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. 2015 Oct 8;10(10):e0139520.
doi: 10.1371/journal.pone.0139520. eCollection 2015.

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

Swanhild U Meyer  1 , Stefan Krebs  2 , Christian Thirion  3 , Helmut Blum  2 , Sabine Krause  4 , Michael W Pfaffl  1
Affiliations

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

Swanhild U Meyer et al. PLoS One. .

Abstract

Introduction: TNF-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 findings: This 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.

Conclusions: This 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.

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Conflict of interest statement

Competing Interests: CT’s affiliation to SIRION Biotech GmbH does not alter the authors' adherence to all PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Experimental set up and analyses of expression profiling data.
(A) Schematic overview of sampling. MB: myoblasts cultured in growth medium. MT: myotubes cultured in differentiation medium. TNF: myotubes cultured in differentiation medium with TNF-α. IGF: myotubes cultured in differentiation medium with IGF-1. (B) Gene expression data were analyzed by hierarchical clustering, principal component analysis (PCA), and dynamic PCA among others. Moreover, the profiling data were clustered by applying the self-organized tree algorithm (SOTA) as well as pathway association enrichment analyses. Furthermore, genes adversely regulated by differentiation and TNF-α were identified. Finally, TNF-α-regulated genes were compared with results of other studies. The study identified novel insights into the gene expression mechanisms and kinetics of early skeletal myocyte differentiation and how this is modified by TNF-α.
Fig 2
Fig 2. Transcriptomic signatures of myoblast differentiation and TNF-α or IGF1 treatment.
Principal component analyses (PCA) of mRNA expression profiling data after (A) 4 h, (D) 12 h, or (G) 24 h of induction of differentiation with TNF-α, IGF1, or control treatment showing nonambiguous genes are depicted. Axes show principal components (PC) 1, PC 2, and PC 3. PCA revealed separation of treatment groups. Light blue indicates myoblasts, green marks myotubes, red distinguishes myotubes treated with TNF-α, whereas dark blue indicates myotubes treated with IGF1. The proportions of variance for the first six components of principal component analysis are depicted for the effect of (B) 4 h, (E) 12 h, and (H) 24 h after induction of differentiation and the respective treatments. Most of the variance is described by PC 1 followed by PC 2 and PC 3. PC 1 explaines most of the variance of myocyte differentiation while PC 2 represented most of the variance induced by TNF-α whereas PC 3 characterized most of the variance caused by IGF1 treatment. Moreover, results from dynamic principal component analyses (dPCA) (group selection myoblasts) are shown for gene expressions (C) 4 h, (F) 12 h, and (I) 24 h after induction of differentiation and treatment. DPCA identified a minimal subset of genes, which could describe the treatment effects (see Table 2) and separate the effects by principal components.
Fig 3
Fig 3. Coregulation of gene sets during myogenic differentiation as well as TNF-α and IGF1 treatment.
Self-organizing tree algorithm (SOTA) analysis of gene expression data within the first 72 h of differentiation as well as TNF-α and IGF1 treatment revealed the following clusters of gene sets: (A) cluster A contained 335 genes upregulated during very early differentiation and (B) cluster B comprised 351 genes upregulated during later differentiation. (C) Genes totaling 172, which were downregulated during very early differentiation, were summarized by cluster C. (D) Genes induced by TNF-α but suppressed during late myotubes were visualized by cluster C implying eight genes. (E) Cluster E included 40 genes specifically induced by TNF-α. (F) Forty genes downregulated later during differentiation were represented by cluster F. Gene identities and signal transduction pathway associations of genes within the individual SOTA clusters are depicted in S4 Table. Furthermore, clusters G, H, and I bear the minority of genes and are depicted in S2 Fig.
Fig 4
Fig 4. Western Blot analysis of differentially expressed genes.
(A) Chk1, (B) Emi1/Fbxo5, (C) Mybl2 protein were detected by western blot analysis. Histone H3 served as the normalization control. Murine mouse muscle cells were cultured for 48 h in growth medium (lane 1), differentiation medium (lane 2), or differentiation medium supplemented with TNF-α (lane 3) or IGF1 (lane 4), respectively. (B) The specificity of the double band between 40 and 50 kDa was confirmed by peptide competition of the Emi/Fbxo5 antibody epitope (S4 Fig).
Fig 5
Fig 5. Novel genes and pathways in skeletal myocyte differentiation and TNF-α response.
We identified genes and pathway associations, which have not been described before in skeletal myocyte differentiation or have been reported to have a different regulation than the one observed in the current study. A plus indicates upregulation during differentiation and a minus indicates downregulation. Moreover, we show genes which are inversely regulated by TNF-α, but have not been defined before, to be regulated in skeletal myocyte differentiation and response to TNF-α.
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The “Muscle Tissue Culture Collection” is part of the German network on muscular dystrophies (MD-NET, service structure S1, 01GM0601) funded by the German Ministry of Education and Research (BMBF, Bonn, Germany, http://www.bmbf.de/). The Muscle Tissue Culture Collection is a partner of EuroBioBank (www.eurobiobank.org) and TREAT-NMD (EC, 6th FP, proposal #036825). This work was an individual research project within the MD-NET and was supported by a grant from the German Federal Ministry of Education and Research [01GM0887] which was granted to CT. SIRION Biotech GmbH provided support in the form of salary for author CT, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contributions’ section.

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