Molecular and cellular regulation of adaptation to exercise pdf

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PLOS Genetics 9 4 : Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. Paradoxically, those displaying most hypertrophy exhibited an inhibited mTOR activation signature, including the striking down-regulation of 70 rRNAs. For age, we found that differential gene-expression methods do not produce consistent molecular differences between young versus old individuals.

Skeletal muscle energy metabolism during exercise

PLOS Genetics 9 4 : Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. Paradoxically, those displaying most hypertrophy exhibited an inhibited mTOR activation signature, including the striking down-regulation of 70 rRNAs. For age, we found that differential gene-expression methods do not produce consistent molecular differences between young versus old individuals.

The RNA signatures from multiple compounds all targeting serotonin, DNA topoisomerase antagonism, and RXR activation were significantly related to the muscle age-related genes. We conclude that human muscle age-related molecular processes appear distinct from the processes regulated by those of physical activity. A fundamental challenge for modern medicine is to generate new strategies to cope with the rising proportion of older people within society, as unaddressed it will make many health care systems financially unviable.

Ageing impacts both quality of life and longevity through reduced musculoskeletal function. Because the details of such interactions will be uniquely human, we aimed to produce the first reproducible global molecular profile of human muscle age, one that could be validated across independent clinical cohorts to ensure its general applicability.

We combined this analysis with extensive data on the impact of exercise training on human muscle phenotype to then identify the processes predominately associated with age and not environment. We were able to identify unique gene pathways associated with human muscle growth and age and were able to conclude that human muscle age-related molecular processes appear distinct from the processes directly regulated by those of physical activity. PLoS Genet 9 3 : e This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

CB was partially funded by the John W. Barton Chair in Genetics and Nutrition. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist. Discovery of the biological determinants of muscle mass and functional molecular phenotypes has substantial bearing on the fields of human performance e. Resistance exercise RE training RET is an effective intervention to increase muscle mass in many, but not all people, and thus provides an excellent opportunity to study gene-network regulation during muscle hypertrophy and the proposed relationship to muscle aging.

Many exogenous factors may influence RET-induced hypertrophy including manipulation of exercise volume [7] , intensity [8] and adequate macronutrient availability [9] all of which interact with an individual's genotype to determine muscle growth.

Establishing the molecular diagnostics that enable a personalized approach to tackle ageing has great appeal. There are however conflicting data regarding the importance of mTOR regulation protein phosphorylation or target gene mRNA responses or its up-stream regulators, and acute anabolic or chronic growth responses to resistance exercise [12] — [17] reported from the same laboratories, indicating that important biological rather than methodological issues remain to be identified.

Perhaps the most convincing observation in favor of a more divergent regulation of muscle growth, is the fact that disparate exercise modes e. RET vs. This suggests that the molecules, so far studied, are pleiotropic and in our opinion probably important for any type of tissue remodeling, regardless of the final physiological phenotype [22].

Therefore, a more innovative approach is needed to define links between molecules and ensuing in vivo physiological adaptations, than can be achieved with targeted western-based molecular profiling. Exercise training has also been postulated as a key tool to reverse the impact of ageing on human skeletal muscle phenotypes [23] , [24].

Similarly, we reported muscle hypertrophy ranging from 0. Both of these RET studies reported that the outcome of supervised progressive RET did not relate to pre-existing differences in characteristics i.

In recent years, we have focused on using the heterogeneous responses to exercise training and OMIC techniques to uncover molecular networks regulated by EET [33] , [29] or generate molecular predictors of trainability [34] , directly in humans.

The aim of the present work is to produce the first reproducible molecular signature of human muscle age, and examine how such a profile relates to new and established exercise adaptation gene networks.

We generated new gene-chip profiles from muscle samples derived from two independent clinical cohorts, with a continuous range of ages 18—79 y and which originate from distinct environments UK and USA and which were independently processed in the laboratory. Finally, Ingenuity's new IPA up-stream analysis tool [35] was used to identify key features, within these novel age and exercise signatures, to provide independent and robust molecular insight into the heterogeneous nature of muscle hypertrophy, and human muscle age.

For logical reasons we used only the 38 subjects that demonstrated a training effect [31]. A Forty-four subjects completed a 20 wk supervised resistance exercise training program RET and biopsy RNA was profiled before and 72 hr after training.

