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Record W2795279724 · doi:10.1113/jp275766

Revealing the ‘obscurin’: mapping the path to new discovery with the phosphoproteome

2018· letter· en· W2795279724 on OpenAlexaffabout
Marcus Waskiw‐Ford, Hugo J. W. Fung, Erica Gavel, Olivera Ivanisevic, Julia M. Malowany, Michael A. Rosenblat

Bibliographic record

VenueThe Journal of Physiology · 2018
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhosphoproteomicsPhosphorylationProtein turnoverProteomicsBiologySkeletal muscleCell biologyProtein phosphorylationProtein subunitComputational biologyProtein biosynthesisProtein kinase ABioinformaticsBiochemistryAnatomyGene

Abstract

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Skeletal muscle is a vital tissue for health and functionality and is constantly ‘turning over’ through the reciprocal processes of protein synthesis and protein breakdown. Exercise markedly increases protein turnover as a means to repair and remodel the composition of skeletal muscle. However, the mechanisms by which the mechanical stimulus of exercise is converted into the biochemical signals that elicit changes in protein turnover remain incompletely understood. Over the past decades, research has focused on a network of proteins within the mammalian target of rapamycin (mTOR) signalling cascade, which is a pathway intimately linked to protein turnover via its dual regulation of mRNA translation (i.e. protein synthesis) and proteolysis (i.e. protein breakdown). The traditional top-down approach to study this pathway involves western blots to estimate the activity of known kinases (via changes in phosphorylation) following the application of an external stimulus (exercise, nutrition, etc.). This method has significantly advanced our understanding of a number of intracellular signalling processes associated with protein turnover, but given its general reliance on established proteins the potential for discovery via this approach is arguably nearing its limit. Novel ‘omics’ methods (e.g. proteomics and the recent phosphoproteomics) take a non-targeted, bottom-up approach, which involves assessing vast protein–protein interactions and phosphorylation events that occur within a tissue. As the complexity of the intracellular signalling network becomes increasingly evident, it seems likely that the bottom-up technique will be necessary to make novel discoveries in skeletal muscle. Recently, Potts et al. (2017) published what we believe to be a seminal paper in The Journal of Physiology. This study utilized functional proteomic approaches to investigate the effects of electrically evoked maximal-intensity contractions (MICs) on the regulation of intracellular signalling networks within rat skeletal muscle. By utilizing MICs, the authors were able to circumvent the generally non-physiological hypertrophy models (e.g. synergist ablation) commonly used in rodents to better model a more physiological acute bout of resistance exercise. Thus, the MICs would ultimately induce a physiological increase in muscle protein turnover, which would more broadly align with human physiology responses, in order to determine its effects on protein phosphorylation (phosphoproteomics). Using a unilateral model, six male C57BL6 mice, 8–10 weeks old, underwent MIC for 10 sets of six contractions in their right tibialis anterior with the contralateral side remaining a rested control. Muscle was collected from both legs 1 h post-exercise to capture the period in which mTOR signalling is thought to be maximal (Potts et al. 2017). The muscle tissue was then subjected to bottom-up workflows that enabled the identification of phosphopeptides that had experienced significant MIC-induced alterations. These major MIC-induced phosphopeptides were then profiled back to parent proteins and analysed using various protein databases to identify their cellular functions and associated pathways. The novelty of this approach was that over half of the MIC-regulated phosphorylation sites identified following analyses were non-annotated, implying that many phosphoproteins involved in the processes that control skeletal muscle turnover had yet to be elucidated. This ultimately provides new opportunities for discovery and further interrogation of these previously uncharacterized phosphorylation events and protein interactions via top-down approaches. The annotated phosphoproteins observed by Potts and colleagues (2017) could be consistent with our understanding of some of the biological processes thought to be important during skeletal muscle remodelling. For example, Z-band streaming, a marker of muscle damage, is generally increased after a novel resistance exercise stimulus, which could have been reflected by the fact that MIC-induced phosphorylation events occurring at the Z-disc were especially prominent. It is interesting to note that obscurin, which experienced significant MIC-induced phosphorylation in the present study, had been previously observed within terminal Z-discs of new sarcomeres during myofibrillogenesis (Manring et al. 2017). These findings suggest that, particularly during myogenesis, obscurin has a crucial role in the organization and alignment of myofilaments. Although we believe the findings by Potts and colleagues (2017) support a role for obscurin in the remodelling of already developed skeletal muscle in response to muscle damage, additional research could better frame the physiological significance of