The autoimmune disease myasthenia gravis (MG), characterized by autoantibody-mediated impairment of neuronal transmission at the neuromuscular junction (NMJ), has an incidence and prevalence of minimal regional variation. Epidemiological investigations have indicated that the global prevalence of MG ranges from 40 to 180 cases per million individuals, while the yearly incidence falls within the range of 4–12 cases per million individuals (1). According to statistical data, it has been shown that around 80% of patients diagnosed with MG have acetylcholine receptor (AChR) antibody-positive. Conversely, a smaller proportion of individuals have muscle-specific kinase (MuSK) antibody-positive or lipoprotein receptor-related protein 4 (LRP4) antibody-positive as well (2). Fatigue and partial or systemic muscle weakness are caused by the NMJ being degraded by these antibodies. These symptoms serve as the primary clinical indicators of MG (3, 4). Patients diagnosed with MG are categorized into two main subtypes, ocular myasthenia gravis (OMG) and generalized myasthenia gravis (GMG), based on the specific muscles that are affected (5). Approximately 30–80% of individuals diagnosed with OMG are likely to progress to GMG over a span of 2 years (6). In addition to ptosis and diplopia, these people exhibit bulbar symptoms, limb weakness, and possibly respiratory failure (4). Hence, prompt detection and management through novel therapeutic interventions is critical for the treatment of such individuals.

The gut microbiota, a highly intricate and dynamic microbial population, resides within the gastrointestinal system of humans. Over the past decades, there has been a significant surge in scientific interest surrounding gut microbiota due to its strong association with immunology, inflammation, nutritional intake, and a wide range of disorders (7, 8). Emerging scientific evidence shows that the gastrointestinal microbiota greatly influences the development and progression of MG. Multiple case–control studies conducted on individuals with MG have shown alterations in the gut microbiota composition upon comparison to a healthy control group. These alterations primarily manifest as changes in the relative abundances of specific bacterial taxa (9, 10). An animal experiment using fecal microbiota transplantation (FMT) showed that, after immunized using the same classical MG modeling approach (experimental autoimmune MG) (11), MG microbiome mice (MMb mice, germ-free mice colonized with the fecal samples from MG patients) resulted in significant impaired motor ability, such as shorter total distance traveled in open field test (OFT), compared to healthy microbiota mice (HMb mice, germ-free mice colonized with the fecal samples from healthy controls). However, this effect could reversed by colonizing with the fecal samples from MG patients and healthy controls in germ-free mice (12). This study raises the possibility that gut microbiota may play a role in MG development. Nevertheless, the existing evidence from conventional epidemiological studies needs to be more adequate in addressing the complexities arising from biases, reverse causality, small sample sizes, and ethical concerns that restrict experimental research. Furthermore, observational studies may not account for potential confounding factors such as comorbidities, medications, and microbiomes at various anatomical sites (13). Consequently, the causal link of the gastrointestinal microbiome with the development of MG remains unexplored. The disentanglement of this causality holds substantial clinical significance, as it has the potential to bridge the existing gaps between classic epidemiological research and emerging novel biomarkers. By establishing the particular association between gut microbiota and MG, valuable insights can be gained, leading to innovative diagnostic and treatment options.

The causal relationship between an outcome and an exposure is investigated by Mendelian randomization (MR) (14), in which single nucleotide polymorphisms (SNPs) are regarded as instrumental variables (IVs) to assess the causality (15). SNPs follow the notion that genetic variants are distributed randomly during meiosis; hence, the possible consequences of reverse causation and the influence of confounding variables are removed. The causal relationship between autoimmune conditions, such as rheumatoid arthritis (16), systemic lupus erythematosus (17), and inflammatory bowel disease (18) with gastrointestinal microbiome has also been investigated. The present research used a two-sample MR approach to examine a possible causal relationship between gastrointestinal microbiota and MG using a genome-wide association study (GWAS) dataset.

This study employed a two-sample MR analysis to explore the causal impact of gastrointestinal microbiota on MG. Results indicate a suggestive causal relationship between specific microbiota and MG. Notably, the phylum Actinobacteria, class Gammaproteobacteria, and the families Defluviitaleaceae, Family XIII, and Peptococcaceae appeared to have protective roles against MG. In contrast, the order Mollicutes RF9 and the genus Faecalibacterium emerged as potential risk factors. Furthermore, the analysis found no reverse causal relationships between MG and these seven microbiota taxa. These insights could be instrumental in developing novel therapeutic and preventive strategies for MG.

