Unveiling the Role of CD27+ Memory B Cells in Primary Biliary Cholangitis: A Mendelian Randomization Study

doi:10.21203/rs.3.rs-3775230/v1

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Unveiling the Role of CD27⁺ Memory B Cells in Primary Biliary Cholangitis: A Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-3775230/v1

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This study performed a two-sample Mendelian randomization (MR) approach based on genome-wide association study (GWAS) summary statistics to investigate the causal relationship between immune cells and primary biliary cholangitis (PBC). A total of 731 immune cell traits were evaluated for association with PBC to identify diagnostic biomarkers and potential therapeutic targets. GWAS data on immune cell traits and PBC were collected with Single Nucleotide Polymorphisms (SNPs) used as instrumental variables. The IVW model showed a causal relationship between elevated levels of four CD27⁺ memory B cell types and increased risk of PBC (P_FDR < 0.05). Specifically, CD27 expression on CD24⁺ CD27⁺ B cells, IgD⁺ CD24⁺ B cells, IgD⁻ CD38^dim B cells and unswitched memory B cells showed associations with PBC risk. However, no causal relationship between PBC and these immune cell traits was observed in the reverse MR analysis. Differential gene analysis from the Gene Expression Omnibus (GEO) database, Spearman correlation analysis and enrichment analysis further supported the association between CD27⁺ memory B cells and PBC risk. These findings suggest that CD27 + memory B cells play a role in the pathogenesis of PBC and may serve as important targets for diagnostic and therapy strategies.

Health sciences/Diseases/Immunological disorders

Biological sciences/Genetics

Health sciences/Biomarkers

Health sciences/Risk factors

Health sciences/Medical research/Epidemiology

Health sciences/Medical research/Genetics research

Primary biliary cholangitis

Immune cells

Mendelian randomization

CD27

Memory B cell

Primary biliary cholangitis (PBC) is a chronic progressive autoimmune cholestatic liver disease primarily affecting the liver. It is characterized by the accumulation of bile in the liver due to non-suppurative inflammation of the small intrahepatic bile ducts, which can ultimately lead to liver fibrosis and cirrhosis ^1,2. PBC predominantly affects middle-aged and elderly women, with an estimated global annual incidence of nearly 100,000 new cases ³. Typical signs consist of tiredness, pruritus, and an increase in levels of serum alkaline phosphatase and gamma-glutamyl transferase without any symptoms. Anti-mitochondrial antibody (AMA), specifically the M2 subtype (AMA-M2), is known for its high sensitivity and specificity in accurately detecting PBC ⁴. Other immunological features include elevated levels of immunoglobulin M, anti-glycoprotein 210, and/or anti-soluble acidic nuclear phosphoprotein antibodies ². At present, ursodeoxycholic acid (UDCA) is the primary therapy for PBC, although there is still a lack of successful immunotherapies for PBC ⁵.

The precise cause and development of PBC remain elusive, yet it is believed to entail a complex interplay of genetic and environmental factors that disrupt the immune system, involving various immune cells and cytokines ^5,6. The loss of immune tolerance in the liver is proposed as a key driver of PBC whereby abnormal immune system activation causes damage to the epithelial cells of the small bile ducts. The immune response comprises the innate immune system, dominantly regulated by macrophages, natural killer cells, and natural killer T cells, and the adaptive immune system, mainly regulated by Th1 cells, Th17 cells, and B lymphocytes ^7,8. The pathology of liver bile ducts entails substantial contributions from both CD4⁺ and CD8⁺ T cells. However, in instances where self-tolerance is compromised, CD8⁺ T cells are specifically responsible for targeting and causing damage to the bile ducts ⁹. The AMA-M2 antibodies that PBC patients produce primarily target mitochondrial antigens in the liver bile ducts, which are involved in antigen recognition and immune cell activation ^10,11. Despite this, there are still many unresolved questions concerning the immunological mechanisms behind the pathogenesis of PBC, and further research into the immunopathogenesis of PBC will provide new insights into the diagnosis, monitoring and development of new treatments for PBC.

