# Atypical gut microbial ecosystem from athletes with very high exercise capacity improves insulin sensitivity and muscle glycogen store in mice

David Martin, Mathis Bonneau, Luz Orfila, Mathieu Horeau, Mathilde Hazon, Romain Demay, Emmanuelle Lecommandeur, Rufin Boumpoutou, Arthur Guillotel, Pierrick Guillemot, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

David Martin, Mathis Bonneau, Luz Orfila, Mathieu Horeau, Mathilde Hazon, et al. Atypical gut microbial ecosystem from athletes with very high exercise capacity improves insulin sensitivity and muscle glycogen store in mice. Cell Reports, 2025, 44 (4), pp.115448. 10.1016/j.celrep.2025.115448. hal-05033053

HAL Id: hal-05033053

https://hal.science/hal-05033053v1

Submitted on 14 Apr 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
Cell Reports

# Atypical gut microbial ecosystem from athletes with very high exercise capacity improves insulin sensitivity and muscle glycogen store in mice

## Graphical abstract

<figure><img src="image_3.png" /></figure>

## Authors

David Martin, Mathis Bonneau,
Luz Orfila, ..., Anne Cuzol, Valérie Monbet,
Frédéric Derbré

## Correspondence

valerie.monbet@univ-rennes.fr (V.M.),
frederic.derbre@univ-rennes2.fr (F.D.)

## In brief

Martin et al. demonstrate that the gut bacterial ecosystem of highly trained athletes is atypical, characterized by reduced bacterial diversity, density, and functional richness but higher levels of fecal SCFAs. When transplanted into mice, this microbiota improves insulin sensitivity and increases muscle glycogen stores, suggesting a potential metabolic advantage.

## Highlights

* Gut bacterial ecosystem is linked to host's exercise capacity independent of food habits

* Despite reduced bacterial diversity, endurance athletes exhibit higher fecal SCFA levels

* Gut microbiota from athletes improves insulin sensitivity of transplanted mice

* The gut bacterial ecosystem might be driven by access to energy resources left by the host
Report

# Atypical gut microbial ecosystem from athletes with very high exercise capacity improves insulin sensitivity and muscle glycogen store in mice

David Martin,<fnref n="1" />,<fnref n="2" /> Mathis Bonneau,<fnref n="1" /> Luz Orfila,<fnref n="1" /> Mathieu Horeau,<fnref n="1" /> Mathilde Hazon,<fnref n="3" /> Romain Demay,<fnref n="1" />
Emmanuelle Lecommandeur,<fnref n="4" /> Rufin Boumpoutou,<fnref n="1" />,<fnref n="5" /> Arthur Guillotel,<fnref n="1" />,<fnref n="6" /> Pierrick Guillemot,<fnref n="7" /> Mikael Croyal,<fnref n="8" />,<fnref n="9" />
Pierre Cressard,<fnref n="3" /> Chrystèle Cressard,<fnref n="3" /> Anne Cuzol,<fnref n="10" /> Valérie Monbet,<fnref n="2" />,<fnref n="11" />,* and Frédéric Derbré<fnref n="1" />,<fnref n="11" />,<fnref n="12" />,*

SUMMARY

Although the gut microbiota is known to act as a bridge between dietary nutrients and the body's energy needs, the interactions between the gut microbiota, host energy metabolism, and exercise capacity remain uncertain. Here, we characterized the gut microbiota ecosystem in a cohort of healthy normo-weight humans with highly heterogeneous aerobic exercise capacities and closely related body composition and food habits. While our data support the idea that the bacterial ecosystem appears to be modestly altered between individuals with low-to-high exercise capacities and close food habits, we report that gut bacterial α diversity, density, and functional richness are significantly reduced in athletes with very high exercise capacity. By using fecal microbiota transplantation, we report that the engraftment of gut microbiota from athletes with very high exercise capacity improves insulin sensitivity and muscle glycogen stores into transplanted mice, which highlights promising therapeutic perspectives in fecal transplantation from human donors selected based on exercise capacity traits.

INTRODUCTION

Aerobic exercise capacity is now considered a powerful predictor of health status and risk of mortality.<fnref n="1" />,<fnref n="2" /> It refers to the maximum amount of physical exertion that an individual can sustain over a prolonged period of time and depends on both the maximal muscle strength and the capacity to transform energy from nutrients into mechanical power.<fnref n="3" /> Aerobic exercise capacity can be assessed by measuring maximal oxygen consumption (VO₂max) and fat oxidation (FO), two physiological traits determined through indirect calorimetry. VO₂max characterizes the maximum energy an organism can produce and sustain during incremental exercise. The percentage of FO during submaximal fasting exercise provides insight into the contribution of carbohydrates and lipids to energy supply, with low FO generally reflecting low metabolic flexibility.<fnref n="4" />

The gut bacterial ecosystem plays a key role in the host's energy metabolism<fnref n="5" />,<fnref n="6" /> and potentially in the host's aerobic exercise capacity. In germ-free or antibiotic-treated (ATB) mice, the reduction of gut bacterial density directly impairs aerobic exercise capacity,<fnref n="7" />⁻<fnref n="9" /> especially due to a concomitant reduction in intestinal absorption of glucose, muscle glycogen stores, and motivation to exercise.<fnref n="7" />,<fnref n="8" />,<fnref n="10" /> Moreover, the absence or reduction of short-chain fatty acids (SCFAs) production in an organism lacking a gut microbiota or exhibiting a dysbiotic gut microbiota displays the crucial role of SCFAs in these alterations,<fnref n="11" />,<fnref n="12" /> and studies further highlight that SCFA supplementation improves energy metabolism and exercise capacity in mice.<fnref n="9" />,¹³,¹⁴ In humans, cross-sectional studies demonstrate that elite athletes have greater gut microbiota diversity and that some bacterial populations are over-represented compared to less active individuals.¹⁵,¹⁶ Longitudinal studies further support that gut microbiota composition undergoes significant changes after a few

<footnote n="1">¹Laboratory “Movement Sport and Health Sciences”, University of Rennes 2/ENS Rennes, Rennes, France</footnote>

<footnote n="2">²IRMAR - UMR CNRS 6625, University of Rennes, Rennes, France</footnote>

<footnote n="3">³Nahibu, Rennes, France</footnote>

<footnote n="4">⁴ELS Consulting, Rennes, France</footnote>

<footnote n="5">⁵Rennes Ortho Sport, Polyclinique Saint Laurent, Rennes, France</footnote>

<footnote n="6">⁶Stade Rennais Football Club, Rennes, France</footnote>

<footnote n="7">⁷CHU Pontchaillou, Rennes, France</footnote>

<footnote n="8">⁸Institut du thorax, Nantes Université, CNRS, INSERM, Nantes, France</footnote>

<footnote n="9">⁹UMS 016, UMS 3556, Nantes Université, INSERM, CNRS, Nantes, France</footnote>

<footnote n="10">¹⁰IUT Vannes, University of South Brittany, Vannes, France</footnote>

<footnote n="11">¹¹These authors contributed equally</footnote>

<footnote n="12">¹²Lead contact</footnote>

<footnote>*Correspondence: valerie.monbet@univ-rennes.fr (V.M.), frederic.derbre@univ-rennes2.fr (F.D.)</footnote>

<footnote>https://doi.org/10.1016/j.celrep.2025.115448</footnote>
weeks of aerobic-based exercise training.¹⁷,¹⁸ However, all of these clinical studies show some limitations, including the lack of consideration of dietary habits and BMI, which are known to modulate the gut microbiota composition and function.¹⁹

Here, we report our characterization of the relationship between aerobic exercise capacity and the gut bacterial ecosystem in humans, ranging from inactive to elite endurance athletes, matched on body composition and dietary habits. Our data highlight that aerobic exercise capacity is associated with the gut bacterial ecosystem independent of dietary habits. Surprisingly, we observe that humans with very high aerobic exercise capacity exhibit reduced gut microbiota diversity, density, and functional pathway abundance but show higher fecal SCFA content. Using fecal microbiota transplantation (FMT) from humans of our cohort to ATB-pretreated mice, we next demonstrate that the gut microbiota associated with aerobic exercise capacity of donors affects the insulin sensitivity and muscle glycogen store in transplanted mice, highlighting the essential role of the gut microbiota associated with a donor's aerobic exercise capacity in the changes in metabolic profile of the FMT recipients.

## RESULTS

### Aerobic exercise capacity is associated with gut microbiota communities independent of food and relative nutrient intake

To explore whether aerobic exercise capacity affects gut microbiota composition in healthy humans, we formed a cohort of 50 young males, named EXOMIC, to create a physiological and biological database of humans with very low to very high aerobic exercise capacity, including non-athletes (NoAs), elite soccer players (ESP), and elite cyclists (ECs). To best limit the differences in food habits between experimental groups, we preselected the potential participants by matching their food intake (recorded by the short-form food frequency questionnaire, 21 items) to correspond qualitatively to the food intake of the ECs (The preselection pipeline is described in the STAR Methods). The participants were recruited only after successfully meeting all requirements of the preselection pipeline and inclusion criteria. All participants were of normal weight (Table S1). They completed a long-form food frequency questionnaire [FFQ] (130 items) during the first visit (Figure S1) and performed two sessions of exercise testing during the second and third visits. The experimental design is illustrated in Figure 1A. During these sessions, indirect calorimetry was used to estimate energy expenditure during maximal (VO₂max) and submaximal (exercise energy expenditure [EEE]) exercises as well as the proportion of energy supplied by FO and carbohydrate oxidation (CHO) during submaximal exercise. Participants presented VO₂max levels ranging from 35 to 85 mL/min/kg, EEE ranging from 8 to 18 kcal/min (Figure 1B), and FO ranging from 0% to 85%, indicating a highly heterogeneous cohort in terms of aerobic exercise capacity (Figure 1C). With maximal FO measured during submaximal exercise being a good indicator of the metabolic flexibility of participants and related to fat mass storage,⁴,²⁰,²¹ we logically report a negative correlation between FO and body fat percentage (r = -0.55, p < 0.001; Figure 1C).

Between the two exercise sessions of the experimental design, all participants provided their fecal samples to characterize the gut bacterial ecosystem using metagenomics sequencing.²² We first observe that the two coordinates of the principal-coordinates analysis (PCoA) of the pairwise dissimilarity matrix of samples (i.e., Bray-Curtis dissimilarity metric) account for 40% of the variability in gut microbiota composition within the EXOMIC cohort (Figure 1D). Specifically, individuals with negative coordinates on PCo1 are predominantly ECs and exhibit a gut microbiota composition characterized by a high relative abundance of species belonging to the *Prevotella* genus, mainly represented by the high level of *Prevotella copri* (Figure 1D). Participants with positive coordinates on PCo1 are mostly NoAs and present a gut microbiota composition characterized by a high relative abundance of species belonging to the *Bacteroides* genus, associated with high abundance of *Bacteroides uniformis*, *Bacteroides vulgatus*, and *Faecalibacterium prausnitzii* (Figure 1D).

In Figures 1E and 1F, each line symbolizes variables characterizing a participant included in the EXOMIC cohort. The ranking of participants based on their coordinates on PCo1 reveals two distinct patterns of gut bacterial composition, close to the *Bacteroides* and *Prevotella* enterotype concept.²³ Figure 1F represents the environmental variables associated with each gut microbiota composition, ordered by the participants' coordinates on PCo1. We first estimated the 95% of the confidence intervals for the r correlation coefficients between host variables (i.e., macronutrients, physiological variables, body composition, SCFAs, and diversity indexes) and coordinates of participants (i.e., PCo1 and PCo2), which summarize the variation of gut microbiota composition within the EXOMIC cohort. Our results reveal that relative macronutrient intake, including carbohydrates (CHO), fiber, proteins, and lipids, exhibit a weak and non-significant association with the coordinates of the gut microbiota composition's PCoA (Figure 1G). We refined our analysis by showing no association between relative nutrient intakes with the coordinates of the gut microbiota composition's PCoA, except for some minerals, trace elements, and vitamins that are significantly related to the second coordinate (Figure S3A). Employing non-parametric redundancy analysis (canonical analysis of principal coordinates [CAP]), we measured how strongly host variables are linked to the composition of the bacterial ecosystem, computing the explained variance and its associated *p* value. CAP confirmed no discernible association between food or relative nutrient intakes and gut microbiota compositions (Figures S3B-S3E). Conversely, aerobic exercise capacity traits are significantly linked with 12% of the gut bacterial composition (*p* = 0.01; Figure S3F). Notably, VO₂max, EEE, and FO display positive and significant associations with the first coordinate of the gut microbiota composition's PCoA (*p* < 0.05; Figure 1G). Together, these findings support the theory that aerobic exercise capacity is associated with gut microbiota composition independent of food or relative nutrient intake.

