Assessing the causal relationship between immune cell and Parkinson's disease by bi-directional  Mendelian randomization analysis

doi:10.21203/rs.3.rs-4823563/v1

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Assessing the causal relationship between immune cell and Parkinson's disease by bi-directional Mendelian randomization analysis

https://doi.org/10.21203/rs.3.rs-4823563/v1

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Previous research has highlighted the significant role of immune cell features in the development and progression of Parkinson's disease (PD), though a direct causal relationship has yet to be established. In our study, we utilized genome-wide association study (GWAS) data involving 731 immune cell traits as exposure factors and GWAS data on PD as outcomes to conduct a bidirectional Mendelian randomization (MR) analysis, primarily using the inverse variance weighted (IVW) method. Our findings revealed that specific traits in classical dendritic cells (cDC), such as Myeloid dendritic cells (MDC) %DC, CD62L- DC %DC, and CD86 + MDC %DC, are positively associated with increased PD susceptibility. Similarly, B cell AC in the TBNK panel and HVEM on CD45RA- CD4 + in the Maturation stages of T cell panel also showed a heightened probability of PD. Conversely, CD45 on HLA DR + CD8br indicated a potential decrease in PD risk. This study establishes a causal link between certain circulating immune cell traits and PD, providing a foundation for further research into the immunological mechanisms of PD and potential immune therapies.

Parkinson's disease

Immune cell

Mendelian randomization

Causal association

neuroinflammation

Parkinson's disease (PD) is the second most frequently occurring degenerative disorder of the central nervous system (CNS), with over 6 million people affected worldwide. As the typical human life expectancy grows, the frequency of Parkinson's disease is also increasing rapidly. The primary clinical symptoms of PD encompass resting tremors, muscle rigidity, slowed movement, postural and balance disorders, as well as autonomic and psychiatric abnormalities. These symptoms result in varying degrees of disability for patients and impose significant psychological and economic burdens on patients and their families(Dorsey et al., 2018; Feigin et al., 2019; Tolosa et al., 2021; Ning et al., 2022).

Despite the pathogenesis of PD involving genetic factors, environmental factors, and aging of the nervous system. (Billingsley et al., 2018; Johnson et al., 2019), a rising number of biological evidence Indicates that the immune response could Have a pivotal role in the development and progression of the PD(Harms et al., 2021; Williams et al., 2021; Zhu et al., 2022). Studies by Real et al. have demonstrated that in PD patients, microglial cells, autoantigen-presenting cells, and autoreactive T lymphocytes are overactivated in both the CNS and peripheral nervous systems(PNS), The activated immune cells(IC) promote the production of cytokines. (Reale et al., 2009; Garretti et al., 2019). For instance, the suppressive effect of regulatory T cells(Treg cells) on the proliferation and triggering of effector T cells is diminished, leading to the induction of more cytokines by effector T cells(Kondělková et al., 2010; Saunders et al., 2012; Kustrimovic et al., 2018). Additional studies have demonstrated that IC and released cytokines can weaken the tight intercellular junctions within endothelial cells of the blood-brain barrier (BBB), augmenting its permeability. This condition allows easier passage of IC and cytokines into the CNS(Varatharaj and Galea, 2017; Garretti et al., 2019). In addition, inflammation of the BBB endothelial cells upregulates the level of adhesion molecules, which accumulates monocytes, T cells, and proteins, promoting the infiltration of immune cells, cytokines, and antibodies into the CNS(Kondělková et al., 2010; Daneman and Engelhardt, 2017). Importantly, in the CNS, proliferation of microglia and the antigen presentation attract T cells, which can directly decimate dopaminergic neurons(Garretti et al., 2019). Therefore, it is credible to suggest that the onset of PD is mediated by peripheral immune responses.

Mendelian randomization(MR) analysis is a research means that employs genetic instrumental variables(IV) as intermediaries to clarify whether there is a cause-and-effect connection between risk factors and disease (Emdin et al., 2017a). From one perspective, genes are randomly assigned and freely combined during gametogenesis, effectively averting confounding influence and reverse causation. from another perspective, the results of MR studies are consistent with numerous basic research and clinical studies. For instance, the associations between smoking and lung cancer, hypertension, hyperlipidemia, and hyperglycemia with ischemic stroke have been confirmed by MR (Valdes-Marquez et al., 2019; Benn et al., 2021; Zhou et al., 2021; Yang et al., 2022). Accordingly, we leverage MR to probe the causal relationships between 731 IC traits and PD, which is fundamental for uncovering the immune mechanisms of PD.

