Tumor Necrosis Factor Alpha and Insulin-Like Growth Factor 1 Induced Modifications of the Gene Expression Kinetics of Differentiating Skeletal Muscle Cells

Abstract

IntroductionTNF-α levels are increased during muscle wasting and chronic muscle degeneration and regeneration processes, which are characteristic for primary muscle disorders. Pathologically increased TNF-α levels have a negative effect on muscle cell differentiation efficiency, while IGF1 can have a positive effect; therefore, we intended to elucidate the impact of TNF-α and IGF1 on gene expression during the early stages of skeletal muscle cell differentiation.Methodology/Principal FindingsThis study presents gene expression data of the murine skeletal muscle cells PMI28 during myogenic differentiation or differentiation with TNF-α or IGF1 exposure at 0 h, 4 h, 12 h, 24 h, and 72 h after induction. Our study detected significant coregulation of gene sets involved in myoblast differentiation or in the response to TNF-α. Gene expression data revealed a time- and treatment-dependent regulation of signaling pathways, which are prominent in myogenic differentiation. We identified enrichment of pathways, which have not been specifically linked to myoblast differentiation such as doublecortin-like kinase pathway associations as well as enrichment of specific semaphorin isoforms. Moreover to the best of our knowledge, this is the first description of a specific inverse regulation of the following genes in myoblast differentiation and response to TNF-α: Aknad1, Cmbl, Sepp1, Ndst4, Tecrl, Unc13c, Spats2l, Lix1, Csdc2, Cpa1, Parm1, Serpinb2, Aspn, Fibin, Slc40a1, Nrk, and Mybpc1. We identified a gene subset (Nfkbia, Nfkb2, Mmp9, Mef2c, Gpx, and Pgam2), which is robustly regulated by TNF-α across independent myogenic differentiation studies.ConclusionsThis is the largest dataset revealing the impact of TNF-α or IGF1 treatment on gene expression kinetics of early in vitro skeletal myoblast differentiation. We identified novel mRNAs, which have not yet been associated with skeletal muscle differentiation or response to TNF-α. Results of this study may facilitate the understanding of transcriptomic networks underlying inhibited muscle differentiation in inflammatory diseases.

PGC‐1α4 gene expression is suppressed by the IL‐6—MEK—ERK 1/2 MAPK signalling axis and altered by resistance exercise, obesity and muscle injury

“…, Meyer et al . ). Experimental concentrations of IL‐6 frequently range from 5 to 100 ng/mL in vitro for studying IL‐6 signalling (Yuen et al .…”

“…, Meyer et al . ). To verify efficacy of inhibitors on protein phosphorylation, treatments were performed for 30 min.…”

“…Concentrations of recombinant proteins IL-6 and TNFa were determined by a combination of examining plasma IL-6 levels in pathological conditions and previous use of the recombinant proteins in vivo and in vitro. Doses of IL-6 and TNF-a were selected from previous literature and optimized in our laboratory for these experiments (Yuen et al 2009, Bhatnagar et al 2010, Bonetto et al 2012, De Larichaudy et al 2012, Podbregar et al 2013, White et al 2013, Meyer et al 2015. Experimental concentrations of IL-6 frequently range from 5 to 100 ng/mL in vitro for studying IL-6 signalling (Yuen et al 2009, Bonetto et al 2012, White et al 2013).…”

“…Experimental concentrations of IL-6 frequently range from 5 to 100 ng/mL in vitro for studying IL-6 signalling (Yuen et al 2009, Bonetto et al 2012, White et al 2013). Furthermore, experimental concentrations of TNFa in vitro range from 5 to 100 ng/mL (Bhatnagar et al 2010, De Larichaudy et al 2012, Podbregar et al 2013, Meyer et al 2015. To verify efficacy of inhibitors on protein phosphorylation, treatments were performed for 30 min.…”

Exercise-related genes analysis of Mongolian Horse

“…According to the analysis of GO, KEGG and individual gene function, we subsequently put our interest in exercise-related genes of Abaga horse. Twenty-one genes may involve in exercise of Abaga horse while their functions embodied vasoconstriction ( HTR2B ) (Launay et al 2002; Bevilacqua et al 2010; Meira et al 2014), angiogenesis ( CDH5 ) (Sauteur et al 2014), cardiac contraction ( KCNQ1 ) (Jespersen et al 2005; Brown et al 2015; Pedersen et al 2017), cardiac development and muscle structure ( ENAH ) (Franzini-Armstrong 1973; Benz et al 2013), muscle growth ( PIH1D1, SMURF1 ) (Inoue et al 2010; Ponsuksili et al 2014; Dalbo et al 2013), myogenic differentiation ( UNC13C ) (Meyer et al 2015; Langlois and Cowan 2017), skeletal muscle function ( ATP1A3 ) (Aughey et al 2007; Brashear et al 2007), femur strength and bone mineral density ( PPP2R5B, PPP6R3 ) (Alam et al 2009; Medina-Gomez et al 2017), osteoclast growth ( PTPRE, RHOBTB1 ) (Chiusaroli et al 2004; Song et al 2014), chondrogenesis (SCFD) (DeLise et al 2000; Hou et al 2017), lipid and carbohydrate metabolism ( PPARD, GCG, TCF7L2, GALNT13 ) (Yi et al 2005; Bevilacqua et al 2010; Park et al 2012; Ahmetov and Fedotovskaya 2015; Giordano Attianese and Desvergne 2015; Ropka-Molik et al 2017), exercise stress-induced response ( CD69, EIF4G3 ) (Testi et al 1989; Gradi et al 1998; Cappelli et al 2007; Morabito et al 2016), exercise coordination ( GRM1 ) (Conquet et al 1994; Bossi et al 2017) and height ( VGLL4 ) (Gabriel et al 2016). These genes of positive selection were presented simultaneously in Abaga horse, which may be a reason that it runs rapider than Wushen horse.…”