Obesity induced Ext1 reduction mediates the occurrence of NAFLD

Wang, Mengxiao; Li, Bingbing; Qin, Fujian; Ye, Junmei; Jin, Liang

doi:10.1016/j.bbrc.2021.12.017

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Exostosin glycosyltransferase 1 (Ext1) is the highest prioritized gene. Ext1 is down-regulated by a high-fat diet and this downregulation is considered to exacerbate ER stress and NAFLD progression [31]. Other prioritized genes include fat storage-inducing transmembrane protein 1 (Fitm1) and hyaluronan synthase 2 (Has2).…”

Section: Gene Prioritization and Functional Analysismentioning

confidence: 99%

Mapping QTL underlying body weight changes that act at different times during high-fat diet challenge in collaborative cross mice

Abu-Toamih-Atami,

Lone,

Binenbaum

et al. 2024

Preprint

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Background Over one billion people worldwide suffer from obesity, and the number is continually rising. This epidemic is partly caused by the modern lifestyle, which is defined by excessive eating of food high in calories and little physical activity. However, genetic variation sets the stage and affects how the disease develops and advances. Animal models, especially mice models, are crucial to identifying the genetic components of complex disorders and exploring the potential applications of these genetic findings. The body weight of the animals used in research is often measured regularly to monitor their health. Only endpoint measurements, like ultimate body weight, are often examined in quantitative trait locus (QTL) studies; time series data, such as weekly or biweekly body weight, are usually disregarded. QTL mapping utilizing biweekly body weight measurements may be particularly intriguing in examining body weight gain in obesity research and identifying more genes connected with obesity and related metabolic problems. Results This study is focused on identifying QTL underlying body weight changes by analyzing biweekly weight measurements in collaborative cross (CC) mice maintained on a high-fat diet for 12 weeks. QTL analysis, utilizing 525 mice from 55 CC lines (308 male and 217 female), revealed genome-wide significant QTL on different chromosomes for body weight changes over 12 weeks. This study unveiled 62 body weight QTLs, among which 28 novels associated with defined traits were observed and found not reported previously. In addition, 34 more QTLs were fine-mapped as the genomic interval positions of these were previously identified. Conclusions These findings illuminate genomic regions influencing body weight in CC mice and emphasize the utility of time series data in uncovering novel genetic factors.

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Section: Gene Prioritization and Functional Analysismentioning

confidence: 99%

Mapping QTL underlying body weight changes that act at different times during high-fat diet challenge in collaborative cross mice

Abu-Toamih-Atami,

Lone,

Binenbaum

et al. 2024

Preprint

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“…The NAFLD model was established using C57BL/6 male mice according to previous studies. 19,20 In a short, regular diet was replaced with a diet containing 60 kcal% fat. In addition, the sex of the mice did not hinder the establishment of animal model.…”

Section: Functional Enrichment Analysis Of Hub Irdegsmentioning

confidence: 99%

Identification and Validation of Hub Immune-Related Genes in Non-Alcoholic Fatty Liver Disease

Li¹,

Kou²,

Sun³

et al. 2023

IJGM

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Background: Nonalcoholic fatty liver disease (NAFLD) is the most common progressive liver disease worldwide. It can cause liver cancer and possibly death. Abnormal immune infiltration is involved in the progression of NAFLD. The aim of this study was to identify and validate the hub immune-related genes in NAFLD. Methods: Microarray data were downloaded from Gene Expression Omnibus, and immune-related differentially expressed genes (IRDEGs) were obtained. A protein-protein interaction network was used to further screen. The diagnostic value of the IRDEGs was evaluated by receiver operating characteristic curves. Differences in immune infiltration levels were analyzed using single-sample gene set enrichment analysis. Hub IRDEGs were identified by correlation analysis with immune infiltration levels. Finally, molecular experiments were used to confirm the expression of the hub IRDEGs and explore their roles in NAFLD. Results: We obtained 18 IRDEGs. Five hub genes were further identified by protein-protein interaction network, receiver operating characteristic curves and correlation analysis: AQP9, BACH2, CD4, IL17RE and S100A9. Based on functional enrichment analysis, the hub genes were enriched primarily in many immune-related pathways. In NAFLD, AQP9, CD4, and IL17RE expression was significantly reduced, whereas BACH2 and S100A9 expression was elevated. PCR, oil red O staining and triglyceride detection revealed that the knock-down of BACH2 and S100A9 reduced lipid accumulation in NAFLD cells. Conclusion:This study provided insight into the profile of immune infiltration underlying NAFLD and identified AQP9, BACH2, CD4, IL17RE and S100A9 as ancillary diagnostic indicators of NAFLD. And BACH2 and S100A9 might be therapeutic targets for NAFLD.

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Identification of candidate biomarkers and signaling pathways associated with Alzheimer’s disease using bioinformatics analysis of next generation sequencing data

Vastrad,

Vastrad

2024

Preprint

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Alzheimer's disease (AD) is the most common cause of dementia, and one of the most common health problems all over the world. However, the specific molecular mechanisms of AD have not been fully investigated. The current investigation aimed to elucidate potential key candidate genes and signaling pathways in AD. Next generation sequencing (NGS) dataset GSE203206 was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 39 AD samples and 8 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R bioconductor package. These DEGs were subsequently investigated by Gene ontology (GO) and pathway enrichment analysis, and a protein-protein interaction (PPI) network and modules were constructed and analyzed. The miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify key miRNAs and TFs. The receiver operating characteristic (ROC) curve analysis was performed to estimate the clinical diagnostic value of the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between AD and normal control samples. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in response to stimulus, cytoplasm, small molecule binding and signal transduction, whereas down regulated genes were mainly enriched in multicellular organism development, cell junction, ion binding and cardiac conduction. The PPI network contained 4886 nodes and 10342 edges. HSP90AA1, FN1, KIT, YAP1, LSM2, SKP1, EIF5A2, TAF9, DDX39B and CDK7 were identified as the top hub genes. The regulatory network analysis revealed that microRNA (miRNA) hsa-mir-545-3p and hsa-miR-548f-5p, and transcription factor (TF) PLAG1 and MEF2A might be involved in the development of AD. These findings provide new insights into the pathogenesis of AD. The hub genes, miRNAs and TFs have the potential to be used as diagnostic and therapeutic markers.

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Obesity induced Ext1 reduction mediates the occurrence of NAFLD

Cited by 3 publications

References 23 publications

Mapping QTL underlying body weight changes that act at different times during high-fat diet challenge in collaborative cross mice

Mapping QTL underlying body weight changes that act at different times during high-fat diet challenge in collaborative cross mice

Identification and Validation of Hub Immune-Related Genes in Non-Alcoholic Fatty Liver Disease

Identification of candidate biomarkers and signaling pathways associated with Alzheimer’s disease using bioinformatics analysis of next generation sequencing data

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