Microbiota dysbiosis influences immune system and muscle pathophysiology of dystrophin-deficient mice

Abstract

Duchenne muscular dystrophy (DMD) is a progressive severe muscle-wasting disease caused by mutations in DMD, encoding dystrophin, that leads to loss of muscle function with cardiac/respiratory failure and premature death. Since dystrophic muscles are sensed by infiltrating inflammatory cells and gut microbial communities can cause immune dysregulation and metabolic syndrome, we sought to investigate whether intestinal bacteria support the muscle immune response in mdx dystrophic murine model. We highlighted a strong correlation between DMD disease features and the relative abundance of Prevotella. Furthermore, the absence of gut microbes through the generation of mdx germ-free animal model, as well as modulation of the microbial community structure by antibiotic treatment, influenced muscle immunity and fibrosis. Intestinal colonization of mdx mice with eubiotic microbiota was sufficient to reduce inflammation and improve muscle pathology and function. This work identifies a potential role for the gut microbiota in the pathogenesis of DMD.

Keywords: Duchenne muscular dystrophy; T-lymphocytes; gut microbiota; immunity; skeletal muscle metabolism.

Figure 1. Colon characterization of 3m mdx mice

1. Representative images of H&E staining of colon from 3m C57Bl (n = 4) and mdx (n = 4) mice. High magnification (scale bar: 20 μm) and low magnification (scale bar: 200 μm).

2. Mucus layer, area between yellow dash lines; crypt length, yellow‐headed arrow. Scale bar: 100 μm.

3. Mucus layer thickness and crypt length were quantified for n = 4 mice per group (with pooled samples of n = 60 for mucus layer thickness and n = 80 for crypt length).

4. Short‐chain fatty acid fecal quantification of 3m C57Bl (n = 3) and mdx (n = 4–5) mice.

5. Colon images captured with the iMScope TRIO described altered pattern of expression of different phosphatidylcholines (PC) and lysophosphatidylcholines (LysoPC) (as indicated by m/z values) in 3m mdx mice (n = 3). Scale bar: 50 μm. For each lipid, the mean intensities measured at 12 positions throughout colon images are shown on the right side where bars are mean ± SEM (n = 3).

6. Cropped images of representative WB showing the expression of proteins involved in inflammation and fibrosis in colon tissues of 3m C57Bl (n = 3) and 3m mdx (n = 5). Densitometric analyses of protein expression was shown as ratio to actin.

Data information: Data are presented as mean ± SD (*P < 0.05; **P < 0.01, ***P < 0.001, ****P < 0.0001; Student's t‐test). Source data are available online for this figure.

Figure 2. Microbiome analysis of 3m mdx mice

1. Observed number of enriched ASVs in 3m C57Bl (n = 6) (maximum: 666; median: 583.67; minimum: 475) and 3m mdx (n = 8) (maximum: 522; median: 465.625; minimum: 404). Data are presented as the exact number of ASVs (*P < 0.05; Student's t‐test).

2. PCA of beta‐diversity of 3m C57Bl (n = 6) and 3m mdx (n = 7) as measured by Unweighted UniFrac distance and Bray–Curtis dissimilarity.

3. Mean relative abundance at genus level among groups. All genera with relative abundance < 0.1% are reported together and labeled as “others.”

4. Volcano plots of 3m C57Bl (n = 6) and 3m mdx (n = 7) showing the significantly enriched bacterial amplicon sequence variants (ASVs) (with P < 0.05) by the DEseq2 analysis. The names of the significantly enriched bacterial ASVs classified to the genus level and P < 0.005 are reported. All P‐values were false discovery rate–corrected.

5. Random forest analysis. The top 20 bacterial genera with the highest discriminatory power sorted by mean decrease GINI value are showed.