The study of the gut microbiome’s influence on immunity has attracted significant attention in recent years (43). A growing body of research indicates a strong association between dysbiosis of the gastrointestinal microbiome and the progression of MG (44, 45). The primary emphasis of the comparative study conducted on the MG and healthy control group pertained to the diversity and abundance of the gut microbiota, as well as examining the metabolites produced by the gut microbiota. High alpha-phylogenetic diversity, typically indicative of good health (46), was found to be reduced in MG patients compared to healthy controls (9, 12), although some studies report no significant difference in alpha-phylogenetic diversity between these groups (10, 47). This discrepancy highlights the need for further research to establish a definitive link between gut microbiota diversity and MG. Previous studies have shown that the abundance of the gut microbiota differed between the MG group and the healthy control group. For instance, a case–control study demonstrated that the MG group exhibited reduced levels of the genera Clostridium and Eubacterium and species F. prausnitzii. In contrast, higher levels of phyla Proteobacteria and Bacteroidetes and genera Streptococcus and Parasutterella were observed in the MG group (9). The insufficiency of FOXP3⁺ CD4+ regulatory T cells (T_reg cells) is central to comprehending intestinal microbiota dysbiosis’s role in MG’s pathogenesis. T_reg cells are a subpopulation of CD4⁺ T cells that express both the CD25 and FOXP3 transcription factors. These cells are crucial in preserving immunological tolerance and immune homeostasis (48). The alterations in the relative proportions of bacterial taxa within the MG group can influence the differentiation of FOXP3⁺ CD4⁺ T_reg cells. As an example, the genus Clostridium has the potential to enhance the production of 2,3-dioxygenase and TGF-β1, hence potentially facilitating the differentiation of naive CD4⁺ T cells into FOXP3⁺ CD4⁺ T_reg cells (49). In contrast, there was a significant decrease in the abundance of the genus Clostridium within the MG group in comparison to the healthy control (9). This leads to insufficient frequency of FOXP3⁺ CD4⁺ T_reg cells, thereby weakening their ability to inhibit the production of autoantibodies. Short-chain fatty acids (SCFAs), non-nutritional substances produced by gut microbiota, play significant roles in physiological regulation. Dysbiosis in the gut microbiota can alter SCFA production, particularly propionate and butyrate, known for their immunoregulatory functions, including the augmentation of FOXP3⁺ CD4⁺ T_reg cells (50). A study indicated that SCFA levels in MG patients were lower compared to healthy controls (9). Therefore, the gut microbiota may enhance the population of FOXP3⁺ CD4⁺ T_reg cells through increased SCFA production, thereby potentially exerting a protective effect against MG.

Early diagnosis of MG, which is crucial for effective treatment, may benefit from identifying microbial biomarkers, as recent studies suggest a link between gut microbiota and MG (5). Zheng et al. reported that MG subjects typically showed reduced operational taxonomic units (OTUs) associated with the Lachnospiraceae and Ruminococcaceae families (12). Additionally, animal studies have demonstrated that a probiotics mixture (IRT5)—comprising Streptococcus thermophilus, Lactobacillus reuter, Bifidobacterium bifidium, Lactobacillus acidophilus, and Lactobacillus casei—can mitigate pro-inflammatory responses and lower AChR antibody levels in MG rat models by increasing Foxp3⁺CD4⁺T_reg cells (51). However, our study did not replicate these findings, possibly due to the limited sample size. It was observed that the phylum Actinobacteria, class Gammaproteobacteria, and families Defluviitaleaceae, Family XIII, and Peptococcaceae may have protective effects against MG. Actinobacteria is a diverse group of Gram-positive bacteria (52), with several studies demonstrating potential advantages for human health. Research of Moris et al. (10) showed that phylum Actinobacteria were less abundant in patients with MG than in the healthy control group. This research is consistent with our MR analysis. However, the exact mechanism of the protective effect of phylum Actinobacteria against MG is currently unknown. In addition, it is worth noting that the current body of research on the correlation between the other four gut microbiota taxa and MG is limited. Therefore, it is imperative to use caution when interpreting the findings, and further investigation is required to elucidate their potential significance in the context of MG. Future research should investigate the biological mechanisms linking these gut microbiota taxa with MG, potentially offering novel strategies for its prevention and treatment.

