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. 2026 Feb 13;105(7):e47588. doi: 10.1097/MD.0000000000047588

Mendelian randomization followed by single-cell RNA sequencing exploration identifies effector memory CD4+ T cells as a risk factor for myasthenia gravis

Zhaolin Ren b, Hao He a, Zhichun Cai a, Peijun Li a, Zhou Liu a, Haihong Zhou a,*
PMCID: PMC12908807  PMID: 41686610

Abstract

Myasthenia gravis (MG) is an autoimmune neuromuscular junction disease classified as early onset MG (EOMG), which typically occurs before the age of 50 years, and late-onset MG (LOMG), which occurs after the age of 50 years. The contribution of different immune cell subsets to MG pathogenesis is not fully understood. To address this, we conducted 2-sample Mendelian randomization (MR) analyses incorporating 731 peripheral immunophenotypes associated with MG. To further explore cellular mechanisms, we analyzed an existing single-cell RNA sequencing (scRNA-seq) dataset derived from the peripheral blood of patients with MG during and after the myasthenic crisis phase. In MR analyses, IVW results suggested that 42 immunophenotypes were risk or protective factors for LOMG, whereas 28 immunophenotypes were involved in EOMG. Comprehensive sensitivity analyses confirmed the absence of heterogeneity and horizontal pleiotropy. Among these, the proportion of effector memory CD4 + T cells emerged as the most significant factor associated with an increased risk of LOMG. Single-cell analyses revealed an expansion of these cells in patients with LOMG during the myasthenic crisis stage compared with the stable stage, with functional annotations showing strong similarities to activated CD4 + T cells treated with TGF-β and IL-4. This study identified effector memory CD4 + T cells as a significant risk factor for LOMG through MR analyses and further explored an existing scRNA-seq dataset. The role of effector memory CD4 + T cells, potentially mediated by TGF-β and IL-4, in LOMG pathogenesis and disease exacerbation, highlighting the need for further functional studies.

Keywords: effector memory CD4+ T cells, Mendelian randomization, myasthenia gravis, single-cell RNA sequencing

1. Introduction

Myasthenia gravis (MG) is an autoimmune neuromuscular junction disease characterized by weakness and fatigue.[1] It affects 10 to 20 individuals per 100,000 people and exhibits a bimodal distribution in age, occurring most frequently among women under 40 years and men over 60 years.[2] An IgG antibody targeting the extracellular domain of an antigen present at the neuromuscular junction’s motor endplate is responsible for causing MG. Notably, approximately 80% of patients have autoantibodies directed against the acetylcholine receptor (AchR).[3] AChR antibody-positive MG can be classified as early onset MG (EOMG), which typically occurs before the age of 50 years, and late-onset MG (LOMG), which occurs after the age of 50 years.[4] Conventional therapeutic approaches for AchR-MG include intravenous immunoglobulin, nonsteroidal immunosuppressive agents, and acetylcholinesterase inhibitors.[5] A growing number of molecular therapies are being explored, such as B-cell depletion agents and T cells with chimeric antigen receptors.[6]

Autoimmune diseases are characterized by aberrant B cell and T cell reactivity to normal host constituents, with T cells playing a pivotal role in both pathogenesis and genetic susceptibility through the involvement of multiple genes that regulate immune cell functions.[7] Early single-cell sequencing studies in patients with MG found increased heterogeneity in B cells, with CD180 − B cells linked to disease activity and anti-AChR antibodies.[8] Another single-cell study found an increase in VISTA + monocytes in patients with MG, with upregulation of the antigen presentation and processing pathways.[9] The upregulation of CD40, HLA-DR, and CD64 in monocytes is associated with MG development, indicating that altered monocyte function may be a risk factor.[10] Myasthenic crisis (MC) is the most severe form of MG, marked by rapid disease progression that results in respiratory failure and poses life-threatening risks. Vigorous inflammatory CD4 + T signatures were identified in MC and correlated with clinical severity.[11] Single-cell studies of MC patients have revealed that FCGR3B + monocytes exhibit distinct pro-inflammatory signals during and after crisis, with oligoclonal expansions in B cells with high autoimmune activity and persistent exhaustion of CD4 + and CD8 + T cells.[12] EOMG and LOMG may involve distinct immune mechanisms, with CD28 and CTLA-4 co-regulating T cell activation and proliferation, which play a significant role in the pathogenesis of LOMG.[13]

