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D'Souza, A. C.

Publications and source records attributed to D'Souza, A. C..

2 recordsLinked to original sources

Dynamic proteomic profiling reveals protein-specific regulation of synthesis rates underpinning the divergent adaptation of human muscle to endurance and resistance training

Endurance (END) or resistance exercise (RE) training results in adaptations that give rise to distinct skeletal muscle phenotypes. Hallmarks of RE include increases in muscle fibre and muscle cross-sectional area and strength, whereas END increases mitochondrial content. Such distinct phenotypes arise from differential metabolic and mechanical signal transduction, transcriptional, and protein translation pathways, culminating in exercise mode-specific adaptations in the muscle proteome. However, little empirical data exist on the protein-specific dynamic responses underlying training-mode-specific adaptations in humans. Using a model of unilateral exercise combined with stable isotope labelling with deuterium oxide, we measured changes in synthesis and abundance from baseline and during early (week 1) and later (week 10) periods of adaptation to END and RE training in young healthy adults (n = 14; 8 female, 6 male; 20 {+/-} 1 y, 70 {+/-} 10 kg). We quantified changes in the abundance (n = 1146 proteins) and synthesis (n = 247 proteins) profiles of skeletal muscle across a 5-day pre-training baseline period and during early and later adaptation to RE and END. Abundance profiling revealed mode-specific proteome remodelling, whereby RE increased ribosomal and contractile protein networks, whereas END increased mitochondrial inner membrane proteins after 10 weeks of training. The protein-specific synthesis rates of 119 proteins showed training-induced differences (P < 0.1 and log2 fold change > 1), including subsets of structural proteins that responded differently to RE and END training modes. Notably, distinct Z-disc proteins, such as XIRP1 (RE-specific) and LDB3 (END-specific), exhibited mode-specific regulation despite sharing a similar subcellular localisation. We report, for the first time, that divergent phenotypic adaptations to RE and END extend beyond changes in bulk fraction-specific synthesis rates and are regulated by training-mode-specific adaptations in distinct protein subsets within similar subcellular protein locations.

physiology↗

Network-based modelling reveals cell-type enriched patterns of non-coding RNA regulation during human skeletal muscle remodelling

A majority of human genes produce non-protein-coding RNA (ncRNA), and some have roles in development and disease. Neither ncRNA nor human skeletal muscle is ideally studied using short-read sequencing, so we used a customised RNA pipeline and network modelling to study cell-type specific ncRNA responses during muscle growth at scale. We completed five human resistance-training studies (n=144 subjects), identifying 61% who successfully accrued muscle-mass. We produced 288 transcriptome-wide profiles and found 110 ncRNAs linked to muscle growth in vivo, while a transcriptome-driven network model demonstrated interactions via a number of discrete functional pathways and single-cell types. This analysis included established hypertrophy-related ncRNAs, including CYTOR - which was leukocyte-associated (FDR = 4.9 x10-7). Novel hypertrophy-linked ncRNAs included PPP1CB-DT (myofibril assembly genes, FDR = 8.15 x 10-8), and EEF1A1P24 and TMSB4XP8 (vascular remodelling and angiogenesis genes, FDR = 2.77 x 10-5). We also discovered that hypertrophy lncRNA MYREM shows a specific myonuclear expression pattern in vivo. Our multi-layered analyses established that single-cell-associated ncRNA are identifiable from bulk muscle transcriptomic data and that hypertrophy-linked ncRNA genes mediate their association with muscle growth via multiple cell types and a set of interacting pathways. One Sentence SummaryWe used an optimised transcriptomic strategy to identify a set of ncRNA genes regulated during skeletal muscle hypertrophy in one hundred and forty-four people, with network modelling and spatial imaging providing biological context.

bioinformatics↗