A comprehensive knowledgebase of known and predicted human genetic variants associated with COVID-19 susceptibility and severity

Host genetic susceptibility is a key risk factor for severe illness associated with COVID-19. Despite numerous studies of COVID-19 host genetics, our knowledge of COVID-19-associated variants is still limited, and there is no resource comprising all the published variants and categorizing them based on their confidence level. Also, there are currently no computational tools available to predict novel COVID-19 severity variants. Therefore, we collated 820 host genetic variants reported to affect COVID-19 susceptibility by means of a systematic literature search and confidence evaluation, and obtained 196 high-confidence variants. We then developed the first machine learning classifier of severe COVID-19 variants to perform a genome-wide prediction of COVID-19 severity for 82,468,698 missense variants in the human genome. We further evaluated the classifier's predictions using feature importance analyses to investigate the biological properties of COVID-19 susceptibility variants, which identified conservation scores as the most impactful predictive features. The results of enrichment analyses revealed that genes carrying high-confidence COVID-19 susceptibility variants shared pathways, networks, diseases and biological functions, with the immune system and infectious disease being the most significant categories. Additionally, we investigated the pleiotropic effects of COVID-19-associated variants using phenome-wide association studies (PheWAS) in ~40,000 BioMe BioBank genotyped individuals, revealing pre-existing conditions that could serve to increase the risk of severe COVID-19 such as chronic liver disease and thromboembolism. Lastly, we generated a web-based interface for exploring, downloading and submitting genetic variants associated with COVID-19 susceptibility for use in both research and clinical settings (https://itanlab.shinyapps.io/COVID19webpage/). Taken together, our work provides the most comprehensive COVID-19 host genetics knowledgebase to date for the known and predicted genetic determinants of severe COVID-19, a resource that should further contribute to our understanding of the biology underlying COVID-19 susceptibility and facilitate the identification of individuals at high risk for severe COVID-19.Copyright © 2023 Elsevier Inc.

Pleiotropic Effects of Nitric Oxide on SARS-CoV-2 Infections

Infection by beta-coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coron-avirus-2) alters the homeostasis of the vascular endothelium, promoting an inflammatory state which causes damage and favors the prothrombotic state. The direct viral cytotoxicity induced by the SARS-CoV-2 leads to endothelial cell death;thus, altering the vessel functions. Moreover, SARS-CoV infection induces endothelial dysfunction (ED) and reduces the levels of nitric oxide (NO);thus, aggravating the vascular injuries, which promotes thrombotic events due to an altera-tion in the homeostasis. NO is a pleiotropic molecule that induces vasodilation, regulates the immune response, inhibits platelet aggregation, and decreases the cellular adhesion to vascular en-dothelium. Moreover, NO acts directly against invasive agents, exhibiting antibacterial, antiviral, and antifungal activity. High levels of NO result in an increase in the ED, causing an inflammatory amplification that aggravates the disease through undesirable positive feedback. The objective of this review was to present and discuss the involvement of NO on ED in SARS-CoV-2 infections. This review may also highlight new perspectives for therapeutic interventions through the supple-mentation of exogenous NO. The maintenance of homeostatic NO levels could represent a useful approach in the prevention of coronavirus-induced ED.Copyright © 2021 Bentham Science Publishers.

Shared genetic influences between blood analyte levels and risk of severe COVID-19.

Genome-wide association studies (GWASs) show that genetic factors contribute to the risk of severe coronavirus disease 2019 (COVID-19) and blood analyte levels. Here, we utilize GWAS summary statistics to study the shared genetic influences (pleiotropy) between severe COVID-19 and 344 blood analytes at the genome, gene, and single-nucleotide polymorphism (SNP) levels. Our pleiotropy analyses genetically link blood levels of 71 analytes to severe COVID-19 in at least one of the three levels of investigation-suggesting shared biological mechanisms or causal relationships. Six analytes (alanine aminotransferase, alkaline phosphatase, apolipoprotein B, C-reactive protein, triglycerides, and urate) display evidence of pleiotropy with severe COVID-19 at all three levels. Causality analyses indicate that higher triglycerides levels causally increase the risk of severe COVID-19, thereby providing important support for the use of lipid-lowering drugs such as statins and fibrates to prevent severe COVID-19.

