Effects of submaximal training on the gut microbiota of forage-only horses

Effects of submaximal training on the gut microbiota of forage-only horses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of submaximal training on the gut microbiota of forage-only horses Thayná Da Cruz Paduan-Silva, Pâmela Aparecida Maldaner Pereira, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7835900/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Physical activity is recognized as a modulator of the intestinal microbial metabolism in humans and may also influence the microbiota of horses subjected to physical conditioning. While the physiological benefits of submaximal training are well-documented, its effects on the intestinal microbiota of horses that are fed a forage-only diet remain insufficiently understood. This study evaluated the fecal microbial composition of pasture-kept horses at three timepoints: before training (Submax), after seven weeks of exercise (Week7), and after 30 days of recovery (Trev30). Compositional analysis of the microbiota was explored by sequencing the V4 region of the 16S rRNA gene. A stable forage-only diet, confirmed by bromatological analysis, indicates the observed microbial shifts were primarily exercise-induced. Significant temporal dynamics in beta diversity (p = 0.001) indicated a marked shift in microbial community structure after training (Week7), with a subsequent partial restoration after recovery (Trev30). No significant changes in alpha diversity were detected (p > 0.05). A transient increase in the phylum Proteobacteria at Week7, which decreased by Trev30, suggested microbial adaptation to exercise-induced metabolic demands. LEfSe analysis identified the genera Rummeliibacillus (trained) and Hungateiclostridiaceae (controls) as discriminant between groups. Fecal pH showed a slight reduction after seven weeks, without statistical differences, and returned to equilibrium values in Trev30. Submaximal training induced a reversible modulation of the equine gut microbiota, demonstrating its adaptive capacity while maintaining overall ecological stability. fecal horse microbiome horse physiology microbial diversity 16S rRNA sequencing equine exercise Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Equine microbiome research typically compares healthy and diseased individuals to identify microbial features associated with each state. The relationship between chronic physical exercise and health has been widely documented in several species, including horses ( 1 – 4 ), indicating that regular and moderate aerobic exercise is positively correlated with overall animal health ( 5 , 6 ). Long-term training acts as a modulator of body homeostasis by inducing adaptations that involve multiple systems, including the immune, metabolic, cardiac and gut–brain axes ( 7 , 8 ). These adaptations encompass changes in inflammatory regulation, energy utilization, and the activity of bioactive compounds derived from the gut microbiota. Regular exercise also enhances carbohydrate utilization efficiency while contributing to the modulation of metabolic pathways related to inflammation in peripheral and intestinal tissues ( 8 ). Sport horses often follow carbohydrate-rich diets that provide a continuous source of energy during prolonged exercise ( 9 , 10 ). The substantial metabolic demands of equine athletes often necessitate a diet where over 40% comprises starch-rich, cereal-grain concentrates to meet their caloric needs. Excessive intake of easily fermentable carbohydrates such as starch may alter the composition and function of the gut microbiota. A reduction in microbial diversity and a decrease in the production of beneficial by-products, such as short-chain fatty acids (SCFAs), are associated with intestinal disorders, including colitis ( 9 , 10 ). This highlights the importance of gut microbiota in maintaining equine health, particularly in the context of physical exercise and specific diets. The development of physiologically aligned equine nutrition is critical. These feeding strategies are essential for preventing widespread health disorders and reducing the economic burden of veterinary care. The equine gut microbiota may play a crucial role in overall physiology, influencing not only metabolic health but also athletic performance( 11 , 12 ). Studies have indicated that a healthy microbial ecosystem in horses involves a large diversity of microorganisms, with some species being dominant and others performing specific metabolic functions( 13 , 14 ). The interaction between training and the gut microbiota demonstrates that moderate exercise can favor the increase of beneficial bacteria such as Roseburia and Faecalibacterium , which produce fatty acids essential for intestinal health ( 15 ). Furthermore, the well-established gut–microbiota–exercise axis shows that physical activity modulates intestinal microbial composition, and microbial metabolites such as SCFAs, particularly butyrate, exert important systemic effects on intestinal barrier integrity, mitochondrial function, and athletic performance ( 3 , 8 ). Evidence from rodent and human studies has shown that exercise can alter gut microbial composition ( 16 , 17 ). These results provide evidence for a beneficial impact of exercise on gut microbiota. In contrast, intensive training in horses may disrupt dominant phyla such as Firmicutes, Bacteroidetes , and Proteobacteria , mainly attributable to physiological stress and inflammation ( 18 , 19 ). In this context, submaximal endurance-training program (SETP) emerges as an alternative conditioning strategy for horses, serving as a preparatory phase for more intense exercise. It is hypothesized that, by inducing responses in the gut microbiota, submaximal training may increase microbial diversity and modulate microbial metabolism. The current study assessed the impact of submaximal training on the gut microbiota of horses fed exclusively on forage under tropical climate. The SETP used in this study aimed to prepare horses for a series of 30-minute continuous exercise tests designed to determine the maximal lactate steady state (MLSS), the gold standard for assessing aerobic fitness ( 20 ). In this study, MLSS was defined as the external workload at which plasma lactate concentration [La − ] did not increase by more than 1 mM during the last 20 minutes of constant-load exercise ( 21 ). Materials and Methods Ethical approval All experimental procedures were approved by the Ethics Committee on Animal Use (CEUA) of UNESP – Universidade Estadual Paulista, under protocol number 2310/21. Animals and diet The study was conducted with fifteen horses, including seven Purebred Arabians and eight crossbred horses (eleven mares and four geldings), aged between 3 and 22 years, with a mean body mass of 418 ± 53 kg. None of the horses had a history of gastrointestinal disease in the previous six months. Of the fifteen horses, ten were subjected to a submaximal endurance-training program (SETP). At the same time, five remained in the control group, performing only voluntary exercise through spontaneous locomotor activity on pasture. Horses were weighed at the beginning of the first week and at the end of the last week of training using a digital livestock scale (MGR-3000 Junior®, Toledo do Brasil Indústria de Balanças Ltda, São Bernardo do Campo, Brazil). All animals were maintained on rotational pasture, consisting of natural forages Tanzania ( Panicum maximum ) and Massai ( Panicum maximum × Panicum infestum ), with free access to mineral salt. To ensure nutritional control throughout the experiment, weekly collections of forage samples were carried out from the paddocks used for feeding. Samples underwent bromatological analysis, and the stability of the key nutritional parameters during the experimental period was evaluated through statistical analysis. Fecal sample collection Fecal samples were collected, by the same researcher (T.C.P. Silva) , directly from the rectum of the animals using palpation gloves and placed in sterile collection containers. Samples were immediately stored in an insulated container and transported to the laboratory, where they were kept at −80 °C until DNA extraction. Sampling time points To evaluate the effects of SETP, fecal samples were collected at three time points during the experiment. The first collection was performed before the beginning of the SETP (Submax), establishing baseline values for comparison with subsequent stages. After seven weeks of training, the second collection (Week7) was carried out to identify possible physiological and metabolic changes associated with training. The third and final collection was performed 30 days after the end of SETP (Trev30), allowing the analysis of the organism’s responses to the recovery period. To control variables and improve the interpretation of training effects, equivalent collections were performed in the horses belonging to the control group, which, as mentioned above, did not undergo the SETP. These samples were identified as SubmaxC (baseline), Week7C (seven weeks), and Trev30C (30 days of recovery). Fecal pH analysis Fecal pH was assessed at all sampling time points using a benchtop pH meter (Digimed—DM 22, São Paulo, Brazil). For the analysis, 30 g of fresh feces were diluted in 30 mL of ultrapure type I water supplied by the Milli-Q® system(22). Submaximal training Training of the horses was monitored to quantify internal load. Heart rate (HR) was measured during all training sessions using a Polar heart rate monitor (receiver M430 with specific Polar H-10 equine transmitter, Polar Electro®, Kempele, Finland). This device has a sampling rate of 128 Hz and was recently validated for use in horses (23). The training protocol lasted six weeks, with three weekly sessions of 12 minutes each. During the first three weeks, animals underwent an adaptation phase consisting of 2 minutes of warm-up at 1.5 m/s, followed by 10 minutes of treadmill exercise aimed at maintaining a heart rate of approximately 130 bpm. The treadmill was inclined at 5%, and the speed was adjusted according to the HR response of each animal. When HR fell below 125 bpm, speed increased in increments of 0.1 m/s, whereas when HR exceeded 140 bpm, speed was decreased by the same interval. In the following three weeks, training intensity progressively increased. Horses continued with a 2-minute warm-up at 1.5 m/s and a 5% inclined, but the target HR was raised to 160 bpm during the 10 minutes of exercise. As in the initial phase, treadmill speed was adjusted based on HR response: if HR dropped below 155 bpm, speed was increased in 0.1 m/s increments, and if HR exceeded 170 bpm, speed decreased proportionally. This protocol was designed to optimize the animals’ aerobic fitness by providing a submaximal, progressive, and controlled training regimen