Search Results (2,393)

DNA methylation is a key epigenetic mechanism linking nutritional exposures to gene regulation and downstream phenotypes. Both undernutrition and overnutrition are associated with distinct methylation signatures, some of which persist beyond the initial exposure window and may relate to long-term metabolic, immune, and neurodevelopmental outcomes. However, the extent to which these associations reflect causal mechanisms, adaptive responses, or secondary effects remains unresolved. Here, we synthesize current evidence on how nutrition influences DNA methylation across the life course, integrating biochemical pathways, metabolic signaling, and microbiome-derived processes within a unified framework. We highlight how these diverse inputs converge on core regulatory axes, including methyl donor availability, enzyme activity, and chromatin context. We then evaluate emerging long-read sequencing, single-cell methylomics, deconvolution strategies, and multi-omic integration methodologies that are improving cellular resolution and enabling a more mechanistic interpretation of nutritional epigenetic variation. Despite these advances, major challenges remain, including tissue specificity, measurement limitations, and the difficulty of distinguishing causation from correlation in observational data. We argue that progress will depend on longitudinal and interventional study designs, improved causal inference frameworks, and integration of functional validation with high-resolution molecular profiling. Addressing these challenges will be critical for determining whether nutrition-associated methylation changes represent biomarkers, mediators, or causal drivers of disease, and for translating epigenetic insights into precision nutrition strategies. Full article

Diabetic foot ulcer (DFU) affects approximately 25% of diabetic patients and represents the leading cause of non-traumatic lower extremity amputation. Neutrophil extracellular traps (NETs) contribute to chronic inflammation; however, their mechanistic role in DFU healing failure remains incompletely characterized. This study integrated bulk RNA sequencing (GSE143735, n = 9) and single-cell RNA sequencing (scRNA-seq; GSE165816, n = 11) datasets to investigate NET-related transcriptional programs. Differential expression analysis identified 96 differentially expressed genes, with significant NET pathway enrichment in non-healers (normalized enrichment score = 4.35, false discovery rate q < 0.001). Analysis of 33,654 single cells revealed elevated NET activity scores in neutrophils from non-healing wounds (p = 4.73 × 10⁻¹⁵⁹). Four neutrophil subpopulations were identified, with the NETs-high subset expanded in non-healers (43.1% versus 15.4%). Cell–cell communication analysis demonstrated enhanced S100A8/A9–RAGE and IL1B–IL1R signaling in the non-healing state. A six-gene signature (S100A8, S100A9, MPO, ELANE, NCF1, HMGB1) achieved an area under the receiver operating characteristic curve of 0.750 for healing prediction under leave-one-out cross-validation. These findings implicate NET pathway activation as a potential driver of DFU healing impairment and identify candidate prognostic biomarkers warranting prospective validation. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD), a leading cause of chronic liver disease, encompasses a continuum from steatosis to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis, and hepatocellular carcinoma. This review aimed to synthesize current evidence on how metabolomic, lipidomic, and spatial multi-omic approaches illuminate MASLD pathogenesis and support precision hepatology. Methods: A structured narrative review was conducted through searches of PubMed, Scopus, and Web of Science, complemented by manual screening of key references. Studies were prioritized when they addressed MASLD biology, metabolic rewiring, lipid remodeling, mitochondrial dysfunction, inflammatory and fibrogenic pathways, gut–liver–adipose crosstalk, biomarker development, or therapeutic monitoring. Results: The reviewed evidence identifies MASLD as a systemic metabolic disorder shaped by excess lipid flux, enhanced de novo lipogenesis, impaired mitochondrial adaptation, oxidative and endoplasmic reticulum stress, sterile inflammation, and hepatic stellate-cell activation. Recurrent metabolomic signatures include altered amino acid, fatty acids, bile acid, and microbial co-metabolite pathways. Lipidomic studies consistently implicate depletion of protective polyunsaturated fatty acids, lysophosphatidylcholines, and phosphatidylcholines, in association with accumulation of diacylglycerols and ceramides, in the transition from steatosis to MASH and fibrosis. Emerging spatial and multi-omic analyses further resolve cell-specific metabolic niches involving hepatocytes, macrophages, endothelial cells, and stellate cells. Conclusions: Metabolomics provides a mechanistic and translational bridge between molecular injury, histological progression, and non-invasive risk stratification in MASLD. Future progress requires standardized analytical workflows, longitudinal validation, causal pathway interrogation, and integration with imaging, genetics, microbiome profiling, and treatment-response phenotyping. Clinical implementation will require standardized platforms, transparent metabolite identification, external validation across diverse populations, cost-effectiveness analyses, and regulatory-grade evidence of clinical utility. Full article

