Causal Relationships between Immune Cells and Rheumatoid Arthritis

doi:10.21203/rs.3.rs-3567767/v1

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Causal Relationships between Immune Cells and Rheumatoid Arthritis

https://doi.org/10.21203/rs.3.rs-3567767/v1

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Background

When it comes to the beginning, course, diagnosis, and management of rheumatoid arthritis (RA), immune cells (ICs) are crucial. However, because of the intricate relationships that exist between RA and the immune system, it is difficult to determine how a particular class or type of immune cell affects RA, and research on this topic is seldom able to rule out the possibility that RA will interfere with the reversal action of ICs.

Methods

To investigate and screen out the impacts on RA from 731 ICs, we mostly used MR analysis using IVW (inverse variance weighted), Weighted median, and MR-Egger regression methods. Then, the inverse effect of RA on ICs was explored by reversed MR. Finally, MR-Egger intercept, Cochran's Q test, and “Leave-one-out” are conducted to measure the degrees of of pleiotropy and heterogeneity.

Results

9 immune phenotypes were found significantly linked to RA risk: IgD- CD27- B cell %B cell (OR = 1.17 95%CI = 1.06–1.29), dendritic cell absolute count (OR = 1.07 95%CI = 1.02–1.13), CD86 + myeloid dendritic cell absolute count (OR = 1.06 95%CI = 1.02–1.11), CD25 on memory B cell (OR = 1.08 95%CI = 1.02–1.13), CD27 on memory B cell(OR = 0.91 95%CI = 0.85–0.98), CD3 on effector memory CD4 + T cell (OR = 0.97 95%CI = 0.94-1.00), CD25 on CD4 + T cell (OR = 0.93 95%CI = 0.87–0.98), CD45 on CD33br HLA DR + CD14dim(OR = 1.04 95%CI = 1.01–1.06), CD11b on CD33br HLA DR + CD14dim(OR = 0.95 95%CI = 0.91–0.99). While only RA on CD25 on memory B cell (OR = 0.95 95%CI = 0.90-1.00) and CD45 on CD33br HLA DR + CD14dim (OR = 0.92 95%CI = 0.87–0.98) were found in reverse MR. Besides, the results of MR-Egger intercept suggest that there exists no evidence of horizontal pleiotropy.

Conclusion

Our study identified an association between specific immune cells and RA, and this association could guide the future diagnosis and treatment of RA.

Rheumatoid Arthritis

Immune Cells

Mendelian Randomization Study

GWAS

Rheumatoid arthritis (RA) is an autoimmune illness that affects around 1% of people worldwide[1]. The disease is typically featured with synovial vascular panesis, which mainly damage to joints and extra-articular system like digestive system, heart kidney even nervous system[2]. However, the diagnosis and treatment of RA are mostly focused on the end stage due to the lack of early diagnosis markers. Therefore, earlier detection of risk factors and early diagnosis may facilitate to control the progress of disease before severe complications occur and polish the prognosis of RA patients.

As agents of the immune system and in certain autoimmune disorders, immune cells (ICs) are essential in healthy humans. Patients with RA typically experience a transition from immune health to autoimmunity then relentless tissue inflammation in the end. [3]. T cells are central players in every stage from the breakdown of autoimmune tolerance to tissue inflammation. Both the aberrant T-cell differentiation and the short lifespan of naïve RA CD4 T-cells with shortened telomere sequences have led to an intense inflammatory response in the synovial tissue environment [4, 5]. Macrophages also play an important part in the RA synovial environment. M1- and M2-polarized macrophages play proinflammatory and anti-inflammatory roles, respectively, but also transform between each other in the inflammatory environment [6]. Furthermore, the involvement of several ICs, including B cells, myeloid cells, and dendritic cells, is essential for the progress of RA[7, 8]. However, due to the abundance of subtypes and interactions between ICs, it is still difficult to define the effect of a specific cell type on RA.，Moreover, the inverse effect of RA on ICs makes it difficult to avoid confounding the relevant studies.

