Gene Expression Profiling of Skeletal Muscles

Abstract

Next-generation sequencing provides an opportunity for an in-depth biocomputational analysis to identify gene expression patterns between soleus and tibialis anterior, two well-characterized skeletal muscles, and analyze their gene expression profiling. RNA read counts were analyzed for differential gene expression using the R package edgeR. Differentially expressed genes were filtered using a false discovery rate of less than 0.05 c, a fold-change value of more than twenty, and an association with overrepresented pathways based on the Reactome pathway over-representation analysis tool. Most of the differentially expressed genes associated with soleus are coded for components of lipid metabolism and unique contractile elements. Differentially expressed genes associated with tibialis anterior encoded mostly for glucose and glycogen metabolic pathway regulatory enzymes and calcium-sensitive contractile components. These gene expression distinctions partly explain the genetic basis for skeletal muscle specialization, and they may help to explain skeletal muscle susceptibility to disease and drugs and further refine tissue engineering approaches.

Thigh muscle metabolic response is linked to feed efficiency and meat characteristics in slow-growing chicken

“…The MYH10 protein (Myosin Heavy Chain 10) regulates cytokinesis, cell motility, and cell polarity ( Ridge et al, 2017 ). PGK1 (phosphoglycerate kinase) is known for the first ATP-yielding step via a PGK-catalyzed reaction, which participates in the reversible reaction from 1,3-bisphosphoglycerat to ADP to form 3-phosphoglycerate and ATP, catalyzed by phosphoglycerate kinase ( Alto et al, 2021 ). Kierans and Taylor (2021) reported that the PGK 1 gene was upregulated under hypoxia conditions, suggesting that an increase in glycolytic activity during hypoxia stress may help preserve bioenergetic balance.…”

Systematic identification of aberrant non-coding RNAs and their mediated modules in rotator cuff tears

“…However, the transcripts that we identified may also show the inherent differences between supraspinatus muscles and subscapularis muscles. Recent studies have investigated the properties of different muscles by identifying differentially expressed genes between different muscles ( Alto et al, 2021 ; Smith et al, 2022 ); Terry et al revealed that an average of 13% of transcripts were differentially expressed between any two skeletal muscles ( Terry et al, 2018 ). Therefore, further transcriptome sequencing of normal supraspinatus muscles and normal subscapularis muscles is required to confirm that the dysregulation of these transcripts is associated with RCT rather than being caused by tissue differences.…”

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

“…In relation to studying the downstream effects of dystrophin deficiency, a holistic systems biological analysis of the complex pathogenesis of DMD would greatly enhance our insights into organ crosstalk and encompass an integrative multi-omics approach. This integromics strategy would ideally consist of genomics [ 440 , 441 , 442 ], transcriptomics [ 443 , 444 , 445 ], top-down proteomics [ 17 , 59 , 60 , 61 ], bottom-up proteomics [ 46 , 107 ], subproteomics [ 108 , 109 , 110 , 213 ], the proteomic evaluation of PTMs [ 117 , 291 , 446 , 447 ], metabolomics [ 448 , 449 ], lipidomics [ 450 , 451 , 452 ], glycomics [ 453 ], immunomics [ 454 ], secretomics [ 218 , 219 , 220 , 221 , 455 ] and high-throughput cytomics [ 158 , 172 , 456 , 457 ], as summarized in Figure 6 .…”

“…In relation to studying the downstream effects of dystrophin deficiency, a holist systems biological analysis of the complex pathogenesis of DMD would greatly enhanc our insights into organ crosstalk and encompass an integrative multi-omics approac This integromics strategy would ideally consist of genomics [440][441][442], transcriptomi [443][444][445], top-down proteomics [17,[59][60][61], bottom-up proteomics [46,107], subprot omics [108][109][110]213], the proteomic evaluation of PTMs [117,291,446,447], metabolomi [448,449], lipidomics [450][451][452], glycomics [453], immunomics [454], secretomics [218 221,455] and high-throughput cytomics [158,172,456,457], as summarized in Figure 6. Importantly, the integration of proteomic data sets from both bottom-up and top down approaches would be extremely helpful in identifying and characterizing specifi proteoforms in highly complex tissue systems [458,459].…”