Aqueous two-phase systems (ATPSs) provide a versatile, fully aqueous platform for probing solute–water interactions and protein structure. This review first surveys the diversity and phase behavior of biphasic aqueous systems formed by polymers and salts. We describe how phase diagrams characterize ATPS formation and composition and how both polymer chemistry and salt identity, rather than molecular size alone, govern phase separation by modulating the solvent properties of water. Building on a modified binodal model, we show that phase separation and solute partitioning can be understood in terms of changes in aqueous solvent dipolarity/polarizability, hydrogen-bond donor/acceptor properties, hydrophobicity, and electrostatics, quantified via solvatochromic probes and homologous solute series. These measurements underpin solvent interaction analysis (SIA), in which the partition coefficients of small molecules and proteins across panels of ATPSs are used to generate “structural signatures” that sensitively report on amino acid substitutions, conformational changes, aggregation, ligand binding, osmolyte effects, and post-translational modifications, independent of protein size. We discuss how SIA can be implemented in vial-, plate-, and microfluidic formats and combined with diverse analytical readouts (HPLC, MS, colorimetric assays, and immunoassays), and we contrast this structure-focused approach with conventional concentration-only proteomic and biomarker strategies. Particular emphasis is placed on structure-based biomarker discovery, where disease-relevant shifts in proteoform distributions—especially glycosylation changes—are often more informative than bulk protein levels and where SIA can complement or simplify complex glycomics and top-down proteomics workflows. As a case study, we describe the recently FDA-approved IsoPSA assay, which applies SIA principles to prostate-specific antigen by measuring cancer-associated structural alterations in circulating PSA via its partition behavior in a proprietary ATPS. IsoPSA generates a single index that discriminates between high-grade prostate cancer and benign and low-grade conditions. Prospective, longitudinal, and MRI-integrated clinical studies demonstrate that IsoPSA improves pre-biopsy risk stratification, reduces unnecessary biopsies, and provides robust negative and positive predictive values within the PSA “gray zone.” Collectively, the data support aqueous solvent interaction analysis as a broadly applicable, mechanistically grounded technology for protein characterization, drug–protein interaction studies, and structure-centric biomarker development, exemplified by the clinical translation of IsoPSA. Full article

Field cancerization describes molecular alterations occurring in histologically normal tissues surrounding tumors that may contribute to cancer initiation and progression. In prostate cancer (PCa), the molecular characteristics of peritumoral tissue (PTT) remain incompletely understood. Because microRNAs (miRNAs) play key roles in gene regulation, tumor progression, and microenvironmental remodeling, we investigated miRNA expression patterns and regulatory networks across benign tissue (BT), PTT, and tumor tissue (TT). Small RNA sequencing was performed on matched formalin-fixed paraffin-embedded samples from 40 patients with PCa. Differential expression analysis was conducted using DESeq2, adjusting for age and Gleason grade, while functional enrichment analysis and weighted gene co-expression network analysis (WGCNA) were used to identify dysregulated pathways and conserved miRNA modules. PTT exhibited a molecular profile intermediate between BT and TT, consistent with a field cancerization effect. Compared with BT, 102 miRNAs were differentially expressed in TT and 57 in PTT, with 39 miRNAs (68% of the PTT-associated miRNAs) overlapping the tumor signature. Shared dysregulated pathways included PI3K–Akt, p53, and HIF-1 signaling; whereas, PTT showed additional enrichment in pathways related to epigenetic regulation (Polycomb Repressive Complex) and cellular stress responses (mitophagy, protein processing in ER) exclusively through up-regulated miRNAs; no pathways were uniquely enriched from down-regulated miRNAs in PTT. WGCNA identified conserved miRNA modules enriched for members of the let-7, miR-200, miR-103/107, and miR-106a~363 families, which have established roles in epithelial–mesenchymal transition, tumor progression, and microenvironmental remodeling. Collectively, these findings demonstrate that histologically benign peritumoral tissues harbor tumor-associated miRNA programs and regulatory networks that closely resemble those observed in prostate tumors, providing molecular evidence of field cancerization in PCa and identifying potential miRNA-mediated mechanisms relevant to disease progression and biomarker development. Full article