### Gut microbiota composition associated with high aerobic exercise capacity is also related to high fecal propionate and valerate content

Fecal metabolites, particularly SCFAs, are the final products of the microbial activities within the gut. Here, we report a significant
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Aerobic exercise capacity is associated with gut microbiota communities independent of food and nutrient intake</figcaption></figure>

(A–D) Experimental design (A), EEE in relation to $VO_{2max}$ (B), fat mass relative to FO during submaximal exercise (C), and PCoA of human gut microbiota composition (D). Each participant is colored according to the *Bacteroides/Prevotella* ratio, with purple points representing participants with the highest abundance of *Prevotella* species and green points representing those with the highest abundance of *Bacteroides* species. Bar plots show the strongest correlations (Spearman's *r*) between species and PCoA coordinates.

(E) Ranking of participants based on their coordinates on PCo1 (1 line = 1 participant, right).

(F) Heatmap of relative macronutrient intake, aerobic exercise capacity traits, body composition, fecal SCFA content, and gut microbiota composition diversity (1 line = 1 participant). The width of each point is normalized by the intra-variable maximum.

(G) 95% CI of the Spearman's *r* correlation between variables with the participant coordinates in PCo1 (dark purple, blue, gray, orange, and green) and the coordinates of the participants in PCo2 (light purple, blue, gray, orange, and green). The coordinates were estimated using PCoA (D).

(H) Correlations between exercise traits, gut microbiota diversity, density, and fecal SCFA content.

Statistical significance of correlations: \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001.

association of 15% between fecal SFCA content and gut microbiota composition (ρ = 0.01; Figure S3G) and a significant association between fecal SFCA content and aerobic exercise capacity (Figure 1H). Notably, fecal valerate and propionate content exhibit the strongest association with the first coordinate of the gut microbiota composition PCoA (Figure 1G), mainly driven by *P. copri* abundance (Figure 1D). A low *Bacteroides/Prevotella* ratio is known to favor SCFA production from various complex

polysaccharide sources,²⁴ with the *Prevotella* genus generally associated with a non-western diet, where starch fermentation leads to an increase in fecal SCFA content.¹²,²⁵ Here, we observe, within the EXOMIC cohort, that the proportion of starch intake is not associated with fecal valerate and propionate content (Figure S4), suggesting that the host's aerobic exercise capacity could impact fecal SCFA production independent of fiber intake. Interestingly, *Phascolarctobacterium succinatutens*, well known
to transform succinate into propionate,²⁶ is the second main bacterium associated with the first coordinate of the gut microbiota composition PCoA (Figure 1D). While P. copri appears to be a succinate producer rather than a propionate producer,²⁷ it seems consistent that both fecal propionate content and these species are associated with the first coordinate of the gut microbiota composition PCoA. These associations between (1) high aerobic exercise capacity traits, (2) gut microbiota composition dominated by P. copri and P. succinatutens, and (3) high fecal propionate content are consistent with previous studies supporting the theory that propionate supplementation improves endurance capacity in mice²⁸ as well as resting FO and energy expenditure in fasted humans.²⁹

**High aerobic exercise capacity is negatively associated with gut microbiota α diversity, bacterial density, and gut microbiota functional abundance**

Over the past decade, both observational and longitudinal clinical studies in humans with low to moderate aerobic exercise capacity have demonstrated a positive correlation between increases in or higher basal levels of V̇O₂max and gut microbiota α diversity.³⁰,³¹ This study seeks to refine this understanding by examining individuals with very high aerobic exercise capacity, where data remains relatively sparse and inconsistencies persist across studies. Here, our data support the theory that gut microbiota α diversity (Shannon) would ultimately follow an inverted-U-shape relationship with V̇O₂max (Figure 2A). In the EXOMIC cohort, individuals having an aerobic exercise capacity between 38 and 66 mL/min/kg aligns with the widely accepted concept that higher aerobic exercise capacity is generally associated with a higher bacterial α diversity and fecal SCFA content (quantiles 1–3; Figure 2A). However, our data also support the theory that a decrease in α diversity (Shannon) occurs among individuals with very high aerobic exercise capacity. Indeed, hosts with the highest V̇O₂max (i.e., belonging to the last quantile with values higher than 66 mL/min/kg) show the lowest gut bacterial α diversity, bacterial density, and functional richness (Figures 2A–2C) while they exhibit fecal SCFA levels comparable to hosts with V̇O₂max between 47 and 66 mL/min/kg (Figures 2D–2F). Additionally, a negative relationship between FO and gut bacterial α diversity (Shannon) is also reported (Figures S5A–S5C), while hosts with the highest FO rates exhibit elevated fecal SCFA content (Figures S5D–S5F). In accordance with these data, we also observe that individuals engaging in more than 9 h of moderate-intensity activity exhibit reduced gut microbiota α diversity (Shannon: Figure S6B), whereas time spent on high-intensity activity shows no association with α diversity. We finally observe that substantial fecal metabolites (i.e., amino acids and bile acids) exhibit a trend to be negatively associated with gut microbiota richness (Figure S7A). All of these results appear paradoxical, since bacterial density is generally considered an indirect indicator of the gut microbiota's capacity to produce metabolites,³² also linked to an improvement in aerobic exercise capacity.³³

To refine our analysis, we explored gut microbiota functionality by using the HuMaNn3 pipeline.²² To this purpose, the normalized count data (counts per million) have been aggregated into corresponding pathways or super-pathways based

on their MetaCyc classifications. We observe that the ability of hosts to consume energy during maximal exercise is inversely proportional to the richness of gut microbiota functionality (*p* < 0.05; Figure 2C). Specifically, several classes of metabolic pathways, including fermentation and carbohydrates degradation, show a negative association with the hosts' aerobic exercise capacity traits, especially V̇O₂max (Figure 2G). We then calculated the ratio between gut microbiota capacity to produce energy from available nutrients (i.e., abundance of pathways involved in the generation of precursor metabolites and energy) and its capacity to degrade substrates to serve as sources of nutrients to their own growth (i.e., degradation/utilization/assimilation pathway abundance). Interestingly, this ratio is positively associated with the host's ability to consume energy during maximal exercise (i.e., V̇O₂max, r = 0.36, *p* = 0.01; Figure 2H). Notably, the ratio of glycolysis and fermentation pathways (i.e., pyruvate producers and fermenters) to carbohydrate degradation pathways (i.e., degradation, storage, and utilization of CHO essential for bacterial growth) is also positively associated with V̇O₂max (r = 0.34, *p* = 0.02; Figure 2I). We found the same relation with the FO (Figures S5G and S5H). Together, these findings support the theory that hosts with very high aerobic exercise capacity traits exhibit a reduction in gut functionality richness, but this reduction seems to be associated with remodeling of the gut bacterial ecosystem and its metabolic pathways, favoring bacterial energy production from available nutrients over the production of energy from complex molecules (e.g., starch and fiber).

**FMT from humans with high to very high aerobic exercise capacity elicits a distinct gut microbiota composition in transplant recipient mice**

To evaluate whether the atypical gut bacterial ecosystem observed in humans with very high aerobic exercise capacity could directly affect the host's energy metabolism and endurance capacity, we transplanted gut microbiota from humans with high to very high aerobic exercise capacity into ATB-pre-treated mice (Figure 3A). We selected 8 physically active and non-sedentary donors exhibiting distinct aerobic exercise capacity traits (Table S2). Each mouse received fecal microbiota from a unique donor (3 mice per donor) during 3 consecutive days and then once per week during the next 2 weeks (Figure 3A). The fecal bacterial density, species engraftment rates, and Bray-Curtis similarity between human donors and transplant recipient mice post FMT, as well as the donor's gut microbiota relative abundance fractions reported post FMT, are in accordance with current references to consider successful FMT engraftment in humans,³⁴,³⁵ thus supporting the effectiveness of FMT from human donors to transplanted mice in our experiments (Figure S8). We next investigated the link between the traits of a donor's aerobic exercise capacity, related to a specific gut microbiota, with the gut microbiota composition expressed in the transplant recipient mice by using the CAP method. We show that V̇O₂max, FO, and EEE are associated with 17% of the variability of the gut microbiota compositions of transplant recipient mice (Figure 3B; *p* < 0.01). The second axis of the canonical analysis (CAP2) refers to the mouse gut microbiota composition explained by FO and EEE of the human donors (Figure 3B). Here,
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 2.** Metagenomic and metabolomic data support selective pressure between gut microbiota and host energy metabolism</figcaption></figure>

Colors indicate α diversity (Shannon, green), bacterial density (blue), and functional richness (red).

(A–C) Local regression between VO₂max and gut microbiota α diversity, bacterial density, and functionality abundance. The dashed line and associated boxplots show gut microbiota α diversity, bacterial density, and functionality abundance by VO₂max quantiles (Kruskal-Wallis followed by Dunn's test).

(D–F) Local regression between VO₂max and total fecal SCFA, propionate, and valerate content. The dashed line and associated boxplots show total fecal SCFA, propionate, and valerate content by VO₂max quantiles (Kruskal-Wallis followed by Dunn's test).

(G) Spearman's r correlations between gut functional pathways with aerobic exercise capacity traits and SCFAs.

(H and I) Local regression for energy metabolites and CHO pathways with VO₂max. Dark lines represent local regression centers with dashed 95% CIs. The raw data of VO₂max and functional richness respects the normality, while it is not the case for the α diversity, bacterial density and fecal SCFA levels.

Statistical significance of correlations/differences: \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001.

we specifically identified *Parabacteroides merdae* among the bacterial species in transplant recipient mice that are well associated with the CAP2 (Figure 3B). We confirmed that this bacterial species is also associated with aerobic exercise capacity traits in the EXOMIC cohort (Figure S9C). Together, these findings highlight that atypical gut microbiota compositions are transferred from humans to transplanted mice depending on the aerobic exercise capacity traits of the donors.

**The gut bacterial ecosystem from human donors affects insulin sensitivity and muscle glycogen stores in transplant recipient mice, depending on the donor's aerobic exercise capacity**

Five weeks post FMT, we assessed the running endurance capacity, the homeostasis model assessment of insulin resistance (HOMA-IR) score, and skeletal muscle glycogen content to characterize the insulin sensitivity of each transplant
A

<figure><img src="image_8.png" /></figure>

B

<figure><img src="image_9.png" /></figure>

C

<figure><img src="image_10.png" /></figure>

D

<figure><img src="image_11.png" /></figure>

E

Gut microbiota composition

<figure><img src="image_12.png" /></figure>

F

Heatmap of individual features

<figure><img src="image_13.png" /></figure>

H

<figure><img src="image_14.png" /></figure>

I

<figure><img src="image_15.png" /></figure>

J

<figure><img src="image_16.png" /><figcaption>Figure 3. The gut bacterial ecosystem from human donors affects insulin sensitivity and muscle glycogen stores in mice 5 weeks after transplantation, depending on the donor's aerobic exercise capacity</figcaption></figure>

(A) Experimental FMT design for antibiotic-pretreated mice using human donors with varying aerobic exercise capacity.

(B) Canonical analysis of principal coordinates (CAP) linking mouse gut microbiota and donor exercise traits (blue arrows). Key bacterial species associated with CAP's second axis are shown.

(C) Muscle glycogen content in relation to HOMA-IR.

(D) Abdominal fat mass in relation to HOMA-IR.

(E) Ranking of mice based on their coordinates on CAP2 (1 line = 1 mouse).