Mendelian randomization (MR), based on mendelian independent distribution law widely used in epidemiological research, is able to utilizes genetic variation to explore the causality between exposure and outcome[9]. The causal direction in MR analysis is settled and less susceptible to influence, thus minimizing unmeasured confounders and avoiding reverse causality bias in observational researches [10]. This study selected MR analysis to find the causality between all ICs and RA.

2.1 Research design

Using two-sample MR analysis, the causal relationships between 731 ICs and RA are investigated. RA is considered an outcome factor, while 731 ICs serve as exposure factors. Meanwhile, in order to explore the counteracting effect of RA on ICs, we aligned the identities of the two and then performed reverse MR analysis following the MR statement[11].

2.2 Source of data

A large meta-analysis of genome-wide association studies (GWAS) was used to identify genetic variations linked to RA. This included 14,361 RA and 43,923 control (58,284 samples) with 13,108,512 SNPs[12]. The diagnostic criteria of RA accord to the American College of Rheumatology's 1987 RA Diagnostic Criteria[13]. 731 ICs is derived from other GWAS (accession numbers from ebi-a-GCST 0001391 to ebi-a-GCST 0002121) containing immune signatures: median fluorescence intensities (MFI)=389, relative cell (RC)=192, absolute cell (AC)=118, and morphological parameters (MP)=32. Among them, MFI, AC and RC contain TBNK (T cells, B cells, natural killer cells), monocytes, myeloid cells, CDCs, etc. while MP contains only CDC and TBNK panels. [14]. The data of GWAS from 3,757 European individuals, using a reference panel based on Sardinian sequences, estimated approximately 22 million high-density arrays genotyped with SNPs. Pooled data selected from European ancestry in order to decline bias of population stratification.

2.3 Selection of instrumental variables

The assumption of single nucleotide polymorphisms (SNPs) was that SNPs were significantly related with ICs but not associated with RA. Only ICs can affect RA.

Then, we extracted SNPs as instrumental variables (IVs) corelated with 731 ICs in genome-wide significant levels (1 × 10^-5). And for RA, we adjusted the significance level to 5 × 10^-8. Threshold are set for linkage disequilibrium at R² < 0.001 within 10000 kb distance to exclude the effect of linkage disequilibrium. In addition, SNPs related to the results were eliminated and harmonized by Catalog, Phenoscanner, and R packages to exclude palindromic SNPs and incompatible SNPs. Finally, the strength of IV was assessed using the F-statistic value. There exists a significant bias in the causal judgment if the F value is less than 10[15].

2.4.MR analysis

2.4.1. Effect size evaluation: The fixed effect inverse variance weighting (IVW) approach is the primary method used for two-sample MR studies, which assumes that validity of each SNP and can be used to pool heterogeneity-free MR data [16]. Then, we executed a chain of sensitivity analysis for further confirmation. The MR-Egger regression approach was used to evaluate the IVW findings' dependability; nonetheless, the results demonstrated relatively poor accuracy. Lastly, the weighted median approach was used as a supplementary method to confirm the precision of the outcome[17].

2.4.2. Assessment of heterogeneity and horizontal pleiotropy: The MR-Egger intercept test is used to determine horizontal pleiotropy in order to validate our findings. Horizontal pleiotropy is present when the MR-Egger intercept has a p-value of less than 0.05, and vice versa[18]. Cochran's Q test which evaluates heterogeneity in causal effect sizes among genetic instruments quantitatively was then executed. Random effect IVW was used rather than fixed effect IVW in the event that the original hypothesis was not accepted. Finally, the "Leave-one-out" analysis investigated the magnitude of the impact of the selected SNPs on the outcomes by exclusion of single SNPs.

95% confidence intervals (CI) and odds ratios (OR) are used to express the results. The R software's "TwoSampleMR," "Mendelian-Randomization," and "ggplot2" packages are used for statistical analysis and graphical displays.

