolescents. After filtering the relevant studies, meta-analysis was used to quantify the association of FTO gene SNPs within different genetic inheritance models. RESULTS We have identified 32 eligible studies with 14,930 obese/overweight cases and 24,765 healthy controls. Our recessive model showed a significant association with rs9939609 (OR: 1.56, 95% CI: 1.20; 2.02, p < 0.01) and rs1421085 (OR: 1.77, 95% CI: 1.14; 2.75, p < 0.01). Besides, in the homozygote model, rs1421085 showed the highest association (OR: 2.32, 95% CI: 1.38; 3.89, p < 0.01) with the risk of obesity in a population of children and adolescents. Moreover, there are other SNPs of FTO genes, such as rs9921255, rs9928094 and rs9930333, which showed a positive association with obesity and overweight. However, their effects were evaluated in very few numbers of studies. CONCLUSION In this study, we have found that the FTO rs9939609 and rs1421085 are associated to an increased risk of obesity among children and adolescents. Besides, the findings of this study further reaffirmed the established link between rs9939609 and obesity in children and adolescents.
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Affiliation(s)
- Maryam Eghbali
- Endocrine Research Center, Institute of Endocrinology and MetabolismIran University of Medical SciencesTehranIran
| | - Azadeh Mottaghi
- Research Center for Prevention of Cardiovascular Diseases, Endocrinology & Metabolism, Institute of Endocrinology MetabolismIran University of Medical SciencesTehranIran
| | - Sara Taghizadeh
- Translational Ophthalmology Research CenterTehran University of Medical SciencesTehranIran
| | - Sara Cheraghi
- Endocrine Research Center, Institute of Endocrinology and MetabolismIran University of Medical SciencesTehranIran
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Steinwand S, Stacher Hörndli C, Ferris E, Emery J, Gonzalez Murcia JD, Cristina Rodriguez A, Leydsman TC, Chaix A, Thomas A, Davey C, Gregg C. Conserved Noncoding Cis-Elements Associated with Hibernation Modulate Metabolic and Behavioral Adaptations in Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600851. [PMID: 38979203 PMCID: PMC11230392 DOI: 10.1101/2024.06.26.600851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Our study elucidates functional roles for conserved cis-elements associated with the evolution of mammalian hibernation. Genomic analyses found topologically associated domains (TADs) that disproportionately accumulated convergent genomic changes in hibernators, including the TAD for the Fat Mass & Obesity (Fto) locus. Some hibernation-linked cis-elements in this TAD form regulatory contacts with multiple neighboring genes. Knockout mice for these cis-elements exhibit Fto, Irx3, and Irx5 gene expression changes, impacting hundreds of genes downstream. Profiles of pre-torpor, torpor, and post-torpor phenotypes found distinct roles for each cis-element in metabolic control, while a high caloric diet uncovered different obesogenic effects. One cis-element promoting a lean phenotype influences foraging behaviors throughout life, affecting specific behavioral sequences. Thus, convergent evolution in hibernators pinpoints functional genetic mechanisms of mammalian metabolic control.
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Affiliation(s)
- Susan Steinwand
- Department of Neurobiology, University of Utah; Salt Lake City, 84105, USA
| | | | - Elliott Ferris
- Department of Neurobiology, University of Utah; Salt Lake City, 84105, USA
| | - Jared Emery
- Department of Neurobiology, University of Utah; Salt Lake City, 84105, USA
| | | | | | - Tyler C. Leydsman
- Department of Neurobiology, University of Utah; Salt Lake City, 84105, USA
| | - Amandine Chaix
- Department of Nutrition and Integrative Physiology, University of Utah; Salt Lake City, 84105, USA
| | - Alun Thomas
- Division of Epidemiology, University of Utah; Salt Lake City, 84105, USA
- Study Design and Biostatistics Center, University of Utah; Salt Lake City, 84105, USA
| | - Crystal Davey
- Mutation Generation & Detection Core Facility, University of Utah; Salt Lake City, 84105, USA
| | - Christopher Gregg
- Department of Neurobiology, University of Utah; Salt Lake City, 84105, USA
- Department of Human Genetics, University of Utah; Salt Lake City, 84105, USA
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29
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Sharma T, Badaruddoza B. Genetic association of FTO gene polymorphisms with obesity and its related phenotypes: A case-control study. J Cardiovasc Thorac Res 2024; 16:102-112. [PMID: 39253342 PMCID: PMC11380751 DOI: 10.34172/jcvtr.33038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/11/2024] [Indexed: 09/11/2024] Open
Abstract
Introduction FTO gene belongs to the non-heme Fe (II) and 2 oxoglutarate-dependent dioxygenase superfamily. Polymorphisms within the first intron of the FTO gene have been examined across various populations, yielding disparate findings.The present study aimed to determine the impact of two intronic polymorphisms FTO 30685T/G (rs17817449) and -23525T/A (rs9939609) on the risk of obesity in Punjab, India. Methods Genotypic and biochemical analysis were done for 671 unrelated participants (obese=333 and non-obese=338) (age≥18 years). Genotyping of the polymorphisms was done by PCR-RFLP method. However, 50% of the samples were sequenced by Sanger sequencing. Results Both the FTO variants 30685 (TT vs GG: odds ratio (OR), 2.30; 95% confidence interval (CI), 1.39-3.79) and -23525 (TT vs AA: odds ratio (OR), 2.78; 95% confidence interval (CI), 1.37-5.64) showed substantial risk towards obesity by conferring it 2 times and 3 times, respectively. The analysis by logistic regression showed a significant association for both the variants 30685T/G (rs17817449) and -23525T/A (rs9939609) (OR=2.29; 95%CI: 1.47-3.57) and (OR=5.25; 95% CI: 2.68-10.28) under the recessive genetic model, respectively. The haplotype combination TA (30685; -23525) develops a 4 times risk for obesity (P=0.0001). Among obese, the G allele of 30685T/G and A- allele of -23525T/A showed variance in Body mass index (BMI), waist circumference (WC), waist-to-height ratio(WHtR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and triglyceride(TG). Conclusion The present investigation indicated that both the FTO 30685T/G (rs17817449) and -23525T/A (rs9939609) polymorphisms have a key impact on an individual's vulnerability to obesity in this population.