This study aimed to explore the potential role of immune cells in the pathogenesis and progression of MG. The Mendelian randomization (MR) method employs genetic variants to evaluate the causal relationships between exposure and outcome, minimizing bias from reverse causality and confounding.[14] Guided by the STROBE-MR guidelines, we first conducted a comprehensive MR analysis to identify the potential causal immune cell subsets associated with MG.[15] Single-cell RNA sequencing (scRNA-seq) technology enables gene expression profiling at the individual cell level, allowing for detailed analysis of complex cell populations.[16] Based on the most significantly associated immune phenotype identified in the MR analyses, we further analyzed a publicly available scRNA-seq dataset to conduct a follow-up investigation. This approach enables a deeper exploration of the role of this implicated immune subset in MG.

2. Materials and methods

2.1. Study design

The roles of 731 peripheral immunophenotypes in EOMG and LOMG were determined using 2-sample MR analyses. ScRNA-seq was employed to complement the findings of the MR analyses by providing an additional cellular-level context, thereby offering deeper insights into the potential mechanisms underlying the observed genetic associations.

2.2. Data sources

The exposure data, comprising 731 peripheral immune phenotypes, such as T, B, and myeloid cells, were sourced from the IEU OpenGWAS dataset, which included 3757 Europeans.[17] Immunophenotypes, including IDs, traits, populations, and sample sizes, are detailed in Supplementary Table 1, Supplemental Digital Content, https://links.lww.com/MD/R363. Data from 1873 AChR antibody-positive MG patients (595 EOMG and 1278 LOMG) and 36,370 healthy individuals were sourced from the IEU OpenGWAS dataset.[18] Single-cell RNA sequencing data from 4 peripheral blood samples of 2 AChR antibody-positive LOMG patients (70 and 73 years old, male), collected during and post-MC, were obtained from the Gene Expression Omnibus database (accession code GSE222427).[12]

2.3. MR analyses

All analyses were conducted using the TwoSampleMR package (version 0.5.7), strictly adhering to the guidelines provided by the developers. Instrumental variables (IVs) were chosen following 3 MR assumptions: a strong link to exposure, independence from confounding factors, and connection to the outcome solely via exposure.[19,20] Given the limited number of IVs that met the strict threshold (P <5 × 10−8), a more inclusive threshold (P <1 × 10−5) was adopted. Single nucleotide polymorphism (SNP) pruning within a 10,000 kb window and an r2 threshold of <.001 ensured IV independence. Selected SNPs were checked for confounders using the PhenoScanner database.[21,22] An F-statistic >10 indicated no weak IV bias and was calculated as F = (beta/se)2.[23] Five common MR methods were used: inverse-variance weighted (IVW), Egger regression, weighted median, simple mode, and weighted mode.[24–27] IVW was used as the primary method to identify positive results, with P <.05 considered as suggestive evidence. After multiple testing corrections, associations with P <.05 were deemed significant. An odds ratio (OR) >1 indicated that exposure was a risk factor, whereas an OR <1 suggested that it was protective. Cochran Q-statistic and the MR-Egger intercept test assessed IV heterogeneity, with a Q-statistic P >.05, indicating no heterogeneity. Horizontal pleiotropy was assessed using the MR-Egger intercept and MR-PRESSO global tests, with P >.05 indicating no pleiotropy.[28] A sequential leave-one-out analysis revealed SNP heterogeneity.