3.1.MR analysis

Assuming no heterogeneity, we first used fixed effect IVW. Findings from the MR in three approach indicate that IgD- CD27- B cell %B cell (OR=1.17 95%CI=1.06-1.29), dendritic cell absolute count (OR=1.07 95%CI=1.02-1.13), CD86+ myeloid dendritic cell absolute count (OR=1.06 95%CI=1.02-1.11), CD25 on memory B cell (OR=1.08 95%CI=1.02-1.13), CD45 on CD33br HLA DR+ CD14dim(OR=1.04 95%CI=1.01-1.06) are risk factors for RA while CD27 on memory B cell(OR=0.91 95%CI=0.85-0.98), CD3 on effector memory CD4+ T cell (OR=0.97 95%CI=0.94-1.00), CD25 on CD4+ T cell (OR=0.93 95%CI=0.87-0.98), CD11b on CD33br HLA DR+ CD14dim(OR=0.95 95%CI=0.91-0.99) are protective factors for RA (three methods P＜0.05). Figure1 shows the results of all above.

3.2 Assessment of horizontal pleiotropy and heterogeneity

All MR-Egger intercepts (P > 0.05) were statistically insignificant, indicating no genetic pleiotropy bias of the results (Figure2). Some P value of ICs Cochran's Q test was below 0.05, so random effects model IVW was thus validated in each exposure (Figure2). Furthermore, no SNP was exhibited to affects the overall estimate of causality through "Leave-one-out" confirming the reliability of our study's findings (Figure3).

3.3 Reverse MR

To ascertain RA's opposite causal impact on the aforementioned ICs, we also conducted a reversed MR study with the same methodology as before for verification (Figure4). Only CD25 on memory B cell (OR=0.95 95%CI=0.90-1.00) and CD45 on CD33br HLA DR+ CD14dim (OR=0.92 95%CI=0.87-0.98) were counteracted by RA (Figure5). and this effect is all negative under the both IVW method validation but not significant under the other methods (P＞0.05). Besides, the P value of MR-Egger regression and “Leave-one-out" demonstrated the result of reverse MR is reliable (Figure6).

In this study, causal relationship from ICs to RA was found through MR analysis based on large public GWAS data. Our results suggest a positive association and causal factor between 5 types of ICs and RA as well as 4 types of ICs with negative association significantly. In the reverse MR analysis, RA is associated with changes in two ICs of these.

First, dendritic cell absolute count and CD86 + myeloid dendritic cell absolute count increases the risk of RA. As a particular kind of antigen-presenting cells, dendritic cells (DCs) connect innate and adaptive immune responses. [19]. In RA, DC is thought to activate a special type of T cells (self-peptide-reactive inflammatory T cells) and B cells, which in turn produce autoantibodies [20, 21]. Currently, DCs are divided into two main functional cellular subpopulations: conventional myeloid DCs (cDCs) and plasmacytoid DCs (pDCs), which are distinguished by the expression of myeloid markers CD1c and CD141.[22]. When DCs recognize inflammatory stimuli (e.g., PAMP, DAMP, etc.), they start a signaling cascade that helps to activate the transcription factor pathway known as nuclear factor κB (NF-κB)., and in synovial and synovial fluid tissues of RA, these DCs are usually mature, with high concentrations and overexpression of NF-κB [23, 24]. DCs not only secrete chemokines CCL3, CCL17, CXCL10 and CXCL19 that allow T cells to be recruited in the RA synovium, but also secrete proinflammatory cytokines like: IL-1β, IL-6, IL-23 and IL-12 to promote the differentiation of CD4 T cells into T helper type 1 (Th1) and Th17, which play a role in RA disease.[25–27]. In addition, CD86 expression is considered as a sign of DC immaturity, and Moret's team found significantly high CD86 expression on myeloid DCs in RA synovial membranes, corroborating their strong T-cell stimulatory capacity. [25].

