Introduction

The human microbiome’s combined genetic load surpasses that of human genes with bacterial protein-coding genes estimated as being over 300 times more abundant. Most (>90%) of the human microbiota reside in the gastro-intestinal tract.1 Alterations in the gut microbiota may be influential in neurological diseases, including multiple sclerosis (MS).2 The gut microbiota regulates the immune system and contributes to the maturation and modulation of the CNS, including myelination, via multiple complex mechanisms.3 MS is considered an immune-mediated and neurodegenerative disease, with the CNS being the primary target. While both genetic and early-life environmental exposures are implicated in triggering MS, current knowledge surrounding these exposures remains incomplete. Animal models of CNS demyelination provide a proof-of-principle that the gut microbiota influence CNS-directed immune responses.4, 5 Studies involving persons with MS, while still limited in size, suggest that compared with controls, subtle differences in key gut microbial taxa exist.6

The concept of a “period of risk” during which the inciting biology is triggered is important when considering risk factors for chronic disease. A symptomatic prodromal period, possibly extending for years before clinical MS onset in adults, has recently been recognized.7 Further, childhood and adolescence are key periods of risk exposure for MS. As such, analysis of the gut microbiota in pediatric-onset MS patients represents a unique opportunity to examine pathological processes closer to actual risk acquisition. Children and youth have accrued fewer confounding exposures, such as medications and medical comorbidities compared to adults, permitting a unique window into the native gut microbiota.8

We compared the gut microbiota from stool samples of well-characterized persons with pediatric-onset MS and unaffected controls in a case-control study, taking into consideration any prior disease-modifying drug (DMD) exposure, and capturing key features seldom considered in MS studies, including other medications, dietary supplements, and stool consistency (the Bristol Stool Scale).8, 9 In addition, we included another disease group––participants with monophasic acquired demyelinating syndromes (monoADS)––to serve as an additional comparator to the chronic disease, MS. Generalizability of main findings was sought in an independent case-control cohort of pediatric-onset MS and unaffected controls.

Methods Study design and participants

This case-control study was embedded within two larger prospective North American studies of pediatric-onset MS and related demyelinating diseases. Participants ≤21 years old who provided a stool sample and had monoADS or MS (McDonald criteria, 2017) and symptom onset (first clinical attack) <18 years or were an unaffected control were eligible. MonoADS was defined as an initial acute clinical episode of symptoms involving the CNS, with evidence of inflammatory demyelination and no new clinical or MRI findings of recurrent demyelination (median observation from first symptom onset = 9.1 years, range = 3.1–12.9 years).10 Unaffected controls had no known neurological or (auto) immune-related condition (headache/migraine, asthma, and allergies were permissible) and were recruited using a mixed-methods approach (e.g., via general pediatric clinic posters, and web-based advertising), with the aim of enrolling age, sex, race, and geographical location representative individuals.

Informed assent/consent were obtained from participants/guardians. Ethical approval was obtained from each institution’s research ethics board.

The main and complementary analyses were conducted for the ‘Canada-USA cohort’ which comprised MS cases, monoADS, and unaffected controls enrolled from four Canadian and one USA site (Children's Hospital of Philadelphia), between 11/2015 and 03/2018 through the Canadian Pediatric Demyelinating Disease Network. A second, independent “USA-only cohort” comprised MS cases and unaffected controls enrolled from eight USA sites, between 06/2012 and 03/2018 through the US Network of Pediatric MS Centers was used to test generalizability of findings.

