Exploring the Causal Role of Gut Microbiota and Inflammatory Proteins in Neuromyelitis Optica Spectrum Disorder: a Mendelian randomization study with mediation analysis

doi:10.21203/rs.3.rs-5234042/v1

Download PDF

Research Article

Exploring the Causal Role of Gut Microbiota and Inflammatory Proteins in Neuromyelitis Optica Spectrum Disorder: a Mendelian randomization study with mediation analysis

https://doi.org/10.21203/rs.3.rs-5234042/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Observational studies have highlighted a strong association between gut microbiota (GM) and the onset of neuromyelitis optica spectrum disorder (NMOSD), however, the causality remains unclear.

Method: A Mendelian randomization (MR) study was conducted to determine the causal effect of GM on NMOSD and to explore the role of inflammatory proteins as mediators. Genetic data from GWAS were analyzed using the Inverse Variance Weighted (IVW) method, alongside weighted median andMR-Egger methods. Sensitivity analyses, including MR-Egger intercept, leave-one-out, Cochrane’s Q-test, and MR-PRESSO, were performed to validate the MR estimations. Two-sample MR and reverse MR were utilized to assess causality and potential reverse causation.

Result: The MR analysis revealed nine significant causal links between GM and NMOSD. Four inflammatory proteins were also found to have positive causal associations with NMOSD. Sensitivity analyses confirmed these findings. However, mediated MR analysis did not find evidence supporting inflammatory proteins as mediators in the GM-NMOSD pathway.

Conclusion: The study presents evidence for a causal relationship between GM, inflammatory proteins, and NMOSD. Notably, inflammatory proteins do not mediate the pathway from GM to NMOSD, contributing to a deeper understanding of the interplay between GM and NMOSD.

gut microbiota

neuromyelitis optica spectrum disorder

Mendelian randomization

inflammatory proteins

autoimmune disease

Neuromyelitis optica spectrum disorder (NMOSD) is a chronic autoimmune inflammatory disease of the central nervous system (CNS) caused by demyelinating lesions mainly in optical nerves and spinal cord, which may result in severe motor and sensory deficits[1; 2]. It has an estimated worldwide prevalence of 0.5 to 4.4 cases per 100,000 people[3]. The course of NMOSD is repetitive and poses a significant economic burden to patients, especially female patients[1; 4]. Notably, serum immunoglobulin G (IgG) antibodies against astrocyte aquaporin 4 (AQP4) have been found in 60 to 90% patients with NMOSD[5]. AQP4-Ab binds to AQP4 expressed on the blood-brain barrier (BBB) astrocytes and causes complement-dependent cellular toxicity and infiltration of neutrophils, eosinophils, and cytokines[6]. Disruption of the BBB leads to oligodendrocyte death, loss of myelin texture, and neuronal damage[7]. Despite the aforementioned potential mechanisms, the initial pathophysiologic triggers that lead to the onset of the disease and result in antibody production remain controversial.

Gut microbiota (GM) is the largest known commensal microbial community in the human body that comprises viruses, fungi, bacteria, and protozoa[8], and contains 4 trillion microorganisms[9] and 150,000 microbial genomes[10]. As the level of sequencing technologies and research progresses, a deeper understanding of the gut flora has opened up new perspectives for exploring disease mechanisms[11]. Recently, emerging evidence suggests an essential role of GM in the progression of degenerative neurological disorders through the gut microbiota-brain axis [12; 13]. NMOSD patients were more likely to have antibody responses against H. pylori and gastrointestinal antigens, according to Long et al. [14; 15]. Furthermore, there is an observed increase in the abundance of Streptococcus, Aristobacter spp., Haemophilus, Veronica, Aeromonas butyricola, and Rhodococcus in patients with NMOSD [16; 17]. Intestinal mucosal barrier and GM dysfunction may be due to an imbalance in the host immune system [18; 19; 20]. Moreover, undigested food, toxins, microbial products, pro-inflammatory cells, and cytokines may disrupt the intestinal mucosal barrier, allowing pathogens to enter the circulation, disrupting the BBB and inducing an immune response. As a result of disruption of the BBB, these pathogens migrate to the CNS, causing demyelination, axonal loss, and damage to other tissues in the CNS[21; 22]. However, GMs may contribute to the development of NMOSD through the AQP4-related pathway. Study conducted by Cree et al. found that The AQP4 amino acids 63 to 76 contain 90% homology with amino acids 207 to 216 within the Clostridium perfringens adenosine triphosphate-binding cassette (ABC) transporter protein permease [23; 24]. Additionally, there was an increased level of interleukin (IL)-1β, IL-6, and IL-17 produced by CD4 (+) T-helper cells in peripheral blood mononuclear cells in patients with optic neuromyelitis stimulated by E. coli. They were positive for these levels, which correlated with the patients' disability scores[25]. Recent studies have separately explored the possible relationship between some gut microbiota and the development of NMOSD. However, there is still a need for a comprehensive study that explores this relationship.

By using the genome-wide association studies (GWAS) summary statistics, Mendelian randomization (MR) can identify causal relationships between exposures and outcomes by utilizing genetic variability as instrumental variables (IVs)[26]. MR studies can avoid reverse causation and confounding factors that exist in the vast majority of traditional observational studies [27]. In this study, we comprehensively assessed the possible causal associations of gut microbiomes, inflammatory proteins, immune cells and NMOSD, and explored whether the mediating effects of inflammatory proteins and immune cells in the pathways between gut microbiomes and NMOSD to better understand the preventive and therapeutic potential of GM in NMOSD.

2.1 Ethics Statement

No ethical approval was required for the present study because all data were based on an online-published GWAS analysis that was publicly available. Approval for all of these studies was obtained from the relevant institutional review committees.

2.2 Study Design

Bidirectional two-sample MR was performed to elucidate the causal association between the GM, inflammatory proteins and NMOSD. Then exploring mediating effects through mediated Mendelian analyses. Figure 1 shows the overview of study design. Three fundamental assumptions must be satisfied in MR analysis: (1) the IVs exhibit a robust association with the exposure factors; (2) No correlation between confounding variables and IVs; (3) IVs can affect outcomes only through exposure factors[28].

Figure 1. Workflow showing our two-sample bidirectional Mendelian Randomization (MR) analysis. GWAS: genome-wide association study; IVs: instrumental variables; IVW: inverse variance weighted; MR-PRESSO: Mendelian randomization pleiotropy residual sum and outlier; SNPs: single nucleotide polymorphisms.

2.3 Data Sources

Genetic variations within the GM were derived from the comprehensive GWAS conducted by the MiBioGen consortium. This consortium boasts the largest collection of published research on gut microbiome genetics to date [29]. The study meticulously procured gene sequencing data from a substantial cohort of 18,340 individuals, encompassing data from 24 distinct cohort studies across 11 countries globally. To standardize the datasets, each was streamlined to a uniform 10,000 reads per sample and subsequently classified using direct classification bins. The study's inclusion criteria were stringent, requiring a minimum cohort size of three and a valid sample size exceeding 3,000 individuals. Within each cohort, binary trait locus mapping (mbBTL) analyses were conducted, encompassing a comprehensive scope of 196 taxa and 122,110 variant loci [30]. The genetic data for 91 inflammatory proteins came from the previously GWAS [31; 32].