Mendelian randomization (MR) studies utilize genetic variation as instrumental variables (IVs) to assess causal relationships between exposure factors and outcomes ¹². By utilizing this method, the challenges of confounding variables and reverse causation often faced in observational research are effectively addressed, leading to more dependable outcomes in terms of causal inference ¹³.

By analysing summary statistics from publicly available genome-wide association studies (GWAS), two-sample MR studies can assess the associations between 731 immune cell traits and PBC. This analysis provides clues regarding the influence of different immune factors on PBC, opening up potential leads for further investigations into the development and management of PBC.

2.1 Selection of IVs for MR Analysis

The GWAS data for MR analysis of PBC were summarized in Table 1. The flowchart of the study design was shown in Fig. 1, and detailed information on the IVs used in the MR analysis was provided in Supplementary Table S1.

Table 1

Summary of GWAS data sources
Phenotypes	GWAS Catalog	Year	Population	Sample size	Number of SNPs	PMID
731 Immune Cell Traits	GCST90001391 - GCST90002121	2020	European	3757	20,143,392	32929287 ¹⁴
Primary Biliary Cholangitis	GCST90061440	2021	European	24,510	5,004,018	34033851 ¹⁵

2.2 Causal Influence of Immune Cell Traits on PBC Risk

The result showed the findings of the MR analysis regarding the influence of immune cell characteristics on the risk of PBC (Supplementary Table S2). Based on the IVW results, a causal effect of 45 immune cell traits on PBC risk was detected at a significant level of difference (P_IVW < 0.05), with 34 immune cell traits showing an increased level and 11 immune cell traits showing a decreased level, potentially triggering PBC risk. These immune cells included 12 distributed in B cells, 4 in mature T cells, 3 in cDCs, 3 in monocytes, 13 in myeloid cells, 6 in Treg and 4 in TBNK panels.

After FDR adjustment (P_FDR < 0.05), a causal effect of 4 immune cell traits on PBC was still detected. The traits of these immune cells included CD27 on CD24⁺ CD27⁺ B cells, CD27 on IgD⁺ CD24⁺ B cells, CD27 on IgD⁻ CD38^dim B cells, and CD27 on unswitched memory B cells. Interestingly, all four of these immune cell traits were MFI traits, located on the B cell panel and defined as memory B cells.

Specifically, using the IVW method, an odds ratio (OR) estimate of 1.130 (95% CI = 1.062 ~ 1.203, P = 0.0001, P_FDR = 0.0168) was observed for CD27 on CD24⁺ CD27⁺ B cells, and similar results were observed using the MR Egger (OR = 1.180, 95% CI = 1.033 ~ 1.348, P = 0.0450), weighted median (OR = 1.157, 95% CI = 1.071 ~ 1.249, P = 0.0002), simple mode (OR = 1.081, 95% CI = 0.937 ~ 1.248, P = 0.3162) and weighted mode (OR = 1.161, 95% CI = 1.075 ~ 1.254, P = 0.005). For CD27 on IgD⁺ CD24⁺ B cells, the IVW estimate for PBC risk was OR = 1.212 (95% CI = 1.100 ~ 1.336, P = 0.0001, P_FDR = 0.0166), while the MR Egger (OR = 1.091, 95% CI = 0.901 ~ 1.320, P = 0.4378), weighted median (OR = 1.204, 95% CI = 1.106 ~ 1.311, P = 2×10^− 5), simple mode (OR = 1.166, 95% CI = 0.985 ~ 1.381, P = 0.1493) and weighted mode (OR = 1.207, 95% CI = 1.108 ~ 1.314, P = 0.0124) showed similar results. The IVW estimate for CD27 on IgD⁻ CD38^dim B cells and its impact on PBC risk was OR = 1.149 (95% CI = 1.078 ~ 1.224, P = 2×10^− 5, P_FDR = 0.0114), while the MR Egger (OR = 1.167, 95% CI = 1.052 ~ 1.296, P = 0. 0270), weighted median (OR = 1.167, 95% CI = 1.088 ~ 1.088, P = 1×10^− 5), simple mode (OR = 1.131, 95% CI = 0.982 ~ 1.302, P = 0.1306) and weighted mode (OR = 1.166, 95% CI = 1.078 ~ 1.078, P = 0.0064) methods showed similar results. For CD27 on unswitched memory B cells, the IVW estimate for PBC risk was OR = 1.162 (95% CI = 1.162 ~ 1.247, P = 3×10^− 5, P_FDR = 0.0114), while the MR Egger (OR = 1.158, 95% CI = 1.015 ~ 1.320, P = 0.0563), weighted median (OR = 1. 169, 95% CI = 1.068 ~ 1.278, P = 0.0007), simple mode (OR = 1.078, 95% CI = 0.902 ~ 1.289, P = 0.4261) and weighted mode (OR = 1.194, 95% CI = 1.088 ~ 1.310, P = 0.0038) showed similar results (Fig. 2). These results suggest that all different MR analysis methods indicated that these four CD27⁺ memory B cell types were potential risk factors for PBC, highlighting the potential importance of CD27⁺ memory B cells in PBC.

