The Role of Multiple Sclerosis Therapies on the Dynamic of Human Gut Microbiota

doi:10.21203/rs.3.rs-2075054/v2

Gut microbiota, the total microorganisms in our gastrointestinal tract, might have an implication in multiple sclerosis (MS), a demyelinating neurological disease. Our study included 50 MS patients and 21 healthy controls (HC). Twenty patients received a disease modifying therapy (DMT), interferon beta1a or teriflunomide, 19 DMT combined with homeopathy and 11 patients accepted only homeopathy. We collected in total 142 gut samples, two for each individual: at the study enrolment and eight weeks after treatment. We compared MS patients’ microbiome with HC, we analysed its evolution in time and the effect of interferon beta1a, teriflunomide and homeopathy. There was no difference in alpha diversity, only two beta diversity results related to homeopathy. Compared to HC, untreated MS patients had a decrease of Actinobacteria, Bifidobacterium, Faecalibacterium prauznitzii and increased Prevotella stercorea, while treated patients presented lowered Ruminococcus and Clostridium. Compared to the initial sample, treated MS patients had a decrease of Lachnospiraceae and Ruminococcus and an increased Enterococcus faecalis. Eubacterium oxidoreducens was reduced after homeopathic treatment. The study revealed that MS patients may present dysbiosis. Treatment with interferon beta1a, teriflunomide or homeopathy implied several taxonomic changes. DMTs and homeopathy might influence the gut microbiota.

multiple sclerosis

gut microbiota

dysbiosis

disease modifying therapy

microbiome

Multiple sclerosis patients have a distinct microbiota compared to healthy controls.
Two months treatment with either interferon beta1a, teriflunomide or homeopathy implies various taxonomic changes for the patients’ microbiota.

The human gut microbiota is composed of a large diversity of microorganisms that reside inside our gastrointestinal tract and it is taxonomically classified into species, genus and phyla, containing both beneficial and pathogenic microbes (1, 2). It is mostly shaped in early life of childhood depending on several factors, such as the type of delivery, milk feeding, weaning and medication. After the first three years of life, it reaches a partial stability, with continuous subtle changes caused by external factors: diet, exercise, body mass index, environmental and medication exposure (1). A microbiome is formed of the total genes, proteins, metabolites of all organisms (3).

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system, where genetic susceptibilities meet environmental factors. It includes impaired blood-brain barrier integrity, inflammation, demyelination, oligodendrocyte loss, gliosis and axonal degeneration (4). Recent studies outline that MS patients have a distinct microbiome composition compared to healthy controls (HC) (5) .

Due to the fact that the microbiome is unique for each individual and contains an enormous variety of microorganisms, it is difficult to describe a specific healthy microbiota (1).¹ A healthy microbiota provides many essential functions, including gut permeability and motility, vitamins synthesis, absorption, but also the development of innate immune system (6).⁶ Based on this role in immunomodulation, recent studies indicate a possible connection between the microbiome and some neurological immune-mediated pathologies (5).

The concept of gut microbiota-brain axis refers to different communication mechanisms between the gastrointestinal and nervous system(7). Microbiota can influence the central nervous system (CNS) using neural networks, with the modulation of different neurotransmitters (such as gamma aminobutyric acid, serotonin, dopamine, histamine) and via the sympathetic or parasympathetic nervous system, especially by the vagus nerve. Another mechanism is the endocrine pathway, when the hypothalamic-pituitary-adrenal axis releases corticoids, as a response to stress, which can alter the microbiota composition and increase intestinal permeability. Gut microorganisms can release metabolites (lipopolysaccharide, peptidoglycan). Also, the microbes can alter the nervous system through immunoregulation. This includes impaired antigen presentation, cytokines and lymphocytes production, differentiating different types of T cells in the gut-associated lymphoid tissue (GALT) (6). The short chain fatty acids (SCFAs) metabolism is involved in microglial cells activation and the blood-brain barrier leakage (7).