The NMOSD data for this study were obtained from the GWAS catalog website dataset (https://www.ebi.ac.uk/). The data included 215 patients diagnosed with NMOSD, of whom 132 were aquaporin 4 antibody-positive, and a control group with a sample size of 1,244[33]. NMOSD patients in this study were diagnosed based on the 2006 diagnostic criteria, which includes transverse myelitis, optic neuritis, and any two of the following three conditions: (1) longitudinally extensive lesions; (2) brain magnetic resonance imaging inconsistent with multiple sclerosis; (3) seropositive for AQP4-IgG antibody [34].

Inflammatory proteins data were extracted from the EBI GWAS Catalog, with accession numbers spanning from GCST90274758 to GCST90274848 (https://www.ebi.ac.uk/gwas/home). This dataset was inclusive of 11 well-defined cohorts and comprised a substantial participant pool of 14,824 individuals. Genome-wide genetic data for 91 plasma proteins were analyzed utilizing the Olink Target-96 Inflammation panel[31].

2.4 IV selection

Given the scarcity of SNPs identified under the stringent genome-wide significance threshold of p < 1×10^-8, we opted for a more lenient locus-wide significance threshold of p < 1×10^-5 to procure a broader array of SNPs to serve as IVs [35], and an aggregation procedure with a strict threshold (r²< 0.001, kb = 10,000) was performed to ensure IV independence. In instances where linkage disequilibrium (LD) was detected (r²> 0.001), we prioritized the selection of the SNP exhibiting the most significant p-value when confronted with high LD [36]. In addition, palindromic SNPs were also removed. To avoid the potential of weak instrumental bias, we calculated F-statistic value using the following formula (F = beta²/se²), with a value >10 indicating sufficient strength [27]. We then further searched the PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/) to exclude SNPs associated with other features.

2.5 MR analysis

Two-sample MR approach was utilized to assess the causal association between GM and NMOSD. The primary analytic method employed was the inverse variance weighted (IVW) method, which amalgamates individual SNP-specific Wald estimates (obtained by dividing the SNP effect size (β) for the outcome by the SNP effect size (β) for exposure) and synthesizes these to provide a comprehensive assessment of the exposure's impact on the SNP. Fixed or random effects models were selected based on the presence of heterogeneity. When horizontal pleiotropy is absent, IVW prevents the confounders’ effects and achieves unbiased estimation [37]. The odds ratio (OR) reflects the causal link between GM and NMOSD, correlating an increased risk of NMOSD with per SD increase in GM levels. The p-value underwent adjustment based on the false discovery rate (FDR) to mitigate the impact of multiple comparisons on the study's results. We also applied other methods such as MR-Egger, weighted median, weighted mode, and MR polytomous residual sums and outliers (MR-PRESSO), to assess the robustness of MR findings, and the consistent direction of all methods indicated high confidence in the evidence [38]. MR-Egger regression test is a meta-regression of SNP exposure associations with SNP outcome associations with a non-fixed y-axis intercept. This method detects and corrects for directional pleiotropy, but the results may be affected by peripheral genetic variables [37; 39]. The weighted median method produces reliable causality estimates when there is a 50% invalid IV [40]. Compared with MR-Egger regression approach, it demonstrates enhanced causality detection, reduced deviation, and a diminished likelihood of type I error, when the instrument strength is not directly influenced by the effect of interest (InSIDE) [41]. The MR-PRESSO method employs global testing to identify horizontal pleiotropy, scrutinizing whether the causality estimates are influenced by the exclusion of peripheral SNPs (p < 0.05) [42].

2.6 Sensitivity analysis

Sensitivity analysis was conducted to detect the robustness of the MR estimates. Heterogeneity among genetic variants was assessed by Cochran’s Q test and I² statistics. MR-PRESSO was utilized to determine potential outlier SNPs, while directional pleiotropy was estimated using the MR-Egger intercept method [43]. The scatter plots depicted the association between BC and meningioma, and the slope represents estimated positive or negative causal effects. Forest plots present causal estimates obtained from each genetic variant, visualizing the heterogeneity of MR results. A funnel plot depicted the distribution of SNPs, and a symmetric distribution indicates no horizontal pleiotropy. Leave-one-out plots were performed multiple times of MR analysis to test whether individual outliers strongly drove causal effects by excluding one SNP in turn. In addition, reverse MR was conducted to rule out potential reverse causality.

2.7 Intermediary effect

Two-step MR approach was utilized for intermediation analysis, through which the total effect could be divided into direct and indirect effects (33961203). The impact of GMs on NMOSD after accounting for potential mediators is denoted as the direct effect; while the intermediary effect from potential mediators is considered indirect effect. We considered the existence of a potential mediator when the following conditions were met: (1) GM was correlated with the inflammatory proteins (β1); (2) inflammatory protein was correlated with NMOSD (β2); and (3) GM was correlated with NMOSD, with no requirement of adjusting mediators (β3). The percentage of mediation was calculated with the ‘Product of coefficient’ by applying the following formula: (β1 × β2)/(β3). All the analyses were conducted using MendelR package (7.8.0) in R software (version 4.3.1).

3.1 Selection of IVs

At the locus-wide significance threshold, a comprehensive analysis identified 2,559 SNPs as the definitive IVs for 196 GMs, with a stringent p-value threshold of less than 1×10^-5. The distribution of these SNPs across different taxonomic levels was as follows: nine were associated with phylum, 16 with class, 20 with order, 119 with genera, and 32 with family. For NMOSD patients, we found 229 SNPs with 24 GMs associated with all NMOSD patients. Additionally, all F-statistics exceeded 10, indicating no weak IV bias. Detailed information of Gut microbiota and selected IVs are shown in Table S1-2.

3.2 MR analysis

Heatmap plot demonstrated the results based on the MR analysis of 196 GM and NMOSD (Figure 2). Detailed analysis information was shown in Table S3 and Table 1. Using IVW approach, genetically predicted phylum Tenericutes (OR = 2.73, 95%CI 1.07 to 6.99, p = 0.0357); class Mollicutes (OR = 2.73, 95%CI 1.07 to 6.99, p = 0.0357); genus Eubacterium rectale group (OR = 4.47, 95%CI 1.01 to 19.86, p = 0.0487); genus Barnesiella (OR = 2.95, 95%CI 1.09 to 7.98, p = 0.03); genus Eubacterium xylanophilum group (OR = 3.66, 95%CI 1.08 to 12.41, p = 0.037); and genus Ruminococcus torques group (OR = 5.05, 95%CI 1.32 to 19.31, p = 0.0179) were positively associated with the risk of NMOSD; while genetically predicted family Clostridiales vadin BB60 group (OR=0.38, 95%CI: 0.16 to 0.88, p = 0.0244); genus Eggerthella (OR=0.41, 95%CI: 0.19 to 0.88, p = 0.0214); and genus Intestinibacter (OR = 0.35, 95%CI 0.13 to 0.91, p = 0.0308) were negatively correlated with NMOSD risk (Table 1). Supplementary methods such as weight median, weight mode, and MR-Egger also showed the same directions, indicating the robustness of the MR results.