the role of obscurin in muscle remodelling. To this end, it would be insightful to study phosphoprotein signatures (such as that of obscurin) at multiple time points during chronic resistance training to examine their activation pattern as a function of training status. For example, Damas et al. (2016) reported that muscle damage, as suggested by the incidence of Z-band streaming, gradually faded as training progressed, and that as the muscle continued to undergo resistance exercise, the myofibrillar protein synthetic response altered focus from muscle repair to increasing muscle cross-sectional area. If obscurin indeed plays a major role in skeletal muscle remodelling at the Z-disc in the setting of increased muscle damage, the data from Damas et al. (2016) could suggest that obscurin would play a lesser role post-exercise in a trained muscle as it becomes more resistant to exercise-induced muscle damage. It is well known that nutrition can markedly enhance the anabolic effects of exercise. For example, elevations in plasma amino acid concentrations from protein and/or amino acid ingestion can potentiate the increase in post-exercise muscle protein synthesis whereas any concomitant rise in insulin can attenuate muscle protein breakdown (Atherton & Smith, 2012). Ultimately, these physiological responses are coordinated by extensive changes in intracellular signalling that converge on and diverge from mTOR activation, which itself is responsive to muscle contraction alone. This latter point is supported by reactome analysis from Potts and colleagues (2017) indicating that signalling via insulin and mTOR were amongst the most highly active pathways immediately post-exercise, at least in the post-absorptive state. However, humans spend most of their waking hours in some degree of postprandial metabolism and are generally advocated to consume protein to enhance post-exercise anabolism. Thus, the exercise-induced increase in muscle protein breakdown, which largely provides the requisite amino acid building blocks to support protein synthesis under suboptimal anabolic conditions, would be attenuated by dietary amino acids and/or feeding-induced increases in insulin. Collectively, this postprandial state could subsequently decrease ubiquitin-mediated proteolysis in the MIC-induced phosphoproteome observed by Potts and colleagues (2017). However, it is important to note that these ‘snapshot’ assessments of intracellular signalling may be dissociated from kinetic measures of muscle protein turnover, especially in response to insulin-mediated alterations of the mTOR pathway. For example, Greenhaff and coworkers (2008) showed that increased insulin availability in healthy young men significantly enhanced intracellular anabolic (i.e. mTOR-related) signalling in the absence of corresponding stable isotope-determined rates of muscle protein synthesis or breakdown. It is possible that the greater number of (novel) targets of the phosphoproteome may help mitigate and/or explain these discrepancies between anabolic signalling and muscle protein metabolism. In this regard, a combination of the top-down approach with stable isotope methods to measure the individual synthesis rates of key proteins identified in the MIC-induced phosphoproteome (e.g. via 2-D gel electrophoresis and tandem mass spectrometry) could help elucidate the physiological impact of these acute signalling events. It is entirely possible that the phosphorylation of certain proteins measured at discrete time points is less related to muscle protein turnover than the rates of synthesis of these proteins measured over several hours. Evidently, our knowledge of the link between the mechanical stimuli of exercise and the ensuing intracellular signalling that leads to skeletal muscle remodelling is continually evolving. Phosphoproteomics has the potential to advance our understanding of this process but remains an underdeveloped field. The plethora of proteins involved in the MIC-regulated phosphoproteome, particularly the proportion not annotated, highlights that commonly used methods (e.g. western blots) are not fully capturing the intricacies of the intracellular signalling networks that coordinate muscle protein turnover. Potts and colleagues (2017) have demonstrated the utility of studying the MIC-regulated phosphoproteome by illustrating the magnitude of post-exercise intracellular protein activity and highlighting key players that may warrant more attention. An emerging protein of interest, obscurin, and its purported significance to the remodelling of skeletal muscle is an early example. Investigating the phosphoproteome of resistance exercise in the setting of variations in training and/or nutritional status may also further our understanding of how skeletal muscle remodels under different conditions. Indeed, in view of the extensive and novel dataset provided by Potts and colleagues (2017), mapping the pathway to new discovery may start with the exercise-regulated phosphoproteome. The authors declare no conflict of interest and do not have any financial disclosures. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. We appreciate the helpful discussions in EXS5531 at the University of Toronto and the feedback of Dr Daniel Moore during the preparation of this manuscript.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.221
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2018
Admission routes2
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