A Dynamic Transcriptional Analysis Reveals IL-6 Axis as a Prominent Mediator of Surgical Acute Response in Non-ischemic Mouse Heart

Abstract

BackgroundIschemic heart diseases are a major cause of death worldwide. Different animal models, including cardiac surgery, have been developed over time. Unfortunately, the surgery models have been reported to trigger an important inflammatory response that might be an effect modifier, where involved molecular processes have not been fully elucidated yet.ObjectiveWe sought to perform a thorough characterization of the sham effect in the myocardium and identify the interfering inflammatory reaction in order to avoid misinterpretation of the data via systems biology approaches.Methods and ResultsWe combined a comprehensive analytical pipeline of mRNAseq dataset and systems biology analysis to characterize the acute phase response of mouse myocardium at 0 min, 45 min, and 24 h after surgery to better characterize the molecular processes inadvertently induced in sham animals. Our analysis showed that the surgical intervention induced 1209 differentially expressed transcripts (DETs). The clustering of positively co-regulated transcript modules at 45 min fingerprinted the activation of signalization pathways, while positively co-regulated genes at 24 h identified the recruitment of neutrophils and the differentiation of macrophages. In addition, we combined the prediction of transcription factors (TF) regulating DETs with protein-protein interaction networks built from these TFs to predict the molecular network which have induced the DETs. By mean of this retro-analysis of processes upstream gene transcription, we revealed a major role of the Il-6 pathway and further confirmed a significant increase in circulating IL-6 at 45 min after surgery.ConclusionThis study suggests that a strong induction of the IL-6 axis occurs in sham animals over the first 24 h and leads to the induction of inflammation and tissues’ homeostasis processes.

Transcriptomic analysis of SEL1L and HRD1 knockout cell lines reveals multifaceted roles of SEL1L beyond the ER quality control

“…Differential gene expression analysis between wild type and knockout samples was performed using the R package DESeq2 (version 1.38.3). Raw read counts from RNA-seq were used as input and DESeq2 work ow was applied to estimate the size factors, dispersion parameters, and fold changes for each gene (Love et al, 2014;Badawi et al, 2019). Low count genes (less than 10 in at least one sample) were ltered using independent ltering method to adjust for multiple testing.…”