Combined proteomics and metabolomics analysis reveal the effect of a training course on the immune function of Chinese elite short-track speed skaters

INTRODUCTION

The aim of this study was to combine proteomics and metabolomics to evaluate the immune system of short-track speed skaters (STSS) before and after a training course. Our research focused on changes in urinary proteins and metabolites that have the potential to serve as indicators for training load.

METHODS

Urine samples were collected from 21 elite STSS (13 male and 8 female) of the China National Team before and immediately after one training course. First-beat sports sensor was used to monitor the training load. Proteomic detection was performed using a Thermo UltiMate 3000 ultra high performence chromatography nano liquid chromatograph and an Orbitrap Exploris 480 mass spectrometer. MSstats (R package) was used for the statistical evaluation of significant differences in proteins from the samples. Two filtration criteria (fold change [FC] > 2 and p < 0.05) were used to identify the differential expressed proteins. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis for differential proteins was performed to identify the pathways involved. Nontargeted metabolomic detection was performed using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS_) with an ACQUITY 2D UPLC plus Q Exactive (QE) hybrid Quadrupole-Orbitrap mass spectrometer. Differential metabolites were identified using non-parametric statistical methods (Wilcox's rank test). Two filtration criteria (FC > 1.2 and p < 0.05) were used to identify differential metabolites. Combined analysis of proteomic and metabolomics were performed on the "Wu Kong" platform. Correlation analysis was performed using Spearman's rank correlation coefficient.

RESULTS

(1) The most upregulated proteins were immune-related proteins, including complement proteins (C9, C4-B, and C9) and immunoglobulins (IgA, IgM, and IgG). The most downregulated proteins were osteopontin (OPN) and CD44 in urine. The correlation analysis showed that the content of OPN and CD44 (the receptor for OPN) in urine were significantly negatively correlated with the upregulated immune-related proteins. The content of OPN and CD44 is sex-dependent and negatively correlated with the training load. (2) The most upregulated metabolites included lactate, cortisol, inosine, glutamine, argininosuccinate (the precursor for arginine synthesis), 3-methyl-2-oxobutyrate (the catabolite of valine), 3-methyl-2-oxovalerate (the catabolite of isoleucine), and 4-methyl-2-oxopentanoate (the catabolite of leucine), which is sex-dependent and negatively correlated with OPN and CD44. (3) The joint analysis revealed five main related pathways, including the immune and innate immune systems. The enriched immune-related proteins included complements, immunoglobulins, and protein catabolism-related proteins. The enriched immune-related metabolites included cAMP, N-acetylgalactosamine, and glutamate. (4) There is a significant negative correlation between the content of OPN and CD44 in urine and the training load.

CONCLUSION

One training course can lead to the activation of the immune system and a sex-dependent decrease in the content of OPN and CD44. Training load has a significant and negative correlation with the content of OPN and CD44, suggesting that OPN and CD44 could be potential indicators for training load.

Metabolomic profiling of elite female soccer players: urinary biomarkers over a championship season

INTRODUCTION

In soccer, most studies evaluate metabolic profile changes in male athletes, often using data from a single match. Given the current landscape of women's soccer and the effects of biological sex on the physiological response and adaptation to exercise, more studies targeting female athletes and analyzing pre- and post-game moments throughout the season are necessary.

OBJECTIVES

To describe the metabolomics profile of female soccer athletes from an elite team in Brazil. The study observed the separation of groups in three pre- and post-game moments and identified the discriminating metabolites.

METHODS

The study included 14 female soccer athletes. Urine samples were collected and analyzed using Nuclear Magnetic Resonance in pre-game and immediate post-game moments over three national championship games. The metabolomics data were then used to generate OPLS-DA and VIP plots.

RESULTS

Forty-three metabolites were identified in the samples. OPLS-DA analyses demonstrated a progressive separation between pre-post conditions, as supported by an increasing Q2 value (0.534, 0.625, and 0.899 for games 1, 2 and 3, respectively) and the first component value (20.2% and 19.1% in games 1 and 2 vs. 29.9% in game 3). Eight out of the fifteen most discriminating metabolites appeared consistently across the three games: glycine, formate, citrate, 3-hydroxyvalerate, glycolic acid, trimethylamine, urea, and dimethylglycine.