6. Relative abundance of different genus in 3m C57Bl (n = 6) and 3m mdx (n = 7). Prevotella: 3m C57Bl: maximum: 0.28438734; median: 0.162312199; minimum: 0. 3m mdx: maximum: 4.650449086; median: 1.928565583; minimum: 0.308938765. Alistipes: 3m C57Bl: maximum: 2.381488226; median: 1.622780995; minimum: 0.52990159. 3m mdx: maximum: 24.79693926; median: 11.74146327; minimum: 5.71434417. Parasutterella: 3m C57Bl: maximum: 1.491499069; median: 0.923747366; minimum: 0.390776848. 3m mdx: maximum: 0.114573317; median: 0.036571394; minimum: 0.002045952. Rikenella: 3m C57Bl: maximum: 0.202549256; median: 0.124121093; minimum: 0.067516419. 3m mdx: maximum: 0; median: 0; minimum: 0. *P < 0.05; Student's t‐test.

7. Multidimensional scaling analysis of small intestinal metabolomic profiles from of 3m C57Bl (n = 4) and 3m mdx (n = 3) mice calculated by samples' distance similarities (Bray‐Curtis) with the most discriminatory metabolites (top variable importance in projection score) identified.

8. Concentration of the significantly different metabolites isolated from the small intestinal content of 3m C57Bl (n = 4) and 3m mdx (n = 3) mice. *P < 0.05, **P < 0.01 and ***P < 0.001; Wilcoxon rank‐sum test.

Source data are available online for this figure.

Figure 3. Metabolic maps of 3m mdx mice

1. The iPath3.0 representation of KEGG metabolic pathways inferred from Piphillin analysis significantly upregulated (in red) or downregulated (in blue) in 3m C57Bl (n = 6) versus 3m mdx (n = 8) mice. Nodes in the map colored in green, yellow, and orange correspond to acetate, propionate, and butyrate, respectively. Line thickness represents the level of statistical significance for the inferred pathways; thick lines with FDR‐corrected P‐value < 0.05, thin lines with nominal P‐value < 0.05.

2. Predicted metagenomic gene content of the key enzymes catalyzing the final steps for the production of microbiota‐derived SCFAs in GI of 3m C57Bl (n = 5/6) and 3m mdx (n = 7/8) mice. Data are presented as mean ± SD (**P < 0.01, ***P < 0.001; Kruskal–Wallis test).

Source data are available online for this figure.

Figure 4. Characterization of gut tissue metabolome in 3m mdx mice and following antibiotics treatment

1. Partial least square discriminant analysis (PLS‐DA) models score plot used to evaluate the differences among 3m C57B1 (in gray), 3m mdx mice (in green) and 3m mdx+ABX (dark purple), with n = 4 each.

2. Relevant metabolites (top variable importance in projection score) in the corresponding PLS‐DA separation, in blue metabolites with a negative fold change and in red metabolites with a positive fold change.

3. Heatmap showing all the relevant metabolites concentration change among the groups. Both metabolites and classes were clusterized according to the Wald method. In blue metabolites' concentration with a negative fold change and in red metabolites' concentration with a positive fold change.

4. Metabolic pathways involving the relevant metabolites obtained using the MetPa algorithm. The color and size of each circle are based on the P‐value and pathway impact value, respectively. The x‐axis represents the pathway impact, and the y‐axis represents the −log of P values from the pathway enrichment analysis for the key differential metabolites of 3m mdx and 3m mdx+ABX mice.

5. Fecal content quantification of SCFAs in 3m mdx and 3m mdx+ABX mice (n = 5 per group). Data equal to 0. Data are presented as mean ± SD (**P < 0.01, ***P < 0.001; Student's t‐test).

Source data are available online for this figure.

Figure EV1. Correlation between bacterial genera and immunity

Heatmap of Spearman's rho correlations between the relative abundance of the most represented bacterial genera (with relative abundance > 0.1%) in the gut microbiota of 3m mdx animals (n = 3–8) with the indicated metabolites and immunological parameters. The significant correlations with FDR‐corrected P‐value < 0.1 are indicated with bubbles. Spearman correlation plots for the significant correlations between Prevotella and the indicated immunological parameters are also shown. Abbreviations used in the Figure: sp, spleen‐derived; msc, muscle‐derived; CEMEM, central memory cells; EFFECT, effector cells.Source data are available online for this figure.

Figure 5. Microbiota depletion induces modulation of immune cells

1. A–D

   FACS analysis of spleen and muscle homogenates from 3m C57Bl (n = 4), mdx (n = 5) and mdx+ABX (n = 7) mice demonstrates no significant alteration of CD45⁺CD11b⁺CD4⁻CD8⁻ myeloid cells (A and C) and few differences in CD4⁺ or CD8⁺ naïve (CD62L⁺ CD44⁻), central memory (CD62L⁺ CD44⁺) and effector (CD62L⁻ CD44⁺) T cells (B and D).