Cohort characteristics were captured for participants primarily through standardized forms and questionnaires administered to the participant/caregiver by trained coordinators at stool sample collection (details of data sources and categorization of variables are in Supplementary Methods). Briefly, these included demographics: age, sex, country of birth/residence, and race (white, non-white); clinical: comorbidities, body mass index (BMI = height(kg)/weight(m)2), cigarette smoking (active or passive), medication use (any prior DMD use for MS, and, in the 30 days pre-stool sample, any other medication/dietary supplement, defined using the World Health Organization’s Anatomical Therapeutic Chemical classification system, level 4 (Supplementary Methods)). Participants/caregivers completed the Block Kids Food Screener (NutritionQuest©)11 and, for the Canada-USA cohort, the Bristol Stool Scale,9 adapted for children. The validated food screener captured the prior week’s diet,11 reported as the percentage caloric intake of protein, fat, and carbohydrate and total grams of fiber. The seven-point ordinal Bristol Stool Scale captures stool consistency, considered a useful reflection of the gut ecosystem, and is associated with gut microbiota composition9 and was categorized into: hard (types 1–2); medium (3–5); or loose (6–7).

Stool sample collection, sequencing, and bioinformatics

A common protocol was used for stool sample collection. The following were not permitted: antibiotics or corticosteroids within 30 days pre-stool sample; any history of cytotoxic immunosuppressant use or major bowel-related comorbidity (e.g., inflammatory bowel disease, IBD). The same collection kits were used for all participants, with stool shipped on ice before −80°C storage in the central laboratories (University of Manitoba IBD Clinical/Research Centre, Winnipeg, Canada or UCSF, USA), with all sequencing performed together (batched) at the National Microbiology Laboratory, Winnipeg. Dry ice was used for cross-border shipping (USA to Canada) to prevent thawing.

DNA was extracted from stool fecal punches using the Zymo Quick-DNA™ Fecal/Soil Microbe Miniprep Kit (D6010). The 16S rRNA gene (V4 region) was amplified in triplicate, combined, purified, and pooled in equimolar concentrations. Sequencing was performed via the Illumina MiSeq platform (reagent kitv.3, 2 × 300 bp base-pair run),12 with paired-end reads trimmed to 252 bp and clustered into amplicon sequence variants (ASVs) using Deblur (v.1.1.0) and QIIME2 (Quantitative Insights Into Microbial Ecology;v.2019.4).13, 14 Data were normalized using the median of ratios method (R-package DESeq2; Differential Expression of Sequencing data) or rarefied to 16,181 sequences for alpha- and beta-diversity analyses.

Alpha and beta-diversity were examined as evenness, richness (Shannon, Margalef's index, Chao1), and weighted UniFrac.15 Gut microbiota network analyses (genus-level) used the R-package SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference, neighbourhood mode), when present in ≥80% of samples.16 Network connectivity were quantified as degrees and betweenness.17 The five most connected taxa were annotated and described. Predicted metagenome functions were generated using the validated Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) algorithm, summarized as metabolic pathways (MetaCyc database).18, 19

Statistical analyses

Cohort characteristics were described. The gut microbiota metrics were compared by disease, and then DMD status (grouped as three categories: MS, controls, monoADS; then four: MS [DMD-naïve, exposed], controls, monoADS). Alpha-diversity, network metrics (connectivity and betweenness), and the metabolic pathway relative abundances were compared between groups using nonparametric tests (Kruskal−Wallis [KW] rank sum test, Holm-adjusted [adj .] p-values). Beta-diversity was similarly explored using permutational multivariate analysis of variance (PERMANOVA). The relative abundance of individual ASVs was compared between groups at the phylum, genus, and species-level, using sex and age at stool sample (continuous) adjusted negative binomial models. Findings were expressed as crude and adjusted-rate ratios (aRR) and 95% confidence intervals (95%CI), along with p and Q-values (false discovery rate adjusted p-values).

To guide future studies, complementary analyses were performed for the Canada-USA cohort, with alpha and beta-diversity compared by: sex, age at stool sample, race, country of residence, Bristol Stool Scale, BMI, dietary intake (protein, carbohydrate, fiber, and fat), and other medications/dietary supplements), categorized as shown in the Supplementary Methods. Finally, key main analyses (alpha-, beta-diversity and genus, and species-level comparisons) were performed using a similar approach for the pediatric-onset MS cases (DMD-naïve and exposed) and unaffected controls within the independently acquired USA-only cohort. Statistical analyses were performed using R (V.4.0.2).