3.3 Sensitivity analysis

Series of sensitivity analyses were conducted to see if the results were robust when more than four SNPs were used as IVs. After completing all sensitivity analyses, we found that the results obtained were robust (Figure 3 and Table S4,5). In addition, MR-Egger intercept test and MR-PROSSO global test reported no pleiotropy in MR estimates (p > 0.05, Table 1). There was no observed heterogeneity between the selected IVs and NMOSDs based on results of the Cochran’s Q test (p > 0.05, Table 1). In the leave-one-out analysis, we found that none of the risk estimates between NMOSD and specific bacterial taxa were due to a single SNP. The results were still valid after the exclusion of a more influential SNP (Figure S1-9). None of the GMs passed the FDR correction in multiple comparisons. We then further investigated the reverse causality using subtypes of NMOSD as exposure and significant GMs as outcomes. After removing linkage disequilibrium, SNPs associated with NMOSD, were obtained from the GWAS database, with each SNP having an F statistic >10. The results were shown in Table S6, no reverse causality was observed between NMOSD and GMs (p>0.05).

3.4 Bi‑directional causal effects of inflammatory proteins and NMOSD

We performed MR analysis of inflammatory proteins with NMOSD, and detailed results are shown in Table S7. The results show significant causal effects of Macrophage colony-stimulating factor 1 levels ( OR = 4.20, 95%CI 1.65 to 10.67, p = 0.00256), C-X-C motif chemokine 11 levels ( OR=1.78, 95%CI: 1.01 to 3.16, p = 0.0478), SIR2-like protein 2 levels (OR=5.17, 95%CI: 1.72 to 15.52, p = 0.00338) and Tumor necrosis factor receptor superfamily member 9 levels (OR=2.30, 95%CI: 1.12 to 4.71, p = 0.0234) on NMOSD. There was no evidence of heterogeneity or pleiotropy. Detailed heterogeneity and pleiotropy results are available in the Table S8-10.

3.5 Mediation analysis

Based on the requirement of mediating role, we performed MR analysis of GM with significant results versus inflammatory proteins with significant results, as detailed in Table S4 and Table S8, and finally four inflammatory proteins have the potential to mediate roles in gut flora and NMOSD. We then selected the GM, inflammatory proteins that were significantly associated with NMOSD for MR analysis, and obtained the parts where GM was significantly associated with inflammatory proteins, respectively. The results of further mediating MR analyses, as shown in Table S11, suggest that there is no mediating role of the GM and NMOSD pathways among the known inflammatory proteins.

This MR analysis is the first to investigate the causal relationship between the GM, inflammatory proteins and NMOSD, and reported an increased relative abundance of genes in the specific genera of GM was associated with a lower risk of NMOSD patients, i.e., phylum Tenericutes, class Mollicutes, genus Eubacterium rectale group, genus Barnesiella, genus Eubacterium xylanophilum group, and genus Ruminococcus torques group were positively associated with the risk of NMOSD. Family Clostridiales vadin BB60 group, genus Eggerthella, and genus Intestinibacter were negatively related to the risk of NMOSD.

In recent years, the influence of gut microorganisms on the pathophysiological mechanisms of the gut-brain axis has been emphasized, and it has been found that gut microorganisms influence the development of neuroendocrine and immune-related diseases through the intestinal barrier and the immune system[44]. The structural and functional integrity of the intestinal barrier influences the regulation of intestinal homeostasis, and any damage, including the GMs or drugs can lead to disruption of intestinal homeostasis, which in turn triggers a series of inflammatory responses that disrupt the intestinal barrier [45; 46]. When the mucosal barrier is disrupted, potential pathogens such as microorganisms or endotoxins come into direct contact with immune cells such as T and B lymphocytes, plasma cells, mast cells, and macrophages in the lamina propria. B-lymphocytes receive stimulation from bacterial or viral antigens, and metabolites and are converted to plasma cells, thereby producing and releasing immunoglobulins[47]. An increased percentage of CD38- and CD138-positive cells (plasma cells) in patients with NMOSD was found in a study by Cui et al. [16; 48]. In addition, the number of CD68-positive cells (macrophages) increased, suggesting that macrophages also play a role in phagocytosis and antigen presentation[49]. Studies have proved that mast cells can release a large number of pro-inflammatory molecules that can damage the tight junction and induce a systemic pro-inflammatory immune response[50; 51; 52; 53]. With the disruption of mucosal barriers, pathogens can escape from the intestinal sites and survive at extra-intestinal sites[54; 55]. Because of the breached barrier, pathogens are transferred to the circulatory system, wherein they induce a chronic or acute inflammatory response increasing host susceptibility to various types of disease[45]. Prolonged presence of molecules and bacteria of intestinal origin in the vicinity of the BBB may compromise the its integrity and thus lead to its collapse[16; 56]. Additionally, Cui et al. observed a decreased level of three types of gap junction proteins (zonula occludens-1, occludin, and claudin-1) in patients with NMOSD, which may result in abnormal intestinal mucosal barrier permeability[16; 57; 58]. TEM data also showed that cellular gaps were significantly more expansive in these patients, possibly related to a reduction in gap junction proteins[16]. Several studies have shown that most patients with NMOSD have high BBB permeability[59; 60], suggesting that the damage to the gut barrier simultaneously disrupts the integrity and permeability of the BBB[46; 61; 62].

