Genomic and transcriptomic predictors of triglyceride response to regular exercise.

AIM: We performed genome-wide and transcriptome-wide profiling to identify genes and single nucleotide polymorphisms (SNPs) associated with the response of triglycerides (TG) to exercise training. METHODS: Plasma TG levels were measured before and after a 20-week endurance training programme in 478 white participants from the HERITAGE Family Study. Illumina HumanCNV370-Quad v3.0 BeadChips were genotyped using the Illumina BeadStation 500GX platform. Affymetrix HG-U133+2 arrays were used to quantitate gene expression levels from baseline muscle biopsies of a subset of participants (N=52). Genome-wide association study (GWAS) analysis was performed using MERLIN, while transcriptomic predictor models were developed using the R-package GALGO. RESULTS: The GWAS results showed that eight SNPs were associated with TG training-response (ΔTG) at p<9.9×10(-6), while another 31 SNPs showed p values <1×10(-4). In multivariate regression models, the top 10 SNPs explained 32.0% of the variance in ΔTG, while conditional heritability analysis showed that four SNPs statistically accounted for all of the heritability of ΔTG. A molecular signature based on the baseline expression of 11 genes predicted 27% of ΔTG in HERITAGE, which was validated in an independent study. A composite SNP score based on the top four SNPs, each from the genomic and transcriptomic analyses, was the strongest predictor of ΔTG (R(2)=0.14, p=3.0×10(-68)). CONCLUSIONS: Our results indicate that skeletal muscle transcript abundance at 11 genes and SNPs at a number of loci contribute to TG response to exercise training. Combining data from genomics and transcriptomics analyses identified a SNP-based gene signature that should be further tested in independent samples.

Genomics and transcriptomics landscapes associated to changes in insulin sensitivity in response to endurance exercise training.

Despite good adherence to supervised endurance exercise training (EET), some individuals experience no or little improvement in peripheral insulin sensitivity. The genetic and molecular mechanisms underlying this phenomenon are currently not understood. By investigating genome-wide variants associated with baseline and exercise-induced changes (∆) in insulin sensitivity index (Si) in healthy volunteers, we have identified novel candidate genes whose mouse knockouts phenotypes were consistent with a causative effect on Si. An integrative analysis of functional genomic and transcriptomic profiles suggests genetic variants have an aggregate effect on baseline Si and ∆Si, focused around cholinergic signalling, including downstream calcium and chemokine signalling. The identification of calcium regulated MEF2A transcription factor as the most statistically significant candidate driving the transcriptional signature associated to ∆Si further strengthens the relevance of calcium signalling in EET mediated Si response.

miRNAs in human subcutaneous adipose tissue: Effects of weight loss induced by hypocaloric diet and exercise.

OBJECTIVE: Obesity is central in the development of insulin resistance. However, the underlying mechanisms still need elucidation. Dysregulated microRNAs (miRNAs; post-transcriptional regulators) in adipose tissue may present an important link. METHODS: The miRNA expression in subcutaneous adipose tissue from 19 individuals with severe obesity (10 women and 9 men) before and after a 15-week weight loss intervention was studied using genome-wide microarray analysis. The microarray results were validated with RT-qPCR, and pathway enrichment analysis of in silico predicted targets was performed to elucidate the biological consequences of the miRNA dysregulation. Lastly, the messenger RNA (mRNA) and/or protein expression of multiple predicted targets as well as several proteins involved in lipolysis were investigated. RESULTS: The intervention led to upregulation of miR-29a-3p and miR-29a-5p and downregulation of miR-20b-5p. The mRNA and protein expression of predicted targets was not significantly affected by the intervention. However, negative correlations between miR-20b-5p and the protein levels of its predicted target, acyl-CoA synthetase long-chain family member 1, were observed. Several other miRNA-target relationships correlated negatively, indicating possible miRNA regulation, including miR-29a-3p and lipoprotein lipase mRNA levels. Proteins involved in lipolysis were not affected by the intervention. CONCLUSIONS: Weight loss influenced several miRNAs, some of which were negatively correlated with predicted targets. These dysregulated miRNAs may affect adipocytokine signaling and forkhead box protein O signaling.

Dysregulation of a novel miR-23b/27b-p53 axis impairs muscle stem cell differentiation of humans with type 2 diabetes.

OBJECTIVE: MicroRNAs (miRNAs) are increasingly recognized as fine-tuning regulators of metabolism, and are dysregulated in several disease conditions. With their capacity to rapidly change gene expression, miRNAs are also important regulators of development and cell differentiation. In the current study, we describe an impaired myogenic capacity of muscle stem cells isolated from humans with type 2 diabetes (T2DM) and assess whether this phenotype is regulated by miRNAs. METHODS: We measured global miRNA expression during in vitro differentiation of muscle stem cells derived from T2DM patients and healthy controls. RESULTS: The mir-23b/27b cluster was downregulated in the cells of the patients, and a pro-myogenic effect of these miRNAs was mediated through the p53 pathway, which was concordantly dysregulated in the muscle cells derived from humans with T2DM. CONCLUSIONS: Our results indicate that we have identified a novel pathway for coordination of myogenesis, the miR-23b/27b-p53 axis that, when dysregulated, potentially contributes to a sustained muscular dysfunction in T2DM.

The Role of Eif6 in Skeletal Muscle Homeostasis Revealed by Endurance Training Co-expression Networks.