Complementary and alternative medicine, including homeopathy, is used for up to 30% of patients, mostly associated with conventional forms of therapy, to relieve general symptoms, pain, spasticity, sensitivity problems, walking impairment, fatigue, memory loss (8). Its effectiveness is not widely studied, but it is estimated at 23–53%, with up to 70–80% of patients declaring improvement in their general state (9).

Homeopathy’s principle “let like be treated by like” is based on the idea that an illness can be treated by substances which trigger the same pathological symptoms in healthy individuals. Homeopathic remedies are highly diluted natural substances, which are prescribed on a case-by-case basis, taking into account other aspects apart from specific medical signs, as well (10). Some symptoms in MS are frequently ameliorated by some substances: Causticum for urinary disfunction, Phosphorus for optic neuritis, Cuprum metallicum and Nux vomica for spasms, Secale for sensory symptoms (10).

As presented further on in this manuscript, a large number of studies outlined that MS patients have a distinct microbiome compared to HC and that MS treatments can modify the gut microbiome, but homeopathy has not been studied in the field of MS microbiome. Our hypothesis is that homeopathic treatments may imply a distinct microbiome pattern.

The aims of this study are to investigate if MS patients’ microbiome changes due to immunomodulatory drugs and complementary homeopathic treatments. Also, the differences between MS and HC’s microbiome were assessed.

2.1. Subjects

The study was prospective, longitudinal, analytical, observational and case-control type. It included 50 patients diagnosed with MS, from the Neurology Department in The Emergency Hospital Cluj-Napoca, between January 2019 to May 2020. Twenty-one HC were recruited. The ethical authorization was obtained and each subject signed an informed consent.

The MS group included adult patients with confirmed RRMS or CIS, according to the revised McDonald diagnosis criteria (11), with an EDSS score of maximum 5 points, who haven’t undergone DMT in at least a year prior to enrolling in the study. We excluded patients with progressive MS, unclear diagnostic criteria, active gastrointestinal pathologies, who were pregnant or breastfeeding or who underwent long term treatment with probiotics, antivirals, antibiotics, non-steroidal anti-inflammatory drugs or proton-pumps inhibitors in the past 6 months. The HC group was composed of healthy individuals without MS, matched for age and gender with the MS patients, with no other active gastrointestinal disorder, cancer, autoimmune disease, and who weren’t taking any chronic treatment. All subjects declared that no major changes occurred in their diet or lifestyle during the study.

After providing the first gut sample, our patients were prescribed treatment with DMT, as a mutual consent, depending on several factors, such as the disease severity, patient comorbidities, drug safety profile and also its accessibility in our country (12). According to our patients’ disease characteristics, they received either oral teriflunomide 14 mg daily or intramuscular interferon beta 1a, 30 µg / 0.5 ml once a week. Some patients agreed to receive complementary homeopathic treatment, in addition to the standard therapy. A small group refused the DMT, either they did not agree with the conventional therapy at all or they wanted a delay in initiating this therapy. However, they accepted the homeopathic treatment as an option.

The homeopathic treatment included an individualized scheme, according to each patient’s MS symptoms and other psychological and life aspects which implies a specific pattern.

A more diluted solution is considered to have a higher potency (13). The potency choice is related to the health level: a lower health level implies a low potency, such as 30C, for a longer period of time, up to 3 months, while a 200CH treatment is limited for a few days. The remedies were prescribed by a neurologist accredited in Homeopathy and they were taken daily, 7 granules sublingually, 3 consecutive days for the patients assigned with a concentration of 200CH and 1–3 months for those with the 30CH concentration.

2.2. Sample collection

All subjects provided fecal samples corresponding to the laboratory instructions: at any moment of the day, with no special restrictions, using special stool containers. The provided samples were stored at minus 20 degrees Celsius and then shipped to the laboratory for the DNA extraction. Each subject collected two fecal samples: one before treatment (sample 1, S1) and one two months after starting the therapy or study enrollment (sample 2, S2).