GMs play a pivotal role in the progression of NMOSD. Gong et al. found that patients with NMOSD had more Streptococcus spp. as fecal microorganisms than healthy subjects [17]. Streptococcus spp. is a part of the normal intestinal flora of the human body and are classified into different species, of which many may also be present in sterile sites causing invasive infections such as bacterial endocarditis, pneumonia, and meningitis[63; 64; 65]. A study by Cui et al. found that in addition to Streptococcus, patients with NMOSD are also enriched with other pro-inflammatory bacteria such as Granulocystis spp., Aspergillus spp., and Vibrio desulfuricans that may also be involved in this pathologic process and play a role in the inflammatory process[16]. Tenericutes (Eubacterium flexneri) belongs to one of the placental-specific flora [66], and Li et al. showed that Tenericutes abundance was positively correlated with IL-6 and TNF-α [67], and that these molecules not only damage the tight junction (TJ), but also induce a systemic pro-inflammatory immune response[53]. Eubacterium rectale group belongs to the genus Eubacterium, which is also a part of the core GMs of humans. Eubacterium have been recognized for their crucial involvement in maintaining energy balance, regulating colonic movement, modulating the immune system, and mitigating inflammation within the intestine[68]. Similarly, the Eubacterium rectale group positively correlated with pro-inflammatory cytokine levels[69] and was linearly associated with radiation-induced intestinal damage[70]. Cree et al. showed that AQP4-specific T-cells cross-react with the adenosine triphosphate-binding cassette transporter protein of Clostridium perfringens, which shares sequence homology with AQP4 [23; 71]. In addition, short-chain fatty acids (SCFAs), produced through the fermentation of dietary fiber by gut microorganisms, have the ability to inhibit histone deacetylase (HDAC) activity. This inhibition promotes the development of regulatory T cells and also supports the proliferation of precursor cells for macrophages. Influencing CD8+ T-cell function by regulating cellular metabolism has led to a paradoxical role for SCFA in NMOSD[72; 73]. A positive correlation was previously shown between Ruminococcus torques and SCFA levels [74]. Butyric acid is a type of SCFA, and butyric acid stimulates GFR receptors and reduces inflammation through limiting the production of inflammatory proteins such as NF-B, IL-1 and IL-6 [75; 76]. Current studies have suggested that Barnesiella and Eubacterium rectale play an essential role in butyric acid production [77; 78]. Our study found the abundance of all three genera correlates with the development of NMOSD. Additionally, our results suggest that the genus Eggerthella is protective against NMOSD. A growing body of evidence collectively emphasizes the association of Eggerthella with a variety of autoimmune diseases such as asthma[79], multiple sclerosis[80]. For example, Eggerthella lenta may increase bile acid metabolites and taurodeoxycholic acid (TDCA) in mice. Both of these can activate oncogenic MAPK/ERK pathway leading to intestinal barrier dysfunction in order to achieve oncogenic effects that trigger colorectal cancer [81]. Since the incidence and development of NMOSD may be affected by bile acids and their metabolites [82], the genus Eggerthella may be associated with NMOSD, but the deeper mechanism requires to be further explored. We also found statistically significant differences between class Mollicutes, genus Intestinibacter[83], Clostridiales vadin BB60 and NMOSD, there are fewer previous studies on the association of in these bacteria with the disease. The relevant directional content needs to be explored in further research studies.

There is growing evidence that the GM could be a potential therapeutic target for treating NMOSD. Dietary control, probiotic nutrition, and fecal flora transplantation have shown promise in improving the efficacy of NMOSD treatment while reducing complications.[84; 85]. Studies by Wu et al. showed that fecal flora transplantation can positively influence the structural composition and functionality of the GM by increasing the level of SCFAs. This discovery provides a new direction for treating neuroimmune diseases in humans[84]. Therefore, our research focuses on exploring therapeutic potential of the specific GM involved in NMOSD and their mechanisms of action, which may translate into possible prevention for NMOSD.

This study boasts several strengths. Firstly, contrasting with traditional epidemiological research, our MR analysis is notably resilient to the influence of confounding variables and the risk of reverse causality. Additionally, we implemented rigorous quality control measures and sensitivity analyses to ensure the reliability of our MR estimates. Furthermore, a stringent FDR correction was applied throughout the MR analysis to significantly reduce the potential for type I errors. Lastly, we conducted MR analyses on the relationship between inflammatory proteins and NMOSD, delving into the potential mediating role of these proteins in the connection between genetic markers and the disease. However, some limitations should also be noted. First, since our GWAS summary data on the GM are at the level of class, order, phylum, family, and genus, some specific GM that relates to more subtle levels (e.g., strain level or species) may be missed and overlooked by this analysis. Second, factors such as the host’s genetics, gender, mode of delivery, surrounding environmental factors, medications, diseases, and dietary habits may affect the diversity of the GM [86; 87; 88], which may have had some impact on our findings. Third, all participants in the dataset used in the study were of European ethnicity, limiting the results to other ethnicities.

This MR study comprehensively assessed the causal relationship between GM, inflammatory proteins, and NMOSD. Specifically, we found no evidence for a mediating effect of inflammatory factors between GM and NMOSD. This research not only enriches the genetic understanding of NMOSD from a novel perspective but also paves the way for innovative approaches to prevention and treatment strategies in the future.

6 Ethics approval and consent to participate

7 Consent for publication

The authors confirmed that this study was based on published data and that informed consent had been obtained from all individual participants for previous studies and did not need to be obtained repeatedly.

8 Availability of data and materials

The data enrolled in the study could be found in the manuscript and the supplementary materials.

9 Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

10 Funding

This work is supported byNational Natural Science Foundation of China (No 82171294).

11 Authors' contributions

ZQY and YJQ were the principal investigators. ZW and JW were involved in selecting the article topic. ZQY and YYY developed the search strategy and analysis plan. SJY collected the data and wrote the article. YJQ analyzed the data and prepared the figures. The manuscript underwent revision and language refinement by ZQC and JW. The final submitted paper was reviewed and endorsed by all authors.

12 Acknowledgements

The author would like to acknowledge the participants and investigators of FinnGen study and MiBioGen.