Prostate adenocarcinoma is characterized by substantial inter-patient heterogeneity, limiting the clinical reliability of conventional diagnostic tools, including prostate-specific antigen testing. This limitation underscores the need for robust molecular biomarkers that may complement conventional diagnostic tools, highlighting the urgent need for biomarkers capable of enhancing diagnostic accuracy and enabling more precise risk stratification. In the present study, transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed using an integrative bioinformatics and machine learning pipeline., The proposed workflow was designed as a stepwise and reproducible biomarker prioritization framework in which differential expression analysis, functional enrichment, protein–protein interaction (PPI) based network interpretation, graph-convolutional feature selection, and hybrid ensemble machine learning were sequentially integrated. Differential gene expression analysis was combined with pathway enrichment (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome), protein–protein interaction network construction, and graph-convolutional feature selection. Multiple machine learning algorithms, including Random Forest, Gradient Boosting Machine, Support Vector Classifier, Artificial Neural Network, and AdaBoost, were systematically evaluated. A hybrid ensemble model integrating Gradient Boosting Machine and Random Forest (GBM+RF) was subsequently developed. Model performance was assessed using accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic (ROC) and externally validated using the independent GSE14206 dataset. The analysis revealed a coordinated molecular pattern characterized by dysregulated cell cycle activity and enhanced interferon-mediated immune signaling. Protein–protein interaction analysis identified STAT1 and PLK1 as highly connected network hub genes within immune-related and cell-cycle-associated modules. Among the evaluated models, the hybrid GBM+RF framework achieved the highest predictive performance on the TCGA dataset, with AUC: 0.9526; Accuracy: 97.49%. External validation using the GSE14206 dataset confirmed the robustness of this model (AUC: 0.9156; Accuracy: 91.53%). These findings support a broader multi-gene candidate signature in prostate adenocarcinoma, in which machine learning prioritized genes such as XAF1, APP, RPA3, IFIH1, UBE2D2, RSAD2, KIF2C, and PLK1, while STAT1 and PLK1 provided complementary network-level biological relevance. The proposed framework provides a robust and transferable strategy for biomarker discovery and precision oncology. Full article

Background/Objectives: Clinical outcomes after traumatic brain injury (TBI) remain difficult to predict, highlighting the need for more sensitive diagnostic and prognostic biomarkers, particularly in polytrauma. This study aimed to identify TBI-specific proteomic signatures in plasma and extracellular vesicles (EV)-enriched fractions of critically injured trauma patients. Methods: Seventy-five severely injured adult trauma patients (ISS ≥ 16) were included: isolated severe TBI (TBI; AIS_head ≥ 4, other AIS ≤ 1, n = 23), polytrauma with TBI (PT-TBI; AIS_head ≥ 4, n = 22), and polytrauma without TBI (PT; AIS_head = 0, n = 30). 24 age- and sex-matched healthy volunteers served as controls. Neat plasma and EV-enriched fractions were profiled using HPLC-MS/MS. Differentially expressed proteins (DEP) were analyzed bioinformatically, and associations with clinical parameters were assessed using Spearman’s correlation. Results: The EV-enriched plasma fraction yielded more DEPs than neat plasma (846 vs. 258). Most DEPs were linked to polytrauma, with plasma reflecting metabolism and EVs showing translation and protein catabolism signatures. Among TBI-context proteins, EV-associated NRCAM and AQR and plasma IGHV1-69D, MASP1, PON1, and IGFBP7 remained significantly associated with TBI after adjustment for confounder. Notably, only EV-associated proteins correlated with injury-related clinical parameters. AQR negatively correlated with GCS (r = −0.51, p < 0.0001), while elevated NRCAM, AQR, and plasma MASP1 were associated with neurological deterioration and neurosurgical intervention. Conclusions: EV-enriched plasma proteomics enhances biomarker discovery in polytrauma, revealing distinct pathways and greater sensitivity than whole plasma. While most changes reflect systemic injury, EV-associated NRCAM and AQR, along with plasma MASP1, show TBI-specific associations with neurological status, highlighting their potential as candidate TBI biomarkers for further study. Full article