that allowed individualized prescription of the exercise response. DNA extraction and sequencing DNA was extracted from 250 mg of fecal samples using the Power Fecal Pro DNA Kit (QIAGEN), according to the manufacturer’s instructions. The quality of the extracted DNA was confirmed by electrophoresis on 1% (w/v) agarose gel, run at 80 V for approximately 2 hours. DNA quantification was performed using the Qubit® 2.0 Fluorometer (Thermo Fisher Scientific, MA, USA) with the Qubit dsDNA BR Assay Kit (Invitrogen®), following the manufacturer’s recommendations. The V4 region of the bacterial 16S rRNA gene was amplified by PCR using specific primers: 515F: 5'-GTGCCAGCMGCCGCGGTAA-3' and 806R: 5'-GGACTACHVGGGTWTCTAAT-3' (24). After amplification, DNA fragments were purified from 2% (w/v) agarose gels using the Zymoclean™ Gel DNA Recovery Kit (Zymo Research, Irvine, CA). Libraries were then prepared with the Nextera XT Index Kit v2 (Illumina), quantified, and pooled at equimolar concentrations prior to sequencing. Sequencing was performed on the Illumina MiSeq platform using the MiSeq® v2 Kit (300 cycles) with paired end reads (2 × 150 bp). Next-generation sequencing (NGS) provided detailed data on microbial composition, offering a robust basis for taxonomic and functional analyses of the equine gut microbiota. Bioinformatics and statistical analyses A total of 44 fecal samples were analyzed to characterize the gut microbiota throughout the training period using QIIME2 version 2023.7 (25). Primer sequences were checked with USEARCH11 (26) and removed with cutadapt in QIIME2. The DADA2 plugin was used for reading filtering, chimera removal, and paired end reading merging. Taxonomy was assigned to amplicon sequence variants (ASVs) using the Silva 138.1 database (27,28). To improve data quality, ASVs present in only one sample, as well as those assigned to chloroplasts or mitochondria, were removed. Alpha diversity was assessed using Shannon and observed ASVs indices, whereas beta diversity was calculated using the Bray–Curtis index and visualized by PCoA, with significant differences tested by PERMANOVA, through the MicrobiomeAnalyst platform (29,30). Graphs were generated using the ggplot2 package in RStudio. Relative abundance analyses focused on the most prevalent phyla, families, and genera (>1%). All statistical analyses were performed considering a significance level of 5%. Means and standard deviations were calculated to describe the data. Group comparisons were performed in SAS® (Statistical Analysis System) using the PROC NPAR1WAY procedure with the WILCOXON option, which runs the Kruskal–Wallis test, appropriate for non-parametric data. When the test indicated statistical significance, the DSCF (Dwass–Steel–Critchlow–Fligner) option was applied to automatically perform post hoc multiple comparisons among groups. The experimental design and methodological workflow, including sampling time points, the submaximal training protocol, and analytical steps applied to fecal samples, are summarized in Figure 1. Results The ten horses from the trained group completed the study and MLSS tests. All horses achieved a maximal lactate steady state (MLSS), defined as a [La − ] increase of ≤1 mM in the final 20 minutes of exercise, within three to five sessions. Forages from the paddocks used during the experiment were subjected to weekly bromatological analysis to quantify the main nutritional parameters: dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), non-fiber carbohydrates (NFC), starch, and water-soluble carbohydrates (WSC). Statistical analysis did not reveal significant differences across the different sampling time points for any of the evaluated parameters (p > 0.05). Mean values were 23.6 ± 2.6% DM, 12.3 ± 2.7% CP, 67.4 ± 5.2% NDF, 38.5 ± 2.4% ADF, 12.5 ± 3.1% NFC, 1.8 ± 1.0% starch, 6.0 ± 2.1% WSC, and 58.9 ± 1.9% TDN (mean ± SD across sampling dates), confirming the stability of forage quality during the experimental period (Supplementary Table 1). Metabolizable energy (ME) was estimated to be using a simplified predictive approach, in which crude protein (CP), neutral detergent fiber (NDF), ether extract (EE), and ash (MM) were subtracted from the bromatological composition, following predictive energy evaluation systems for horses (31,32). The mean estimated value was 6.25% of dry matter, which is consistent with the energy profile reported for forage-only diets in training horses (33). The composition of the equine gut microbiota varied throughout the experiment, with predominance of the phyla Firmicutes, Bacteroidota, Verrucomicrobiota , and Euryarchaeota . Less abundant phyla such as Proteobacteria, Actinobacteriota, Spirochaetota, Halobacterota, Planctomycetota , and Cyanobacteria were also identified (Figure 2). Firmicutes and Bacteroidota were the most abundant phyla, whereas Proteobacteria showed significant variation among groups (p = 0.0074, Supplementary Table 2), with higher abundance in Week7_C and Week7 groups, and lower abundance in Submax_C. In terms of bacterial families (Figure 3), the most representative were Lachnospiraceae, Oscillospiraceae , and Christensenellaceae , followed by Rikenellaceae and Prevotellaceae , as well as Methanobacteriacea e and Eggerthellaceae. The distribution of these families varied significantly, with emphasis on Eggerthellaceae , which showed lower abundance in the Submax_C group (control without training) compared with Submax (beginning of submaximal training). In contrast, the Crev30, Trev30, Week7, and Week7_C groups showed intermediate values, with no significant differences among them. The UCG_010 family, predominant in Submax_C, showed a reduction in Trev30, while Week7_C exhibited an intermediate value (Supplementary Table 3). At the genus level, the main bacterial groups, such as Lachnospiraceae_XPB1014 , Rikenellaceae , and Christensenellaceae_R7 , also showed variations according to the experimental group (Figure 4). Lachnospiraceae_XPB1014 exhibited higher relative abundance before training (Submax), but a significant reduction was observed at later sampling points, both in trained and control groups (Supplementary Table 4, Figure 4). The relative abundance of Rikenellaceae showed a slight increase at the final collection point (Trev30) (p = 0.0273), whereas the abundance of UCG_010 showed the opposite trend (p = 0.0255). The results regarding the alpha diversity of the gut microbiota in horses subjected to submaximal training (Figure 5) were assessed using two metrics: Observed ASVs, which measures species richness, and the Shannon index, which considers both richness and evenness of the microbial community. Although minor variations could be observed, there was no statistical evidence indicating changes in alpha diversity across the different experimental phases. Beta diversity of the equine gut microbiota assessed using the Bray–Curtis index, revealed statistically significant differences in microbial composition among the experimental groups (Figure 6, p = 0.001). Statistical analysis (Supplementary Table 5) indicated variation in microbial composition throughout the experimental period. Comparisons between Submax and Crev30 (p = 0.015) and between Submax and Week7_C (p = 0.015) showed significant differences between these groups. Variations were also observed between Submax_C and Trev30 (p = 0.015) and between Submax_C and Week7 (p = 0.015). The comparison between Trev30 and Week7_C yielded a p-value of 0.015, while the comparison between Submax and Submax_C indicated a p-value of 0.02. The Bray–Curtis ordination plot (Figure 6) shows a clear separation among experimental groups, with perceptible clustering according to different stages of training and recovery. In addition to changes in microbial composition, fecal pH varied among groups throughout the experiment (Figure 7, Supplementary Table 6). At baseline, the SubmaxC0 group (control before submaximal training) had a mean pH of 7.00 ± 0.27, which was statistically like the Submax group (before the onset of submaximal training), which recorded the highest value (7.09 ± 0.20). After seven weeks, the Week7_C (control at week 7) and Week7 (seven weeks of training) groups showed mean values of 6.94 ± 0.23 and 6.96 ± 0.29, respectively. The Crev30 group (control after 30 days) exhibited the lowest mean pH (6.55 ± 0.47), which was significantly different from the initial Submax group (p < 0.05), but statistically like the Week7, Week7_C, and Trev30 groups (p ≥ 0.05). The Trev30 group (30 days after the end of training) had a mean pH of 6.96 ± 0.29, with no statistical differences compared to the other groups (p ≥ 0.05). Tukey’s test indicated that the Week7, Week7_C, and Trev30 groups did not differ statistically from each other, whereas the Crev30 group presented a significantly lower pH compared with Submax (p < 0.05), but without significant differences from the other groups (p ≥ 0.05). Additionally, the trained group showed a significant reduction in body mass compared with Submax (p = 0.01, Supplementary Table 7). Biomarker analysis using the LEfSe method (Linear Discriminant Analysis Effect Size) identified two taxa that were differentially abundant between experimental groups. The genus Rummeliibacillus was more strongly associated with trained animals (Week7), whereas the family Hungateiclostridiaceae was more abundant in the control group. Both taxa exhibited LDA scores greater than 2.0, indicating the biological relevance of the observed differences (Figure 8) Discussion This study investigated the impact of submaximal training on the gut microbiota of pasture-only horses, analyzing its composition before training (Submax), after seven weeks of exercise (Week7), and after 30 days of recovery (Trev30). The ten horses from the trained group not only completed the study, but they also performed the MLSS tests (20) demonstrating the effectiveness of our training method. Firmicutes, Bacteroidota, Verrucomicrobiota , and Euryarchaeota were the most abundant phyla across all groups. Proteobacteria increased in Week7, possibly in response to the physiological stress and higher energy demand imposed by training. This phylum includes fast-growing microorganisms capable of metabolizing fermentable substrates, suggesting an adaptation to conditions of greater substrate flux in the intestinal lumen, as previously described in studies on training-induced microbial shifts (3,34,35). The reduction in Proteobacteria observed in Trev30 suggests a return to intestinal homeostasis, possibly associated with the redistribution of blood flow to the digestive tract after cessation of exercise (36). The fact that values in the control group (Week7_C) remained stable reinforces that these changes were directly induced by physical training. Among the predominant families were Lachnospiraceae, WCHB1_41, Oscillospiraceae , and Rikenellaceae , in addition to Eggerthellaceae . These families showed lower abundance in Submax_C and higher abundance in Submax, suggesting that individual differences prior to training may influence the microbial response to exercise. The UCG_010 family, more abundant in Submax_C and reduced in Trev30, may be related to the adaptation of the gut microbiota to the increased metabolic demand during training (14). The maintenance of stability in these families throughout the study may be associated with the fiber-rich diet, which has been described as a factor preserving microbial diversity in horses (10,15). Bromatological analysis of the forages used confirmed that the nutritional composition remained stable throughout the experiment, with no significant variations in crude protein, fiber, starch, or soluble carbohydrate contents. This finding reflects the increased energy demand induced by exercise, leading to efficient mobilization of energy reserves. Recent studies indicate that even without changes in body composition, training improves metabolic function (33)and aerobic capacity (37,38). The maintenance of a forage-only diet ensured a continuous supply of fiber and essential nutrients, which may have contributed to preserving gut microbiota stability. Therefore, the observed body mass loss should be interpreted as a positive physiological adaptation to exercise rather than as a detrimental effect, reinforcing the role of training in the integrated modulation of metabolism, body composition, and gut microbiota (35,36) At the genus level, the higher abundance of Lachnospiraceae_XPB1014 in Submax and its reduction in Crev30 may be related to shifts in the microbial fermentation of structural carbohydrates in response to exercise intensity. Rikenellaceae_RC9 , which decreased in Submax but increased in Week7_C, may have been influenced by environmental and dietary factors, as previous studies indicate that the equine microbiota responds dynamically to variations in management and training(14,36). LEfSe analysis identified Rummeliibacillus and Hungateiclostridiaceae as differentially abundant between groups, reinforcing the role of submaximal training in the specific modulation of the gut microbiota. The enrichment of Rummeliibacillus in trained animals suggests a greater capacity to degrade complex substrates, whereas Hungateiclostridiaceae predominated in controls, reflecting fermentative profiles of horses not subjected to exercise. Similar results have been reported in horses subjected to endurance programs, in which alterations in microbiota composition and function were associated with changes in blood metabolome and transcriptome (36,39). More recent evidence confirms that microbiotas enriched with fiber-degrading and short-chain fatty acid (SCFA)-producing bacteria, such as butyrate producers, are linked to improved athletic performance, suggesting a gut–muscle axis that modulates energy metabolism during exercise (7,40). Complementary studies in animal models and humans indicate that exercise increases microbial diversity and promotes greater abundance of SCFA-producing groups, with potential anti-inflammatory effects and support for metabolic function (8,17). Alpha diversity, measured by the Observed ASVs and Shannon indices, did not show statistical differences among groups, suggesting that training did not reduce microbiota richness. This result corroborates previous findings indicating that, under moderate exercise protocols, alpha diversity of the equine microbiota tends to remain stable (36). However, this does not exclude the possibility of functional alterations in the microbiota, which cannot be captured by analyses based solely on 16S rRNA sequencing (41). Beta diversity, however, varied among groups throughout the experiment, demonstrating that training modulated the structure of the bacterial community. The differences observed between Submax and Crev30, Submax and Week7_C, and Trev30 and Submax_C reinforce that exercise influences the gut microbiota, either through adjustments in the utilization of energy substrates or through the mechanical impact of physical activity on intestinal function (4). Throughout the experiment, fecal pH partially mirrored these changes, showing a reduction after seven weeks of exercise, parallel to the increase in Proteobacteria , and stabilization in Trev30. This dynamic may be associated with SCFA production by Firmicutes and Bacteroidota , particularly the families Lachnospiraceae and Prevotellaceae , which contribute to the regulation of intestinal pH (22). After the 30-day recovery period in Trev30, fecal pH stabilized, returning to baseline values, indicating microbial readjustment following the cessation of training. This re-equilibration after recovery suggests that exercise-induced microbial modulation is reversible and adjusted according to physiological demands (40,42). Taken together, the results demonstrate that submaximal training program induced adaptive and reversible adjustments in the gut microbiota, modulating taxa of metabolic relevance while maintaining overall diversity. These adaptations may impact digestion, nutrient absorption, and immunity, with potential implications for equine performance. The nutritional control throughout the study reinforces that the observed changes were attributable to exercise and not to dietary variations (36). Finally, some limitations should be considered. The seven-week period may not have been sufficient to induce long-term changes. Moreover, 16S rRNA sequencing allows identification of microbial composition but not functional capacities. Future studies should consider longer training protocols and functional approaches to clarify the impacts of exercise on the microbiota and its influence on equine athletic performance. Conclusion Submaximal training promoted adaptive and reversible adjustments in the equine gut microbiota, characterized by temporal variations in microbial composition without long-term impacts on alpha diversity. The increase in Proteobacteria during exercise, followed by its reduction after recovery, suggests a microbial community response to the metabolic demands of physical effort. The stability of alpha diversity and the maintenance of the main bacterial families indicate that the forage-based diet exerted a protective role, preserving microbiota resilience. Differences observed in beta diversity reinforce the modulatory effect of exercise on microbial structure, while variations in fecal pH paralleled these metabolic adjustments, returning to equilibrium values after recovery. Overall, the findings demonstrate that the equine gut microbiota possesses functional plasticity, being able to dynamically respond to physical activity without loss of ecological stability. These results contribute to the understanding of the relationship between exercise, gut health, and athletic performance in horses, and highlight the need for future studies employing functional approaches to elucidate the mechanisms involved. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research was funded by the São Paulo Research Foundation – FAPESP (Grant numbers: 2023/10337-4 and 2020/09633-0). References Willemse E. THE IMPACT OF ENDURANCE EXERCISE ON FECAL INDICATORS OF EQUINE GUT HEALTH. 2019. Evans CC, LePard KJ, Kwak JW, Stancukas MC, Laskowski S, Dougherty J et al. Exercise prevents weight gain and alters the gut microbiota in a mouse model of high fat diet-induced obesity. PLoS ONE. 2014;9(3). Plancade S, Clark A, Philippe C, Helbling JC, Moisan MP, Esquerré D et al. Unraveling the effects of the gut microbiota composition and function on horse endurance physiology. Sci Rep. 2019;9(1). Almeida MLM, Feringer WH, Carvalho JRG, Rodrigues IM, Jordão LR, Fonseca MG et al. Intense exercise and aerobic conditioning associated with chromium or L-carnitine supplementation modified the fecal microbiota of fillies. PLoS ONE. 2016;11(12). Bishwajit G, Tang S, Yaya S, He Z, Feng Z. Lifestyle Behaviors, Subjective Health, and Quality of Life Among Chinese Men Living With Type 2 Diabetes. Am J Mens Health. 2017;11(2):357–64. Kanagasabai T, Riddell MC, Ardern CI. Physical Activity Contributes to Several Sleep-Cardiometabolic Health Relationships. Metab Syndr Relat Disord. 2017;15(1):44–51. Hawley JA, Krook A, Metabolism. One step forward for exercise. Nature Reviews Endocrinology. Volume 12. Nature Publishing Group; 2016. pp. 7–8. Barton W, Penney NC, Cronin O, Garcia-Perez I, Molloy MG, Holmes E, et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut. 2018;67(4):625–33. Harlow BE, Donley TM, Lawrence LM, Flythe MD. Effect of starch source (corn, oats or wheat) and concentration on fermentation by equine faecal microbiota in vitro. J Appl Microbiol. 2015;119(5):1234–44. Willing B, Vörös A, Roos S, Jones C, Jansson A, Lindberg JE. Changes in faecal bacteria associated with concentrate and forage-only diets fed to horses in training. Equine Vet J. 2009;41(9):908–14. Pratt-Phillips S, Munjizun A. Impacts of Adiposity on Exercise Performance in Horses. Volume 13. Animals. MDPI; 2023. Garber A, Hastie P, Murray JA. Factors Influencing Equine Gut Microbiota: Current Knowledge. Journal of Equine Veterinary Science. Volume 88. W.B. Saunders; 2020. Costa MC, Weese JS. The equine intestinal microbiome. Vol. 13, Animal health research reviews / Conference of Research Workers in Animal Diseases. 2012. pp. 121–8. Mach N, Ruet A, Clark A, Bars-Cortina D, Ramayo-Caldas Y, Crisci E et al. Priming for welfare: gut microbiota is associated with equitation conditions and behavior in horse athletes. Sci Rep. 2020;10(1). Julliand V, Grimm P. The Impact of Diet on the Hindgut Microbiome. Journal of Equine Veterinary Science. Volume 52. W.B. Saunders; 2017. pp. 23–8. Janabis AHD, Biddle AS, Klein D, McKeever KH. Exercise training-induced changes in the gut microbiota of Standardbred racehorses. Comp Exerc Physiol. 2016;12(3):119–30. Allen JM, Berg Miller ME, Pence BD, Whitlock K, Nehra V, Gaskins HR et al. Voluntary and forced exercise differentially alters the gut microbiome in C57BL/6J mice. J Appl Physiol [Internet]. 2015;118:1059–66. Available from: http://www.jappl.org O’hara E, Neves ALA, Song Y, Guan LL. The Role of the Gut Microbiome in Cattle Production and Health: Driver or Passenger? AV08CH09_Guan ARjats.cls. Annual Review of Animal Biosciences Downloaded from www.annualreviews.org Guest [Internet]. 2025;12(1):47. Available from: https://doi.org/10.1146/annurev-animal-021419- Theelen MJP, Luiken REC, Wagenaar JA, van Sloet MM, Rossen JWA, Zomer AL. The equine faecal microbiota of healthy horses and ponies in the netherlands: Impact of host and environmental factors. Animals. 