Following SAM analysis of the 38 subjects that demonstrated a clear physiological gain, the gene list was uploaded to the Ingenuity Pathway Analysis database IPA and the up-stream regulators were identified using IPA's new up-stream tool.

We have previously speculated that a core set of gene-networks will be common to all types of exercise and could represent basic determinants of tissue adaptability [22]. Thus while differential expression in the Derby Resistance Exercise Training DRET study clearly identifies a number of tissue remodeling related processes, these are not specific to exercise training modality. Of greater physiological importance was our effort to identify genes which link to the magnitude of muscle hypertrophy in humans.

Quantitative SAM analysis [3] , [36] identified probe-sets and interestingly the majority of genes identified were negatively correlated with gains in lean mass Dataset S2. We identified a number of regulators that could be responsible for regulating the transcriptional signature that correlated to gain in lean mass Dataset S2.

Figure 2A presents the most striking finding, an active rapamycin signature, equating to inhibition of mTOR signaling [37]. In short, subjects that demonstrated the largest gains in lean tissue mass following wks of RET have suppressed mTOR signaling over the training period, a novel and controversial observation all raw data was manually checked for consistency of direction.

Thus these two robust observations are consistent, and notably the signature evidence is based on entirely independent data. B Given the strength of the negative statistical association between the rapamycin signature, we then plotted the data to establish the precise nature of the relationship.

C We selected a representative subset of the genes from Figure 2A and plotted the mean changes with respect to lean mass changes. This established that those with the greatest lean mass actually had a reduction in mTOR related genes with RET and not simple a lesser increase as one might have expected from first inspection of Figure 2A.

The remaining genes Figure 2A belonged to metabolic processes and other facets of protein metabolism or signaling. To more easily appreciate the characteristics of those subjects that were found to have the greatest increase in lean tissue mass combined with a reduction in ribosomal gene abundance, we plotted the quartile response in lean mass Figure 2B.

Baseline lean mass could not explain our observation and in fact the four groups had the same mean age, physiological characteristics, while the highest and lowest quartiles for lean mass gain had exactly the same proportion of males and females Table 1. Thus, our analysis strategy enabled the discovery of an entirely novel in vivo feature of the mTOR growth pathway, while standard differential RNA expression analysis pre vs.

We also plotted the relationship between physiological characteristics, protein-phosphorylation during acute net anabolic situations resistance exercise coupled with feeding with lean mass gain in these subjects using principal component analysis PCA. Selected variables were scaled and principal components 1 and 2 are presented. In Figure 3A , it is abundantly clear that none of the metabolic or physiological characteristics shared variance with the main component capturing lean mass gain variation following RET.

Likewise, while protein kinase abundance or protein kinase activation status varied in a manner consistent with the literature, none of these acute net anabolic responses were correlated to gains in RET induced lean mass or shared variance with gains in lean mass when studied prior to wk RET Figure 3B.

In short, only the unbiased transcriptomics method was able to identify a biological profile distinguishing between high and low responders for lean mass gain.

A Change in lean mass following 20 wk RET and a number of physiological parameters which demonstrated the most variance were scaled to a common value and plotted using principal component analysis in R. Principal component PC 1 captured the major variance in lean mass gains across subjects however none of the commonly postulated physiological parameters varied with lean mass linear regression analysis demonstrated no significant association also.

PC2, the second largest proportion of independent variance also demonstrated no association between factors such as fiber type or age and gains in lean mass. B Phospho-protein signaling 2 hr after a combined exercise and nutrition acute intervention to promote anabolic signaling were scaled and plotted with change in lean mass following 20 wk RET.

Identification of the determinants of skeletal muscle mass has obvious implications for treating age-related sarcopenia. There is no longitudinal molecular analysis of ageing muscle in humans. However using cross-sectional gene-chip data-sets, attempts have been made to identify age-related gene expression changes.

For example, Melov et al. To investigate this issue further, we utilised the Melov et al. We used SAM analysis to compare young with old subjects in each study. We used baseline samples from DRET, 10 young 20—29 y versus 16 old subjects 64—75 y , and finally we re-analysed the Melov et al. Our re-analysis of the GO analysis using DAVID of earlier studies [26] , [39] , using the appropriate back-ground files [40] , also confirmed that mitochondrial RNA changes in ageing cannot be claimed as being a reproducible hallmark of ageing, despite the presumed association with inactivity.