Background and Objectives: Individuals with Parkinson’s disease (PD) experience deficits in working memory (WM) and sustained attention (ATTN), but diagnosing and monitoring these deficits remains challenging. This study compares machine learning (ML) classification models trained on EEG and pupillometry data from WM and ATTN tasks to identify task-specific and shared cognitive biomarkers of PD. Methods: EEG and pupillometry were recorded from PD patients and healthy controls (HC) during a visual change detection WM task and a continuous performance ATTN task. A standardized toolbox extracted 108 features, reduced via PCA and recursive elimination (RE) and classified using an SVM-RBF within a nested, 5-fold cross-validated pipeline, with class balancing (SMOTE) and feature selection performed strictly within training folds to prevent leakage. Results: On internal test folds, WM achieved 71% accuracy (F1 = 0.701) and ATTN achieved 73% (F1 = 0.699); on an independent hold-out set, WM achieved 63% accuracy (F1 = 0.626) and ATTN achieved 70% (F1 = 0.623). ATTN showed higher accuracy and precision, WM showed higher recall, and univariate analyses independently supported several top-ranked features (e.g., theta/beta and alpha/theta ratios); PAI and FAA, though top features in both tasks, reached univariate significance only in ATTN. These results indicate WM and ATTN yield complementary, task-linked neurophysiological signatures relevant to PD classification. Conclusion: At its current stage, this pipeline functions as a research tool for biomarker discovery rather than a clinical diagnostic, though larger, externally validated samples could support future screening and monitoring applications. Full article

Background: Necrotizing enterocolitis (NEC) is a life-threatening intestinal disorder in preterm infants and is strongly associated with gut microbial dysbiosis. However, whether recurrent genus-level dysbiosis patterns can be observed across heterogeneous NEC cohorts and summarized as a compact, interpretable microbial signature remains unclear. Methods: We analyzed two public neonatal gut microbiome cohorts and an independent real-world cohort within a three-stage framework of discovery, contextual analysis, and external validation. Community composition, alpha diversity, beta diversity, and differential genera were evaluated using harmonized genus-level features within each cohort. Machine learning feature prioritization was used to derive a reduced NEC-associated microbial signature. A four-group cohort was then used to examine this signature in relation to disease status, antibiotic exposure, and early postnatal development. The reduced signature was finally examined in our collected samples. Results: NEC-related microbial alterations showed marked heterogeneity at the whole-community level across cohorts, whereas several genus-level directional patterns recurred across datasets. Across datasets, NEC was repeatedly associated with enrichment of several opportunistic genera, including Enterobacter, Klebsiella, Serratia, and Escherichia–Shigella, and depletion of commensal or probiotic-associated taxa such as Bifidobacterium, Lactobacillus, and Pediococcus. Feature prioritization yielded a compact microbial signature that improved discrimination over the unfiltered abundance profile in the discovery cohort (AUC 0.674 vs. 0.59). In the four-group cohort, the NEC_ABT group remained distinct from healthy controls, supporting partial modification rather than normalization of the NEC-associated pattern. In our collected samples, the signature retained directional consistency but showed only modest external discrimination (AUC 0.618). Conclusions: These findings identify recurrent genus-level dysbiosis features associated with NEC across clinically heterogeneous settings. The compact microbial signature may serve as an exploratory biomarker for longitudinal and mechanistic validation, but not as a standalone diagnostic tool. Full article

Substance use disorders (SUDs) are increasingly associated with systemic inflammatory, neuroendocrine, and cardiometabolic dysregulation; however, the biological signatures underlying distinct substance-use patterns remain incompletely characterized. This study compared biomarker profiles among healthy controls and clinical groups comprising alcohol-associated polysubstance users (A + PS), non-alcohol substance users (PS-A), and alcohol-associated polysubstance users with comorbid post-traumatic stress disorder (PTSD+). Plasma levels of interleukin-6 (IL-6), C-reactive protein (CRP), cortisol, and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were evaluated through comparative, multivariate, dose–response, and receiver operating characteristic (ROC) curve analyses. All clinical groups exhibited significantly increased IL-6, CRP, cortisol, and NT-proBNP levels compared with controls, indicating systemic physiological dysregulation associated with substance exposure. Among the evaluated biomarkers, IL-6 demonstrated the most robust and consistent performance across all analytical approaches. Multivariate regression identified alcohol consumption as an independent predictor of IL-6 elevation (p = 0.0003), while dose–response analysis revealed progressive increases in IL-6 according to alcohol consumption severity. ROC analysis further demonstrated that IL-6 exhibited the highest discriminatory performance (AUC = 0.844), outperforming CRP, cortisol, and NT-proBNP in distinguishing substance-using individuals from controls. Collectively, these findings were able to identify IL-6 as a central biomarker associated with substance-related systemic inflammatory dysregulation and support its translational potential as a sensitive indicator of physiological burden associated with chronic alcohol and substance exposure. Full article