In addition, this MR analysis identified the order Mollicutes RF9 and the genus Faecalibacterium, a Gram-positive bacterium notably prevalent in the gut microbiome of healthy adults, as potential risk factors for MG. Different findings emerged from a case–control study in China, which found that Faecalibacterium was significantly less abundant in MG patients than in healthy controls (53). Conversely, another study reported a higher abundance of Faecalibacterium in MG patients relative to those with non-inflammatory neurological conditions (54). Faecalibacterium is typically regarded as beneficial, contributing to immune regulation, gut barrier integrity, and microbiota balance (55, 56). However, some MR studies have identified Faecalibacterium as a risk factor in diseases like female infertility and age-related macular degeneration, with the underlying causes for these discrepancies remaining unclear (57, 58). Future clinical and experimental research is crucial to elucidate the mechanisms linking Faecalibacterium with MG. Additionally, larger and more diverse gut microbiota GWAS datasets are needed to enhance the robustness and objectivity of MR study findings. Notably, Mollicutes, a class of bacteria characterized by the absence of a cell wall and a small genome, are also of interest in this context (59). Studies have shown that changes in microbial composition enable microbes and their metabolites to invade and pass through the gut barrier, evade immune intervention, and enter the circulation, leading to immune activation and chronic systemic inflammation (45). An MR study demonstrated that as Mollicutes lack a cell wall, they may be more invasive and capable of crossing the gut barrier (59). Based on their structural properties, it is hypothesized that Mollicutes, particularly the order Mollicutes RF9, might invade more easily across the intestinal barrier, potentially contributing to MG development. However, due to the lack of direct research studies on the correlation between order Mollicutes RF9 and MG, caution must be exercised in interpreting the findings.

Su et al. (60) also employed MR to explore the causal link between gut microbiota and MG. Their study, using MG-related GWAS data from the FinnGen R9 database, could not differentiate the effects of gut microbiota on various antibody types of MG. In contrast, this study focused exclusively on AChR antibody-positive MG patients; hence, it could not establish causal relationships for other antibody-positive MG types. The differing datasets led to varying results between this study and Su et al.’s, suggesting that gut microbiota may influence different MG types differently. Both studies identified a suggestive causal relationship between gut microbiota and MG, with variations in specific gut microbiota taxa, thereby expanding the scope for future research and strategies to modulate gut microbiota for MG prevention and treatment. Notably, both studies highlighted the potential protective role of the Defluviitaleaceae family against MG, suggesting its significance in MG management.

From a clinical perspective, this study’s findings could contribute to developing innovative prevention and treatment strategies for MG. Potential interventions include small-molecule antibiotics, specific probiotic strains, bioactive metabolites, and fecal microbiota transplantation (FMT) (5, 44). However, as this research area is still nascent, further investigations are required to validate these initial results and identify the most effective approaches. Additionally, the inherent complexity and individual variability of gut microbiota necessitate the consideration of personalized treatments.

However, it is vital to recognize the limitations of the present investigation. Initially, it should be noted that the original research on MG exclusively included individuals with AChR antibody-positive MG. Consequently, the absence of other types of MG patients, such as those with MuSK antibody-positive and LRP4 antibody-positive, precluded us from doing additional subgroup analyses to ascertain a more precise impact of the relationship. Secondly, it is noteworthy that the GWAS summary data used in the present analysis predominantly consisted of individuals of European ancestry. Biased estimations may arise due to this, necessitating caution in generalizing our findings to other ethnic groups. In addition, the assessment of gut microbiota taxa was limited to summary statistics due to the absence of individual-level data. To explore possible variations among groups, further analyses pertaining to population stratification, such as gender and age, might be undertaken. Finally, bacteria are the predominant component in the gut microbiome, but viruses, fungi, and archaea are also present. The causal relationship between other gut microbes and MG could not be explored due to the lack of relevant GWAS data.

In conclusion, this MR study supports a suggestive causal relationship between gut microbiota and MG, offering valuable insights for novel therapeutic and preventive strategies. Future randomized controlled trials are essential to fully understand the role and therapeutic implications of gut microbiota in MG’s pathogenesis. At the same time, this study suffers from limitations such as using only European ancestry and AChR antibody-positive GWAS data, which may diminish the general applicability of our findings. We look forward to more including larger sample size and more ethnic group GWAS data, as well as more individual level GWAS data to meet the requirement of obtaining more comprehensive and reliable findings on the causal relationship between gut microbiota and MG.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Our analyses used published studies or publicly available GWAS abstract data. All studies were conducted with the approval of the appropriate institutional ethics committees, and therefore did not require additional ethical approval.

CM: Writing – original draft. AH: Writing – original draft. ZW: Writing – original draft. XQ: Writing – review & editing. JT: Writing – review & editing.