Disentangling the common genetic architecture and causality of rheumatoid arthritis and systemic lupus erythematosus with COVID-19 outcomes: Genome-wide cross trait analysis and bidirectional Mendelian randomization study

Multi-omics analysis uncovered systemic lupus erythematosus and COVID-19 crosstalk

BACKGROUND

Studies have highlighted a possible crosstalk between the pathogeneses of COVID-19 and systemic lupus erythematosus (SLE); however, the interactive mechanisms remain unclear. We aimed to elucidate the impact of COVID-19 on SLE using clinical information and the underlying mechanisms of both diseases.

METHODS

RNA-seq datasets were used to identify shared hub gene signatures between COVID-19 and SLE, while genome-wide association study datasets were used to delineate the interaction mechanisms of the key signaling pathways. Finally, single-cell RNA-seq datasets were used to determine the primary target cells expressing the shared hub genes and key signaling pathways.

RESULTS

COVID-19 may affect patients with SLE through hematologic involvement and exacerbated inflammatory responses. We identified 14 shared hub genes between COVID-19 and SLE that were significantly associated with interferon (IFN)-I/II. We also screened and obtained four core transcription factors related to these hub genes, confirming the regulatory role of the IFN-I/II-mediated Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway on these hub genes. Further, SLE and COVID-19 can interact via IFN-I/II and IFN-I/II receptors, promoting the levels of monokines, including interleukin (IL)-6/10, tumor necrosis factor-α, and IFN-γ, and elevating the incidence rate and risk of cytokine release syndrome. Therefore, in SLE and COVID-19, both hub genes and core TFs are enriched within monocytes/macrophages.

CONCLUSIONS

The interaction between SLE and COVID-19 promotes the activation of the IFN-I/II-triggered JAK-STAT signaling pathway in monocytes/macrophages. These findings provide a new direction and rationale for diagnosing and treating patients with SLE-COVID-19 comorbidity.

Bidirectional two-sample Mendelian randomization study of differential white blood cell counts and schizophrenia

BACKGROUND

Schizophrenia and white blood cell counts (WBC) are both complex and polygenic traits. Previous evidence suggests that increased WBC are associated with higher all-cause mortality, and other studies have found elevated WBC in first-episode psychosis and chronic schizophrenia. However, these observational findings may be confounded by antipsychotic exposures and their effects on WBC. Mendelian randomization (MR) is a useful method for examining the directions of genetically-predicted relationships between schizophrenia and WBC.

METHODS

We performed a two-sample MR using summary statistics from genome-wide association studies (GWAS) conducted by the Psychiatric Genomics Consortium Schizophrenia Workgroup (N = 130,644) and the Blood Cell Consortium (N = 563,946). The MR methods included inverse variance weighted (IVW), MR Egger, weighted median, MR-PRESSO, contamination mixture, and a novel approach called mixture model reciprocal causal inference (MRCI). False discovery rate was employed to correct for multiple testing.

RESULTS

Multiple MR methods supported bidirectional genetically-predicted relationships between lymphocyte count and schizophrenia: IVW (b = 0.026; FDR p-value = 0.008), MR Egger (b = 0.026; FDR p-value = 0.008), weighted median (b = 0.013; FDR p-value = 0.049), and MR-PRESSO (b = 0.014; FDR p-value = 0.010) in the forward direction, and IVW (OR = 1.100; FDR p-value = 0.021), MR Egger (OR = 1.231; FDR p-value < 0.001), weighted median (OR = 1.136; FDR p-value = 0.006) and MRCI (OR = 1.260; FDR p-value = 0.026) in the reverse direction. MR Egger (OR = 1.171; FDR p-value < 0.001) and MRCI (OR = 1.154; FDR p-value = 0.026) both suggested genetically-predicted eosinophil count is associated with schizophrenia, but MR Egger (b = 0.060; FDR p-value = 0.010) and contamination mixture (b = -0.013; FDR p-value = 0.045) gave ambiguous results on whether genetically predicted liability to schizophrenia would be associated with eosinophil count. MR Egger (b = 0.044; FDR p-value = 0.010) and MR-PRESSO (b = 0.009; FDR p-value = 0.045) supported genetically predicted liability to schizophrenia is associated with elevated monocyte count, and the opposite direction was also indicated by MR Egger (OR = 1.231; FDR p-value = 0.045). Lastly, unidirectional genetic liability from schizophrenia to neutrophil count were proposed by MR-PRESSO (b = 0.011; FDR p-value = 0.028) and contamination mixture (b = 0.011; FDR p-value = 0.045) method.