Regular endurance training improves muscle oxidative capacity and reduces the risk of age-related disorders. Understanding the molecular networks underlying this phenomenon is crucial. Here, by exploiting the power of computational modeling, we show that endurance training induces profound changes in gene regulatory networks linking signaling and selective control of translation to energy metabolism and tissue remodeling. We discovered that knockdown of the mTOR-independent factor Eif6, which we predicted to be a key regulator of this process, affects mitochondrial respiration efficiency, ROS production, and exercise performance. Our work demonstrates the validity of a data-driven approach to understanding muscle homeostasis.

Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective.

The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of "omics" technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to "learn" biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed.

eIF6 coordinates insulin sensitivity and lipid metabolism by coupling translation to transcription.

Insulin regulates glycaemia, lipogenesis and increases mRNA translation. Cells with reduced eukaryotic initiation factor 6 (eIF6) do not increase translation in response to insulin. The role of insulin-regulated translation is unknown. Here we show that reduction of insulin-regulated translation in mice heterozygous for eIF6 results in normal glycaemia, but less blood cholesterol and triglycerides. eIF6 controls fatty acid synthesis and glycolysis in a cell autonomous fashion. eIF6 acts by exerting translational control of adipogenic transcription factors like C/EBPß, C/EBPδ and ATF4 that have G/C rich or uORF sequences in their 5' UTR. The outcome of the translational activation by eIF6 is a reshaping of gene expression with increased levels of lipogenic and glycolytic enzymes. Finally, eIF6 levels modulate histone acetylation and amounts of rate-limiting fatty acid synthase (Fasn) mRNA. Since obesity, type 2 diabetes, and cancer require a Fasn-driven lipogenic state, we propose that eIF6 could be a therapeutic target for these diseases.

A systems biology approach reveals a link between systemic cytokines and skeletal muscle energy metabolism in a rodent smoking model and human COPD.

BACKGROUND: A relatively large percentage of patients with chronic obstructive pulmonary disease (COPD) develop systemic co-morbidities that affect prognosis, among which muscle wasting is particularly debilitating. Despite significant research effort, the pathophysiology of this important extrapulmonary manifestation is still unclear. A key question that remains unanswered is to what extent systemic inflammatory mediators might play a role in this pathology. Cigarette smoke (CS) is the main risk factor for developing COPD and therefore animal models chronically exposed to CS have been proposed for mechanistic studies and biomarker discovery. Although mice have been successfully used as a pre-clinical in vivo model to study the pulmonary effects of acute and chronic CS exposure, data suggest that they may be inadequate models for studying the effects of CS on peripheral muscle function. In contrast, recent findings indicate that the guinea pig model (Cavia porcellus) may better mimic muscle wasting. METHODS: We have used a systems biology approach to compare the transcriptional profile of hindlimb skeletal muscles from a Guinea pig rodent model exposed to CS and/or chronic hypoxia to COPD patients with muscle wasting. RESULTS: We show that guinea pigs exposed to long-term CS accurately reflect most of the transcriptional changes observed in dysfunctional limb muscle of severe COPD patients when compared to matched controls. Using network inference, we could then show that the expression profile in whole lung of genes encoding for soluble inflammatory mediators is informative of the molecular state of skeletal muscles in the guinea pig smoking model. Finally, we show that CXCL10 and CXCL9, two of the candidate systemic cytokines identified using this pre-clinical model, are indeed detected at significantly higher levels in serum of COPD patients, and that their serum protein level is inversely correlated with the expression of aerobic energy metabolism genes in skeletal muscle. CONCLUSIONS: We conclude that CXCL10 and CXCL9 are promising candidate inflammatory signals linked to the regulation of central metabolism genes in skeletal muscles. On a methodological level, our work also shows that a system level analysis of animal models of diseases can be very effective to generate clinically relevant hypothesis.

High responders to resistance exercise training demonstrate differential regulation of skeletal muscle microRNA expression.

MicroRNAs (miRNA), small noncoding RNA molecules, may regulate protein synthesis, while resistance exercise training (RT) is an efficient strategy for stimulating muscle protein synthesis in vivo. However, RT increases muscle mass, with a very wide range of effectiveness in humans. We therefore determined the expression level of 21 abundant miRNAs to determine whether variation in these miRNAs was able to explain the variation in RT-induced gains in muscle mass. Vastus lateralis biopsies were obtained from the top and bottom ∼20% of responders from 56 young men who undertook a 5 day/wk RT program for 12 wk. Training-induced muscle mass gain was determined by dual-energy X-ray absorptiometry, and fiber size was evaluated by histochemistry. The expression level of each miRNA was quantified using TaqMan-based quantitative PCR, with the analysis carried out in a blinded manner. Gene ontology and target gene profiling were used to predict the potential biological implications. Of the 21 mature miRNAs examined, 17 were stable during RT in both groups. However, miR-378, miR-29a, miR-26a, and miR-451 were differentially expressed between low and high responders. miR-378, miR-29a, and miR-26a were downregulated in low responders and unchanged in high responders, while miR-451 was upregulated only in low responders. Interestingly, the training-induced change in miR-378 abundance was positively correlated with muscle mass gains in vivo. Gene ontology analysis of the target gene list of miR-378, miR-29a, miR-26a, and miR-451, from the weighted cumulative context ranking methodology, indicated that miRNA changes in the low responders may be compensatory, reflecting a failure to "activate" growth and remodeling genes. We report, for the first time, that RT-induced hypertrophy in human skeletal muscle is associated with selected changes in miRNA abundance. Our analysis indicates that miRNAs may play a role in the phenotypic change and pronounced intergroup variation in the RT response.