2.3. The 16rRNA sequencing and statistical analysis

The metagenomic analysis of the universally present 16S subunit ribosomal RNA (rRNA) gene used specific hypervariable regions (V1-V3, V3-V4, ITS1&ITS2) throughout the Illumina MiSeq platform. The first results are the OTUs, microorganisms that have a DNA similarity of at least 97% to a laboratory database and then, classified into specific taxa, with their frequency and relative abundance.

Gut microbiota diversity is a mathematical measure of variability, characterized by richness (the number of different species) and evenness (the uniformity of different species). For alpha diversity, the diversity within the sample, the Chao index is an estimator of species richness. Shannon and Simpson are parameters for both richness and evenness, their values increase with the number of species and a more even distribution (14).

Using the frequency matrices, each combination of pairs was analyzed using the Wilcoxon Rank-Sum Test. For beta diversity, the diversity between samples, we used the Bray-Curtis and the Jaccard indexes, where the graphical distance between them indicates the difference. Principal coordinate analysis (PCoA) was obtained from relative abundance matrices. Linear Discriminant Analysis Effect Size (LEfSe) figures were calculated with an alpha value of 0.05 and a logarithmic linear discriminant analysis (LDA) score threshold of 2.0. Relative Abundance Stacked Bars and heatmaps were also generated.

The MS group consisted of 31 females and 19 males, with a median age of 30.5 9.9 years, 44 of them being diagnosed withrelapsing-remitting MS (RRMS) and 6 with clinically isolated syndrome (CIS), a mean Expanded Disability Status Scale (EDSS) score of 1.8 points and a mean disease duration of 3.4 years. All the MS specific characteristics are presented in table 1. The majority of them were naive (with no previous disease modifying treatment (DMT)), only 4 patients received treatment that was interrupted in the previous year.

The HC group was assembled considering people with similar demographic features as the MS group: 62% females and 38% males, a median age of 28 9.7 years.

All subjects were separated into four different groups, presented in table 2. Group 1 (G1) received a form of DMT: either interferon beta1a (subgroup G1A), or teriflunomide (subgroup G1B). Group 2 (G2) agreed on a combination of DMT and homeopathy: subgroup G2A with interferon beta1a and homeopathy, subgroup G2B with teriflunomide and homeopathy. Group 4 (G4) was composed of patients who refused conventional therapy and accepted homeopathic treatment. Group 5 (G5) was formed of HC.

Regarding the homeopathic treatment distribution, each patient received a personalized medication. Both groups 2 and 4 had patients receiving one of the following: Phosphorus, Lycopodium, Natrium muriaticum, Lac Caninum, Nux Vomica, Lachezis, Nitric acid, Rhus Toxicodendron and Tarentula Hispanica. In group 2 there were also patients receiving Pulsatilla, Calcarea Carbonica, Sulphur, Ignatia, Aconitum or Causticum.

Table 1. Multiple sclerosis (MS) specific characteristics of our cohort

Characteristics	MS patients (n=50)
MS type, n (%)
RRMS	44 (88%)
CIS	6 (12%)
EDSS score, mean (range)	1.8 (0-5)
Confirmed disease duration, mean (SD) (y)	3.4 (6.2)
Number of relapses, mean (SD)	2.5 (2.13)
MMSE score, mean	29.4
Treatment, n (%)
teriflunomide	10 (14.28%)
interferon beta1a	10 (14.28%)
teriflunomide+ homeopathy	10 (14.28%)
interferon beta+ homeopathy	9 (12.8%)
homeopathy	11 (15.7)

Abbreviations: MS=multiple sclerosis, n=number of patients, SD=standard deviation, y=years, RRMS= relapsing-remitting MS, CIS= clinically isolated syndrome, EDSS=Expanded Disability Status Scale, MMSE=Mini-Mental State Exam.