Results

In total, 109 participants in the Canada-USA cohort and 93 in the USA-only cohort fulfilled inclusion criteria. Characteristics are shown in Tables 1 and 2. In both cohorts, the MS cases/controls were similar in age at stool sample procurement (averaging 16.5/15.1 years for Canada-USA and 15.9/15.6 years for USA-only). Females represented 73%–75% of MS cases and 58%–69% of controls across both cohorts. As expected, monoADS participants were younger at symptom onset and at stool sample procurement versus the MS cases and/or controls; 56% were female (Table 1, Canada-USA cohort). The average dietary metrics were rather similar across groups in both cohorts, as were the Bristol Stool Scale scores in the Canada-USA cohort.

Table 1. Characteristics of the pediatric-onset multiple sclerosis cases, unaffected controls, and monophasic acquired demyelinating syndrome participants, the Canada-USA cohort. Characteristic, n (%) unless stated otherwise Multiple sclerosis cases, n = 32 Unaffected controls, n = 36 Monophasic demyelinating syndrome participants, n = 41 Female 24 (75%) 21 (58%) 23 (56%) Age at symptom onset, years: mean (SD; range) 14.0 (3.9; 4–17) – 6.9 (3.9; <1–14.6) Age at stool sample collection, years: mean (SD; range) 16.5 (3.7; 5–21) 15.1 (3.44; 7–21) 13.8 (4.2; 5–21) Self-identified race:1 White 17 (61%) 13 (41%) 31 (78%)

Birth country:2 North America

(Canada or USA)

22 (79%) 31 (91%) 35 (90%) Country of residence (at stool collection): Canada 21 (66%) 27 (75%) 38 (93%) USA 11 (34%) 9 (25%) 3 (7%) Atopy-related condition (dermatitis, psoriasis, asthma, or allergies): present 8 (25%) 7 (19%) 17 (41%) Other comorbidity:3 present 2 (6%) 1 (3%) 1 (2%) Disease-modifying drug (DMD) exposure status:4 ever/never 23 (72%)/9 (28%) – – Ever beta-interferon 11 (34%) – – Ever glatiramer acetate 7 (22%) – – Ever dimethyl fumarate 4 (13%) – – Other4 4 Any other medication (excl. DMDs, incl. vitamins, supplements) 30 days pre-stool sample:5 yes 27 (84%) 16 (44%) 28 (68%) Mean and total number of different drug classes per child 2.0; total 21 0.8; total 17 1.1; total 13 Any vitamin or dietary supplement:6 yes 26 (81%) 10 (28%) 27 (66%) Mean and total number of different vitamin or dietary supplements6 1.2; total 7 0.4; total 5 1.0; total 7 Bristol Stool Scale:7 median (IQR) 3 (2.5–4) 4 (3–4) 3 (3–4) Hard (types 1–2) 8 (26%) 7 (20%) 9 (23%) Medium (types 3–5) 21 (68%) 27 (77%) 28 (72%) Loose (types 6–7) 2 (6%) 1 (3%) 2 (5%) BMI:8 crude median (range) 22.8 (13.8–36.3) 19.9 (13.2–29.9) 19.7 (14.0–30.0) Overweight/obese (≥85th percentile) 5 (16%) 6 (18%) 5 (12%) Cigarette smoking (passive or active) ever pre-stool sample 2 1 1 Block Kids Screener:9 dietary intake per day, median % protein caloric intake (range) 16% (8–23) 16% (10–24) 18% (13–26) % fat caloric intake (range) 34% (28–51) 34% (23–45) 35% (26–43) % carbohydrate caloric intake (range) 50% (28–63) 50% (35–68) 50% (33–64) Grams of fiber (range) 9 (3–20) 11 (4–29) 10 (4–25) Total with available/valid diet data n = 90 26 34 30 Percentage calculated with the denominator reflecting individuals with non-missing data for that variable; ADS = acquired demyelinating syndrome; BMI = body mass index; DMD = disease modifying drugs; excl. = excluding; MS = multiple sclerosis; SD = standard deviation. Antibiotic use: by design, no participant had used a systemic antibiotic within 30 days pre-stool sample. Only one participant (with monophasic acquired demyelinating syndrome) had a record of antibiotic use within 3 months pre-stool sample (i.e., >30 to 90 days pre-stool sample). Additional numbers shown below ordered as “MS/controls/ADS”. Totals exceed 23 (72%) of those ever DMD-exposed pre-stool sample as three MS cases were exposed to >1 DMD (the most recent pre-stool sample is shown first: natalizumab, beta-interferon [IFNB]; IFNB, dimethyl fumarate [DMF]; teriflunomide, DMF). Table 2. Characteristics of the pediatric multiple sclerosis (MS) cases and unaffected controls from the USA Network of Pediatric MS Center’s microbiome study. Characteristic, n (%) unless stated otherwise MS cases = 51 Unaffected controls, n = 42 Female 37 (73%) 29 (69%) Age at symptom onset, years: mean (SD; range) 14.5 (2.2; 8.6–17.9) – Age at stool sample collection, years: mean (SD; range) 15.9 (2.1; 9.6–19.7) 15.6 (2.8; 8.1–20.7) Disease duration at stool sample collection, mean (SD; range)

