Biology subjects

McColl, T. J.

Publications and source records attributed to McColl, T. J..

4 recordsLinked to original sources

bioRxiv2025

Bioinformatic analysis of differentially expressed long non-coding RNAs in skeletal muscle following aerobic and resistance exercise

Emerging evidence suggests that long non-coding RNA (lncRNA) molecules influence the adaptive response to exercise, but how lncRNA responses differ between endurance and resistance exercise modalities is poorly understood. The purpose of this study was to bioinformatically infer the expression of lncRNA in skeletal muscle following acute aerobic exercise (AE) and resistance exercise (RE). We downloaded publicly available RNA-seq data, performed a differential expression analysis, and compared lncRNA expression profiles between different exercise types (AE vs. RE) at three time points: baseline, 1 hour post-exercise, and 4 hours post-exercise. We observed distinct lncRNA profiles between acute AE and RE at different time points, suggesting that lncRNA perform different roles in controlling the response to different exercise modalities in skeletal muscle. Future studies should investigate the specific roles of these lncRNAs in the response to acute exercise in skeletal muscle.

bioinformatics↗

bioRxiv2025

Bioinformatic inference of the exercise-responsive control of p70 S6 kinase through RPS6KB1 expression

Previous research suggests that the absolute levels of p70 S6 kinase (p70S6K) are a key determinant of the rate of skeletal muscle protein synthesis (MPS). p70S6K levels are in part determined by the transcriptional control of the gene encoding p70S6K, RPS6KB1, but the molecular mechanisms governing its expression are poorly understood. The purpose of this study was to infer the molecular regulatory network governing RPS6KB1 expression. We applied a novel bioinformatic network inference algorithm called CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) to infer the signaling network downstream of canonical exercise sensors controlling RPS6KB1-specific transcription factors (TFs) after acute aerobic (AE) or resistance exercise (RE). CARNIVAL integrates a prior knowledge network, TF and signaling pathway activities inferred from transcriptomic data, and perturbation targets to predict the network that best explains the data. The networks revealed intracellular sensors and hormone receptors controlling RPS6KB1-specific TFs. Both exercise types resulted in AMPK-mediated SNAI1 regulation, but HIF1A was distinctly controlled (AE: PHD1-3, FIH; RE: AMPK). AE controlled FOXA1 via insulin, TGF-{beta}, and myostatin signalling, while RE controlled CEBPA via MAP3Ks. Our study is the first to apply a comprehensive bioinformatic network inference algorithm to infer causal exercise-responsive signaling networks. The results of our analysis motivate experimentally testable hypotheses pertaining to the molecular control of RPS6KB1 transcription in human skeletal muscle in response to aerobic and resistance exercise.

bioinformatics↗

bioRxiv2025

Exploring the multifactorial causes and therapeutic strategies for anabolic resistance in sarcopenia: A systems modeling study

BackgroundSarcopenia is the progressive loss of skeletal muscle mass, strength, and function with age, driven by dysregulation in the rates of muscle protein synthesis (MPS) and breakdown (MPB). Although MPB contributes to net protein balance (NB), a primary contributor of sarcopenia is anabolic resistance, defined as the blunted MPS response to anabolic stimuli such as feeding. While candidate mechanisms of anabolic resistance have been identified, none singularly accounts for the observed reduction in MPS. Instead, multiple mechanisms likely act simultaneously and interactively to suppress MPS. Studying these interactions experimentally is challenging. Mathematical modeling is well suited to analyzing complex biological phenomena such as anabolic resistance. MethodsWe analyzed a previously developed kinetic model of leucine-mediated signaling and protein metabolism in human skeletal muscle to systematically investigate potential mechanisms contributing to anabolic resistance. Using global sensitivity analysis, we identified key controllers of MPS, MPB, and NB. We then simulated amino acid feeding in older adults, classified the responses as either anabolic sensitive or resistant, and compared the resulting parameter distributions of the two groups. We next performed targeted analysis to evaluate the effects of individual and combined putative mechanisms of anabolic resistance on muscle metabolism. Finally, we simulated therapeutic interventions aimed at restoring muscle metabolism. ResultsThe sensitivity analysis revealed that MPS and MPB are primarily controlled by their proximal signaling processes, while NB is largely driven by MPS dynamics. Exploratory simulations showed that several parameters and signaling protein concentrations, particularly those controlling MPS, differed significantly between anabolic sensitive and resistant groups. The targeted simulations indicated that multiple dysregulated mechanisms were required to account for the experimentally observed reductions in MPS in older adults. Therapy simulations showed that single-target interventions could largely restore MPS when isolated mechanisms were perturbed (e.g., increasing mTORC1 sensitivity, enhancing p70S6K levels), but a multifactorial approach was required to recover muscle metabolism when all anabolic resistance mechanisms were present. ConclusionThis study highlights the multifactorial nature of anabolic resistance and the implications for therapy. Specifically, the results motivate new hypotheses regarding the mechanisms most likely to be impaired and argue for multi-target therapeutic strategies to help restore muscle protein metabolism in aging.

systems biology↗

bioRxiv2023

Kinetic modeling of leucine-mediated signaling and protein metabolism in human skeletal muscle

Skeletal muscle protein levels are governed by the relative rates of muscle protein synthesis (MPS) and breakdown (MPB). The mechanisms controlling these rates are complex and their integrated behaviors are challenging to study through experiments alone. The purpose of this study was to develop and analyze a kinetic model of leucine-mediated mTOR signaling and protein metabolism in the skeletal muscle of young adults. Our model amalgamates published cellular-level models of the IRS1-PI3K-Akt-mTORC1 signaling system and of skeletal-muscle leucine kinetics with physiological-level models of leucine digestion and transport and insulin dynamics. The model satisfactorily predicts experimental data from diverse leucine feeding protocols. Model analysis revealed that basal levels of p70S6K are a primary determinant of MPS, insulin signaling substantially affects muscle net protein balance via its effects on MPB, and p70S6K-mediated feedback of mTORC1 signaling reduces MPS in a dose-dependent manner.

systems biology↗