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Affiliation(s)
- Tanmayi Sharma
- Department of Human Genetics, Guru Nanak Dev University, Amritsar-143 005, Punjab, India
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30
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Xu H, Gupta S, Dinsmore I, Kollu A, Cawley AM, Anwar MY, Chen HH, Petty LE, Seshadri S, Graff M, Below P, Brody JA, Chittoor G, Fisher-Hoch SP, Heard-Costa NL, Levy D, Lin H, Loos RJF, Mccormick JB, Rotter JI, Mirshahi T, Still CD, Destefano A, Cupples LA, Mohlke KL, North KE, Justice AE, Liu CT. Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.11.24308730. [PMID: 38903089 PMCID: PMC11188121 DOI: 10.1101/2024.06.11.24308730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA
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Affiliation(s)
- Hanfei Xu
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
| | - Shreyash Gupta
- Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Ian Dinsmore
- Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Abbey Kollu
- Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
| | - Anne Marie Cawley
- Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Mohammad Y. Anwar
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Hung-Hsin Chen
- Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
| | - Lauren E. Petty
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
| | - Sudha Seshadri
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
- Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
| | - Misa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Piper Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
| | - Jennifer A. Brody
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
| | - Geetha Chittoor
- Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Susan P. Fisher-Hoch
- Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
| | - Nancy L. Heard-Costa
- Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
| | - Ruth JF. Loos
- Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
| | - Joseph B. Mccormick
- Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
| | - Tooraj Mirshahi
- Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Christopher D. Still
- Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Anita Destefano
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
- Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
| | - L. Adrienne Cupples
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
| | - Karen L Mohlke
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Anne E. Justice
- Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
| | - Ching-Ti Liu
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
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31
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Ghosh S, Bouchard C. Considerations on efforts needed to improve our understanding of the genetics of obesity. Int J Obes (Lond) 2024:10.1038/s41366-024-01528-0. [PMID: 38849463 DOI: 10.1038/s41366-024-01528-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/18/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Sujoy Ghosh
- Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
| | - Claude Bouchard
- Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
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32
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Son JE. Genetics, pharmacotherapy, and dietary interventions in childhood obesity. JOURNAL OF PHARMACY & PHARMACEUTICAL SCIENCES : A PUBLICATION OF THE CANADIAN SOCIETY FOR PHARMACEUTICAL SCIENCES, SOCIETE CANADIENNE DES SCIENCES PHARMACEUTIQUES 2024; 27:12861. [PMID: 38863827 PMCID: PMC11165095 DOI: 10.3389/jpps.2024.12861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 05/16/2024] [Indexed: 06/13/2024]
Abstract
Childhood obesity has emerged as a major global health issue, contributing to the increased prevalence of chronic conditions and adversely affecting the quality of life and future prospects of affected individuals, thereby presenting a substantial societal challenge. This complex condition, influenced by the interplay of genetic predispositions and environmental factors, is characterized by excessive energy intake due to uncontrolled appetite regulation and a Westernized diet. Managing obesity in childhood requires specific considerations compared with adulthood, given the vulnerability of the critical juvenile-adolescent period to toxicity and developmental defects. Consequently, common treatment options for adult obesity may not directly apply to younger populations. Therefore, research on childhood obesity has focused on genetic defects in regulating energy intake, alongside pharmacotherapy and dietary interventions as management approaches, with an emphasis on safety concerns. This review aims to summarize canonical knowledge and recent findings on genetic factors contributing to childhood obesity. Additionally, it assesses the efficacy and safety of existing pharmacotherapies and dietary interventions and suggests future research directions. By providing a comprehensive understanding of the complex dynamics of childhood obesity, this review aims to offer insights into more targeted and effective strategies for addressing this condition, including personalized healthcare solutions.
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Affiliation(s)
- Joe Eun Son
- School of Food Science and Biotechnology, Research Institute of Tailored Food Technology, Kyungpook National University, Daegu, Republic of Korea
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33
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Górczyńska-Kosiorz S, Lejawa M, Goławski M, Tomaszewska A, Fronczek M, Maksym B, Banach M, Osadnik T. The Impact of Haplotypes of the FTO Gene, Lifestyle, and Dietary Patterns on BMI and Metabolic Syndrome in Polish Young Adult Men. Nutrients 2024; 16:1615. [PMID: 38892547 PMCID: PMC11174437 DOI: 10.3390/nu16111615] [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: 04/19/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Variants in fat mass and the obesity-associated protein (FTO) gene have long been recognized as the most significant genetic predictors of body fat mass and obesity. Nevertheless, despite the overall evidence, there are conflicting reports regarding the correlation between different polymorphisms of the FTO gene and body mass index (BMI). Additionally, it is unclear whether FTO influences metabolic syndrome (MetS) through mechanisms other than BMI's impact. In this work, we aimed to analyze the impact of the following FTO polymorphisms on the BMI as well as MetS components in a population of young adult men. METHODS The patient group consisted of 279 Polish young adult men aged 28.92 (4.28) recruited for the MAGNETIC trial. The single-nucleotide polymorphisms (SNPs), located in the first intron of the FTO gene, were genotyped, and the results were used to identify "protective" and "risk" haplotypes and diplotypes based on the literature data. Laboratory, as well as anthropometric measurements regarding MetS, were performed. Measured MetS components included those used in the definition in accordance with the current guidelines. Data regarding dietary patterns were also collected, and principal components of the dietary patterns were identified. RESULTS No statistically significant correlations were identified between the analyzed FTO diplotypes and BMI (p = 0.53) or other MetS components (waist circumference p = 0.55; triglycerides p = 0.72; HDL cholesterol p = 0.33; blood glucose p = 0.20; systolic blood pressure p = 0.06; diastolic blood pressure p = 0.21). Stratification by the level of physical activity or adherence to the dietary patterns also did not result in any statistically significant result. CONCLUSIONS Some studies have shown that FTO SNPs such as rs1421085, rs1121980, rs8050136, rs9939609, and rs9930506 have an impact on the BMI or other MetS components; nevertheless, this was not replicated in this study of Polish young adult males.
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Affiliation(s)
- Sylwia Górczyńska-Kosiorz
- Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland
| | - Mateusz Lejawa
- Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland; (M.L.); (M.G.); (M.F.); (B.M.); (T.O.)
| | - Marcin Goławski
- Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland; (M.L.); (M.G.); (M.F.); (B.M.); (T.O.)
| | - Agnieszka Tomaszewska
- Prenatal Diagnostic and Genetic Clinic, Medical Center, Medical University of Silesia, 41-800 Zabrze, Poland;
| | - Martyna Fronczek
- Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland; (M.L.); (M.G.); (M.F.); (B.M.); (T.O.)
| | - Beata Maksym
- Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland; (M.L.); (M.G.); (M.F.); (B.M.); (T.O.)
| | - Maciej Banach
- Department of Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), 90-549 Lodz, Poland;
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Carnegie 591, Baltimore, MD 21287, USA
| | - Tadeusz Osadnik
- Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland; (M.L.); (M.G.); (M.F.); (B.M.); (T.O.)