CONCLUSION

The main difference between the three games was the increasing separation between groups throughout the championship. Since the higher VIP-scores metabolites are linked to energy and protein metabolism, this separation may be attributed several factors, one being the accumulation of fatigue.

The Relationship between Changes in MYBPC3 Single-Nucleotide Polymorphism-Associated Metabolites and Elite Athletes' Adaptive Cardiac Function

Athletic performance is a multifactorial trait influenced by a complex interaction of environmental and genetic factors. Over the last decades, understanding and improving elite athletes' endurance and performance has become a real challenge for scientists. Significant tools include but are not limited to the development of molecular methods for talent identification, personalized exercise training, dietary requirements, prevention of exercise-related diseases, as well as the recognition of the structure and function of the genome in elite athletes. Investigating the genetic markers and phenotypes has become critical for elite endurance surveillance. The identification of genetic variants contributing to a predisposition for excellence in certain types of athletic activities has been difficult despite the relatively high genetic inheritance of athlete status. Metabolomics can potentially represent a useful approach for gaining a thorough understanding of various physiological states and for clarifying disorders caused by strength-endurance physical exercise. Based on a previous GWAS study, this manuscript aims to discuss the association of specific single-nucleotide polymorphisms (SNPs) located in the MYBPC3 gene encoding for cardiac MyBP-C protein with endurance athlete status. MYBPC3 is linked to elite athlete heart remodeling during or after exercise, but it could also be linked to the phenotype of cardiac hypertrophy (HCM). To make the distinction between both phenotypes, specific metabolites that are influenced by variants in the MYBPC3 gene are analyzed in relation to elite athletic performance and HCM. These include theophylline, ursodeoxycholate, quinate, and decanoyl-carnitine. According to the analysis of effect size, theophylline, quinate, and decanoyl carnitine increase with endurance while decreasing with cardiovascular disease, whereas ursodeoxycholate increases with cardiovascular disease. In conclusion, and based on our metabolomics data, the specific effects on athletic performance for each MYBPC3 SNP-associated metabolite are discussed.

Exercise Metabolome: Insights for Health and Performance

Exercise has many benefits for physical and mental well-being. Metabolomics research has allowed scientists to study the impact of exercise on the body by analyzing metabolites released by tissues such as skeletal muscle, bone, and the liver. Endurance training increases mitochondrial content and oxidative enzymes, while resistance training increases muscle fiber and glycolytic enzymes. Acute endurance exercise affects amino acid metabolism, fat metabolism, cellular energy metabolism, and cofactor and vitamin metabolism. Subacute endurance exercise alters amino acid metabolism, lipid metabolism, and nucleotide metabolism. Chronic endurance exercise improves lipid metabolism and changes amino acid metabolism. Acute resistance exercise changes several metabolic pathways, including anaerobic processes and muscular strength. Chronic resistance exercise affects metabolic pathways, resulting in skeletal muscle adaptations. Combined endurance-resistance exercise alters lipid metabolism, carbohydrate metabolism, and amino acid metabolism, increasing anaerobic metabolic capacity and fatigue resistance. Studying exercise-induced metabolites is a growing field, and further research can uncover the underlying metabolic mechanisms and help tailor exercise programs for optimal health and performance.

Serum Metabolome Adaptations Following 12 Weeks of High-Intensity Interval Training or Moderate-Intensity Continuous Training in Obese Older Adults

Physical activity can be effective in preventing some of the adverse effects of aging on health. High-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) are beneficial interventions for the quality of life of obese older individuals. The understanding of all possible metabolic mechanisms underlying these beneficial changes has not yet been established. The aim of this study was to analyze changes in the serum metabolome after 12 weeks of HIIT and MICT in obese older adults. Thirty-eight participants performed either HIIT (n = 26) or MICT (n = 12) three times per week for 12 weeks. Serum metabolites as well as clinical and biological parameters were assessed before and after the 12-week intervention. Among the 364 metabolites and ratio of metabolites identified, 51 metabolites changed significantly following the 12-week intervention. Out of them, 21 significantly changed following HIIT intervention and 18 significantly changed following MICT. Associations with clinical and biological adaptations revealed that changes in acyl-alkyl-phosphatidylcholine (PCae) (22:1) correlated positively with changes in handgrip strength in the HIIT group (r = 0.52, p < 0.01). A negative correlation was also observed between 2-oxoglutaric acid and HOMA-IR (r = -0.44, p < 0.01) when considering both groups together (HIIT and MICT). This metabolite also correlated positively with quantitative insulin-sensitivity check index (QUICKI) in both groups together (r = 0.46, p < 0.01) and the HIIT group (r = 0.51, p < 0.01). Additionally, in the MICT group, fumaric acid was positively correlated with triglyceride levels (r = 0.73, p < 0.01) and acetylcarnitine correlated positively with low-density lipoprotein (LDL) cholesterol (r = 0.81, p < 0.01). These four metabolites might represent potential metabolites of interest concerning muscle strength, glycemic parameters, as well as lipid profile parameters, and hence, for a potential healthy aging. Future studies are needed to confirm the association between these metabolites and a healthy aging.