2021;11(6). Littiere TO, Costa GB, Sales NAA, Carvalho JRG, Rodriguez IDM, Ramos GV et al. Evaluating plasma lactate running speed derived parameters for predicting maximal lactate steady state in teaching horses. J Equine Vet Sci. 2025;147. Heck H, Wackerhage H. The origin of the maximal lactate steady state (MLSS). Volume 16. Medicine and Rehabilitation. BioMed Central Ltd;: BMC Sports Science; 2024. Van den Berg M, Hoskin SO, Rogers CW, Grinberg A. Fecal pH and Microbial Populations in Thoroughbred Horses During Transition from Pasture to Concentrate Feeding. J Equine Vet Sci. 2013;33(4):215–22. Haughan J, Manriquez M, Cohen ND, Robinson MA, de Solis CN. Comparison of novel wearable cardiac monitors to yield accurate exercising ECG and heart rate data in horses. Comp Exerc Physiol [Internet]. 2021;17(5):457–66. Available from: https://brill.com/view/journals/cep/17/5/article-p457_6.xml Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108(SUPPL 1):4516–22. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology. Volume 37. Nature Publishing Group; 2019. pp. 852–7. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6(1). Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D1. Lu S, Deng H, Zhou C, Du Z, Guo X, Cheng Y, et al. Enhancement of β-Caryophyllene Biosynthesis in Saccharomyces cerevisiae via Synergistic Evolution of β-Caryophyllene Synthase and Engineering the Chassis. ACS Synth Biol. 2023;12(6):1696–707. Dhariwal A, Chong J, Habib S, King IL, Agellon LB, Xia J. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 2017;45(W1):W180–8. National Research Council. Nutrient Requirements of Horses [Internet]. 6th revised edition. Washington, D.C.: National Academies Press. 2007. Available from: http://www.nap.edu/catalog/11653 Martin-Rosset W, Andueza D, Vermorel M. Prediction of the metabolisable energy content of forages and mixed diets for horses: validation and comparison of two evaluation systems. Animal. 2021;15(2). Jansson A, Lindberg JE. A forage-only diet alters the metabolic response of horses in training. Animal. 2012;6(12):1939–46. O’Donnell MM, Harris HMB, Jeffery IB, Claesson MJ, Younge B, O’Toole PW, et al. The core faecal bacterial microbiome of Irish Thoroughbred racehorses. Lett Appl Microbiol. 2013;57(6):492–501. Gubert C, Kong G, Renoir T, Hannan AJ. Exercise, diet and stress as modulators of gut microbiota: Implications for neurodegenerative diseases. Neurobiology of Disease. Volume 134. Academic Press Inc.; 2020. Górniak W, Cholewińska P, Szeligowska N, Wołoszyńska M, Soroko M, Czyż K. Effect of intense exercise on the level of bacteroidetes and firmicutes phyla in the digestive system of thoroughbred racehorses. Animals. 2021;11(2):1–9. Klein DJ, McKeever KH, Mirek ET, Anthony TG. Metabolomic Response of Equine Skeletal Muscle to Acute Fatiguing Exercise and Training. Front Physiol. 2020;11. Garland A, Van Doorn DA, Van Den Boom R, Roelfsema E, McCrae P, Boast M et al. 32 Exercise versus feed restriction: What is the most efficient weight loss option for your fat horse? J Equine Vet Sci [Internet]. 2023;124:104334. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0737080623001247 Mach N, Midoux C, Leclercq S, Pennarun S, Le Moyec L, Rué O et al. Mining the equine gut metagenome: poorly-characterized taxa associated with cardiovascular fitness in endurance athletes. Commun Biol. 2022;5(1). Li C, Li X, Liu K, Xu J, Yu J, Liu Z et al. Multiomic analysis of different horse breeds reveals that gut microbial butyrate enhances racehorse athletic performance. NPJ Biofilms Microbiomes. 2025;11(1). Silvers BL, Leatherwood JL, Bradbery AN, Guy CP, Martinez RE, Much ML, et al. 23 Forced exercise on gut microbiota and cytokines in young horses. J Equine Vet Sci. 2021;100:103486. Dougal K, de la Fuente G, Harris PA, Girdwood SE, Pinloche E, Newbold CJ. Identification of a Core Bacterial Community within the Large Intestine of the Horse. PLoS ONE. 2013;8(10). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1forage.xlsx SupplementaryTable2phylum.xlsx SupplementaryTable3family.xlsx SupplementaryTable4Genus.xlsx SupplementaryTable5Betadiversity.xlsx SupplementaryTable6pH.xlsx SupplementaryTable7weight.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7835900","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538032246,"identity":"af15c70a-bce6-4826-bcb2-9d0ca4e6cc4f","order_by":0,"name":"Thayná Da Cruz 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1","display":"","copyAsset":false,"role":"figure","size":156580,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic overview of the biological experiment and analytical pipeline. Horses kept on pasture were sampled at three time points: Submax (pre-training), Week7 (post-training), and Trev30 (30-day recovery), together with the respective equivalent control groups (Submax_C, Week7_C, and Trev30_C). The protocol consisted of seven weeks of submaximal treadmill exercise. Fecal samples were processed for DNA extraction, 16S rRNA sequencing, and bioinformatics analyses of microbial diversity and taxa abundance.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/b0cb43242c6e82f8f7033012.png"},{"id":95082234,"identity":"3b5e0ed0-7e87-42dc-b4d8-f2f07b49db21","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28353,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance distribution of the main bacterial phyla across groups over time. The bar plot illustrates changes in the composition of the equine gut microbiota in response to submaximal physical training.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/05df8f283b408cede1d95ba1.png"},{"id":95082235,"identity":"3ee0997c-5a70-4942-b4cd-e586fbd253bd","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56592,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of bacterial families across groups over time. The bar plot illustrates variations in the composition of the equine gut microbiota in response to submaximal physical training.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/580d18e41e071e7a59a9afd4.png"},{"id":95082240,"identity":"2a2bceee-c26f-4618-8d7b-f4c69a6b588c","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60308,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of bacterial genera across groups over time in response to submaximal physical training.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/c042835b32f7f945bf65d77b.png"},{"id":95223023,"identity":"79ffb09a-0532-45b1-b904-e22bedffe07e","added_by":"auto","created_at":"2025-11-05 16:21:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":47855,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity of the equine gut microbiota assessed by Observed ASVs and Shannon index throughout the submaximal training protocol.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/9993549da5f9ed8c843a68df.png"},{"id":95082247,"identity":"00198a17-b2ea-4893-a89b-7515466315f5","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":37395,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Coordinates Analysis (PCoA) based on the Bray–Curtis index of microbial community composition in horses subjected to submaximal training.Análise de coordenadas principais (PCoA), por meio do índice de Bray-Curtis para amostras microbiológicas avaliada em relação ao treinamento submáximo.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/57495ceb3d2a1bee70a883be.png"},{"id":95082248,"identity":"325a4d91-bd0a-444b-b599-5e90463617c2","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":25832,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in fecal pH of horses subjected to submaximal training. Values represent mean pH for each group: Submax_C (control before submaximal training), Submax (before the onset of submaximal training), Week7_C (control at week 7), Week7 (seven weeks of training), Crev30 (control after 30 days), and Trev30 (30 days of recovery). Different letters above the bars indicate statistically significant differences among groups according to Tukey’s test (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/2985bf3ee234036e0473c4ed.png"},{"id":95223015,"identity":"cb76a469-de29-4744-af3d-37c4e7d9274e","added_by":"auto","created_at":"2025-11-05 16:21:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":34562,"visible":true,"origin":"","legend":"\u003cp\u003eLEfSe analysis showing discriminant taxa of the fecal microbiota in horses subjected to submaximal training. The genus \u003cem\u003eRummeliibacillus\u003c/em\u003e was associated with the trained group, whereas the family Hungateiclostridiaceae was more abundant in the control group.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/e0c634b431b56f1e1afcd62b.png"},{"id":99793542,"identity":"41de1f4b-9c04-45ea-8123-52bc7f2ef417","added_by":"auto","created_at":"2026-01-08 13:31:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":960801,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/2535a38c-d042-45b5-bce4-81c2732af259.pdf"},{"id":95082238,"identity":"a917cb73-0a4b-4b77-9971-4414a02f3e45","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11638,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1forage.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/e6a8bfb70b0a1e71402cfd5b.xlsx"},{"id":95082237,"identity":"1712f3e4-702a-49ed-899d-d3688e63b528","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14746,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2phylum.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/22c344a00e7fb199110ebdbc.xlsx"},{"id":95082244,"identity":"6e872f7b-df05-4188-bf1a-77e3169f1bcc","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15352,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3family.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/8ef31a9e415b7437da71a053.xlsx"},{"id":95082245,"identity":"337399e9-4b32-4395-a239-34f353f9c8c6","added_by":"auto","created_at":"2025-11-04 06:31:22","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18195,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4Genus.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/c09d9ad6c3868cc4688b7eb4.xlsx"},{"id":95223768,"identity":"69c70371-693e-44c3-9b37-7870c06079fc","added_by":"auto","created_at":"2025-11-05 16:22:47","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9706,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5Betadiversity.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/5ffbeb15ca30ec4d4bcd45b6.xlsx"},{"id":95223648,"identity":"7420e7f4-fd25-4370-88aa-a7f2f762e01f","added_by":"auto","created_at":"2025-11-05 16:22:36","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9938,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6pH.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/b220e76a6c6ec3456c81691c.xlsx"},{"id":95223201,"identity":"0632e6c3-c66c-42dd-a5a1-53d8ab5b8f09","added_by":"auto","created_at":"2025-11-05 16:21:50","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9577,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7weight.