Challenge of Administering Antidiabetic Medications to Patients with Diabetes Who Have COVID-19

The Coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), is a previously unrecognized viral illness with high infectivity that has sparked a global crisis. Poorly controlled diabetes was demonstrated to be a crucial risk factor for poor COVID-19 outcomes. COVID-19 infections are associated with severe metabolic dysfunctions, new-onset diabetes, and increased thrombotic events against the backdrop of aberrant endothelial function. The current body of evidence suggests that when hyperglycemia interacts with other risk factors, it might modify immune and inflammatory responses such that individuals become susceptible to severe COVID-19 infection and worse outcomes including higher mortality. Apart from their glucose-lowering actions, the pleiotropic effects of antidiabetic medications can inhibit viral action, attenuate endothelial dysfunction, ameliorate oxidant effects, and modulate inflammatory and immune responses during COVID-19 infections. These actions make antidiabetic medications feasible candidates for drug repurposing to combat the SARS-CoV-2-induced tsunami in diabetic COVID-19 patients. This review discusses the association between diabetes and COVID-19, pathophysiology of the disease in diabetes, and therapeutic potential of antidiabetic medications for diabetic patients during the current COVID-19 pandemic. Given the short history of human infection with SARS-CoV-2, the information provided by recent studies is limited. Hence, further investigations of the optimal management of patients with diabetes who are affected by COVID-19 are warranted.

A novel penalized inverse-variance weighted estimator for Mendelian randomization with applications to COVID-19 outcomes.

Mendelian randomization utilizes genetic variants as instrumental variables (IVs) to estimate the causal effect of an exposure variable on an outcome of interest even in the presence of unmeasured confounders. However, the popular inverse-variance weighted (IVW) estimator could be biased in the presence of weak IVs, a common challenge in MR studies. In this article, we develop a novel penalized inverse-variance weighted (pIVW) estimator, which adjusts the original IVW estimator to account for the weak IV issue by using a penalization approach to prevent the denominator of the pIVW estimator from being close to zero. Moreover, we adjust the variance estimation of the pIVW estimator to account for the presence of balanced horizontal pleiotropy. We show that the recently proposed debiased IVW (dIVW) estimator is a special case of our proposed pIVW estimator. We further prove that the pIVW estimator has smaller bias and variance than the dIVW estimator under some regularity conditions. We also conduct extensive simulation studies to demonstrate the performance of the proposed pIVW estimator. Furthermore, we apply the pIVW estimator to estimate the causal effects of five obesity-related exposures on three coronavirus disease 2019 (COVID-19) outcomes. Notably, we find that hypertensive disease is associated with an increased risk of hospitalized COVID-19; and peripheral vascular disease and higher body mass index are associated with increased risks of COVID-19 infection, hospitalized COVID-19, and critically ill COVID-19.

Pleiotropic effects of heparins, potential interest in SARS-CoV-2 infections

Heparins are made up of glycosaminoglycan chains that we do have known and used for decades for their anticoagulant properties mainly anti-Xa and anti-IIa. In recent years, apart from anticoagulant effects, attention was drawn to the pleiotropic effects of heparins. Some of these effects are potentially useful in the management of patients infected with Covid-19. Indeed in vitro studies reveal many potential side effects of heparins that could be useful in such viral infections, and clinical studies reinforce the interest and the effectiveness of heparin in patients infected with this virus.

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights.

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

Lipid Lowering Effects of Herbal Supplements: A Review

As cardiovascular diseases are still a major cause of death in most countries, it is still relevant to look into treatment of such diseases. Dyslipidemia is one of the important identified risk factors for cardiovascular diseases. As this is largely driven by lifestyle and diet, it may be difficult to control it with lifestyle modifications alone. Currently, Statins remains to be the mainstay therapy for dyslipidemia but this is also met by problems within certain patient population. The drug may be contraindicated in certain patient groups;some patients tend to not respond to Statins;while certain patients may not tolerate the adverse events. This study looked into available literature on studies done on dyslipidemia using plant-based formulations using randomized clinical trial. Based on the review conducted, there are several plant-based formations with potential to be similar in efficacy to Statins. Some of the plants used are abundant or may be easily sourced. With the increasing popularity of food supplements or nutraceuticals, exploration on the potential of plant-based products is attractive. Despite the promising results of some studies, these will need further investigations and targeting a larger population size. Formulation options may need to be explored also focused on its stability. © RJPT All right reserved.

