Abstract
Excessive fat deposition can cause chicken health problem, and affect production efficiency by causing great economic losses to the industry. However, the molecular underpinnings of the complex adiposity trait remain elusive. In the current study, we constructed and compared the gene co-expression networks on four transcriptome profiling datasets, from two chicken lines under divergent selection for abdominal fat contents, in an attempt to dissect network compositions underlying adipose tissue growth and development. After functional enrichment analysis, nine network modules important to adipogenesis were discovered to be involved in lipid metabolism, PPAR and insulin signaling pathways, and contained hub genes related to adipogenesis, cell cycle, inflammation, and protein synthesis. Moreover, after additional functional annotation and network module comparisons, common sub-modules of similar functionality for chicken fat deposition were identified for different chicken lines, apart from modules specific to each chicken line. We further validated the lysosome pathway, and found TFEB and its downstream target genes showed similar expression patterns along with chicken preadipocyte differentiation. Our findings could provide novel insights into the genetic basis of complex adiposity traits, as well as human obesity and related metabolic diseases.
Highlights
The global obesity pandemic and related metabolic syndromes are currently devastating the human society, by threatening human health and decreasing life expectancy (Blüher, 2019)
Gene co-expression networks were built for two different chicken lines both under divergent selection for abdominal fat content, and common network modules with similar functionality were discovered to be of potential importance for adiposity
We identified network modules significantly associated with fat deposition in chicken lines with different genetic backgrounds, but all under divergent selection for the same trait of interest, abdominal fat content (Guo et al, 2011; Resnyk et al, 2013, 2017)
Summary
The global obesity pandemic and related metabolic syndromes are currently devastating the human society, by threatening human health and decreasing life expectancy (Blüher, 2019). To find effective therapy for metabolic diseases, current research efforts employ large-scale genomics and systems biology methods, to understand better the biology and physiology of adipose tissues (Dyar et al, 2018; Guan et al, 2018; Pellegrinelli et al, 2018; Zhao et al, 2018; Brial et al, 2019; Justice et al, 2019; van der Klaauw et al, 2019). As one method of integrative analysis on high-throughput transcriptome profiling and a variety of other omics data, helps discover successfully the structural and functional gene modules and molecular signaling pathways for human diseases (Barabási and Oltvai, 2004). Integrative genomics methods are effective in combining different omics data (e.g., transcripomics, proteomics, metabolomics), to pinpoint key biochemical and molecular biomarkers (Lee et al, 2016; Emilsson et al, 2018; Ghaemi et al, 2019). Network methods were employed on the investigation of growth and reproduction traits (Zhang et al, 2017; Wang et al, 2018)
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