1.3 years (1.1; 0.1–5.4)

16.2 months (13.3; 1.1–65.3)

– Self-identified race: White 35

34

1 missing

Disease-modifying drug (DMD) exposure status: ever/never 33 (65%)/18 (35%) – Ever beta-interferon 10 (30%) – Ever glatiramer acetate 20 (61%) – Ever dimethyl fumarate 3 (9%) – Ever natalizumab 6 (18%) BMI: crude median (range) 25.0 (17.4–47.0)

22.0 (9.0–43.9)

1 missing

Overweight/obese (≥85th percentile) 9 5 Block Kids Screener: dietary intake per day, median % protein caloric intake (range) 16.9 (10.2–25.7) 17.3 (12.0–25.7) % fat caloric intake (range) 35.5 (21.2–44.9) 36.2 (25.0–47.0) % carbohydrate caloric intake (range) 47.1 (32.5–67.3) 47.7 (29.8–65.6) Grams of fiber (range) 10.1 (1.8–25.1) 12.1 (2.9–23.5) Total with available/valid diet data n = 46 n = 40 Percentage calculated with the denominator reflecting individuals with non-missing data for that variable; BMI = body mass index; DMD = disease modifying drugs; MS = multiple sclerosis; SD = standard deviation. Beta-interferon products used included: −1a (IM or SC), −1b (SC), and peginterferon beta−1a; 6 MS cases had been exposed to 2 different DMDs (the most common sequential combination was for a beta-interferon or glatiramer acetate followed by natalizumab; n = 3 participants).

All cases had relapsing-remitting MS, and the mean disease duration (from symptom onset) at stool sample = 30.1 months (SD:35.1) in the Canada-USA cohort and 16.2 months (SD:13.3) in the USA-only. Approximately one-third of MS cases had never used a DMD prior to stool sample procurement [nine (28%) Canada-USA and 18 (35%) USA-only]. Beta-interferon or glatiramer acetate were most commonly used (Tables 1 and 2).

For the Canada-USA cohort, the mean number of non-DMD medication/supplement classes used in the previous 30 days = 2.0 for MS, 1.1 for monoADS and 0.8 for control. The most common were vitamins/dietary supplements, with >80% (n = 26) of MS cases, 66% (n = 27) of monoADS and 28% (n = 10) of controls taking ≥one. Atopy was common, affecting 32 (29%) of the participants, but only 4 (4%) had any other comorbidity (Table 1).