Circadian disruption has been linked to impaired male fecundity, but its association with semen molecular phenotypes and circadian genes remains unclear. We analyzed 441 men from the Male Reproductive Health in Chongqing College Students cohort to assess whether social jetlag, an indicator of circadian disruption, was associated with whole-semen mitochondrial DNA copy number (mtDNAcn), an emerging biomarker of male fecundity. Core circadian genes related to mtDNAcn were screened using genetic polymorphism data. A light-cycle phase-shifting mouse model, Cry1-knockout mice, and testicular Cry1 re-expression models were used for experimental validation, with histology, transcriptomics, single-cell data, and proteomics analyses used to explore mechanisms. Social jetlag was associated with higher mtDNAcn in men (1.29-fold, p = 0.026), with a concordant increase in circadian-disrupted mice (1.33-fold, p = 0.010). Among core circadian genes, CRY1 showed the strongest association with mtDNAcn (p = 0.048). Cry1 knockout elevated mtDNAcn (2.18-fold, p < 0.001), whereas testicular Cry1 re-expression reduced it toward wild-type levels. Circadian disruption and Cry1 deficiency were accompanied by seminiferous epithelial disorganization, spermatogenesis-related transcriptomic changes, and altered mitochondrial pathway signatures. To our knowledge, this study is the first to identify whole-semen mtDNAcn as a circadian-disruption-associated molecular phenotype and supports CRY1 as a candidate regulator. Full article

Renal cell carcinoma (RCC) is the most common malignancy of the urinary system, characterized by high incidence, mortality, and resistance to therapy. Its molecular heterogeneity presents challenges for effective precision treatment. RCC is highly heterogeneous, yet treatment guidelines rely predominantly on kidney renal clear cell carcinoma (KIRC) studies, neglecting other molecular subtypes, which limits therapy personalization for non-KIRC patients. This study aimed to explore the role of small ubiquitin-like modifier (SUMOylation)-associated genes in the progression and prognosis of RCC and its subtypes. We identified 298 SUMOylation-associated differentially expressed genes (DEGs), including 151 RCC-specific genes after excluding expression changes attributable to RCC subtype-specific variation. Ten core genes (PRKCG, PRKCQ, PRKCD, IRS4, SLC2A4, SLC27A1, SLC27A4, LCN2, S100A7, and S100A9) were identified, with LCN2 expression not only discriminates tumor from normal tissue but also separates KIRC from KICH/KIRP, proposing LCN2 as a potential second-step biomarker for KIRC identification on top of traditional histology. Inter-subtype RCC heterogeneity represented a key factor limiting predictive performance of the six prognostic signature genes (CREB3L1, GNA11, PFKM, PPARGC1A, PPP2R2C and PRKCG). Its 5-year AUC exceeded 0.7 for every individual RCC subtype in the TCGA training cohort, with pooled 5-year AUCs of 0.61 (TCGA training cohort) and 0.67 (independent PCAWG validation cohort). The prognostic risk model demonstrated strong predictive performance, with a C-index of 0.791 before calibration and 0.774 after calibration. Importantly, the C-index remained above 0.75 throughout the 60-month follow-up period, indicating stable and robust long-term prognostic accuracy. High-risk patients exhibited greater immune cell infiltration, indicating potential for immunotherapy. Following secondary screening, three RCC cell lines (BFTC909, CAKI1, and CAL54) and five target genes (PRKCD, SLC27A1, SLC27A4, LCN2 and GNA11) were identified as optimal candidates for subsequent mechanistic investigations. This study uncovers the prognostic and functional relevance of SUMOylation in RCC and offers a novel framework for biomarker development, therapeutic targeting, and immunotherapeutic stratification. Full article