J. Beekman, A. Keisler, O. Pedraza, M. Haramura, A. Gianella-Borradori, E. Katz, J.N. Ratchford, G. Barron, L.J. Cook, J.M. Behne, T.F. Blaschke, T.J. Smith, and M.R. Yeaman, Neuromyelitis optica spectrum disorder: Patient experience and quality of life. Neurol Neuroimmunol Neuroinflamm 6 (2019) e580.
C.T. Tan, Z. Mao, W. Qiu, X. Hu, D.M. Wingerchuk, and B.G. Weinshenker, International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology 86 (2016) 491-2.
M.A. Mealy, D.M. Wingerchuk, J. Palace, B.M. Greenberg, and M. Levy, Comparison of relapse and treatment failure rates among patients with neuromyelitis optica: multicenter study of treatment efficacy. JAMA Neurol 71 (2014) 324-30.
S.J. Pittock, and C.F. Lucchinetti, Neuromyelitis optica and the evolving spectrum of autoimmune aquaporin-4 channelopathies: a decade later. Ann N Y Acad Sci 1366 (2016) 20-39.
N. Borisow, M. Mori, S. Kuwabara, M. Scheel, and F. Paul, Diagnosis and Treatment of NMO Spectrum Disorder and MOG-Encephalomyelitis. Front Neurol 9 (2018) 888.
S.M. Mueller, K.M. White, S.B. Fass, S. Chen, Z. Shi, X. Ge, J.A. Engelbach, S.H. Gaines, A.R. Bice, M.J. Vasek, J.R. Garbow, J.P. Culver, Z. Martinez-Lozada, M. Cohen-Salmon, J.D. Dougherty, and D. Sapkota, Evaluation of gliovascular functions of Aqp4 readthrough isoforms. bioRxiv (2023).
X.Y. Yao, M.C. Gao, S.W. Bai, L. Xie, Y.Y. Song, J. Ding, Y.F. Wu, C.R. Xue, Y. Hao, Y. Zhang, and Y.T. Guan, Enlarged perivascular spaces, neuroinflammation and neurological dysfunction in NMOSD patients. Front Immunol 13 (2022) 966781.
A.N. Shkoporov, and C. Hill, Bacteriophages of the Human Gut: The "Known Unknown" of the Microbiome. Cell Host Microbe 25 (2019) 195-209.
R. Sender, S. Fuchs, and R. Milo, Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans. Cell 164 (2016) 337-40.
E. Pasolli, F. Asnicar, S. Manara, M. Zolfo, N. Karcher, F. Armanini, F. Beghini, P. Manghi, A. Tett, P. Ghensi, M.C. Collado, B.L. Rice, C. DuLong, X.C. Morgan, C.D. Golden, C. Quince, C. Huttenhower, and N. Segata, Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle. Cell 176 (2019) 649-662.e20.
W.M. de Vos, H. Tilg, M. Van Hul, and P.D. Cani, Gut microbiome and health: mechanistic insights. Gut 71 (2022) 1020-1032.
S.M. Collins, M. Surette, and P. Bercik, The interplay between the intestinal microbiota and the brain. Nat Rev Microbiol 10 (2012) 735-42.
H. Tremlett, K.C. Bauer, S. Appel-Cresswell, B.B. Finlay, and E. Waubant, The gut microbiome in human neurological disease: A review. Ann Neurol 81 (2017) 369-382.
Y. Long, C. Gao, W. Qiu, X. Hu, Y. Shu, F. Peng, and Z. Lu, Helicobacter pylori infection in Neuromyelitis Optica and Multiple Sclerosis. Neuroimmunomodulation 20 (2013) 107-12.
M. Banati, P. Csecsei, E. Koszegi, H.H. Nielsen, G. Suto, L. Bors, A. Trauninger, T. Csepany, C. Rozsa, G. Jakab, T. Molnar, A. Berthele, S.R. Kalluri, T. Berki, and Z. Illes, Antibody response against gastrointestinal antigens in demyelinating diseases of the central nervous system. Eur J Neurol 20 (2013) 1492-5.
C. Cui, S. Tan, L. Tao, J. Gong, Y. Chang, Y. Wang, P. Fan, D. He, Y. Ruan, and W. Qiu, Intestinal Barrier Breakdown and Mucosal Microbiota Disturbance in Neuromyelitis Optical Spectrum Disorders. Front Immunol 11 (2020) 2101.
J. Gong, W. Qiu, Q. Zeng, X. Liu, X. Sun, H. Li, Y. Yang, A. Wu, J. Bao, Y. Wang, Y. Shu, X. Hu, J.A. Bellanti, S.G. Zheng, Y. Lu, and Z. Lu, Lack of short-chain fatty acids and overgrowth of opportunistic pathogens define dysbiosis of neuromyelitis optica spectrum disorders: A Chinese pilot study. Mult Scler 25 (2019) 1316-1325.
S.C. Liang, X.Y. Tan, D.P. Luxenberg, R. Karim, K. Dunussi-Joannopoulos, M. Collins, and L.A. Fouser, Interleukin (IL)-22 and IL-17 are coexpressed by Th17 cells and cooperatively enhance expression of antimicrobial peptides. J Exp Med 203 (2006) 2271-9.
Y. Shimada, M. Kinoshita, K. Harada, M. Mizutani, K. Masahata, H. Kayama, and K. Takeda, Commensal bacteria-dependent indole production enhances epithelial barrier function in the colon. PLoS One 8 (2013) e80604.
R. Okumura, and K. Takeda, Maintenance of intestinal homeostasis by mucosal barriers. Inflamm Regen 38 (2018) 5.
W.W. Kamphuis, C. Derada Troletti, A. Reijerkerk, I.A. Romero, and H.E. de Vries, The blood-brain barrier in multiple sclerosis: microRNAs as key regulators. CNS Neurol Disord Drug Targets 14 (2015) 157-67.
G.G. Ortiz, F.P. Pacheco-Moisés, M. Macías-Islas, L.J. Flores-Alvarado, M.A. Mireles-Ramírez, E.D. González-Renovato, V.E. Hernández-Navarro, A.L. Sánchez-López, and M.A. Alatorre-Jiménez, Role of the blood-brain barrier in multiple sclerosis. Arch Med Res 45 (2014) 687-97.
M. Varrin-Doyer, C.M. Spencer, U. Schulze-Topphoff, P.A. Nelson, R.M. Stroud, B.A. Cree, and S.S. Zamvil, Aquaporin 4-specific T cells in neuromyelitis optica exhibit a Th17 bias and recognize Clostridium ABC transporter. Ann Neurol 72 (2012) 53-64.
B.A. Cree, C.M. Spencer, M. Varrin-Doyer, S.E. Baranzini, and S.S. Zamvil, Gut microbiome analysis in neuromyelitis optica reveals overabundance of Clostridium perfringens. Ann Neurol 80 (2016) 443-7.
P.O. Barros, U.C. Linhares, B. Teixeira, T.M. Kasahara, T.B. Ferreira, R. Alvarenga, J. Hygino, R.G. Silva-Filho, V.C. Bittencourt, R.M. Andrade, A.F. Andrade, and C.A. Bento, High in vitro immune reactivity to Escherichia coli in neuromyelitis optica patients is correlated with both neurological disabilities and elevated plasma lipopolysaccharide levels. Hum Immunol 74 (2013) 1080-7.
G. Davey Smith, and G. Hemani, Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 23 (2014) R89-98.