(F) Heatmap of metabolic profile, body composition, food intake, and gut microbiota composition diversity (Shannon and richness). The width of each point is normalized by the intra-variable maximum.

(G) 95% CI for correlations with CAP1 (dark) and CAP2 (light).

(H and I) Local regression between gut α diversity (Shannon) and HOMA-IR or muscle glycogen.

(J) Correlations between gut microbiota functional families and metabolic traits, running endurance capacity, and body composition.

Statistical significance of correlations: \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001.

recipient mouse (Table S3). We first observe that HOMA-IR score is negatively correlated with skeletal muscle glycogen content (Pearson's r = -0.71, *p* < 0.001; Figure 3C) and tends to be positively associated with relative abdominal fat mass

(Pearson's r = 0.35, *p* = 0.09; Figure 3D), indicating a consistent metabolic profile in the transplant recipient mice.³⁶

In Figures 3E and 3F, each line symbolizes variables characterizing a transplant recipient mouse. The mouse gut microbiota
compositions are ordered depending on their positions in the second axis of the CAP (i.e., linked to the FO and the EEE of the donors). Figure 3F shows a heatmap of functional and metabolic variables (i.e., running endurance capacity, insulin sensitivity, and body composition) across all transplant recipient mice. We next determined the 95% confidence interval (CI) of the r correlation between the second coordinate of the gut microbiota composition CAP and the physiological variables of the transplanted mice. Here, we show that muscle glycogen content is inversely correlated with the second coordinate of the mouse gut microbiota composition CAP (r = -0.49, *p* < 0.05; Figure 3G), indicating that mice with a gut microbiota composition with negative coordinates in CAP2 exhibit a higher muscle glycogen level compared to those with positive coordinates. Since muscle glycogen content is directly associated with host insulin sensitivity,³⁷ we logically observe that the HOMA-IR score is positively correlated with CAP2 (r = 0.41, *p* < 0.05; Figure 3G), meaning that mice with a gut microbiota composition with negative coordinates in CAP2 exhibit better whole-body insulin sensitivity compared to those with positive coordinates. In parallel to the results, we report positive correlations between HOMA-IR score and gut microbiota α diversity in transplant recipient mice (r = 0.41, *p* = 0.046; Figure 3H), whereas muscle glycogen content is negatively correlated with gut microbiota α diversity (r = -0.36, *p* = 0.08; Figure 3I). These results suggest that the metabolic benefits of gut microbiota transplantation from human donors with very high aerobic exercise capacity do not necessarily depend on the gut microbiota α diversity, confirming the paradox between gut bacterial diversity and aerobic exercise capacity that we observed within the EXOMIC cohort.

We finally explored gut metabolic functionality and its relationship with running endurance capacity, insulin sensitivity, and muscle glycogen content of transplant recipient mice. The functional capacity of the gut was assessed using the same methodology as employed in the EXOMIC cohort. We observe a positive correlation of pyruvate fermentation into acetate and lactate with running endurance capacity, while these pathways are negatively associated with HOMA-IR score (*p* < 0.05; Figure 3J). We also found significant negative correlations between pathways of pyruvate synthesis (glycolysis IV, glycolysis from glucose) and the HOMA-IR score (*p* < 0.05; Figure 3J) and logically positive correlations with muscle glycogen content (*p* < 0.05; Figure 3J). Our results are consistent with previous studies reporting that high levels of the gut functional pathways involved in glycolysis increase the availability of pyruvate,³⁸ which is essential for the functional pathways of fermentation-synthetizing SCFAs from pyruvate degradation.³⁹ All of these findings highlight that transplantation of gut microbiota associated with specific donor characteristics (here, aerobic exercise capacity traits) can significantly remodel gut microbiota composition and exert metabolic effects in transplant recipients.

## DISCUSSION

During the last decade, substantial observational and interventional studies have been conducted to characterize the impact of physical activity on the gut microbiota ecosystem in humans,

providing contradictory results concerning the effects on gut microbiota composition α diversity in men and women.¹⁶,³⁰,⁴⁰,⁴¹ The first main confounding factor to explain such discrepancies is the assessment of aerobic exercise capacity traits, which could be evaluated from an indirect questionnaire,¹⁶ physiological variables,³⁰ or accelerometers.⁴² When diet is taken into account, several variables characterizing food habits or nutrient intake are also generally identified as confounding factors, making it difficult to isolate the effects of exercise capacity on gut microbiota composition.³⁰,⁴⁰ In our prospective clinical study, we rigorously recruited participants with low aerobic exercise capacity traits by quantitatively matching their food intake with that of ECs. Our results support the theory that the differences in gut bacterial ecosystems reported previously in the literature between humans with low to high exercise capacities is largely attributable to food habits rather than physical activity levels and associated aerobic exercise capacity. Overall, our data highlight that, for populations that do not present extreme exercise capacity (i.e., very low or very high), the effects of the host's aerobic exercise capacity on the gut bacterial ecosystem appear to be very modest compared to those of dietary habits.

On the contrary, our results support the theory that the gut bacterial ecosystem is remodeled in individuals with a very high ability to consume energy and who preferentially use fat during exercise. In these athletes, we report a clear reduction of gut microbiota diversity with a bacterial ecosystem dominated by *P. copri*, a bacterium featuring polysaccharide utilization loci and genes encoding carbohydrate utilization enzymes, enabling it to efficiently convert dietary fiber into SCFAs.⁴³ This atypical bacterial ecosystem in athletes is associated with high fecal propionate levels, in accordance with the role of this SCFA in aerobic exercise capacity and FO.²⁸,²⁹ We also report that the ratio between functional pathways characterizing the ability of bacteria to produce energy from available nutrients and those reflecting their capacity to degrade nutrients as substrate sources for growth is higher in humans with a very high ability to consume energy. Together, these findings support the theory that the association between host energy metabolism and the gut bacterial ecosystem could be driven by access to energy resources for the gut bacteria ecosystem, depending on physical activity levels and the host's aerobic exercise capacity. If the energy pressure related to regular physical exercise exerted by the host becomes too high, the gut bacterial ecosystem seems to specialize to cope with this situation, with some collateral implications, including a reduction in bacterial diversity and density.

FMT has been recognized as a therapeutic tool to treat diverse non-communicable diseases, including obesity, diabetes, neuropsychiatric disorders, and inflammatory bowel disease.⁴⁴ One of the current major issues is to determine which clinical or metabolic characteristics the donor must possess to observe clinical and/or metabolic effects in the recipient. Numerous studies have already assessed the impact of FMT from donors with distinct metabolic profiles (i.e., mice, brown bears, and humans), reporting benefits on transplanted mice concerning glucose tolerance, insulin sensitivity, and lipid metabolism.⁴⁵⁻⁴⁷ While aerobic exercise capacity is a clinical hallmark of health status,¹,² it is not yet considered a donor clinical parameter that could induce metabolic benefits in recipients.
However, a few recent studies have highlighted that FMT from exercise-trained donor mice induces an improvement in the metabolic profile of the transplant recipient mice compared to those from sedentary donor mice.⁴⁸,⁴⁹ In the present study, we intended, for the first time, to evaluate the consequences of FMT from human donors with high to very high exercise capacity on both metabolic parameters and exercise capacity in transplant recipient mice. Here, we show that the donors' aerobic exercise capacity traits partly explain the variability in gut microbiota composition in mice, supporting a causal inference between gut microbiota remodeling by aerobic exercise capacity in humans and gut microbiota composition in transplant recipient mice. We observe that donors with very high aerobic exercise capacity promote, in the transplant recipient mice, an overabundance of *Akkermansia muciniphila*, *P. merdae*, and *B. vulgatus*, recognized to induce metabolic benefits for the host.⁵⁰,⁵¹ Interestingly, these bacterial ecosystems are associated with improvements in whole-body insulin sensitivity and in muscle glycogen stores, supporting metabolic benefits associated with the transfer of the gut bacterial ecosystem from donors with very high aerobic exercise capacity. Interestingly, mice exhibiting greater insulin sensitivity and higher muscle glycogen stores display a reduction of α diversity (Shannon) and a higher level of SCFA-producing pathways, further supporting the hypothesis of specialization of the gut bacterial ecosystem in favor of metabolite generation but detrimental to bacterial growth and diversity.

From a therapeutic perspective, we selected human donors with high to very high $VO_{2max}$ and normal weight, constituting typical lean, healthy, very physically active donors. Despite gut microbiota remodeling and metabolic adaptations, the running endurance capacity of transplant recipient mice remained unaffected. Importantly, these mice did not perform regular physical exercise during the 5 weeks of the FMT protocol, and the gut bacterial ecosystem they received was consequently not subjected to the high energy demands related to physical exercise. These results suggest that gut microbiota remodeling from donors with high aerobic exercise capacity alone is not sufficient to improve the running endurance capacity of recipients. Further experiments appear to be necessary to determine whether FMT from donors with high aerobic exercise capacity could potentially enhance performance when the host, whether human or mouse, simultaneously increases energy needs by engaging in regular physical exercise training. Overall, all of these findings open promising research perspectives: (1) to improve the management of the gut microbiota ecosystem of elite athletes and patients performing adapted physical activity for therapeutic purposes and (2) to personalize FMT in patients treated for non-communicable diseases by including aerobic exercise capacity in the clinical criteria for donor selection.

## Limitations of the study

One of the aims of this study was to provide new insights into the gut microbiome of athletes while minimizing confounding factors such as body composition and nutrient or food intake. To achieve this, we recorded the nutrient and food intake of participants, controlling the dietary quality of NoAs based on the dietary patterns of ECs. The higher caloric intake of ESPs and

cyclists remains a confounding factor, but reducing their caloric intake could have led to negative energy balance and undernutrition, considering their daily energy needs for training and competition.

## RESOURCE AVAILABILITY

### Lead contact

Requests for further information, resources, and reagents should be directed to and will be fulfilled by the lead contact, Frédéric Derbré (frédéric.derbre@univ-rennes2.fr).

### Materials availability

This study did not generate new unique reagents.

### Data and code availability

* Raw metagenomics sequencing data have been deposited into the NCBI under accession number PRJNA1115089.

* The code and other data (metabolomics, food intake, and physiological data) used for the computational analyses described in this study are available at GitHub (https://github.com/DJrMartin/Gut-Microbiota-and-Physical-Activity.git).

* Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

## ACKNOWLEDGMENTS

We thank all participants and coaches from Stade Rennais FC and Sojasun Espoir for their committed efforts in participating in the present study. F.D. acknowledges fundings from the Brittany Council (N°21004090), the Société Française de Nutrition (SFN), and Nahibu. V.M. and F.D. acknowledge funding from the University of Rennes. D.M., F.D., and V.M. acknowledge funding from the ANR within the framework of the PIA EUR DIGISPORT project (ANR-18-EURE-0022).

## AUTHOR CONTRIBUTIONS

Conceptualization and methodology, D.M., M.B., A.C., V.M., and F.D.; data curation, D.M., M.B., M. Horeau, L.O., R.D., M. Hazon, E.L., R.B., A.G., P.G., M.C., and F.D.; formal analysis and investigation, D.M., M.B., M. Horeau, A.C., V.M., and F.D.; statistical analysis, D.M., A.C., and V.M.; interpretation of data, D.M., M.B., V.M., and F.D.; writing – original draft, D.M.; writing – review & editing, V.M. and F.D.; funding acquisition, P.C., C.C., V.M., and F.D.; project administration, V.M. and F.D.; supervision, V.M. and F.D. D.M., F.D., and V.M. had unrestricted access to all data. All authors agreed to submit the manuscript, read and approved the final draft, and take full responsibility for its content.

## DECLARATION OF INTERESTS

M.H., P.C., and C.C. are employees and shareholders of Nahibu.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* KEY RESOURCES TABLE

* EXPERIMENTAL MODEL AND SUBJECT DETAILS

* Human clinical study

* Mouse experiments

* METHOD DETAILS

* Human clinical study

* Mouse experiments

* Metagenomics data

* Metabolomic data

* QUANTIFICATION AND STATISTICAL ANALYSIS
* Data normalization

* Exploratory methods on bacterial ecosystem

* Interpretive methods on bacterial ecosystem

* ADDITIONAL RESOURCES

## SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2025.115448.

Received: October 17, 2024

Revised: January 13, 2025

Accepted: February 28, 2025

Published: March 27, 2025

## REFERENCES

1. Myers, J., Prakash, M., Froelicher, V., Do, D., Partington, S., and Atwood, J.E. (2002). Exercise Capacity and Mortality among Men Referred for Exercise Testing. N. Engl. J. Med. 346, 793–801. https://doi.org/10.1056/NEJMoa011858.