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Littleton SH, Trang KB, Volpe CM, Cook K, DeBruyne N, Maguire JA, Weidekamp MA, Hodge KM, Boehm K, Lu S, Chesi A, Bradfield JP, Pippin JA, Anderson SA, Wells AD, Pahl MC, Grant SFA. Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3' UTR of FAIM2. CELL GENOMICS 2024; 4:100556. [PMID: 38697123 PMCID: PMC11099382 DOI: 10.1016/j.xgen.2024.100556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 03/21/2024] [Accepted: 04/08/2024] [Indexed: 05/04/2024]
Abstract
The ch12q13 locus is among the most significant childhood obesity loci identified in genome-wide association studies. This locus resides in a non-coding region within FAIM2; thus, the underlying causal variant(s) presumably influence disease susceptibility via cis-regulation. We implicated rs7132908 as a putative causal variant by leveraging our in-house 3D genomic data and public domain datasets. Using a luciferase reporter assay, we observed allele-specific cis-regulatory activity of the immediate region harboring rs7132908. We generated isogenic human embryonic stem cell lines homozygous for either rs7132908 allele to assess changes in gene expression and chromatin accessibility throughout a differentiation to hypothalamic neurons, a key cell type known to regulate feeding behavior. The rs7132908 obesity risk allele influenced expression of FAIM2 and other genes and decreased the proportion of neurons produced by differentiation. We have functionally validated rs7132908 as a causal obesity variant that temporally regulates nearby effector genes and influences neurodevelopment and survival.
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Affiliation(s)
- Sheridan H Littleton
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Khanh B Trang
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Christina M Volpe
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kieona Cook
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Nicole DeBruyne
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jean Ann Maguire
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Mary Ann Weidekamp
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kenyaita M Hodge
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Keith Boehm
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jonathan P Bradfield
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Quantinuum Research LLC, San Diego, CA 92101, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Stewart A Anderson
- Department of Child and Adolescent Psychiatry, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
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Roumi Z, Salimi Z, Mahmoudi Z, Mobarakeh KA, Ladaninezhad M, Zeinalabedini M, Keshavarz Mohammadian M, Shamsi‐Goushki A, Saeedirad Z, Bahar B, Khoshdooz S, Kalantari N, Azizi Tabesh G, Doaei S, Gholamalizadeh M. Efficacy of a Comprehensive Weight Reduction Intervention in Male Adolescents With Different FTO Genotypes. Endocrinol Diabetes Metab 2024; 7:e00483. [PMID: 38556726 PMCID: PMC10982462 DOI: 10.1002/edm2.483] [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: 12/20/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND The FTO gene polymorphisms may influence the effects of lifestyle interventions on obesity. The present study aimed to assess the influence of the rs9930506 FTO gene polymorphism on the success of a comprehensive weight loss intervention in male adolescents with overweight and obesity. METHODS This study was carried out on 96 adolescent boys with overweight and obesity who were randomly assigned to the intervention (n = 53) and control (n = 43) groups. The blood samples of the participants were collected, and the FTO gene was genotyped for the rs9930506 polymorphism. A comprehensive lifestyle intervention including changes in diet and physical activity was performed for 8 weeks in the intervention group. RESULTS Following the lifestyle intervention, BMI and fat mass decreased significantly in the intervention group compared with the control group (both p < 0.05), while no change was found in weight, height or body muscle percentage between the groups. The participants in the intervention group with the AA/AG genotype and not in carriers of the GG genotype had a significantly higher reduction in BMI (-1.21 vs. 1.87 kg/m2, F = 4.07, p < 0.05) compared with the control group. CONCLUSION The intervention in individuals with the AA/AG genotype has been significantly effective in weight loss compared with the control group. The intervention had no association effect on anthropometric indices in adolescents with the GG genotype of the FTO rs9930506 polymorphism. TRIAL REGISTRATION Name of the registry: National Nutrition and Food Technology Research Institute; Trial registration number: IRCT2016020925699N2; Date of registration: 24/04/2016; URL of trial registry record: https://www.irct.ir/trial/21447.
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Affiliation(s)
- Zahra Roumi
- Department of Nutrition, Science and Research BranchIslamic Azad UniversityTehranIran
| | - Zahra Salimi
- Nutrition and Metabolic Diseases Research CenterAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Zahra Mahmoudi
- Department of Nutrition, Science and Research BranchIslamic Azad UniversityTehranIran
| | - Khadijeh Abbasi Mobarakeh
- Food Security Research Center and Department of Community Nutrition, School of Nutrition and Food ScienceIsfahan University of Medical SciencesIsfahanIran
| | - Maryam Ladaninezhad
- School of Nutritional Sciences and DieteticsTehran University of Medical SciencesTehranIran
| | - Mobina Zeinalabedini
- Department of Cellular and Molecular Nutrition, School of Nutritional Sciences and DieteticsTehran University of Medical SciencesTehranIran
| | | | - Ali Shamsi‐Goushki
- Department of Nutrition, School of MedicineMashhad University of Medical SciencesMashhadIran
| | - Zahra Saeedirad
- Department of Clinical Nutrition and DieteticsTehran University of Medical SciencesTehranIran
| | - Bojlul Bahar
- Nutrition Sciences and Applied Food Safety Studies, Research Centre for Global Development, School of Sport & Health SciencesUniversity of Central LancashirePrestonUK
| | - Sara Khoshdooz
- Faculty of MedicineGuilan University of Medical ScienceRashtIran
| | - Naser Kalantari
- Department of Community Nutrition, Faculty of Nutrition and Food TechnologyNational Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical SciencesTehranIran
| | - Ghasem Azizi Tabesh
- Genomic Research CenterShahid Beheshti University of Medical SciencesTehranIran
| | - Saeid Doaei
- Department of Community Nutrition, Faculty of Nutrition and Food TechnologyNational Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical SciencesTehranIran
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Agrawal P, Kaur J, Singh J, Rasane P, Sharma K, Bhadariya V, Kaur S, Kumar V. Genetics, Nutrition, and Health: A New Frontier in Disease Prevention. JOURNAL OF THE AMERICAN NUTRITION ASSOCIATION 2024; 43:326-338. [PMID: 38015713 DOI: 10.1080/27697061.2023.2284997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
The field of nutrition research has traditionally focused on the effects of macronutrients and micronutrients on the body. However, it has become evident that individuals have unique genetic makeups that influence their response to food. Nutritional genomics, which includes nutrigenetics and nutrigenomics, explores the interaction between an individual's genetic makeup, diet, and health outcomes. Nutrigenetics studies the impact of genetic variation on an individual's response to dietary nutrients, while nutrigenomics investigates how dietary components affect gene regulation and expression. These disciplines seek to understand the impact of diet on the genome, transcriptome, proteome, and metabolome. It provides insights into the mechanisms underlying the effect of diet on gene expression. Nutrients can cause the modification of genetic expression through epigenetic changes, such as DNA methylation and histone modifications. The aim of nutrigenomics is to create personalized diets based on the unique metabolic profile of an individual, gut microbiome, and genetic makeup to prevent diseases and promote health. Nutrigenomics has the potential to revolutionize the field of nutrition by combining the practicality of personalized nutrition with knowledge of genetic factors underlying health and disease. Thus, nutrigenomics offers a promising approach to improving health outcomes (in terms of disease prevention) through personalized nutrition strategies based on an individual's genetic and metabolic characteristics.