CONCLUSION

This MR study utilised multiple MR methods to obtain results suggesting bidirectional genetic genetically-predicted relationships for elevated lymphocyte counts and schizophrenia risk. In addition, moderate evidence also showed bidirectional genetically-predicted relationships between schizophrenia and monocyte counts, and unidirectional effect from genetic liability for eosinophil count to schizophrenia and from genetic liability for schizophrenia to neutrophil count. The influence of schizophrenia to eosinophil count is less certain. Our findings support the role of WBC in schizophrenia and concur with the hypothesis of neuroinflammation in schizophrenia.

Causality between COVID-19 and multiple myeloma: a two-sample Mendelian randomization study and Bayesian co-localization

Infection is the leading cause of morbidity and mortality in patients with multiple myeloma (MM). Studying the relationship between different traits of Coronavirus 2019 (COVID-19) and MM is critical for the management and treatment of MM patients with COVID-19. But all the studies on the relationship so far were observational and the results were also contradictory. Using the latest publicly available COVID-19 genome-wide association studies (GWAS) data, we performed a bidirectional Mendelian randomization (MR) analysis of the causality between MM and different traits of COVID-19 (SARS-CoV-2 infection, COVID-19 hospitalization, and severe COVID-19) and use multi-trait analysis of GWAS(MTAG) to identify new associated SNPs in MM. We performed co-localization analysis to reveal potential causal pathways between diseases and over-representation enrichment analysis to find involved biological pathways. IVW results showed SARS-CoV-2 infection and COVID-19 hospitalization increased risk of MM. In the reverse analysis, the causal relationship was not found between MM for each of the different symptoms of COVID-19. Co-localization analysis identified LZTFL1, MUC4, OAS1, HLA-C, SLC22A31, FDX2, and MAPT as genes involved in COVID-19-mediated causation of MM. These genes were mainly related to immune function, glycosylation modifications and virus defense. Three novel MM-related SNPs were found through MTAG, which may regulate the expression of B3GNT6. This is the first study to use MR to explore the causality between different traits of COVID-19 and MM. The results of our two-way MR analysis found that SARS-CoV-2 infection and COVID-19 hospitalization increased the susceptibility of MM.

Comprehensive bioinformatics analysis and systems biology approaches to identify the interplay between COVID-19 and pericarditis

Background

Increasing evidence indicating that coronavirus disease 2019 (COVID-19) increased the incidence and related risks of pericarditis and whether COVID-19 vaccine is related to pericarditis has triggered research and discussion. However, mechanisms behind the link between COVID-19 and pericarditis are still unknown. The objective of this study was to further elucidate the molecular mechanisms of COVID-19 with pericarditis at the gene level using bioinformatics analysis.

Methods

Genes associated with COVID-19 and pericarditis were collected from databases using limited screening criteria and intersected to identify the common genes of COVID-19 and pericarditis. Subsequently, gene ontology, pathway enrichment, protein-protein interaction, and immune infiltration analyses were conducted. Finally, TF-gene, gene-miRNA, gene-disease, protein-chemical, and protein-drug interaction networks were constructed based on hub gene identification.

Results

A total of 313 common genes were selected, and enrichment analyses were performed to determine their biological functions and signaling pathways. Eight hub genes (IL-1β, CD8A, IL-10, CD4, IL-6, TLR4, CCL2, and PTPRC) were identified using the protein-protein interaction network, and immune infiltration analysis was then carried out to examine the functional relationship between the eight hub genes and immune cells as well as changes in immune cells in disease. Transcription factors, miRNAs, diseases, chemicals, and drugs with high correlation with hub genes were predicted using bioinformatics analysis.

Conclusions

This study revealed a common gene interaction network between COVID-19 and pericarditis. The screened functional pathways, hub genes, potential compounds, and drugs provided new insights for further research on COVID-19 associated with pericarditis.

Interpretation of Neurodegenerative GWAS Risk Alleles in Microglia and their Interplay with Other Cell Types

Microglia have been implicated in numerous neurodegenerative and neuroinflammatory disorders; however, the causal contribution of this immune cell type is frequently debated. Genetic studies offer a unique vantage point in that they infer causality over a secondary consequence. Genome-wide association studies (GWASs) have identified hundreds of loci in the genome that are associated with susceptibility to neurodegenerative disorders. GWAS studies implicate microglia in the pathogenesis of Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and to a lesser degree suggest a role for microglia in vascular dementia (VaD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS), and other neurodegenerative and neuropsychiatric disorders. The contribution and function of GWAS risk loci on disease progression is an ongoing field of study, in which large genomic datasets, and an extensive framework of computational tools, have proven to be crucial. Several GWAS risk loci are shared between disorders, pointing towards common pleiotropic mechanisms. In this chapter, we introduce key concepts in GWAS and post-GWAS interpretation of neurodegenerative disorders, with a focus on GWAS risk genes implicated in microglia, their interplay with other cell types and shared convergence of GWAS risk loci on microglia.

