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. 2017 Jul;47(7):1188-1199.
doi: 10.1002/eji.201646792. Epub 2017 Jun 6.

CD44 Deletion Leading to Attenuation of Experimental Autoimmune Encephalomyelitis Results From Alterations in Gut Microbiome in Mice

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CD44 Deletion Leading to Attenuation of Experimental Autoimmune Encephalomyelitis Results From Alterations in Gut Microbiome in Mice

Kumaraswamy Naidu Chitrala et al. Eur J Immunol. .
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Dysbiosis in gut microbiome has been shown to be associated with inflammatory and autoimmune diseases. Previous studies from our laboratory demonstrated the pivotal role played by CD44 in the regulation of EAE, a murine model of multiple sclerosis. In the current study, we determined whether these effects resulted from an alteration in gut microbiota and the short-chain fatty acid (SCFA) production in CD44 knockout (CD44KO) mice. Fecal transfer from naïve CD44KO but not C57BL/6 wild type (CD44WT) mice, into EAE-induced CD44WT mice, led to significant amelioration of EAE. High-throughput bacterial 16S rRNA gene sequencing, followed by clustering sequences into operational taxonomic units (OTUs) and biochemical analysis, revealed that EAE-induced CD44KO mice showed significant diversity, richness, and evenness when compared to EAE-induced CD44WT mice at the phylum level, with dominant Bacteroidetes (68.5%) and low Firmicutes (26.8%). Further, data showed a significant change in the abundance of SCFAs, propionic acid, and i-butyric acid in EAE-CD44KO compared to EAE-CD44WT mice. In conclusion, our results demonstrate that the attenuation of EAE seen following CD44 gene deletion in mice may result from alterations in the gut microbiota and SCFAs. Furthermore, our studies also demonstrate that the phenotype of gene knock-out animals may be shaped by gut microbiota.

Keywords: CD44; EAE; Metagenomics; Microbiota; Short-chain fatty acids.

Conflict of interest statement

The authors declare no competing financial interests.


Figure 1
Figure 1. Effect of fecal transfer from CD44 KO or WT mice into EAE-induced WT mice
(A) Naïve C57BL/6 mice were immunized with MOG35-55 peptide to induce EAE along with 200 ng of pertussis toxin (day 0) and 400 ng of pertussis toxin (day 2). Mice were treated with streptomycin and ampicillin (day 3, 4) followed by fecal transfer from naïve CD44WT or CD44KO mice (day 5, 6). Control groups were given either antibiotics only (abx) or sterile 0.9% NaCl solution (Nacl), without fecal transfer from either donors. (B) Clinical scores were recorded daily after EAE induction and fecal transfer. The values are shown as mean ± SEM. (C) EAE was induced in C57BL/6 mice as described in Materials and Methods. All mice were treated with antibiotics on day 4. Mice received fecal transfers as follows: EAE/WT: WT mice with EAE that received feces from WT mice; n=7, and EAE/CD44 KO: WT mice with EAE that received feces from CD44 KO mice; n=7. On day 18, spleen cells were double-stained for CD4 and Foxp3 or IL-17 and cells were analyzed by flow cytometry (D) Shows the mean± SEM data with asterisks (*) representing significant difference (< 0.05) between the two groups.
Figure 2
Figure 2. Diversity of the fecal microbiota in mice
Microbiota from the following groups of mice were analyzed: Naïve-CD44WT (NaWT; n=1), EAE-CD44WT (EAEWT; n=3), Naïve-CD44KO (NaKO; n=1) and EAE-CD44KO (EAEKO; n=3). For EAE induction, mice were immunized with MOG35-55 peptide to induce EAE along with 200 ng of pertussis toxin (day 0) and 400 ng of pertussis toxin (day 2). (A) shows the violin plots with median and quartile ranges of the Shannon Index in each group (B) shows the box-and-whisker plot for the probability of two individuals taken at random from each group belonging to the same OTU (C) shows the Circos plot representing the phylum-level taxonomic classification of OTUs in each group. From the outside to the inside track: OTUs are arranged by percentage abundances. Colors in tracks represent each phyla belonging to each group. (D) Principal components analysis (PCA) shows a close relationship between the Naïve and EAEKOs, both of which are distinct from the naïve and EAEWTs. The percentage of variation between the groups was explained by the principal coordinates indicated on the axis.
Figure 3
Figure 3. Fecal microbiota composition in mice
The composition of microbiota in each sample is based on the RDP taxonomic assignment of the 16S sequences (A) Represents Stacked area chart showing the relative abundance percentages of 351 diverse genera in each group. Blank represents the OTU sequences that are unclassified (B) Stacked bar charts representing the proportional abundance of major genera in the fecal microbiota of each group (C) Proportion of OTU percentages in each significant genus. Data represent mean± SD and comparison between EAE WTs (n=3) vs EAE KOs (n=3): ** P <0.01, *** P <0.001, Student’s t-test.
Figure 4
Figure 4. Metagenomic analysis of the fecal microbiota in mice
Shows the phylogenetic tree of the metagenomic sequences from EAEWT and EAEKO mice created according to the M5NR annotations at the (A) species level (C) species strain level. Color coding in the phylogenetic tree is on the basis of hits per bacterial sequence in the metagenomes. The data were compared to M5Nr using the default settings. Heat map depicting the relationships between the fecal microbiota belonging to EAEWT and EAEKO at (B) species level (D) species strain level, is depicted. These data were calculated for EAEWT1 (mg-rast id 4694317.3), EAEWT2 (mg-rast id 4694313.3), EAEWT3 (mg-rast id 4694310.3), EAEKO1 (mg-rast id 4694315.3), EAEKO2 (mg-rast id 4694314.3), EAEKO3 (mg-rast id 4694312.3), NaWT (mg-rast id 4694316.3) and NaKO (mg-rast id 4694311.3). The data was normalized to the values between 0 and 1. (E) Percentage abundance of species in Bacteroides genus showing significant change between EAE induced WTs and the KOs in each genus. Data are mean± SD from EAEWTs (n=3) and EAE KOs (n=3). * P <0.05, ** P <0.01, *** P <0.001, Student’s t-test.
Figure 5
Figure 5. Functional classification and short chain fatty acid analysis of the fecal microbiota
(A) Represents the functional profiles of the OTUs distributed in the EAEWT and EAEKO. Each functional category is represented in the KEGG Color Codes. Shows a change in the functional profile from the metabolism of carbohydrates, cofactors and vitamins (EAEWT) to the metabolism of nucleotides (EAEKO) (B) Heat map indicating the relative abundance of the core pathways across the EAEWT and EAEKO. The color indicator ranges from a non-abundant pathway (0, light orange) to relatively high abundant pathway (2, dark orange). (C) Relative percentage abundance of SCFAs produced by the fecal microbiota (* P < 0.05, **** P < 0.0001, ANOVA) from WT EAE (n=4), KO EAE (n=4), and EAE-induced mice given feces from CD44KO donors (Fecal Transfer, n=4). (D) Concentration of SCFAs produced by the microbiota in the fecal contents (data are mean ± SD, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, Student’s t-test).

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