S. Grover, M.F. Del Greco, C.M. Stein, and A. Ziegler, Mendelian Randomization. Methods Mol Biol 1666 (2017) 581-628.
N.M. Davies, M.V. Holmes, and G. Davey Smith, Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj 362 (2018) k601.
A. Kurilshikov, C. Medina-Gomez, R. Bacigalupe, D. Radjabzadeh, J. Wang, A. Demirkan, C.I. Le Roy, J.A. Raygoza Garay, C.T. Finnicum, X. Liu, D.V. Zhernakova, M.J. Bonder, T.H. Hansen, F. Frost, M.C. Rühlemann, W. Turpin, J.Y. Moon, H.N. Kim, K. Lüll, E. Barkan, S.A. Shah, M. Fornage, J. Szopinska-Tokov, Z.D. Wallen, D. Borisevich, L. Agreus, A. Andreasson, C. Bang, L. Bedrani, J.T. Bell, H. Bisgaard, M. Boehnke, D.I. Boomsma, R.D. Burk, A. Claringbould, K. Croitoru, G.E. Davies, C.M. van Duijn, L. Duijts, G. Falony, J. Fu, A. van der Graaf, T. Hansen, G. Homuth, D.A. Hughes, R.G. Ijzerman, M.A. Jackson, V.W.V. Jaddoe, M. Joossens, T. Jørgensen, D. Keszthelyi, R. Knight, M. Laakso, M. Laudes, L.J. Launer, W. Lieb, A.J. Lusis, A.A.M. Masclee, H.A. Moll, Z. Mujagic, Q. Qibin, D. Rothschild, H. Shin, S.J. Sørensen, C.J. Steves, J. Thorsen, N.J. Timpson, R.Y. Tito, S. Vieira-Silva, U. Völker, H. Völzke, U. Võsa, K.H. Wade, S. Walter, K. Watanabe, S. Weiss, F.U. Weiss, O. Weissbrod, H.J. Westra, G. Willemsen, H. Payami, D. Jonkers, A. Arias Vasquez, E.J.C. de Geus, K.A. Meyer, J. Stokholm, E. Segal, E. Org, C. Wijmenga, H.L. Kim, R.C. Kaplan, T.D. Spector, A.G. Uitterlinden, F. Rivadeneira, A. Franke, M.M. Lerch, L. Franke, S. Sanna, M. D'Amato, O. Pedersen, et al., Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet 53 (2021) 156-165.
A. Kurilshikov, C. Medina-Gomez, R. Bacigalupe, D. Radjabzadeh, J. Wang, A. Demirkan, C.I. Le Roy, J.A. Raygoza Garay, C.T. Finnicum, X. Liu, D.V. Zhernakova, M.J. Bonder, T.H. Hansen, F. Frost, M.C. Ruhlemann, W. Turpin, J.Y. Moon, H.N. Kim, K. Lull, E. Barkan, S.A. Shah, M. Fornage, J. Szopinska-Tokov, Z.D. Wallen, D. Borisevich, L. Agreus, A. Andreasson, C. Bang, L. Bedrani, J.T. Bell, H. Bisgaard, M. Boehnke, D.I. Boomsma, R.D. Burk, A. Claringbould, K. Croitoru, G.E. Davies, C.M. van Duijn, L. Duijts, G. Falony, J. Fu, A. van der Graaf, T. Hansen, G. Homuth, D.A. Hughes, R.G. Ijzerman, M.A. Jackson, V.W.V. Jaddoe, M. Joossens, T. Jorgensen, D. Keszthelyi, R. Knight, M. Laakso, M. Laudes, L.J. Launer, W. Lieb, A.J. Lusis, A.A.M. Masclee, H.A. Moll, Z. Mujagic, Q. Qibin, D. Rothschild, H. Shin, S.J. Sorensen, C.J. Steves, J. Thorsen, N.J. Timpson, R.Y. Tito, S. Vieira-Silva, U. Volker, H. Volzke, U. Vosa, K.H. Wade, S. Walter, K. Watanabe, S. Weiss, F.U. Weiss, O. Weissbrod, H.J. Westra, G. Willemsen, H. Payami, D. Jonkers, A. Arias Vasquez, E.J.C. de Geus, K.A. Meyer, J. Stokholm, E. Segal, E. Org, C. Wijmenga, H.L. Kim, R.C. Kaplan, T.D. Spector, A.G. Uitterlinden, F. Rivadeneira, A. Franke, M.M. Lerch, L. Franke, S. Sanna, M. D'Amato, O. Pedersen, et al., Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet 53 (2021) 156-165.
J.H. Zhao, D. Stacey, N. Eriksson, E. Macdonald-Dunlop, A.K. Hedman, A. Kalnapenkis, S. Enroth, D. Cozzetto, J. Digby-Bell, J. Marten, L. Folkersen, C. Herder, L. Jonsson, S.E. Bergen, C. Gieger, E.J. Needham, P. Surendran, T. Estonian Biobank Research, D.S. Paul, O. Polasek, B. Thorand, H. Grallert, M. Roden, U. Vosa, T. Esko, C. Hayward, A. Johansson, U. Gyllensten, N. Powell, O. Hansson, N. Mattsson-Carlgren, P.K. Joshi, J. Danesh, L. Padyukov, L. Klareskog, M. Landen, J.F. Wilson, A. Siegbahn, L. Wallentin, A. Malarstig, A.S. Butterworth, and J.E. Peters, Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat Immunol 24 (2023) 1540-1551.
V. Orru, M. Steri, C. Sidore, M. Marongiu, V. Serra, S. Olla, G. Sole, S. Lai, M. Dei, A. Mulas, F. Virdis, M.G. Piras, M. Lobina, M. Marongiu, M. Pitzalis, F. Deidda, A. Loizedda, S. Onano, M. Zoledziewska, S. Sawcer, M. Devoto, M. Gorospe, G.R. Abecasis, M. Floris, M. Pala, D. Schlessinger, E. Fiorillo, and F. Cucca, Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat Genet 52 (2020) 1036-1045.
K. Estrada, C.W. Whelan, F. Zhao, P. Bronson, R.E. Handsaker, C. Sun, J.P. Carulli, T. Harris, R.M. Ransohoff, S.A. McCarroll, A.G. Day-Williams, B.M. Greenberg, and D.G. MacArthur, A whole-genome sequence study identifies genetic risk factors for neuromyelitis optica. Nat Commun 9 (2018) 1929.
D.M. Wingerchuk, V.A. Lennon, S.J. Pittock, C.F. Lucchinetti, and B.G. Weinshenker, Revised diagnostic criteria for neuromyelitis optica. Neurology 66 (2006) 1485-9.
N. Li, Y. Wang, P. Wei, Y. Min, M. Yu, G. Zhou, G. Yuan, J. Sun, H. Dai, E. Zhou, W. He, M. Sheng, K. Gao, M. Zheng, W. Sun, D. Zhou, and L. Zhang, Causal Effects of Specific Gut Microbiota on Chronic Kidney Diseases and Renal Function-A Two-Sample Mendelian Randomization Study. Nutrients 15 (2023).
N. Xie, Z. Wang, Q. Shu, X. Liang, J. Wang, K. Wu, Y. Nie, Y. Shi, D. Fan, and J. Wu, Association between Gut Microbiota and Digestive System Cancers: A Bidirectional Two-Sample Mendelian Randomization Study. Nutrients 15 (2023).
K. Xiang, P. Wang, Z. Xu, Y.Q. Hu, Y.S. He, Y. Chen, Y.T. Feng, K.J. Yin, J.X. Huang, J. Wang, Z.D. Wu, X.K. Yang, D.G. Wang, D.Q. Ye, and H.F. Pan, Causal Effects of Gut Microbiome on Systemic Lupus Erythematosus: A Two-Sample Mendelian Randomization Study. Front Immunol 12 (2021) 667097.
J. Cai, Z. Wei, M. Chen, L. He, H. Wang, M. Li, and Y. Peng, Socioeconomic status, individual behaviors and risk for mental disorders: A Mendelian randomization study. Eur Psychiatry 65 (2022) e28.
D.R. Morris, G.T. Jones, M.V. Holmes, M.J. Bown, R. Bulbulia, T.P. Singh, and J. Golledge, Genetic Predisposition to Diabetes and Abdominal Aortic Aneurysm: A Two Stage Mendelian Randomisation Study. Eur J Vasc Endovasc Surg 63 (2022) 512-519.
