The Genetic Interface of Immunity and COVID-19: Insights from a Two-Sample Mendelian Randomization Approach

doi:10.21203/rs.3.rs-3941833/v1

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Research Article

The Genetic Interface of Immunity and COVID-19: Insights from a Two-Sample Mendelian Randomization Approach

https://doi.org/10.21203/rs.3.rs-3941833/v1

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Introduction

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as a global public health emergency since late 2019. Immune cells are crucial for host defense against viral infection and disease progression. However, the specific immune cell characteristics that influence susceptibility to COVID-19 remain unclear. This study aimed to investigate the causal relationship between immune cell signatures and COVID-19 using MR analysis.

Materials and Methods

This study utilized publicly available genetic datasets from the COVID-19 Host Genetics Initiative and the Blueprint Consortium and applied a two-sample Mendelian randomization approach to examine the association between 731 immune cell signatures and the risk of COVID-19. We included four types of immune signatures: median fluorescence intensity (MFI), relative cell count (RC), absolute cell count (AC), and morphological parameter (MP) data. We used the inverse-variance weighted (IVW) method as the primary analysis and performed several sensitivity analyses to test the robustness of the results.

Results

Our analysis revealed 30 distinct immune cell characteristics that were directly associated with the risk of COVID-19, including CD4 + regulatory T cells (CD4 + Treg cells), CCR2 + CD14- CD16 + monocytes, CD86 + plasmacytoid DC AC, CCR2 + plasmacytoid DC (CCR2 + pDC), CCR2 + CD62L + plasmacytoid DC (CCR2 + CD62L + pDC), and CD80 + CD62L + plasmacytoid DC (CD80 + pDC). However, among these findings, only the expression of CCR2 on CD14-CD16 + monocytes had a significant impact (P = 0.0249, OR = 1.0427, 95% CI=[1.0053, 1.0814]) on immune cell attributes in the context of COVID-19. Sensitivity analyses confirmed the validity of the IVW results and ruled out the possibility of horizontal pleiotropy.

Conclusion

Through two-sample Mendelian randomization analysis, we demonstrated a significant causal relationship between specific immune cell characteristics and the risk of COVID-19. These findings provide important genetic evidence for the development of future vaccines and treatment strategies.

731 immune cell signatures

coronavirus disease 2019

Mendelian randomization

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has posed unprecedented challenges to global health systems since its emergence in late 2019[1, 2]. Immune cells have been identified as pivotal players in the body’s response to viruses, and their role in both defense and disease progression is of significant interest to researchers[3]. A study published in Cell revealed that, in addition to respiratory epithelial cells, a variety of immune cells, including neutrophils, macrophages, plasma cells, T cells, and natural killer cells, were found to contain viral RNA, suggesting active transcription and replication of the virus within these cells[4]. This finding suggested that the range of host cells available for the virus was broader than previously thought, potentially contributing to its high transmissibility.

Further investigations have highlighted the complexity of the immune response to SARS-CoV-2 infection. A comprehensive review in Nature Reviews Immunology discussed the trinity of COVID-19 infection, immunity, inflammation, and intervention, emphasizing the interplay between the virus and the immune system[5]. This review details how SARS-CoV-2 infection and subsequent destruction of lung cells induce a cascade of local immune responses, leading to the recruitment of macrophages and monocytes and the release of cytokines. This process typically resolves the infection; however, dysfunctional immune responses can lead to severe lung damage and systemic pathological reactions.

Ongoing research underscores the importance of understanding the specific immune cell characteristics associated with COVID-19 risk[6–8]. Our study used a two-sample Mendelian randomization approach to elucidate the causal relationship between immune cell signatures and COVID-19 and aimed to contribute to the growing body of knowledge in this critical area of research.

Research design

Our study used two-sample Mendelian randomization to assess the causal link between 731 immune signatures and COVID-19, ensuring that the genetic variations met three key assumptions for valid instrumental variables[9, 10].

Genome-wide association study (GWAS) data sources for COVID-19

The “Risteys·U22_COVID19_CONFIRMED” is a web endpoint of the Finnish Registry (FinRegistry) that provides data on confirmed COVID-19 cases. This endpoint allows users to access a variety of information related to confirmed COVID-19 cases, including information associated with medical and pharmaceutical codes. Additionally, the endpoint offers a tool called CodeWAS, which explores the associations between the endpoint and all medical and pharmaceutical codes. The cohort of U22_COVID19_CONFIRMED included 8,838 matched patients with COVID-19 and 88,378 matched controls. These data were constructed by matching birth year and sex, and Fisher’s test was conducted on all medical and pharmaceutical codes between these patients and controls[11].

Immunity-wide GWAS data sources

The GWAS Catalog offers open access to a vast array of immune trait statistics, featuring 731 immunophenotypes across parameters such as cell counts and antigen levels. This dataset, derived from 3,757 Europeans, was analysed for 22 million SNPs using a Sardinian reference panel, with meticulous adjustments for demographic covariates[9, 12].

