TY - JOUR
T1 - Interplay of miRNA-TF-Gene Through a Novel Six-node Feed-forward Loop Identified Inflammatory Genes as Key Regulators in Type-2 Diabetes
AU - Bhat, Gayathri Shama
AU - Keshav, Tarakad Ranganatha
AU - Hariharapura, Raghu Chandrashekar
AU - Fayaz, Shaik Mahammad Abdul
N1 - Publisher Copyright:
© 2024 The Author(s).
PY - 2024
Y1 - 2024
N2 - Background: Intricacy in the pathological processes of type 2 diabetes (T2D) invites a need to understand gene regulation at the systems level. However, deciphering the complex gene modulation requires regulatory network construction. Objective: The study aims to construct a six-node feed-forward loop (FFL) to analyze all the diverse inter-and intra-interactions between microRNAs (miRNA) and transcription factors (TF) involved in gene regulation. Methods: The study included 644 genes, 64 TF, and 448 miRNA. A cumulative hypergeometric test was employed to identify the significant miRNA-miRNA and miRNA-TF interaction pairs. In addition, experimentally proven TF-TF pairs were incorporated for the first time in the regulatory network to discern gene regulation. The networks were analyzed to identify crucial genes involved in T2D. Following this, gene ontology was predicted to recognize the biological function that is crucial in T2D. Results: In T2D, the lowest gene regulation for a composite FFL occurs through a four-node FFL variant1 (TF-miRNA-miRNA-Gene, n=14) and the highest regulation via a five-node FFL variant2 (TF-TF-miRNA-Gene, n=353). However, the maximum gene regulation occurs via six-node miRNA FFL (miRNA-miRNA-TF-TF-gene-gene, n=23987). Subnetworks derived from the six-node miRNATF-gene regulatory networks identified interactions among TP53 and NFkB, hsa-miR-125-5p and hsamiR-155-5p. Conclusion: The core regulation occurs through TP53, NFkB, hsa-miR-125-5p, and hsa-miR-155-5p FFL implicating the association of inflammation in the pathogenesis of T2D, which occurs majorly via six-node miRNA FFL. Thus regulatory network provides broader insights into the pathogenesis of T2D and can be extended to study the inflammatory mechanisms in various infections.
AB - Background: Intricacy in the pathological processes of type 2 diabetes (T2D) invites a need to understand gene regulation at the systems level. However, deciphering the complex gene modulation requires regulatory network construction. Objective: The study aims to construct a six-node feed-forward loop (FFL) to analyze all the diverse inter-and intra-interactions between microRNAs (miRNA) and transcription factors (TF) involved in gene regulation. Methods: The study included 644 genes, 64 TF, and 448 miRNA. A cumulative hypergeometric test was employed to identify the significant miRNA-miRNA and miRNA-TF interaction pairs. In addition, experimentally proven TF-TF pairs were incorporated for the first time in the regulatory network to discern gene regulation. The networks were analyzed to identify crucial genes involved in T2D. Following this, gene ontology was predicted to recognize the biological function that is crucial in T2D. Results: In T2D, the lowest gene regulation for a composite FFL occurs through a four-node FFL variant1 (TF-miRNA-miRNA-Gene, n=14) and the highest regulation via a five-node FFL variant2 (TF-TF-miRNA-Gene, n=353). However, the maximum gene regulation occurs via six-node miRNA FFL (miRNA-miRNA-TF-TF-gene-gene, n=23987). Subnetworks derived from the six-node miRNATF-gene regulatory networks identified interactions among TP53 and NFkB, hsa-miR-125-5p and hsamiR-155-5p. Conclusion: The core regulation occurs through TP53, NFkB, hsa-miR-125-5p, and hsa-miR-155-5p FFL implicating the association of inflammation in the pathogenesis of T2D, which occurs majorly via six-node miRNA FFL. Thus regulatory network provides broader insights into the pathogenesis of T2D and can be extended to study the inflammatory mechanisms in various infections.
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U2 - 10.2174/1574893618666230731164002
DO - 10.2174/1574893618666230731164002
M3 - Article
AN - SCOPUS:85185528976
SN - 1574-8936
VL - 19
SP - 73
EP - 92
JO - Current Bioinformatics
JF - Current Bioinformatics
IS - 1
ER -