2. Mandsager, K., Harb, S., Cremer, P., Phelan, D., Nissen, S.E., and Jaber, W. (2018). Association of Cardiorespiratory Fitness With Long-term Mortality Among Adults Undergoing Exercise Treadmill Testing. JAMA Netw. Open 1, e183605. https://doi.org/10.1001/jamanetworkopen.2018.3605.

3. Goldstein, R.E. (1990). Exercise Capacity. In Clinical Methods: The History, Physical, and Laboratory Examinations, H.K. Walker, W.D. Hall, and J.W. Hurst, eds. (Butterworths).

4. Goodpaster, B.H., and Sparks, L.M. (2017). Metabolic Flexibility in Health and Disease. Cell Metab. 25, 1027–1036. https://doi.org/10.1016/j.cmet.2017.04.015.

5. Liu, J., Tan, Y., Cheng, H., Zhang, D., Feng, W., and Peng, C. (2022). Functions of Gut Microbiota Metabolites, Current Status and Future Perspectives. Aging Dis. 13, 1106–1126. https://doi.org/10.14336/AD.2022.0104.

6. Takeuchi, T., Kubota, T., Nakanishi, Y., Tsugawa, H., Suda, W., Kwon, A.T.-J., Yazaki, J., Ikeda, K., Nemoto, S., Mochizuki, Y., et al. (2023). Gut microbial carbohydrate metabolism contributes to insulin resistance. Nature 621, 389–395. https://doi.org/10.1038/s41586-023-06466-x.

7. Dohnalová, L., Lundgren, P., Carty, J.R.E., Goldstein, N., Wenski, S.L., Nanudorn, P., Thiengmag, S., Huang, K.-P., Litichevskiy, L., Descamps, H.C., et al. (2022). A microbiome-dependent gut-brain pathway regulates motivation for exercise. Nature 612, 739–747. https://doi.org/10.1038/s41586-022-05525-z.

8. Nay, K., Jollet, M., Goustard, B., Baati, N., Vernus, B., Pontones, M., Lefeuvre-Orfila, L., Bendavid, C., Rué, O., Mariadassou, M., et al. (2019). Gut bacteria are critical for optimal muscle function: a potential link with glucose homeostasis. Am. J. Physiol. Endocrinol. Metab. 317, E158–E171. https://doi.org/10.1152/ajpendo.00521.2018.

9. Okamoto, T., Morino, K., Ugi, S., Nakagawa, F., Lemecha, M., Ida, S., Ohashi, N., Sato, D., Fujita, Y., and Maegawa, H. (2019). Microbiome potentializes endurance exercise through intestinal acetate production. Am. J. Physiol. Endocrinol. Metab. 316, E956–E966. https://doi.org/10.1152/ajpendo.00510.2018.

10. McNamara, M.P., Cadney, M.D., Castro, A.A., Hillis, D.A., Kallini, K.M., Macbeth, J.C., Schmill, M.P., Schwartz, N.E., Hsiao, A., and Garland, T. (2022). Oral antibiotics reduce voluntary exercise behavior in athletic mice. Behav. Processes 199, 104650. https://doi.org/10.1016/j.beproc.2022.104650.

11. Hawley, J.A. (2020). Microbiota and muscle highway - two way traffic. Nat. Rev. Endocrinol. 16, 71–72. https://doi.org/10.1038/s41574-019-0291-6.

12. den Besten, G., van Eunen, K., Groen, A.K., Venema, K., Reijngoud, D.-J., and Bakker, B.M. (2013). The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J. Lipid Res. 54, 2325–2340. https://doi.org/10.1194/jlr.R036012.

13. Shimizu, H., Masujima, Y., Ushiroda, C., Mizushima, R., Taira, S., Ohue-Kitano, R., and Kimura, I. (2019). Dietary short-chain fatty acid intake improves the hepatic metabolic condition via FFAR3. Sci. Rep. 9, 16574. https://doi.org/10.1038/s41598-019-53242-x.

14. Ismaeel, A., Valentino, T.R., Burke, B., Goh, J., Saliu, T.P., Albathi, F., Owen, A., McCarthy, J.J., and Wen, Y. (2023). Acetate and succinate benefit host muscle energetics as exercise-associated post-biotics. Physiol. Rep. 11, e15848. https://doi.org/10.14814/phy2.15848.

15. Petersen, L.M., Bautista, E.J., Nguyen, H., Hanson, B.M., Chen, L., Lek, S.H., Sodergren, E., and Weinstock, G.M. (2017). Community characteristics of the gut microbiomes of competitive cyclists. Microbiome 5, 98. https://doi.org/10.1186/s40168-017-0320-4.

16. Barton, W., Penney, N.C., Cronin, O., Garcia-Perez, I., Molloy, M.G., Holmes, E., Shanahan, F., Cotter, P.D., and O'Sullivan, O. (2018). The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut 67, 625–633. https://doi.org/10.1136/gutjnl-2016-313627.

17. Allen, J.M., Mailing, L.J., Niemiro, G.M., Moore, R., Cook, M.D., White, B.A., Holscher, H.D., and Woods, J.A. (2018). Exercise Alters Gut Microbiota Composition and Function in Lean and Obese Humans. Med. Sci. Sports Exerc. 50, 747–757. https://doi.org/10.1249/MSS.0000000000001495.

18. Barton, W., Cronin, O., Garcia-Perez, I., Whiston, R., Holmes, E., Woods, T., Molloy, C.B., Molloy, M.G., Shanahan, F., Cotter, P.D., and O'Sullivan, O. (2021). The effects of sustained fitness improvement on the gut microbiome: A longitudinal, repeated measures case-study approach. Transl. Sports Med. 4, 174–192. https://doi.org/10.1002/tsm2.215.

19. Greenhalgh, K., Meyer, K.M., Aagaard, K.M., and Wilmes, P. (2016). The human gut microbiome in health: establishment and resilience of microbiota over a lifetime. Environ. Microbiol. 18, 2103–2116. https://doi.org/10.1111/1462-2920.13318.

20. Zurlo, F., Lillioja, S., Esposito-Del Puente, A., Nyomba, B.L., Raz, I., Saad, M.F., Swinburn, B.A., Knowler, W.C., Bogardus, C., and Ravussin, E. (1990). Low ratio of fat to carbohydrate oxidation as predictor of weight gain: study of 24-h RQ. Am. J. Physiol. 259, E650–E657. https://doi.org/10.1152/ajpendo.1990.259.5.E650.

21. Astrup, A. (2011). The relevance of increased fat oxidation for body-weight management: metabolic inflexibility in the predisposition to weight gain. Obes. Rev. 12, 859–865. https://doi.org/10.1111/j.1467-789X.2011.00894.x.

22. Beghini, F., McIver, L.J., Blanco-Miguez, A., Dubois, L., Asnicar, F., Maharjan, S., Mailyan, A., Manghi, P., Scholz, M., Thomas, A.M., et al. (2021). Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife 10, e65088. https://doi.org/10.7554/eflife.65088.

23. Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D.R., Fernandes, G.R., Tap, J., Bruls, T., Batto, J.-M., et al. (2011). Enterotypes of the human gut microbiome. Nature 473, 174–180. https://doi.org/10.1038/nature09944.

24. Chen, T., Long, W., Zhang, C., Liu, S., Zhao, L., and Hamaker, B.R. (2017). Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota. Sci. Rep. 7, 2594. https://doi.org/10.1038/s41598-017-02995-4.

25. Christensen, L., Vuholm, S., Roager, H.M., Nielsen, D.S., Krych, L., Kristensen, M., Astrup, A., and Hjorth, M.F. (2019). *Prevotella* Abundance Predicts Weight Loss Success in Healthy, Overweight Adults Consuming a Whole-Grain Diet Ad Libitum: A Post Hoc Analysis of a 6-Wk Randomized Controlled Trial. J. Nutr. 149, 2174–2181. https://doi.org/10.1093/jn/nxz198.

26. Watanabe, Y., Nagai, F., and Morotomi, M. (2012). Characterization of Phascolarctobacterium succinatutens sp. nov., an asaccharolytic, succinate-utilizing bacterium isolated from human feces. Appl. Environ. Microbiol. 78, 511–518. https://doi.org/10.1128/AEM.06035-11.
27. De Vadder, F., Kovatcheva-Datchary, P., Zitoun, C., Duchampt, A., Bäckhed, F., and Mithieux, G. (2016). Microbiota-Produced Succinate Improves Glucose Homeostasis via Intestinal Gluconeogenesis. Cell Metab. 24, 151–157. https://doi.org/10.1016/j.cmet.2016.06.013.

28. Scheiman, J., Luber, J.M., Chavkin, T.A., MacDonald, T., Tung, A., Pham, L.-D., Wibowo, M.C., Wurth, R.C., Punthambaker, S., Tierney, B.T., et al. (2019). Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism. Nat. Med. 25, 1104–1109. https://doi.org/10.1038/s41591-019-0485-4.

29. Chambers, E.S., Byrne, C.S., Aspey, K., Chen, Y., Khan, S., Morrison, D.J., and Frost, G. (2018). Acute oral sodium propionate supplementation raises resting energy expenditure and lipid oxidation in fasted humans. Diabetes Obes. Metab. 20, 1034–1039. https://doi.org/10.1111/dom.13159.

30. Estaki, M., Pither, J., Baumeister, P., Little, J.P., Gill, S.K., Ghosh, S., Ahmadi-Vand, Z., Marsden, K.R., and Gibson, D.L. (2016). Cardiorespiratory fitness as a predictor of intestinal microbial diversity and distinct metagenomic functions. Microbiome 4, 42. https://doi.org/10.1186/s40168-016-0189-7.

31. Dupuit, M., Rance, M., Morel, C., Bouillon, P., Boscaro, A., Martin, V., Vazeille, E., Barnich, N., Chassaing, B., and Boisseau, N. (2022). Effect of concurrent training on body composition and gut microbiota in postmenopausal women with overweight or obesity. Med. Sci. Sports Exerc. 54, 517–529.

32. Corbin, K.D., Carnero, E.A., Dirks, B., Igudesman, D., Yi, F., Marcus, A., Davis, T.L., Pratley, R.E., Rittmann, B.E., Krajmalnik-Brown, R., and Smith, S.R. (2023). Reprogramming the Human Gut Microbiome Reduces Dietary Energy Harvest. Preprint at Res. Sq. rs.3.rs-2382790. https://doi.org/10.21203/rs.3.rs-2382790/v1.

33. Akbuğa-Schön, T., Suzuki, T.A., Jakob, D., Vu, D.L., Waters, J.L., and Ley, R.E. (2023). The keystone gut species Christensenella minuta boosts gut microbial biomass and voluntary physical activity in mice. mBio 15, e02836-23. https://doi.org/10.1128/mbio.02836-23.

34. Ianiro, G., Punčochár, M., Karcher, N., Porcari, S., Armanini, F., Asnicar, F., Beghini, F., Blanco-Míguez, A., Cumbo, F., Manghi, P., et al. (2022). Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases. Nat. Med. 28, 1913–1923. https://doi.org/10.1038/s41591-022-01964-3.

35. Podlesny, D., Durdevic, M., Paramsothy, S., Kaakoush, N.O., Högenauer, C., Gorkiewicz, G., Walter, J., and Fricke, W.F. (2022). Identification of clinical and ecological determinants of strain engraftment after fecal microbiota transplantation using metagenomics. Cell Rep. Med. 3, 100711. https://doi.org/10.1016/j.xcrm.2022.100711.