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7835900/v1/7c865a02ec55a34337b07d1c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of submaximal training on the gut microbiota of forage-only horses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEquine microbiome research typically compares healthy and diseased individuals to identify microbial features associated with each state. The relationship between chronic physical exercise and health has been widely documented in several species, including horses (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), indicating that regular and moderate aerobic exercise is positively correlated with overall animal health (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Long-term training acts as a modulator of body homeostasis by inducing adaptations that involve multiple systems, including the immune, metabolic, cardiac and gut\u0026ndash;brain axes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These adaptations encompass changes in inflammatory regulation, energy utilization, and the activity of bioactive compounds derived from the gut microbiota. Regular exercise also enhances carbohydrate utilization efficiency while contributing to the modulation of metabolic pathways related to inflammation in peripheral and intestinal tissues (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Sport horses often follow carbohydrate-rich diets that provide a continuous source of energy during prolonged exercise (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The substantial metabolic demands of equine athletes often necessitate a diet where over 40% comprises starch-rich, cereal-grain concentrates to meet their caloric needs. Excessive intake of easily fermentable carbohydrates such as starch may alter the composition and function of the gut microbiota. A reduction in microbial diversity and a decrease in the production of beneficial by-products, such as short-chain fatty acids (SCFAs), are associated with intestinal disorders, including colitis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This highlights the importance of gut microbiota in maintaining equine health, particularly in the context of physical exercise and specific diets. The development of physiologically aligned equine nutrition is critical. These feeding strategies are essential for preventing widespread health disorders and reducing the economic burden of veterinary care.\u003c/p\u003e\u003cp\u003eThe equine gut microbiota may play a crucial role in overall physiology, influencing not only metabolic health but also athletic performance(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Studies have indicated that a healthy microbial ecosystem in horses involves a large diversity of microorganisms, with some species being dominant and others performing specific metabolic functions(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The interaction between training and the gut microbiota demonstrates that moderate exercise can favor the increase of beneficial bacteria such as \u003cem\u003eRoseburia\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e, which produce fatty acids essential for intestinal health (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Furthermore, the well-established gut\u0026ndash;microbiota\u0026ndash;exercise axis shows that physical activity modulates intestinal microbial composition, and microbial metabolites such as SCFAs, particularly butyrate, exert important systemic effects on intestinal barrier integrity, mitochondrial function, and athletic performance (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Evidence from rodent and human studies has shown that exercise can alter gut microbial composition (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). These results provide evidence for a beneficial impact of exercise on gut microbiota. In contrast, intensive training in horses may disrupt dominant phyla such as \u003cem\u003eFirmicutes, Bacteroidetes\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e, mainly attributable to physiological stress and inflammation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this context, submaximal endurance-training program (SETP) emerges as an alternative conditioning strategy for horses, serving as a preparatory phase for more intense exercise. It is hypothesized that, by inducing responses in the gut microbiota, submaximal training may increase microbial diversity and modulate microbial metabolism. The current study assessed the impact of submaximal training on the gut microbiota of horses fed exclusively on forage under tropical climate. The SETP used in this study aimed to prepare horses for a series of 30-minute continuous exercise tests designed to determine the maximal lactate steady state (MLSS), the gold standard for assessing aerobic fitness (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In this study, MLSS was defined as the external workload at which plasma lactate concentration [La\u003csup\u003e\u0026minus;\u003c/sup\u003e] did not increase by more than 1 mM during the last 20 minutes of constant-load exercise (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003eEthical approval\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAll experimental procedures were approved by the Ethics Committee on Animal Use (CEUA) of UNESP \u0026ndash; Universidade Estadual Paulista, under protocol number 2310/21.\u003c/p\u003e\n\u003ch2\u003eAnimals and diet\u003c/h2\u003e\n\u003cp\u003eThe study was conducted with fifteen horses, including seven Purebred Arabians and eight crossbred horses (eleven mares and four geldings), aged between 3 and 22 years, with a mean body mass of 418 \u0026plusmn; 53 kg. None of the horses had a history of gastrointestinal disease in the previous six months. Of the fifteen horses, ten were subjected to a submaximal endurance-training program (SETP). At the same time, five remained in the control group, performing only voluntary exercise through spontaneous locomotor activity on pasture. Horses were weighed at the beginning of the first week and at the end of the last week of training using a digital livestock scale (MGR-3000 Junior\u0026reg;, Toledo do Brasil Ind\u0026uacute;stria de Balan\u0026ccedil;as Ltda, S\u0026atilde;o Bernardo do Campo, Brazil). All animals were maintained on rotational pasture, consisting of natural forages \u003cem\u003eTanzania\u003c/em\u003e (\u003cem\u003ePanicum maximum\u003c/em\u003e) and \u003cem\u003eMassai\u003c/em\u003e (\u003cem\u003ePanicum maximum \u0026times; Panicum infestum\u003c/em\u003e), with free access to mineral salt. To ensure nutritional control throughout the experiment, weekly collections of forage samples were carried out from the paddocks used for feeding. Samples underwent bromatological analysis, and the stability of the key nutritional parameters during the experimental period was evaluated through statistical analysis.\u003c/p\u003e\n\u003ch2\u003eFecal sample collection\u003c/h2\u003e\n\u003cp\u003eFecal samples were collected, by the same researcher (T.C.P. Silva)\u003ca id=\"_anchor_1\" href=\"#_msocom_1\" language=\"JavaScript\" name=\"_msoanchor_1\"\u003e\u003c/a\u003e, directly from the rectum of the animals using palpation gloves and placed in sterile collection containers. Samples were immediately stored in an insulated container and transported to the laboratory, where they were kept at \u0026minus;80 \u0026deg;C until DNA extraction.\u003c/p\u003e\n\u003ch2\u003eSampling time points\u003c/h2\u003e\n\u003cp\u003eTo evaluate the effects of SETP, fecal samples were collected at three time points during the experiment. The first collection was performed before the beginning of the SETP (Submax), establishing baseline values for comparison with subsequent stages. After seven weeks of training, the second collection (Week7) was carried out to identify possible physiological and metabolic changes associated with training. The third and final collection was performed 30 days after the end of SETP (Trev30), allowing the analysis of the organism\u0026rsquo;s responses to the recovery period. To control variables and improve the interpretation of training effects, equivalent collections were performed in the horses belonging to the control group, which, as mentioned above, did not undergo the SETP. These samples were identified as SubmaxC (baseline), Week7C (seven weeks), and Trev30C (30 days of recovery).\u003c/p\u003e\n\u003ch2\u003eFecal pH analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFecal pH was assessed at all sampling time points using a benchtop pH meter (Digimed\u0026mdash;DM 22, S\u0026atilde;o Paulo, Brazil). For the analysis, 30 g of fresh feces were diluted in 30 mL of ultrapure type I water supplied by the Milli-Q\u0026reg; system(22).\u003c/p\u003e\n\u003ch2\u003eSubmaximal training\u003c/h2\u003e\n\u003cp\u003eTraining of the horses was monitored to quantify internal load. Heart rate (HR) was measured during all training sessions using a Polar heart rate monitor (receiver M430 with specific Polar H-10 equine transmitter, Polar Electro\u0026reg;, Kempele, Finland). This device has a sampling rate of 128 Hz and was recently validated for use in horses\u0026nbsp;(23).\u0026nbsp;The training protocol lasted six weeks, with three weekly sessions of 12 minutes each. During the first three weeks, animals underwent an adaptation phase consisting of 2 minutes of warm-up at 1.5 m/s, followed by 10 minutes of treadmill exercise aimed at maintaining a heart rate of approximately 130 bpm. The treadmill was inclined at 5%, and the speed was adjusted according to the HR response of each animal. When HR fell below 125 bpm, speed increased in increments of 0.1 m/s, whereas when HR exceeded 140 bpm, speed was decreased by the same interval. In the following three weeks, training intensity progressively increased. Horses continued with a 2-minute warm-up at 1.5 m/s and a 5% inclined, but the target HR was raised to 160 bpm during the 10 minutes of exercise. As in the initial phase, treadmill speed was adjusted based on HR response: if HR dropped below 155 bpm, speed was increased in 0.1 m/s increments, and if HR exceeded 170 bpm, speed decreased proportionally. This protocol was designed to optimize the animals\u0026rsquo; aerobic fitness by providing a submaximal, progressive, and controlled training regimen that allowed individualized prescription of the exercise response.\u003c/p\u003e\n\u003ch2\u003eDNA extraction and sequencing\u003c/h2\u003e\n\u003cp\u003eDNA was extracted from 250 mg of fecal samples using the Power Fecal Pro DNA Kit (QIAGEN), according to the manufacturer\u0026rsquo;s instructions. The quality of the extracted DNA was confirmed by electrophoresis on 1% (w/v) agarose gel, run at 80 V for approximately 2 hours. DNA quantification was performed using the Qubit\u0026reg; 2.0 Fluorometer (Thermo Fisher Scientific, MA, USA) with the Qubit dsDNA BR Assay Kit (Invitrogen\u0026reg;), following the manufacturer\u0026rsquo;s recommendations.