Transcriptomic Signatures and Upstream Regulation in Human Skeletal Muscle Adapted to Disuse and Aerobic Exercise

Alternative transcription start sites contribute to acute-stress-induced transcriptome response in human skeletal muscle

Background

More than half of human protein-coding genes have an alternative transcription start site (TSS). We aimed to investigate the contribution of alternative TSSs to the acute-stress-induced transcriptome response in human tissue (skeletal muscle) using the cap analysis of gene expression approach. TSSs were examined at baseline and during recovery after acute stress (a cycling exercise).

Results

We identified 44,680 CAGE TSS clusters (including 3764 first defined) belonging to 12,268 genes and annotated for the first time 290 TSSs belonging to 163 genes. The transcriptome dynamically changes during the first hours after acute stress; the change in the expression of 10% of genes was associated with the activation of alternative TSSs, indicating differential TSSs usage. The majority of the alternative TSSs do not increase proteome complexity suggesting that the function of thousands of alternative TSSs is associated with the fine regulation of mRNA isoform expression from a gene due to the transcription factor-specific activation of various alternative TSSs. We identified individual muscle promoter regions for each TSS using muscle open chromatin data (ATAC-seq and DNase-seq). Then, using the positional weight matrix approach we predicted time course activation of “classic” transcription factors involved in response of skeletal muscle to contractile activity, as well as diversity of less/un-investigated factors.

Conclusions

Transcriptome response induced by acute stress related to activation of the alternative TSSs indicates that differential TSSs usage is an essential mechanism of fine regulation of gene response to stress stimulus. A comprehensive resource of accurate TSSs and individual promoter regions for each TSS in muscle was created. This resource together with the positional weight matrix approach can be used to accurate prediction of TFs in any gene(s) of interest involved in the response to various stimuli, interventions or pathological conditions in human skeletal muscle.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00399-8.

Relationship between gene expression networks and muscle contractile physiology differences in Anolis lizards

Muscles facilitate most animal behavior, from eating to fleeing. However, to generate the variation in behavior necessary for survival, different muscles must perform differently; for instance, sprinting requires multiple rapid muscle contractions, whereas biting may require fewer contractions but greater force. Here, we use a transcriptomic approach to identify genes associated with variation in muscle contractile physiology among different muscles from the same individual. We measured differential gene expression between a leg and jaw muscle of Anolis lizards known to differ in muscle contractile physiology and performance. For each individual, one muscle was used to measure muscle contractile physiology, including contractile velocity (V_max and V₄₀), specific tension, power ratio, and twitch time, whereas the contralateral muscle was used to extract RNA for transcriptomic sequencing. Using the transcriptomic data, we found clear clustering of muscle type. Expression of genes clustered in gene ontology (GO) terms related to muscle contraction and extracellular matrix was, on average, negatively correlated with V_max and slower twitch times but positively correlated to power ratio and V₄₀. Conversely, genes related to the GO terms related to aerobic respiration were downregulated in muscles with higher power ratio and V₄₀, and over-expressed as twitch time decreased. Determining the molecular mechanisms that underlie variation in muscle contractile physiology can begin to explain how organisms are able to optimize behavior under variable conditions. Future studies pursuing the effects of differential gene expression across muscle types in different environments might inform researchers about how differences develop across species, populations, and individuals varying in ecological history.

Molecular Big Data in Sports Sciences: State-of-Art and Future Prospects of OMICS-Based Sports Sciences

Together with environment and experience (that is to say, diet and training), the biological and genetic make-up of an athlete plays a major role in exercise physiology. Sports genomics has shown, indeed, that some DNA single nucleotide polymorphisms (SNPs) can be associated with athlete performance and level (such as elite/world-class athletic status), having an impact on physical activity behavior, endurance, strength, power, speed, flexibility, energetic expenditure, neuromuscular coordination, metabolic and cardio-respiratory fitness, among others, as well as with psychological traits. Athletic phenotype is complex and depends on the combination of different traits and characteristics: as such, it requires a “complex science,” like that of metadata and multi-OMICS profiles. Several projects and trials (like ELITE, GAMES, Gene SMART, GENESIS, and POWERGENE) are aimed at discovering genomics-based biomarkers with an adequate predictive power. Sports genomics could enable to optimize and maximize physical performance, as well as it could predict the risk of sports-related injuries. Exercise has a profound impact on proteome too. Proteomics can assess both from a qualitative and quantitative point of view the modifications induced by training. Recently, scholars have assessed the epigenetics changes in athletes. Summarizing, the different omics specialties seem to converge in a unique approach, termed sportomics or athlomics and defined as a “holistic and top-down,” “non-hypothesis-driven research on an individual’s metabolite changes during sports and exercise” (the Athlome Project Consortium and the Santorini Declaration) Not only sportomics includes metabonomics/metabolomics, but relying on the athlete’s biological passport or profile, it would enable the systematic study of sports-induced changes and effects at any level (genome, transcriptome, proteome, etc.). However, the wealth of data is so huge and massive and heterogenous that new computational algorithms and protocols are needed, more computational power is required as well as new strategies for properly and effectively combining and integrating data.

