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Coexpression Network Analysis Of Time-Course Transcriptional Response During Cutaneous Wound Healing In A Murine Model Of Diabetes
Abstract   Open access   Peer reviewed

Coexpression Network Analysis Of Time-Course Transcriptional Response During Cutaneous Wound Healing In A Murine Model Of Diabetes

S. Nassiri, E. A. Grice, M. Palma, K. Pourrezaei and I. Zakeri
Wound repair and regeneration, v 25(4), pp A9-A9
Jul 2017
url
https://doi.org/10.1111/wrr.12573View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze) Open

Abstract

Extraction of valuable information from the vast pool of data provided by wholetranscriptome profiling remains challenging and is a subject of ongoing research. Coexpression network analysis has emerged as a powerful systems biology approach for analyzing transcriptome data. However, the application of coexpression network analysis in wound healing research is impeded by the lack of a robust statistical measure of coexpression applicable to time-series data arising in wound healing research. Herein, we proposed and implemented a functional approach to coexpression network analysis of transcriptional response during impaired cutaneous wound healing in a murine model of diabetes (Leprdb; db/ db). Using dynamic correlation, a functional analog of Pearson’s product-moment correlation, pairwise correlation among transcripts was calculated. The correlation matrix was then converted into a scale-free coexpression network, and groups of coexpressed transcripts, also known as modules, were identified within the network. To explore the modular architecture of the network, enrichment analysis using Gene Ontology annotation was performed, and the association of modules with external traits, namely phenotype (either db/db or db/1) and time (day 0, 3, 7, 14, or 21) was assessed. The analysis above demonstrated that intramodular transcripts with high degree of connectivity, commonly referred to as hub transcripts, are highly enriched in evolutionary conserved genes and exhibit high levels of homology between mouse and human. More interestingly, it also showed that the hub transcripts are among high-confidence targets of microRNAs previously identified to be implicated in diabetic wound healing. Taken together, we generated a transcriptional coexpression network of impaired healing in diabetic mice, and linked its systems-level attributes to the underlying molecular aspects of wound healing. We showed that cutaneous wound healing could be characterized by transcriptionally coordinated modules and intramodular hubs, and importantly, established direct association between hubs and validated drivers of impaired diabetic wound healing

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