Table 2. Group naming convention

Group	Number of subjects	Subgroup	Number of subjects
G1 (DMT)	20	G1A (interferon beta1a)	10
G1 (DMT)	20	G1B (teriflunomide)	10
G2 (DMT+ homeopathy)	19	G2A (interferon beta1a+ homeopathy)	9
G2 (DMT+ homeopathy)	19	G2B (teriflunomide+ homeopathy)	10
G4 (homeopathy)	11
G5 (HC)	21
G1G2G4 (all MS patients)	50

Table 2 summarizes the acronyms we use for groups identification.
Abbreviations: DMT=disease modifying treatment, HC=healthy controls

3.1. Microbiome diversity

We studied overall differences in microbial composition by using alpha and beta diversity. There was no statistically significant difference in alpha diversity between MS patients and HC before (p=0.85) and after the treatment (p=0.95), between other group combinations ,for sample 2 (S2): G1 vs G2 (p=0.89), G2 vs G4 (p=0.98), G1 vs G4 (p=0.64), and their subgroups, G2A vs G2B (p=0.23), G1A vs G2A (p=0.06) between sample 1 (S1) and S2 for MS patients (p=0.93), G2 (p=0.38) or G4 (p=0.79). Also, there was no difference in alpha diversity between the MS patients treated with DMT (G1G2) and the patients not following DMT (G4), p=0.81. All p values are presented in table 3.

As we analysed beta diversity, there was a difference for S2 between G4 and G2B (p=0.007) (figure 1) and between G4 and G1A (p=0.012), but no statistically significant difference between the MS patients and HC or between the two samples for MS patients (all p<0.05). Other results are outlined in table 3 and fig.1* (*supplementary data files).

Table 3. Overview of all p values for microbial diversity

Comparison	Alpha diversity			Beta diversity
Comparison	Chao	Shannon	Simpson	Bray-Curtis	Jaccard
G5vsG1G2G4(S1)	p=0.85	p=0.33	p=0.55	p=0.15	p=0.13
G5vsG1G2G4(S2)	p=0.95	p=0.16	p=0.39	p=0.14	p=0.12
G1vsG2(S2)	p=0.89	p=0.69	p=0.59	p=0.85	p=0.9
G2vsG4(S2)	p=0.98	p=0.97	p=0.87	p=0.12	p=0.2
G1vsG4(S2)	p=0.64	p=0.8	p=1	p=0.15	p=0.13
G1G2vsG4(S2)	p=0.81	p=0.91	p=0.91	p=0.19	p=0.2
G1AvsG2A(S2)	p=0.06	p=1	p=0.84	p=0.06	p=0.06
G1BvsG2B(S2)	p=0.13	p=0.68	p=0.68	p=0.23	p=0.35
G2AvsG4(S2)	p=0.47	p=0.41	p=0.66	p=0.06	p=0.06
G2BvsG4(S2)	p=0.55	p=0.39	p=0.47	p=0.007	p=0.012
G1AvsG4(S2)	p=0.2	p=0.35	p=0.72	p=0.012	p=0.016
G1BvsG4(S2)	p=0.62	p=0.62	p=0.72	p=0.62	p=0.33
G2AvsG2B(S2)	p=0.23	p=0.14	p=0.14	p=0.06	p=0.11
S1vsS2(G1G2G4)	p=0.93	p=0.26	p=0.36	p=0.88	p=0.93
S1vsS2(G2)	p=0.38	p=0.6	p=0.69	p=0.95	p=0.99
S1vsS2(G4)	p=0.79	p=0.4	p=0.44	p=0.98	p=0.98
S1vsS2(G2A)	p=0.96	p=0.86	p=0.8	p=0.87	p=0.93
S1vsS2(G2B)	p=0.24	p=0.58	p=0.68	p=0.96	p=0.98

*Values in bold are statistically significant. This table presents specific comparisons between our cohorts and diversity indexes applied, along with the p value of each test.

Abbreviations: S1=sample 1; S2=sample 2;

3.2. Taxonomic differences

The 16S analysis results include the frequency and relative abundance of the species.

For the MS cohort, we identified 51 Operational Taxonomic Units (OTUs) at the phylum level, with the majority composing of Firmicutes (49.45%), Bacteroidetes (34.3%), Actinobacteria (4.77%) and Proteobacteria (3.6%). At the species level, we analysed 1505 OTUs, with the highest relative abundance of Prevotella copri (10.75%) and Bacteroides (10.3%), followed by Faecalibacterium prausnitzii (6.63%) and Blautia (4.49%).