36. Zou, K., Hinkley, J.M., Park, S., Zheng, D., Jones, T.E., Pories, W.J., Hornby, P.J., Lenhard, J., Dohm, G.L., and Houmard, J.A. (2019). Altered tricarboxylic acid cycle flux in primary myotubes from severely obese humans. Int. J. Obes. 43, 895–905. https://doi.org/10.1038/s41366-018-0137-7.

37. Petersen, M.C., and Shulman, G.I. (2018). Mechanisms of Insulin Action and Insulin Resistance. Physiol. Rev. 98, 2133–2223. https://doi.org/10.1152/physrev.00063.2017.

38. Macfarlane, S., and Macfarlane, G.T. (2003). Regulation of short-chain fatty acid production. Proc. Nutr. Soc. 62, 67–72. https://doi.org/10.1016/j.pns2002207.

39. Louis, P., and Flint, H.J. (2017). Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 19, 29–41. https://doi.org/10.1111/1462-2920.13589.

40. Clarke, S.F., Murphy, E.F., O'Sullivan, O., Lucey, A.J., Humphreys, M., Horgan, A., Hayes, P., O'Reilly, M., Jeffery, I.B., Wood-Martin, R., et al. (2014). Exercise and associated dietary extremes impact on gut microbial diversity. Gut 63, 1913–1920. https://doi.org/10.1136/gutjnl-2013-306541.

41. Shah, S., Mu, C., Moossavi, S., Shen-Tu, G., Schlicht, K., Rohmann, N., Geisler, C., Laudes, M., Franke, A., Züllig, T., et al. (2023). Physical activity-induced alterations of the gut microbiota are BMI dependent. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 37, e22882. https://doi.org/10.1096/fj.202201571R.

42. Baldanzi, G., Sayols-Baixeras, S., Ekblom-Bak, E., Ekblom, Ö., Dekkers, K.F., Hammar, U., Nguyen, D., Ahmad, S., Ericson, U., Arvidsson, D., et al. (2024). Accelerometer-based physical activity is associated with the gut microbiota in 8416 individuals in SCAPIS. EBioMedicine 100, 104989. https://doi.org/10.1016/j.ebiom.2024.104989.

43. Accetto, T., and Avgüstin, G. (2015). Polysaccharide utilization locus and CAZYme genome repertoires reveal diverse ecological adaptation of Prevotella species. Syst. Appl. Microbiol. 38, 453–461. https://doi.org/10.1016/j.syapm.2015.07.007.

44. Hanssen, N.M.J., de Vos, W.M., and Nieuwdorp, M. (2021). Fecal microbiota transplantation in human metabolic diseases: From a murky past to a bright future? Cell Metab. 33, 1098–1110. https://doi.org/10.1016/j.cmet.2021.05.005.

45. de Groot, P., Scheithauer, T., Bakker, G.J., Prodan, A., Levin, E., Khan, M.T., Herrema, H., Ackermans, M., Serlie, M.J.M., de Brauw, M., et al. (2020). Donor metabolic characteristics drive effects of faecal microbiota transplantation on recipient insulin sensitivity, energy expenditure and intestinal transit time. Gut 69, 502–512. https://doi.org/10.1136/gutjnl-2019-318320.

46. Kootte, R.S., Levin, E., Salojärvi, J., Smits, L.P., Hartstra, A.V., Udayapan, S.D., Hermes, G., Bouter, K.E., Koopen, A.M., Holst, J.J., et al. (2017). Improvement of Insulin Sensitivity after Lean Donor Feces in Metabolic Syndrome Is Driven by Baseline Intestinal Microbiota Composition. Cell Metab. 26, 611–619.e6. https://doi.org/10.1016/j.cmet.2017.09.008.

47. Sommer, F., Ståhlman, M., Ilkayeva, O., Arnemo, J.M., Kindberg, J., Josefsson, J., Newgard, C.B., Fröbert, O., and Bäckhed, F. (2016). The Gut Microbiota Modulates Energy Metabolism in the Hibernating Brown Bear Ursus arctos. Cell Rep. 14, 1655–1661. https://doi.org/10.1016/j.cel-rep.2016.01.026.

48. Aoi, W., Inoue, R., Mizushima, K., Honda, A., Björnholm, M., Takagi, T., and Naito, Y. (2023). Exercise-acclimated microbiota improves skeletal muscle metabolism via circulating bile acid deconjugation. iScience 26, 106251. https://doi.org/10.1016/j.isci.2023.106251.

49. Lai, Z.-L., Tseng, C.-H., Ho, H.J., Cheung, C.K.Y., Lin, J.-Y., Chen, Y.-J., Cheng, F.-C., Hsu, Y.-C., Lin, J.-T., El-Omar, E.M., and Wu, C.Y. (2018). Fecal microbiota transplantation confers beneficial metabolic effects of diet and exercise on diet-induced obese mice. Sci. Rep. 8, 15625. https://doi.org/10.1038/s41598-018-33893-y.

50. Qiao, S., Liu, C., Sun, L., Wang, T., Dai, H., Wang, K., Bao, L., Li, H., Wang, W., Liu, S.-J., and Liu, H. (2022). Gut Parabacteroides merdae protects against cardiovascular damage by enhancing branched-chain amino acid catabolism. Nat. Metab. 4, 1271–1286. https://doi.org/10.1038/s42255-022-00649-y.

51. Dao, M.C., Everard, A., Aron-Wisnewsky, J., Sokolovska, N., Prifti, E., Verger, E.O., Kayser, B.D., Levenez, F., Chilloux, J., Hoyles, L., et al. (2016). Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut 65, 426–436. https://doi.org/10.1136/gutjnl-2014-308778.

52. Mulligan, A.A., Luben, R.N., Bhaniani, A., Parry-Smith, D.J., O'Connor, L., Khawaja, A.P., Forouhi, N.G., and Khaw, K.-T.; EPIC-Norfolk FFQ Study (2014). A new tool for converting food frequency questionnaire data into nutrient and food group values: FETA research methods and availability. BMJ Open 4, e004503. https://doi.org/10.1136/bmjopen-2013-004503.

53. Cleghorn, C.L., Harrison, R.A., Ransley, J.K., Wilkinson, S., Thomas, J., and Cade, J.E. (2016). Can a dietary quality score derived from a short-form FFQ assess dietary quality in UK adult population surveys? Public Health Nutr. 19, 2915–2923. https://doi.org/10.1017/S1368980016001099.

54. Bull, F.C., Maslin, T.S., and Armstrong, T. (2009). Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J. Phys. Act. Health 6, 790–804. https://doi.org/10.1123/jphs.6.6.790.
<figure><img src="image_17.png" /></figure>

55. Durnin, J.V., and Womersley, J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br. J. Nutr. 32, 77-97. https://doi.org/10.1079/bjn19740060.

56. Frayn, K.N. (1983). Calculation of substrate oxidation rates in vivo from gaseous exchange. J. Appl. Physiol. 55, 628-634. https://doi.org/10.1152/jappl.1983.55.2.628.

57. Matthews, D.R., Hosker, J.P., Rudenski, A.S., Naylor, B.A., Treacher, D.F., and Turner, R.C. (1985). Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412-419. https://doi.org/10.1007/BF00280883.

58. Adamo, K.B., and Graham, T.E. (1998). Comparison of traditional measurements with macroglycogen and proglycogen analysis of muscle glycogen. J. Appl. Physiol. 84, 908-913. https://doi.org/10.1152/jappl.1998.84.3.908.

59. Cuffaro, B., Assohoun, A.L.W., Boutillier, D., Peucelle, V., Desramaut, J., Boudebbouze, S., Croyal, M., Waligora-Dupriet, A.-J., Rhimi, M., Grangette, C., and Maguin, E. (2021). Identification of New Potential Biotherapeutics from Human Gut Microbiota-Derived Bacteria. Microorganisms 9, 565. https://doi.org/10.3390/microorganisms9030565.

60. Wargny, M., Croyal, M., Ragot, S., Gand, E., Jacobi, D., Trochu, J.N., Prieur, X., Le May, C., Goronflot, T., Cariou, B., et al. (2022). Nutritional biomarkers and heart failure requiring hospitalization in patients with type 2 diabetes: the SURDIAGENE cohort. Cardiovasc. Diabetol. 21, 101. https://doi.org/10.1186/s12933-022-01505-9.

61. Brochard, C., Bouguen, G., Olivier, R., Durand, T., Henno, S., Peyronnet, B., Pagenault, M., Lefèvre, C., Boudry, G., Croyal, M., et al. (2022). Altered epithelial barrier functions in the colon of patients with spina bifida. Sci. Rep. 12, 7196. https://doi.org/10.1038/s41598-022-11289-3.

62. Borcard, D., Gillet, F., and Legendre, P. (2018). Numerical Ecology with R (Springer International Publishing). https://doi.org/10.1007/978-3-319-71404-2.

63. Xia, Y., Sun, J., and Chen, D.-G. (2018). Statistical Analysis of Microbiome Data with R (Springer). https://doi.org/10.1007/978-981-13-1534-3.

64. Anderson, M.J., and Willis, T.J. (2003). Canonical Analysis of Principal Coordinates: A Useful Method of Constrained Ordination for Ecology. Ecology 84, 511-525. https://doi.org/10.1890/0012-9658(2003)084[0511:CAO PCA]2.0.CO;2.
## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3">Chemicals, peptides, and recombinant proteins</td></tr><tr><td>GOD-PAP solution</td><td>Bio-labo, Maizy, France</td><td>Ref. 87409</td></tr><tr><td>SYBR® Green Supermix</td><td>Bio-Rad Laboratories, Inc</td><td>#1708880</td></tr><tr><td>Ampicilin Sodium salt</td><td>Sigma Aldrich</td><td>A9518-256</td></tr><tr><td>Colistin Sulfate</td><td>Chem-Impex INT'L INC</td><td>Cat #00068 (25G)</td></tr><tr><td>Streptomycin Sulfate salt</td><td>Sigma-Aldrich</td><td>S6501-50G</td></tr><tr><td>Vancomycin hydrochloride</td><td>Chem-Impex INT'L INC</td><td>Cat #0035 (5G)</td></tr><tr><td>Dextril-maltose</td><td>MP Biomedicals</td><td>Cat #960048</td></tr><tr><td>D-(+)-Trehalose dihydrate</td><td>Sigma-Aldrich</td><td>90210-50G</td></tr><tr><td>Perchloric Acid</td><td>Fisher Chemical</td><td>A511-P500</td></tr><tr><td>Hydrochloric Acid</td><td>Fisher Chemical</td><td>H/1200/PB15</td></tr><tr><td>Lists of Chemicals reagents for the Metabolomic detection in ANNEX</td><td></td><td>N/A</td></tr><tr><td>Hydrochloric acid 37%</td><td>Sigma-Aldrich</td><td>258148</td></tr><tr><td>Diethylether</td><td>Biosolve</td><td>05280501</td></tr><tr><td>Sodium hydroxide</td><td>Merck</td><td>6482</td></tr><tr><td>Methanol</td><td>Biosolve</td><td>0013684102BS</td></tr><tr><td>Distilled water</td><td>Dutscher</td><td>069811</td></tr><tr><td>Acetonitrile</td><td>Biosolve</td><td>0001204102BS</td></tr><tr><td>Ethyl acetate</td><td>Biosolve</td><td>0005400602BS</td></tr><tr><td>Formic acid 99%</td><td>Biosolve</td><td>00069141A8BS</td></tr><tr><td>Ultrapure water</td><td>Biosolve</td><td>0023214102BS</td></tr><tr><td>Ammonium formate</td><td>Biosolve</td><td>0001984153BS</td></tr><tr><td>Isopropanol</td><td>Biosolve</td><td>0016264102BS</td></tr><tr><td>1-(tert-butyldimethylsilyl)imidazole</td><td>Sigma-Aldrich</td><td>250236</td></tr><tr><td>Tris(2-carboxyethyl)phosphine hydrochloride</td><td>Sigma-Aldrich</td><td>646547</td></tr><tr><td>Acetyle chloride</td><td>Eluka</td><td>00990</td></tr><tr><td>Butanol</td><td>Sigma-Aldrich</td><td>B7906</td></tr><tr><td>Acetate</td><td>Sigma-Aldrich</td><td>33209</td></tr><tr><td>Butyrate sodium salt</td><td>Sigma-Aldrich</td><td>B5887</td></tr><tr><td>Isobutyrate</td><td>Sigma-Aldrich</td><td>I1754</td></tr><tr><td>Propionate sodium salt</td><td>Sigma-Aldrich</td><td>P1880</td></tr><tr><td>Valerate</td><td>Sigma-Aldrich</td><td>75054</td></tr><tr><td><sup>2</sup>H<sub>3</sub>-acetate</td><td>Sigma-Aldrich</td><td>17.607-9</td></tr><tr><td><sup>13</sup>C-butyrate</td><td>Sigma-Aldrich</td><td>29.265-6</td></tr><tr><td><sup>2</sup>H<sub>5</sub>-propionate</td><td>Isotec</td><td>615749</td></tr><tr><td><sup>2</sup>H<sub>9</sub>-valerate</td><td>Sigma-Aldrich</td><td>493201</td></tr><tr><td>Cholic acid</td><td>Sigma-Aldrich</td><td>700212P</td></tr><tr><td>Desoxycholic acid</td><td>Sigma-Aldrich</td><td>D2510</td></tr><tr><td>Chenodesoxycholic acid</td><td>Sigma-Aldrich</td><td>700198P</td></tr><tr><td>Ursodesoxycholic acid</td><td>Sigma-Aldrich</td><td>700199P</td></tr><tr><td>Hyodesoxycholic acid</td><td>Sigma-Aldrich</td><td>700214P</td></tr><tr><td>Lithocholic acid</td><td>Sigma-Aldrich</td><td>700218P</td></tr><tr><td><sup>2</sup>H<sub>4</sub>-cholic acid</td><td>Sigma-Aldrich</td><td>614149</td></tr><tr><td>Amino acid standard</td><td>Sigma-Aldrich</td><td>AAS18</td></tr></tbody></table>

