Gene expression and fiber type variations in repeated vastus lateralis biopsies.

INTRODUCTION: Muscle sample collection can introduce variation in any measured variable due to inter- and intramuscle variation. We investigated the variation in gene expression and fiber type composition after repeated biopsy sampling from the vastus lateralis muscle. METHODS: Six subjects donated 3 tissue samples each. One hour after baseline sampling from 1 vastus lateralis muscle, samples from both vastus lateralis muscles were obtained. RESULTS: The fiber type composition differed between biopsies taken from the same leg. There were no within-subject differences in gene expression between the 3 biopsies. Multivariate analysis supports a model in which gene expression differs significantly between individuals but is not affected by repeated muscle biopsy sampling from the same subject. CONCLUSION: One vastus lateralis muscle sample per subject is sufficient to establish a reliable baseline for comparing gene expression representing selected pathways over time within the same individual.

Life style intervention in moderately overweight individuals is associated with decreased levels of cathepsins L and S in plasma.

BACKGROUND: Adipose tissue cells produce cathepsins L and S, which have proatherogenic effects. Obesity is strongly linked to atherogenesis, cardiovascular morbidity, and mortality. OBJECTIVE: The aim of the present study was to see if life style interventions/weight reduction could decrease cathepsin L and S levels in blood plasma. METHOD: Study subjects (n=31) were recruited to a life style intervention program aiming at increased physical activity, more healthy eating habits, and weight reduction for most of the participants. Blood samples were collected at inclusion and after 4 and 8 weeks. RESULTS: Cathepsin L was significantly reduced at 4 weeks (p<0.0001) and 8 weeks (p=0.0004). A similar reduction was also seen for cathepsin S at 4 weeks (p=0.03) and 8 weeks (p=0.008). No significant change in fractalkine values was observed at 4 weeks (p=0.58), but a significant increase was apparent at 8 weeks (p=0.0002). CONCLUSION: The intervention program resulted in significant reductions of cathepsin L and S levels in plasma after 4 and 8 weeks of intervention.

Metabolic adaptations in skeletal muscle, adipose tissue, and whole-body oxidative capacity in response to resistance training.

PURPOSE: The effects of resistance training on mitochondrial biogenesis and oxidative capacity in skeletal muscle are not fully characterized, and even less is known about alterations in adipose tissue. We aimed to investigate adaptations in oxidative metabolism in skeletal muscle and adipose tissue after 8 weeks of heavy resistance training in apparently healthy young men. METHODS: Expression of genes linked to oxidative metabolism in the skeletal muscle and adipose tissue was assessed before and after the training program. Body composition, peak oxygen uptake (VO2 peak), fat oxidation, activity of mitochondrial enzyme in muscle, and serum adiponectin levels were also determined before and after resistance training. RESULTS: In muscle, the expression of the genes AdipoR1 and COX4 increased after resistance training (9 and 13 %, respectively), whereas the expression levels of the genes PGC-1α, SIRT1, TFAM, CPT1b, and FNDC5 did not change. In adipose tissue, the expression of the genes SIRT1 and CPT1b decreased after training (20 and 23 %, respectively). There was an increase in lean mass (from 59.7 ± 6.1 to 61.9 ± 6.2 kg), VO2 peak (from 49.7 ± 5.5 to 56.3 ± 5.0 ml/kg/min), and fat oxidation (from 6.8 ± 2.1 to 9.1 ± 2.7 mg/kg fat-free mass/min) after training, whereas serum adiponectin levels decreased significantly and enzyme activity of citrate synthase and 3-hydroxyacyl-CoA dehydrogenase did not change. CONCLUSION: Despite significant increases in VO2 peak, fat oxidation, and lean mass following resistance training, the total effect on gene expression and enzyme activity linked to oxidative metabolism was moderate.

Physical fitness level is reflected by alterations in the human plasma metabolome.