We analysed taxonomic differences between our MS cohort (G1G2G4) and HC (G5) before any treatment (S1). Untreated MS patients presented, compared to HC, increased relative abundance of Lentisphaerae phylum (p=0.005), Prevotella stercorea species (p=0.02) (Bacteroidetes phylum). They had a decreased level of Actinobacteria phylum (p=0.01), with its species Bifidobacterium (p=0.01) and Bifidobacterium adolescentis (p=0.007), also decreased level of Bacteroides coprophilus (p=0.02) (from Bacteroidetes phylum). Firmicutes phylum has reduced relative abundance of Faecalibacterium prausnitzii (p=0.03), Lachnospiraceae (p=0.01), Staphylococcus hominis (p=0.02) and Staphylococcus bacterium (p=0.02). Proteobacteria phylum has a decreased in Haemophilus (p=0.04) and Escherichia coli (p=0.04). The other statistically significant differences are presented in figure 2A, fig.2*-4*.

When we analysed the second samples, the results for the MS treated patients compared to the HC were slightly different, with reduced Bifidobacterium (p=0.02), Ruminococcus (p=0.04) and Clostridiales (p=0.01) and a higher prevalence of Gemella (p=0.04), Megaspherae (p=0.02) and Prevotella stercorea (p=0.02) for MS patients compared to HC (figure 2B,fig.5*-7*).

We analysed different comparisons between groups after a two months treatment. When analysing the microbiota of the MS patients who received DMT (G1) compared to the patients undergoing complementary homeopathic treatment (G2), we outlined the following results. G1 had , compared to G2, enriched relative abundance for Catenibacterium (p=0.02), Lachnospiraceae (p=0.02), Sharpea (p=0.02) and Gammaproteobacteria (p=0.02) and decreased level of Shuttleworthia (p=0.04), Cytophaga (p=0.03) and Rumen (p=0.02). The G1A subgroup is more abundant in Cyanobacteria (p=0.002), Catenibacterium (p=0.01), Alloprevotella (p=0.03), Lachnospiraceae (p=0.003), Clostridium (p=0.01), Prevotella copri (p=0.04), Prevotella stercorea (p=0.03) compared to G2A, after treatment. On the other side, G2A shows higher levels of Proteobacteria (p=0.02), Escherichia shigella (p=0.01), Barnesiella (p=0.03), Serratia (p=0.008), Lactobacillus zeae (p=0.02). In the G1B group Escherichia shigella (p=0.007), Lactobacillus (p=0.01), Enterobacter (p=0.02), Enterococcus faecalis (p=0.03) is more prevalent than in G2B, while G2B shows higher signs of Rickenellaceae (p=0.01) and Lachnospiraceae (p=0.02). We also compared the two groups having received homeopathic treatment, G2 and G4. We outlined that G2 presented a higher relative abundance for Megasphaera (p=0.04), Eubacterium oxidoreducens (p=0.02), Veillonellaceae (p=0.02) and Gardnerella (p=0.02), compared to G4. G4 was enriched in Faecalibacterium prausnitzii (p=0.01), Akkermansia muciniphila (p=0.02), Lachnospiraceae (p=0.003), Bacteroides acidifaciens (p=0.04) and pectinophilus (p=0.03), Veilonella (p=0.01), compared to G2 (fig.8*-20*).

Another point of interest in our research was to emphasize how the microbiome changes in time, from the moment the patients were prescribed a therapeutic scheme (S1) until two months after the treatment began (S2). For the MS cohort, the taxonomic changes are shown in figure 3 with the most abundant 25 organisms. The statistically significant differences between the two samples were more prevalent for Firmicutes phylum, as there were identified reduced levels for Ruminococcus (p=0.03), Oscillospira (p=0.0004), Anaerotruncus (p=0.02), Lachnoclostridium (p=0.01), Lachnospiraceae (p=0.02) and Eubacterium oxidoreducens (p=0.04) in the MS patients after treatment compared to the baseline sample. The MS treated patients presented more diverse bacteria changes, with an increase in Firmicutes phylum (Sulfobacillus p=0.02, Enterococcus faecalis p=0.02), Bacteroidetes (Hymenobacter p=0.04), Proteobacteria (p=0.04), Actinobacteria (Amycolatopsis p=0.04), Fusobacterium (Leptotrichiaceae p=0.04) after treatment compared to before (fig.21*-25*).