\u0026nbsp;The V4 region of the bacterial 16S rRNA gene was amplified by PCR using specific primers: 515F: 5\u0026apos;-GTGCCAGCMGCCGCGGTAA-3\u0026apos; and 806R: 5\u0026apos;-GGACTACHVGGGTWTCTAAT-3\u0026apos; (24). After amplification, DNA fragments were purified from 2% (w/v) agarose gels using the Zymoclean\u0026trade; Gel DNA Recovery Kit (Zymo Research, Irvine, CA). Libraries were then prepared with the Nextera XT Index Kit v2 (Illumina), quantified, and pooled at equimolar concentrations prior to sequencing.\u003c/p\u003e\n\u003cp\u003eSequencing was performed on the Illumina MiSeq platform using the MiSeq\u0026reg; v2 Kit (300 cycles) with paired end reads (2 \u0026times; 150 bp). Next-generation sequencing (NGS) provided detailed data on microbial composition, offering a robust basis for taxonomic and functional analyses of the equine gut microbiota.\u003c/p\u003e\n\u003ch2\u003eBioinformatics and statistical analyses\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eA total of 44 fecal samples were analyzed to characterize the gut microbiota throughout the training period using QIIME2 version 2023.7 (25). Primer sequences were checked with USEARCH11 (26) and removed with \u003cem\u003ecutadapt\u003c/em\u003e in QIIME2. The DADA2 plugin was used for reading filtering, chimera removal, and paired end reading merging. Taxonomy was assigned to amplicon sequence variants (ASVs) using the Silva 138.1 database (27,28).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo improve data quality, ASVs present in only one sample, as well as those assigned to chloroplasts or mitochondria, were removed. Alpha diversity was assessed using Shannon and observed ASVs indices, whereas beta diversity was calculated using the Bray\u0026ndash;Curtis index and visualized by PCoA, with significant differences tested by PERMANOVA, through the MicrobiomeAnalyst platform (29,30). Graphs were generated using the \u003cem\u003eggplot2\u003c/em\u003e package in RStudio. Relative abundance analyses focused on the most prevalent phyla, families, and genera (\u0026gt;1%). All statistical analyses were performed considering a significance level of 5%. Means and standard deviations were calculated to describe the data. Group comparisons were performed in SAS\u0026reg; (Statistical Analysis System) using the PROC NPAR1WAY procedure with the WILCOXON option, which runs the Kruskal\u0026ndash;Wallis test, appropriate for non-parametric data. When the test indicated statistical significance, the DSCF (Dwass\u0026ndash;Steel\u0026ndash;Critchlow\u0026ndash;Fligner) option was applied to automatically perform post hoc multiple comparisons among groups. The experimental design and methodological workflow, including sampling time points, the submaximal training protocol, and analytical steps applied to fecal samples, are summarized in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#_msoanchor_1\"\u003e\u003c/a\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe ten horses from the trained group completed the study and MLSS tests. All horses achieved a maximal lactate steady state (MLSS), defined as a [La\u003csup\u003e\u0026minus;\u003c/sup\u003e] increase of \u0026le;1 mM in the final 20 minutes of exercise, within three to five sessions. Forages from the paddocks used during the experiment were subjected to weekly bromatological analysis to quantify the main nutritional parameters: dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), non-fiber carbohydrates (NFC), starch, and water-soluble carbohydrates (WSC). Statistical analysis did not reveal significant differences across the different sampling time points for any of the evaluated parameters (p \u0026gt; 0.05). Mean values were 23.6 \u0026plusmn; 2.6% DM, 12.3 \u0026plusmn; 2.7% CP, 67.4 \u0026plusmn; 5.2% NDF, 38.5 \u0026plusmn; 2.4% ADF, 12.5 \u0026plusmn; 3.1% NFC, 1.8 \u0026plusmn; 1.0% starch, 6.0 \u0026plusmn; 2.1% WSC, and 58.9 \u0026plusmn; 1.9% TDN (mean \u0026plusmn; SD across sampling dates), confirming the stability of forage quality during the experimental period (Supplementary Table 1).\u0026nbsp;Metabolizable energy (ME) was estimated to be using a simplified predictive approach, in which crude protein (CP), neutral detergent fiber (NDF), ether extract (EE), and ash (MM) were subtracted from the bromatological composition, following predictive energy evaluation systems for horses (31,32). The mean estimated value was 6.25% of dry matter, which is consistent with the energy profile reported for forage-only diets in training horses (33).\u003c/p\u003e\n\u003cp\u003eThe composition of the equine gut microbiota varied throughout the experiment, with predominance of the phyla \u003cem\u003eFirmicutes, Bacteroidota, Verrucomicrobiota\u003c/em\u003e, and \u003cem\u003eEuryarchaeota\u003c/em\u003e. Less abundant phyla such as \u003cem\u003eProteobacteria, Actinobacteriota, Spirochaetota, Halobacterota, Planctomycetota\u003c/em\u003e, and \u003cem\u003eCyanobacteria\u003c/em\u003e were also identified (Figure 2). \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e were the most abundant phyla, whereas \u003cem\u003eProteobacteria\u003c/em\u003e showed significant variation among groups (p = 0.0074, Supplementary Table 2), with higher abundance in Week7_C and Week7 groups, and lower abundance in Submax_C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of bacterial families (Figure 3), the most representative were \u003cem\u003eLachnospiraceae, Oscillospiraceae\u003c/em\u003e, and \u003cem\u003eChristensenellaceae\u003c/em\u003e, followed by \u003cem\u003eRikenellaceae\u003c/em\u003e and \u003cem\u003ePrevotellaceae\u003c/em\u003e, as well as \u003cem\u003eMethanobacteriacea\u003c/em\u003ee and \u003cem\u003eEggerthellaceae.\u003c/em\u003e The distribution of these families varied significantly, with emphasis on \u003cem\u003eEggerthellaceae\u003c/em\u003e, which showed lower abundance in the Submax_C group (control without training) compared with Submax (beginning of submaximal training). In contrast, the Crev30, Trev30, Week7, and Week7_C groups showed intermediate values, with no significant differences among them. The UCG_010 family, predominant in Submax_C, showed a reduction in Trev30, while Week7_C exhibited an intermediate value (Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003eAt the genus level, the main bacterial groups, such as \u003cem\u003eLachnospiraceae_XPB1014\u003c/em\u003e, \u003cem\u003eRikenellaceae\u003c/em\u003e, and \u003cem\u003eChristensenellaceae_R7\u003c/em\u003e, also showed variations according to the experimental group (Figure 4). \u003cem\u003eLachnospiraceae_XPB1014\u003c/em\u003e exhibited higher relative abundance before training (Submax), but a significant reduction was observed at later sampling points, both in trained and control groups (Supplementary Table 4, Figure 4). The relative abundance of \u003cem\u003eRikenellaceae\u003c/em\u003e showed a slight increase at the final collection point (Trev30) (p = 0.0273), whereas the abundance of UCG_010 showed the opposite trend (p = 0.0255).\u003c/p\u003e\n\u003cp\u003eThe results regarding the alpha diversity of the gut microbiota in horses subjected to submaximal training (Figure 5) were assessed using two metrics: Observed ASVs, which measures species richness, and the Shannon index, which considers both richness and evenness of the microbial community. Although minor variations could be observed, there was no statistical evidence indicating changes in alpha diversity across the different experimental phases.\u003c/p\u003e\n\u003cp\u003eBeta diversity of the equine gut microbiota assessed using the Bray\u0026ndash;Curtis index, revealed statistically significant differences in microbial composition among the experimental groups (Figure 6, p = 0.001). Statistical analysis (Supplementary Table 5) indicated variation in microbial composition throughout the experimental period. Comparisons between Submax and Crev30 (p = 0.015) and between Submax and Week7_C (p = 0.015) showed significant differences between these groups. Variations were also observed between Submax_C and Trev30 (p = 0.015) and between Submax_C and Week7 (p = 0.015). The comparison between Trev30 and Week7_C yielded a p-value of 0.015, while the comparison between Submax and Submax_C indicated a p-value of 0.02. The Bray\u0026ndash;Curtis ordination plot (Figure 6) shows a clear separation among experimental groups, with perceptible clustering according to different stages of training and recovery.\u003c/p\u003e\n\u003cp\u003eIn addition to changes in microbial composition, fecal pH varied among groups throughout the experiment (Figure 7, Supplementary Table 6). At baseline, the SubmaxC0 group (control before submaximal training) had a mean pH of 7.00 \u0026plusmn; 0.27, which was statistically like the Submax group (before the onset of submaximal training), which recorded the highest value (7.09 \u0026plusmn; 0.20). After seven weeks, the Week7_C (control at week 7) and Week7 (seven weeks of training) groups showed mean values of 6.94 \u0026plusmn; 0.23 and 6.96 \u0026plusmn; 0.29, respectively. The Crev30 group (control after 30 days) exhibited the lowest mean pH (6.55 \u0026plusmn; 0.47), which was significantly different from the initial Submax group (p \u0026lt; 0.05), but statistically like the Week7, Week7_C, and Trev30 groups (p \u0026ge; 0.05). The Trev30 group (30 days after the end of training) had a mean pH of 6.96 \u0026plusmn; 0.29, with no statistical differences compared to the other groups (p \u0026ge; 0.05). Tukey\u0026rsquo;s test indicated that the Week7, Week7_C, and Trev30 groups did not differ statistically from each other, whereas the Crev30 group presented a significantly lower pH compared with Submax (p \u0026lt; 0.05), but without significant differences from the other groups (p \u0026ge; 0.05). Additionally, the trained group showed a significant reduction in body mass compared with Submax (p = 0.01, Supplementary Table 7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBiomarker analysis using the LEfSe method (Linear Discriminant Analysis Effect Size) identified two taxa that were differentially abundant between experimental groups. The genus \u003cem\u003eRummeliibacillus\u003c/em\u003e was more strongly associated with trained animals (Week7), whereas the family \u003cem\u003eHungateiclostridiaceae\u003c/em\u003e was more abundant in the control group. Both taxa exhibited LDA scores greater than 2.0, indicating the biological relevance of the observed differences (Figure 8)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the impact of submaximal training on the gut microbiota of pasture-only horses, analyzing its composition before training (Submax), after seven weeks of exercise (Week7), and after 30 days of recovery (Trev30). The ten horses from the trained group not only completed the study, but they also performed the MLSS tests (20) demonstrating the effectiveness of our training method.