2.3 Sensitivity Analysis of Immune Cell Traits in PBC Risk

Furthermore, the MR-Egger intercepts provided evidence against the presence of horizontal pleiotropy in relation to the four immune cell trait associations mentioned above. No heterogeneity was found in the current study according to Cochran's Q test. The robustness of the observed causal associations was demonstrated in the detailed information provided by the sensitivity analysis (Table 2). No outliers were detected by the MR-PRESSO test. The results of the MR-Steiger directional test confirmed the accuracy of our estimates of causal direction. It demonstrated the consistency of the findings as evidenced by the scatter plots (Fig. 3a-d), and the causality was not confounded by any single SNP, as confirmed by the results of the leave-one-out approach (Fig. 3e-h).

Table 2

Sensitivity analysis results of causal effects of immune cells on PBC
Trait.exposure	Outcome	F-statistic	Pval (IVW)	Pval (MR egger intercept)	Outliers (MR-PRESSO)	Steiger Test	Pval (Steiger Test)
CD27 on CD24⁺ CD27⁺ B cell	PBC	130.9301	0.4469	0.495	NA	TRUE	9.47E-183
CD27 on IgD⁺ CD24⁺B cell	PBC	146.0403	0.2167	0.3052	NA	TRUE	9.64E-113
CD27 on IgD⁻ CD38^dim B cell	PBC	151.1385	0.6911	0.7198	NA	TRUE	4.85E-187
CD27 on unswitched memory B cell	PBC	85.1383	0.4832	0.95	NA	TRUE	8.09E-145

2.4 Causal Influence of PBC on Cell Traits Risk

The reverse MR analysis used SNPs linked to PBC as instrumental variables for exposure and examined immune cell traits as outcomes (Supplementary Table S3). The hypothesised causal effect of PBC on immune cell traits was estimated. However, the results indicated a lack of significant causal associations between PBC and the four immune cell traits mentioned above (Supplementary Fig. S1). Sensitivity analysis revealed no differences in heterogeneity and pleiotropy (Supplementary Table S4). These results suggest that PBC does not have a causal effect on these four CD27⁺ memory B cell traits.

2.5 CD27 Expression and its Association with PBC Risk

To further validate the impact of CD27⁺ memory B cells on PBC, we also examined the expression of CD27 in PBC. The gene expression profile dataset GSE79850 was obtained from the Gene Expression Omnibus (GEO) database. It consisted of 7 normal samples, 7 low-risk PBC samples and 9 high-risk PBC samples. Principal component analysis (PCA) was performed to assess differences between samples, which showed limited variation within groups but notable variation between groups (Fig. 4a). Differential expression gene (DEG) analysis was then performed between the different groups (Supplementary Table S5). Applying the criteria of P < 0.05 and |logFC| ≥ 2, a total of 116 DEGs were discovered when comparing the low-risk PBC group with the normal group. Among these DEGs, 61 genes were upregulated and 55 genes were downregulated (Fig. 4b).When comparing the high-risk PBC group with the normal group, we identified a total of 192 DEGs, consisting of 139 genes that were upregulated and 52 genes that were downregulated (Fig. 4c). Among these DEGs from both comparisons, 75 genes were commonly differentially expressed (Fig. 4d). Notably, CD27 was observed as one of the significantly upregulated genes associated with increasing PBC risk, serving as a marker for memory B cells (Fig. 4e).