2. E

   FACS analysis of spleen of 3m C57Bl (n = 7), mdx (n = 9) and GFmdx (n = 5) mice revealed similar proportions of CD4⁺ and CD8⁺ T cells but reduced activated CD44⁺ T cells in GFmdx mice. Representative plots are depicted.

3. F

   Graphs show cumulative frequencies of CD4⁺ and CD8⁺ T cells on live cells of 3m C57Bl (n = 7), mdx (n = 4) and GFmdx (n = 5) mice. Representative dot plots and cumulative frequencies of splenic CD4⁺GITR⁺CD25⁺ Treg. Frequencies of effector CD44⁺ T cells were significantly decreased in spleen of GFmdx mice.

4. G

   Representative dot‐plots showing the proportion of muscle‐infiltrating CD45⁺ cells of 3m C57Bl (n = 6), mdx (n = 6) and GFmdx (n = 5) mice. Cumulative frequencies of muscle‐infiltrating CD45⁺ cells are shown.

5. H

   Representative images of TA muscles from 3m mdx and GFmdx mice stained for CD45 (in green), isolectin (in red), and phalloidin (in purple). Nuclei were counterstained with DAPI (in blue). Scale bar: 10 μm.

6. I

   Absolute number of CD3⁺ inflammatory cells (white arrows) were quantified in n = 12 images of TA of 3m C57Bl, 3m mdx, and 3m GFmdx mice (n = 6 each). CD3 staining is shown in green and DAPI in blue. Scale bars: 50 μm.

Data information: Data are presented as mean ± SD. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 ordinary one‐way ANOVA, Tukey's multiple‐comparison test. The comparisons among the averages of CD3⁺ cells were evaluated using unpaired t‐test. Source data are available online for this figure.

Figure 6. Muscle homeostasis of 3m mdx mice is influenced by microbiota

1. A, B

   RNA datasets clustering (A) and convergently up‐ and downregulated genes (B) of muscles of 3m mdx (n = 3), mdx+ABX (n = 3) and GFmdx (n = 3) mice.

2. C

   Gene ontology (GO) analysis on both groups of convergent genes.

3. D

   RT‐qPCR analysis of TA muscles of two independent experiments with 3m mdx (n = 4), mdx+ABX (n = 4), and GFmdx (n = 5) mice determined the expression of myogenic markers.

4. E

   Representative Gomori‐modified staining and quantification of myofiber area and relative frequency of the myofiber cross‐sectional area (CSA) expressed as the frequency distribution of the TA muscles of 3m C57Bl (n = 4), 3m mdx (n = 4), mdx+ABX (n = 4) and GFmdx mice (n = 5). Pooled samples for each group with n = 6,240 for 3m C57Bl; n = 6,001 for 3m mdx; n = 10,556 for 3m mdx+ABX; n = 23,059 for GFmdx. For morphometric analysis, images were quantified with Image J software for each mouse. Scale bars: 50 μm.

5. F

   Quantification of the fibrotic area from Gomori stained images (pooled samples for each group with n = 223 for 3m C57Bl, 3m mdx and 3m mdx+ABX; n = 215 for GFmdx) and RT‐qPCR analysis of Col1a (two independent experiments with n = 4 animals each group).

6. G

   Representative images of skeletal muscle showed the distribution and composition of the myosin heavy chain (MyHC) isoforms (Type IIa, Type IIx, and Type IIb).

7. H, I

   Graph portrays (H) the percentage of myofibers expressing different MyHC isoforms and (I) myofibers area per type of MyHC in TAs of 3m mdx (n = 4), mdx+ABX (n = 4), and GFmdx (n = 5) mice (n = 12 images per animal).

8. J

   Representative SDH staining and quantification of percentage of SDH⁺ myofibers of TAs from 3m mdx (n = 4), mdx+ABX (n = 4), and GFmdx mice (n = 5) (n = 12 images per animal). Scale bars: 50 μm.

