Omics Sciences in Regular Physical Activity
Abstract
1. Introduction
2. Bibliographic Search Method
3. Role of Intensity, Volume, and Type of Activity on Regular Physical Exercise
4. Role of Exerkines on Regular Physical Exercise
5. Omics Sciences
5.1. Genomics
5.2. Epigenomics
5.2.1. DNA Methylation
5.2.2. Histone Post-Translational Modification
Author | Year | Aerobic or Anaerobic Exercise | Main Findings | Main Molecules Identified |
---|---|---|---|---|
Dimauro et al. [34] | 2020 | Both | Exercise influences DNA methylation and histone modifications. | Histones, DNA methylation, and redox-related molecules during exercise. |
Jacques et al. [35] | 2019 | Both | DNA methylation and histone changes in human skeletal muscle following exercise. | DNA hypomethylation marks genes such as PGC-1a, TFAM, MEF2A, and PDK4, along with histone modifications such as H3K36 and H3K9/14 acetylation. |
Voisin et al. [36] | 2015 | Both | Exercise training affects DNA methylation. | p15, ASC, PGC-1α, PDK4, TFAM, PPAR-δ, citrate synthase, MEF2A, MYOD1. |
Landen et al. [37] | 2023 | Both | Physiological and molecular sex differences in muscle response to exercise training. | DNA methylation of hormone-related transcription factors as the androgen receptor and the estrogen receptor. |
Fabre et al. [38] | 2018 | Both | DNA methylation in human adipose tissue after exercise. | DNA methylation in ADRA2A, FOSL1, METRNL, RARA, CBLB, GPR132, and RELT. |
Geiger et al. [39] | 2024 | Both | DNA methylation of exercise-responsive genes differs between trained and untrained individuals. | Exercise-responsive genes MYH7 and MYL3, high methylation in transcription factors such as FOXO3, CREB5, and PGC-1α. |
Turner et al. [40] | 2020 | Both | DNA methylation of HOX genes. | Hypermethylated state in genes KIF15, DYRK2, FHL2, MRPS33, ABCA17P, and HOX genes. |
Li et al. [44] | 2020 | Both | Glis1 induces epigenome-metabolome signaling cascade influenced by exercise. | Glis1 and modulation of glycolytic gene expression. |
5.3. Transcriptomics
Author | Year | Aerobic or Anaerobic Exercise | Main Findings | Main Molecules Identified |
---|---|---|---|---|
De Sanctis et al. [51] | 2021 | Aerobic | Non-coding RNAs differentiating in skeletal muscles in elderly trained in endurance vs. resistance exercise. | Non-coding RNAs: miR-20a-5p and miR-106-5p shared 108 mRNA involved in the regulation of TGF-beta, p53, FoxO, Hippo, WNT, MAPK, and HIF-1. |
Hecksteden et al. [52] | 2016 | Aerobic | Non-coding RNAs in sports training. | Non-coding RNAs hsa-miR 513b-5p, hsa-miR-140-5p, hsa-miR-650, and hsa-miR 3620-3p regulate vascular endothelial growth factor A pathway. |
Domańska-Senderowska et al. [53] | 2019 | Both | MicroRNA profiles and their adaptive response to exercise training. | Non-coding RNAs microRNA-1, -21, -23a, -124, -125b, -133a/b, -144, -145, -206, -486, and -696 regulate the IGF1/PI3K/AKT/mTOR signaling pathway. |
Dimauro et al. [34] | 2020 | Both | Exercise in redox homeostasis and epigenetic regulation in skeletal muscle. | Non-coding RNAs: miR-1, miR-16, miR-21, miR-26a, miR-29a, miR-126, miR-133a, miR-133b, and miR-206, miR-210, miR-221, 328, miR-378, miR-451, miR-494, miR-21, miR-221, miR-20a, miR-146a, miR-133, and miR-222. |
5.4. Metabolomics
5.5. Proteomics
6. Bioinformatic Tools
7. Conclusions
8. Limitations
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Keywords | Total | 2014–2024 | Free Full Text | English | Humans | 19–44 Years Old | Records After Duplicate Remove |
---|---|---|---|---|---|---|---|
Omics AND exercise | 392 | 349 | 282 | 281 | 145 | 30 | 40 |
Omics AND PA | 509 | 456 | 362 | 360 | 187 | 40 | |
Genomics AND exercise | 7135 | 4901 | 3527 | 3520 | 2163 | 634 | 868 |
Genomics AND PA | 12,178 | 7510 | 5441 | 5427 | 3126 | 845 | |
Epigenomics AND exercise | 281 | 251 | 178 | 177 | 133 | 34 | 44 |
Epigenomics AND PA | 426 | 380 | 282 | 277 | 199 | 43 | |
Transcriptomics AND exercise | 1760 | 1340 | 1057 | 1057 | 481 | 129 | 153 |