2.4. Single-cell RNA sequencing analyses

All analyses were conducted using the Seurat package.[29] The process included data reading, dimensionality reduction, clustering, cell-type annotation, proportion statistics, differential gene expression analysis, and functional pathway investigation. The downloaded data were subjected to quality control, log-normalization, and scaling for further analysis. Variable genes were identified across the 4 samples using the FindVariableGenes function. The integration of these samples utilized an anchoring approach to mitigate batch effects while preserving the biological variability.[30] Clustering was performed using the FindClusters and FindNeighbor functions. Uniform manifold approximation and projection (UMAP) was used for data visualization. Cluster-specific marker genes were identified using Seurat FindAllMarkers function with the Wilcoxon rank-sum test (min. pct = 0.25, logfc.threshold = 0.25). The 20 most variable genes in each cluster were then selected. Cell-type annotations for each cluster were determined using marker genes from the CellMarker database and existing literature.[31] Statistical analyses were performed to determine the number and proportion of each cell type. Cells of interest were extracted for further clustering and annotation. Differentially expressed genes between groups were identified using a log-fold change threshold of 0.25, expression in at least 50% of cells, and an adjusted P-value of <.05. Functional analysis of specific cell types was conducted using the fgsea package for pathway analysis based on the significant differentially expressed genes.[32]

2.5. Statistics and visualization

R software (version 4.2.3) was used for conducting the MR analyses, single-cell RNA sequencing data analyses, and result visualization. Statistical analyses were performed using the relevant R package, with a significance threshold set at P <.05. The Bonferroni method was used for multiple testing corrections, with thresholds set at P <.05/n, where n represents the number of immunophenotypes in each category.[33] Visualization of results and combining plots were achieved using R packages such as RColorBrewer, paletteer, tidyverse, patchwork, and ggplot2.

3. Results

3.1. Roles of immunophenotypes in MG revealed by MR analyses

In the MR analyses, we identified 2 to 597 single SNPs as IVs associated with 731 peripheral immunophenotypes, with LOMG and EOMG as outcomes. These IVs demonstrated a stronger correlation with the exposure variable (P < 1 × 10−5) than with the outcome variable (P >5 × 10−5), with all F-statistics surpassing 10. Detailed information on these SNPs is provided in Tables S2–3, Supplemental Digital Content, https://links.lww.com/MD/R363. Tables S4–5, Supplemental Digital Content, https://links.lww.com/MD/R363 present the results of the 5 MR methods. The IVW results identified a number of immunophenotypes associated with MG, with 42 potentially linked to LOMG (Fig. S1, Supplemental Digital Content, https://links.lww.com/MD/R364) and 28 to EOMG (Fig. S2, Supplemental Digital Content, https://links.lww.com/MD/R364). Among these, several immune cell subsets demonstrated significant associations, including switched memory B cells, IgD- CD27- B cells, CD14 + CD16 + monocytes, and regulatory T cells, which emerged as either risk or protective factors across the analyses. Notably, the immunophenotype Effector Memory CD4+ T cell % T cell exhibited the most significant risk with LOMG after Bonferroni correction (P = .0000152, OR = 1.333, 95% confidence interval = 1.170–1.519). The IVW Q test and MR-Egger Q test both had P >.05, indicating no heterogeneity. The MR-Egger intercept test and the MR-PRESSO global test has P >.05, indicating no evidence of horizontal pleiotropy. Table S6, Supplemental Digital Content, https://links.lww.com/MD/R363 provides details of the sensitivity analysis. Figure 1 presents the MR effects of Effector Memory CD4 + T cells on LOMG evaluated using various MR methods, including visual assessments through funnel plots and leave-one-out plots.

Figure 1.

Figure 1.

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Effector memory CD4 + T cell percentage as a potential risk factor for LOMG. (A) The forest plot presented the results of 5 MR methods, including the number of SNPs, P-value, and OR accompanied by a 95% CI. (B–E) The scatter plot, forest plot, funnel plot, and leave-one-out plot collectively demonstrated the robustness and consistency of the MR estimates. CI = confidence interval, LOMG = late-onset MG, MR = Mendelian randomization. OR = odds ratio, SNPs = single nucleotide polymorphisms.