3.1 Genome-wide association study (GWAS) data sources

The GWAS data on IC traits is obtained from the GWAS Catalog-EMBL-EBI database, with ID numbers extending from GCST90001391 to GCST90002121. This data is based on a study of 3,757 Europeans from Sardinia, analyzing More than a hundred types of absolute cell counts, Nearly four hundred types of surface antigen median fluorescence intensities, Over 30 types of morphological parameters, and Approximately 200 types of relative counts (ratios between cell levels) through flow cytometry, alongside 22 million single nucleotide polymorphisms (SNPs), providing better insights into the association between IC traits and illness, revealing many potential therapeutic targets for immune-related diseases(Orrù et al., 2020).

The GWAS data on PD is sourced from the International PD Genomics Consortium and was published in 2019 in the GWAS Catalog - EMBL-EBI database, identified as ieu-b-7. This data is derived from a study of Europeans, covering a total of 482,730 samples. Approximately 17.89 million SNPs associated with PD were analyzed from these samples

3.2 MR method

3.2.1 Selection of IVs

Firstly, the IV chosen for MR analysis should meet the following criteria: (a)The chosen IVs must correlate with IC. (b) The selected IVs should only influence PD through the exposure factor. (c) The selected IVs are not allowed to affect PD through confounding variables. (Emdin et al., 2017b). Secondly, we found that different screening thresholds were set for the IVs selected for MR studies, ranging from p < 1 × 10^-5 to p < 5 × 10^-8. Considering that some IC GWAS date had fewer than three independent SNPs at a higher genome-wide significance level, we chose a higher threshold (p < 1 × 10^-5) to gather more SNPs(Choi et al., 2019; Kennedy et al., 2020; Sanchez-Roige et al., 2021; Meng et al., 2023). Thirdly, we set the linkage disequilibrium coefficient r^2 < 0.001 and a clumping window size = 10,000 kb for screening SNPs to eliminate bias caused by linkage disequilibrium(Slatkin, 2008). Fourthly, we excluded weak IVs having an F-statistic value lower than 10 to avoid bias in the outcomes resulting from weak instruments (Burgess et al., 2011).(Burgess et al., 2011).

3.2.2 statistical method for MR analysis

Firstly, all statistical analyses were conducted utilizing R software version 4.4.1. Secondly, all GWAS date are publicly available, so no special ethical review is required. Thirdly, we used GWAS data of IC traits as exposure variables and GWAS data of PD as outcome variables to conduct two-sample MR analysis in batches, used the IVW method as the primary approach to evaluate the cause-and-effect relationship between IC traits and PD, and The stability of the IVW results was verified using Weighted Mode, Weighted Median, Simple Median, and MR Egger methods(Burgess et al., 2013; Su et al., 2023). Fourthly, IVW results were corrected by Bonferroni to eliminate the interference of false positive results(Curtin and Schulz, 1998). Fifthly, we used Cochran’s Q statistics and funnel plots to evaluate the heterogeneity of the IVs(Greco M et al., 2015). The Egger regression intercept and MR-PRESSO analyses were employed to determine whether the causal relationship between IC traits and PD was influenced by horizontal pleiotropy of the IVs(Bowden et al., 2015; Verbanck et al., 2018). Sixthly, we performed a leave-one-out analysis, sequentially removing each instrumental variable to further test the robustness of the MR analysis results(Sudlow et al., 2015). Finally, we used GWAS data of PD as the exposure in a reverse MR analysis, taking GWAS data of IC traits significantly associated with PD (after Bonferroni correction) as the outcome. This was done to examine whether PD influences IC subtypes.

4.1 Causal effects of IC on PD

Using IVW as the principal analytical method, we performed MR to assess the causal connection between IC traits and PD. After excluding IC phenotypes with no significant causal association, those with horizontal pleiotropy, those with inconsistent Odds Ratio (OR) effect values across five analytical methods, and those with reverse causality, out of 731 traits, we pinpointed six IC traits with causal ties to PD at a P _{(False Discovery Rate (FDR))} = 0.05 significance level. These results cover different cell populations, including three in cDC panel, namely MDC %DC, CD62L- DC %DC, and CD86 + MDC %DC; two in TBNK panel, namely B cell AC and CD45 on HLA DR + CD8br; and HVEM on CD45RA- CD4 + in Maturation stages of the T cell panel. However, the reverse MR of PD and IC traits did not reveal a significant causal connection between PD and these six IC traits.