Transcriptomics AND PA | 2391 | 1826 | 1427 | 1426 | 643 | 150 | |
Metabolomics AND exercise | 1768 | 1605 | 1188 | 1183 | 653 | 221 | 287 |
Metabolomics AND PA | 2328 | 2133 | 1592 | 1585 | 854 | 286 | |
Proteomics AND exercise | 1328 | 1069 | 781 | 778 | 359 | 102 | 124 |
Proteomics AND PA | 1981 | 1574 | 1160 | 1153 | 536 | 122 |
Author | Year | Aerobic or Anaerobic Exercise | Main Findings | Main Molecules Identified |
---|---|---|---|---|
Warburg et al. [9] | 2017 | Both | Health benefits, cardiovascular improvements, and longevity. | Not specific to molecules, focused on general health benefits and physiological effects of exercise. |
Nederveen et al. [10] | 2020 | Both | Role of extracellular vesicles and exosomes in exercise science in cell communication and adaptation. | Exosomes, extracellular vesicles, and various proteins involved in cellular communication post-exercise. |
Nayor et al. [11] | 2020 | Both | Metabolic response to acute exercise in middle-aged adults. | Metabolites: amino acids, lipids, glucose, lactate, and several hormones involved in metabolic response. |
Aldiss et al. [12] | 2018 | Aerobic | Influence of exercise on adipose tissue. | Brown adipose tissue markers, UCP1 (uncoupling protein 1), and other adipokines involved in thermogenesis. |
Morville et al. [13] | 2020 | Both | Metabolic shifts based on exercise type. | Metabolites: amino acids (e.g., BCAA), fatty acids, glucose, and other metabolites linked to exercise metabolism. |
Kim et al. [14] | 2022 | Both | Genetic factors influencing human performance to endurance and resistance exercise. | Genes: PPARs (peroxisome proliferator-activated receptors), FNDC5 (fibronectin type III domain-containing protein 5), and ACE (angiotensin-converting enzyme). |
Roberts et al. [15] | 2024 | Anaerobic | Molecular signatures in human skeletal muscle in response to resistance training. | Proteins: MYH (myosin heavy chains), actin, PGC-1α (peroxisome proliferator-activated receptor gamma coactivator), IGF-1 (insulin-like growth factor 1). |
Author | Year | Aerobic or Anaerobic Exercise | Main Findings | Main Molecules Identified |
---|---|---|---|---|
Nederveen et al. [10] | 2020 | Both | Role of extracellular vesicles in tissue communication and adaptation. | Exosomes, extracellular vesicles, miRNAs, and proteins involved in cell signaling. |
Sabaratnam et al. [16] | 2022 | Both | Exercise-induced organ crosstalk involving muscle. | Myokines (e.g., IL-6, irisin), adipokines, and cytokines involved in muscle–organ interactions. |
Thomou et al. [17] | 2017 | Both | Circulating miRNAs from adipose tissue regulate gene expression in distant tissues. | miRNAs from adipose tissue, including miR-30a-5p, miR-27a-3p, and miR-155. |
Whitham et al. [18] | 2018 | Both | Extracellular vesicles as mediators of tissue crosstalk during exercise. | Extracellular vesicles, miRNAs, proteins like IL-6, and adipokines. |
Severinsen et al. [19] | 2020 | Both | The emerging roles of myokines in muscle–organ crosstalk, inflammation, metabolism, and muscle growth. | Myokines (e.g., IL-6, irisin, FGF21), and adipokines influencing metabolism. |
Pourteymour et al. [20] | 2017 | Aerobic | Identification of novel exercise-regulated myokines of human skeletal muscle. | Myokines: IL-6, irisin, FGF21. |
Catoire et al. [21] | 2014 | Both | Identification of exercise-induced myokines and their regulatory role in metabolism. | Myokines: IL-6, irisin, FGF21, and other factors regulating fat metabolism and muscle growth. |
Chow et al. [22] | 2022 | Both | Role of exerkines on released signaling molecules during exercise. | IL-6, FGF21, irisin, BDNF influencing metabolism, stress responses, and disease. |
Pucino et al. [23] | 2017 | Aerobic | Lactate as a key molecule released during exercise. | Lactate. |
Sá Filho et al. [24] | 2023 | Both | Lactate as mediator on mental health. | Lactate, neurotransmitters, and miRNAs involved in brain-muscle communication. |