The G2 group presented a lower relative abundance in some Firmicutes components (Oscillospira p=0.01, Lachnoclostridium p=0.03, Lachnospiraceae p=0.03) and Proteobacteria (Helicobacteraceae p=0.01, Undibacterium p=0.03) after treatment, compared to baseline. The group with only homeopathic treatment (G4) had reduced Eubacterium oxidoreducens after treatment, compared to the first sample (p=0.03) (fig.25*-32*).

Our data revealed no major changes in diversity between MS patients and HC, before or after any kind of treatment. Compared to HC, untreated patients presented an increase for Prevotella stercorea and decreased levels in Actinobacteria and Faecalibacterium prausnitzii. The taxonomic changes after two months of treatment were slightly different, with increased Gemella levels and decreased Ruminococcus ones for MS subjects. Comparisons between groups after treatment emphasize different taxonomic modifications. For example, patients treated with homeopathic treatment had an increase for Faecalibacterium prausnitzii, Akkermansia muciniphila and Bacteroides compared to combination therapy. Also, there were beta diversity significant results for treatment with homeopathy versus homeopathy combined with teriflunomide and also for homeopathy versus interferon beta1a. Analyzing changes that have occured in time, between the two samples, we noticed a decrease of Ruminococcus, Lachnospiraceae and Eubacterium oxidoreducens levels among MS treated patients compared to their baseline sample. Homeopathy induced in time the reduction of Eubacterium exidoreducens.

There is a consensus that a healthy microbiota is characterized by a balance between the microorganisms and the host, with a large diversity, resilience and stability, in order to maintain the host homeostasis and immune functions (15). Although external factors can rapidly modify the microbiota, healthy stable bacteria return to their original composition and change only with persistent habits. A higher biodiversity means higher differences in species and thus, more biological functions they can exert, a better stability, the ability to resist to changes and to recover (16). Dysbiosis refers to an imbalance in bacterial composition, with an increase in harmful microorganisms and a decrease in the beneficial species with a change towards an inflammatory state (15).

We notice in our MS cohort’s microbiota profiles an increase in Firmicutes and Actinobacteria and a decrease in Bacteroidetes phylum. These changes are specific for a typical Western diet high in fats and sugar, where Firmicutes are more capable to extract energy from food, promoting weight gain. On the other hand, a diet based on complex carbohydrates, rich in fibers promotes the increase of Bacteroidetes and benefic metabolites (17).

MS therapies could modulate the gut microbiota, but evidence is not well established in human studies so far. Interferon beta inhibits T cells and proinflammatory cytokines, stimulates Tregs and suppressive B cells, modulates the interaction between the microbes and epithelial cells and stabilize the intestinal barrier by upregulating tight junction proteins in endothelial cells. Teriflunomide inhibits dihydroorotate dehydrogenase, pyrimidine synthesis and proinflammatory cytokines and could influence the gut microbiome by suppressing the STAT-6 signaling pathway, increasing specific T reg cells (2,4).

4.1. Comparison between MS patients and HC at baseline

In our study, none of the alpha or beta diversity metrics differed significantly between the MS patients and the HC, before initiating treatment. This suggests that the microbiota of the MS patients doesn’t show sign of unusual trends, corresponding with literature research (5). At the taxonomy level, we observed several organisms which had a significant different relative abundance between the MS cases and the HC. Actinobacteria is a beneficial microorganism which has a role in the formation of the immune system (18) and it is lower among MS patients. Bifidobacterium is decreased among our MS cohort. The literature provides conflicting data regarding its role in immune diseases, but most studies have shown that Bifidobacterium induces an anti-inflammatory immune response (19,20). Prevotella genus is a well-studied bacteria in MS, implicated in the phytoestrogen metabolism, generally accepted as less prevalent in MS and more present for treated patients (21,22).Our MS patients presented higher levels of one species compared to HC, before or after treatment, Prevotella stercorea , but no relevant data on the Prevotella genus. This is probably due to the fact that this genus has several species with different roles (5).

