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Biology subjects

Landen, S.

Publications and source records attributed to Landen, S..

7 recordsLinked to original sources

Molecular Landscape of Modality-Specific Exercise Adaptation in Human Skeletal Muscle through Large-Scale Multi-OMICs Integration

We conducted a large-scale, statistically powered, meta-analysis of exercise adaptations in human skeletal muscles, integrating epigenetic, transcriptomic, transcription factors, and proteomic data across 12 independent cohorts comprising over 1000 participants and 2340 human muscle samples. Our study identified distinctive signatures associated with maximal oxygen consumption (VO2max), and identified five genes robustly intersecting multi-OMIC layers. Notably, transcription factors predominantly functioned as activators across these layers, regulating expression of target genes irrespective of whether DNA methylation levels were low or high, indicating a synergistic effect between TFs and the methylome. Analysis of distinct exercise modalities (aerobic and resistance exercise) revealed unique gene pathways, contrasting with patterns observed in inactivity (muscle disuse) studies. These findings offer a comprehensive understanding of exercise and modality-specific adaptations, shedding light on muscle health and the molecular mechanisms associated with cardiorespiratory fitness, aging, and disease prevention.

molecular biology↗

ACTN3 genotype influences androgen response in skeletal muscle

AbstractAndrogens are vital for the maintenance of muscle mass and their anabolic effects are primarily exerted through the androgen receptor (AR). Accumulating evidence in humans and mice suggests that circulating androgens, AR and androgen response are influenced by ACTN3 (- actinin-3), also known as "the gene for speed". One in 5 people worldwide are -actinin-3 deficient due to homozygous inheritance of a common null polymorphism (577X) in ACTN3. In this study, we show that -actinin-3 deficiency decreases baseline AR in skeletal muscles of mice and humans, in both males and females, and that AR expression directly correlates with ACTN3 in a dosage dependent manner. We further demonstrate in Actn3 knockout mice that - actinin-3 deficiency increases muscle wasting induced by androgen deprivation and reduces the muscle hypertrophic response to dihydrotestosterone and this is mediated by differential activation of pathways regulating amino acid metabolism, intracellular transport, MAPK signalling, autophagy, mitochondrial activity and calcineurin signalling. Gene set enrichment and protein analyses indicate that the absence of -actinin-3 results in a failure to coactivate many of these pathways in response to changes in androgens, and relies on leveraging mitochondrial remodelling and calcineurin signalling to restore muscle homeostasis. We further identified 7 genes that are androgen sensitive and -actinin-3-dependent in expression, and whose functions correspond to these processes. Our results highlight the pivotal role of - actinin-3 in various processes associated with the regulation of protein turnover and muscle mass, and suggest that ACTN3 genotype is a genetic modifier of androgen action in skeletal muscle.

genetics↗

The influence of Sex on microRNA expression in Human Skeletal Muscle

IntroductionSex differences in microRNA (miRNA) expression profiles have been found across multiple tissues. Skeletal muscle is one of the top tissues that underpin sex-based differences, yet there is limited research into whether there are sex differences in miRNA expression in skeletal muscle. Further, there is limited literature investigating potential differences between males and females in skeletal muscle miRNA expression following exercise, a well-known modulator of miRNA expression. Therefore, the aim of this study was to investigate the effect of sex on miRNA expression in skeletal muscle at baseline and after an acute bout of exercise. MethodsMiRNAs were measured using Taqman(R)miRNA arrays in skeletal muscle of 42 healthy participants from the GeneSMART study (24 males and 20 females aged 18-45 yrs). Differentially expressed miRNAs were identified using mixed linear models adjusted for age. Experimentally validated miRNA gene targets enriched in skeletal muscle were identified in-silico. Over representation analysis was conducted to identify enriched pathways. TransmiR V.2 was used to identify transcription factor (TF)-miR regulatory networks using CHIP-derived data. We further profiled the effects of two sex-biased miRNAs overexpressed in human primary muscle cells lines derived from male and female donors to understand the transcriptome targeted by these miRNAs and investigate and potential sex-specific effects. ResultsA total of 80 miRNAs were differentially expressed in skeletal muscle between the sexes, with 61 miRNAs responding differently to the exercise between the sexes. Sex-biased miRNA gene targets were enriched for muscle-related processes including proliferation and differentiation of the muscle cells and numerous metabolic pathways, suggesting that miRNAs are playing a role in programming sex differences in skeletal muscle. Over-expression of sex-biased miRNAs miRNA-30a and miRNA-30c resulted in profound changes to gene expression profiles that were partly specific to the sex of the cell donor in human primary skeletal muscle cells. ConclusionWe found sex-differences in the expression profile of skeletal muscle miRNAs at baseline and in response to exercise. These miRNAs target regulatory pathways essential to skeletal muscle development and metabolism, suggesting that miRNAs play a profound but highly complex role in programming sex-differences in the skeletal muscle phenotype.