Canada-USA cohort Gut diversity and taxa-level findings by disease and DMD status

Alpha and beta-diversity did not differ by disease status (across the three groups compared: MS monoADS, controls) or by DMD status (four groups: MS DMD-exposed/naïve, monoADS, controls), all p > 0.1. Figure 1 depicts richness, all other diversity metrics are shown in Table 3.

Gut microbiota alpha diversity (richness) for the pediatric-onset multiple sclerosis cases (DMD-exposed or naïve), monophasic acquired demyelinating syndromes (monoADS), and unaffected control participants for the Canada-USA cohort. Margalef's richness index: (S − 1)/ln(n), where S is the number of taxa, and n is the number of individuals. ASV data were rarefied. Box-and-whisker plots: thick black horizontal line = median; horizontal edges of box depict Q1 and Q3 (interquartile range); the ends of the whiskers represent one and a half times the interquartile range (1.5*IQR); circles = individual outliers. For example, as depicted, the median richness for each of the participant groups were 18.0 for the multiple sclerosis, 19.2 for the monophasic acquired demyelinating syndrome (ADS) and 18.8 for the unaffected controls. The clinical relevance of these slight differences is unknown. None of the comparisons were statistically significant (all p > 0.5). The overall group p-values shown here are based on the Kruskal–Wallis test and the pairwise p-values are based on the Dunn’s Kruskal–Wallis with a Holm adjustment for multiple comparisons: (1) Overall group p = 0.507; MS cases versus controls (p = 0.737), ADS versus controls (p = 1.00), MS versus ADS (p = 0.583). (2) Overall group p = 0.521; MS cases DMD-exposed versus controls (p = 0.761), MS cases DMD-naïve versus controls (p = 0.783), MS DMD-exposed versus naïve (p = 1.00).

Table 3. Alpha and beta diversity metrics for the gut microbiota of pediatric-onset multiple sclerosis (MS) cases, unaffected controls and monophasic acquired demyelinating syndromes (ADS) participants in the Canada-USA cohort. Alpha diversity, median (quartiles) MS cases, n = 32 Unaffected controls, n = 36 ADS, n = 41 MS versus controls versus ADS MS cases only Comparisons by DMD and disease status DMD exposed, n = 23 DMD naïve, n = 9 DMD exposed versus naïve MS cases DMD naïve MS cases versus DMD exposed MS cases versus controls Richness (number of observed ASVs; Margalef index1) 18.2 (15.9, 22.1) 19.7 (16.7, 22.9) 19.7 (14.5, 22.5) p = 0.507KW 17.6 (16.2, 21.0) 20.1 (13.8, 22.9) p = 0.675MW p = 0.455KW Evenness (Shannon) 0.682 (0.634, 0.706) 0.680 (0.643, 0.693) 0.690 (0.669, 0.715) p = 0.268 KW 0.680 (0.634, 0.719) 0.684 (0.642, 0.689) p = 0.438MW p = 0.766 KW Chao1 (a richness estimate/assesses importance of rare ASVs) 216 (174, 257) 236 (201, 260) 214 (167, 260) p = 0.4942 KW 208 (180, 245) 232 (151, 280) p = 0.722MW p = 0.497KW Beta diversity derived from Rsquared,weighted UniFrac; PERMANOVA 1.70%; p = 0.4982 2.10%; p = 0.6862 2.00%; p = 0.7902 ADS = acquired demyelinating syndromes; DMD = disease modifying drugs; MS = multiple sclerosis. Gray shading = not applicable. KW Kruskal–Wallis; MW Mann–Whitney test. Findings not shown for the following comparisons (all p-values were derived from two tests): (1) MS cases versus controls, ADS versus controls, MS versus ADS; (2) MS cases DMD exposed versus controls, MS cases DMD naive versus controls, MS DMD exposed versus naïve (all p > 0.05 based on Dunn’s Kruskal–Wallis with a Holm adjustment for multiple comparisons). Diversity metrics shown to 3 significant figures. ASV data were rarefied.

