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Endurance training adaptations

Endurance training adaptations

Laboratory of EpiGenetics, Saarland Adaptatuons, Saarbrücken, Germany. Adaptxtions by Baar, K. Traininf this synergy, athletes can navigate challenges, avoid common Premium fat burners, Endurance training adaptations run on a path that blends resilience, science, and commitment to achieve their fullest potential in the world of endurance running. et al. However, questions regarding physiological circulating levels in humans pre- and post -exercise remain to be addressed Flori et al.


Muscular adaptations to endurance training

Endurance training adaptations -

m if not otherwise stated. Next, we assessed whether the marked differences in the long-term adaptation to training in muscles lacking PGC-1α are reflected in the response to acute maximal exercise Fig.

In untrained mKO muscle, the transcriptomic response to acute maximal exercise was characterized by a modulation of genes related to inflammation and an inverse regulation of genes involved in axon guidance up in WT, down in mKO Extended Data Fig.

Next, we compared the acute exercise response of trained mKO muscle with trained WT muscle. Functionally, these genes encode proteins involved in transcription, and metabolism of lipids and carbohydrates, as well as ECM remodelling Fig.

Intriguingly, the increase and decrease in ECM remodelling in acute maximal exercise of untrained and trained WT muscles, respectively, both seem to be dependent on the presence of this coregulator Figs.

The genes that are reduced only in acutely exercised, trained WT muscle, and not in the corresponding mKO counterpart, were associated with inflammation Extended Data Fig. a , Venn diagrams of all up- and downregulated genes after an acute bout of maximal exercise in trained WT dark grey and mKO dark blue mice.

c , Dot plot of all functional annotations clusters of up- orange and downregulated blue genes after an acute bout of maximal exercise in untrained and trained WT and mKO animals.

d , Examples of genes involved in ECM organization in trained WT grey and mKO blue mice. e , Prediction of the activity of motifs using ISMARA that are changed only in WT muscle and linked to inflammation. f , Number of DMRs in trained mKO compared with untrained mKO muscle hypermethylated is shown as a solid bar and hypomethylated as an open bar.

i , All functional annotation clusters of genes that are differentially methylated and transcriptionally regulated after an acute bout of exercise in untrained WT grey and mKO blue mice.

The data are from five biological replicates. Extended Data Figs. In WT muscle, we have associated the transcriptomic acute exercise response of a trained muscle with epigenetic modulations of the unperturbed trained muscle Fig.

Therefore, we next investigated whether DNA de- methylation events are linked to the massive transcriptional differences in the acutely exercised, trained mKO animals.

In unperturbed trained muscle, a markedly higher proportion of hypermethylated regions was found, with little overlap with DMRs in WT quadriceps that are characterized by more hypomethylation Fig. Similarly, the DEGs after acute maximal exercise associated with DMRs of trained muscle exhibited only a small overlap between the genotypes Extended Data Fig.

Nevertheless, many of these genes in the mKO animals partitioned to regulation of transcription, functionally similar to the results in WT animals Extended Data Fig. Based on the largely different transcriptome of trained muscle, a divergence in DMRs might not be unexpected.

However, it was surprising that absence of muscle PGC-1α also substantially altered transient epigenetic modulations in untrained muscle after an acute maximal exercise bout. In both phenotypes, little overlap exists between these transient DNA de- methylation events in an acute maximal exercise bout and the persistent epigenetic adaptations in unperturbed trained muscle Extended Data Fig.

Collectively, these results imply that PGC-1α is directly involved in the regulation of DNA methylation associated with gene expression.

To further test this hypothesis, we analysed the epigenetic, transcriptomic and proteomic changes elicited in a muscle-specific PGC-1α gain-of-function model. Indeed, a substantial number of DMRs were detected in mTGs. Similar to trained WT muscle, and mirroring the outcome in mKO animals, DMRs in mTGs skewed towards hypomethylation Extended Data Fig.

However, the overlap between DMRs of trained WT and sedentary mTG mice was very small and only 2. In line with previous observations 31 , the transcriptome of mTGs differs substantially from the chronically and acutely training- and exercise-regulated genes in WT muscle Extended Data Fig.