(Continued on next page)
Continued

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td>L-asparagine</td><td>Sigma-Aldrich</td><td>A0884</td></tr><tr><td>L-glutamine</td><td>Sigma-Aldrich</td><td>G3126</td></tr><tr><td>L-tryptophane</td><td>Sigma-Aldrich</td><td>93659</td></tr><tr><td>5-aminovaleric acid</td><td>Sigma-Aldrich</td><td>123188</td></tr><tr><td>Anthranilic acid</td><td>Sigma-Aldrich</td><td>A89855</td></tr><tr><td>5-HIAA</td><td>Sigma-Aldrich</td><td>H8876</td></tr><tr><td>Kynurenic acid</td><td>Sigma-Aldrich</td><td>K3375</td></tr><tr><td>Quinolinic</td><td>Sigma-Aldrich</td><td>P63204</td></tr><tr><td>Xanthurenic acid</td><td>Sigma-Aldrich</td><td>D120804</td></tr><tr><td>ADMA (symmetric dimethylarginine) dihydrochloride</td><td>Sigma-Aldrich</td><td>D4268</td></tr><tr><td>L-citrulline</td><td>Sigma-Aldrich</td><td>C7629</td></tr><tr><td>DL-cystathionine</td><td>Sigma-Aldrich</td><td>C3633</td></tr><tr><td>Cysteine-ylglycine</td><td>Bachem</td><td>G3755</td></tr><tr><td>Dimethylglycine</td><td>Sigma-Aldrich</td><td>D1156</td></tr><tr><td>Homoarginine hydrochloride</td><td>Sigma-Aldrich</td><td>H1007</td></tr><tr><td>DL-Homocysteine</td><td>Sigma-Aldrich</td><td>44925</td></tr><tr><td>3-hydroxy-DL-kynurenine</td><td>Sigma-Aldrich</td><td>H1771</td></tr><tr><td>5-hydroxy-L-tryptophan</td><td>Sigma-Aldrich</td><td>H9772</td></tr><tr><td>L-kynurenine</td><td>Sigma-Aldrich</td><td>K8625</td></tr><tr><td>NMMA (N-monomethylarginine) acetate</td><td>Sigma-Aldrich</td><td>M7033</td></tr><tr><td>L-ornithine monohydrochloride</td><td>Sigma-Aldrich</td><td>O2375</td></tr><tr><td>Sarcosine</td><td>Sigma-Aldrich</td><td>131776</td></tr><tr><td>SDMA (asymmetric dimethylarginine) sulfonate</td><td>Sigma-Aldrich</td><td>D0390</td></tr><tr><td><sup>15</sup>N-aspartate</td><td>Sigma-Aldrich</td><td>332135</td></tr><tr><td><sup>15</sup>N-glutamate</td><td>Cambridge Isotope Lab</td><td>NLM-135-SP</td></tr><tr><td><sup>13</sup>C-alanine</td><td>Cambridge Isotope Lab</td><td>CLM-116.1</td></tr><tr><td><sup>13</sup>C-arginine</td><td>MassTRAC INC</td><td>NA</td></tr><tr><td><sup>15</sup>N<sub>2</sub>-asparagine</td><td>Sigma-Aldrich</td><td>641960</td></tr><tr><td><sup>2</sup>H<sub>3</sub>-cysteine</td><td>Sigma-Aldrich</td><td>701424</td></tr><tr><td><sup>15</sup>N-glutamine</td><td>Sigma-Aldrich</td><td>48680-9</td></tr><tr><td><sup>13</sup>C-glycine</td><td>Isotec</td><td>279420</td></tr><tr><td><sup>15</sup>N<sub>3</sub>-histidine</td><td>Sigma-Aldrich</td><td>574368</td></tr><tr><td><sup>15</sup>N-leucine</td><td>Tracer Technologies, INC</td><td>DMB-II-83</td></tr><tr><td><sup>13</sup>C<sub>6</sub>-lysine</td><td>Cortecnet</td><td>CC1720P025</td></tr><tr><td><sup>13</sup>C, <sup>2</sup>H<sub>3</sub>-methionine</td><td>Sigma-Aldrich</td><td>299154</td></tr><tr><td><sup>2</sup>H<sub>3</sub>-phenylalanine</td><td>Sigma-Aldrich</td><td>615870</td></tr><tr><td><sup>13</sup>C<sub>4</sub>-proline</td><td>Sigma-Aldrich</td><td>604933</td></tr><tr><td><sup>15</sup>N-serine</td><td>Sigma-Aldrich</td><td>609005</td></tr><tr><td><sup>15</sup>N-threonine</td><td>Sigma-Aldrich</td><td>609099</td></tr><tr><td><sup>2</sup>H<sub>5</sub>-tryptophan</td><td>Sigma-Aldrich</td><td>615862</td></tr><tr><td><sup>15</sup>N-tyrosine</td><td>Cambridge Isotope Lab</td><td>NLM-590</td></tr><tr><td><sup>13</sup>C-valine</td><td>Eurisotop</td><td>C102PS</td></tr><tr><td><sup>13</sup>C<sub>6</sub>- anthranilic acid</td><td>Sigma-Aldrich</td><td>709530</td></tr><tr><td><sup>2</sup>H<sub>5</sub>-kynurenic acid</td><td>Sigma-Aldrich</td><td>793477</td></tr><tr><td><sup>2</sup>H<sub>7</sub>-ADMA hydrochloride</td><td>Cambridge Isotope Lab</td><td>DLM-7476</td></tr><tr><td><sup>2</sup>H<sub>7</sub>-L-citrulline</td><td>CDN Isotopes</td><td>D-7306</td></tr><tr><td><sup>2</sup>H<sub>6</sub>- dimethylglycine hydrochloride</td><td>Sigma-Aldrich</td><td>592471</td></tr></tbody></table>

(Continued on next page)
Continued

<table><thead><tr><td>REAGENT or RESOURCE</td><td>SOURCE</td><td>IDENTIFIER</td></tr></thead><tbody><tr><td><sup>13</sup>C<sub>3</sub>, <sup>15</sup>N-3-hydroxy-DL-kynurenine</td><td>Sigma-Aldrich</td><td>903450</td></tr><tr><td><sup>13</sup>C<sub>5</sub>-L-ornithine hydrochloride</td><td>Sigma-Aldrich</td><td>736147</td></tr></tbody></table>

Critical commercial assays

<table><tr><td>Quick-DNA Fecal/Soil Microbe Mini Prep Kit</td><td>ZymoResearch</td><td>Cat #D6010</td></tr><tr><td>Rat/Mouse Insulin ELISA</td><td>Merk Millipore</td><td>Cat #EZRMI-13K</td></tr></table>

Deposited data

<table><tr><td>Metagenomics sequencing data</td><td>This paper</td><td>NCBI: PRJNA1115089</td></tr><tr><td>Other Raw data (Metabolomic data, Food Intakes, Nutrient Intakes and Physiological data). Raw Data of Transfected Mice.</td><td>This paper</td><td>https://github.com/DJrMartin/Gut-Microbiota-and-Physical-Activity.git</td></tr></table>

Experimental models: Organisms/strains

<table><tr><td>Homo Sapiens</td><td>Bretagne, France</td><td>Taxon ID: 9606</td></tr><tr><td>Mouse: WT: C57BL/6J male mice</td><td>Janvier labs, France</td><td>Taxon ID: 10090, RRID:MGI:3028467</td></tr></table>

Oligonucleotides

<table><tr><td>F-bact1369/5'-CGGTGAATACGTTCCCGG-3'</td><td></td><td>N/A</td></tr><tr><td>R-prok1492/5'-TACGGCTACCTTGTTACGACTT-3'</td><td></td><td>N/A</td></tr></table>

Software and algorithms

<table><tr><td>MetaPhLan 3</td><td>Beghini et al., 2021<sup>22</sup></td><td>RRID:SCR_004915</td></tr><tr><td>HuManN3</td><td>Beghini et al., 2021<sup>22</sup></td><td>N/A</td></tr><tr><td>ape R package</td><td>https://cran.r-project.org/web/packages/ape/ape.pdf</td><td>N/A</td></tr><tr><td>vegan R package</td><td>https://cran.r-project.org/web/packages/vegan/vegan.pdf</td><td>N/A</td></tr><tr><td>FETA software: Food Frequency Questionary</td><td>Mulligan et al., 2014<sup>52</sup></td><td>N/A</td></tr><tr><td>Short Form Food Frequency Questionary</td><td>Cleghorn et al., 2016<sup>53</sup></td><td>N/A</td></tr><tr><td>Skanlt Software for microplate readers</td><td>ThermoFisher</td><td>Cat #5187139</td></tr></table>

Others

<table><tr><td>CFX Real-Time detection system</td><td>Bio-Rad Laboratories, Inc</td><td>1855201, RRID:SCR_018064</td></tr><tr><td>ECG Custo 300 BT</td><td>Custo-Med</td><td>COR_CUSTO300</td></tr><tr><td>Freestyle Papillon Vision</td><td>Abbott</td><td>3401097918655</td></tr><tr><td>Global Physical activity Questionnaire</td><td>Bull, Maslin, et Armstrong 2009</td><td>https://www.who.int/publications/m/item/global-physical-activity-questionnaire</td></tr><tr><td>Lactate Pro 2</td><td>Arkray</td><td>10010718</td></tr><tr><td>Mice Food - S8189-S095</td><td>Janvier Labs</td><td>MP-AL-S8189-S095</td></tr><tr><td>Ultima CardiO</td><td>Medgraphics</td><td>https://mgcdiagnostics.com/fr/produits/ultima-ccm-calorimetre-indirect</td></tr><tr><td>Skinfold Caliber Body Fat</td><td>Harpenden® forceps</td><td>98002-00</td></tr><tr><td>Treadmill - Interchangeable Lane</td><td>Ugo Basile®</td><td>47300/47350</td></tr><tr><td>Assembly for Mice and Rat</td><td></td><td></td></tr><tr><td>Varioskan™ LUX multimode microplate reader</td><td>ThermoFisher</td><td>15340757</td></tr></table>