Brooks et al. [25] | 2022 | Both | Lactate as a myokine influencing metabolism during exercise. | Lactate, IL-6, FGF21, and other myokines. |
Pal et al. [27] | 2014 | Both | Role of IL-6 in metabolic regulation after exercise. | IL-6, signaling molecules related to muscle metabolism. |
Author | Year | Aerobic or Anaerobic Exercise | Main Findings | Main Molecules Identified |
---|---|---|---|---|
Tanisawa et al. [28] | 2020 | Both | Genetic factors influencing physical performance and adaptation to exercise. | ACE, PPARA, ACTN3, VEGF. |
Pitsiladis et al. [29] | 2016 | Both | Markers that predict athletic performance. | ACTN3, ACE, PPARA, VEGF, FTO. |
Semenova et al. [30] | 2023 | Both | Genes involved in athletic performance, endurance, strength, and recovery. | Genetic variants in ACTN3, ACE, BDNF, PPARA, and myostatin (MSTN). |
Ahmetov et al. [31] | 2016 | Both | Polymorphisms in genes related to endurance and strength. | ACE, ACTN3, VEGF, FTO, myostatin, PPARA. |
Durmic et al. [32] | 2017 | Anaerobic | Polymorphisms in ACE and ACTN3 genes in response to acute exercise. | ACE (Angiotensin-converting enzyme), ACTN3 (Alpha-actinin-3). |
Wang et al. [33] | 2017 | Both | COL1A1 gene polymorphisms and their association with tendon and ligament injuries in athletes. | COL1A1 gene polymorphisms. |
Sample | Advantages | Disadvantages |
---|---|---|
Blood | Allows the study of all endogenous metabolites secreted by different tissues and could be used for all methods of analysis. | Very invasive, difficult to evaluate the origin of metabolites identified, can be easily degraded due to the presence of enzymes in the sample. |
Saliva | Easy to pick up and handle. Reflects the state of the body. | Could be affected by the condition of mouth, such as the presence of bacteria, and may show lower concentrations of endogenous metabolites compared to blood. |
Urine | Easy to pick up and handle. Contains stable metabolites and both endogenous and exogenous compounds. | Could be affected by diet and external factors such as bacteria. The presence of urea and salts can be a problem for MS. |
Tissue | Allows the study local metabolites detectable at high concentrations | Very invasive, limitation in the amount of sample that can be taken. |
Author | Year | Aerobic or Anaerobic Exercise | Main Findings | Main Molecules Identified |
---|---|---|---|---|
Bongiovanni et al. [55] | 2019 | Both | “Sportomics” linked to performance analysis. | Summary of the most important studies of metabolites related to exercise performance and biomarkers for sports performance. |
Nieman et al. [57] | 2020 | Both | Exercise immunology. | Lipid and Krebs cycle metabolites, n-6/n-3PUFA oxylipins. |
Vella et al. [60] | 2019 | Aerobic | Lipid profile responses to resistance exercise. | Lipid profiles: cyclooxygenase (COX)-derived thromboxanes and prostaglandins (PGE2), lipoxygenase (LOX), monohydroxy-eicosatetraenoic acids (5-HETE, 12-HETE, 15-HETE), monohydroxy-docosahexaenoic acids (4 HDoHE, 7-HDoHE, 14-HDoHE). CYP pathway-derived epoxy- and dihydroxy-eicosa trienoic acids (5,6-EpETrE, 11,12-DiHETrE and 14,15-DiHETrE). |
Egan et al. [62] | 2016 | Aerobic | Exercise metabolism and its effects on muscle energy systems and performance. | Metabolites related to aerobic metabolism, lipid oxidation, glucose metabolism, and mitochondrial function. |
Santone et al. [63] | 2014 | Both | Saliva metabolomics by NMR in sport performance. | Salivary metabolites, amino acids, lipids, and lactate, glucose, glycerol, and citrate. |
Luti et al. [64] | 2023 | Both | Chronic training effects on metabolic and proteomic responses to exercise. | Salivary metabolites, amino acids (lysine, valine, glycine, tyrosine) citric acid, taurine. |
Ntovas et al. [65] | 2022 | Both | Effects of physical exercise on saliva composition. | Salivary metabolites, biomarkers of exercise intensity (alpha amylase, salivary cortisol IgA, IgM, IgG), melatonin, lactate, testosterone, and oral health indicators. |