J. Bowden, G. Davey Smith, P.C. Haycock, and S. Burgess, Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40 (2016) 304-14.
F.P. Hartwig, G. Davey Smith, and J. Bowden, Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46 (2017) 1985-1998.
M. Verbanck, C.Y. Chen, B. Neale, and R. Do, Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50 (2018) 693-698.
W. Li, A. Ren, Q. Qin, L. Zhao, Q. Peng, R. Ma, and S. Luo, Causal associations between human gut microbiota and cholelithiasis: a mendelian randomization study. Front Cell Infect Microbiol 13 (2023) 1169119.
C.R. Camara-Lemarroy, L.M. Metz, and V.W. Yong, Focus on the gut-brain axis: Multiple sclerosis, the intestinal barrier and the microbiome. World J Gastroenterol 24 (2018) 4217-4223.
R. Nagpal, and H. Yadav, Bacterial Translocation from the Gut to the Distant Organs: An Overview. Ann Nutr Metab 71 Suppl 1 (2017) 11-16.
M.C. Houser, and M.G. Tansey, The gut-brain axis: is intestinal inflammation a silent driver of Parkinson's disease pathogenesis? NPJ Parkinsons Dis 3 (2017) 3.
P. Blanc, L. Moro-Sibilot, L. Barthly, F. Jagot, S. This, S. de Bernard, L. Buffat, S. Dussurgey, R. Colisson, E. Hobeika, T. Fest, M. Taillardet, O. Thaunat, A. Sicard, P. Mondière, L. Genestier, S.L. Nutt, and T. Defrance, Mature IgM-expressing plasma cells sense antigen and develop competence for cytokine production upon antigenic challenge. Nat Commun 7 (2016) 13600.
B. Ribourtout, and M. Zandecki, Plasma cell morphology in multiple myeloma and related disorders. Morphologie 99 (2015) 38-62.
T.E. Taher, J. Bystrom, V.H. Ong, D.A. Isenberg, Y. Renaudineau, D.J. Abraham, and R.A. Mageed, Intracellular B Lymphocyte Signalling and the Regulation of Humoral Immunity and Autoimmunity. Clin Rev Allergy Immunol 53 (2017) 237-264.
S. Polyzoidis, T. Koletsa, S. Panagiotidou, K. Ashkan, and T.C. Theoharides, Mast cells in meningiomas and brain inflammation. J Neuroinflammation 12 (2015) 170.
D. González-de-Olano, and I. Álvarez-Twose, Mast Cells as Key Players in Allergy and Inflammation. J Investig Allergol Clin Immunol 28 (2018) 365-378.
C. Mauri, and M. Menon, Human regulatory B cells in health and disease: therapeutic potential. J Clin Invest 127 (2017) 772-779.
M. Al-Asmakh, and L. Hedin, Microbiota and the control of blood-tissue barriers. Tissue Barriers 3 (2015) e1039691.
T. Shao, C. Zhao, F. Li, Z. Gu, L. Liu, L. Zhang, Y. Wang, L. He, Y. Liu, Q. Liu, Y. Chen, H. Donde, R. Wang, V.R. Jala, S. Barve, S.Y. Chen, X. Zhang, Y. Chen, C.J. McClain, and W. Feng, Intestinal HIF-1α deletion exacerbates alcoholic liver disease by inducing intestinal dysbiosis and barrier dysfunction. J Hepatol 69 (2018) 886-895.
N. Thevaranjan, A. Puchta, C. Schulz, A. Naidoo, J.C. Szamosi, C.P. Verschoor, D. Loukov, L.P. Schenck, J. Jury, K.P. Foley, J.D. Schertzer, M.J. Larché, D.J. Davidson, E.F. Verdú, M.G. Surette, and D.M.E. Bowdish, Age-Associated Microbial Dysbiosis Promotes Intestinal Permeability, Systemic Inflammation, and Macrophage Dysfunction. Cell Host Microbe 23 (2018) 570.
J. Huppert, D. Closhen, A. Croxford, R. White, P. Kulig, E. Pietrowski, I. Bechmann, B. Becher, H.J. Luhmann, A. Waisman, and C.R. Kuhlmann, Cellular mechanisms of IL-17-induced blood-brain barrier disruption. Faseb j 24 (2010) 1023-34.
Z.M. Slifer, and A.T. Blikslager, The Integral Role of Tight Junction Proteins in the Repair of Injured Intestinal Epithelium. Int J Mol Sci 21 (2020).
N. Bertiaux-Vandaële, S.B. Youmba, L. Belmonte, S. Lecleire, M. Antonietti, G. Gourcerol, A.M. Leroi, P. Déchelotte, J.F. Ménard, P. Ducrotté, and M. Coëffier, The expression and the cellular distribution of the tight junction proteins are altered in irritable bowel syndrome patients with differences according to the disease subtype. Am J Gastroenterol 106 (2011) 2165-73.
Y. Wang, M. Zhu, C. Liu, J. Han, W. Lang, Y. Gao, C. Lu, S. Wang, S. Hou, N. Zheng, D. Wang, Y. Chen, Y. Zhang, H.L. Zhang, and J. Zhu, Blood Brain Barrier Permeability Could Be a Biomarker to Predict Severity of Neuromyelitis Optica Spectrum Disorders: A Retrospective Analysis. Front Neurol 9 (2018) 648.
S. Liang, Q. Qin, Y. Tang, W. Liao, Y. Yang, J. He, and L. Li, Impact of blood-brain barrier disruption on newly diagnosed neuromyelitis optica spectrum disorder symptoms and prognosis. Ann Palliat Med 9 (2020) 324-330.
G.B. Rogers, D.J. Keating, R.L. Young, M.L. Wong, J. Licinio, and S. Wesselingh, From gut dysbiosis to altered brain function and mental illness: mechanisms and pathways. Mol Psychiatry 21 (2016) 738-48.
A. Vojdani, J. Lambert, and E. Vojdani, The Gut-Brain Axis: Autoimmune and Neuroimmune Disorders. Altern Ther Health Med 22 (2016) 31-46.
T.S. Shailaja, K.A. Sathiavathy, and G. Unni, Infective endocarditis caused by Granulicatella adiacens. Indian Heart J 65 (2013) 447-9.
J.S. Cargill, K.S. Scott, D. Gascoyne-Binzi, and J.A.T. Sandoe, Granulicatella infection: diagnosis and management. J Med Microbiol 61 (2012) 755-761.
T. Parks, L. Barrett, and N. Jones, Invasive streptococcal disease: a review for clinicians. Br Med Bull 115 (2015) 77-89.
K. Aagaard, J. Ma, K.M. Antony, R. Ganu, J. Petrosino, and J. Versalovic, The placenta harbors a unique microbiome. Sci Transl Med 6 (2014) 237ra65.
K. Li, L. Zhang, J. Xue, X. Yang, X. Dong, L. Sha, H. Lei, X. Zhang, L. Zhu, Z. Wang, X. Li, H. Wang, P. Liu, Y. Dong, and L. He, Dietary inulin alleviates diverse stages of type 2 diabetes mellitus via anti-inflammation and modulating gut microbiota in db/db mice. Food Funct 10 (2019) 1915-1927.