Characteristics of the gut microbiome in esports players compared with those in physical education students and professional athletes

Introduction

Esports is a category of competitive video games that, in many aspects, may be similar to traditional sports; however, the gut microbiota composition of players has not been yet studied.

Materials and methods

Here, we investigated the composition and function of the gut microbiota, as well as short chain fatty acids (SCFAs), and amino acids, in a group of 109 well-characterized Polish male esports players. The results were compared with two reference groups: 25 endurance athletes and 36 healthy students of physical education. DNA and metabolites isolated from fecal samples were analyzed using shotgun metagenomic sequencing and mass spectrometry, respectively. Physical activity and nutritional measures were evaluated by questionnaire.

Results

Although anthropometric, physical activity and nutritional measures differentiated esports players from students, there were no differences in bacterial diversity, the Bacteroidetes/Firmicutes ratio, the composition of enterotype clusters, metagenome functional content, or SCFA concentrations. However, there were significant differences between esports players and students with respect to nine bacterial species and nine amino acids. By contrast, all of the above-mentioned measures differentiated professional athletes from esports players and students, with 45 bacteria differentiating professional athletes from the former and 31 from the latter. The only species differentiating all three experimental groups was Parabacteroides distasonis, showing the lowest and highest abundance in esports players and athletes, respectively.

Conclusion

Our study confirms the marked impact of intense exercise training on gut microbial structure and function. Differences in lifestyle and dietary habits between esports players and physical education students appear to not have a major effect on the gut microbiota.

Biomarkers of post-match recovery in semi-professional and professional football (soccer)

High-level football (soccer) players face intense physical demands that result in acute and residual fatigue, impairing their physical performance in subsequent matches. Further, top-class players are frequently exposed to match-congested periods where sufficient recovery times are not achievable. To evaluate training and recovery strategies, the monitoring of players' recovery profiles is crucial. Along with performance and neuro-mechanical impairments, match-induced fatigue causes metabolic disturbances denoted by changes in chemical analytes that can be quantified in different body fluids such as blood, saliva, and urine, thus acting as biomarkers. The monitoring of these molecules might supplement performance, neuromuscular and cognitive measurements to guide coaches and trainers during the recovery period. The present narrative review aims to comprehensively review the scientific literature on biomarkers of post-match recovery in semi-professional and professional football players as well as provide an outlook on the role that metabolomic studies might play in this field of research. Overall, no single gold-standard biomarker of match-induced fatigue exists, and a range of metabolites are available to assess different aspects of post-match recovery. The use of biomarker panels might be suitable to simultaneously monitoring these broad physiological processes, yet further research on fluctuations of different analytes throughout post-match recovery is warranted. Although important efforts have been made to address the high interindividual heterogeneity of available markers, limitations inherent to these markers might compromise the information they provide to guide recovery protocols. Further research on metabolomics might benefit from evaluating the long-term recovery period from a high-level football match to shed light upon new biomarkers of post-match recovery.

