Swanhild U. Meyer

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. 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. 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.

Skeletal muscle cell differentiation is impaired by elevated levels of the inflammatory cytokine tumor necrosis factor-α (TNF-α) with pathological significance in chronic diseases or inherited muscle disorders. Insulin like growth factor-1 (IGF1) positively regulates muscle cell differentiation. Both, TNF-α and IGF1 affect gene and microRNA (miRNA) expression in this process. However, computational prediction of miRNA-mRNA relations is challenged by false positives and targets which might be irrelevant in the respective cellular transcriptome context. Thus, this study is focused on functional information about miRNA affected target transcripts by integrating miRNA and mRNA expression profiling data. Murine skeletal myocytes PMI28 were differentiated for 24 hours with concomitant TNF-α or IGF1 treatment. Both, mRNA and miRNA expression profiling was performed. The data-driven integration of target prediction and paired mRNA/miRNA expression profiling data revealed that i) the quantity of predicted miRNA-mRNA relations was reduced, ii) miRNA targets with a function in cell cycle and axon guidance were enriched, iii) differential regulation of anti-differentiation miR-155-5p and miR-29b-3p as well as pro-differentiation miR-335-3p, miR-335-5p, miR-322-3p, and miR-322-5p seemed to be of primary importance during skeletal myoblast differentiation compared to the other miRNAs, iv) the abundance of targets and affected biological processes was miRNA specific, and v) subsets of miRNAs may collectively regulate gene expression. Joint analysis of mRNA and miRNA profiling data increased the process-specificity and quality of predicted relations by statistically selecting miRNA-target interactions. Moreover, this study revealed miRNA-specific predominant biological implications in skeletal muscle cell differentiation and in response to TNF-α or IGF1 treatment. Furthermore, myoblast differentiation-associated miRNAs are suggested to collectively regulate gene clusters and targets associated with enriched specific gene ontology terms or pathways. Predicted miRNA functions of this study provide novel insights into defective regulation at the transcriptomic level during myocyte proliferation and differentiation due to inflammatory stimuli.

Elevated levels of the inflammatory cytokine TNF-α are common in chronic diseases or inherited or degenerative muscle disorders and can lead to muscle wasting. By contrast, IGF1 has a growth promoting effect on skeletal muscle. The molecular mechanisms mediating the effect of TNF-α and IGF1 on muscle cell differentiation are not completely understood. Muscle cell proliferation and differentiation are regulated by microRNAs (miRNAs) which play a dominant role in this process. This study aims at elucidating how TNF-α or IGF1 regulate microRNA expression to affect myoblast differentiation and myotube formation. In this study, we analyzed the impact of TNF-α or IGF1 treatment on miRNA expression in myogenic cells. Results reveal that i) TNF-α and IGF1 regulate miRNA expression during skeletal muscle cell differentiation in vitro, ii) microRNA targets can mediate the negative effect of TNF-α on fusion capacity of skeletal myoblasts by targeting genes associated with axon guidance, MAPK signalling, focal adhesion, and neurotrophin signalling pathway, iii) inhibition of miR-155 in combination with overexpression of miR-503 partially abrogates the inhibitory effect of TNF-α on myotube formation, and iv) MAPK/ERK inhibition might participate in modulating the effect of TNF-α and IGF1 on miRNA abundance. The inhibitory effects of TNF-α or the growth promoting effects of IGF1 on skeletal muscle differentiation include the deregulation of known muscle-regulatory miRNAs as well as miRNAs which have not yet been associated with skeletal muscle differentiation or response to TNF-α or IGF1. This study indicates that miRNAs are mediators of the inhibitory effect of TNF-α on myoblast differentiation. We show that intervention at the miRNA level can ameliorate the negative effect of TNF-α by promoting myoblast differentiation. Moreover, we cautiously suggest that TNF-α or IGF1 modulate the miRNA biogenesis of some miRNAs via MAPK/ERK signalling. Finally, this study identifies indicative biomarkers of myoblast differentiation and cytokine influence and points to novel RNA targets.

Adequate normalization minimizes the effects of systematic technical variations and is a prerequisite for getting meaningful biological changes. However, there is inconsistency about miRNA normalization performances and recommendations. Thus, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of two distinct and commonly used miRNA profiling platforms. We included data from miRNA profiling analyses derived from a hybridization-based platform (Agilent Technologies) and an RT-qPCR platform (Applied Biosystems). Furthermore, we validated a subset of miRNAs by individual RT-qPCR assays. Our analyses incorporated data from the effect of differentiation and tumor necrosis factor alpha treatment on primary human skeletal muscle cells and a murine skeletal muscle cell line. Distinct normalization methods differed in their impact on (i) standard deviations, (ii) the area under the receiver operating characteristic (ROC) curve, (iii) the similarity of differential expression. Loess, loessM, and quantile analysis were most effective in minimizing standard deviations on the Agilent and TLDA platform. Moreover, loess, loessM, invariant selection and generalized procrustes analysis increased the area under the ROC curve, a measure for the statistical performance of a test. The Jaccard index revealed that inter-platform concordance of differential expression tended to be increased by loess, loessM, quantile, and GPA normalization of AGL and TLDA data as well as RGI normalization of TLDA data. We recommend the application of loess, or loessM, and GPA normalization for miRNA Agilent arrays and qPCR cards as these normalization approaches showed to (i) effectively reduce standard deviations, (ii) increase sensitivity and accuracy of differential miRNA expression detection as well as (iii) increase inter-platform concordance. Results showed the successful adoption of loessM and generalized procrustes analysis to one-color miRNA profiling experiments.

MicroRNA (miRNA) profiling is a first important step in elucidating miRNA functions. Real time quantitative PCR (RT-qPCR) and microarray hybridization approaches as well as ultra high throughput sequencing of miRNAs (small RNA-seq) are popular and widely used profiling methods. All of these profiling approaches face significant introduction of bias. Normalization, often an underestimated aspect of data processing, can minimize systematic technical or experimental variation and thus has significant impact on the detection of differentially expressed miRNAs. At present, there is no consensus normalization method for any of the three miRNA profiling approach. Several normalization techniques are currently in use, of which some are similar to mRNA profiling normalization methods, while others are specifically modified or developed for miRNA data. The characteristic nature of miRNA molecules, their composition and the resulting data distribution of profiling experiments challenges the selection of adequate normalization techniques. Based on miRNA profiling studies and comparative studies on normalization methods and their performances, this review provides a critical overview of commonly used and newly developed normalization methods for miRNA RT-qPCR, miRNA hybridization microarray, and small RNA-seq datasets. Emphasis is laid on the complexity, the importance and the potential for further optimization of normalization techniques for miRNA profiling datasets.