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In silico identification of genes involved in chronic metabolic acidosis

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Aims: Chronic metabolic acidosis (CMA) refers to increased plasma acidity due to disturbed acid‐base equilibrium in human body. CMA leads to many dysfunctions including disorders of intestinal metabolism and barrier… Click to show full abstract

Aims: Chronic metabolic acidosis (CMA) refers to increased plasma acidity due to disturbed acid‐base equilibrium in human body. CMA leads to many dysfunctions including disorders of intestinal metabolism and barrier functions. The human body responds to these intestinal dysfunctions by creating a compensatory mechanism at genomic level in intestinal epithelial cells. This study was to identify the molecular pathways involved in metabolic dysfunction and compensatory adaptations in intestinal epithelium during CMA. Main methods: In silico approaches were utilized to characterize a set of 88 differentially expressed genes (DEGs) from intestinal cells of rat CMA model. Interaction networks were constructed for DEGs by GeneMANIA and hub genes as well as enriched clusters in the network were screened using GLay. Gene Ontology (GO) was used for enriching functions in each cluster. Key findings: Four gene hubs, i.e., trefoil factor 1, 5‐hydroxytryptamine (serotonin) receptor 5a, solute carrier family 6 (neurotransmitter transporter), member 11, and glutamate receptor, ionotropic, n‐methyl D‐aspartate 2b, exhibiting the highest node degree were predicted. Six biologically related gene clusters were also predicted. Functional enrichment of GO terms predicted neurological processes such as neurological system process regulation and nerve impulse transmission which are related to negative and positive regulation of digestive system processes., intestinal motility and absorption and maintenance of gastrointestinal epithelium. Significance: The study predicted several important genomic pathways that potentially play significant roles in metabolic disruptions or compensatory adaptations of intestinal epithelium induced by CMA. The results provide a further insight into underlying molecular mechanisms associated with CMA.

Keywords: genes involved; silico identification; identification genes; chronic metabolic; metabolic acidosis

Journal Title: Life Sciences
Year Published: 2018

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