9. K

   Tetanic force of TA muscle of 3m C57Bl (n = 4), mdx (n = 4), mdx+ABX (n = 4), and GFmdx (n = 5) mice.

10. L

    ALT, AST, and CPK serum levels were measured in 3m mdx (n = 4), mdx+ABX (n = 4), and GFmdx mice (n = 5) (two independent experiments).

Data information: Data are presented as mean ± SD (*P < 0.05, **P < 0.01, ***P < 0.001; ****P < 0.0001, ordinary one‐way ANOVA, Tukey's multiple‐comparison test for WB and non‐parametric test followed by Kruskal–Wallis test for RT‐qPCR). Source data are available online for this figure.

Figure EV2. Gene and protein expression in muscles from 3m mdx, mdx+ABX, and GFmdx

1. A–K

   Cropped images of representative WB and RT‐qPCR analysis of TA muscle of 3m mdx (n = 3/4), 3m mdx+ABX (n = 3/4), and 3m GFmdx (n = 5) showing the expression of the proteins specifically involved in inflammation/fibrosis (A), skeletal muscle metabolism (B–E), mitochondrial biogenesis (F and G), calcium conducting channels (H and I), autophagy (J), and nicotinic acetylcholine receptors (K). Densitometric data were normalized on vinculin and expressed as mean ± SD. Data are presented as mean ± SD (*P < 0.05, **P < 0.01, ***P < 0.001; ****P < 0.0001, ordinary one‐way ANOVA, Tukey's multiple‐comparison test for WB and non‐parametric test followed by Kruskal–Wallis test for RT‐qPCR).

Source data are available online for this figure.

Figure EV3. Gene set enrichment analysis of 3m C57bl, mdx, mdx+ABX, and GFmdx mice RNA sequencing data

The annotated dataset “Hallmark” collection by the Molecular Signatures Database (MSigDB) was used as a reference. A green background refers to positive Normalized Enrichment Score (NES) (enrichment in positive phenotype, or upregulation); a red background refers to negative NES (enrichment in negative phenotype, or downregulation). FDR, false discovery rate. Genes involved in inflammatory response, epithelial‐to‐mesenchymal transition, complement activity, angiogenesis, and interferon‐γ response are upregulated in 3m mdx (n = 3) versus age‐matched C57Bl (n = 3) mice (top lane). Genes involved in G2M checkpoint transition, interferon‐α and ‐γ response, E2F transcriptional activity and myogenesis are downregulated in 3m mdx+ABX (n = 3) versus age‐matched mdx (n = 3) mice (mid lane). Genes involved in adipogenesis, fatty acid metabolism, and cholesterol homeostasis are upregulated in 3m GFmdx (n = 3) versus age‐matched mdx (n = 3) mice; conversely, genes involved in interferon‐alpha response, E2F transcriptional activity, and inflammatory response are downregulated in the 3m GFmdx versus age‐matched mdx mice (bottom lane).Source data are available online for this figure.

Figure 7. Effects of dysbiotic microbiota of mdx on intestine, spleen and muscle inflammation and muscle function

1. A

   FACS analysis of T cell subsets from lamina propria in 3m C57Bl (n = 5), mdx (n = 5), and ABX‐mdx^FMT_C57Bl (n = 5/6) showing decrease in CD3⁺ T cells in ABX‐mdx^FMT_C57Bl. Infiltrating CD3⁺CD4⁺, and regulatory CD69⁺ subsets of CD4⁺ and CD8⁺ were decreased in ABX‐mdx^FMT_C57Bl. Eubiotic FMT in mdx modulates T helper response, with reductions in the cumulative frequencies of CD4⁺ IFNγ⁺ (Th1) and CD4⁺ IL‐10⁺ cells in ABX‐mdx^FMT_C57Bl. Data are presented as mean ± SD (*P < 0.05; **P < 0.01 ordinary one‐way ANOVA, Tukey's multiple‐comparison test).

