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. 2022 Mar 14;2(3):201-210.
doi: 10.1007/s43657-022-00049-y. eCollection 2022 Jun.

Global Quantitative Proteomics Analysis Reveals the Downstream Signaling Networks of Msx1 and Msx2 in Myoblast Differentiation

Guoqiang Zhou  1 , Shuangping Ma  1 , Ming Yang  1 , Yenan Yang  1
Affiliations

Global Quantitative Proteomics Analysis Reveals the Downstream Signaling Networks of Msx1 and Msx2 in Myoblast Differentiation

Guoqiang Zhou et al. Phenomics. .

Abstract

The msh homeobox 1 (Msx1) and msh homeobox 2 (Msx2) coordinate in myoblast differentiation and also contribute to muscle defects if altered during development. Deciphering the downstream signaling networks of Msx1 and Msx2 in myoblast differentiation will help us to understand the molecular events that contribute to muscle defects. Here, the proteomics characteristics in Msx1- and Msx2-mediated myoblast differentiation was evaluated using isobaric tags for the relative and absolute quantification labeling technique (iTRAQ). The downstream regulatory proteins of Msx1- and Msx2-mediated differentiation were identified. Bioinformatics analysis revealed that these proteins were primarily associated with xenobiotic metabolism by cytochrome P450, fatty acid degradation, glycolysis/gluconeogenesis, arginine and proline metabolism, and apoptosis. In addition, our data show Acta1 was probably a core of the downstream regulatory networks of Msx1 and Msx2 in myoblast differentiation.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00049-y.

Keywords: Msx1; Msx2; Myoblast differentiation; Quantitative proteomics.

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Conflict of interest statement

Conflict of interestThe authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Forced expression of Msx1 and Msx2 inhibits C2C12 cells differentiation. a Myotube formation assay. b Western blotting against MHC, Msx1 and Msx2. The scale bars represent 50 µm
Fig. 2
Fig. 2
Sample collection and the proteomics analysis a The workflow of quantitative proteomics. b The principal component analysis (PCA) of different samples. c Correlation analysis of different samples. d Vocano plot of differentially expressed proteins from our study
Fig. 3
Fig. 3
Venn and Heatmap analysis of DEPs a Overlap of all DEPs. b Venn of DEPs before induced differentiation. c Venn of DEPs after induced differentiation. d Heatmap of common DEPs before induced differentiation. e Heatmap of common DEPs after induced differentiation
Fig. 4
Fig. 4
Gene ontology analysis a GO analysis of DEPs for Msx1_B compared to WT_B. b GO analysis of DEPs for Msx2_B compared to WT_B. c GO analysis of DEPs for Msx1_A compared to WT_A. d GO analysis of DEPs for Msx2_A compared to WT_A
Fig. 5
Fig. 5
KEGG analysis and PPIs networks a KEGG pathway analysis of all DEPs. b PPIs networks of DEPs for Msx1_B compared to WT_B. c PPIs networks of DEPs for Msx2_B compared to WT_B. d PPIs networks of DEPs for Msx1_A compared to WT_A. e PPIs networks of DEPs for Msx2_A compared to WT_A
See this image and copyright information in PMC

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