A. Mukherjee, C. Lordan, R.P. Ross, and P.D. Cotter, Gut microbes from the phylogenetically diverse genus Eubacterium and their various contributions to gut health. Gut Microbes 12 (2020) 1802866.
X. Gong, L. Liu, X. Li, J. Xiong, J. Xu, D. Mao, and L. Liu, Neuroprotection of cannabidiol in epileptic rats: Gut microbiome and metabolome sequencing. Front Nutr 9 (2022) 1028459.
T.S. Zhao, L.W. Xie, S. Cai, J.Y. Xu, H. Zhou, L.F. Tang, C. Yang, S. Fang, M. Li, and Y. Tian, Dysbiosis of Gut Microbiota Is Associated With the Progression of Radiation-Induced Intestinal Injury and Is Alleviated by Oral Compound Probiotics in Mouse Model. Front Cell Infect Microbiol 11 (2021) 717636.
S.S. Zamvil, C.M. Spencer, S.E. Baranzini, and B.A.C. Cree, The Gut Microbiome in Neuromyelitis Optica. Neurotherapeutics 15 (2018) 92-101.
N. Arpaia, C. Campbell, X. Fan, S. Dikiy, J. van der Veeken, P. deRoos, H. Liu, J.R. Cross, K. Pfeffer, P.J. Coffer, and A.Y. Rudensky, Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 504 (2013) 451-5.
A. Trompette, E.S. Gollwitzer, C. Pattaroni, I.C. Lopez-Mejia, E. Riva, J. Pernot, N. Ubags, L. Fajas, L.P. Nicod, and B.J. Marsland, Dietary Fiber Confers Protection against Flu by Shaping Ly6c(-) Patrolling Monocyte Hematopoiesis and CD8(+) T Cell Metabolism. Immunity 48 (2018) 992-1005.e8.
L. Zhang, Y. Ouyang, H. Li, L. Shen, Y. Ni, Q. Fang, G. Wu, L. Qian, Y. Xiao, J. Zhang, P. Yin, G. Panagiotou, G. Xu, J. Ye, and W. Jia, Metabolic phenotypes and the gut microbiota in response to dietary resistant starch type 2 in normal-weight subjects: a randomized crossover trial. Sci Rep 9 (2019) 4736.
G. Chen, X. Ran, B. Li, Y. Li, D. He, B. Huang, S. Fu, J. Liu, and W. Wang, Sodium Butyrate Inhibits Inflammation and Maintains Epithelium Barrier Integrity in a TNBS-induced Inflammatory Bowel Disease Mice Model. EBioMedicine 30 (2018) 317-325.
J.C. Clemente, J. Manasson, and J.U. Scher, The role of the gut microbiome in systemic inflammatory disease. Bmj 360 (2018) j5145.
P. Louis, K.P. Scott, S.H. Duncan, and H.J. Flint, Understanding the effects of diet on bacterial metabolism in the large intestine. J Appl Microbiol 102 (2007) 1197-208.
M. Sakamoto, A. Takagaki, K. Matsumoto, Y. Kato, K. Goto, and Y. Benno, Butyricimonas synergistica gen. nov., sp. nov. and Butyricimonas virosa sp. nov., butyric acid-producing bacteria in the family 'Porphyromonadaceae' isolated from rat faeces. Int J Syst Evol Microbiol 59 (2009) 1748-53.
Q. Wang, F. Li, B. Liang, Y. Liang, S. Chen, X. Mo, Y. Ju, H. Zhao, H. Jia, T.D. Spector, H. Xie, and R. Guo, A metagenome-wide association study of gut microbiota in asthma in UK adults. BMC Microbiol 18 (2018) 114.
E. Cekanaviciute, B.B. Yoo, T.F. Runia, J.W. Debelius, S. Singh, C.A. Nelson, R. Kanner, Y. Bencosme, Y.K. Lee, S.L. Hauser, E. Crabtree-Hartman, I.K. Sand, M. Gacias, Y. Zhu, P. Casaccia, B.A.C. Cree, R. Knight, S.K. Mazmanian, and S.E. Baranzini, Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models. Proc Natl Acad Sci U S A 114 (2017) 10713-10718.
X. Bai, H. Wei, W. Liu, O.O. Coker, H. Gou, C. Liu, L. Zhao, C. Li, Y. Zhou, G. Wang, W. Kang, E.K. Ng, and J. Yu, Cigarette smoke promotes colorectal cancer through modulation of gut microbiota and related metabolites. Gut 71 (2022) 2439-2450.
X. Cheng, L. Zhou, Z. Li, S. Shen, Y. Zhao, C. Liu, X. Zhong, Y. Chang, A.G. Kermode, and W. Qiu, Gut Microbiome and Bile Acid Metabolism Induced the Activation of CXCR5+ CD4+ T Follicular Helper Cells to Participate in Neuromyelitis Optica Spectrum Disorder Recurrence. Front Immunol 13 (2022) 827865.
J. Rodriguez, S. Hiel, and N.M. Delzenne, Metformin: old friend, new ways of action-implication of the gut microbiome? Curr Opin Clin Nutr Metab Care 21 (2018) 294-301.
N. Wu, X. Li, H. Ma, X. Zhang, B. Liu, Y. Wang, Q. Zheng, and X. Fan, The role of the gut microbiota and fecal microbiota transplantation in neuroimmune diseases. Front Neurol 14 (2023) 1108738.
A. Dunalska, K. Saramak, and N. Szejko, The Role of Gut Microbiome in the Pathogenesis of Multiple Sclerosis and Related Disorders. Cells 12 (2023).
P.B. Eckburg, E.M. Bik, C.N. Bernstein, E. Purdom, L. Dethlefsen, M. Sargent, S.R. Gill, K.E. Nelson, and D.A. Relman, Diversity of the human intestinal microbial flora. Science 308 (2005) 1635-8.
S. Schloissnig, M. Arumugam, S. Sunagawa, M. Mitreva, J. Tap, A. Zhu, A. Waller, D.R. Mende, J.R. Kultima, J. Martin, K. Kota, S.R. Sunyaev, G.M. Weinstock, and P. Bork, Genomic variation landscape of the human gut microbiome. Nature 493 (2013) 45-50.
E.A. Franzosa, K. Huang, J.F. Meadow, D. Gevers, K.P. Lemon, B.J. Bohannan, and C. Huttenhower, Identifying personal microbiomes using metagenomic codes. Proc Natl Acad Sci U S A 112 (2015) E2930-8.

Table 1 is available in the Supplementary Files section.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Exploring the Causal Role of Gut Microbiota and Inflammatory Proteins in Neuromyelitis Optica Spectrum Disorder: a Mendelian randomization study with mediation analysis

Status:

Version 1

Abstract

Figures

1 Introduction

2 Methods

2.1 Ethics Statement

2.2 Study Design

2.3 Data Sources

2.4 IV selection

2.5 MR analysis

2.6 Sensitivity analysis

2.7 Intermediary effect

3 Results

3.1 Selection of IVs

3.2 MR analysis

3.3 Sensitivity analysis

3.4 Bi‑directional causal effects of inflammatory proteins and NMOSD

3.5 Mediation analysis

4 Discussion

5 Conclusion

Declarations

6 Ethics approval and consent to participate

7 Consent for publication

8 Availability of data and materials

9 Competing interests

10 Funding

11 Authors' contributions

12 Acknowledgements

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1