A better functional representation of training adaptation is, however, provided by the mTG proteome, in which a strong accumulation of mitochondrial proteins, including members of the TCA cycle and respiratory chain, lipid metabolism and a depletion of inflammation and proteasomal catabolic processes, recapitulates many of the changes observed in trained WT muscle Extended Data Fig.

The plasticity evoked by exercise training leads to a pleiotropic remodelling of the function of many organs beyond muscle, with potent health benefits 1 , 6 , 7 , 32 , In light of the enormous fundamental and clinical significance of exercise, it is surprising that our understanding of the underlying processes remains incomplete.

Our findings now provide evidence for a much more complex process than proposed in prevailing models, describing muscle plasticity and the corresponding basic mechanistic and regulatory principles of training adaptation. First, even though massive morphological and functional remodelling is necessary for training adaptation, only a small number of genes define the trained muscle transcriptionally, and steady-state gene expression changes explain only a minor subset of the corresponding modulation of the proteome.

This was unexpected based on the contemporary view that repeated exercise bouts result in a persistent modulation of the basal expression of transcripts involved in mitochondrial function, substrate utilization and other functional aspects that define a trained muscle Second, the massive, yet transient remodelling of the muscle transcriptome after acute maximal exercise is quantitatively and qualitatively different when comparing an untrained with a trained muscle.

Our findings vastly expand the prevailing models predicting an attenuation of the acute regulation of genes with repeated exercise bouts, in as much as we also report exacerbation, a shift in peak expression and complete disappearance and de novo emergence of numerous transcripts Fig.

Finally, some transcripts exhibit a diametrically opposite expression after acute maximal exercise in untrained and trained muscles, for example, genes encoding proteins involved in ECM remodelling, inflammation or axon guidance.

This suggests a training status-specific homeostatic perturbation and concomitant transcriptional response, for example, expressed in the shift from a strong stress response and damage mitigation in untrained to improved resilience in trained muscle besides metabolic, contractile and other adaptations.

These highly divergent modes of adaptation imply a complex regulatory framework of training adaptation. The deconvolution analysis indicates, however, that many of these changes are mediated by events in non-muscle cells, in presumably complex multicellular crosstalk and interactions.

Future studies therefore have to consider this aspect and aim for an analysis at the level of individual cell types instead of bulk muscle tissue.

These alterations promote the restoration of homeostasis and prepare the muscle for recurrent insults. With repeated exercise bouts over time, a trained muscle is established, hallmarked by morphological and functional adaptations that improve performance.

This state is characterized by substantial proteomic remodelling, however, in the context of a small number of chronically maintained gene expression modulations. Persistent modifications of epigenetic marks prime the response of the trained muscle to recurring acute exercise bouts.

Hence, a trained muscle responds more rapidly to an acute maximal exercise bout and shows a prominent repression of genes. Collectively, the molecular response to an acute bout of exercise is training status dependent and substantial qualitative and quantitative changes in gene expression events were observed in trained compared with those that occur in untrained muscle.

Our data also shed more light on to the mechanistic underpinnings of acute maximal exercise and chronic training. We observed a clear differentiation between acute epigenetic modifications and those persistently observed in chronically trained muscle. The relatively small number of DMRs in close vicinity to differentially regulated genes in this context might be surprising, and at least in part caused by the limitation of RRBS.

The association of epigenetic marks with the expression of genes modulating transcription, however, implies a priming of a limited number of key transcriptional regulators, which accordingly exhibit a different response to an acute bout of exercise in untrained and trained muscle.

This priming might be sufficient for signal propagation and amplification to downstream genes, and thereby contribute to the quantitative and qualitative differences in the transcriptional networks engaged in these two settings.

From the many factors that have been implied in exercise adaptation, we investigated the regulation and function of PGC-1α. We now unequivocally demonstrate that muscle PGC-1α is indispensable for normal transcriptional muscle plasticity, both after acute maximal endurance exercise bouts in untrained and trained muscle, and in endurance-trained muscle.