physiology↗

Exercise rejuvenates the skeletal muscle methylome and transcriptome in humans

Exercise training prevents age-related decline in muscle function. Targeting epigenetic aging is a promising actionable mechanism and late-life exercise mitigates epigenetic aging in rodent muscle. Whether exercise training can decelerate, or reverse epigenetic aging in humans is unknown. Here, we performed a powerful meta-analysis of the methylome and transcriptome of an unprecedented number of human skeletal muscle samples (n = 3,176). We show that: 1) individuals with higher baseline aerobic fitness have younger epigenetic and transcriptomic profiles, 2) exercise training leads to significant shifts of epigenetic and transcriptomic patterns towards a younger profile, and 3) muscle disuse "ages" the transcriptome. Higher fitness levels were associated with attenuated differential methylation and transcription during aging. Furthermore, both epigenetic and transcriptomic profiles shifted towards a younger state after exercise training interventions, while the transcriptome shifted towards an older state after forced muscle disuse. We demonstrate that exercise training targets many of the age-related transcripts and DNA methylation loci to maintain younger methylome and transcriptome profiles, specifically in genes related to muscle structure, metabolism and mitochondrial function. Our comprehensive analysis will inform future studies aiming to identify the best combination of therapeutics and exercise regimes to optimize longevity.

molecular biology↗

DNA methylation and proteomics integration uncover dose-dependent group and individual responses to exercise in human skeletal muscle

ObjectiveExercise is a major regulator of muscle metabolism, and health benefits acquired by exercise are a result of molecular shifts occurring across multiple OMIC levels (i.e. epigenome, transcriptome, proteome). Identifying robust targets associated with exercise response, at both group and individual levels, is therefore important to develop health guidelines and targeted health interventions. MethodsTwenty, apparently healthy, moderately trained (VO2 max= 51.0{+/-}10.6 mL{middle dot}min-1{middle dot}kg-1) males (age range= 18-45yrs) from the Gene SMART (Skeletal Muscle Adaptive Responses to Training) study completed a 12-week High-Intensity Interval Training (HIIT) intervention. Muscle biopsies were collected at baseline and after 4, 8, and 12 weeks of HIIT. High throughput DNA methylation ([~]850 CpG sites), and proteomic ([~]3000 proteins) analyses were conducted at all-time points. Mixed-models were applied to estimate group and individual changes, and methylome and proteome integration was conducted using a holistic multilevel approach with the mixOmics package. ResultsSignificant shifts in the methylome (residual analysis) and proteome profiles were observed after 12 weeks of HIIT. 461 proteins significantly changed over time (at 4, 8, and 12 weeks), whilst only one differentially methylated position (DMP) was changed (adj.p-value <0.05). K-means analysis revealed clear protein clustering exhibiting similar changes over time. Individual responses to training were observed in 101 proteins. Seven proteins had a large effect-sizes >0.5, among them are two novel exercise-related proteins, LYRM7 and EPN1. Integration analysis uncovered bidirectional relationships between the methylome and proteome. ConclusionsWe showed a significant influence of HIIT on the epigenome and proteome in human muscle, and uncovered groups of proteins clustering according to similar patterns across the exercise intervention. Individual responses to exercise were observed in the proteome with novel mitochondrial and metabolic proteins consistently changed across individuals. Future work is required to elucidate the role of such proteins in response to exercise as well as to investigate the mechanisms associating genes and proteins in response to exercise.

molecular biology↗

Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle.