Re-analysis of the Melov et al. This gene-set is also known to reflect physical activity [29] and inactivity in humans [41] and thus it shouldn't be attributed to age per se anyway. To consolidate the conclusion that there was no common finding across these three studies, we entered the individual gene-lists in a gene ontology analysis to evaluate if some common pathways were enriched in each list, even if the member genes differed.

Only 1 ontological group was common to 2 from the 3-gene lists and it related to mitochondrial processes indicating that even when the older subjects have a lower physical capacity a decline in mitochondrial genes is not always a prominent feature of age-related changes. Several attempts have been to define a set of genes that differ in skeletal muscle between young and old human subjects. We re-analysed three of the most robust and largest human studies with common methods, including our new study, and contrasted the genes identified to be differentially regulated using SAM analysis and Gene Ontology analysis.

No common pattern of differential gene expression could be found using this analysis method indicating that no prior gene signature for muscle ageing can be considered a reliable marker of muscle age in humans.

Gene ontology analysis found that both the Trappe and Melov data sets had modest enrichment in mitochondrial genes, which were down-regulated with age however this was not true for the DRET study and both Melov and Trappe data-sets had elderly with much lower physical fitness levels making it impossible to attribute these changes to age per se with differential expression analysis.

Therefore, an alternative approach to identify age-related gene expression profiles in human muscle was required. To achieve this we utilised QSAM, which we have previously applied and validated to some extent for human studies [3]. We applied QSAM to generate a list of muscle transcript levels, which correlated with subject age 20—75 yr , with correction for multiple testing.

This allowed us to identify genes that either negatively or positively associated with the subject age. This has not been attempted before because previous studies did not have a sufficiently wide and continuous range of ages to generate such data [23] , [24] , [26] , [39]. However, such an analysis would be of limited value if some of the observations could not be independently reproduced, using a distinct set of clinical samples.

Each mediator orchestrated a set of either positive yellow or negatively green age-correlated genes such that both overlap and direction were similar to the literature-constructed networks. Critically, these networks were not significantly related to the lean-mass associated gene-list Figure 5B or differentially regulated by either acute RE Figure S4A or chronic endurance exercise Figure S4B. Thus it is unlikely that these new age-related observations reflect simple confounding factors, such as exercise training or being physically active.

Mitochondrial genes were not a feature of this linear age vs gene analysis. We then mapped the Affymetrix probe-sets to the IPA database and examined the up-stream analysis output.

B We noted that some members of these age-related networks were also associated with lean mass gains in humans. Clearly some responses can be causal, some may be purely correlative and some may represent compensatory events.

Again, no clear relationship with acute exercise or endurance training was apparent Figure S5 , while a closer association with genes related to gains in lean mass was noted Figure 6B with some key exceptions. Furthermore, large differences in gene expression still existed when comparing the age groups and the pre and post-training samples in the Trappe dataset data not shown.

The age-related expression signature was also related to RNA signatures in the Broad Connectivity database, including multiple serotonin antagonists and appears opposite to DNA topoisomerase inhibition Dataset S4. Finally, we examined whether the age-related genes were over represented at genomic loci using Positional enrichment analysis [43].

Both Chromosome 1 q12 and 13 q B A few members of these age-related networks were also associated with lean mass gains in humans and this included mTOR regulated genes, which were negatively associated with increasing age and thus in contrast to the lean-mass association. A plot of selected genes found to be over-represented at 1q12 and 13q Genes at these loci included proteins that are known to influence mTOR related signalling. Muscle hypertrophy is the most recognized adaptation to RET.

Enzymes And Cellular Regulation Model 2 Quizlet

The regulatory processes in cells are typically organized into complex genetic networks. However, it is still unclear how this network structure modulates the evolution of cellular regulation. One would expect that mutations in central and highly connected modules of a network so-called hubs would often result in a breakdown and therefore be an evolutionary dead end. However, a new study by Koubkova-Yu and colleagues finds that in some circumstances, altering a hub can offer a quick evolutionary advantage. Specifically, changes in a hub can induce significant phenotypic changes that allow organisms to move away from a local fitness peak, whereas the fitness defects caused by the perturbed hub can be mitigated by mutations in its interaction partners.

