1
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Gruber T, Lechner F, Murat C, Contreras RE, Sanchez-Quant E, Miok V, Makris K, Le Thuc O, González-García I, García-Clave E, Althammer F, Krabichler Q, DeCamp LM, Jones RG, Lutter D, Williams RH, Pfluger PT, Müller TD, Woods SC, Pospisilik JA, Martinez-Jimenez CP, Tschöp MH, Grinevich V, García-Cáceres C. High-calorie diets uncouple hypothalamic oxytocin neurons from a gut-to-brain satiation pathway via κ-opioid signaling. Cell Rep 2023; 42:113305. [PMID: 37864798 PMCID: PMC10636643 DOI: 10.1016/j.celrep.2023.113305] [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: 08/04/2022] [Revised: 08/21/2023] [Accepted: 10/04/2023] [Indexed: 10/23/2023] Open
Abstract
Oxytocin-expressing paraventricular hypothalamic neurons (PVNOT neurons) integrate afferent signals from the gut, including cholecystokinin (CCK), to adjust whole-body energy homeostasis. However, the molecular underpinnings by which PVNOT neurons orchestrate gut-to-brain feeding control remain unclear. Here, we show that mice undergoing selective ablation of PVNOT neurons fail to reduce food intake in response to CCK and develop hyperphagic obesity on a chow diet. Notably, exposing wild-type mice to a high-fat/high-sugar (HFHS) diet recapitulates this insensitivity toward CCK, which is linked to diet-induced transcriptional and electrophysiological aberrations specifically in PVNOT neurons. Restoring OT pathways in diet-induced obese (DIO) mice via chemogenetics or polypharmacology sufficiently re-establishes CCK's anorexigenic effects. Last, by single-cell profiling, we identify a specialized PVNOT neuronal subpopulation with increased κ-opioid signaling under an HFHS diet, which restrains their CCK-evoked activation. In sum, we document a (patho)mechanism by which PVNOT signaling uncouples a gut-brain satiation pathway under obesogenic conditions.
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Affiliation(s)
- Tim Gruber
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Department of Metabolism and Nutritional Programming, Van Andel Institute, Grand Rapids, MI 49506, USA; Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49506, USA.
| | - Franziska Lechner
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Cahuê Murat
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Raian E Contreras
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Eva Sanchez-Quant
- Helmholtz Pioneer Campus (HPC), Helmholtz Zentrum München, Neuherberg, Germany
| | - Viktorian Miok
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Konstantinos Makris
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Computational Discovery Research, Institute for Diabetes and Obesity (IDO), Helmholtz Diabetes Center (HDC), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ophélia Le Thuc
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Ismael González-García
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Elena García-Clave
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | | | - Quirin Krabichler
- Department of Neuropeptide Research in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Lisa M DeCamp
- Department of Metabolism and Nutritional Programming, Van Andel Institute, Grand Rapids, MI 49506, USA
| | - Russell G Jones
- Department of Metabolism and Nutritional Programming, Van Andel Institute, Grand Rapids, MI 49506, USA
| | - Dominik Lutter
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Computational Discovery Research, Institute for Diabetes and Obesity (IDO), Helmholtz Diabetes Center (HDC), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Rhiannan H Williams
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute for Neurogenomics, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Paul T Pfluger
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Research Unit NeuroBiology of Diabetes, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Neurobiology of Diabetes, TUM School of Medicine, Technical University Munich, 80333 Munich, Germany
| | - Timo D Müller
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Department of Pharmacology and Experimental Therapy, Institute for Experimental and Clinical Pharmacology and Toxicology, Eberhard Karls Hospitals and Clinics, Tübingen, Germany
| | - Stephen C Woods
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - John Andrew Pospisilik
- Department of Metabolism and Nutritional Programming, Van Andel Institute, Grand Rapids, MI 49506, USA; Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49506, USA
| | - Celia P Martinez-Jimenez
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49506, USA; TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Matthias H Tschöp
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Division of Metabolic Diseases, Department of Medicine, Technische Universität, Munich, Germany
| | - Valery Grinevich
- Department of Neuropeptide Research in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Center for Neuroinflammation and Cardiometabolic Diseases, Georgia State University, Atlanta, GA, USA.
| | - Cristina García-Cáceres
- Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Medizinische Klinik und Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität München, 80336 Munich, Germany.