Some bacterial metabolites can directly influence the central nervous system, such as short-chain fatty acid (SCFA) which has an immunosuppressive role in gut mucosa. Faecalibacterium prausnitzii and Bacteroides coprophilus participate in SCFA metabolism (15). Bacteroides is a beneficial bacterium promoting IL-10 and is generally present in lower levels in MS cases.Faecalibacterium prauznitzii is established as a marker for a healthy microbiota and it is depleted among MS patients, as it can be observed in literature (23) and also in our cohort. All these differences in bacterial taxonomy compared to the HC suggest a dysbiosis for MS microbiota.

4.2. Comparison between groups after treatment

When analyzing if the treatment (of any kind) for MS significantly modifies the microbiome, we outlined that there is no major change in alpha or beta diversity for the MS patients compared to the HC, in the two months after beginning any form of therapy, as suggested by literature research (5). Regarding the taxonomy changes, Ruminococcus is a beneficial bacterium and it is usually restored in MS cases after DMT (24), our data reflects a lower relative abundance among the MS patients compared to the HC for the second sample. Clostridium is a butyrate producing bacteria involved in the production of regulatory T cells and the IL-10 anti-inflammatory cytokine (25) and it is reduced among MS patients after treatment.

The differences between groups after two months of therapy outline a possible intervention of confounders: DMT or homeopathic treatment on gut microbiome. Homeopathy might have an influence on gut microbiota, as our data suggests. Changes between patients who received any form of DMT (G1) and a combination of DMT and homeopathy (G2) are subtle, but we notice a large number of significant differences regarding the bacterial relative abundance between subgroups, with changes partially inconsistent and contradictory. The pair G1A and G2A outlines a possible influence of homeopathy on interferon beta1a treated patients, while the G1B and G2B pair suggests its influence on teriflunomide patients.

Compared to combination therapy, patients receiving homeopathy presented an increase in Akkermansia muciniphila, a controversial species, with an unknown role in immune maturation, elevated among untreated MS patients (26). In vitro experiments outline its pro-inflammatory role, but in vivo studies have not reproduced these effects, but the opposite, it might have a benefic function for some metabolic disorders. Although the therapeutic choice did not influence alpha diversity for any group, we found isolated differences in beta diversity, similar to our literature research (5). Our findings were related to homeopathy and interferon beta1A as treatments that could modify the microbiome diversity. The beta diversity of the group who received homeopathic treatment was different than the group treated with a combination of homeopathy and teriflunomide. Also, the homeopathy-treated group had a different beta diversity than the group treated with interferon beta1A, all p values are presented in table 3. With a statistically unsignificant p value of 0.06, close to 0.05, the group with interferon treatment had a minor change in beta diversity compared to the interferon and homeopathy group. These results suggest that homeopathy might have a role in the beta diversity of MS patients, but further studies are required.

Analyzing relative abundance in the selected phyla and species, we notice a decrease for Firmicutes for the group receiving the combination therapy compared to other groups, but not statistically significant. Firmicutes are related to shorter relapse times in MS (27), thus combining these two therapies could be beneficial in delaying the disease progress.

The two beneficial bacteria involved in the SCFAs metabolism, Bacteroides and Faecalibacterium prauznitzii, have similar relative abundances for the HC and the homeopathy group, of approximately 12% for Bacteroides and respectively 7% for Faecalibacterium. This suggests that homeopathic treatment might have a role in maintaining the balance of these healthy microorganisms.