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFirmicutes, Bacteroidota, Verrucomicrobiota\u003c/em\u003e, and \u003cem\u003eEuryarchaeota\u003c/em\u003e were the most abundant phyla across all groups. \u003cem\u003eProteobacteria\u003c/em\u003e increased in Week7, possibly in response to the physiological stress and higher energy demand imposed by training. This phylum includes fast-growing microorganisms capable of metabolizing fermentable substrates, suggesting an adaptation to conditions of greater substrate flux in the intestinal lumen, as previously described in studies on training-induced microbial shifts (3,34,35). The reduction in \u003cem\u003eProteobacteria\u003c/em\u003e observed in Trev30 suggests a return to intestinal homeostasis, possibly associated with the redistribution of blood flow to the digestive tract after cessation of exercise (36). The fact that values in the control group (Week7_C) remained stable reinforces that these changes were directly induced by physical training.\u003c/p\u003e\n\u003cp\u003eAmong the predominant families were \u003cem\u003eLachnospiraceae, WCHB1_41, Oscillospiraceae\u003c/em\u003e, and \u003cem\u003eRikenellaceae\u003c/em\u003e, in addition to \u003cem\u003eEggerthellaceae\u003c/em\u003e. These families showed lower abundance in Submax_C and higher abundance in Submax, suggesting that individual differences prior to training may influence the microbial response to exercise. The \u003cem\u003eUCG_010\u003c/em\u003e family, more abundant in Submax_C and reduced in Trev30, may be related to the adaptation of the gut microbiota to the increased metabolic demand during training (14). The maintenance of stability in these families throughout the study may be associated with the fiber-rich diet, which has been described as a factor preserving microbial diversity in horses (10,15). Bromatological analysis of the forages used confirmed that the nutritional composition remained stable throughout the experiment, with no significant variations in crude protein, fiber, starch, or soluble carbohydrate contents.\u003c/p\u003e\n\u003cp\u003eThis finding reflects the increased energy demand induced by exercise, leading to efficient mobilization of energy reserves. Recent studies indicate that even without changes in body composition, training improves metabolic function \u0026nbsp;(33)and aerobic capacity (37,38). The maintenance of a forage-only diet ensured a continuous supply of fiber and essential nutrients, which may have contributed to preserving gut microbiota stability. Therefore, the observed body mass loss should be interpreted as a positive physiological adaptation to exercise rather than as a detrimental effect, reinforcing the role of training in the integrated modulation of metabolism, body composition, and gut microbiota (35,36)\u003c/p\u003e\n\u003cp\u003eAt the genus level, the higher abundance of \u003cem\u003eLachnospiraceae_XPB1014\u003c/em\u003e in Submax and its reduction in Crev30 may be related to shifts in the microbial fermentation of structural carbohydrates in response to exercise intensity. \u003cem\u003eRikenellaceae_RC9\u003c/em\u003e, which decreased in Submax but increased in Week7_C, may have been influenced by environmental and dietary factors, as previous studies indicate that the equine microbiota responds dynamically to variations in management and training(14,36).\u003c/p\u003e\n\u003cp\u003eLEfSe analysis identified \u003cem\u003eRummeliibacillus\u003c/em\u003e and \u003cem\u003eHungateiclostridiaceae\u003c/em\u003e as differentially abundant between groups, reinforcing the role of submaximal training in the specific modulation of the gut microbiota. The enrichment of \u003cem\u003eRummeliibacillus\u003c/em\u003e in trained animals suggests a greater capacity to degrade complex substrates, whereas \u003cem\u003eHungateiclostridiaceae\u003c/em\u003e predominated in controls, reflecting fermentative profiles of horses not subjected to exercise. Similar results have been reported in horses subjected to endurance programs, in which alterations in microbiota composition and function were associated with changes in blood metabolome and transcriptome (36,39). More recent evidence confirms that microbiotas enriched with fiber-degrading and short-chain fatty acid (SCFA)-producing bacteria, such as butyrate producers, are linked to improved athletic performance, suggesting a gut\u0026ndash;muscle axis that modulates energy metabolism during exercise (7,40). Complementary studies in animal models and humans indicate that exercise increases microbial diversity and promotes greater abundance of SCFA-producing groups, with potential anti-inflammatory effects and support for metabolic function (8,17).\u003c/p\u003e\n\u003cp\u003eAlpha diversity, measured by the Observed ASVs and Shannon indices, did not show statistical differences among groups, suggesting that training did not reduce microbiota richness. This result corroborates previous findings indicating that, under moderate exercise protocols, alpha diversity of the equine microbiota tends to remain stable (36). However, this does not exclude the possibility of functional alterations in the microbiota, which cannot be captured by analyses based solely on 16S rRNA sequencing (41). Beta diversity, however, varied among groups throughout the experiment, demonstrating that training modulated the structure of the bacterial community. The differences observed between Submax and Crev30, Submax and Week7_C, and Trev30 and Submax_C reinforce that exercise influences the gut microbiota, either through adjustments in the utilization of energy substrates or through the mechanical impact of physical activity on intestinal function (4).\u003c/p\u003e\n\u003cp\u003eThroughout the experiment, fecal pH partially mirrored these changes, showing a reduction after seven weeks of exercise, parallel to the increase \u003cem\u003ein Proteobacteria\u003c/em\u003e, and stabilization in Trev30. This dynamic may be associated with SCFA production by \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e, particularly the families \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003ePrevotellaceae\u003c/em\u003e, which contribute to the regulation of intestinal pH (22). After the 30-day recovery period in Trev30, fecal pH stabilized, returning to baseline values, indicating microbial readjustment following the cessation of training. This re-equilibration after recovery suggests that exercise-induced microbial modulation is reversible and adjusted according to physiological demands (40,42).\u003c/p\u003e\n\u003cp\u003eTaken together, the results demonstrate that submaximal training program induced adaptive and reversible adjustments in the gut microbiota, modulating taxa of metabolic relevance while maintaining overall diversity. These adaptations may impact digestion, nutrient absorption, and immunity, with potential implications for equine performance. The nutritional control throughout the study reinforces that the observed changes were attributable to exercise and not to dietary variations (36). Finally, some limitations should be considered. The seven-week period may not have been sufficient to induce long-term changes. Moreover, 16S rRNA sequencing allows identification of microbial composition but not functional capacities. Future studies should consider longer training protocols and functional approaches to clarify the impacts of exercise on the microbiota and its influence on equine athletic performance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Submaximal training promoted adaptive and reversible adjustments in the equine gut microbiota, characterized by temporal variations in microbial composition without long-term impacts on alpha diversity. The increase in \u003cem\u003eProteobacteria\u003c/em\u003e during exercise, followed by its reduction after recovery, suggests a microbial community response to the metabolic demands of physical effort. The stability of alpha diversity and the maintenance of the main bacterial families indicate that the forage-based diet exerted a protective role, preserving microbiota resilience. Differences observed in beta diversity reinforce the modulatory effect of exercise on microbial structure, while variations in fecal pH paralleled these metabolic adjustments, returning to equilibrium values after recovery. Overall, the findings demonstrate that the equine gut microbiota possesses functional plasticity, being able to dynamically respond to physical activity without loss of ecological stability. These results contribute to the understanding of the relationship between exercise, gut health, and athletic performance in horses, and highlight the need for future studies employing functional approaches to elucidate the mechanisms involved.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by the S\u0026atilde;o Paulo Research Foundation \u0026ndash; FAPESP (Grant numbers: 2023/10337-4 and 2020/09633-0).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWillemse E. THE IMPACT OF ENDURANCE EXERCISE ON FECAL INDICATORS OF EQUINE GUT HEALTH. 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvans CC, LePard KJ, Kwak JW, Stancukas MC, Laskowski S, Dougherty J et al. Exercise prevents weight gain and alters the gut microbiota in a mouse model of high fat diet-induced obesity. PLoS ONE. 2014;9(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePlancade S, Clark A, Philippe C, Helbling JC, Moisan MP, Esquerr\u0026eacute; D et al. Unraveling the effects of the gut microbiota composition and function on horse endurance physiology. Sci Rep. 2019;9(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlmeida MLM, Feringer WH, Carvalho JRG, Rodrigues IM, Jord\u0026atilde;o LR, Fonseca MG et al. Intense exercise and aerobic conditioning associated with chromium or L-carnitine supplementation modified the fecal microbiota of fillies. PLoS ONE. 2016;11(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBishwajit G, Tang S, Yaya S, He Z, Feng Z. Lifestyle Behaviors, Subjective Health, and Quality of Life Among Chinese Men Living With Type 2 Diabetes. Am J Mens Health. 2017;11(2):357\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanagasabai T, Riddell MC, Ardern CI. Physical Activity Contributes to Several Sleep-Cardiometabolic Health Relationships. Metab Syndr Relat Disord. 