Instrumental variable (IV) selection

We set the significance threshold for instrumental variables (IVs) related to each immune trait at 1×10^− 5[9, 13]. The clumping function within the “TwoSampleMR” software package (version 0.5.7) was utilized to refine the single nucleotide polymorphisms (SNPs), with a linkage disequilibrium (LD) r2 threshold of less than 0.001 within a 100 kb range. These LD r2 values were determined based on the 1000 Genomes Project as the reference panel[14]. Subsequently, the MR-PRESSO test was employed to detect potential horizontal pleiotropy and remove outliers to exclude the effects of pleiotropy. Finally, to assess the strength of each IV and prevent weak instrumental bias, we calculated the proportion of phenotypic variation explained (PVE) and the F statistic for each IV[15].

Statistical analysis

Primary Mendelian randomization (MR) analyses were conducted using the inverse variance weighted (IVW) method complemented by MREgger regression, simple mode, weighted mode, and weighted median approaches[16]. These techniques are designed to identify and adjust for potential pleiotropy and biases. Heterogeneity among instrumental variables was evaluated using Cochran’s Q statistic[9]. Upon rejecting the null hypothesis, the model was adjusted from fixed-effects IVW to random-effects IVW. The MREgger method was employed to assess horizontal pleiotropy, indicated by a statistically significant intercept [17]. The MR pleiotropy residual sum and outlier (MR-PRESSO) method was also utilized to identify and correct for horizontal pleiotropic outliers [18]. All analyses were meticulously performed using R software version 4.3.1.

In this study, we investigated the causal effects of four types of immune traits (MFI, RC, AC, and MP) on COVID-19 using Mendelian randomization analysis. We identified 30 immunophenotypes that were significantly associated with COVID-19 (P < 0.05), 18 of which were risk factors and 12 of which were protective factors (Figs. 1–5). We also performed MREgger and MR-PRESSO tests to rule out any potential presence of horizontal pleiotropy for these 30 associations.

Impact of B-cell subsets on COVID-19: A Mendelian randomization study

We performed a Mendelian randomization analysis to examine the causal impact of B-cell subsets and phenotypes on COVID-19 susceptibility. We found that CD27 expression on Unswitched Memory B cells, CD24 expression on IgD + CD24 + B cells, CD27 expression on CD20- CD38- B cells, and HLA-DR expression on IgD + CD24- B cells were associated with a decreased likelihood of COVID-19, with odds ratios (ORs) ranging from 0.9018 to 0.9542 and 95% confidence intervals (CIs) excluding 1. In contrast, IgD + CD24 + B cells and the percentage of Memory B cells were associated with an increased risk of COVID-19, with ORs of 1.1798 and 1.0918, respectively, and 95% CIs excluding 1.

Investigating the Relationship between Dendritic Cell Subsets and COVID-19 Outcomes

We analysed the associations between COVID-19 and various dendritic cell (DC) subsets and phenotypes. We identified eight unique DC subsets and phenotypes that correlated with COVID-19. The percentage of CD11c + HLA DR + + monocytes, the absolute count of CD86 + plasmacytoid DCs, and the expression of CCR2 and CD80 on plasmacytoid DCs were potential protective factors against COVID-19, with ORs ranging from 0.8571 to 0.9245 and 95% CIs excluding 1. In contrast, CD86 expression in granulocytes was a risk factor for COVID-19, with an OR of 1.1481 and a 95% CI excluding 1. The most significant association was observed for CCR2 expression on plasmacytoid DCs, with a P value of 0.0003.

Examining the Role of Treg Cell Markers in COVID-19 Prognosis

We identified three Treg cell markers that are significantly associated with COVID-19. The expression of CD8 on CD28-CD8 + T cells was a potential protective factor against COVID-19, with an OR of 0.8787 and a 95% CI of 0.7773 to 0.9933. Conversely, CD4 + regulatory T cells (CD4 + on CD4 + Treg cells) and CD4 + expression on activated and secreted Treg cells were risk factors for COVID-19, with ORs of 1.0862 and 1.063, respectively, and 95% CIs excluding 1.

Correlation analysis of monocytes and COVID-19 incidence status

We also examined the correlation between four monocyte subsets and COVID-19 incidence. CCR2 on the CD14- CD16 + monocyte subset was significantly associated with COVID-19 incidence, indicating an increased risk, with an OR of 1.0666 and a 95% CI of 1.0030–1.1342.

Based on large amounts of publicly available genetic data, we explored causal associations between 731 immune cell traits and COVID-19. To our knowledge, this is the first MR analysis to explore the causal relationship between all immunophenotypes and COVID-19. Our IVW analysis revealed that a total of 30 types of immune cells are significantly related to COVID-19. The 30 immunophenotypes linked to COVID-19 were distributed across seven distinct panels: the Treg panel, TANK panel, myeloid cell panel, monocyte panel, maturation stages of T-cell panel, conventional dendritic cell (cDC) panel, and B-cell panel.