2.6 Functional Role and Pathway Enrichment of CD27 Related Genes in PBC

To investigate the functional role of CD27 in PBC, Spearman correlation analysis was performed on the genes associated with PBC (Fig. 4f and Supplementary Table S6), then CD27-related genes with significant differential expression were identified (Fig. 4g), followed by enrichment analysis (Fig. 4h and Supplementary Table S7). The results showed that the GO functions consisted of BP, MF and CC. The top three GO-BP terms were mainly associated with signalling pathways related to leukocyte migration, promotion of cytokine production, and leukocyte cell adhesion. The GO-CC analysis revealed notable enrichment in the outer surface of the plasma membrane, immunological synapse, and MHC class II protein complex. The GO-MF terms included immune receptor activity, chemokine receptor binding, and cytokine activity. It also showed the three most significantly enriched KEGG pathways, which included cytokine-cytokine receptor interaction, viral protein interaction with cytokine and cytokine receptor, and Th1 and Th2 cell differentiation signalling pathways. The results were consistent with the ability of memory B cells and plasma cells to specifically recognise antigens while also controlling the immune response of leukocytes.

2.7 Current Drugs Targeting CD27

Finally, the status of drug development targeting CD27 was investigated. The DSigDB database was searched for drug candidates predicted to modulate CD27. The results showed that there are currently no drugs or effective kinase inhibitors available for clinical use. However, the database provided several recommended drugs for further research (Table 3).

Table 3

Predictive drugs for CD27 in DSigDB databas
Source	Chemical Name	Function
FDA Approved	-	-
Kinase Inhibitors	-	-
CMAP	Chlorhexidine	Up-regulated CD27
BOSS	Isoguanine	Predicted
BOSS	AGN-PC-0JHFVD	Predicted
CTD	Paraquat	Predicted
CTD	Arsenic	Predicted
CTD	Simvastatin	Predicted
CTD	Bortezomib	Predicted
CTD	Trichloroethylene	Predicted
CTD	PhIP	Predicted
CTD	Zoledronic acid	Predicted

Abbreviation: FDA: Food and Drug Administration; CMAP: Connectivity map; BOSS: Biomedical Object Search System; CTD: Toxicogenomics Database.

At present, the complex and ambiguous role of the immune system in the progression of PBC necessitates the development of targeted immunotherapy. The genetic perspective had systematically elucidated the mechanisms by which immune cells contribute to the development and progression of PBC. By conducting a comprehensive genetic analysis based on extensive GWAS summary data, this study postulated that immune cells have the ability to influence susceptibility to PBC. Utilising SNPs as instrumental variables and incorporating a two-sample MR method, the study successfully demonstrated a causal relationship between four CD27⁺ B cell traits (CD24⁺ CD27⁺, IgD⁺ CD24⁺, IgD⁻ CD38^dim, and unswitched memory) and PBC, as evidenced by P_FDR < 0.05. The findings indicated that an increase in these four immune cell traits was associated with an increased risk of PBC, although their causal effect on PBC was not statistically significant.

B cells can be classified into distinct subsets, including naive B cells, activated B cells and memory B cells, based on their response to antigen stimulation. Among these subsets, unswitched memory B cells, switched memory B cells and plasmablasts expressed the protein CD27 ¹⁶. In particular, mature memory B cells were characterised by the co-expression of CD27 and CD24, with CD27 present on CD24⁺ CD27⁺ cells. Additionally, unswitched memory B cells, identified by the presence of CD27 on IgD⁺ CD24⁺ cells, played a critical role, particularly in the early stages of the immune response. Similarly, the presence of CD27 on IgD⁻ CD38^dim was indicative of unswitched memory B cells, which were involved in long-term immune responses and lacked IgD expression while exhibiting reduced levels of CD38 expression. The presence of CD27 on unswitched memory B cells was associated with the maintenance of IgM and IgD and may influence the response to specific antigens ¹⁴. CD27, a marker of memory B cells, played a crucial role in the regulation of immune memory, antibody production and immune response, thereby maintaining the normal functioning of the immune system and effectively combating infections ¹⁷. All four immune cell types identified were characterised as memory B cells expressing CD27.