Analysis of Teaching Tactics Characteristics of Track and Field Sports Training in Colleges and Universities Based on Deep Neural Network

In the analysis of the teaching tactical characteristics of track and field sports training in colleges and universities, the teaching tactical characteristics are not quantified, which leads to the low key degree of determining the influencing factor indicators in colleges and universities, and the error in the evaluation of the teaching tactical characteristics of track and field sports training is large. Therefore, this paper designs a method to analyze the tactical characteristics of college track and field sports training teaching based on deep neural network. Firstly, by analyzing the current situation of track and field sports training teaching in colleges and universities, it determines the areas that need to be improved in teaching. Then, by determining the factors of teaching environment, the core competitiveness of track and field teams, and the teaching ability of track and field coaches, these factors are determined as the key characteristics, the data basis is analyzed, and the unified data quantitative processing is carried out to determine the key factor indexes affecting the analysis of tactical characteristics. Finally, the deep neural network is introduced to construct the evaluation model of the tactical characteristics of college track and field sports training teaching, and the characteristic analysis results are further modified with the help of cascade noise reduction self-encoder to complete the analysis of the tactical characteristics of college track and field sports training teaching. The experimental results show that the proposed method can effectively analyze the teaching tactical characteristics of track and field sports training in colleges and universities and improve the performance of the evaluation of the teaching tactical characteristics of track and field sports training.

A Metabolomic Approach and Traditional Physical Assessments to Compare U22 Soccer Players According to Their Competitive Level

The purpose of this study was to use traditional physical assessments combined with a metabolomic approach to compare the anthropometric, physical fitness level, and serum fasting metabolic profile among U22 soccer players at different competitive levels. In the experimental design, two teams of male U22 soccer were evaluated (non-elite = 20 athletes, competing in a regional division; elite = 16 athletes, competing in the first division of the national U22 youth league). Earlobe blood samples were collected, and metabolites were extracted after overnight fasting (12 h). Untargeted metabolomics through Liquid Chromatograph Mass Spectrometry (LC-MS) analysis and anthropometric evaluation were performed. Critical velocity was applied to determine aerobic (CV) and anaerobic (ARC) capacity. Height (non-elite = 174.4 ± 7.0 cm; elite = 176.5 ± 7.0 cm), body mass index (non-elite = 22.1 ± 2.4 kg/m2; elite = 21.9 ± 2.3 kg/m2), body mass (non-elite = 67.1 ± 8.8 kg; elite = 68.5 ± 10.1 kg), lean body mass (non-elite = 59.3 ± 7.1 kg; elite = 61.1 ± 7.9 kg), body fat (non-elite = 7.8 ± 2.4 kg; elite = 7.3 ± 2.4 kg), body fat percentage (non-elite = 11.4 ± 2.4%; elite = 10.5 ± 1.7%), hematocrit (non-elite = 50.2 ± 4.0%; elite = 51.0 ± 4.0%), CV (non-elite = 3.1 ± 0.4 m/s; elite = 3.0 ± 0.2 m/s), and ARC (non-elite = 129.6 ± 55.7 m; elite = 161.5 ± 61.0 m) showed no significant differences between the elite and non-elite teams, while the multivariate Partial Least Squares Discriminant Analysis (PLS-DA) model revealed a separation between the elite and non-elite athletes. Nineteen metabolites with importance for projection (VIP) >1.0 were annotated as belonging to the glycerolipid, sterol lipid, fatty acyl, flavonoid, and glycerophospholipid classes. Metabolites with a high relative abundance in the elite group were related in the literature to a better level of aerobic power, greater efficiency in the recovery process, and improvement of mood, immunity, decision making, and accuracy, in addition to acting in mitochondrial preservation and electron transport chain maintenance. In conclusion, although classical physical assessments were not able to distinguish the teams at different competitive levels, the metabolomics approach successfully indicated differences between the fasting metabolic profiles of elite and non-elite teams.

Metabolomics in Exercise and Sports: A Systematic Review

BACKGROUND

Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed.

OBJECTIVE

This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics.

METHODS

We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020.

RESULTS

Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies.

CONCLUSION

Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.

Urinary Proteomics of Simulated Firefighting Tasks and Its Relation to Fitness Parameters

Firefighting rescues are high-hazard activities accompanied by uncertainty, urgency, and complexity. Knowledge of the metabolic characteristics during firefighting rescues is of great value. The purpose of this study was to explore the firefighting-induced physiological responses in greater depth. The urine samples of ten firefighters were collected before and after the simulated firefighting, and the proteins in urine samples were identified by the liquid chromatography–mass spectroscopy. Blood lactate and heart rate were measured. There were 360 proteins up-regulated and 265 proteins downregulated after this simulated firefighting. Changes in protein expression were significantly related to acute inflammatory responses, immune responses, complement activation, and oxidative stress. Beta-2-microglobulin (r = 0.76, p < 0.05) and von Willebrand factors (r = 0.81, p < 0.01) were positively correlated with heart rate during simulated firefighting, and carbonic anhydrase 1 (r = 0.67, p < 0.05) were positively correlated with blood lactate after simulated firefighting. These results illustrated that Beta-2-microglobulin, von Willebrand, and carbonic anhydrase 1 could be regarded as important indicators to evaluate exercise intensity for firefighters.