2017;15(1):44\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHawley JA, Krook A, Metabolism. One step forward for exercise. Nature Reviews Endocrinology. Volume 12. Nature Publishing Group; 2016. pp. 7\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarton W, Penney NC, Cronin O, Garcia-Perez I, Molloy MG, Holmes E, et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut. 2018;67(4):625\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarlow BE, Donley TM, Lawrence LM, Flythe MD. Effect of starch source (corn, oats or wheat) and concentration on fermentation by equine faecal microbiota in vitro. J Appl Microbiol. 2015;119(5):1234\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilling B, V\u0026ouml;r\u0026ouml;s A, Roos S, Jones C, Jansson A, Lindberg JE. Changes in faecal bacteria associated with concentrate and forage-only diets fed to horses in training. Equine Vet J. 2009;41(9):908\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePratt-Phillips S, Munjizun A. Impacts of Adiposity on Exercise Performance in Horses. Volume 13. Animals. MDPI; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarber A, Hastie P, Murray JA. Factors Influencing Equine Gut Microbiota: Current Knowledge. Journal of Equine Veterinary Science. Volume 88. W.B. Saunders; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCosta MC, Weese JS. The equine intestinal microbiome. Vol. 13, Animal health research reviews / Conference of Research Workers in Animal Diseases. 2012. pp. 121\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMach N, Ruet A, Clark A, Bars-Cortina D, Ramayo-Caldas Y, Crisci E et al. Priming for welfare: gut microbiota is associated with equitation conditions and behavior in horse athletes. Sci Rep. 2020;10(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJulliand V, Grimm P. The Impact of Diet on the Hindgut Microbiome. Journal of Equine Veterinary Science. Volume 52. W.B. Saunders; 2017. pp. 23\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanabis AHD, Biddle AS, Klein D, McKeever KH. Exercise training-induced changes in the gut microbiota of Standardbred racehorses. Comp Exerc Physiol. 2016;12(3):119\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen JM, Berg Miller ME, Pence BD, Whitlock K, Nehra V, Gaskins HR et al. Voluntary and forced exercise differentially alters the gut microbiome in C57BL/6J mice. J Appl Physiol [Internet]. 2015;118:1059\u0026ndash;66. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.jappl.org\u003c/span\u003e\u003cspan address=\"http://www.jappl.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;hara E, Neves ALA, Song Y, Guan LL. The Role of the Gut Microbiome in Cattle Production and Health: Driver or Passenger? AV08CH09_Guan ARjats.cls. Annual Review of Animal Biosciences Downloaded from www.annualreviews.org Guest [Internet]. 2025;12(1):47. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-animal-021419-\u003c/span\u003e\u003cspan address=\"10.1146/annurev-animal-021419-\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTheelen MJP, Luiken REC, Wagenaar JA, van Sloet MM, Rossen JWA, Zomer AL. The equine faecal microbiota of healthy horses and ponies in the netherlands: Impact of host and environmental factors. Animals. 2021;11(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLittiere TO, Costa GB, Sales NAA, Carvalho JRG, Rodriguez IDM, Ramos GV et al. Evaluating plasma lactate running speed derived parameters for predicting maximal lactate steady state in teaching horses. J Equine Vet Sci. 2025;147.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeck H, Wackerhage H. The origin of the maximal lactate steady state (MLSS). Volume 16. Medicine and Rehabilitation. BioMed Central Ltd;: BMC Sports Science; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan den Berg M, Hoskin SO, Rogers CW, Grinberg A. Fecal pH and Microbial Populations in Thoroughbred Horses During Transition from Pasture to Concentrate Feeding. J Equine Vet Sci. 2013;33(4):215\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaughan J, Manriquez M, Cohen ND, Robinson MA, de Solis CN. Comparison of novel wearable cardiac monitors to yield accurate exercising ECG and heart rate data in horses. Comp Exerc Physiol [Internet]. 2021;17(5):457\u0026ndash;66. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://brill.com/view/journals/cep/17/5/article-p457_6.xml\u003c/span\u003e\u003cspan address=\"https://brill.com/view/journals/cep/17/5/article-p457_6.xml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108(SUPPL 1):4516\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology. Volume 37. Nature Publishing Group; 2019. pp. 852\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2\u0026rsquo;s q2-feature-classifier plugin. Microbiome. 2018;6(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu S, Deng H, Zhou C, Du Z, Guo X, Cheng Y, et al. Enhancement of β-Caryophyllene Biosynthesis in \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e via Synergistic Evolution of β-Caryophyllene Synthase and Engineering the Chassis. ACS Synth Biol. 2023;12(6):1696\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhariwal A, Chong J, Habib S, King IL, Agellon LB, Xia J. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 2017;45(W1):W180\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Research Council. Nutrient Requirements of Horses [Internet]. 6th revised edition. Washington, D.C.: National Academies Press. 2007. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nap.edu/catalog/11653\u003c/span\u003e\u003cspan address=\"http://www.nap.edu/catalog/11653\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartin-Rosset W, Andueza D, Vermorel M. Prediction of the metabolisable energy content of forages and mixed diets for horses: validation and comparison of two evaluation systems. Animal. 2021;15(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJansson A, Lindberg JE. A forage-only diet alters the metabolic response of horses in training. Animal. 2012;6(12):1939\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Donnell MM, Harris HMB, Jeffery IB, Claesson MJ, Younge B, O\u0026rsquo;Toole PW, et al. The core faecal bacterial microbiome of Irish Thoroughbred racehorses. Lett Appl Microbiol. 2013;57(6):492\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGubert C, Kong G, Renoir T, Hannan AJ. Exercise, diet and stress as modulators of gut microbiota: Implications for neurodegenerative diseases. Neurobiology of Disease. Volume 134. Academic Press Inc.; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026oacute;rniak W, Cholewińska P, Szeligowska N, Wołoszyńska M, Soroko M, Czyż K. Effect of intense exercise on the level of bacteroidetes and firmicutes phyla in the digestive system of thoroughbred racehorses. Animals. 2021;11(2):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlein DJ, McKeever KH, Mirek ET, Anthony TG. Metabolomic Response of Equine Skeletal Muscle to Acute Fatiguing Exercise and Training. Front Physiol. 2020;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarland A, Van Doorn DA, Van Den Boom R, Roelfsema E, McCrae P, Boast M et al. 32 Exercise versus feed restriction: What is the most efficient weight loss option for your fat horse? J Equine Vet Sci [Internet]. 2023;124:104334. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S0737080623001247\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/S0737080623001247\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMach N, Midoux C, Leclercq S, Pennarun S, Le Moyec L, Ru\u0026eacute; O et al. Mining the equine gut metagenome: poorly-characterized taxa associated with cardiovascular fitness in endurance athletes. Commun Biol. 2022;5(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi C, Li X, Liu K, Xu J, Yu J, Liu Z et al. Multiomic analysis of different horse breeds reveals that gut microbial butyrate enhances racehorse athletic performance. NPJ Biofilms Microbiomes. 2025;11(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSilvers BL, Leatherwood JL, Bradbery AN, Guy CP, Martinez RE, Much ML, et al. 23 Forced exercise on gut microbiota and cytokines in young horses. J Equine Vet Sci. 2021;100:103486.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDougal K, de la Fuente G, Harris PA, Girdwood SE, Pinloche E, Newbold CJ. Identification of a Core Bacterial Community within the Large Intestine of the Horse. PLoS ONE. 2013;8(10).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"fecal horse microbiome, horse physiology, microbial diversity, 16S rRNA sequencing, equine exercise","lastPublishedDoi":"10.21203/rs.3.rs-7835900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7835900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhysical activity is recognized as a modulator of the intestinal microbial metabolism in humans and may also influence the microbiota of horses subjected to physical conditioning. While the physiological benefits of submaximal training are well-documented, its effects on the intestinal microbiota of horses that are fed a forage-only diet remain insufficiently understood. This study evaluated the fecal microbial composition of pasture-kept horses at three timepoints: before training (Submax), after seven weeks of exercise (Week7), and after 30 days of recovery (Trev30). Compositional analysis of the microbiota was explored by sequencing the V4 region of the 16S rRNA gene. A stable forage-only diet, confirmed by bromatological analysis, indicates the observed microbial shifts were primarily exercise-induced. Significant temporal dynamics in beta diversity (p\u0026thinsp;=\u0026thinsp;0.001) indicated a marked shift in microbial community structure after training (Week7), with a subsequent partial restoration after recovery (Trev30). No significant changes in alpha diversity were detected (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A transient increase in the phylum Proteobacteria at Week7, which decreased by Trev30, suggested microbial adaptation to exercise-induced metabolic demands. LEfSe analysis identified the genera \u003cem\u003eRummeliibacillus\u003c/em\u003e (trained) and \u003cem\u003eHungateiclostridiaceae\u003c/em\u003e (controls) as discriminant between groups. Fecal pH showed a slight reduction after seven weeks, without statistical differences, and returned to equilibrium values in Trev30. Submaximal training induced a reversible modulation of the equine gut microbiota, demonstrating its adaptive capacity while maintaining overall ecological stability.\u003c/p\u003e","manuscriptTitle":"Effects of submaximal training on the gut microbiota of forage-only horses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 06:31:17","doi":"10.21203/rs.3.rs-7835900/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ce315ff-e1e1-4596-8c5c-d9ad8aaff782","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-06T12:09:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 06:31:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7835900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7835900","identity":"rs-7835900","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}