Pitti et al. [67] | 2019 | Both | Salivary metabolome changes due to exercise-induced stress. | Exercise-induced metabolites: amino acids, acetate, creatine, dimethylamine, ethanol, ethanolamine, formate, fumarate, glycerol, lactate, ornithine. |
Bongiovanni et al. [68] | 2022 | Both | Metabolomics in team-sport athletes. | Metabolites in urine and fecal samples: adenine, creatine, glutamine, carnitine, arachidonic acid, plasmalogen, cortisol, lysine, tyrosine, leucine, valine, and isoleucine, glutamate, beta-citryl-glutamate, 5-oxoproline, fatty acid-carnitines and acylated carnitines, trimethylamine-N-oxide (TMAO), dimethylglycine, O-acetyl carnitine, proline, betaine, acetoacetate, 3-hydroxy-isovaleric acid, acetone, N-methyl nicotinate, N-methyl nicotinamide, phenylacetylglutamine (PAG), 3-methylhistidine, trimethylamine (TMA), short-chain fatty acids, as well as methylamine, glycerate, allantoin, and succinate. |
Author | Year | Aerobic or Anaerobic Exercise | Main Findings | Main Molecules Identified |
---|---|---|---|---|
Franco-Martínez et al. [71] | 2020 | Anaerobic | Salivary proteome in response to acute exercise in men and women. | Men: keratin, ceruloplasmin, IgLambda, catalase, suprabasin, annexin A1. |
Balfoussia et al. [72] | 2014 | Anaerobic | Plasma proteome under extreme physical stress. | Plasma proteins (albumin, fibrinogen), stress response proteins, oxidative stress biomarkers. |
Gehlert et al. [74] | 2016 | Aerobic | Small ubiquitin-related modifier (SUMO)-1 in human myofibers. | SUMO-1 myofiber proteins: Lamina-A, actina, perinuclear region of myonuclei. |
Gorini et al. [75] | 2019 | Both | Protein oxidation during exercise. | Oxidized plasma and muscle proteins (carbonylated proteins). |
Gholamnezhad et al. [76] | 2020 | Both | Signaling pathways and muscle recovery. | Exercise adaptation-related proteins, signaling molecules (Rapamycin, myostatin/Smad) recovery biomarkers. |
Wilson et al. [77] | 2018 | Both | Phosphoproteomics. | Phosphorylated proteins, signaling molecules (kinases). |
Tool | Main Functionality | Key Features | Best for |
---|---|---|---|
MetaboAnalyst | Web-based platform for comprehensive analysis of metabolomics data | - Data preprocessing - Statistical analysis - Pathway analysis - Visualization tools | Metabolomics data analysis and visualization |
MergeOmics | Integration and analysis of multiomics data (e.g., genomics, proteomics, and metabolomics) | - Multiomics integration - Differential analysis - Pathway enrichment analysis | Multiomics data integration and analysis |
Cytoscape | Open-source software for network analysis and visualization | - Network visualization - Integration of omics data into networks - Supports large networks | Network analysis, visualization, and biological data integration |
InCroMAP | Network-based analysis for integrative omics data analysis (omics and clinical data) | - Multiomics integration - Clinical outcome prediction - Network analysis | Integrative omics and clinical data analysis |
3Omics | Tool for the integrative analysis of multiomics data (omics, clinical, and phenotypic) | - Multiomics data integration - Predictive modeling - Pathway analysis - Data visualization | Integrative analysis of multiomics data with clinical information |
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Militello, R.; Luti, S.; Modesti, A. Omics Sciences in Regular Physical Activity. Int. J. Mol. Sci. 2025, 26, 5529. https://doi.org/10.3390/ijms26125529
Militello R, Luti S, Modesti A. Omics Sciences in Regular Physical Activity. International Journal of Molecular Sciences. 2025; 26(12):5529. https://doi.org/10.3390/ijms26125529
Chicago/Turabian StyleMilitello, Rosamaria, Simone Luti, and Alessandra Modesti. 2025. "Omics Sciences in Regular Physical Activity" International Journal of Molecular Sciences 26, no. 12: 5529. https://doi.org/10.3390/ijms26125529
APA StyleMilitello, R., Luti, S., & Modesti, A. (2025). Omics Sciences in Regular Physical Activity. International Journal of Molecular Sciences, 26(12), 5529. https://doi.org/10.3390/ijms26125529