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Affiliation(s)
- Piyush Agrawal
- Department of Food Technology and Nutrition, School of Agriculture, Lovely Professional University, Phagwara, India
| | - Jaspreet Kaur
- Department of Food Technology and Nutrition, School of Agriculture, Lovely Professional University, Phagwara, India
| | - Jyoti Singh
- Department of Food Technology and Nutrition, School of Agriculture, Lovely Professional University, Phagwara, India
| | - Prasad Rasane
- Department of Food Technology and Nutrition, School of Agriculture, Lovely Professional University, Phagwara, India
| | - Kartik Sharma
- Faculty of Agro-Industry, Prince of Songkla University, Songkla, Thailand
| | - Vishesh Bhadariya
- School of Chemical Engineering, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Sawinder Kaur
- Department of Food Technology and Nutrition, School of Agriculture, Lovely Professional University, Phagwara, India
| | - Vikas Kumar
- Department of Food Science and Technology, Punjab Agricultural University, Ludhiana, India
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37
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Chen J, Xiao WC, Zhao JJ, Heitkamp M, Chen DF, Shan R, Yang ZR, Liu Z. FTO genotype and body mass index reduction in childhood obesity interventions: A systematic review and meta-analysis. Obes Rev 2024; 25:e13715. [PMID: 38320834 DOI: 10.1111/obr.13715] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 10/27/2023] [Accepted: 01/07/2024] [Indexed: 04/18/2024]
Abstract
Numerous guidelines have called for personalized interventions to address childhood obesity. The role of fat mass and obesity-associated gene (FTO) in the risk of childhood obesity has been summarized. However, it remains unclear whether FTO could influence individual responses to obesity interventions, especially in children. To address this, we systematically reviewed 12,255 records across 10 databases/registers and included 13 lifestyle-based obesity interventions (3980 children with overweight/obesity) reporting changes in body mass index (BMI) Z-score, BMI, waist circumference, waist-to-hip ratio, and body fat percentage after interventions. These obesity-related outcomes were first compared between children carrying different FTO genotypes (rs9939609 or its proxy) and then synthesized by random-effect meta-analysis models. The results from single-group interventions showed no evidence of associations between FTO risk allele and changes in obesity-related outcomes after interventions (e.g., BMI Z-score: -0.01; 95% CI: -0.04, 0.01). The results from controlled trials showed that associations between the FTO risk allele and changes in obesity-related outcomes did not differ by intervention/control group. To conclude, the FTO risk allele might play a minor role in the response to obesity interventions among children. Future studies might pay more attention to the accumulation effect of multiple genes in the intervention process among children.
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Affiliation(s)
- Jing Chen
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
| | - Wu-Cai Xiao
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
| | - Jia-Jun Zhao
- Department of Nutrition, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Melanie Heitkamp
- Department of Prevention and Sports Medicine, University Hospital "Klinikum rechts der Isar," Technical University of Munich, Munich, Germany
| | - Da-Fang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Rui Shan
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
| | - Zhi-Rui Yang
- Department of Hematology, The Fifth Medical Center, The Chinese PLA General Hospital, Beijing, China
| | - Zheng Liu
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
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Bhattarai KR, Mobley RJ, Barnett KR, Ferguson DC, Hansen BS, Diedrich JD, Bergeron BP, Yoshimura S, Yang W, Crews KR, Manring CS, Jabbour E, Paietta E, Litzow MR, Kornblau SM, Stock W, Inaba H, Jeha S, Pui CH, Cheng C, Pruett-Miller SM, Relling MV, Yang JJ, Evans WE, Savic D. Investigation of inherited noncoding genetic variation impacting the pharmacogenomics of childhood acute lymphoblastic leukemia treatment. Nat Commun 2024; 15:3681. [PMID: 38693155 PMCID: PMC11063049 DOI: 10.1038/s41467-024-48124-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
Defining genetic factors impacting chemotherapy failure can help to better predict response and identify drug resistance mechanisms. However, there is limited understanding of the contribution of inherited noncoding genetic variation on inter-individual differences in chemotherapy response in childhood acute lymphoblastic leukemia (ALL). Here we map inherited noncoding variants associated with treatment outcome and/or chemotherapeutic drug resistance to ALL cis-regulatory elements and investigate their gene regulatory potential and target gene connectivity using massively parallel reporter assays and three-dimensional chromatin looping assays, respectively. We identify 54 variants with transcriptional effects and high-confidence gene connectivity. Additionally, functional interrogation of the top variant, rs1247117, reveals changes in chromatin accessibility, PU.1 binding affinity and gene expression, and deletion of the genomic interval containing rs1247117 sensitizes cells to vincristine. Together, these data demonstrate that noncoding regulatory variants associated with diverse pharmacological traits harbor significant effects on allele-specific transcriptional activity and impact sensitivity to antileukemic agents.
