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Table_1_Whole Transcriptome Analysis of Obese Adipose Tissue Suggests u001kfc.1 as a Potential Regulator to Glucose Homeostasis.docx (667.26 kB)

Table_1_Whole Transcriptome Analysis of Obese Adipose Tissue Suggests u001kfc.1 as a Potential Regulator to Glucose Homeostasis.docx

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posted on 2019-11-21, 04:33 authored by Linlin Yang, Xing Wang, Huaibin Guo, Wanxing Zhang, Wei Wang, Huijuan Ma

Long non-coding RNA (LncRNAs) are newly highlighted key factors controlling brown adipogenesis and development, but their regulatory effect to white adipocyte is still merely understood. Deciphering their underlying mechanism could be a novel way to discovering potential targets of obesity. Therefore, we conducted a whole transcriptome analysis in white adipose tissue from obese patients for the first time. Six obese patients and five control subjects were selected for microarray assay. Differentially expressed coding genes (DEGs), targets of lncRNAs, and alternatively spliced genes in obesity group were systematically compared in a functional framework based on a global gene regulatory network. It was demonstrated that all the three kinds of transcripts were enriched in pathways related to glucose metabolism while only DEGs showed closer proximity to neuro-endocrine-immune system. Thus, a lncRNA-regulated core network was constructed by a stepwise strategy using DEGs as seed nodes. From the core network, we identified a decreased lncRNA, uc001kfc.1, as potential cis-regulator for phosphatase and tensin homolog (PTEN) to enhance insulin sensitivity of white adipocytes in obese patients. We further validated the down-regulation of uc001kfc.1 and PTEN in an independent testing sample set enrolling 22 subjects via qRT-PCR. Although whether the decreased uc001kfc.1 correlated with low risk of diabetes deserved to be examined in an expanded cohort with long-term follow-up visit, the present study highlighted the potential of lncRNA regulating glucose homeostasis in human adipose tissue from a global perspective. With further improvement, such network-based analyzing protocol proposed in this study could be applied to interpreting function of more lncRNAs from other whole transcriptome data.

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