Using Metabolomics to Differentiate Player Positions in Elite Male Basketball Games: A Pilot Study

Purpose: The current study compared metabolic profiles and movement patterns between different player positions and explored relationships between indicators of internal and external loads during elite male basketball games.

Methods: Five main players from 14 basketball teams (n = 70) were selected as subjects and defined as backcourt (positions 1–3) or frontcourt (positions 4–5) players. Video-based time motion analysis (VBTMA) was performed based on players’ individual maximal speeds. Movements were classified into high and low intensity running with and without ball, high and low intensity shuffling, static effort and jumps. Saliva samples were collected before and after 40-min basketball games with metabolomics data being analyzed by multivariate statistics. Independent t-tests were used to compare VBTMA.

Results: Frequency, duration, and distance of high and low intensity running and -shuffling were higher in backcourt players, whereas static effort duration and frequency as well as jump frequency were higher in frontcourt players (all p ≤ 0.05). The levels of taurine, succinic acid, citric acid, pyruvate, glycerol, acetoacetic acid, acetone, and hypoxanthine were higher in backcourt players, while lactate, alanine, 3-methylhistidine were higher and methionine was lower in frontcourt players (all p < 0.05). High intensity running with ball was significantly associated by acetylecholine, hopoxanthine, histidine, lactic acid and leucine in backcourt players (p < 0.05).

Conclusion: We demonstrate different metabolic profiles of backcourt and frontcourt players during elite male basketball games; while aerobic metabolic changes are more present in backcourt players, frontcourt players showed lager changes in anaerobic metabolic pathways due to more static movements.

Establishment of a post-race biomarkers database and application of pathway analysis to identify potential biomarkers in post-race equine plasma

In the context of doping control, conventional direct chemical testing detects only a limited scope of target substances in equine biological samples. To expand the ability to detect doping agents and their detection windows, metabolomics has recently become a common approach for monitoring alteration of biomarkers caused by doping agents in relevant metabolic pathways. In horse racing, remarkable changes in metabolic profiles between the rest state and racing are likely to affect the identification of doping biomarkers. Previously, we reported a limited number of significantly upregulated metabolites after racing, based on a non-targeted metabolomics approach using out-of-competition and post-race equine plasma samples. In this study, we performed a more thorough analysis of the data set, using pathway analysis to establish a post-race biomarkers database (PBD) that includes upregulated and downregulated metabolites, their fold changes, and relevant pathways, with the main objective of improving our understanding of changes in physiological status related to horse racing. Statistical analysis of the PBD revealed that two peak combinations of pentadecanoyl carnitine/galactosylglycerol (P/G) and heptadecanoyl carnitine/galactosylglycerol (H/G) could be used as potential biomarkers for the discrimination of the rest and post-race groups. To demonstrate the applicability of the PBD, we validated the post-race biomarkers P/G and H/G (highly involved in lipid metabolism) by a single-blind test. This strategy, which combines establishment of a biomarker database with pathway analysis, represents a powerful tool for discovering potential doping biomarkers in the future.

Long distance running - Can bioprofiling predict success in endurance athletes?

The TransEuropeFootRace (TEFR) was one of the most extreme multistage competitions worldwide. The ultramarathon took the runners over a distance of 4487 km, from Bari, Italy, to the North Cape, Norway, in 64 days. The participating ultra-long-distance runners had to complete almost two marathons per day (~70 km). The race was accompanied by a research team analysing adaptations of different organ systems of the human body that were exposed to a chronic lack of regeneration time. Here, we analyzed runner's urine using mass spectrometric profiling of thousands of low-molecular weight compounds. The results indicated that pre-race molecular factors can predict finishers and separate them from nonfinishers already before the race. These observations were related to the training volume as finishers ran about twice as many kilometers per week before TEFR than nonfinishers, thus apparently achieving a higher performance level and resistance against overuse. While this hypothesis needs to be validated in future long-distance races, the bioprofiling experiments suggest that the competition readiness of the runners is measurable and might be adjustable.