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Human clinical study

The EXOMIC study is a pilot clinical study that took place at the laboratory “Movement, Sport and health Sciences” (M2S), Rennes, France in 2022. 50 male participants with low to very high aerobic exercise capacity were recruited from an elite cyclist team competing at the highest French national level (Elite cyclists, n = 14), the academic structure of Stade Rennais FC, competing in the highest French national league (elite soccer players, n = 15), and the student community of Rennes (Non-athletes, n = 21). We selected elite soccer players and cyclists based on the nature of their training loads. Cyclists primarily engage in continuous moderate-intensity exercise, whereas soccer players predominantly perform intermittent high-intensity physical activities. Cyclists generally achieve higher VO₂max and fat oxidation rates during moderate-intensity exercise compared to soccer players, reflecting the
endurance-oriented nature of their sport. Due to the intermittent demands of the activity, soccer players generally exhibit distinct metabolic profile, with a higher proportion of individuals showing high carbohydrate oxidation rates during moderate-intensity exercise. Non-athlete participants were recruited from our local student community to fit with the food intakes of elite cyclists. A preselection pipeline was designed to identify non-athlete volunteers whose food and nutrient intakes aligned with those of the elite cyclists group. Dietary intake data was collected using a Short Form Food Frequency Questionnaire (FFQ), previously validated by Cleghorn and colleagues.⁵³ Additionally, the weekly physical activity levels of potential participants were recorded. The goal was to recruit 10 participants below the WHO's physical activity recommendations, and 10 above these recommendations (≥ 600 MET-min/week). The preselection process involved three main steps: (1) Latent Space Projection: Volunteers were mapped into a latent space (via Principal Coordinates Analysis) based on the absolute food intake compositions of cyclist groups. We visually checked that the volunteer was more or less close to the center of the elite cyclists group. (2) Nutrient Intake Matching: The absolute nutrient intakes of the first 13 cyclists in the cohort were estimated. Non-athlete participants were included if their macronutrient intakes fell within the mean ±2 standard deviations of the cyclists' macronutrient intakes. (3) Activity Profiling: Volunteers' physical activity levels were analyzed to compare with the WHO's physical activity recommendations. The preselection algorithm is available in the Github associated with this article: https://github.com/DJrMartin/Gut-Microbiota-and-Physical-Activity/blob/main/Preselect_pipeline.html. Inclusion criteria were being healthy, between 18 and 30 years of age, BMI (in kg/m2) between 18 and 25, non-smoker and covered by medical insurance (more details on inclusion and exclusion criteria are available on ClinicalTrials.gov: NCT05220657). Each participant carried out three visits at the M2S lab: (1) a medical inclusion visit (2) a visit to perform an incremental maximal exercise test and (3) a last visit to perform a submaximal exercise test in fasted state. All participants gave their written informed consent. For each participant, the three visits were performed during a period of 15 days or less.

## Mouse experiments

C57BL/6J male mice (8-week-old; Janvier Labs, France) were randomly divided into the following two experimental groups: control group (CTL, *n* = 6) versus mice transplanted (*n* = 12) by heterogeneous human donors (*n* = 4). One donor has been used for three mice. Mice were maintained on a 12h/12-h dark-light cycle in a temperature-controlled room in individual cages with filter lids and had ad libitum access to food and water. The diet (MP-AL-S8189-S095, Janvier Labs) was standardized and identical for all groups. Except for running tests, mice were housed in individual standard cages without wheels, and were not subjected to daily exercise. Two independent campaigns of experiments were conducted to obtain a large sample size (n CTL mice = 12; n transplanted mice = 24; n Donors = 8). These animal experiments received approval from the Rennes committee on Ethics in Research (authorization APAFIS #39632-2022120109328170 v5) in accordance with the European directives (86/609/European Economic Community).

## METHOD DETAILS

### Human clinical study

#### Medical inclusion visit (visit n° 1) and fecal samples collection

The first visit in the M2S lab consisted in a one-to-one interview including a medical exam with a physician. Each participant completed the food frequency questionnaire to estimate daily food and nutrient intakes (FFQ).⁵² Daily energy expenditure was also calculated on a weekly basis (MET-minutes per week) using the Global Physical Activity Questionnaire (GPAQ).⁵⁴ Body Mass Index (BMI) was calculated as body mass in kilograms divided by height in square meters. Percentage body fat (%FM) was measured by skinfold measurement using Harpenden forceps based on the 4-skinfolds method of Durnin and Womersley.⁵⁵ Body density (BD) was calculated using the same authors' equation based on skinfold measurements and the subject's age: $BD = C - [M (Log10 Σ (Skinfolds))], where C and M are coefficients varying according to sex and age.⁵⁵ The %FM was then calculated from BD using Siri's equation: %FM = 495/BD - 450. One fecal sample was collected for each participant during the 15 days following the medical inclusion, before the last visit to the laboratory. Each participant was required to collect a fecal sample after at least 24 h without engaging in strenuous exercise, training, or testing sessions. These samples were briefly stored by participants in a DNA blocker buffer for a maximum of one day before being frozen at -80°C in our partner laboratory (Nahibu, Rennes).

#### Incremental maximal exercise test (visit n° 2)

During the second visit to the laboratory, the participants performed an incremental test on erg-cycle until reaching voluntary exhaustion to determine maximal oxygen uptake and the mechanical power developed for ventilatory thresholds 1 and 2. V̇ O2 and V̇ CO2 were continuously monitored during the test using an indirect calorimetry system (Ultima CardiO2, Medgraphics, United Kingdom). Heart rate and ECG signals were also continuously monitored during the test (12-lead Digital Holter Recorder). For soccer players and cyclists, the test began with a 3-min warm-up at 150 W, followed by 2 min at 175 W. For non-athletes' participants, the test began with a 3 min warm-up at 100 W, followed by 2 min at 150 W. Then, the power was increased by 25 W every 2 min until the subject could no longer maintain this power. Lactate blood concentrations were measured after each stage to determine lactate thresholds 1 and 2 using Lactate Pro 2 (Arkray, UK).

#### Submaximal exercise test in fasting state (visit n° 3)

During the last visit to the laboratory, the participants performed a submaximal test on erg-cycle in a fasting state (between 7 and 10 a.m.) to determine carbohydrate and fat oxidation (FO). V̇ O2 and V̇ CO2 were continuously monitored during the test using an indirect calorimetry system (Ultima CardiO2, Medgraphics, United Kingdom). Heart rate and ECG signals were also continuously monitored during the test in a fasting state.
monitored during the test (12-lead Digital Holter Recorder). For all participants, the test began with a 4-min warm-up at a power of 60 W, followed by 10 min at the power developed at ventilatory threshold 1. A second stage, corresponding to 90% of the power developed at ventilatory threshold 2, was then performed during 10 min. The minute between 8 and 9 min of each stage was used to calculate absolute rates of fat and carbohydrate oxidation (g·min⁻¹) using stoichiometric equations,⁵⁶ based on the assumption that the excretion of urinary nitrogen was negligible. The proportion of fat oxidation in the total energy expenditure (FO, %) and the total energy expenditure during exercise (EEE, kcal/min) were calculated by summing the energy expenditure from both carbohydrate and fat oxidation.

## Mouse experiments

### Fecal material transplantation (FMT) from humans to mice

After 10 days of treatment with an oral cocktail of broad-spectrum antibiotics (1 mg/mL ampicillin, 5 mg/mL streptomycin, 1 mg/mL colistin, and 45 g/mL vancomycin in drinking water), mice were randomly assigned to FMT or control (CTL) groups. Drinking water containing antibiotics was changed every 60 h during the 10-day treatment period. Mice selected for transplantation (n = 24) received 100 µg of fecal material by oral gavage every morning during 3 days, then once per week for the next two weeks. The fecal material was provided by the human donors from the EXOMIC cohort and preserved into a Maltodextrin/Trehalose (3:1) saline solution. In parallel, CTL mice (n = 12) underwent natural reseeding following the same 10-day antibiotic treatment. These CTL mice received two times soiled litter from their own litter, collected just before the antibiotic treatment. Fecal samples from FMT and CTL mice were individually collected before and after the antibiotic treatment, and 5 weeks post-FMT. 5 weeks post-FMT and overnight fasting, mice were weighed and glycemia was measured using Blood Glucose Monitoring System (Freestyle Papillon Vision, Abbott, France). Mice were then deeply anesthetized with a ketamine-xylazine-butorphanol cocktail. Intracardiac blood was collected into dry tubes, and the mice were euthanized by cardiac exsanguination. Blood was then centrifuged (1500 g, 10 min, 4°C) for serum sampling. The liver, gastrocnemius muscles and abdominal fat mass were dissected, weighed and then frozen in liquid nitrogen.

### Donor selection

We selected eight human donors depending on their aerobic exercise capacity traits rather than their group affiliations. Our objective was to focus on participants with high to very high exercise capacity in order to explore the metabolic impact of microbiota transplantation from active, healthy individuals versus very high exercise capacity athletes. The selection was performed to ensure significant heterogeneity in two key aerobic exercise capacity traits: maximal energy expenditure during exercise ($V̇O_{2max}$) and substrate preference during submaximal exercise (%FO). Three participants had a $V̇O_{2max} > 70$ mL/min/kg (A, B, and F), two between 60 and 70 mL/min/kg (C and G), and three between 55 and 60 mL/min/kg (D, E, and H). Among these eight donors, four exhibited low fat oxidation during exercise (C, D, E, and H), while the other four showed high fat oxidation (A, B, F, and G) (Table S2).

### Running endurance capacity

All mice performed a submaximal running test on a treadmill (Ugo Basile, Gemonio, Italy) before and after antibiotic treatment, and then each week during the next 2 weeks. Running endurance capacity was determined by the time until exhaustion from a test adapted from Okamoto et al.⁹ Briefly, the running speed started at 10 m/min for the first 10 min and then increased by 1 m/min every 14 min until reaching 24 m/min or exhaustion. Exhaustion was defined as the inability to maintain the normal running position and/or after five consecutive seconds in contact with the shock grid (0.2 mA) at the rear of the treadmill.

### Serum parameters

Glycemia was measured using Automated Beckman Coulter (Beckman Coulter, Brea, CA). Serum insulin concentrations were measured by enzyme-linked immunosorbent assay (ELISA) according to manufacturer's instructions (Millipore, St Louis, MO, USA). Insulin sensitivity was determined by calculating HOMA-IR according to the following formula⁵⁷: $HOMA-IR = [fasting glucose (mmol/L)] \times [fasting insulin (U/ml)]/22.5$.

### Muscle glycogen content

The acid-hydrolysis method used for glycogen quantification was adapted from the protocols described by Adamo and Graham.⁵⁸ Frozen gastrocnemius muscle powder (20–30 mg) was placed in ice-cooled 6% perchloric acid and 1 M hydrochloric acid. Samples were then boiled at 100°C for 2 h, cooled on ice for 10 min, and centrifuged (1,300 revolutions/min for 5 min). Supernatant (12 µL) was removed from each sample, diluted in 200L of GOD-PAP solution (Biolabo, Maizy, France), and incubated in the dark at 37°C for 20 min to determine the glucose concentration. Absorbance was measured at 500 nm using a microplate reader (Varioskan LUX multimode microplate reader).

## Metagenomics data

### Fecal DNA extraction

Total cellular DNA was extracted from 0.1 g of mouse or human fecal material using the Quick-DNA Fecal/Soil Microbe Mini Prep Kit (ZymoResearch, CA, USA). Fecal samples were homogenized in the supplied cell suspension solution. A cell lysis/denaturing solution and 0.1-mm-diameter silica beads were then added, and the samples were mixed at maximum speed in a Beadbeater (Biospec, Bartlesville) for 1h at 4°C.

### Fecal DNA quantification (bacterial density)

The bacterial density (total amount of bacterial DNA) present in fecal samples of each mouse of human participant was evaluated by real-time qPCR targeting “all bacteria”16S rRNA genes using the universal primers F-bact1369 and R-prok1492. Experiments were
performed using CFX Real-Time detection system (Bio-Rad Laboratories) with a final volume of 10 µL containing 5 µL of bacterial DNA (diluted at 1/2000), 0.2 µL of primers (10 µM), and 4.8 µL of SYBR Green Super-mix (Bio-Rad Laboratories Inc., USA). All qPCR assays were performed in duplicate using the following cycling conditions: 50°C for 2 min and then 95°C for 2 min followed by 35 cycles of 95°C for 3 s and 60°C for 30 s. For the quantification, the E. coli DNA standard curve was generated by plotting the threshold cycles (Ct) versus bacterial quantity.