Background: Colorectal cancer (CRC) is driven by genetic alterations, epigenetic dysregulation and tumor microenvironment remodeling. Histone modification reader proteins serve as key epigenetic regulators of anti-tumor immunity, yet their synergistic immune networks, combined prognostic roles and immune subtype heterogeneity remain poorly understood. Methods: Here, we integrated multi-omics data and graph attention networks (GAT) to systematically screen prognostic-associated histone reader genes. We then conducted analyses using the immunoassay pipeline and ultimately identified hub immune-related genes validated in independent external cohorts. Additional analyses, including single-cell RNA sequencing (scRNA-seq), molecular docking and multiple in silico functional assays, were performed based on retrospective public datasets. Results: Four core genes (CUL7, GPC1, NFYA, SLC25A5) exhibited robust prognostic performance and strong correlations with anti-tumor immunity. Both core and auxiliary genes participate in critical metabolic and immune pathways. Candidate drugs present differential binding affinity for their encoded proteins, with sapitinib designated as a promising agent. Conclusions: This work constructs an epigenetic immune regulatory network and a four-gene signature, offering promising biomarkers and actionable therapeutic targets for precision immunotherapy against CRC. Full article

Background/Objectives: Major depressive disorder (MDD) has been increasingly associated with alterations of the gut microbiome through the microbiota–gut–brain axis. However, published findings remain highly heterogeneous, limiting identification of reproducible microbial signatures associated with depression. This systematic review aimed to evaluate reproducible taxonomic and functional gut microbiome alterations in patients with MDD compared with healthy controls. Methods: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science Core Collection, and the Cochrane Library for studies published between January 2016 and December 2025. Observational human studies evaluating gut microbiome composition in adults with clinically diagnosed MDD and healthy control groups were included. Methodological quality was assessed using the Newcastle-Ottawa Scale. Due to substantial methodological heterogeneity, findings were synthesized using structured qualitative narrative analysis. Results: Sixteen observational studies were included in the qualitative synthesis. Findings related to alpha diversity were inconsistent across studies, whereas beta diversity alterations demonstrated greater reproducibility across independent cohorts. The most recurrent microbiome pattern involved depletion of short-chain fatty acid (SCFA)-producing bacteria, particularly Faecalibacterium and Roseburia, together with recurrent alterations affecting members of the Ruminococcaceae, Lachnospiraceae, and Clostridia groups. Functional microbiome alterations demonstrated greater consistency than higher-level taxonomic findings and included reduced butyrate synthesis pathways, dysregulated amino acid and tryptophan metabolism, increased lipopolysaccharide biosynthesis, and enrichment of pro-inflammatory microbial signatures. Antidepressant-naïve cohorts generally demonstrated more homogeneous dysbiosis patterns than mixed-treated populations. Conclusions: Current evidence suggests that functional gut microbiome dysregulation may represent a more reproducible biological feature of MDD than isolated taxonomic alterations alone. However, substantial heterogeneity in study design, participant characteristics, sequencing methodologies, and analytical approaches continues to limit clinical translation. Large-scale longitudinal multi-omics studies using standardized methodologies are required to clarify the role of the gut microbiome in depressive disorders and to evaluate the potential utility of microbiome-based biomarkers and interventions in mental health and public health practice. Full article

Recovery in elite athletes represents a critical determinant of performance and health outcomes. The gut microbiota has been proposed as a modulating factor for recovery through anti-inflammatory mechanisms, oxidative stress management, sleep regulation, and biosynthetic potential for essential micronutrients. This review examines the mechanisms linking gut microbiota composition and function to athletic recovery and critically evaluates the evidence supporting its application in sports medicine. Athletes appear to harbor a more enriched microbial biosynthetic potential, with substantially greater numbers of high-biological-impact synthases involved in the production of vitamins, amino acids, and bioactive metabolites. Short-chain fatty acids, particularly butyrate and propionate, have demonstrated anti-inflammatory effects in preclinical studies, with emerging evidence in humans. The gut–brain axis has been proposed to modulate recovery by regulating neurotransmitter production and controlling circadian rhythms. Sport-associated microbial signatures seem to reflect metabolic demands, with endurance athletes showing enrichment for Prevotella and Veillonella, while strength athletes tend to harbor higher levels of proteolytic bacteria. Probiotic interventions with multi-strain Lactobacillus and Bifidobacterium formulations have reported reductions in inflammatory markers, improvements in oxidative stress biomarkers, and enhanced sleep quality in small-scale randomized controlled trials involving athletic populations, and improvements in self-reported sleep quality in a controlled, non-randomized study in elite athletes. Optimizing gut microbiota composition and function offers a promising complementary strategy for enhancing recovery in elite athletes. Potential applications that require prospective validation include sport-specific probiotic interventions, nutritional strategies to enhance short-chain fatty acid production, and the integration of microbiota assessment with traditional recovery monitoring. Further research is needed to establish standardized protocols and identify predictive biomarkers of individual response to microbiota-targeted interventions. Full article