Myostatin regulates energy homeostasis through autocrine- and paracrine-mediated microenvironment communications[BAT]
Myostatin (MSTN) has been discovered as a critical regulator of muscle mass. Recently, there has been an increasing interest in its functions in metabolism. Here, we specific knocked out MSTN in brown adipose tissue (BAT) (MSTN?UCP1), and found that the MSTN?UCP1 mice gained more weight than controls on high-fat diet, with progressive hepatosteatosis, and impaired skeletal muscle activity. RNA-seq analysis indicated signatures of mitochondrial dysfunction and inflammation in MSTN-ablation BAT. Further studies demonstrated that KLF4 is required for the metabolic phenotypes and FGF21 contributes to the microenvironment communication between adipocytes and macrophages induced by loss of MSTN in BAT. Moreover, MSTN-SMAD2/3-p38 signaling pathway mediated the expression of KLF4 and FGF21 in adipocytes. Taken together, brown adipocytes-derived MSTN governs metabolic niche in BAT and regulates systemic energy homeostasis. Overall design: The brown adipose tissue (BAT) tissue samples from each group were collected for RNA sequencing. Total RNA was isolated using the Trizol method following the manufacturer's instructions. The quality of the RNA was assessed using the Agilent 2100 Bioanalyzer with the RNA 6000 Nano Kit from Agilent Technologies, Santa Clara, CA, USA. cDNA libraries for each sample were constructed as previously described [35713959]. The libraries were sequenced on the BGIseq500 platform (BGI-Shenzhen, China) using 150 bp paired-end reads with a target of 30 million reads per sample.The raw sequencing data was filtered using trim-galore (v0.6.7) to remove reads containing sequencing adapters and low-quality bases. The resulting clean reads were aligned to the reference genome (mm10) using STAR (v2.5.2b). Gene expression quantification was performed using RSEM (v1.2.28) with commands rsem-calculate-expression --paired-end -p 20 –alignments samples.bam star_index.files. Differential gene expression analysis was conducted to compare MSTN?UCP1 to MSTNFlox/Flox. DESeq2 (v1.42.0) was used for differential expression analysis, considering genes with an adjusted P value < 0.05 and |FC| > 1.5 as significant. Furthermore, KEGG pathway analysis was conducted using differentially expressed genes in, while gene-set enrichment analysis was performed using the obtained log2FC values to detect pathways enriched with profiling genes.