2. B

   FACS analysis of spleen and muscle homogenates from 3m C57Bl (n = 5), mdx (n = 5), and ABX‐mdx^FMT_C57Bl (n = 5/6). Analysis of the spleen revealed downregulation of Ly6C⁺ inflammatory monocytes and F4/80⁺ macrophages in ABX‐mdx^FMT_C57Bl. Eubiotic FMT in mdx mice determined a decrease of CD4⁺/CD8⁺ CD44⁺CD62L effector and GITR⁺CD4⁺ T‐cells in ABX‐mdx^FMT_C57Bl. Gut‐derived CCR9⁺CD8⁺TEM⁺ cells were increased in ABX‐mdx^FMT_C57Bl. Data are presented as mean ± SD (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001, ordinary one‐way ANOVA, Tukey's multiple‐comparison test).

3. C

   Graphs showing cumulative frequencies of infiltrating CD45⁺CD4⁺ and CD45⁺CD8⁺ cells in muscles of 3m C57Bl (n = 5), mdx (n = 5) and ABX‐mdx^FMT_C57Bl (n = 5/6) were decreased in ABX‐mdx^FMT_C57Bl related to mdx mice. Data are presented as mean ± SD (*P < 0.05; **P < 0.01, ordinary one‐way ANOVA, Tukey's multiple‐comparison test).

4. D, E

   Representative H&E staining (D) and quantification of myofiber area with Image J software (E) of TA muscles from 3m C57Bl (n = 5), mdx (n = 5) and ABX‐mdx^FMT_C57Bl (n = 6). Scale bars for H&E: 200 μm.

5. F

   Measurement of ALT, and CPK in the serum of 3m C57Bl (n = 5), mdx (n = 5), and ABX‐mdx^FMT_C57Bl (n = 6).

6. G

   Tetanic force of TA muscles from mdx (n = 5) and ABX‐mdx^FMT_C57Bl (n = 6).

7. H

   Representative images of skeletal muscle showed the distribution and composition of MyHC isoforms (Type IIa, Type IIx, and Type IIb). Scale bar: 50 μm.

8. I

   Graph portrays the percentage of myofibers expressing different MyHC isoforms. n = 12 images were analyzed for each mouse.

9. J, K

   Representative SDH staining and quantification of percentage of SDH⁺ myofibers of TA muscles from mdx (n = 5) and ABX‐mdx^FMT_C57Bl (n = 6) (n = 12 images per mouse). Scale bar: 200 μm.

10. L, M

    Representative image of CD31 (in cyan), α‐SMA (in green), and isolectin (in red) staining and their quantification in TA muscles from mdx (n = 5) and ABX‐mdx^FMT_C57Bl (n = 6) mice. Scale bar: 500 μm.

Data information: Data for tetanic force, ALT and CPK concentration, and staining quantification are presented as mean ± SD (*P < 0.05, **P < 0.01, ***P < 0.001; ****P < 0.0001, one‐way ANOVA, Tukey's multiple‐comparison test). Source data are available online for this figure.

Figure EV4. Effects of dysbiotic microbiota of mdx on intestine, spleen and muscle inflammation

1. A

   FACS analysis of colon lamina propria of mdx (n = 5) and ABX‐mdx^FMT_C57Bl (n = 5/6) for quantification of T cell subsets.

2. B

   Representative plots of FACS analysis for the expression of CCR9 in ABX‐mdx^FMT_C57Bl and ABX‐C57Bl^FMT_mdx are depicted. The numbers within the panels indicate the percentage of each population of live cells. Each analysis included at least 5–10 × 10⁴ events for each gate.

3. C, D

   FACS analysis of T cells of the spleen (C) and granulocyte, monocyte and macrophage of muscle (D) tissues from mdx (n = 5) and ABX‐mdx^FMT_C57Bl (n = 5/6).

4. E

   Serum levels of AST and GLUC3 in 3m C57Bl (n = 5), mdx (n = 5) and ABX‐mdx^FMT_C57Bl (n = 6).

Data information: Data are presented as mean ± SD (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 ordinary one‐way ANOVA, Tukey's multiple‐comparison test). Source data are available online for this figure.

Figure EV5. Principal component analysis (PCA) biplot of samples and analyzed variables from spleen, gut, and muscle

The biplot shows the PCA scores of the explanatory variables as vectors and samples colored according to treatment and genetic backgrounds. The color intensity of the vectors (lines) shows the strength of their contribution to each PC. Vectors pointing in similar directions indicate positively correlated variables, and vectors pointing in opposite directions indicate negatively correlated variables.