4.3. Comparison in time (between the second and the first sample)

Microbiome changes in time were analyzed for the MS cohort, where the results outline the effects of all treatments applied in our study: DMT, homeopathy or a combination of both. MS treated patients presented more diverse bacterial changes, from multiple phylum, which suggests that treatment may have a positive influence on microbiota in general. Patients had a lower relative abundance of Lachnospiraceae, Ruminococcus, Eubacterium oxidoreducens after treatment, as expected by literature research (5). Lachnospiraceae is involved in the reduction of mucosal permeability through SCFAs production and increased expression of tight junction proteins in epithelial cells (1).

For the two groups including homeopathy, G2 and G4, the bacteria more prevalent before treatment were attenuated in the second sample. This suggests that homeopathic treatment or its combination with DMT had an impact on gut microbiota after two months of treatment.

Homeopathic treatment has not been studied on gut microbiota among MS patients and no relevant studies were found to compare our findings with. Our patients who received only a homeopathic treatment presented a relative stability after 2 months of treatment, with the only statistically significant modification of Eubacterium oxidoreducens, less abundant in the second sample. Also, in the group treated with a combination of therapies, there are a few bacterial species that were less abundant in the second sample. These results suggest that homeopathic treated patients might have a more stable microbiome.

4.4. Strengths and limitations

Our study has a contribution in this medical field because it analyses gut samples in dynamics, comparing not only treatment exposure but also the changes that occur in time. Also, it is unique by also introducing homeopathic treatment as a possible con-founder.

As possible limitation factors, all environmental and dietary compounds that can interfere and modify the microbiota in two months cannot be excluded or quantified. Further studies are needed.

MS microbiota is characterized by dysbiosis, with various taxonomic changes compared to HC. In untreated MS patients we find a higher abundance of Prevotella stercorea and a reduction of Actinobacteria, Bifidobacterium and Faecalibacterium prauznitzii. Treatment with interferon beta1a, teriflunomide or homeopathy implied several taxonomic changes. MS treated patients had a different microbiota compared to HC, with reduced Bifidobacterium, Ruminococcus and Clostridiales and a higher prevalence of Gemella, Megaspherae and Prevotella stercorea.

Comparing the second with the first sample, we outlined the modifications in MS microbiota in time, such as decreased Lachnospiraceae and Ruminococcus and increased Enterococcus faecalis. These dynamic changes may be due to MS treatment.

Overall, treatment exposure had a small influence on microbiome diversity. The only statistically significant diversity modifications we found implied the effect of homeopathic treatment on beta diversity indexes, compared to the effect of immunomodulatory drugs. Eubacterium oxidoreducens was also reduced after homeopathic treatment. Our results suggest that homeopathic treatment has also a role in influencing the gut microbiota.

This study outlines the important role of gut microbiota in MS and brings up further pathways to be studied. The microbiome is a vast ecosystem, influenced by numerous factors which cannot be completely quantified. Our project focused on dynamic changes in microbiota and the possible influence of some specific DMTs, as well as homeopathy as a complementary and alternative medicine used in clinical practice.

Funding: This research received no external funding.

Ethical approval: The study was conducted in accordance with the Declaration of Helsinki, and approved by Cluj Emergency County Hospital Ethics Committee, code 2394/28.01.2020.

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement: The data and other information of this study are available from the corresponding author upon request, due to privacy issues.

Acknowledgments: We appreciate the help of CosmosID laboratory for analyzing our samples and for statistical analysis support.

Conflicts of Interest: The authors declare no conflict of interest.

Author Contribution: Conceptualization, V.V., G.V., D.F.M., A-D.B.; methodology and resources, V.V., C.N., A.C.P., D.B-V., C.V.; formal analysis, C.N., A.C.P.; data curation, S.C.V.; writing-original draft preparation, V.V., C.N., S.C.V.; writing- review & editing, V.V., G.V., S.C.V.; supervision D.F.M. All authors approved the published version of the manuscript.

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No competing interests reported.

supplementary.pdf

The Role of Multiple Sclerosis Therapies on the Dynamic of Human Gut Microbiota

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Version 2

Abstract

Figures

Key Messages

1. Introduction

2. Materials And Methods

2.1. Subjects

2.2. Sample collection

2.3. The 16rRNA sequencing and statistical analysis

3. Results

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 2