Recent studies have highlighted the role of unswitched memory B cells in the immune response to COVID-19. These cells, characterized by CD27 expression, are important for maintaining a rapid secondary immune response and have been associated with a protective effect against COVID-19[19].The presence of CD27 on CD20- CD38- B cells also suggests a potential role in adaptive immunity, possibly through the generation of memory cells or long-lived plasma cells that produce antibodies against SARS-CoV-2[20] The expression of CD24 on IgD + CD24 + B cells has been linked to a protective association, indicating their involvement in early stages of B cell activation and differentiation[21]. Conversely, an increased risk of COVID-19 has been observed with higher percentages of IgD + CD24 + B cells and Memory B cells, which may reflect a dysregulated immune response or an imbalance in B cell subsets leading to inadequate protection[22–24].

Recent studies have shown that CD11c + dendritic cells and plasmacytoid DCs are activated by viral infections and retain their T cell-stimulatory capability, which is essential for an effective immune response[25]. The expression of CCR2 and CD80 on plasmacytoid DCs, as well as CD86 in granulocytes, has been implicated in the modulation of immune responses during viral infections, including COVID-19[26, 27]. The activation and function of CD11c + HLA DR + + monocytes are significant, as they are involved in antigen presentation and the initiation of the immune response[28]. The absolute count of CD86 + plasmacytoid DCs is also noteworthy, as these cells are key producers of type I interferons, which play a vital role in antiviral defense. Furthermore, the expression of CCR2 on CD62L + plasmacytoid DCs has been associated with their migration and positioning within the immune system, which can influence the overall immune response to COVID-19[29].

Regulatory T cells (Tregs) on CD4 + and CD8 + cells play -multifaceted roles in COVID-19, including maintaining immune homeostasis, suppressing excessive immune responses, and reducing tissue damage[23]. In COVID-19, the balance and function of Tregs may be affected, leading to an imbalance between regulatory and cytotoxic CD4 + T cells. This imbalance may be associated with the severity and progression of COVID-19[30].

The CD14- CD16 + monocytes represent a subset of non-classical monocytes that play a pivotal role in inflammatory and immune responses. Infection with COVID-19 can lead to aberrant activation of the immune system, where the activation and migration of monocytes are key components of the pathological process. Studies have shown that CD16 + monocytes from COVID-19 patients exhibit transcriptional changes indicative of enhanced cell activation and the induction of a migratory phenotype[31].

The expression of CD11b on CD14 + monocytes has been suggested as a potential biomarker for the severity of COVID-19. A study found that the frequencies of CD11b + CD33 + HLA-DR-CD14 + cells in peripheral blood could serve as severity immune biomarkers in COVID-19, indicating that higher levels of these cells are associated with more severe disease outcomes[32].

T-cell markers such as HVEM, CD4, and CD8 on subsets of cells, such as EM CD4+, CD8br, and DN T cells, play crucial roles in COVID-19 immunity. Its expression affects T-cell activation, memory, and regulatory functions, influencing the effectiveness of the immune response against the virus and disease severity. Understanding these dynamics is key for targeted immunotherapy development[33–35].

Recent studies have focused on how alterations in these subsets can affect the severity and outcome of COVID-19. For instance, a decrease in specific lymphocyte T-cell subsets, such as CD4 + and CD8 + T cells, has been associated with an increased risk of severe outcomes, including the need for mechanical ventilation and increased mortality rates[36]. These findings suggest that monitoring the levels of these cells could be critical for identifying patients at higher risk and tailoring their treatment accordingly.

The use of Mendelian randomization in this study is particularly noteworthy. Moreover, genetic variations can be used as a proxy for modifiable risk factors (in this case, immune cell characteristics) to determine whether there is a causal effect on the outcome (risk of COVID-19). This method helps to overcome some of the limitations of traditional observational studies, such as confounding factors and reverse causation[37, 38].

In conclusion, through rigorous statistical methods, this study provides a new perspective for understanding the complex relationship between COVID-19 and the immune system, laying the foundation for future medical research and public health interventions. This also reaffirms the important role of genetics in modern medical research.

Author contributions

All the authors participated in the study design, interpretation of the studies and analysis of the data and review of the manuscript; YH and LH wrote the manuscript.

Conflict of interest statement

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Ethical Approval

Not applicable.

Acknowledgement

Not applicable.

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No competing interests reported.

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Version 1

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You are reading this latest preprint version

The Genetic Interface of Immunity and COVID-19: Insights from a Two-Sample Mendelian Randomization Approach

Status:

Version 1

Abstract

Introduction

Materials and Methods

Results

Conclusion

Figures

Introduction

Materials and Methods Study design

Research design

Genome-wide association study (GWAS) data sources for COVID-19

Immunity-wide GWAS data sources

Instrumental variable (IV) selection

Statistical analysis

Results

Impact of B-cell subsets on COVID-19: A Mendelian randomization study

Investigating the Relationship between Dendritic Cell Subsets and COVID-19 Outcomes

Examining the Role of Treg Cell Markers in COVID-19 Prognosis

Correlation analysis of monocytes and COVID-19 incidence status

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1