An excessive energy intake combined with a low level of physical activity induces detrimental processes involved in disease development, e.g. type 2 diabetes and cardiovascular disease. However the underlying mechanisms for regulation of metabolic capacity and fitness status remain unclear. Metabolomics involves global studies of the metabolic reactions in an organism or cell. Thus hypotheses regarding biochemical events can be generated to increase the understanding of disease development and thereby aid in the development of novel treatments or preventions. We present the first standardized intervention study focusing on characterizing the human metabolome in relation to moderate differences in cardiorespiratory fitness. Gas chromatography-time of flight/mass spectrometry (GC-TOF/MS) was used to characterize 460 plasma samples from 27 individuals divided into two groups based on physical fitness level (VO(2)max). Multi- and univariate between group comparisons based on 197 metabolites were carried out in samples collected at rest prior to any intervention, over time following a nutritional load or a standardized exercise scheme, with and without nutritional load. We detected decreased levels of gamma-tocopherol (GT), a vitamin E isomer, in response to a high fitness level, whereas the opposite was seen for the alpha isomer (AT). In addition, the high fitness level was associated with elevated ω3-PUFA (DHA, 22 : 6ω3) and a decrease in ω6-PUFA (18 : 2ω6) as well as in saturated (16 : 0, 18 : 0), monounsaturated (18 : 1) and trans (16 : 1) fatty acids. We thus hypothesize that high fitness status induces an increased cardiorespiratory inflammatory and antioxidant defense system, more prone to deal with the inflammatory response following exercise and nutrition intake.

Validated and predictive processing of gas chromatography-mass spectrometry based metabolomics data for large scale screening studies, diagnostics and metabolite pattern verification.

The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern.

Predictive metabolomics evaluation of nutrition-modulated metabolic stress responses in human blood serum during the early recovery phase of strenuous physical exercise.

We have investigated whether postexercise ingestion of carbohydrates in combination with proteins generates a different systemic metabolic response, as compared to the sole ingestion of carbohydrate or water, in the early recovery phase following exercise. In addition, metabolic patterns related to fitness level were studied together with individual responses to nutritional modulation. Twenty-four male subjects were exposed to 90 min of ergometer-cycling. Each participant was subject to four identical test-sessions, including ingestion of one of four beverages (water, low-carbohydrate beverage, high-carbohydrate beverage, and low-carbohydrate-protein beverage (LCHO-P)) immediately after cycling. Blood was collected at six time points, one pre- and five postexercise. Extracted blood serum was subject to metabolomic characterization by gas chromatography/time-of-flight mass spectrometry (GC-TOF MS). Data was processed using hierarchical multivariate curve resolution (HMCR), and multivariate statistical analysis was carried out using orthogonal partial least-squares (OPLS). Predictive metabolomics, including predictive HMCR and OPLS classification, was applied to ensure efficient sample processing and validation of detected metabolic patterns. Separation of subjects in relation to ingested beverage was detected and interpreted. Pseudouridine was suggested as a novel marker for pro-anabolic effect following LCHO-P ingestion, which was supported by the detected decrease of the catabolic marker 3-methylhistidine. Separation of subjects according to fitness level was achieved, and nutritional modulation by LCHO-P was shown to improve the metabolic status of less fit subjects in the recovery phase. In addition, the potential of the methodology for detection of early signs of insulin resistance was also demonstrated.

A multivariate screening strategy for investigating metabolic effects of strenuous physical exercise in human serum.

A novel hypothesis-free multivariate screening methodology for the study of human exercise metabolism in blood serum is presented. Serum gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) data was processed using hierarchical multivariate curve resolution (H-MCR), and orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to model the systematic variation related to the acute effect of strenuous exercise. Potential metabolic biomarkers were identified using data base comparisons. Extensive validation was carried out including predictive H-MCR, 7-fold full cross-validation, and predictions for the OPLS-DA model, variable permutation for highlighting interesting metabolites, and pairwise t tests for examining the significance of metabolites. The concentration changes of potential biomarkers were verified in the raw GC/TOFMS data. In total, 420 potential metabolites were resolved in the serum samples. On the basis of the relative concentrations of the 420 resolved metabolites, a valid multivariate model for the difference between pre- and post-exercise subjects was obtained. A total of 34 metabolites were highlighted as potential biomarkers, all statistically significant (p < 8.1E-05). As an example, two potential markers were identified as glycerol and asparagine. The concentration changes for these two metabolites were also verified in the raw GC/TOFMS data. The strategy was shown to facilitate interpretation and validation of metabolic interactions in human serum as well as revealing the identity of potential markers for known or novel mechanisms of human exercise physiology. The multivariate way of addressing metabolism studies can help to increase the understanding of the integrative biology behind, as well as unravel new mechanistic explanations in relation to, exercise physiology.