At the taxon-level, 8 phyla, 144 genera, and 228 species had sufficient coverage, based on their presence in 80% of samples, for modelling by disease status, with nominal significance (p < 0.05) reached for 5 (63%), 44 (31%), and 60 (26%), respectively (Tables S1–S3).

Overall, five phyla, 11 genera and four species reached significance (p,Q < 0.05) in either the disease or DMD status comparisons. Of the five phyla (p,Q < 0.05) – Actinobacteria, Firmicutes, Fusobacteria, Patescibacteria, Verrucomicrobia, three differed between the MS cases and controls (Table S1). Compared to controls, cases were depleted for Actinobacteria (aRR = 0.57;95%CI:0.36–0.91, p,Q < 0.035) and Firmicutes (aRR = 0.66;95%CI:0.46–0.95, p,Q < 0.038), while enriched for Verrucomicrobia (aRR = 13.9;95%CI:2.6–73.8); p,Q < 0.05), with the latter differing by DMD status, being higher in the DMD-exposed, but not naïve, MS cases (p,Q < 0.05). Other group differences also emerged, for example, compared to monoADS, MS cases had a fourfold higher relative abundance of Patescibacteria (a recently identified superphylum; aRR = 4.2;95%CI:1.6–11.2, p = 0.004,Q = 0.01). This higher abundance remained consistent for both the DMD-exposed and naïve MS cases, although only the former reached significance (p,Q < 0.035 vs. monoADS).

Eleven genera were identified (p,Q < 0.05): Actinomyces, Anaerosporobacter, Bacteroides, Enterorhabdus, (Eubacterium) eligens, Pseudomonas, Ruminococcaceae NK4A214-group, Ruminococcaceae UCG−003 and three uncultured/unnamed taxa within Lachnospiraceae, Ruminococcaceae and Clostridiales vadin BB60 (Fig. 2A,C, and Table S2). Four genera Anaerosporobacter, Ruminococcaceae UCG−003, Clostridiales vadin BB60, Pseudomonas differentiated MS cases from the others, with the latter two influenced by the cases’ DMD exposure status. Both Anaerosporobacter and Ruminococcaceae UCG−003 were lower in MS versus either controls or monoADS (for Anaerosporobacter both aRR<0.01, p,Q < 0.05; for Ruminococcaceae UCG−003, the respective aRR = 0.23;95%CI:0.09–0.59, p,Q < 0.05 and 0.35;95%CI:0.14–0.90, p < 0.05, but Q > 0.05), with the direction of findings for both genera consistent regardless of the MS cases’ DMD exposure. For Clostridiales vadin BB60, both DMD-naïve and exposed MS cases were lower in relative abundance versus monoADS (p,Q < 0.01 and p = 0.001, but Q = 0.06, respectively), while the DMD-naïve MS cases were lower versus both the controls (aRR = 0.02;95%CIs:0.00–0.17, p,Q < 0.04) and the DMD-exposed cases (p < 0.05, although Q > 0.05). Pseudomonas was also lower for the DMD-naïve versus exposed MS cases (p,Q < 0.03), while MS cases were enriched versus monoADS (aRR = 16.5;95%CI:4.3–62.5, p,Q < 0.007) which remained significant for the DMD-exposed MS cases only (vs. monoADS, p,Q < 0.004).

