Immune Response Is Key to Genetic Mechanisms of SARS-CoV-2 Infection With Psychiatric Disorders Based on Differential Gene Expression Pattern Analysis

Genetic analyses point to alterations in immune-related pathways underpinning the association between psychiatric disorders and COVID-19

Current literature suggests that people with psychiatric disorders have a higher risk of COVID-19 infection and a worse prognosis of the disease. We aimed to study the genetic contribution to these associations across seven psychiatric disorders as well as a general psychopathology factor (P-factor) and determine whether these are unique or shared across psychiatric disorders using statistical genetic techniques. Using the largest available genome-wide association studies (GWAS), we found a significant genetic overlap between depression, ADHD, PTSD, and the P-factor with both COVID-19 infection and hospitalization, and between anxiety and COVID-19 hospitalization. We used pairwise GWAS to examine this overlap on a fine-grained scale and identified specific regions of the genome shared between several psychiatric disorders, the P-factor, and COVID-19. Gene-based analysis in these genomic regions suggested possible links with immune-related pathways such as thyroid homeostasis, inflammation, and stress response. Finally, we show preliminary evidence for causal associations between depression, ADHD, PTSD, and the P-factor, and higher COVID-19 infection and hospitalization using Mendelian Randomization and Latent Causal Variable methods. Our results support the hypothesis that the relationship between psychiatric disorders and COVID-19 risk is likely due to shared alterations in immune-related pathways and is not a result of environmental factors alone, shedding light on potentially viable therapeutic targets.

Inflammation and viral infection as disease modifiers in schizophrenia

Numerous studies have now implicated a role for inflammation in schizophrenia. However, many aspects surrounding this aspect of the disease are still controversial. This controversy has been driven by conflicting evidence on the role of both pro-and anti-inflammatory factors and by often contentious findings concerning cytokine and immune cell profiles in the central nervous system and periphery. Current evidence supports the point that interleukin-6 is elevated in CSF, but does not support activation of microglia, resident macrophage-like cells in the brain. Furthermore, the mechanisms involving transit of the peripheral immune system factors across the blood brain barrier to central parenchyma have still not been completely elucidated. This process appears to involve perivascular macrophages and accompanying dendritic cells retained in the parenchyma by the chemokine and cytokine composition of the surrounding milieu. In addition, a number of studies have shown that this can be modulated by infection with viruses such as herpes simplex virus type I which may disrupt antigen presentation in the perivascular space, with long-lasting consequences. In this review article, we discuss the role of inflammation and viral infection as potential disease modifiers in schizophrenia. The primary viral hit may occur in the fetus in utero, transforming the immune response regulatory T-cells or the virus may secondarily remain latent in immune cells or neurons and modify further immune responses in the developing individual. It is hoped that unraveling this pathway further and solidifying our understanding of the pathophysiological mechanisms involved will pave the way for future studies aimed at identification and implementation of new biomarkers and drug targets. This may facilitate the development of more effective personalized therapies for individuals suffering with schizophrenia.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Bioinformatics and systems biology approaches to identify the effects of COVID-19 on neurodegenerative diseases: A review

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing coronavirus disease (COVID-19), has been devastated by COVID-19 in an increasing number of countries and health care systems around the world since its announcement of a global pandemic on 11 March 2020. During the pandemic, emerging novel viral mutant variants have caused multiple outbreaks of COVID-19 around the world and are prone to genetic evolution, causing serious damage to human health. As confirmed cases of COVID-19 spread rapidly, there is evidence that SARS-CoV-2 infection involves the central nervous system (CNS) and peripheral nervous system (PNS), directly or indirectly damaging neurons and further leading to neurodegenerative diseases (ND), but the molecular mechanisms of ND and CVOID-19 are unknown. We employed transcriptomic profiling to detect several major diseases of ND: Alzheimer 's disease (AD), Parkinson' s disease (PD), and multiple sclerosis (MS) common pathways and molecular biomarkers in association with COVID-19, helping to understand the link between ND and COVID-19. There were 14, 30 and 19 differentially expressed genes (DEGs) between COVID-19 and Alzheimer 's disease (AD), Parkinson' s disease (PD) and multiple sclerosis (MS), respectively; enrichment analysis showed that MAPK, IL-17, PI3K-Akt and other signaling pathways were significantly expressed; the hub genes (HGs) of DEGs between ND and COVID-19 were CRH, SST, TAC1, SLC32A1, GAD2, GAD1, VIP and SYP. Analysis of transcriptome data suggests multiple co-morbid mechanisms between COVID-19 and AD, PD, and MS, providing new ideas and therapeutic strategies for clinical prevention and treatment of COVID-19 and ND.

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Mendelian randomization study on the causal effects of COVID-19 on childhood intelligence

Although individuals with coronavirus disease 2019 (COVID‐19) are known to be at increased risk for other conditions resulting from pathogenic changes (including metaplastic or anaplastic) in the lungs and other organs and organ systems, it is still unknown whether COVID‐19 affects childhood intelligence. The present two‐sample Mendelian randomization study aims to identify the genetic causal link between COVID‐19 and childhood intelligence. Four COVID‐19 genetic instrumental variants (IVs) were chosen from the largest genome‐wide association studies (GWAS) for COVID‐19 (hospitalized vs. population) (6406 cases and 902 088 controls of European ancestry). The largest childhood intelligence GWAS (n = 12 441 individuals of European ancestry) was used to evaluate the effect of the identified COVID‐19‐associated genetic IVs on childhood intelligence. We found that as the genetic susceptibility to COVID‐19 increased, childhood intelligence followed a decreasing trend, according to mr_egger (β = −0.156; p = 0.601; odds ratio [OR] = 0.856; 95% confidence interval [CI]: 0.522–1.405), simple mode (β = −0.126; p = 0.240; OR = 0.882; 95% CI: 0.745–1.044), and weighted mode (β = −0.121; p = 0.226; OR = 0.886; 95% CI: 0.758–1.036) analyses. This trend was further demonstrated by the weighted median (β = −0.134; p = 0.031; OR = 0.875; 95% CI: 0.774–0.988) and the inverse variance weighted (β = −0.152; p = 0.004; OR = 0.859; 95% CI: 0.776–0.952). Our analysis suggests a causal link between genetically increased COVID‐19 and decreased childhood intelligence. Thus, COVID‐19 may be a risk factor for declines in childhood intelligence.