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2
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Díez-Del-Molino D, Dehasque M, Chacón-Duque JC, Pečnerová P, Tikhonov A, Protopopov A, Plotnikov V, Kanellidou F, Nikolskiy P, Mortensen P, Danilov GK, Vartanyan S, Gilbert MTP, Lister AM, Heintzman PD, van der Valk T, Dalén L. Genomics of adaptive evolution in the woolly mammoth. Curr Biol 2023; 33:1753-1764.e4. [PMID: 37030294 DOI: 10.1016/j.cub.2023.03.084] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/24/2023] [Accepted: 03/29/2023] [Indexed: 04/10/2023]
Abstract
Ancient genomes provide a tool to investigate the genetic basis of adaptations in extinct organisms. However, the identification of species-specific fixed genetic variants requires the analysis of genomes from multiple individuals. Moreover, the long-term scale of adaptive evolution coupled with the short-term nature of traditional time series data has made it difficult to assess when different adaptations evolved. Here, we analyze 23 woolly mammoth genomes, including one of the oldest known specimens at 700,000 years old, to identify fixed derived non-synonymous mutations unique to the species and to obtain estimates of when these mutations evolved. We find that at the time of its origin, the woolly mammoth had already acquired a broad spectrum of positively selected genes, including ones associated with hair and skin development, fat storage and metabolism, and immune system function. Our results also suggest that these phenotypes continued to evolve during the last 700,000 years, but through positive selection on different sets of genes. Finally, we also identify additional genes that underwent comparatively recent positive selection, including multiple genes related to skeletal morphology and body size, as well as one gene that may have contributed to the small ear size in Late Quaternary woolly mammoths.
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Affiliation(s)
- David Díez-Del-Molino
- Centre for Palaeogenetics, 10691 Stockholm, Sweden; Department of Zoology, Stockholm University, 10691 Stockholm, Sweden; Department of Bioinformatics and Genetics, Swedish Museum of Natural History, 10405 Stockholm, Sweden.
| | - Marianne Dehasque
- Centre for Palaeogenetics, 10691 Stockholm, Sweden; Department of Zoology, Stockholm University, 10691 Stockholm, Sweden; Department of Bioinformatics and Genetics, Swedish Museum of Natural History, 10405 Stockholm, Sweden
| | - J Camilo Chacón-Duque
- Centre for Palaeogenetics, 10691 Stockholm, Sweden; Department of Archaeology and Classical Studies, Stockholm University, 10691 Stockholm, Sweden
| | - Patrícia Pečnerová
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, 10405 Stockholm, Sweden; Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Alexei Tikhonov
- Zoological Institute of the Russian Academy of Sciences, 190121 Saint Petersburg, Russia
| | | | | | - Foteini Kanellidou
- Centre for Palaeogenetics, 10691 Stockholm, Sweden; Department of Microbiology, Tumor and Cell Biology, Clinical Genomics Facility, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Pavel Nikolskiy
- Geological Institute, Russian Academy of Sciences, 119017 Moscow, Russia
| | - Peter Mortensen
- Department of Zoology, Swedish Museum of Natural History, 10405 Stockholm, Sweden
| | - Gleb K Danilov
- Peter the Great Museum of Anthropology and Ethnography, Kunstkamera, Russian Academy of Sciences, 199034 Saint-Petersburg, Russia
| | - Sergey Vartanyan
- North-East Interdisciplinary Scientific Research Institute N.A. Shilo, Far East Branch, Russian Academy of Sciences (NEISRI FEB RAS), 685000 Magadan, Russia
| | - M Thomas P Gilbert
- Center for Evolutionary Hologenomics, GLOBE Institute, Faculty of Health and Medical Sciences, 1353 Copenhagen, Denmark; University Museum NTNU, 7012 Trondheim, Norway
| | | | - Peter D Heintzman
- Centre for Palaeogenetics, 10691 Stockholm, Sweden; Department of Geological Sciences, Stockholm University, 11418 Stockholm, Sweden
| | - Tom van der Valk
- Centre for Palaeogenetics, 10691 Stockholm, Sweden; Department of Bioinformatics and Genetics, Swedish Museum of Natural History, 10405 Stockholm, Sweden; Science for Life Laboratory, 17165 Stockholm, Sweden
| | - Love Dalén
- Centre for Palaeogenetics, 10691 Stockholm, Sweden; Department of Zoology, Stockholm University, 10691 Stockholm, Sweden; Department of Bioinformatics and Genetics, Swedish Museum of Natural History, 10405 Stockholm, Sweden.