Heatmaps summarizing gut microbiota genus-level findings (ASV counts) expressed as sex and age-adjusted rate ratios for the pediatric-onset multiple sclerosis (MS), monophasic acquired demyelinating syndrome (ADS) and unaffected control participants for the Canada-USA cohort. (A) Three-groups compared: multiple sclerosis, ADS, and controls. (B) Three-groups compared: multiple sclerosis, ADS, and controls, overlaid with a hierarchical cluster analysis. (C) Four-groups compared: multiple sclerosis (DMD-naïve and exposed), ADS, and controls. (D) Four-groups compared: multiple sclerosis (DMD-naïve and exposed), ADS, and controls, overlaid with a hierarchical cluster analysis. ADS = monophasic acquired demyelinating syndrome, DMD = disease-modifying drug, MS = pediatric-onset multiple sclerosis; MS DMD-naïve = MS case has never been exposed to a DMD at the time of the stool sample. Each Panel summarizes age and sex-adjusted RRs derived from a single negative binomial regression model for each genus (two models in total, one for three group comparison, and another for four group comparisons), with only the RRs reaching nominal significance (p S1–S3) for unadjusted and adjusted models). For each comparison, the second group forms the reference. *p p p p Q 22 Biological relevance is inferred from the clusters (rather than being directly assessed).

Other genera appeared particularly relevant in differentiating monoADS participants from the other groups––both Actinomyces and Bacteroides differed versus the MS cases or controls (p < 0.05, although not all Q < 0.05), while no differences emerged when the MS cases and controls were directly compared (p > 0.05). Shared features for both disease groups were observed: versus controls, both Ruminococcaceae-NK4A214 group and (Eubacterium) eligens were lower in MS and monoADS (p,Q < 0.05), while the two disease groups did not differ (p > 0.05). Finally, for the remaining three genera, findings were largely driven by differences in relative abundance between MS and monoADS participants. For example, both Lachnospiraceae and Ruminococcaceae were lower in MS (and the DMD-exposed subgroup) versus monoADS, while the latter were enriched versus controls (all p,Q < 0.05). Conversely, Enterorhabdus was higher in MS versus monoADS (aRR = 26.2;95%CI:4.6–149.3, p,Q < 0.02), with the direction of findings consistent irrespective of the MS cases’ DMD-exposure (p < 0.01). Further, the monoADS participants were depleted versus controls (p,Q < 0.05).

The four identified species (p,Q < 0.05) were housed within the genera: Anaerosporobacter, Enterorhabdus, Pseudomonas, or family Ruminococcaceae (Table S3). Findings largely mirrored the genus-level observations. Briefly, Anaerosporobacter sp. (family Lachnospiraceae) was lower for MS cases versus either controls (p < 0.0001, Q = 0.003) or monoADS (p = 0.00153, p,Q > 0.05), with both aRR<0.02. Compared to monoADS, Enterorhabdus sp. was higher for controls (p,Q < 0.05) and MS cases (p < 0.05, Q > 0.05). Pseudomonas sp., and Ruminococcaceae sp. were both lower in monoADS, particularly versus the DMD-exposed MS cases (p,Q < 0.05).

Gut microbiota network analysis by disease and DMD status

From network analyses, the MS cases, monoADS, and controls’ genus-level gut microbiota did not differ by degree of connectivity or betweenness (p > 0.1, Figs. 3 and 4). However, findings differed by DMD status; the naïve MS cases exhibited a visually distinct gut microbial network (Fig. 3) and had a higher connectivity (betweenness) versus the other three groups (DMD-exposed cases, monoADS, and controls, all p < 0.00007, Fig. 4). Further, annotation of the five most connected taxa suggested distinct patterns across groups (Fig. S1A–E). For example, four of the five most connected taxa (by degrees or betweenness) for controls were short chain fatty-acid (SCFA) producers (all were in the Firmicutes phylum, e.g., Ruminococcaceae family members [UCG-003, UCG-005], Anaerostipes, and Veillonella). Conversely, for the MS cases, several housed microbes commonly cited as opportunistic pathogens (e.g., Actinobacteria [phylum Actinomyces], Gemella, and Leuconostoc [phylum Firmicutes]). Among the most connected taxa (by degrees or betweenness) for the DMD-naïve MS cases, several had been identified relatively recently; all were contained in the phylum Firmicutes: Candidatus Stoquefichus, Mogibacterium, Phocea, and Subdoligranulum.