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Randomized Controlled Trial
. 2024 Jan-Dec;16(1):2416928.
doi: 10.1080/19490976.2024.2416928. Epub 2024 Oct 29.

Exercise-changed gut mycobiome as a potential contributor to metabolic benefits in diabetes prevention: an integrative multi-omics study

Yao Wang  1   2   3 , Jiarui Chen  1   2 , Yueqiong Ni  4 , Yan Liu  1   2 , Xiang Gao  1 , Michael Andrew Tse  1   5 , Gianni Panagiotou  1   2   4   6   7 , Aimin Xu  1   2   8
Affiliations
Randomized Controlled Trial

Exercise-changed gut mycobiome as a potential contributor to metabolic benefits in diabetes prevention: an integrative multi-omics study

Yao Wang et al. Gut Microbes. 2024 Jan-Dec.

Abstract

Background: The importance of gut microbes in mediating the benefits of lifestyle intervention is increasingly recognized. However, compared to the bacterial microbiome, the role of intestinal fungi in exercise remains elusive. With our established randomized controlled trial of exercise intervention in Chinese males with prediabetes (n = 39, ClinicalTrials.gov:NCT03240978), we investigated the dynamics of human gut mycobiome and further interrogated their associations with exercise-elicited outcomes using multi-omics approaches.

Methods: Clinical variations and biological samples were collected before and after training. Fecal fungal composition was analyzed using the internal transcribed spacer 2 (ITS2) sequencing and integrated with paired shotgun metagenomics, untargeted metabolomics, and Olink proteomics.

Results: Twelve weeks of exercise training profoundly promoted fungal ecological diversity and intrakingdom connection. We further identified exercise-responsive genera with potential metabolic benefits, including Verticillium, Sarocladium, and Ceratocystis. Using multi-omics approaches, we elucidated comprehensive associations between changes in gut mycobiome and exercise-shaped metabolic phenotypes, bacterial microbiome, and circulating metabolomics and proteomics profiles. Furthermore, a machine-learning algorithm built using baseline microbial signatures and clinical characteristics predicted exercise responsiveness in improvements of insulin sensitivity, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% CI: 0.85-0.97) in the discovery cohort and of 0.79 (95% CI: 0.74-0.86) in the independent validation cohort (n = 30).

Conclusions: Our findings suggest that intense exercise training significantly remodels the human fungal microbiome composition. Changes in gut fungal composition are associated with the metabolic benefits of exercise, indicating gut mycobiome is a possible molecular transducer of exercise. Moreover, baseline gut fungal signatures predict exercise responsiveness for diabetes prevention, highlighting that targeting the gut mycobiome emerges as a prospective strategy in tailoring personalized training for diabetes prevention.

Keywords: Gut mycobiome; diabetes prevention; exercise training; fungal microbiome; intervention responsiveness; multi-omics; randomized controlled trial.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Physical exercise training reshapes gut fungal composition. Exercise training significantly remodeled overall fungal compositions at the genus level as evidenced in (a) alpha-diversity in the exercise group (Exercise, n = 20) and sedentary control group (CTRL, n = 19) measured by Shannon index; (b) changes of alpha-diversity within each subject, and (c) within-subject beta-diversity (measured by Bray-Curtis dissimilarity) between 0-week and 12-week in two groups. (d) NMDS plot of log2 fold change of genus abundance within subjects during exercise. The confidence ellipses (level = 0.95) were constructed in the exercise group at baseline (blue), after training (purple); and the control group at baseline (red) and after 12 weeks of exercise intervention (green). (e) The relative abundance of fungal phyla in individuals before and after exercise training. (f) The mean value of the fold change of significantly altered fungal genus composition (log10 transformed) in both exercise and control subjects. The fold change was divided by fungal relative abundance at 12 weeks to 0 weeks for each individual. (g) Exercise intervention promoted fungal communication as showed by co-abundance network analysis. The edges indicate statistically significant (p < 0.05) Spearman correlations between species present in at least 50% of subjects. The nodes are colored based on their affiliated phyla. Dashed and solid lines represent correlations before and after exercise, respectively. Lines in orange and blue represent positive and negative correlations.
Figure 2.
Figure 2.
The taxonomic alterations of gut mycobiome are tightly associated with exercise-induced improvement in metabolic health. (a) The influence of host factors on human gut mycobiome at the Bray-Curtis distance was evaluated by permutational multivariate analysis of variance (PERMANOVA, permutation = 999). The bars were colored according to metadata categories. (b) Spearman correlation between the fold changes in significant fungal genus and fold changes in metabolic health after exercise training (n = 20). Significant results (FDR < 0.25) were marked with a plus symbol. *FDR < 0.05, +FDR <0.25.
Figure 3.
Figure 3.
Inter-kingdom linkages between enteric fungi and bacteria in response to exercise intervention. (a) Significant correlation between gut fungal and bacterial α-diversity in Shannon indices at genus levels. Linear trends with a 95% confidence interval were shown. (b) Spearman correlation between the fold change of significant gut fungi and bacteria after exercise (n = 20). Significant correlations were marked with a plus symbol. The cells in red and blue represent positive and negative correlations. Significant correlations were marked with a plus symbol. *FDR <0.05, +FDR <0.25. (c) The number of significant results of each fungus associated with bacterial KOs. The number of enteric fungi-related bacterial KOs categorized in the (d) KEGG pathway and (e) KEGG module.
Figure 4.
Figure 4.
The correlations between changes in gut mycobiome and serum metabolome signatures, circulating proteomics profiles. (a) The pathway enrichment of metabolites which were significantly related to fungal changes. The cells marked in green indicated the involved metabolites in each pathway. The bar chart showed the enrichment ratio for each pathway and P-values from 0 to 0.05 were colored from red to white for each pathway. (b) The heatmap showed the Spearman correlation between the fold change in fungal abundance and serum metabolites and protein abundance (n = 20). The cells marked with plus symbols represent significant results. The cells in red and blue colors represent positive and negative correlations, respectively. *FDR <0.05, +FDR <0.25.
Figure 5.
Figure 5.
Basal gut fungal abundance is associated with exercise-improved outcomes via different mediators. The triangle plot showed the significant mediation effects from the fungal genus (left) to participants’ phenotypes (right) via different metabolites, and serum proteins (middle). The arrows showed the direction of the effect and the corresponding Spearman coefficient and p values were indicated.
Figure 6.
Figure 6.
Baseline gut mycobiome predicts exercise responsiveness in the improvement of insulin sensitivity. (a) The significantly changed fungal genera between exercise responders (R, n = 36) and non-responders (NR, n = 14) after training. (b) The correlation networks among the fold changes in significantly changed genera in R and NR were constructed. Genera were colored according to their affiliated phyla. The correlations in R and NR were connected in solid and dashed edges, respectively. The edges colored from blue to red represented the coefficient value from − 1 to 1 for the significant correlations. (c) The informative feature plots show the importance of the selected mycobiome in the machine-learning model.(d) the pie chart shows the percentage of the importance of informative features used in the prediction model. The pie colors indicate the different catalogs, including fungi, bacteria, and clinical information. (e) The receiver operating characteristic curves and area under the curve (AUROC) of the mycobiome-based predictive models for discriminating R and NR in the discovery cohort (n = 20). (f) AUROC of mycobiome-based predictive models to identify NR in the validation cohort (n = 30).
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