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3
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Hegarty R, Kyrana E, Fitzpatrick E, Dhawan A. Fatty liver disease in children (MAFLD/PeFLD Type 2): unique classification considerations and challenges. Ther Adv Endocrinol Metab 2023; 14:20420188231160388. [PMID: 36968656 PMCID: PMC10034351 DOI: 10.1177/20420188231160388] [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: 09/22/2022] [Accepted: 02/11/2023] [Indexed: 03/24/2023] Open
Abstract
In children, fatty liver disease is a group of disorders that often overlaps with inherited metabolic disorders (IMDs), which requires prompt diagnosis and specific management. Metabolic dysfunction-associated fatty liver disease (MAFLD) or, formerly, non-alcoholic fatty liver disease (NAFLD) is the hepatic component of a multisystemic disease that requires a positive criteria in metabolic dysfunction for diagnosis. However, in children, the diagnosis of MAFLD is one of the exclusions of an IMD [paediatric fatty liver disease (PeFLD) type 1] including the possibility that an IMD can be identified in the future following investigations that may be negative at the time. Therefore, while children with fatty liver with metabolic dysfunction could be classified as MAFLD (PeFLD type 2) and managed that way, those who do not fulfil the criteria for metabolic dysfunction should be considered separately bearing in mind the possibility of identifying a yet undiagnosed IMD (PeFLD type 3). This concept is ever more important in a world where MAFLD is the most common cause of liver disease in children and adolescents in whom about 7% are affected. The disease is only partially understood, and awareness is still lacking outside hepatology and gastroenterology. Despite its increasing pervasiveness, the management is far from a one-size-fits-all. Increasing complexities around the genetic, epigenetic, non-invasive modalities of assessment, psychosocial impacts, therapeutics, and natural history of the disease have meant that an individualised approach is required. This is where the challenge lies so that children with fatty liver are considered on their own merits. The purpose of this review is to give a clinical perspective of fatty liver disease in children with relevance to metabolic dysfunction.
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Affiliation(s)
- Robert Hegarty
- Paediatric Liver, GI and Nutrition Centre, and
MowatLabs, King’s College Hospital, London, UK
| | - Eirini Kyrana
- Paediatric Liver, GI and Nutrition Centre, and
MowatLabs, King’s College Hospital, London, UK
| | - Emer Fitzpatrick
- Department of Gastroenterology, Hepatology and
Nutrition, Our Lady’s Hospital Crumlin, Dublin, Ireland
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4
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Zhang F, Zhang Z, Li Y, Sun Y, Zhou X, Chen X, Sun S. Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease. Front Genet 2022; 13:942153. [PMID: 35910194 PMCID: PMC9330026 DOI: 10.3389/fgene.2022.942153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 06/13/2022] [Indexed: 11/24/2022] Open
Abstract
Objective: Nonalcoholic fatty liver disease (NAFLD) is a serious threat to human health worldwide. In this study, the aim is to analyze diagnosis biomarkers in NAFLD and its relationship with the immune microenvironment based on bioinformatics analysis. Methods: We downloaded microarray datasets (GSE48452 and GSE63067) from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The hub genes were screened by a series of machine learning analyses, such as support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and weighted gene co-expression network analysis (WGCNA). It is worth mentioning that we used the gene enrichment analysis to explore the driver pathways of NAFLD occurrence. Subsequently, the aforementioned genes were validated by external datasets (GSE66676). Moreover, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Finally, the Spearman analysis was used to verify the relationship between hub genes and immune cells. Results: Hub genes (CAMK1D, CENPV, and TRHDE) were identified. In addition, we found that the pathogenesis of NAFLD is mainly related to nutrient metabolism and the immune system. In correlation analysis, CENPV expression had a strong negative correlation with resting memory CD4 T cells, and TRHDE expression had a strong positive correlation with naive B cells. Conclusion: CAMK1D, CENPV, and TRHDE play regulatory roles in NAFLD. In particular, CENPV and TRHDE may regulate the immune microenvironment by mediating resting memory CD4 T cells and naive B cells, respectively, and thus influence disease progression.