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Affiliation(s)
- Kashi Raj Bhattarai
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Robert J Mobley
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Kelly R Barnett
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Daniel C Ferguson
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Baranda S Hansen
- Center for Advanced Genome Engineering, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Cell and Molecular Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jonathan D Diedrich
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Brennan P Bergeron
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Graduate School of Biomedical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Satoshi Yoshimura
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Advanced Pediatric Medicine, Tohoku University School of Medicine, Tokyo, Japan
| | - Wenjian Yang
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Kristine R Crews
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Christopher S Manring
- Alliance Hematologic Malignancy Biorepository; Clara D. Bloomfield Center for Leukemia Outcomes Research, Columbus, OH, 43210, USA
| | - Elias Jabbour
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Mark R Litzow
- Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Steven M Kornblau
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wendy Stock
- Comprehensive Cancer Center, University of Chicago Medicine, Chicago, IL, USA
| | - Hiroto Inaba
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Sima Jeha
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Ching-Hon Pui
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Cheng Cheng
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Shondra M Pruett-Miller
- Center for Advanced Genome Engineering, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Cell and Molecular Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Mary V Relling
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jun J Yang
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Graduate School of Biomedical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Integrated Biomedical Sciences Program, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - William E Evans
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Daniel Savic
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
- Graduate School of Biomedical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
- Integrated Biomedical Sciences Program, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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Gedik H, Peterson R, Chatzinakos C, Dozmorov MG, Vladimirov V, Riley BP, Bacanu SA. A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.14.24305811. [PMID: 38699366 PMCID: PMC11065030 DOI: 10.1101/2024.04.14.24305811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.
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40
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Choi E, Song J, Lee Y, Jeong Y, Jang W. Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study. Hum Genomics 2024; 18:34. [PMID: 38566255 PMCID: PMC10985920 DOI: 10.1186/s40246-024-00591-y] [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: 01/23/2024] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge. RESULTS In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes. CONCLUSIONS Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.
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Affiliation(s)
- Eunyoung Choi
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Jaeseung Song
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Yubin Lee
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Yeonbin Jeong
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea
| | - Wonhee Jang
- Department of Life Sciences, Dongguk University, Seoul, 04620, Republic of Korea.
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41
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Chen Z, Snetkova V, Bower G, Jacinto S, Clock B, Dizehchi A, Barozzi I, Mannion BJ, Alcaina-Caro A, Lopez-Rios J, Dickel DE, Visel A, Pennacchio LA, Kvon EZ. Increased enhancer-promoter interactions during developmental enhancer activation in mammals. Nat Genet 2024; 56:675-685. [PMID: 38509385 PMCID: PMC11203181 DOI: 10.1038/s41588-024-01681-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 02/06/2024] [Indexed: 03/22/2024]
Abstract
Remote enhancers are thought to interact with their target promoters via physical proximity, yet the importance of this proximity for enhancer function remains unclear. Here we investigate the three-dimensional (3D) conformation of enhancers during mammalian development by generating high-resolution tissue-resolved contact maps for nearly a thousand enhancers with characterized in vivo activities in ten murine embryonic tissues. Sixty-one percent of developmental enhancers bypass their neighboring genes, which are often marked by promoter CpG methylation. The majority of enhancers display tissue-specific 3D conformations, and both enhancer-promoter and enhancer-enhancer interactions are moderately but consistently increased upon enhancer activation in vivo. Less than 14% of enhancer-promoter interactions form stably across tissues; however, these invariant interactions form in the absence of the enhancer and are likely mediated by adjacent CTCF binding. Our results highlight the general importance of enhancer-promoter physical proximity for developmental gene activation in mammals.
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Affiliation(s)
- Zhuoxin Chen
- Department of Developmental and Cell Biology, School of the Biological Sciences, University of California, Irvine, CA, USA
| | - Valentina Snetkova
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Grace Bower
- Department of Developmental and Cell Biology, School of the Biological Sciences, University of California, Irvine, CA, USA
| | - Sandra Jacinto
- Department of Developmental and Cell Biology, School of the Biological Sciences, University of California, Irvine, CA, USA
| | - Benjamin Clock
- Department of Developmental and Cell Biology, School of the Biological Sciences, University of California, Irvine, CA, USA
| | - Atrin Dizehchi
- Department of Developmental and Cell Biology, School of the Biological Sciences, University of California, Irvine, CA, USA
| | - Iros Barozzi
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Brandon J Mannion
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Comparative Biochemistry Program, University of California, Berkeley, CA, USA
| | - Ana Alcaina-Caro
- Centro Andaluz de Biología del Desarrollo, CSIC, Universidad Pablo de Olavide, Junta de Andalucía, Seville, Spain
| | - Javier Lopez-Rios
- Centro Andaluz de Biología del Desarrollo, CSIC, Universidad Pablo de Olavide, Junta de Andalucía, Seville, Spain
- School of Health Sciences, Universidad Loyola Andalucía, Seville, Spain
| | - Diane E Dickel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Octant, Inc, Emeryville, CA, USA
| | - Axel Visel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA, USA
- School of Natural Sciences, University of California, Merced, CA, USA
| | - Len A Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Comparative Biochemistry Program, University of California, Berkeley, CA, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA, USA
| | - Evgeny Z Kvon
- Department of Developmental and Cell Biology, School of the Biological Sciences, University of California, Irvine, CA, USA.
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42
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 PMCID: PMC11456345 DOI: 10.1038/s41588-024-01682-1] [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: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Serra F, Nieto-Aliseda A, Fanlo-Escudero L, Rovirosa L, Cabrera-Pasadas M, Lazarenkov A, Urmeneta B, Alcalde-Merino A, Nola EM, Okorokov AL, Fraser P, Graupera M, Castillo SD, Sardina JL, Valencia A, Javierre BM. p53 rapidly restructures 3D chromatin organization to trigger a transcriptional response. Nat Commun 2024; 15:2821. [PMID: 38561401 PMCID: PMC10984980 DOI: 10.1038/s41467-024-46666-1] [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: 06/15/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Activation of the p53 tumor suppressor triggers a transcriptional program to control cellular response to stress. However, the molecular mechanisms by which p53 controls gene transcription are not completely understood. Here, we uncover the critical role of spatio-temporal genome architecture in this process. We demonstrate that p53 drives direct and indirect changes in genome compartments, topologically associating domains, and DNA loops prior to one hour of its activation, which escort the p53 transcriptional program. Focusing on p53-bound enhancers, we report 340 genes directly regulated by p53 over a median distance of 116 kb, with 74% of these genes not previously identified. Finally, we showcase that p53 controls transcription of distal genes through newly formed and pre-existing enhancer-promoter loops in a cohesin dependent manner. Collectively, our findings demonstrate a previously unappreciated architectural role of p53 as regulator at distinct topological layers and provide a reliable set of new p53 direct target genes that may help designs of cancer therapies.