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. An Author Correction to this article was published on 10 September The continual supply of ATP to the fundamental cellular processes that underpin skeletal muscle contraction during exercise is essential for sports performance in events lasting seconds to several hours.

Regulation of gene expression , or gene regulation , [1] includes a wide range of mechanisms that are used by cells to increase or decrease the production of specific gene products protein or RNA. Sophisticated programs of gene expression are widely observed in biology, for example to trigger developmental pathways, respond to environmental stimuli, or adapt to new food sources. Virtually any step of gene expression can be modulated, from transcriptional initiation , to RNA processing , and to the post-translational modification of a protein. Often, one gene regulator controls another, and so on, in a gene regulatory network. Gene regulation is essential for viruses , prokaryotes and eukaryotes as it increases the versatility and adaptability of an organism by allowing the cell to express protein when needed. In multicellular organisms, gene regulation drives cellular differentiation and morphogenesis in the embryo, leading to the creation of different cell types that possess different gene expression profiles from the same genome sequence. Although this does not explain how gene regulation originated, evolutionary biologists include it as a partial explanation of how evolution works at a molecular level , and it is central to the science of evolutionary developmental biology "evo-devo".


Purchase Molecular and Cellular Regulation of Adaptation to Exercise, Volume - 1st Edition. Print Book & E-Book. View all volumes in this series: Progress in Molecular Biology and Translational Science DRM-free (Mobi, PDF, EPub).


Molecular and cellular regulation of adaptation to exercise

Enzymes And Cellular Regulation Model 2 Quizlet Michaelis—Menten kinetics pro-vides a solid framework for enzyme kinetics in vitro, but what about kinetics in cells, where enzymes can be highly. This enzyme causes the breakdown of lactose into glucose and galactose. The cellular metabolism of substrates such as glucose and fatty acids green arrows in the figure generates hydrogens and, specifically, hydrogen carriers — NADH and FADH 2.

Cell non-autonomous regulation of health and longevity

As the demographics of the modern world skew older, understanding and mitigating the effects of aging is increasingly important within biomedical research. Many well-studied pathways that influence aging involve sensory cells, frequently neurons, that signal to peripheral tissues and promote survival during the presence of stress. Importantly, this activation of stress response pathways is often sufficient to improve health and longevity even in the absence of stress. Here, we review the current landscape of research highlighting the importance of cell non-autonomous signaling in modulating aging from C. We also discuss emerging concepts including retrograde signaling, approaches to mapping these networks, and development of potential therapeutics. It is estimated that by the number of US citizens over the age of 65 will reach nearly million, more than twice as many as today United Nations,

Molecular Aspects of Exercise Biology and Exercise Genomics, the latest volume in the Progress in Molecular Biology and Translational Science series includes a comprehensive summary of the evidence accumulated thus far on the molecular and cellular regulation of the various adaptations taking place in response to exercise. Changes in the cellular machinery are described for multiple tissues and organs in terms of signaling pathways, gene expression, and protein abundance. Adaptations to acute exercise as well as exposure to regular exercise are also discussed and considered. Graduate students in exercise physiology and exercise medicine programs, postdoctoral fellows, basic scientists and clinical investigators interested in exercise for the prevention and treatment of common chronic disease associated with a sedentary lifestyle and poor cardiorespiratory fitness. He holds the John W. Barton Sr. Endowed Chair in Genetics and Nutrition.

Enzymes thus play an important role in controlling cellular metabolism. The G 2 checkpoint control mechanism ensures that everything is ready to enter the M mitosis phase and divide. Cell division: Mitosis: M: Cell growth stops at this stage and cellular energy is focused on the orderly division into two daughter cells. Destroy unwanted bacteria through phagocytosis. For most cells this includes appropriate communication with neighboring cells.


2UAB Center for Exercise Medicine and Department of Cell, Developmental, and Integrative complex diseases, whereas biological adaptations that enhance control is exerted, as well as the molecular mechanisms that.


Molecular and Cellular Regulation of Adaptation to Exercise, Volume 135

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