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Affiliation(s)
| | | | | | | | | | | | - Shibo Sun
- *Correspondence: Xiaoning Chen, ; Shibo Sun,
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5
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Xu L, Yin L, Qi Y, Tan X, Gao M, Peng J. 3D disorganization and rearrangement of genome provide insights into pathogenesis of NAFLD by integrated Hi-C, Nanopore, and RNA sequencing. Acta Pharm Sin B 2021; 11:3150-3164. [PMID: 34729306 PMCID: PMC8546856 DOI: 10.1016/j.apsb.2021.03.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 01/30/2021] [Accepted: 02/07/2021] [Indexed: 12/12/2022] Open
Abstract
The three-dimensional (3D) conformation of chromatin is integral to the precise regulation of gene expression. The 3D genome and genomic variations in non-alcoholic fatty liver disease (NAFLD) are largely unknown, despite their key roles in cellular function and physiological processes. High-throughput chromosome conformation capture (Hi-C), Nanopore sequencing, and RNA-sequencing (RNA-seq) assays were performed on the liver of normal and NAFLD mice. A high-resolution 3D chromatin interaction map was generated to examine different 3D genome hierarchies including A/B compartments, topologically associated domains (TADs), and chromatin loops by Hi-C, and whole genome sequencing identifying structural variations (SVs) and copy number variations (CNVs) by Nanopore sequencing. We identified variations in thousands of regions across the genome with respect to 3D chromatin organization and genomic rearrangements, between normal and NAFLD mice, and revealed gene dysregulation frequently accompanied by these variations. Candidate target genes were identified in NAFLD, impacted by genetic rearrangements and spatial organization disruption. Our data provide a high-resolution 3D genome interaction resource for NAFLD investigations, revealed the relationship among genetic rearrangements, spatial organization disruption, and gene regulation, and identified candidate genes associated with these variations implicated in the pathogenesis of NAFLD. The newly findings offer insights into novel mechanisms of NAFLD pathogenesis and can provide a new conceptual framework for NAFLD therapy.
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Key Words
- 3C, chromosome conformation capture
- 3D genome
- 3D, three-dimensional
- ALT, alanine aminotransferase
- AST, aspartate aminotransferase
- Abcg5, ATP-binding cassette sub-family G member 5
- BWA, Burrows-Wheeler Aligner
- CNV, copy number variation
- Camk1d, calcium/calmodulin-dependent protein kinase type 1D
- Chr, chromosome
- Chromatin looping
- DEG, differentially expressed gene
- DEL, deletion
- DI, directionality index
- DUP, duplication
- Elovl6, elongation of very long chain fatty acids protein 6
- FDR, false discovery rate
- FFA, free fatty acid
- Fgfr2, fibroblast growth factor receptor 2
- GCKR, glucokinase regulator
- GO, gene ontology
- GSH, glutathione
- Gadd45g, growth arrest and DNA damage-inducible protein GADD45 gamma
- Grm8, metabotropic glutamate receptor 8
- Gsta1, glutathione S-transferase A1
- H&E, hematoxylin-eosin
- HFD, high-fat diet
- HSD17B13, hydroxysteroid 17-beta dehydrogenase 13
- Hi-C, high-throughput chromosome conformation capture
- IDE, interaction decay exponent
- INS, insertion
- INV, inversion
- IR, inclusion ratio
- IRGM, immunity related GTPase M
- IRS4, insulin receptor substrate 4
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- Kcnma1, calcium-activated potassium channel subunit alpha-1
- LPIN1, lipin 1
- MBOAT7, membrane bound O-acyltransferase domain containing 7
- MDA, malondialdehyde
- NAFLD, non-alcoholic fatty liver disease
- NF1, neurofibromin 1
- NGS, next-generation sequencing
- NOTCH1, notch receptor 1
- ONT, Oxford Nanopore Technologies
- PCA, principal component analysis
- PNPLA3, patatin like phospholipase domain containing 3
- PPP1R3B, protein phosphatase 1 regulatory subunit 3B
- PTEN, phosphatase and tensin homolog
- Pde4b, phosphodiesterase 4B
- Plce1, 1-phosphat-idylinositol 4,5-bisphosphate phosphodiesterase epsilon-1
- Plxnb1, Plexin-B1
- RB1, RB transcriptional corepressor 1
- RNA-seq, RNA-sequencing
- SD, standard deviation
- SOD, superoxide dismutase
- SV, structural variation
- Scd1, acyl-CoA desaturase 1
- Sugct, succinate-hydroxymethylglutarate CoA-transferase
- TAD, topologically associated domain
- TC, total cholesterol
- TG, triglyceride
- TM6SF2, transmembrane 6 superfamily member 2
- TP53, tumor protein p53
- TRA, translocation
- Topologically associated domain
- Transcriptome
- WGS, whole-genome sequencing
- Whole-genome sequencing