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Affiliation(s)
- François Serra
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain
| | | | | | | | - Mónica Cabrera-Pasadas
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | | | - Blanca Urmeneta
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain
| | | | - Emanuele M Nola
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain
| | - Andrei L Okorokov
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Peter Fraser
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Mariona Graupera
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
- CIBERONC, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Jose L Sardina
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain
| | - Alfonso Valencia
- Barcelona Supercomputing Center, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Biola M Javierre
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain.
- Institute for Health Science Research Germans Trias i Pujol, Barcelona, Spain.
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44
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Zhang E, Sun Q, Zhang C, Ma H, Zhang J, Ding Y, Wang G, Jin C, Jin C, Fu Y, Yan C, Zhu M, Wang C, Dai J, Jin G, Hu Z, Shen H, Ma H. Comprehensive functional interrogation of susceptibility loci in GWASs identified KIAA0391 as a novel oncogenic driver via regulating pyroptosis in NSCLC. Cancer Lett 2024; 585:216646. [PMID: 38262497 DOI: 10.1016/j.canlet.2024.216646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
Approximately 51 non-small-cell lung cancer (NSCLC) risk loci have been identified by genome-wide association studies (GWASs). We conducted a high throughput RNA-interference (RNAi) screening to identify the candidate causal genes in NSCLC risk loci. KIAA0391 at 14q13.1 had the highest score and could promote proliferation and metastasis of NSCLC in vitro and in vivo. We next prioritized rs3783313 as a causal variant at 14q13.1, by integrating a large-scale population study consisting of 27,120 lung cancer cases and 27,355 controls, functional annotation, and expression quantitative trait locus (eQTL) analysis. Then we found that rs3783313 could facilitate a promoter-enhancer interaction to upregulate KIAA0391 expression by affecting the affinity of transcription factor NFYA. Mechanistically, KIAA0391 knockdown dramatically influenced pyroptosis-related pathways and increased the expression of CASP1. And KIAA0391 transcriptionally repressed CASP1 by binding to SMAD2 and induced an anti-pyroptosis phenotype, promoting tumorigenesis of NSCLC, which provides new insights and potential target for NSCLC.
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Affiliation(s)
- Erbao Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Qi Sun
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chang Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou Second People's Hospital, Changzhou Medical Center, Nanjing Medical University, Nanjing 211166, China
| | - Huimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Jing Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Yue Ding
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guoqing Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chen Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chenying Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Yating Fu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Caiwang Yan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Cheng Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100142, China.
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100142, China.
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45
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Archana CA, Sekar YS, Suresh KP, Subramaniam S, Sagar N, Rani S, Anandakumar J, Pandey RK, Barman NN, Patil SS. Investigating the Influence of ANTXR2 Gene Mutations on Protective Antigen Binding for Heightened Anthrax Resistance. Genes (Basel) 2024; 15:426. [PMID: 38674361 PMCID: PMC11049084 DOI: 10.3390/genes15040426] [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: 02/23/2024] [Revised: 03/14/2024] [Accepted: 03/18/2024] [Indexed: 04/28/2024] Open
Abstract
Bacillus anthracis is the bacterium responsible for causing the zoonotic disease called anthrax. The disease presents itself in different forms like gastrointestinal, inhalation, and cutaneous. Bacterial spores are tremendously adaptable, can persist for extended periods and occasionally endanger human health. The Anthrax Toxin Receptor-2 (ANTXR2) gene acts as membrane receptor and facilitates the entry of the anthrax toxin into host cells. Additionally, mutations in the ANTXR2 gene have been linked to various autoimmune diseases, including Hyaline Fibromatosis Syndrome (HFS), Ankylosing Spondylitis (AS), Juvenile Hyaline Fibromatosis (JHF), and Infantile Systemic Hyalinosis (ISH). This study delves into the genetic landscape of ANTXR2, aiming to comprehend its associations with diverse disorders, elucidate the impacts of its mutations, and pinpoint minimal non-pathogenic mutations capable of reducing the binding affinity of the ANTXR2 gene with the protective antigen. Recognizing the pivotal role of single-nucleotide polymorphisms (SNPs) in shaping genetic diversity, we conducted computational analyses to discern highly deleterious and tolerated non-synonymous SNPs (nsSNPs) in the ANTXR2 gene. The Mutpred2 server determined that the Arg465Trp alteration in the ANTXR2 gene leads to altered DNA binding (p = 0.22) with a probability of a deleterious mutation of 0.808; notably, among the identified deleterious SNPs, rs368288611 (Arg465Trp) stands out due to its significant impact on altering the DNA-binding ability of ANTXR2. We propose these SNPs as potential candidates for hypertension linked to the ANTXR2 gene, which is implicated in blood pressure regulation. Noteworthy among the tolerated substitutions is rs200536829 (Ala33Ser), recognized as less pathogenic; this highlights its potential as a valuable biomarker, potentially reducing side effects on the host while also reducing binding with the protective antigen protein. Investigating these SNPs holds the potential to correlate with several autoimmune disorders and mitigate the impact of anthrax disease in humans.
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Affiliation(s)
- Chamalapura Ashwathama Archana
- ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Bengaluru 560064, India; (C.A.A.); (Y.S.S.); (N.S.); (S.R.); (J.A.); (S.S.P.)
| | - Yamini Sri Sekar
- ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Bengaluru 560064, India; (C.A.A.); (Y.S.S.); (N.S.); (S.R.); (J.A.); (S.S.P.)
| | - Kuralayanapalya Puttahonnappa Suresh
- ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Bengaluru 560064, India; (C.A.A.); (Y.S.S.); (N.S.); (S.R.); (J.A.); (S.S.P.)
| | | | - Ningegowda Sagar
- ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Bengaluru 560064, India; (C.A.A.); (Y.S.S.); (N.S.); (S.R.); (J.A.); (S.S.P.)
| | - Swati Rani
- ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Bengaluru 560064, India; (C.A.A.); (Y.S.S.); (N.S.); (S.R.); (J.A.); (S.S.P.)
| | - Jayashree Anandakumar
- ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Bengaluru 560064, India; (C.A.A.); (Y.S.S.); (N.S.); (S.R.); (J.A.); (S.S.P.)
| | - Rajan Kumar Pandey
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Solna, Sweden;
| | - Nagendra Nath Barman
- College of Veterinary Science, Assam Agricultural University (AAU), Guwahati 781022, India;
| | - Sharanagouda S. Patil
- ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Bengaluru 560064, India; (C.A.A.); (Y.S.S.); (N.S.); (S.R.); (J.A.); (S.S.P.)