The functional role of CD27⁺ memory B cells in PBC was investigated. When memory B cells were encountered again with the same antigen, they were able to rapidly activate, proliferate and differentiate into plasma cells to mount an immune response. Research showed that the number of memory B cells in the peripheral blood of individuals with PBC, as identified by single-cell mass cytometry, was lower than in the control group ¹⁸. In contrast, high-throughput detection results revealed that the liver tissue of PBC patients had a greater abundance of memory B cells compared to the control group ¹⁹. In addition, there was evidence for the accumulation of CD27⁺ memory B cells and plasma cells in the portal area of the liver in individuals with PBC. These cells interacted with DUOX2⁺ ACE2⁺ small bile duct cells, potentially leading to loss of immune tolerance and progression of PBC ²⁰. Besides, elevated CD27 expression in specific subsets of memory B cells was associated with the CD40 and FCGR2A genetic regions and increases susceptibility to inflammatory bowel disease, particularly Crohn's disease ^21,22. Therefore, a comprehensive study of CD27 expression in liver tissue from low and high risk PBC in the GEO dataset was performed, validating the strong correlation between CD27 expression and the development and progression of PBC, suggesting that CD27⁺ memory B cells play a critical role in the development and progression of PBC.

In the context of treating PBC, immunotherapeutic strategies primarily centered around modulating immune molecules involved in B cell and T cell responses, as well as selectively targeting cytokines and chemokines. Despite the presence of conventional autoantibodies in PBC, various treatment approaches directed towards immune cells had proven unsuccessful. The efficacy of therapies such as B cell depletion therapy and other biological agents had been underwhelming ²³. For instance, previous studies on drugs such as Rituximab, Ustekinumab, and Abatacept, which possess biological properties, yielded inconclusive results and were deemed unsuitable for standard therapy ^24–27. The exploration of the novel medication Baricitinib, which inhibits JAK1 and JAK2, in PBC treatment was discontinued despite its successful application in rheumatoid arthritis treatment ^28,29. Recent experiments conducted on mice indicate that the combination of anti-BAFF and anti-CD20 medications exhibited a significant potential in enhancing the eradication of B cells, resulting in a subsequent reduction in serum levels of AMA, total IgM, and IgG ³⁰. Consequently, targeting CD27⁺ memory B cells through immunotherapy emerges as a promising approach for future interventions in the treatment of PBC, including for patients with AMA-negative PBC.

Although this study utilised a variety of MR analysis methods to infer causality, reliable results were obtained independent of confounding factors such as horizontal pleotropy and other variables. However, it remains important to acknowledge that the study has certain limitations. The immune cell data and PBC data came from different studies, resulting in differences in sample size, quality control methods and ethnic background, which may lead to potential inaccuracies. Although the study performed multiple sensitivity analyses, it was not possible to conduct a comprehensive assessment of horizontal pleiotropy. Furthermore, due to the lack of precise data, a more comprehensive examination of the population was not feasible. Additionally, the study relied solely on European databases, which limits the generalizability of the findings to other ethnic groups. Despite implementing multiple FDR adjustments, the limited sample size may have led to false positive results. Future studies should include more experimental investigations to explore the potential mechanisms between the identified immune signatures and PBC risk.

To conclude, this study employed MR methods to investigate the association between immune cells and PBC. The findings revealed that CD27⁺ memory B cells may have a potential role in the pathogenesis of PBC. Furthermore, through the validation of gene expression data and bioinformatic analysis, this study elucidated the essential immunological functions and pathways in which CD27 may be involved in PBC. Overall, this study held significant importance in advancing our understanding of PBC development and identifying potential therapeutic targets, thereby offering novel perspectives and strategies for the management and treatment of PBC.