3.2. Roles of immune cells in LOMG revealed by scRNA-seq analyses

We analyzed a publicly available scRNA-seq dataset consisting of 4 blood samples comprising 31,399 whole peripheral blood cells and 20,320 genes. The numbers of cells in each sample were as follows: P1AMC (3738 cells), P1MC (8075 cells), P2AMC (10,835 cells), and P2MC (8751 cells). Cells were categorized into 2 groups: during and after MC treatment. By utilizing UMAP analysis to visualize the cells in 2-dimensional space, we were able to identify 24 clusters, each signifying different cellular states, which consistently grouped various cell types into distinct regions (Fig. 2A). The top 20 most highly expressed genes from each cluster were used for cell annotation (Table S7, Supplemental Digital Content, https://links.lww.com/MD/R363). Ten distinct immune cell types have been characterized: monocytes (Mono, clusters 1, 4, 5, 9, 11, 13, 22), CD4 + T cells (CD4T, cluster 2, 14, 19), natural killer cells (NK, cluster 3, 21), CD8 + T cells (CD8T, cluster 6, 7, 17, 18, 20), immature neutrophils (ImNeut, cluster 8), B cells (B, cluster 10, 15), neutrophils (Neut, cluster 12), platelets (Plt, cluster 16), double-negative T cells (DNT, cluster 23), and cycling cells (cyclin, cluster 24). The UMAP visualization of the annotated cells is shown in Figure 2B and C. The cell types were classified based on the expression of specific marker genes (Fig. 2D) as follows: monocytes (CD14, FCGR3A, CD68, and LYZ), CD4 + T cells (CD3D, CD3E, CD3G, CD4), NK cells (NKG7, GNLY, GZMB, GZMA), CD8 + T cells (CD3D, CD3E, CD3G, CD8A, CD8B), immature neutrophils (FCGR3B, CD24), B cells (CD79A, CD79B, MS4A1), neutrophils (DEFA3, CSF3R, S100A8, S100A9), platelets (PPBP), double-negative T cells (CD3D, CD3E, CD3G, negative for CD4, CD8A, CD8B), and cycling cells (MKI67). The specific proportions of each cell type are depicted in Figure 2E. The proportion of CD4 + T cells was consistently elevated during the MC stage compared to the AMC stage in both patients. In contrast, other major immune cell populations did not exhibit a consistent, directional change across these disease stages. Guided by the MR findings and supported by this phenotypic concordance, we therefore focused our subsequent analysis on the transcriptional profile of CD4 + T cells.

Figure 2.

Figure 2.

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Single-cell RNA sequencing characterization of peripheral immune cells from 2 patients during and after MC. (A) UMAP visualization of 24 cellular clusters; (B) UMAP projection of ten cell populations; (C) UMAP representation of sample-specific cell type distributions; (D) Dotplot of 26 marker genes for cell type annotation; (E) Comparative analysis of the proportion of ten immune cell types between 2 groups. AM = After MC, MC = myasthenic crisis, UMAP = uniform manifold approximation and projection.

Furthermore, we reclustered CD4 + T cells into 9 subclusters (Fig. 3A), among which clusters 7 and 9 were annotated as mixed cell types and were subsequently excluded. The top 20 highly expressed genes from each cluster used for cell annotation are presented in Table S8, Supplemental Digital Content, https://links.lww.com/MD/R363. This process allowed us to identify 5 distinct subtypes (Fig. 3B and C): naïve CD4 + T cells (marked by CCR7 and SELL), memory CD4 + T cells (marked by ITGB1, ANXA1, and ANXA2), effector CD4 + T cells (marked by CCL5, GZMA, GZMK, and CXCR3), effector memory CD4 + T cells (marked by KLRB1, CD28, MAF, and IDH2), and regulatory CD4 + T cells (marked by FOXP3 and IL2RA). The proportion of effector memory CD4 + T cells during the MC stage was higher during the MC stage than after the MC stage in both patients (Fig. 3D). Specifically, the proportion in Patient 1 was 10.5% during the MC stage and 8% during the stage after MC, whereas in Patient 2, it was 7% and 2.5%, respectively. Differential gene expression analysis between the MC and AM groups, followed by gene set enrichment analysis, revealed that the functional profile of this subtype showed strong similarity to TGF-β and IL-4-treated activated CD4 + T cells (Fig. 3E).