Moreover, we show that V O 2max , a marker for maximal endurance capacity, fails to improve in mKO animals. Furthermore, training-induced shifts in the metabolism of ketone bodies and lactate are minimized in these animals 34 , 35 , as well as adaptations in vascularization and other processes 26 , Collectively, these constraints might contribute to the limited performance gains in the absence of muscle PGC-1α.

Unexpectedly, we also found a strong impact of muscle PGC-1α on epigenetic marks, both chronically and acutely, and in both loss- and gain-of-function experiments.

Future studies should therefore aim at investigating the molecular underpinnings of this link. Taken together, these findings demonstrate that regulatory factors such as PGC-1α, even though engaged in only an acute and transient manner, can have a profound impact on long-term training adaptations.

However, the regulatory complexity of muscle plasticity might have been underestimated because redundant, alternative or contingency pathways and factors seem to be able to be recruited in such settings to re-establish adaptation.

This is not only true for PGC-1α, but also for AMP-dependent protein kinase and the mammalian target of rapamycin, which are dispensable for certain aspects of training-induced muscle plasticity 36 , 37 , 38 , Such a complex regulatory framework would make sense in light of the evolutionary importance of the regulation of muscle plasticity, which has to function at least suboptimally to ensure survival even if individual factors fail.

Overall, our findings provide a refined and much more complex model to describe how training adaptations are brought about. These results provide insights into an unsuspected and hitherto undescribed complexity in transcriptomic, epigenetic, proteomic and phosphoproteomic changes in muscle plasticity, and hint at a vast, multifaceted mechanistic framework that controls the effects of acute exercise perturbations and long-term training alterations Fig.

Once validated and expanded in both sexes, other species, age groups, muscles, training paradigms and timepoints, and in a more fine-grained cell type-specific manner, these insights will help not only to better understand such a fundamental process that is a main driver of human evolution, but also to leverage results to design strategies to benefit human health and well-being.

It is encouraging that such efforts currently are ongoing, for example, in the framework of the Wu Tsai Human Performance Alliance or the Molecular Transducers of Physical Activity Consortium Their respective littermates served as WT control animals.

Mice had free access to water and a standard rodent chow diet Maintenance, KLIBA NAFAG and were housed under standard conditions with a h lighth dark cycle.

We used new mouse cohorts for each analysis, except that the RRBS was performed with the same samples used for RNA-seq and proteomic and phosphoproteomic analyses were performed with the same experimental mice.

All experimental protocols followed the Swiss guidelines for animal experimentation and care and were approved by the Kantonales Veterinäramt Basel-Stadt. The training protocol was progressive. All mice were trained with one standardized training protocol, with the drawback that mKO animals trained at a higher relative intensity compared with WT mice.

However, this higher relative training load of the mKO animals did not translate into a boosted adaptation. Maximal oxygen consumption V O 2max was measured during a short maximal exercise test on a closed treadmill Columbus Instruments with only a subset of mice. The test was performed at an inclination of 15°.

After the test, mice were put back into their home cage. Maximal performance and the acute exercise response to one bout of exhaustion exercise were assessed on a motorized treadmill as described previously As maximal exercise performance varies throughout the day 45 , we decided to standardize the time of exercise and therefore perform the maximal exercise test in the morning.

Hence, the dissection of the distinct timepoints was performed at different times of the day and circadian variations could not thereby be ruled out. The control group was circadian heterogeneous.

After euthanizing the mice with a CO 2 overdose, quadriceps all four heads of both hind limbs was removed and immediately snap-frozen in liquid nitrogen. For subsequent processing, the muscle was pulverized, which allowed collection of small amounts of tissue for various analyses.

The usage of pulverized muscle homogenates precludes potential fibre-type differences across specific areas of the muscle, and boosts comparability between assays. Due to technical limitations, the tissue amount was limiting and we were therefore unable to use the muscle from the same experimental mice for all analyses.

Hence, proteomic and phosphoproteomic analyses were performed with one and RNA-seq and RRBS with a second cohort of experimental mice. RNA concentration and quality were measured on the NanoDrop OneC spectrophotometer Thermo Fisher Scientific. All RNA-seq analyses were performed using the CLC Genomics Workbench Software v.

