TGF-β Contributes to Impaired Exercise Response by Suppression of Mitochondrial Key Regulators in Skeletal Muscle

Abstract

A substantial number of people at risk of developing type 2 diabetes could not improve insulin sensitivity by physical training intervention. We studied the mechanisms of this impaired exercise response in 20 middle-aged individuals at high risk of developing type 2 diabetes who performed 8 weeks of controlled cycling and walking training at 80% individual Vo2 peak. Participants identified as nonresponders in insulin sensitivity (based on the Matsuda index) did not differ in preintervention parameters compared with high responders. The failure to increase insulin sensitivity after training correlates with impaired upregulation of mitochondrial fuel oxidation genes in skeletal muscle, and with the suppression of the upstream regulators PGC1α and AMPKα2. The muscle transcriptomes of the nonresponders are further characterized by the activation of transforming growth factor (TGF)-β and TGF-β target genes, which is associated with increases in inflammatory and macrophage markers. TGF-β1 as inhibitor of mitochondrial regulators and insulin signaling is validated in human skeletal muscle cells. Activated TGF-β1 signaling downregulates the abundance of PGC1α, AMPKα2, the mitochondrial transcription factor TFAM, and mitochondrial enzymes. Thus, the data suggest that increased TGF-β activity in skeletal muscle can attenuate the improvement of mitochondrial fuel oxidation after training and contribute to the failure to increase insulin sensitivity.

A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease

“…(WG), Sears et al . (SEARS), Heritage (HERTG) and IDEAL (2,22,37,38,46,50–54,,56). The present project involved the production and analysis of 470 new samples profiled on the HTA 2.0 Affymetrix platform along with 144 new samples profiled on the Illumina 12 platform; and 466 of our previously published (22,37,53,54) U133+2 muscle RNA profiles and literature cohorts (where appropriate clinical data was available).…”

“…The HTA 2.0 gene-chip contains 6.9 million short ‘probes’ which are computationally combined into groups (probe-sets) representing individual RNA transcripts (or part of a transcript) using a ‘map’ named the chip definition file (CDF). The CDF combines probe signals irrespective of whether each probe represents an active ‘signal’ in the tissue or cell type being profiled or not (27,36,38,39), i.e. there is no probe-level filtering.…”

“…(WG) and in vivo drug treatment (SEARS) studies in Figure 1) (38,45,56). Further, a subset of 568 muscle gene-chip profiles were paired, from 284 individuals before and after distinct clinical interventions aimed at improving IS (MP, S1–2, S-PD, DRET, IDEAL, HERTG, SEARS and WG, Figure 1) (38,46–48,50,52,53,56–58). Sixty percent of these samples were not included in the baseline fasting CORE-IS analysis (see below), and thus represent entirely independent samples from those used to discover the CORE-IS genes.…”

Biomarkers of subclinical inflammation and increases in glycaemia, insulin resistance and beta-cell function in non-diabetic individuals: the Whitehall II study

“…We focused on hsCRP, IL6, IL1Ra and adiponectin as pro-and anti-inflammatory biomarkers because of their well-established associations with incident type 2 diabetes in prospective studies (1, 2, 31). Based on experimental data and other epidemiological studies, cytokines such as IL1β (32,33,34), tumour necrosis factor (TNF)-α (35,36) and transforming growth factor (TGF)-β (37,38) and chemokines such as monocyte chemoattractant protein-1/chemokine (C-C motif) ligand 2 (MCP-1/CCL2) (39,40) undoubtedly represent interesting candidates because of their impact on insulin sensitivity and/or beta-cell function. However, circulating levels of IL1β are below the limit of detection for a large proportion of individuals in population-based studies with currently available assays, and experimental data on TNFα and insulin resistance do not appear to be translated into an association between circulating levels of this protein and risk of type 2 diabetes in cohort studies (41,42).…”

Omics and Exercise: Global Approaches for Mapping Exercise Biological Networks

“…sions of prevalent literature in this area, recent studies have uncovered novel insights into endurance exercise-regulated muscle transcriptomic network remodeling during recovery (Neubauer et al 2014), protein supplementation (Rowlands et al 2011(Rowlands et al , 2016, and impairments in endurance-trained subjects lacking metabolic responsiveness (i.e., training improvements in insulin sensitivity) (Bohm et al 2016). The human muscle resistance exercise training -responsive transcriptional networks in the settings of aging (Raue et al 2012), aging-associated muscle frailty , stress-induced immune activation (Gordon et al 2012), and combined aerobic training modes (Lundberg et al 2016) are also continuing to be unraveled using transcriptomics.…”