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46
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Koo HJ, Pan W. Are trait-associated genes clustered together in a gene network? Genet Epidemiol 2024. [PMID: 38472164 DOI: 10.1002/gepi.22557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/25/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024]
Abstract
Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES) data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.
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Affiliation(s)
- Hyun Jung Koo
- School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
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47
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Pahl MC, Liu L, Pippin JA, Wagley Y, Boehm K, Hankenson KD, Wells AD, Yang W, Grant SFA. Variant to gene mapping for carpal tunnel syndrome risk loci implicates skeletal muscle regulatory elements. EBioMedicine 2024; 101:105038. [PMID: 38417377 PMCID: PMC10909706 DOI: 10.1016/j.ebiom.2024.105038] [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: 07/25/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/01/2024] Open
Abstract
BACKGROUND Carpal tunnel syndrome (CTS) is a common disorder caused by compression of the median nerve in the wrist, resulting in pain and numbness throughout the hand and forearm. While multiple behavioural and physiological factors influence CTS risk, a growing body of evidence supports a strong genetic contribution. Recent genome-wide association study (GWAS) efforts have reported 53 independent signals associated with CTS. While GWAS can identify genetic loci conferring risk, it does not determine which cell types drive the genetic aetiology of the trait, which variants are "causal" at a given signal, and which effector genes correspond to these non-coding variants. These obstacles limit interpretation of potential disease mechanisms. METHODS We analysed CTS GWAS findings in the context of chromatin conformation between gene promoters and accessible chromatin regions across cellular models of bone, skeletal muscle, adipocytes and neurons. We identified proxy variants in high LD with the lead CTS sentinel SNPs residing in promoter connected open chromatin in the skeletal muscle and bone contexts. FINDINGS We detected significant enrichment for heritability in skeletal muscle myotubes, as well as a weaker correlation in human mesenchymal stem cell-derived osteoblasts. In myotubes, our approach implicated 117 genes contacting 60 proxy variants corresponding to 20 of the 53 GWAS signals. In the osteoblast context we implicated 30 genes contacting 24 proxy variants coinciding with 12 signals, of which 19 genes shared. We subsequently prioritized BZW2 as a candidate effector gene in CTS and implicated it as novel gene that perturbs myocyte differentiation in vitro. INTERPRETATION Taken together our results suggest that the CTS genetic component influences the size, integrity, and organization of multiple tissues surrounding the carpal tunnel, in particular muscle and bone, to predispose the nerve to being compressed in this disease setting. FUNDING This work was supported by NIH Grant UM1 DK126194 (SFAG and WY), R01AG072705 (SFAG & KDH) and the Center for Spatial and Functional Genomics at CHOP (SFAG & ADW). SFAG is supported by the Daniel B. Burke Endowed Chair for Diabetes Research. WY is supported by the Perelman School of Medicine of the University of Pennsylvania.
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Affiliation(s)
- Matthew C Pahl
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lin Liu
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Institute for Diabetes, Obesity & Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia PA19104, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Yadav Wagley
- Orthopaedic Research Laboratories, Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Keith Boehm
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kurt D Hankenson
- Orthopaedic Research Laboratories, Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 3615 Civic Center Boulevard, Philadelphia, PA, USA; Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, 3615 Civic Center Boulevard, Philadelphia, PA, USA
| | - Wenli Yang
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Institute for Diabetes, Obesity & Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia PA19104, USA.
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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48
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Lopez-Yus M, Hörndler C, Borlan S, Bernal-Monterde V, Arbones-Mainar JM. Unraveling Adipose Tissue Dysfunction: Molecular Mechanisms, Novel Biomarkers, and Therapeutic Targets for Liver Fat Deposition. Cells 2024; 13:380. [PMID: 38474344 DOI: 10.3390/cells13050380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/14/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
Adipose tissue (AT), once considered a mere fat storage organ, is now recognized as a dynamic and complex entity crucial for regulating human physiology, including metabolic processes, energy balance, and immune responses. It comprises mainly two types: white adipose tissue (WAT) for energy storage and brown adipose tissue (BAT) for thermogenesis, with beige adipocytes demonstrating the plasticity of these cells. WAT, beyond lipid storage, is involved in various metabolic activities, notably lipogenesis and lipolysis, critical for maintaining energy homeostasis. It also functions as an endocrine organ, secreting adipokines that influence metabolic, inflammatory, and immune processes. However, dysfunction in WAT, especially related to obesity, leads to metabolic disturbances, including the inability to properly store excess lipids, resulting in ectopic fat deposition in organs like the liver, contributing to non-alcoholic fatty liver disease (NAFLD). This narrative review delves into the multifaceted roles of WAT, its composition, metabolic functions, and the pathophysiology of WAT dysfunction. It also explores diagnostic approaches for adipose-related disorders, emphasizing the importance of accurately assessing AT distribution and understanding the complex relationships between fat compartments and metabolic health. Furthermore, it discusses various therapeutic strategies, including innovative therapeutics like adipose-derived mesenchymal stem cells (ADMSCs)-based treatments and gene therapy, highlighting the potential of precision medicine in targeting obesity and its associated complications.