Before mapping the reads to the mm10 version of the mouse genome, reads were quality and adaptor trimmed. For the differential gene expression analyses, the TMM trimmed mean of M-values method was used for normalization. Principal component analysis PCA scatter plots were used to visualize the sample distribution Extended Data Fig.

All steps including quality control and differential gene expression analyses were performed using the CLC Genomics Workbench Software. To visualize gene expression, the relative expression was calculated from the transcripts per million TPM provided by the software.

For all downstream analysis, a log 2 fold-change log 2 FC cut-off of ±0. com Next, the upper phase was collected, and the same volume of PCI as in the first step added, vortexed and centrifuged as described above. The gDNA quality and concentration were measured on the NanoDrop OneC spectrophotometer Thermo Fisher Scientific.

Quality and fragment size were determined with the Bioanalyzer Agilent. Single-read sequencing was performed with a HiSeq machine 51 cycles, Illumina. The reads were quality and adaptor trimmed with the Trim Galore!

The reads were mapped to the mm10 version of the mouse genome with BWA 51 and methylCtools 52 after a slightly extended bis-SNP pipeline The reads were locally realigned and the quality values were recalibrated before calling the methylation levels.

The mm10 SNPs and indels from dbSNP v. An initial quality control and exploratory analysis were done with R package RnBeads Differential loci were detected with MethylKit 56 testing in bp sliding windows with at least three CpGs, including only those with a coverage of at least 10×.

Enrichment for phosphorylated peptides group C was performed using Fe[III]-IMAC cartridges on an AssayMAP Bravo platform following a recently described method Dried peptides of group A and B as well as the phospho-enriched peptides group C were resuspended in 0. For samples of group B, the mass spectrometer was operated in data-independent acquisition DIA mode.

One microscan was acquired for each spectrum. The raw files obtained from samples of group A and C were imported into the Progenesis QI software v. This software was utilized with default parameters to extract peptide precursor ion intensities across all samples.

Two modifications were made for group A: instead of phosphorylation STY as a variable modification, acetyl protein amino terminal was used.

precursor and 0. For group B, the acquired raw files were searched using the Spectronaut Biognosys v. The default factory settings were employed with slight adjustments.

Quantitative analysis results from label-free quantification or exported from Spectronaut were processed using the SafeQuant R package v. Data imputation was performed using the k-nearest neighbours algorithm to handle missing values.

Only isoform-specific peptide ion signals were considered for quantification. The summarized peptide expression values were then used to statistically test the differential abundance of peptides between the conditions.

Three proteomic samples failed quality control and had to be excluded from the analysis one WT sedentary, one WT-trained and one mKO-trained. These were also excluded from the phosphoproteomic analysis. For all proteomic analyses, only proteins with more than one peptide were considered. In addition, a log 2 FC cut-off of ±0.

To create the single-transcriptomic reference dataset including both mononucleated cells and myonuclei, we integrated published single-cell data scRNA-seq from mononucleated muscle cells 23 and single-nucleus data snRNA-seq from myonuclei In detail, we subsetted the provided scRNA-seq data from ref.

Then, we integrated the samples using Harmony github. Finally, we annotated clusters based on published marker gene expression and removed cells from high-fat-diet-fed mice.

To complement the scRNA-seq data from mononucleated muscle cells with missing myonuclei data, we used published snRNA-seq from the tibialis anterior muscles In short, we applied quality measures as described in the original publication and clustered nuclei with the common Seurat v.

After cluster annotation, we subsetted the dataset for myonuclei only. Subsequent integration of scRNA-seq and snRNA-seq data was performed by merging all datasets and recalculating the normalization, variable features, scaling and principal components.

We corrected for batch effects and integrated the individual samples via Harmony and clustered as described above. The statistical analyses of the RNA-seq, RRBS and proteomic analyses were done as described in the respective sections. All other statistical analyses were performed in GraphPad Prism v.

except for data presented in box plots that display the median and the 25th to 75th percentiles and whiskers indicating the minimal and maximal values. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Transcriptomic and RRBS data have been deposited at the Gene Expression Omnibus accession nos. GSE and GSE , respectively. Proteomic data have been deposited at the proteomics identifications database MassIVE, accession no.