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Buchanan VL, Wang Y, Blanco E, Graff M, Albala C, Burrows R, Santos JL, Angel B, Lozoff B, Voruganti VS, Guo X, Taylor KD, Chen YDI, Yao J, Tan J, Downie C, Highland HM, Justice AE, Gahagan S, North KE. Genome-wide association study identifying novel variant for fasting insulin and allelic heterogeneity in known glycemic loci in Chilean adolescents: The Santiago Longitudinal Study. Pediatr Obes 2021; 16:e12765. [PMID: 33381925 PMCID: PMC8711702 DOI: 10.1111/ijpo.12765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 11/25/2020] [Indexed: 12/01/2022]
Abstract
BACKGROUND The genetic underpinnings of glycemic traits have been understudied in adolescent and Hispanic/Latino (H/L) populations in comparison to adults and populations of European ancestry. OBJECTIVE To identify genetic factors underlying glycemic traits in an adolescent H/L population. METHODS We conducted a genome-wide association study (GWAS) of fasting glucose (FG) and fasting insulin (FI) in H/L adolescents from the Santiago Longitudinal Study. RESULTS We identified one novel variant positioned in the CSMD1 gene on chromosome 8 (rs77465890, effect allele frequency = 0.10) that was associated with FI (β = -0.299, SE = 0.054, p = 2.72×10-8 ) and was only slightly attenuated after adjusting for body mass index z-scores (β = -0.252, SE = 0.047, p = 1.03×10-7 ). We demonstrated directionally consistent, but not statistically significant results in African and Hispanic adults of the Population Architecture Using Genomics and Epidemiology Consortium. We also identified secondary signals for two FG loci after conditioning on known variants, which demonstrate allelic heterogeneity in well-known glucose loci. CONCLUSION Our results exemplify the importance of including populations with diverse ancestral origin and adolescent participants in GWAS of glycemic traits to uncover novel risk loci and expand our understanding of disease aetiology.
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Affiliation(s)
- Victoria L Buchanan
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yujie Wang
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Estela Blanco
- Division of Academic General Pediatrics, Child Development and Community Health, University of California at San Diego, San Diego, California, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Cecilia Albala
- Department of Public Health Nutrition, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Raquel Burrows
- Department of Public Health Nutrition, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - José L Santos
- Department of Nutrition, Diabetes and Metabolism, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Bárbara Angel
- Department of Public Health Nutrition, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Betsy Lozoff
- Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, USA
| | - Venkata Saroja Voruganti
- Department of Nutrition and UNC Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, North Carolina, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Carolina Downie
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anne E Justice
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA
| | - Sheila Gahagan
- Division of Academic General Pediatrics, Child Development and Community Health, University of California at San Diego, San Diego, California, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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7
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Mitochondrial Mutations and Genetic Factors Determining NAFLD Risk. Int J Mol Sci 2021; 22:ijms22094459. [PMID: 33923295 PMCID: PMC8123173 DOI: 10.3390/ijms22094459] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 04/21/2021] [Accepted: 04/22/2021] [Indexed: 02/07/2023] Open
Abstract
NAFLD (non-alcoholic fatty liver disease) is a widespread liver disease that is often linked with other life-threatening ailments (metabolic syndrome, insulin resistance, diabetes, cardiovascular disease, atherosclerosis, obesity, and others) and canprogress to more severe forms, such as NASH (non-alcoholic steatohepatitis), cirrhosis, and HCC (hepatocellular carcinoma). In this review, we summarized and analyzed data about single nucleotide polymorphism sites, identified in genes related to NAFLD development and progression. Additionally, the causative role of mitochondrial mutations and mitophagy malfunctions in NAFLD is discussed. The role of mitochondria-related metabolites of the urea cycle as a new non-invasive NAFLD biomarker is discussed. While mitochondria DNA mutations and SNPs (single nucleotide polymorphisms) canbe used as effective diagnostic markers and target for treatments, age and ethnic specificity should be taken into account.