4.1 Research Design

We evaluated the causal association between PBC and 731 immune cell traits (7 categories) using a two-sample MR analysis. For the MR analysis, PBC was considered as the outcome, while the exposure consisted of 731 selected immune cell traits. Subsequently, the positions of the exposure and the outcome were reversed to perform a reverse MR analysis. In causal inference, MR employs genetic variation as IVs to represent the risk factors. These IVs must adhere to three critical assumptions: (1) IVs exhibit a direct association with the exposure; (2) IVs are not influenced by confounding factors; (3) IVs do not affect the outcome through pathways other than the exposure. All summary statistics for GWAS employed in this research were readily available and downloadable to the general public. The original analysis received ethical approval.

4.2 GWAS Data Sources for Immune Cell Traits

Summary statistics for 731 immune cell traits were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/), covering accession numbers GCST90001391 to GCST90002121. The study examined the influence of approximately 22,000,000 variations on 731 immune cell characteristics in 3,757 people from the Sardinian population. A total of 70 loci, of which 53 were newly discovered, exposed 122 exceptional independent association signals that relate to 459 cellular traits (P < 1.28 × 10^− 11). The research involved 389 median fluorescence intensities (MFI) that indicate surface antigen levels, 32 morphological parameters (MP), 192 relative counts (RC) and 118 absolute counts (AC). Included in the MFI, RC and AC traits were cDCs, myeloid cells, monocytes, B cells, mature T cells, Treg cells and TBNK (T cells, B cells and Natural Killer cells) panels ¹⁴. The MP traits included only cDCs and TBNK panels. The current research built on and added to a previous GWAS that looked at 272 traits of immune cells in the blood ³¹. The study was conducted on a population of 1,629 individuals from the Sardinian founder population. After accounting for covariates (specifically, gender, age, and age squared), the association of these 731 immune cell characteristics was examined ³².

4.3 GWAS Data Sources for PBC

Summary statistics for PBC were acquired from the GWAS Catalog (accession numbers GCST90061440). A cross-ethnicity meta-analysis of the complete genome for genetic susceptibility to PBC was conducted, which involved five European ancestry cohorts and two East Asian cohorts, encompassing a total of 10,516 cases and 20,772 controls. The investigation identified 56 genetic regions across the genome, including 20 previously unknown regions, having a significant impact on the immune system. The study revealed that the genetic makeup of PBC was prevalent in both European and East Asian populations. Additionally, computerized screening was utilized to investigate potential drug effectiveness for repurposing in PBC treatment ¹⁵.

4.4 Selection of IVs

For PBC, the significance level was adjusted to 5×10^− 8. For each immune trait examined, the significance level of IVs was set at 1×10^− 5, as stated in recent research ^33–35. The study employed the clumping procedure from the PLINK1.9 package to trim single nucleotide polymorphisms (SNPs) associated with these immune traits ³⁶. Furthermore, the linkage disequilibrium (LD) r² threshold for linkage disequilibrium was established at < 0.001 within a 10000 kb distance, while the LD r² values were computed based on the reference panel of the 1000 Genomes Project ³⁷. The study computed the proportion of phenotypic variance explained (PVE) and F-statistics for each IV to assess their strength and reduce bias. The research identified 7 to 1786 distinct immune phenotype independent variables, which accounted for an average of 0.240%, ranging from 0.004–3.652%, of the variation in their corresponding immune characteristics.

4.5 MR Statistical Analysis

We used various methods, including the inverse variance weighted (IVW) method ³⁸, the weighted median method ³⁹, the MR-egger method ⁴⁰, the simple mode method ⁴¹ and the weighted mode method ⁴² from the TwoSampleMR package ⁴³ to evaluate the causal relationships between 731 immune cell traits and PBC. R², alternatively referred to as PVE, measures the degree to which instrumental variables elucidate the exposure factor. The calculation method for R² was taken from previous literature ⁴⁴. To assess the presence of weak instrumental variable bias and to determine the correlation between the genetic instrument and the exposure factor, the F value was calculated using the formula F = (N-k-1)/k×R²/(1-R²), where N represents the sample size in the GWAS analysis and k represents the number of instrumental variables. The False Discovery Rate (FDR) correction method can help us to control the proportion of false positives generated in multiple testing, thereby improving the reliability of research results. R 4.3.2 software (http //www.Rproject.org) was used for all analyses.