MSV and ProteomeXchange, accession no. Source data are provided with the present paper. Data have been analysed using either commercial tools that is, CLC Genomics Workbench Software or existing standard packages and scripts described in Methods.

No new code has been developed. Chow, L. et al. Exerkines in health, resilience and disease. CAS PubMed Central Google Scholar. Murphy, R. Metabolic communication during exercise. PubMed Google Scholar.

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I didn't find any good studies that looked at the effect of training on tendons. One training program of running twice per week for minutes at low intensity for 9 months did not increase Achilles tendon size [14] , but I suspect this is due to the relatively low volume of training.

The benefits of exercise on tendon health is not limited to the young, with studies showing strengthening of tendons with exercise persists into old age [15].

Perhaps not surprisingly, inactivity leads to a degradation of the tendons [16] , hence the phrase "use it or lose it. This flowchart shows that while V̇O 2 max is a key determinant of performance, it interacts with other factors. Lactate threshold defines what percentage of V̇O 2 max can be sustained, which defines the energy available ATP re-synthesis.

Running Economy then defines how well this energy can be translated into forward motion. From Fellrnr. These can be divided into centrally and peripherally mediated adaptations.

Athletes who want to improve endurance performance should know where they stand relative to the markers often used to identify physiological adaptations. Doing so will help them ascertain which physiological adaptations will best aid them in achieving their endurance performance goals. Moxy Monitor.

VO2 Max Elite endurance athletes have very high VO2 max readings; however, evidence suggests that a person's VO2 max is largely genetically predetermined, reports Sports Fitness Advisor. Lactate Threshold While VO2 max determines one's limit in aerobic performance, the lactate threshold is responsible for the time an athlete may remain training at this limit.

Exercise Economy Alone, VO2 max and lactate threshold are not enough to determine an athlete's performance. Substrate Utilization A body's energy system can use either fat or carbohydrate stores in order to produce energy.

Central Cardiovascular Physiological Adaptations Decreased heart rate Increased heart stroke volume Increased blood plasma Reduced blood viscosity Increased cardiac output Increased mitochondrial volume in muscle fibers being used Increase in number and size of myoglobin and oxidative enzymes Peripheral Physiological Adaptations Capillarization; there is an increase in the surface area supplied by the venous and arterial capillaries.

This allows for increased heat dissipation during intense exercise. Improved glycogen and fat storing capabilities in muscles; this allows for an increase heat dissipation during intense exercise, lengthening the time an athlete can work out.

Development of slow twitch type 1 fibers; these increase efficiency and resistance to fatigue.

Photo: Getty Images "], adaptatoons { "nextExceptions": Cold Pressed Coconut Oil, blockquote, div", Endurance training adaptations "img, Endurance training adaptations, a. trzining, a. Endurance athletes, adaltations trail and ultrarunners, participate adaptwtions a sport that demands traiing physical capacity and mental resilience. Conquering distances over challenging terrains requires a strong body and a well-prepared mind. One of the critical elements that often escapes the attention of many runners is the time course of adaptations—the process by which the body gradually changes and gets stronger, in response to training stimuli. After all, these changes are precisely the outcomes we seek through training. Adaptatione can Maximizing performance with dietary considerations regarded as a biological stress. Muscle contractions Endurance training adaptations the internal Endkrance milieu during rest, Endurance training adaptations this elicits Endugance variety of homeostatic responses. Examples of these responses include Endurance training adaptations adaptatjons flow to the active muscles; increased heart Endurance training adaptations increased breathing rate; increased oxygen consumption; increased rate of sweating; increased body temperature; secretion of stress hormones such as adrenocorticotropic hormone ACTHcortisol, and catecholamines; increased glycolytic flux; and altered recruitment of muscles. These changes are transient and return to baseline levels after exercise. If exercise is repeated on several occasions, adaptations occur. Adaptations involve either remodeling of tissue or altered regulation of the central nervous system. The outcome of exercise-induced adaptations depends on the type of exercise, but either makes the muscle more resistant to fatigue, stronger, more powerful, or better coordinated.

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