The Effect of Sleep Restriction, With or Without Exercise, on Skeletal Muscle Transcriptomic Profiles in Healthy Young Males

BACKGROUND

Inadequate sleep is associated with many detrimental health effects, including increased risk of developing insulin resistance and type 2 diabetes. These effects have been associated with changes to the skeletal muscle transcriptome, although this has not been characterised in response to a period of sleep restriction. Exercise induces a beneficial transcriptional response within skeletal muscle that may counteract some of the negative effects associated with sleep restriction. We hypothesised that sleep restriction would down-regulate transcriptional pathways associated with glucose metabolism, but that performing exercise would mitigate these effects.

METHODS

20 healthy young males were allocated to one of three experimental groups: a Normal Sleep (NS) group (8 h time in bed per night (TIB), for five nights (11 pm - 7 am)), a Sleep Restriction (SR) group (4 h TIB, for five nights (3 am - 7 am)), and a Sleep Restriction and Exercise group (SR+EX) (4 h TIB, for five nights (3 am - 7 am) and three high-intensity interval exercise (HIIE) sessions (performed at 10 am)). RNA sequencing was performed on muscle samples collected pre- and post-intervention. Our data was then compared to skeletal muscle transcriptomic data previously reported following sleep deprivation (24 h without sleep).

RESULTS

Gene set enrichment analysis (GSEA) indicated there was an increased enrichment of inflammatory and immune response related pathways in the SR group post-intervention. However, in the SR+EX group the direction of enrichment in these same pathways occurred in the opposite directions. Despite this, there were no significant changes at the individual gene level from pre- to post-intervention. A set of genes previously shown to be decreased with sleep deprivation was also decreased in the SR group, but increased in the SR+EX group.

CONCLUSION

The alterations to inflammatory and immune related pathways in skeletal muscle, following five nights of sleep restriction, provide insight regarding the transcriptional changes that underpin the detrimental effects associated with sleep loss. Performing three sessions of HIIE during sleep restriction attenuated some of these transcriptional changes. Overall, the transcriptional alterations observed with a moderate period of sleep restriction were less evident than previously reported changes following a period of sleep deprivation.

Effects of Training Status and Exercise Mode on Global Gene Expression in Skeletal Muscle

The aim of this study was to investigate differences in skeletal muscle gene expression of highly trained endurance and strength athletes in comparison to untrained individuals at rest and in response to either an acute bout of endurance or strength exercise. Endurance (ET, n = 8, VO₂max 67 ± 9 mL/kg/min) and strength athletes (ST, n = 8, 5.8 ± 3.0 training years) as well as untrained controls (E-UT and S-UT, each n = 8) performed an acute endurance or strength exercise test. One day before testing (Pre), 30 min (30'Post) and 3 h (180'Post) afterwards, a skeletal muscle biopsy was obtained from the m. vastus lateralis. Skeletal muscle mRNA was isolated and analyzed by Affymetrix-microarray technology. Pathway analyses were performed to evaluate the effects of training status (trained vs. untrained) and exercise mode-specific (ET vs. ST) transcriptional responses. Differences in global skeletal muscle gene expression between trained and untrained were smaller compared to differences in exercise mode. Maximum differences between ET and ST were found between Pre and 180'Post. Pathway analyses showed increased expression of exercise-related genes, such as nuclear transcription factors (NR4A family), metabolism and vascularization (PGC1-α and VEGF-A), and muscle growth/structure (myostatin, IRS1/2 and HIF1-α. The most upregulated genes in response to acute endurance or strength exercise were the NR4A genes (NR4A1, NR4A2, NR4A3). The mode of acute exercise had a significant effect on transcriptional regulation Pre vs. 180'Post. In contrast, the effect of training status on human skeletal muscle gene expression profiles was negligible compared to strength or endurance specialization. The highest variability in gene expression, especially for the NR4A-family, was observed in trained individuals at 180'Post. Assessment of these receptors might be suitable to obtain a deeper understanding of skeletal muscle adaptive processes to develop optimized training strategies.

Electrical Stimulation of Cultured Myotubes in vitro as a Model of Skeletal Muscle Activity: Current State and Future Prospects

Skeletal muscles comprise more than a third of human body mass and critically contribute to regulation of body metabolism. Chronic inactivity reduces metabolic activity and functional capacity of muscles, leading to metabolic and other disorders, reduced life quality and duration. Cellular models based on progenitor cells isolated from human muscle biopsies and then differentiated into mature fibers in vitro can be used to solve a wide range of experimental tasks. The review discusses the aspects of myogenesis dynamics and regulation, which might be important in the development of an adequate cell model. The main function of skeletal muscle is contraction; therefore, electrical stimulation is important for both successful completion of myogenesis and in vitro modeling of major processes induced in the skeletal muscle by acute or regular physical exercise. The review analyzes the drawbacks of such cellular model and possibilities for its optimization, as well as the prospects for its further application to address fundamental aspects of muscle physiology and biochemistry and explore cellular and molecular mechanisms of metabolic diseases.