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Affiliation(s)
- Marta Lopez-Yus
- Adipocyte and Fat Biology Laboratory (AdipoFat), Translational Research Unit, University Hospital Miguel Servet, 50009 Zaragoza, Spain
- Instituto Aragones de Ciencias de la Salud (IACS), 50009 Zaragoza, Spain
- Instituto de Investigación Sanitaria (IIS) Aragon, 50009 Zaragoza, Spain
| | - Carlos Hörndler
- Instituto de Investigación Sanitaria (IIS) Aragon, 50009 Zaragoza, Spain
- Pathology Department, Miguel Servet University Hospital, 50009 Zaragoza, Spain
| | - Sofia Borlan
- General and Digestive Surgery Department, Miguel Servet University Hospital, 50009 Zaragoza, Spain
| | - Vanesa Bernal-Monterde
- Adipocyte and Fat Biology Laboratory (AdipoFat), Translational Research Unit, University Hospital Miguel Servet, 50009 Zaragoza, Spain
- Instituto Aragones de Ciencias de la Salud (IACS), 50009 Zaragoza, Spain
- Gastroenterology Department, Miguel Servet University Hospital, 50009 Zaragoza, Spain
| | - Jose M Arbones-Mainar
- Adipocyte and Fat Biology Laboratory (AdipoFat), Translational Research Unit, University Hospital Miguel Servet, 50009 Zaragoza, Spain
- Instituto Aragones de Ciencias de la Salud (IACS), 50009 Zaragoza, Spain
- Instituto de Investigación Sanitaria (IIS) Aragon, 50009 Zaragoza, Spain
- CIBER Fisiopatología Obesidad y Nutrición (CIBERObn), Instituto Salud Carlos III, 28029 Madrid, Spain
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49
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Farris KM, Senior AM, Sobreira DR, Mitchell RM, Weber ZT, Ingerslev LR, Barrès R, Simpson SJ, Crean AJ, Nobrega MA. Dietary macronutrient composition impacts gene regulation in adipose tissue. Commun Biol 2024; 7:194. [PMID: 38365885 PMCID: PMC10873408 DOI: 10.1038/s42003-024-05876-5] [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: 07/11/2023] [Accepted: 01/30/2024] [Indexed: 02/18/2024] Open
Abstract
Diet is a key lifestyle component that influences metabolic health through several factors, including total energy intake and macronutrient composition. While the impact of caloric intake on gene expression and physiological phenomena in various tissues is well described, the influence of dietary macronutrient composition on these parameters is less well studied. Here, we use the Nutritional Geometry framework to investigate the role of macronutrient composition on metabolic function and gene regulation in adipose tissue. Using ten isocaloric diets that vary systematically in their proportion of energy from fat, protein, and carbohydrates, we find that gene expression and splicing are highly responsive to macronutrient composition, with distinct sets of genes regulated by different macronutrient interactions. Specifically, the expression of many genes associated with Bardet-Biedl syndrome is responsive to dietary fat content. Splicing and expression changes occur in largely separate gene sets, highlighting distinct mechanisms by which dietary composition influences the transcriptome and emphasizing the importance of considering splicing changes to more fully capture the gene regulation response to environmental changes such as diet. Our study provides insight into the gene regulation plasticity of adipose tissue in response to macronutrient composition, beyond the already well-characterized response to caloric intake.
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Affiliation(s)
- Kathryn M Farris
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
| | - Alistair M Senior
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW, 2006, Australia
| | - Débora R Sobreira
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Robert M Mitchell
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Zachary T Weber
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Lars R Ingerslev
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, DK-2200, Copenhagen, Denmark
| | - Romain Barrès
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, DK-2200, Copenhagen, Denmark.
- Institut de Pharmacologie Moléculaire et Cellulaire, Université Côte d'Azur & Centre National pour la Recherche Scientifique (CNRS), Valbonne, 06560, France.
| | - Stephen J Simpson
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW, 2006, Australia.
| | - Angela J Crean
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
| | - Marcelo A Nobrega
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
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Cui Y, Xiao Q, Wang Z, Zhang Q, Liu Y, Hao W, Jiang J, Meng Q, Wei X. 1,2-bis(2,4,6-tribromophenoxy) ethane, a novel brominated flame retardant, disrupts intestinal barrier function via the IRX3/NOS2 axis in rat small intestine. JOURNAL OF HAZARDOUS MATERIALS 2024; 461:132597. [PMID: 37804762 DOI: 10.1016/j.jhazmat.2023.132597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/09/2023]
Abstract
Novel brominated flame retardants are widely used in electronics, textiles, furniture, and other products; they can enter the human body through ingestion and respiration and cause harm to the human body, and have been proven to have potential biological toxicity and accumulation effects. 1,2-bis(2,4,6-tribromophenoxy) ethane (BTBPE) is a widely used novel brominated flame retardant; however, there is a lack of research on its mechanism of toxicity, particularly that of intestinal toxicity. Currently, studies on the functionality of iroquois homeobox 3 (IRX3) are extremely limited. In our study, BTBPE was administered to Sprague-Dawley (SD) rats and rat small intestinal crypt epithelial cells (IEC6 cells) in vivo and in vitro, respectively, and hematoxylin and eosin (HE), immunohistochemical, Alcian blue-periodic acid-Schiff (AB-PAS), CCK8, acridine orange/ethidium bromide (AO/EB), fluorescent probes, qPCR, western blotting, and immunofluorescence analyses were performed. To explore the damage mechanism of BTBPE, we used siRNA to silence IRX3 and iNOs-IN-1 (yeast extract-peptone-wheat; YPW) to inhibit nitric oxide synthase 2 (NOS2). The results showed that BTBPE exposure caused inflammation and necroptosis in the jejunum and ileum, as well as destruction of the tight junctions and mucus layer. Moreover, BTBPE activated the IRX3/NOS2 axis both in vivo and in vitro. Silencing IRX3 or inhibiting NOS2 inhibits necroptosis and restores tight junctions in IEC6 cells. In conclusion, our study found that in the jejunum, ileum, and IEC6 cells, BTBPE exposure caused necroptosis and tight junction destruction by activating the IRX3/NOS2 axis. Blocking the IRX3/NOS2 axis can effectively inhibit necroptosis and restore tight junction. In addition, BTBPE exposure caused inflammation and loss of the mucous layer in the jejunum and ileum. Our study is the first to explore the mechanism of intestinal damage caused by BTBPE exposure and to discover new biological functions regulated by the IRX3/NOS2 axis, providing new research directions for necroptosis and tight junctions.
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Affiliation(s)
- Yuan Cui
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China
| | - Qianqian Xiao
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China
| | - Zhenyu Wang
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China
| | - Qiong Zhang
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China
| | - Yuetong Liu
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China
| | - Weidong Hao
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China
| | - Jianjun Jiang
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China
| | - Qinghe Meng
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China
| | - Xuetao Wei
- Department of Toxicology, School of Public Health, Peking University, Beijing 100191, PR China; Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Beijing 100191, PR China.
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