Systemic lupus erythematosus is associated with lower risk of hepatitis B virus infection: A multivariable Mendelian randomization study in East Asian population

The relationship between systemic lupus erythematosus (SLE) and hepatitis B virus (HBV) infection is still unclear. We conducted a two-sample Mendelian randomization (MR) analysis using summary statistics from genome-wide association studies for SLE and HBV infection in individuals of East Asian ancestry. The inverse-variance weighted (IVW) method, weighted median (WM) method, and MR-Egger method were used to estimate the causal effect of SLE on HBV infection. Additionally, we performed a multivariable MR analysis adjusting for the effects of body mass index and rheumatoid arthritis. This MR study included a total of 225 106 individuals of East Asian ancestry, comprising 5616 cases and 219 490 controls. The IVW method (OR: 0.79, p = 3.34E-08) and the WM method (OR: 0.79, p = 9.09E-06) revealed a causal relationship between genetically predicted SLE and a low risk of HBV infection. The multivariable MR analysis still suggested a low risk of HBV infection associated with SLE (OR: 0.83, p = 2.89E-06). Our MR analysis supports a causal relationship between SLE and a low risk of HBV infection in individuals of East Asian ancestry.

Integrating population-level and cell-based signatures for drug repositioning

Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.

Shared genetic architecture of COVID-19 and Alzheimer's disease

The severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) and the сoronavirus disease 2019 (COVID-19) have become a global health threat. At the height of the pandemic, major efforts were focused on reducing COVID-19-associated morbidity and mortality. Now is the time to study the long-term effects of the pandemic, particularly cognitive impairment associated with long COVID. In recent years much attention has been paid to the possible relationship between COVID-19 and Alzheimer's disease, which is considered a main cause of age-related cognitive impairment. Genetic predisposition was shown for both COVID-19 and Alzheimer's disease. However, the analysis of the similarity of the genetic architecture of these diseases is usually limited to indicating a positive genetic correlation between them. In this review, we have described intrinsic linkages between COVID-19 and Alzheimer's disease, pointed out shared susceptibility genes that were previously identified in genome-wide association studies of both COVID-19 and Alzheimer's disease, and highlighted a panel of SNPs that includes candidate genetic risk markers of the long COVID-associated cognitive impairment.

Graphical Learning and Causal Inference for Drug Repurposing

Gene expression profiles that connect drug perturbations, disease gene expression signatures, and clinical data are important for discovering potential drug repurposing indications. However, the current approach to gene expression reversal has several limitations. First, most methods focus on validating the reversal expression of individual genes. Second, there is a lack of causal approaches for identifying drug repurposing candidates. Third, few methods for passing and summarizing information on a graph have been used for drug repurposing analysis, with classical network propagation and gene set enrichment analysis being the most common. Fourth, there is a lack of graph-valued association analysis, with current approaches using real-valued association analysis one gene at a time to reverse abnormal gene expressions to normal gene expressions. To overcome these limitations, we propose a novel causal inference and graph neural network (GNN)-based framework for identifying drug repurposing candidates. We formulated a causal network as a continuous constrained optimization problem and developed a new algorithm for reconstructing large-scale causal networks of up to 1,000 nodes. We conducted large-scale simulations that demonstrated good false positive and false negative rates. To aggregate and summarize information on both nodes and structure from the spatial domain of the causal network, we used directed acyclic graph neural networks (DAGNN). We also developed a new method for graph regression in which both dependent and independent variables are graphs. We used graph regression to measure the degree to which drugs reverse altered gene expressions of disease to normal levels and to select potential drug repurposing candidates. To illustrate the application of our proposed methods for drug repurposing, we applied them to phase I and II L1000 connectivity map perturbational profiles from the Broad Institute LINCS, which consist of gene-expression profiles for thousands of perturbagens at a variety of time points, doses, and cell lines, as well as disease gene expression data under-expressed and over-expressed in response to SARS-CoV-2.

Genetic Determinants of the Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition that arises from multiple causes, including sepsis, pneumonia, trauma, and severe coronavirus disease 2019 (COVID-19). Given the heterogeneity of causes and the lack of specific therapeutic options, it is crucial to understand the genetic and molecular mechanisms that underlie this condition. The identification of genetic risks and pharmacogenetic loci, which are involved in determining drug responses, could help enhance early patient diagnosis, assist in risk stratification of patients, and reveal novel targets for pharmacological interventions, including possibilities for drug repositioning. Here, we highlight the basis and importance of the most common genetic approaches to understanding the pathogenesis of ARDS and its critical triggers. We summarize the findings of screening common genetic variation via genome-wide association studies and analyses based on other approaches, such as polygenic risk scores, multi-trait analyses, or Mendelian randomization studies. We also provide an overview of results from rare genetic variation studies using Next-Generation Sequencing techniques and their links with inborn errors of immunity. Lastly, we discuss the genetic overlap between severe COVID-19 and ARDS by other causes.