MR analysis results are detailed in Fig. 1. IVW results support that MDC %DC (OR = 1.086, 95%CI = 1.030–1.146, P = 0.002, P_FDR=0.028) may increase the potential for PD. Similar results were also supported by MR Egger (OR = 1.146, P = 0.006), Weighted median (OR = 1.123, P = 0.002), and Weighted mode (OR = 1.125, P = 0.005). Additionally, the IVW results for CD62L- DC %DC (OR = 1.081, 95%CI = 1.037–1.128, P = 0.001, P_FDR=0.008) and other methods like MR Egger (OR = 1.077, P = 0.023), Weighted median (OR = 1.103, P = 0.001), Simple mode (OR = 1.113, P = 0.036), and Weighted mode (OR = 1.091, P = 0.007) also indicate a potential heighten chance of PD. IVW results also support that CD86 + MDC %DC may elevate incidence of PD of PD (OR = 1.064, 95%CI = 1.029–1.101, P = 0.001, P_FDR=0.008), with similar findings from MR Egger (OR = 1.059, P = 0.021), Weighted median (OR = 1.084, P = 0.001), and Weighted mode (OR = 1.064, P = 0.008). IVW results also indicate that B cell AC may greater susceptibility to PD (OR = 1.103,95%CI = 1.034–1.177, P = 0.003, P_FDR=0.030). Furthermore, IVW results suggest that HVEM on CD45RA- CD4+ (OR = 1.060, 95%CI = 1.020–1.102, P = 0.003, P_FDR=0.030) may enhance the risk of PD, similarly supported by Weighted median (OR = 1.065, P = 0.024) and Weighted mode (OR = 1.114, P = 0.030). However, IVW results suggest that CD45 on HLA DR + CD8br may alleviate the risk of PD (OR = 0.927, 95% CI = 0.879–0.978, P = 0.006, P_FDR=0.049).

4.2 Sensitivity analyses result

Table 1 illustrates the results of the heterogeneity analysis, showing that there is no significant heterogeneity among the IVs(P heterogeneity > 0.05). The results of the horizontal pleiotropy analysis are also shown in Table 1. Both the MR Egger regression intercept and MR-PRESSO exclude the possibility of significant genetic horizontal pleiotropy between the 6 IC traits and PD (P _{MR Egger interception} > 0.05 and P _MR−PRESSO > 0.05). Additionally, the scatter plot (Fig. 2), funnel plot (Fig. 3), and leave-one-out curve (Fig. 4) further verify the stability of the MR analysis results.=

This research utilizes genetic IVs as intermediaries to perform MR analysis on extensive GWAS datasets, investigating the causal relationships between 731 IC traits and PD, ultimately, the study found that six immune cell traits—MDC %DC, CD62L- DC %DC, CD86 + MDC %DC, B cell AC, CD45 on HLA DR + CD8br, and HVEM on CD45RA- CD4+—are significantly causally associated with PD.

Our MR analysis suggests that MDC %DC, CD62L- DC %DC, and CD86 + MDC %DC may promote the development of PD. Antonio Ciaramella et al. used flow cytometry to compare circulating blood dendritic cells (DCs) between healthy individuals and PD patients, finding a significant decrease in circulating blood DCs in PD patients (Banchereau and Steinman, 1998; Ciaramella et al., 2013; Bossù et al., 2015). Paola Bossù et al. pointed out that DCs are recruited due to damage to substantia nigra dopaminergic neurons and migrate from peripheral blood to the CNS, resulting in a relative decrease in the frequency of DCs in peripheral blood. Bailey, T. et al. also found that although DCs are rarely seen in the central nervous system, myeloid DCs often appear during neuroinflammation(Bailey et al., 2006; Zozulya et al., 2009). Additionally, while exploring the relationship between the proportion of circulating myeloid DCs and motor symptoms in PD patients, Gallizioli, M. found a negative correlation between the frequency of circulating myeloid DCs and the severity of PD motor symptoms(Ciaramella et al., 2013). Therefore, these studies suggest that peripheral blood myeloid DCs have an inverse relationship with the occurrence of PD. Clearly, this is inconsistent with our research conclusions, and we will discuss this discrepancy at the end of the article.