Nearly all human complex traits and diseases exhibit some degree of sex differences, with epigenetics being one of the main contributing factors. Various tissues display sex differences in DNA methylation, however this has not yet been explored in skeletal muscle, despite skeletal muscle being among the tissues with the most transcriptomic sex differences. For the first time, we investigated the effect of sex on autosomal DNA methylation in human skeletal muscle across three independent cohorts (Gene SMART, FUSION, and GSE38291) using a meta-analysis approach, totalling 369 human muscle samples (222 males, 147 females), and integrated this with known sex-biased transcriptomics. We found 10,240 differentially methylated regions (DMRs) at FDR < 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and substrate metabolism. We then investigated biological factors underlying DNA methylation sex differences and found that circulating hormones were not associated with differential methylation at sex-biased DNA methylation loci, however these sex-specific loci were enriched for binding sites of hormone-related transcription factors (with top TFs including androgen (AR), estrogen (ESR1), and glucocorticoid (NR3C1) receptors). Fibre type proportions were associated with differential methylation across the genome, as well as across 16 % of sex-biased DNA methylation loci (FDR < 0.005). Integration of DNA methylomic results with transcriptomic data from the GTEx database and the FUSION cohort revealed 326 autosomal genes that display sex differences at both the epigenome and transcriptome levels. Importantly, transcriptional sex-biased genes were overrepresented among epigenetic sex-biased genes (p-value = 4.6e-13), suggesting differential DNA methylation and gene expression between male and female muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in antioxidant metabolism, displays male-biased expression as well as lower methylation in males across the three cohorts. In conclusion, we uncovered 8,420 genes that exhibit DNA methylation differences between males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health. SignificanceThe importance of uncovering biological sex differences and their translation to physiology has become increasingly evident. Using a large-scale meta-analysis of three cohorts, we perform the first comparison of genome-wide skeletal muscle DNA methylation between males and females, and identify thousands of genes that display sex-differential methylation. We then explore intrinsic biological factors that may be underlying the DNA methylation sex differences, such as fibre type proportions and sex hormones. Leveraging the GTEx database, we identify hundreds of genes with both sex-differential expression and DNA methylation in skeletal muscle. We further confirm the sex-biased genes with gene expression data from two cohorts included in the methylation meta-analysis. Our study integrates genomewide sex-biased DNA methylation and expression in skeletal muscle, shedding light on distinct sex differences in skeletal muscle.

genomics↗

Meta-analysis of genome-wide DNA methylation and integrative OMICs in human skeletal muscle.

Knowledge of age-related DNA methylation changes in skeletal muscle is limited, yet this tissue is severely affected by aging in humans. Using a large-scale epigenome-wide association study (EWAS) meta-analysis of age in human skeletal muscle from 10 studies (total n = 908 human muscle methylomes), we identified 9,986 differentially methylated regions at a stringent false discovery rate < 0.005, spanning 8,748 unique genes, many of which related to skeletal muscle structure and development. We then integrated the DNA methylation results with known transcriptomic and proteomic age-related changes in skeletal muscle, and found that even though most differentially methylated genes are not altered at the mRNA or protein level, they are nonetheless strongly enriched for genes showing age-related differential expression. We provide here the most comprehensive picture of DNA methylation aging in human skeletal muscle, and have made our results available as an open-access, user-friendly, web-based tool called MetaMeth (https://sarah-voisin.shinyapps.io/MetaMeth/).

genetics↗