## Sequence pre-processing, taxonomic and functional profiling

Fecal metagenomic shotgun sequences from both human and mice experiments underwent a pre-processing pipeline, where sequences were quality filtered using KneadData2 with default parameters.²² MetaPhlAn3 was used for quantitative profiling the taxonomic composition of the microbial communities of all metagenomic samples.²² HUMANn3 was used to profile pathway and gene family abundances.²²

## Metabolomic data

Targeted metabolomic profiling of human fecal samples was performed by the CORSAIRE platform (Biogenouest, Nantes, France).

### Short chain fatty acids

Measurement of short-chain fatty acids (SCFAs) was performed as previously described with slight modifications.⁵⁹ A stock solution of SCFA metabolites was prepared and serially diluted to get ten calibration solutions. A working solution of internal standards was prepared in 0.15 M sodium hydroxide to get the following final concentrations: 75 mmol/L of ²H₃-acetate, 3.8 mmol/L of ²H₅-propionate, 2.5 mmol/L of ¹³C-butyrate, and 0.5 mmol/L of ²H₉-valerate. Fecal samples were weighed, homogenized at 0.1 g/mL in sodium hydroxide solution 0.15 M, and centrifuged 3 min at 7 000 g (4°C). Samples (50 µL of fecal supernatants, serums or standard solutions) were dissolved in 200 µL of sodium hydroxide solution 0.15 M. Twenty microliters of the internal standard solution were added to samples and calibration solutions. After addition of the standards, each sample was acidified with 5 µL of hydroxide chloride 37% and then extracted with 1.7 mL of diethyl ether. Samples were stirred gently for 1h and then centrifuged 2 min (7000 g, 4°C). The organic layers were transferred into 1.5 mL glass vials and SCFAs were derivatized with 20 µL of tert-butyldimethylsilyl imidazole. Samples were incubated for 30 min at 60°C before analysis. Samples were finally analyzed by gas chromatography-mass spectrometry (model 7890A-5975C; Agilent Technologies, Montpellier, France) using a 30 m × 0.25 mm × 0.25 µm capillary column (HP5-MS; Agilent Technologies). The temperature program started at 50°C for 1 min, ramped to 90 °C at 5°C/min and then up to 300 °C at 70°C/min. Selected ion monitoring mode was used to measure SCFA concentrations with ions at mass-to-charge ratio (m/z) 117 (acetate), m/z 120 (²H₃-acetate), m/z 131 (propionate), m/z 136 (²H₅-propionate), m/z 145 (butyrate and isobutyrate), m/z 146 (¹³C-butyrate), m/z 159 (valerate), and m/z 168 (²H₉-valerate). Chromatographic peak area ratios between unlabelled compounds and their respective internal standards constituted the detector responses. Standard solutions were used to plot calibration curves for quantification.

### Amino acids

Amino acid concentrations were determined in fecal samples by liquid chromatography-tandem mass spectrometry (LC-MS/MS) as described previously with slight modifications.⁶⁰ Fecal samples were weighed (50 mg) and dissolved in 500 µL of water. Fecal homogenates were centrifuged for 10 min (10 000 g, 4°C) and supernatants were collected. Individual stock solutions (10 mmol/L) of unlabelled and labeled amino acids were prepared in 0.1 M HCl. A pool of unlabelled standard solutions was prepared and serially diluted in water to obtain seven standard solutions ranging from 0.01 to 100 µmol/L (equivalent to 0.1–100 nmol/g). A pool solution of labeled amino acids (10 µmol/L) was prepared in water. The standard solutions and fecal homogenate samples (20 µL) were then extracted with 100 µL of methanol and 50 µL of the labeled amino acid solution. The samples were mixed and centrifuged (10000 g, 4°C) for 15 min to remove the precipitated proteins. The supernatants were collected and dried under a gentle stream of nitrogen (45°C). The derivatization step was performed by dissolving the dried extract in 100 µL of a freshly prepared butanol solution containing 5% acetyl chloride and kept at 60°C for 30 min. The solvent was then removed under a gentle stream of nitrogen (60°C). The dried samples were dissolved in 100 µL of water containing 0.1% formic acid and 50 µmol/L tris (2-carboxyethyl) phosphine and injected into the LC-MS/MS system. Samples (10 µL) were injected onto an Acquity BEH-C18 column (1.7 µm; 2.1 × 100 mm, Waters Corporation) held at 60°C, and compounds were separated with a linear gradient of mobile phase B (0.1% formic acid in methanol) in mobile phase A (0.1% formic acid in water) at a flow rate of 400 µL/min. Mobile phase B was kept constant at 1% for 0.5 min, linearly increased from 1% to 95% for 4.5 min, kept constant for 1 min, returned to the initial condition over 0.5 min, and kept constant for 1.5 min before the following injection. Target compounds were then detected by the mass spectrometer with the electrospray interface operating in the positive ion mode (capillary voltage, 3 kV; desolvation gas (N2) flow, 650 L/h; desolvation gas temperature, 350°C; source temperature, 120°C). The multiple reaction monitoring mode was applied for MS/MS detection as detailed in the Table S4. Standard solutions were used to plot the calibration curves for quantification.

### Bile acids

Bile acids (BAs) were analyzed in fecal samples using LC-MS/MS as previously described.⁶¹ Fecal samples were weighed (50 mg) and dissolved in 500 µL of water. A pool of reference standard solutions including cholic acid (CA), chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), ursodeoxycholic acid (UDCA), hyodeoxycholic acid (HDCA) and lithocholic acid (LCA) was prepared and serially diluted in water to obtain 7 standard solutions ranging from 0.1 to 100 µg/mL (equivalent to 0.1–100 µg/g). Ten microliters (10 µL) of ²H₄-CA at 100 µg/mL in 50% methanol were added to 500 µL of standard solutions and fecal samples. All samples were then mixed in an ultrasonic bath for 15 min. After complete homogenization, samples were acidified with 25 µL of 37% hydroxide chloride and then extracted with 1.2 mL of ethyl acetate (Biosolve, France). Samples were centrifuged for 10 min (5000 g, 4°C).
Supernatants were collected and dried under a gentle stream of nitrogen. Dried samples were finally dissolved in 600 µL of 50% acetonitrile and injected (10 µL) into the LC-MS/MS. LC-MS/MS analyses were performed using a Xevo TQD mass spectrometer with an electrospray interface and an Acquity H-Class UPLCTM device (Waters Corporation, Milford, MA, USA). Samples (5 µL) were injected into a CORTECS UPLC C18 column (1.6 µm; 2.1 × 100 mm, Waters Corporation) held at 60°C, and compounds were separated with a linear gradient of mobile phase B (50% acetonitrile, 50% isopropanol, 0.1% formic acid, and 10 mM ammonium formate) in mobile phase A (5% acetonitrile, 0.1% formic acid, and 10 mM ammonium formate) at a flow rate of 400 µL/min. Mobile phase B was maintained at a constant volume of 10% for 1 min, linearly increased from 10% to 60% for 9 min, linearly increased from 60% to 95% for 1 min, held constant at 95% for 1 min, returned to the initial condition (10%) over 1 min, and held constant for 1 min before the following injection. Targeted compounds were then detected using the mass spectrometer with the electrospray interface operating in negative ion mode (capillary voltage, 2 kV; desolvation gas (N2) flow rate and temperature, 1000 L/h and 400°C, respectively; source temperature, 150°C). Multiple reaction monitoring mode was applied for MS/MS detection as detailed in the Table S5. Standard solutions were used to plot the calibration curves for quantification.

## QUANTIFICATION AND STATISTICAL ANALYSIS

### Data normalization

For the microbiome data, we performed a Total Sum Scaling (TSS) within each metagenome. In this case, all the participants exhibit a sum of species equal to 1. We summed species at their phylogenetic genus level to 1. By using this new dataset, we were able to calculate the ratio between species belonging to the genus *Prevotella* and species belonging to the genus *Bacteroides*. For the functional capacity of microbial communities, we performed relative abundance normalization by converting raw counts (i.e., Uniref. 90 gene family count data) into relative proportions (counts per million). Next, we mapped the normalized count data into corresponding pathways or super-pathways based on their MetaCyc classifications. For the metabolomic data, we performed a normalization of the fecal metabolite content based on the water content in their fecal material. Consequently, each volunteer exhibited metabolite content (mol) per g of dry feces. For the nutrient intakes, we performed a total sum scaling within participants, except for the caloric intake, where we divided the total energetic intake by the participant's body mass (BM). We calculated caloric intake by multiplying CHO and protein intake by 4 and fat by 9.

### Exploratory methods on bacterial ecosystem

To assess the intra-sample diversity of the bacterial ecosystem, we measured two indices: (A) the number of different species (richness) and (B) the evenness of the gut microbiota (Shannon index). These indices were computed using the diversity function from the *vegan* R package.

To assess the inter-sample diversity of the gut microbiota composition, we applied a Principal Coordinates Analysis<sup>62,63</sup> on the pairwise sample dissimilarity matrix of the gut microbiota compositions using Bray Curtis metrics. We presented the 20 strongest Spearman correlation between species and the two first Principal Coordinates. In other words, the 20 presented species are those that contribute the most to the Principal Coordinates Analysis of the gut microbiota. We also projected the donor's aerobic exercise capacity traits as supplementary variable (Figure 3B). The coordinates were obtained by computing the Spearman's r correlation respectively with PCo1 and PCo2. The computation of pairwise sample dissimilarity matrix was performed using the *vegdist* function from the *vegan* R package, and its representation in the latent space (PCoA) was performed using the *pcoa* function from the *ape* R package.

### Interpretive methods on bacterial ecosystem

Hierarchical Ascending Clustering (HAC) was used to cluster the participants according to their gut microbiota. The pairwise dissimilarity sample matrix essential for HAC is the Bray-Curtis metrics. The dendogram shows the merging sequence. The HAC was computed with the *hclust* function in R, using the *ward.D* agglomerative method.

Correlation describes the strength and direction of the relationship between two quantitative variables. In our results, we consistently presented two statistics: (a) the *p* value of the test where null hypothesis is no correlation, (b) the *r* correlation that provide the direction of the relation. The Pearson's *r* correlation was presented if the data respected normality hypothesis. When this condition is not met, the Spearman's *r* correlation was used. Furthermore, variables not following a normal distribution are presented as rank statistics in the associated plot. The 95% confidence interval was computed by estimating the correlations for 1000 bootstrap samples.

Local linear regression was estimated to assess the variability and heterogeneity in relationships between two variables. Local linear regression is typically estimated by defining a neighborhood around each data point and then fitting a linear model (such as linear regression) within that window to estimate the local relationship between the variables. This was performed using the *loess* function in R.

Canonical Analysis of Principal Coordinates (CAP) is the non-parametric form of the Redundancy Analysis (RDA).<sup>64</sup> It is a multivariate statistical technique exploring the relationships between sets of variables. CAP measures how much environmental variables (i.e., diet, aerobic exercise capacity traits, SCFAs) are linked to the structure and composition of the bacterial ecosystem. CAP is composed of two steps; in the first one, the PCoA of the species variables is performed; in the second step a canonical correlation analysis is computed that maximizes the correlation between the environmental variables and the axis of the PCoA of step 1. The
results of CAP can be interpreted in terms of relationships between environmental variables and bacterial ecosystem. In the results, we obtained two statistics: (a) the adjusted explained variance (R2) of Y (gut microbiota compositional data) by the set of variables X (environmental variables), and (b) the *p* value associated with the test where the null hypothesis is that X is linearly independent of Y. The used function was capscale provided by the vegan R package.

ADDITIONAL RESOURCES

The clinical study was approved by the national Comité de protection des personnes Ouest IV Nantes (ID-RCB: 2021-A02496-35) and registered on [ClinicalTrials.gov](https://ClinicalTrials.gov) under NCT05220657.