However, some research indicates that myeloid-derived DCs penetrate the brain amidst neuroinflammation. Further research demonstrated that upon detecting neuromelanin in peripheral blood, DCs undergo self-activation, transforming into DCs with elevated levels of CD86 and MHCII. These activated DCs not only enhance the secretion of pro-inflammatory cytokines such as IL-6 and TNF-α, but also encourage the proliferation of T and B cells in the lymphocyte response, leading to increased serum levels of anti-neuromelanin antibodies. This chain of events ultimately causes the destruction of dopaminergic neurons that contain neuromelanin and a notable decrease in dopamine within the nigrostriatal pathway. (Banchereau and Steinman, 1998; Dale, 2004; Oberländer et al., 2011). CD62 is a molecule that regulates DC migration and cell adhesion. Research by Pilar Martín et al. found that CD62L-dependent recruitment of blood-derived DCs leads to a dramatic increase in DCs, and injecting CD62L antibodies almost completely inhibited the increase in DC numbers(Martı́n et al., 2002). Combining the aforementioned findings, peripheral blood DCs can increase serum anti-neuromelanin antibodies, which can damage Dopamine-producing neurons located in the substantia nigra. ultimately leading to PD. Based on these studies, we believe that CD62L- DCs can promote the onset of PD. To sum up, the findings suggest that CD62L- DC %DC and CD86 + myeloid DC %DC might be positively correlated with the progression of PD., which corresponds with our MR observations.

The study we performed showed that B cell activating cells are positively associated with the onset of PD. B cell activating cells refer to a class of cells that can activate B cells. Our analysis suggests that these cells may promote the progression of PD. B cell activation promotes the expression of LRRK2, LRRK2 can enhance the Lipopolysaccharide-induced interaction between TRAF6 and LRRK2, phosphorylation processes of MAPK and IκB-α, as well as other processes., leading to the release of TNF-α, thereby inducing local inflammatory responses(Li et al., 2021; Zheng et al., 2022). In such an inflammatory environment, dopaminergic neurons may experience increased apoptosis(Ye et al., 2018), thus promoting the progression of PD. Therefore, the conclusions of these studies are consistent with our MR analysis results.

The analysis we conducted also identified that CD45 on HLA DR + CD8br is inversely related to the development of PD. CD45 on HLA DR + CD8br refers to an activated T cell subset that expresses MHC II, CD45, and CD8 molecules on its surface. Earlier research has identified a rise in CD8 + T cells within the substantia nigra of individuals with PD;α-synuclein-specific CD8 + T cells are also increased.; CD8 + T cells infiltrate the CNS before the aggregation of α-synuclein and the loss of dopaminergic neurons, and the density of CD8 + T cells is positively correlated with neuronal death(Galiano-Landeira et al., 2020; Lindestam Arlehamn et al., 2020; Capelle et al., 2023). Clearly, this conflicts with our MR analysis results. We will further analyze this discrepancy and acknowledge that more research is indispensable to verify this difference.

Our research has additionally shown that HVEM on CD45RA- CD4 + cells is negatively associated with the onset of PD. CD4 + T cells have the potential to promote the formation of IFN-γ, which not only activates microglia to enter a phagocytic state but also promotes the infiltration of peripheral monocytes into the CNS, collectively damaging dopaminergic neurons in the substantia nigra and leading to Parkinson's disease(Drevets et al., 2010; Kreutzfeldt et al., 2013; Williams et al., 2021). However, HVEM on CD45RA- CD4 + refers to mature CD4 + T cells expressing HVEM, which is believed to inhibit the immune response of CD4 + T cells, thus reducing the risk of PD(Watanabe et al., 2003; Villanueva-Romero et al., 2022; Wojciechowicz et al., 2024). Clearly, the conclusions of the aforementioned studies are consistent with our MR analysis results. Additionally, when Zhiwei Song et al. explored the causal relationship between HVEM on CD45RA- CD4 + T cells and Parkinson's disease using different GWAS data for PD, they obtained the same results.