Figure 3.

Figure 3.

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Single-cell RNA sequencing analysis of CD4 + T cells from 2 patients during and after MC. (a) UMAP visualization depicting 9 distinct subclusters of CD4 + T cells; (b) UMAP visualization illustrating 5 subtypes of CD4 + T cells; (c) Dot plot displaying the expression levels of 23 marker genes used for cell type annotation; (d) Comparative analysis of the proportions of effector memory CD4 + T cells in 2 patients during and after the MC stage; (e) GSEA pathway enrichment analysis of effector memory CD4 + T cells. GSEA = gene set enrichment analysis, MC = myasthenic crisis, UMAP = uniform manifold approximation and projection.

4. Discussion

Despite advances in MG research and treatment, critical questions remain, such as identifying the cell types and their functions. In this study, we aimed to investigate the potential role of specific immune cell subtypes in MG using MR analyses with further insights from scRNA-seq datasets. Guided by MR analyses that identified associations between multiple immune cell subsets and MG, which is consistent with prior reports implicating cell types such as B cells and regulatory T cells.[34] Among these, effector memory CD4 + T cells emerged as the most significant risk factor after rigorous multiple testing correction, leading us to prioritize them for further investigation. Subsequent scRNA-seq analysis of MG patients indicated effector memory CD4 + T cells had a higher proportion during the MC stage. Further functional analysis indicated that these cells may be associated with the TGF-β and IL-4 signaling pathways, contributing to disease progression. Nevertheless, it must be acknowledged that they may not be the primary cause of the disease.

Our findings highlight the importance of effector memory CD4 + T cells in MG. Huang et al proposed that elevated CD95 expression in effector memory CD4 + T cells from patients with MG is positively correlated with clinical severity scores, IL-17 levels, and plasma cell frequencies.[35] It underscores the importance of its its activated state and functional pathway in disease progression. Effector memory CD4 + T cells, categorized into various helper T cell (Th) memory subsets, promptly secrete effector cytokines upon reactivation.[36] Follicular Th in MG shifts towards Th2 and Th17 rather than Th1, producing higher levels of cytokines such as IL-4, and its alterations correlate with disease severity and therapeutic efficacy, potentially serving as a biomarker for disease progression.[37]

An observational study showed that CD4 + T cells are essential for long-term antibody responses in MG, as they regulate B-cell antibody production through cytokines such as IL-21, IL-4, and IL-17A.[38] IL-4 is a critical cytokine implicated in the pathogenesis of MG, where it promotes B cell growth, differentiation, class-switching, and autoantibody production.[39] Additionally, IL-4 drives the differentiation of T cells into Th2 cells and broadly influences the growth and differentiation of T cells, natural killercells, and eosinophils, encompassing the promotion of naive Th2 cell differentiation, the regulation of immunoglobulin class switching, the prevention of apoptosis, and the modulation of macrophage plasticity.[40] Given these multifaceted roles, IL-4 may exert a pro-inflammatory effect in MG, particularly through its actions on T and B cells. TGF-β is a multifunctional cytokine crucial for immune regulation.[41] TGF-β regulates immune responses by inhibiting B-cell proliferation, reducing immunoglobulin secretion, decreasing IL-4 production, and preventing the development of autoantigen-specific T cells.[42] However, the role of TGF-β as a pro-inflammatory or anti-inflammatory mediator in vivo depends on the cell type, phenotype, and cytokine environment.[43] The role of TGF-β in MG pathogenesis requires further investigation. It may enhance the anti-inflammatory effects during disease progression and serve as a potential therapeutic target. Putheti et al demonstrated that the combination of TGF-β and IL-4 drives high-level IL-9 expression in activated human memory CD4 + T cells and concomitantly converts inducible regulatory T cells into an IL-9-producing activated state.[44] This specific population, termed Th9 cells, which primarily secrete IL-9, has subsequently been shown by Roostaee et al to possess the functional capacity to induce immunomodulation and tolerance.[45] Thus, TGF-β and IL-4 may play a pivotal role in mediating the immunomodulatory function of CD4 + T cells.

