WGCNA‐Based Identification of Hub Genes and Key Pathways Involved in Nonalcoholic Fatty Liver Disease

Zeng, Folai; Shi, Meijie; Xiao, Hang; Chi, Xiaoling

doi:10.1155/2021/5633211

Cited by 23 publications

(15 citation statements)

References 39 publications

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“…WGCNA uses data from all genes to identify gene sets of interest, rather than genes only showing differential expression, and to analyze significant associations with phenotypes. WGCNA has two main advantages: i.e., loss of fewer genes and the ability to collate many genes into gene sets and identify their association with phenotypes without multiple hypothesis testing [ 40 ]. Therefore, we used WGCNA to analyze the RNA-sequencing data of Cd, salt, K, and N treatments and six dwarf mutants to identify hub genes and their target genes involved in stress adaptation and PH regulation.…”

Section: Resultsmentioning

confidence: 99%

Genome-Wide Identification of Brassicaceae Hormone-Related Transcription Factors and Their Roles in Stress Adaptation and Plant Height Regulation in Allotetraploid Rapeseed

Zheng²,

Liu³

et al. 2022

IJMS

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Phytohormone-related transcription factors (TFs) are involved in regulating stress responses and plant growth. However, systematic analysis of these TFs in Brassicaceae is limited, and their functions in stress adaptation and plant height (PH) regulation remain unclear. In this study, 2115 hormone-related TFs were identified in nine Brassicaceae species. Specific domains were found in several Brassicaceae hormone-related TFs, which may be associated with diverse functions. Syntenic analysis indicated that expansion of these genes was mainly caused by segmental duplication, with whole-genome duplication occurring in some species. Differential expression analysis and gene co-expression network analysis identified seven phytohormone-related TFs (BnaWRKY7, 21, 32, 38, 52, BnaGL3-4, and BnaAREB2-5) as possible key genes for cadmium (Cd) toxicity, salinity stress, and potassium (K) and nitrogen (N) deficiencies. Furthermore, BnaWRKY42 and BnaARR21 may play essential roles in plant height. Weighted gene co-expression network analysis (WGCNA) identified 15 phytohormone-related TFs and their potential target genes regulating stress adaptation and plant height. Among the above genes, BnaWRKY56 and BnaWRKY60 responded to four different stresses simultaneously, and BnaWRKY42 was identified in two dwarf rapeseeds. In summary, several candidate genes for stress resistance (BnaWRKY56 and BnaWRKY60) and plant height (BnaWRKY42) were identified. These findings should help elucidate the biological roles of Brassicaceae hormone-related TFs, and the identified candidate genes should provide a genetic resource for the potential development of stress-tolerant and dwarf oilseed plants.

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Section: Resultsmentioning

confidence: 99%

Genome-Wide Identification of Brassicaceae Hormone-Related Transcription Factors and Their Roles in Stress Adaptation and Plant Height Regulation in Allotetraploid Rapeseed

Zheng²,

Liu³

et al. 2022

IJMS

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“…Wu et al conducted WGCNA on two datasets from GEO and identified hub genes of NAFLD ( Wu et al, 2021 ). Zeng et al used similar methods, but a different dataset to find the hub genes in NAFLD ( Zeng et al, 2021 ). Other bioinformatic tools, such as immune infiltration analysis, were also used in combination with DEG to explore the molecular mechanisms of NASH ( Jiang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Hub Genes Associated With Non-alcoholic Steatohepatitis Using Integrated Bioinformatics Analysis

Meng

Xiong

2022

Front. Genet.