Our MR results indicate that the characteristics of IC associated with PD differ from those reported in the MR study by Zhiwei Songd et al. Specifically, Zhiwei Songd et al. found a negative correlation in their MR analysis of Myeloid DC %DC and PD, which is inconsistent with our findings. Although both we and Zhiwei Songd et al. used the same genetic parameters, our MR analysis utilized a larger-scale PD genome-wide association study dataset and conducted multiple testing corrections on the results. Therefore, our results may be more accurate, but further research is needed to validate these discrepancies(Song et al., 2024).

Although our MR analysis has yielded some results, there are still certain limitations. First, the IV might have potential confounding factors that cannot be entirely excluded, which could introduce bias into the MR analysis results through other pathways. Second, genes may influence diseases through multiple pathways, and the complex mechanisms might lead to discrepancies between the MR analysis results and basic research findings. Third, our study only focuses on immune cell traits, while other potential influencing factors such as environmental and lifestyle factors have not been included in the analysis, which may affect the comprehensiveness of the results. Fourth, our study subjects are limited to Europeans, and specific exposure factors may have different effects in different populations, leading to different conclusions. Finally, the quality and characteristics of genetic data may affect statistical results, such as missing data or outliers which could impact outcomes. Different data sources and collection methods might also influence the results, which could be the reason for discrepancies between the association of certain IC traits with PD and the findings from basic research.

This study conducted a two-sample MR analysis based on published GWAS studies, demonstrating a positive causal relationship between five IC traits and PD, including Myeloid DC %DC, CD62L- DC %DC, CD86 + Myeloid DC %DC, B cell AC, and HVEM on CD45RA- CD4+. Additionally, we found a negative causal relationship between CD45 on HLA DR + CD8br and PD. These findings reveal the complexity of the immune response mechanism in PD, providing new evidence for further research on its immune mechanisms and the exploration of immunotherapy

Conflict of Interest

We 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.

Funding

Our work was funded by the Affiliated Hospital of Southwest Medical University, and Gulin County People's Hospital, grant number GLXNYDFY09 and c

Author Contribution

Q.H participated in the topic selection, statistical analysis, and article writing. X.L contributed to the writing and revision of the article. S. Z was responsible for the production of images. M.Z handled the translation and revision of the article. B.Z mainly focused on the revision of the article. J.L participated in the topic selection, manuscript revision, and provided funding support.

Acknowledgments

First, we sincerely thank Southwest Medical University for providing a convenient environment for

Data Availability Statement

The GWAS data on IC traits is obtained from the GWAS Catalog-EMBL-EBI database, with ID numbers extending from GCST90001391 to GCST90002121. The GWAS data on PD is sourced from the International PD Genomics Consortium and was published in 2019 in the GWAS Catalog - EMBL-EBI database, identified as ieu-b-7.

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Table 1

P _{heterogeneity} represents the results of the heterogeneity analysis, while P _MR−PRESSO and P _{MR Egger interception} represent the results of the horizontal pleiotropy analysis.
P _{MR Egger interception}	P_MR−PRESSO	Trait	Method	P _{heterogeneity}
0.148	0.804	Myeloid DC %DC	MR Egger	0.851
0.148	0.804	Myeloid DC %DC	IVW	0.783
0.830	0.977	CD62L- DC %DC	MR Egger	0.962
0.830	0.977	CD62L- DC %DC	IVW	0.974
0.735	0.446	CD86 + myeloid DC %DC	MR Egger	0.327
0.735	0.446	CD86 + myeloid DC %DC	IVW	0.376
0.410	0.581	B cell AC	MR Egger	0.542
0.410	0.581	B cell AC	IVW	0.558
0.17	0.431	CD45 on HLA DR + CD8br	MR Egger	0.433
0.17	0.431	CD45 on HLA DR + CD8br	IVW	0.362
0.849	0.907	HVEM on CD45RA- CD4+	MR Egger	0.876
0.849	0.907	HVEM on CD45RA- CD4+	IVW	0.900

No competing interests reported.

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Assessing the causal relationship between immune cell and Parkinson's disease by bi-directional Mendelian randomization analysis

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Abstract

Figures

Introduction

Materials and methods

3.1 Genome-wide association study (GWAS) data sources

3.2 MR method

3.2.1 Selection of IVs

3.2.2 statistical method for MR analysis

MR Result

4.1 Causal effects of IC on PD

4.2 Sensitivity analyses result

Discussion

Conclusion

Declarations