4.6 Sensitivity Analysis

To assess heterogeneity among the chosen instruments, the statistical measure of Cochran's Q and its associated P-values were used ⁴⁵. To account for horizontal pleiotropy, the researchers utilised a widely used technique known as MR-Egger. The presence of horizontal pleiotropy ⁴⁰ was indicated by the importance of the intercept term in MR-Egger. In addition, the MR-Presso technique was utilised to detect and recalculate the estimated magnitudes of pleiotropy anomalies using MR pleiotropy residual and outlier analysis ⁴⁶. We used the leave-one-out method to progressively eliminate each SNP and assessed the robustness of the results based on the remaining SNPs. Scatter plots were also used to demonstrate the robustness and lack of heterogeneity in the results.

4.7 Reverse MR analysis

To investigate whether PBC has a causal effect on the immune phenotype, we also conducted a reverse MR analysis using SNPs related to PBC as instrumental variables, with PBC as the exposure and immune phenotype as the outcome, and the analysis method was the same as in the MR analysis.

4.8 PBC Microarray Data

We obtained the GSE79850 mRNA expression profile dataset from the GEO database (http //www.ncbi.nlm.nih.gov/geo/). The GSE79850 study using the GPL19965 mRNA annotation platform consisted of high-risk PBC patients (n = 9) who required liver transplantation, low-risk PBC patients (n = 7) who achieved complete remission with UDCA, and a control group of unaffected livers (n = 7). The data were log2 transformed, followed by PCA analysis and significantly different gene analysis. Significantly differentially expressed genes were identified based on genes with a corrected p-value less than 0.05 and an absolute logFC greater than or equal to 2. The ggplot2 and VennDiagram packages were used to visualise the results. Statistical significance was determined by comparing gene expression levels using one-way ANOVA with a significance threshold of P < 0.05. The clusterProfiler package was used to perform ID conversion on the input molecular list, followed by Spearman correlation analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. The GOplot package was used to calculate the z-score for each enriched term using the numerical values of the given molecules, and terms with a P-value less than 0.05 were considered significantly enriched.

AC Absolute cell

BP Biological process

CC Cellular component

CI Confidence interval

DEG Differentially expressed gene

FDR False discovery rate

GO Gene ontology

GWAS Genome-wide association study

IVs Instrumental variables

IVW Inverse variance weighting

KEGG Kyoto Encyclopedia of Genes and Genomes

LD Linkage disequilibrium

MF Molecular function

MFI Median fluorescence intensities

MP Morphological parameters

MR Mendelian randomization

MR-PRESSO MR pleiotropy residual sum and outlier

OR Odds ratio

PBC Primary biliary cholangitis

RC Relative cell

SNP Single Nucleotide Polymorphisms

UDCA Ursodeoxycholic acid

Acknowledgements

The contributions and sharing of summary data from the GWAS by all participants and researchers are greatly appreciated.

Author Contributions

The study design was conceptualized by Z.H. Data collection and dataset organization were conducted by Z.H. and P.Z.. Z.H. conducted the data analyses. The manuscript was drafted and revised by Z.H., K.H., and Z.S. All authors contributed to the article, reviewed it, and approved the final version for submission.

Data Availability Statement

Summary statistics for 731 immune cell features were obtained from the GWAS Catalogue (https://www.ebi.ac.uk/gwas/), specifically accession numbers GCST90001391 to GCST90002121. GWAS statistics for PBC were obtained from the GWAS Catalogue accession numbers GCST90061440. The GSE79850 mRNA expression profile dataset from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). It is important to note that all statistics used in this study are publicly available and can be easily downloaded. Furthermore, ethical approval was obtained for the original analysis.

Competing Interests Statement

The authors declare no competing interests.

Funding

This research was funded by the National Natural Science Foundation of China No. 82272996 (Z.S.) the Science and Technology Program of Guangzhou No. 202206010081 (Z.S.).

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Unveiling the Role of CD27⁺ Memory B Cells in Primary Biliary Cholangitis: A Mendelian Randomization Study

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