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8
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He Y, Ao N, Yang J, Wang X, Jin S, Du J. The preventive effect of liraglutide on the lipotoxic liver injury via increasing autophagy. Ann Hepatol 2021; 19:44-52. [PMID: 31787541 DOI: 10.1016/j.aohep.2019.06.023] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/25/2019] [Accepted: 06/25/2019] [Indexed: 02/04/2023]
Abstract
INTRODUCTION AND OBJECTIVES The incidence of non-alcoholic fatty liver disease (NAFLD) is increasing. Previous studies indicated that Liraglutide, glucagon-like peptide-1 analogue, could regulate glucose homeostasis as a valuable treatment for Type 2 Diabetes. However, the precise effect of Liraglutide on NAFLD model in rats and the mechanism remains unknown. In this study, we investigated the molecular mechanism by which Liraglutide ameliorates hepatic steatosis in a high-fat diet (HFD)-induced rat model of NAFLD in vivo and in vitro. MATERIALS AND METHODS NALFD rat models and hepatocyte steatosis in HepG2 cells were induced by HFD and palmitate fatty acid treatment, respectively. AMPK inhibitor, Compound C was added in HepG2 cells. Autophagy-related proteins LC3, Beclin1 and Atg7, and AMPK pathway-associated proteins were evaluated by Western blot and RT-PCR. RESULTS Liraglutide enhanced autophagy as showed by the increased expression of the autophagy markers LC3, Beclin1 and Atg7 in HFD rats and HepG2 cells treated with palmitate fatty acid. In vitro, The AMPK inhibitor exhibited an inhibitory effect on Liraglutide-induced autophagy enhancement with the deceased expression of LC3, Beclin1 and Atg7. Additionally, Liraglutide treatment elevated AMPK levels and TSC1, decreased p-mTOR expression. CONCLUSIONS Liraglutide could upregulate autophagy to decrease lipid over-accumulation via the AMPK/mTOR pathway.
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Affiliation(s)
- Yini He
- Department of General Practice, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Na Ao
- Department of Endocrinology, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jing Yang
- Department of Endocrinology, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaochen Wang
- Department of Endocrinology, The People's Hospital of Liaoning Province, Shenyang, Liaoning, China
| | - Shi Jin
- Department of Endocrinology, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jian Du
- Department of Endocrinology, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
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Xia W, Yang N, Li Y. Analysis of Risk Factors for Adverse Cardiovascular Events in Elderly Patients with Acute Myocardial Infarction and Non-Alcoholic Fatty Liver Disease (NAFLD). Med Sci Monit 2020; 26:e922913. [PMID: 32475980 PMCID: PMC7288831 DOI: 10.12659/msm.922913] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Background The present research aimed to explore the risk factors for adverse cardiovascular events in elderly patients with acute myocardial infarction (AMI) combined with NAFLD. Material/Methods We included 325 AMI patients hospitalized in the Department of Cardiology. AMI patients underwent emergency thrombolysis or percutaneous coronary intervention (PCI). AMI patients were classified into NAFLD group and non-NAFLD group. General clinical data, creatinine and myocardial enzyme, GRACE scores of AMI patients were evaluated and compared between two groups. Incidence of adverse cardiovascular events, including ECG instability, hemodynamic instability and death were evaluated. Results Compared to patients in the non-NAFLD group, patients in the NAFLD group had remarkably lower proportions of diabetic patients (p=0.001), coronary heart disease (CHD) patients (p=0.027), and CABG/PCI patients (p<0.001), and had significantly higher EF values (p=0.042). Meanwhile, the proportion of adverse cardiovascular events (ECG instability (p<0.001), hemodynamic instability (p=0.033), and deaths (p=0.016)) in patients in the NAFLD group was significantly higher compared to patients in the non-NAFLD group. Multivariate logistic regression analysis showed that GRACE score >140 (OR: 3.005, 95% CI: 1.504–6.032), EF <35% (OR: 2.649, 95% CI: 1.364–4.346), diabetes (OR: 1.308, 95% CI: 1.072–1.589), and NAFLD (OR: 1.112, 95% CI: 1.043–1.324) were independent predictors for elderly AMI patients’ adverse cardiovascular events. Conclusions The risk for adverse cardiovascular events in elderly acute myocardial infarction patients who also had NAFLD was significantly higher. Therefore, strengthening monitoring and active treatment for elderly AMI patients who also have NAFLD could reduce the incidence of adverse cardiovascular events and improve survival rate prognosis.
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Affiliation(s)
- Wei Xia
- Graduate School, Tianjin Medical University, Tianjin, China (mainland).,Department of Cardiology, Tianjin First Central Hospital, Tianjin, China (mainland)
| | - Ning Yang
- TEDA International Cardiovascular Hospital, Tianjin, China (mainland)
| | - Yuming Li
- Graduate School, Tianjin Medical University, Tianjin, China (mainland).,TEDA International Cardiovascular Hospital, Tianjin, China (mainland)
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