Messing with ßc: A unique receptor with many goals.

Our understanding of the biological role of the ßc family of cytokines has evolved enormously since their initial identification as bone marrow colony stimulating factors in the 1960's. It has become abundantly clear over the intervening decades that this family of cytokines has truly astonishing pleiotropic capacity, capable of regulating not only hematopoiesis but also many other normal and pathological processes such as development, inflammation, allergy and cancer. As noted in the current pandemic, ßc cytokines contribute to the cytokine storm seen in acutely ill COVID-19 patients. Ongoing studies to discover how these cytokines activate their receptor are revealing insights into the fundamental mechanisms that give rise to cytokine pleiotropy and are providing tantalizing glimpses of how discrete signaling pathways may be dissected for activation with novel ligands for therapeutic benefit.

A two-sample robust Bayesian Mendelian Randomization method accounting for linkage disequilibrium and idiosyncratic pleiotropy with applications to the COVID-19 outcomes.

Mendelian randomization (MR) is a statistical method exploiting genetic variants as instrumental variables to estimate the causal effect of modifiable risk factors on an outcome of interest. Despite wide uses of various popular two-sample MR methods based on genome-wide association study summary level data, however, those methods could suffer from potential power loss or/and biased inference when the chosen genetic variants are in linkage disequilibrium (LD), and also have relatively large direct effects on the outcome whose distribution might be heavy-tailed which is commonly referred to as the idiosyncratic pleiotropy phenomenon. To resolve those two issues, we propose a novel Robust Bayesian Mendelian Randomization (RBMR) model that uses the more robust multivariate generalized t$t$ -distribution to model such direct effects in a probabilistic model framework which can also incorporate the LD structure explicitly. The generalized t$t$ -distribution can be represented as a Gaussian scaled mixture so that our model parameters can be estimated by the expectation maximization (EM)-type algorithms. We compute the standard errors by calibrating the evidence lower bound using the likelihood ratio test. Through extensive simulation studies, we show that our RBMR has robust performance compared with other competing methods. We further apply our RBMR method to two benchmark data sets and find that RBMR has smaller bias and standard errors. Using our proposed RBMR method, we find that coronary artery disease is associated with increased risk of critically ill coronavirus disease 2019. We also develop a user-friendly R package RBMR (https://github.com/AnqiWang2021/RBMR) for public use.

Maturation and application of phenome-wide association studies.

In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype-phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits.

An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility.

BACKGROUND: While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility. RESULTS: Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity. CONCLUSIONS: Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb .

Five reasons COVID-19 is less severe in younger age-groups.

The severity of COVID-19 is age-related, with the advantage going to younger age-groups. Five reasons are presented. The first two are well-known, are being actively researched by the broader medical community, and therefore are discussed only briefly here. The third, fourth and fifth reasons derive from evolutionary life history theory, and potentially fill gaps in current understanding of why and how young and old age-groups respond differently to infection with SARS-CoV-2. Age of onset of generalized somatic aging and the timing of its progression are identified as important causes of these disparities, as are specific antagonistic pleiotropic tradeoffs in immune system function. Lay Summary: Covid-19 is less severe in younger age-groups than it is in older age-groups. Five advantages of youth are identified and explained in light of evolutionary life history theory, with a focus on the pattern of aging and specific tradeoffs between early and late immune system function.

Diabetes, inflammation, and the adiponectin paradox: Therapeutic targets in SARS-CoV-2.

Aging and pre-existing conditions in older patients increase severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) severity and its complications, although the causes remain unclear. Apart from acute pulmonary syndrome, Coronavirus 2019 (COVID-19) can increasingly induce chronic conditions. Importantly, SARS-CoV-2 triggers de novo type 2 diabetes mellitus (T2DM) linked to age-associated cardiovascular disease (CVD), cancers, and neurodegeneration. Mechanistically, SARS-CoV-2 induces inflammation, possibly through damage-associated molecular pattern (DAMP) signaling and 'cytokine storm,' causing insulin resistance and the adiponectin (APN) paradox, a phenomenon linking metabolic dysfunction to chronic disease. Accordingly, preventing the APN paradox by suppressing APN-related inflammatory signaling might prove beneficial. A better understanding could uncover novel therapies for SARS-CoV-2 and its chronic disorders.