MR is a robust epidemiological method that minimizes bias from confounding factors and reverse causation, and is commonly found in observational research. ScRNA-seq technology enables the in-depth analysis of complex cell populations and characterization of individual cells. Incorporating MR analyses and insights from scRNA-seq datasets enhances our understanding of the cellular and molecular mechanisms underlying this disease. However, this study had several limitations. First, the scRNA-seq analysis was constrained by a relatively small sample size, which may introduce statistical bias and constrain the generalizability of our findings. This limitation was influenced by several factors, including the challenges in obtaining high-quality samples from MG patients, particularly concerning the clinical phase and autoantibody status matching. The lack of publicly accessible datasets and the refusal of data owners to share their data also contribute to this limitation. Secondly, the functional roles of prioritized cell types remain to be experimentally validated. Future studies should include in vitro and in vivo functional assays to confirm their potential contribution to MG pathogenesis. Third, although MR analyses identified effector memory CD4 + T cells as the most significant phenotype, other phenotypes that did not meet the Bonferroni threshold may still be biologically relevant and merit further exploration.

5. Conclusion

In summary, this study identified effector memory CD4 + T cells as a significant risk factor for LOMG through MR analyses and further explored an existing scRNA-seq dataset. Our results underscore the role of effector memory CD4 + T cells, potentially mediated by TGF-β and IL-4, in LOMG pathogenesis and disease exacerbation, highlighting the need for further functional studies.

Acknowledgments

We extend our appreciation to the participants and researchers involved in the collection and analysis of the GWAS and scRNA-seq data.

Author contributions

Conceptualization: Zhaolin Ren, Zhou Liu, Haihong Zhou.

Data curation: Hao He, Peijun Li.

Formal analysis: Zhaolin Ren, Hao He.

Funding acquisition: Haihong Zhou.

Methodology: Zhou Liu, Haihong Zhou.

Software: Zhaolin Ren, Zhichun Cai.

Validation: Hao He, Zhichun Cai, Peijun Li.

Visualization: Zhaolin Ren, Peijun Li.

Writing – original draft: Zhaolin Ren.

Writing – review & editing: Zhou Liu, Haihong Zhou.

Supplementary Material

Abbreviations:

AchR
acetylcholine receptor
CI
confidence intervals
DEGs
differentially expressed genes
GWAS
genome-wide association studies
IVs
instrumental variables
IVW
inverse-variance weighted
MC
myasthenic crisis
MG
myasthenia gravis
MR
Mendelian randomization
OR
odds ratios
scRNA-seq
single-cell RNA sequencing
SNPs
single nucleotide polymorphisms

This study was financially supported by the National Natural Science Foundation of China (Grant No.82071475).

This study was conducted using published studies and consortia providing publicly available summary statistics. All original studies were approved by the corresponding ethical review boards, and informed consent was obtained from all participants. Therefore, no new ethical review board approval is required.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Ren Z, He H, Cai Z, Li P, Liu Z, Zhou H. Mendelian randomization followed by single-cell RNA sequencing exploration identifies effector memory CD4+ T cells as a risk factor for myasthenia gravis. Medicine 2026;105:7(e47588).

The authors declare no competing financial or personal interests that could influence the findings of this study.

Contributor Information

Zhaolin Ren, Email: 18813358188@163.com.

Hao He, Email: 2160684080@qq.com.

Zhichun Cai, Email: caicai071112@126.com.

Peijun Li, Email: 13414164131@163.com.

Zhou Liu, Email: liuzhou@gdmu.edu.cn.

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