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Background and aims: As a major cause of liver disease worldwide, non-alcoholic fatty liver disease (NAFLD) comprises non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH). Due to the high prevalence and poor prognosis of NASH, it is critical to understand its mechanisms. However, the etiology and mechanisms remain largely unknown. In addition, the gold standard for the diagnosis of NASH is liver biopsy, which is an invasive procedure. Therefore, there is a pressing need to develop noninvasive tests for NASH diagnosis. The goal of the study is to discover key genes involved in NASH development and investigate their value as noninvasive biomarkers.Methods: The Gene Expression Omnibus (GEO) database was used to obtain two datasets encompassing NASH patients and healthy controls. We used weighted gene co-expression network analysis (WGCNA) and differential expression analysis in order to investigate the association between gene sets and clinical features, as well as to discover co-expression modules. A protein-protein interaction (PPI) network was created to extract hub genes. The results were validated using another publicly available dataset and mice treated with a high-fat diet (HFD) and carbon tetrachloride (CCl4).Results: A total of 24 differentially co-expressed genes were selected by WGCNA and differential expression analysis. KEGG analysis indicated most of them were enriched in the focal adhesion pathway. GO analysis showed these genes were mainly enriched in circadian rhythm, aging, angiogenesis and response to drug (biological process), endoplasmic reticulum lumen (cellular component), and protein binding (molecular function). As a result, eight genes (JUN, SERPINE1, GINS2, TYMS, HMMR, IGFBP2, BIRC3, TNFRSF12A) were identified as hub genes. Finally, three genes were found significantly changed in both the validation dataset and the mouse model.Conclusion: Our research discovered genes that have the potential to mediate the process of NASH and might be useful diagnostic biomarkers for the disorder.

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“…MRPL27 encodes a mitochondrial ribosomal protein, and its abnormal function may cause small steric dysfunction and muscle atrophy ( Bogatikov et al, 2020 ). MRPL27 is also regarded as an NAFLD-related hub gene ( Zeng et al, 2021 ). AAR2 is a splicing factor that is a component of the U5 snRNP complex and is required for spliceosome assembly and pre-mRNA splicing ( Weber et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Integrative Analysis of Nanopore and Illumina Sequencing Reveals Alternative Splicing Complexity in Pig Longissimus Dorsi Muscle

Shu

Wang

et al. 2022

Front. Genet.

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Alternative splicing (AS) is a key step in the post-transcriptional regulation of gene expression that can affect intramuscular fat (IMF). In this study, longissimus dorsi muscles from 30 pigs in high- and low- IMF groups were used to perform Oxford Nanopore Technologies (ONT) full-length sequencing and Illumina strand-specific RNA-seq. A total of 43,688 full-length transcripts were identified, with 4,322 novel genes and 30,795 novel transcripts. Using AStalavista, a total of 14,728 AS events were detected in the longissimus dorsi muscle. About 17.79% of the genes produced splicing isoforms, in which exon skipping was the most frequent AS event. By analyzing the expression differences of mRNAs and splicing isoforms, we found that differentially expressed mRNAs with splicing isoforms could participate in skeletal muscle development and fatty acid metabolism, which might determine muscle-related traits. SERBP1, MYL1, TNNT3, and TNNT1 were identified with multiple splicing isoforms, with significant differences in expression. AS events occurring in IFI6 and GADD45G may cause significant differences in gene expression. Other AS events, such as ONT.15153.3, may regulate the function of ART1 by regulating the expression of different transcripts. Moreover, co-expression and protein-protein interaction (PPI) analysis indicated that several genes (MRPL27, AAR2, PYGM, PSMD4, SCNM1, and HNRNPDL) may be related to intramuscular fat. The splicing isoforms investigated in our research provide a reference for the study of alternative splicing regulation of intramuscular fat deposition.

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WGCNA‐Based Identification of Hub Genes and Key Pathways Involved in Nonalcoholic Fatty Liver Disease

Cited by 23 publications

References 39 publications

Genome-Wide Identification of Brassicaceae Hormone-Related Transcription Factors and Their Roles in Stress Adaptation and Plant Height Regulation in Allotetraploid Rapeseed

Genome-Wide Identification of Brassicaceae Hormone-Related Transcription Factors and Their Roles in Stress Adaptation and Plant Height Regulation in Allotetraploid Rapeseed

Identification of Hub Genes Associated With Non-alcoholic Steatohepatitis Using Integrated Bioinformatics Analysis

Integrative Analysis of Nanopore and Illumina Sequencing Reveals Alternative Splicing Complexity in Pig Longissimus Dorsi Muscle

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