This is followed by the protective role of CD3 on effector memory CD4 + T cells and CD25 on CD4 + T cells in RA, which belong to mature and regulatory T cells (Treg), respectively. T cells control the stabilization or progression of the autoimmune stage during RA. The differentiation of naïve CD4 T cells into memory T cells that are highly proliferative, invasive, pro-inflammatory, or relatively quiescent has a significant effect on RA. [28, 29]. Furthermore, evidence for the protective role of mature and functioning CD4 T cells in RA is also provided by the shorter telomere sequences of naïve CD4 T cells and the mitochondrial malfunction of CD4 T cells in RA[30, 31]. Increased numbers or functional upregulation of Treg are thought to help treat autoimmune diseases, while the reverse can enhance immunity to tumors and chronic infection pathogens[32]. However, increased, unchanged and decreased Tregs were observed in RA patients[33–35]. Such differences mainly stem from the limitations of the way Treg is recognized.

Single-cell sequencing revealed that the fraction of IgD-CD27-B cells in the synovium of RA patients was much greater than that in peripheral blood, indicating its critical role in the RA pathogenesis[36]. B-cell accumulation in the synovium is highly associated with increased T-cell activation, while B-cells also secrete LTα and LTβ to maintain T-cell and B-cell infiltration [37, 38]. Unfortunately, there is a paucity of studies on the effects of different subpopulations of memory B cells on RA. Our positive and negative MR analyses showed that CD27 on memory B cell is a protective factor for RA. While CD25 on memory B cell was a risk factor for RA, the presence of RA was associated with a decrease in CD25 on memory B cell. To our knowledge, it is the first time to explore the association of CD25 + B memory cells with RA. Interestingly, patients with rheumatoid arthritis (RA) who had a reduced baseline percentage of CD27 + memory B cells exhibited more favorable results when treated with B-cell depletion therapy (BCDT). [39]. This may be related to the heterogeneity and difficulty in accurately identifying CD27-expressing memory B cells [40].

Finally, CD45 on CD33br HLA DR + CD14dim and CD11b on CD33br HLA DR + CD14dim are belong to the myeloid cell. Myeloid cells are major defenders of pathogens and take effect in facilitating the development of adaptive immunity, especially during acute infections [41]. However, in chronic inflammation or tumors, persistently low concentrations of myeloid growth factors ,combined with some inflammatory mediators, may bring about dysregulation of myeloid differentiation, which in turn leads to undermining of the immune response [42]. This seems to explain the fact that CD45 on CD33br HLA DR + CD14dim increases the risk of RA, while RA also leads to a decrease in its levels. But further mechanisms still need to be elucidated.

In this study, the causal relationship between ICs and RA was analyzed by applying MR, and several ICs associated with RA risk were identified based on the large GWAS data. There are also some limitations to this study: 1. Due to limitations in population selection, validation of this result's applicability to other populations is required.;2. Failure to observe horizontal pleiotropism because of method selection may bias the results.༛3. we have lowered the threshold, so false positive results may occur.

This study shows robust genetic evidence of the complex immune relationship between RA and several immune cells avoiding the bias of confounding and reverse causation like previous observational studies. These ICs are expected to be early diagnostic targets and prognostic indicators for RA in the future.

Ethics approval and consent to participate.

Not applicable.

Consent for publication:

All authors have approved the manuscript and agree with its submission to the BMC Musculoskeletal Disorders.

Availability of data and materials:

The datasets and materials analyzed during this study are available in https://gwas.mrcieu.ac.uk/.

Competing interests:

The author declares no conflict of interest.

Funding:

No funding.

Authors' contributions:

Xin Tan wrote the main manuscript text and prepared figures. All authors reviewed the manuscript.

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No competing interests reported.

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Causal Relationships between Immune Cells and Rheumatoid Arthritis

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