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Low responders to endurance training exhibit impaired hypertrophy and divergent biological process responses in skeletal muscle

ABSTRACT: Divergent skeletal muscle phenotypes result from chronic resistance-type- versus endurance-type contraction, reflecting the principle of training specificity. However, it is unclear whether there is a common set of genetic factors that influence skeletal muscle adaptation to different modes of training. Female rats were obtained from out-bred lines selectively bred from high responders to endurance training (HRT) or low responders to endurance training (LRT; n=6/group; generation 19). Both groups underwent 14 d of synergist ablation to induce functional overload of the plantaris muscle prior to comparison to non-overload controls of the same genotype. RNA sequencing was performed to identify Gene Ontology Biological Processes with differential (LRT vs HRT) gene set enrichment. Running distance, determined well in advance of synergist ablation, increased in response to aerobic training in HRT but not LRT (65 ±26% versus -6 ±18%, respectively, mean ±SD, p<0.0001). The hypertrophy response to functional overload was attenuated in LRT versus HRT (20.1 ±5.6% versus 41.6 ±16.1%, respectively, P = 0.015). There were between-group differences in the magnitude of response of 96 upregulated- and 101 downregulated pathways. A further 27 pathways showed contrasting upregulation or downregulation in LRT versus HRT in response to functional overload. In conclusion, low responders to aerobic endurance training were also low responders for compensatory hypertrophy, and attenuated hypertrophy was associated with differential gene expression. Thus, our findings suggest that genetic factors that underpin aerobic training maladaptation may also dysregulate the transcriptional activity of biological processes that contribute to adaptation to mechanical overload.

ORGANISM(S): Rattus norvegicus

PROVIDER: GSE156724 | GEO | 2020/12/31

REPOSITORIES: GEO

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Project description:The molecular pathways which are activated and contribute to physiological remodeling of skeletal muscle in response to endurance exercise have not been fully characterized. We previously reported that ~800 gene transcripts are regulated following 6 weeks of supervised endurance training in young sedentary males, referred to as the training responsive transcriptome (TRT). Here we utilized this database together with data on biological variation in muscle adaptation to aerobic endurance training in both humans and a novel out-bred rodent model to study the potential regulatory molecules that coordinate this complex network of genes. We identified three DNA sequences representing RUNX1, SOX9, and PAX3 transcription factor binding sites as over-represented in the TRT. In turn, miRNA profiling indicated that several miRNAs targeting RUNX1, SOX9 and PAX3 were down-regulated by endurance training. The TRT was then examined by contrasting subjects who demonstrated the least vs. the greatest improvement in aerobic capacity (low vs. high responders), and at least 100 of the 800 TRT genes were differentially regulated, thus suggesting regulation of these genes may be important for improving aerobic capacity. In high responders, pro-angiogenic and tissue developmental networks emerged as key candidates for coordinating tissue aerobic adaptation. Beyond RNA level validation there were several DNA variants that associated with VO(2)max trainability in the HERITAGE Family Study but these did not pass conservative Bonferroni adjustment. In addition, in a rat model selected across 10 generations for high aerobic training responsiveness, we found that both the TRT and a homologous subset of the human high responder genes were regulated to a greater degree in high responder rodent skeletal muscle. This analysis provides a comprehensive map of the transcriptomic features important for aerobic exercise-induced improvements in maximal oxygen consumption. This data is from skeletal muscle post 6 weeks of endurance exercise training.

Project description:Aerobic exercise capacity is a strong predictor of disease and survivability but the utility of exercise intervention is largely dependent on how one’s genome interacts with an exercise-training environment. A newly developed rat model selectively bred for inherited differences in response to aerobic exercise training shows to be a useful resource to sort out the networks of genes responsible for signalling exercise-induced changes that benefit cardiac function. Animals The study consisted of four groups: 1) HRT trained rats (HRT-T), 2) LRT trained rats (LRT-T) 3) HRT untrained sedentary rats (HRT-S) and 4) LRT untrained sedentary rats (LRT-S) (n=5 in each group). Experimental protocols were approved by the Institutional Animal Research Ethics Council, Norway. Sample collection Left ventricle tissue was snap-frozen in liquid nitrogen and stored at -80oC to time of analysis. RNA isolation RNA was isolated from the left ventricle (20 mg) with the mirVana RNA isolation kit (Ambion) according to the manufacturer's instructions. RNA integrity, purity and quantity were assessed by Bioanalyzer (Agilent Technologies, Santa Clara, US) and Nanodrop (NanoDrop Technologies, Baltimore, US). The concentration of total RNA was measured by Nanodrop with ultraviolet spectrophotometry at 260/280 nm. RNA quality was assessed by electrophoresis on Bioanalyzer chips (Agilent Technologies, Santa Clara, US). Only samples with a 260/280-ratio of more than 1.8, and RNA integrity number (RIN) value above 8 was analyzed. Microarray analysis Samples were hybridized to Applied Biosystems Rat Genome Survey chips v.1.0. All samples were labelled at the same time, and hybridised over two days. The biological groups were balanced between the two hybridisation batches and the samples randomly distributed within this format. Samples were analyzed by the Applied Biosystems 1700 Array Expression System. The following threes comparisons were done: A: HRT-S vs. LRT-S B: HRT-S vs. HRT-T C: LRT-S vs. LRT-T Differentially expressed gene analysis The data files from the AB 1700 Chemiluminescent Microarray Analyzer Software were processed using J-Express Pro v.2.8. The signal intensity values were extracted per spot, and all flagged and control spots were filtered out. Before compiled into an expression profile data matrix, all arrays were quantile normalized to be comparable. Genes with at most 20% missing values were allowed in the final dataset. The signal intensities in the dataset were further log transformed, and missing values were replaced using the method LSimpute Adaptive. The search for differentially expressed genes was performed both on a single gene and gene set level. Rank Product was used to look for differentially expressed genes on a gene by gene basis, while Gene Set Enrichment Analysis (GSEA) was used to look for sets of genes sharing common characteristics that were differentially expressed between the classes examined. Gene sets were created using the Panther Biological Processes (241 gene sets) and Panther Molcular Functions (252 gene sets). This information was extracted from the Applied Biosystems Human Annotation File, dated September 30th 2006, with updated information from a Panther search carried out on February 1st 2008. Gene sets smaller than 15 and larger than 500 were excluded from the GSEA. GSEA First the genes were ranked according to Golub score, which t is the default method for GSEA. Next an enrichment score (ES) was calculated for each gene set. For instance, when calculating the ES for the gene set “T-cell mediated immunity”, GSEA starts by looking at the gene ranked on top of the genelist. If this gene is a member of T-cell mediated immunity, a positive score is added to the ES, otherwise a negative score is added. Then the next gene on the gene list is evaluated and the ES is updated. This process is repeated for every gene in the entire gene list. The green line in Figure 1 shows how the value of ES changes when evaluating the genes in the ranked list. The maximum value obtained during this “walk” is used as ES for the gene set. Also worth noting is that the positive score that is added to the enrichment score is weigthed, so that a higher score is added when a gene higher on the ranked list is found to be a member of the gene set than when a gene lower on the ranked list is marked with a hit. Therefore, a high ES means that the gene set is overrepresented towards the top of the ranked list. Gene sets with less than 10 members or more than 500 members were removed from the analysis.

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Low responders to endurance training exhibit impaired hypertrophy and divergent biological process responses in skeletal muscle

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