COVID-19 outcomes and the human genome

Type I interferon pathway genetic variants in severe COVID-19

Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. According to the World Health Organization (WHO), there have been over 760 million reported cases and over 6 million deaths caused by this disease worldwide. The severity of COVID-19 is based on symptoms presented by the patient and is divided as asymptomatic, mild, moderate, severe, and critical. The manifestations are interconnected with genetic variations. The innate immunity is the quickest response mechanism of an organism against viruses. Type I interferon pathway plays a key role in antiviral responses due to viral replication inhibition in infected cells and adaptive immunity stimulation induced by interferon molecules. Thus, variants in type I interferon pathway's genes are being studied in different COVID-19 manifestations. This review summarizes the role of variants in type I interferon pathway's genes on prognosis and severity progression of COVID-19.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

Covid-19, HLA, and race common link: A novel hypothesis

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) accountable for the coronavirus disease 2019 (Covid-19) prompted a catastrophic pandemic striking millions of people with diverse presentations, from asymptomatic to severe, potentially lethal disease requiring unprecedented levels of specialized care and extraordinary resources that have overwhelmed healthcare systems around the world. In this detailed communication we postulating a novel hypothesis, based on the viral replication and transplantation immunology. This based on reviewing published journal articles and text book chapters to account for variable mortality and degrees of morbidity among various race and origins. Homo sapiens evolution over millions of years, for that the matter the origin of any biologic form of life form initiated by microorganisms. The entire body of a human has several millions of bacterial and viral genomes incorporated over millions of years. Perhaps the answer or a clue lies how compatible a foreign genomic sequence fits into three billion copies of human genome.

Comparison of characteristics and laboratory tests of COVID-19 hematological patients from France and Brazil during the pre-vaccination period: identification of prognostic profiles for survival

INTRODUCTION

COVID-19 disease presentation is heterogeneous, from asymptomatic up to severe life-threatening forms. Getting further insights into patients with specific diseases is of particular interest. We aimed to identify profiles of hematology patients hospitalized with COVID-19 that would be associated with survival and to assess the differences between cohorts METHODS: A binational cohort of 263 patients with COVID-19 and hematological disease was studied in Paris, France and São Paulo, Brazil. Patient profiles were based on age, comorbidities, biological measurements, COVID-19 symptoms and hematological disease characteristics. A semi-supervised learning method with a survival endpoint was first used, following which, a classifier was identified to allow the classification of patients using only baseline information MAIN RESULTS: Two profiles of patients were identified, one being young patients with few comorbidities and low C-reactive protein (CRP), D-dimers, lactate dehydrogenase (LDH) and creatinine levels, and the other, older patients, with several comorbidities and high levels of the 4 biology markers. The profiles were strongly associated with survival (p < 0.0001), even after adjusting for age (p = 0.0002). The 30-day survival rate was 77.1% in the first profiles, versus 46.7% in the second. The Brazilian analysis emphasized the importance of age, while the French focused on the comorbidities CONCLUSION: This analysis showed the importance of CRP, LHD and creatinine in the COVID-19 presentation and prognosis, whatever the geographic origin of the patients.

Association of Toll-like receptor-4 polymorphism with SARS CoV-2 infection in Kurdish Population

Genetic variations are critical for understanding clinical outcomes of infections including server acute respiratory syndrome coronavirus 2 (SARS CoV-2). The immunological reactions of human immune genes with SARS CoV-2 have been under investigation. Toll-like receptors (TLRs), a group of proteins, are important for microbial detections including bacteria and viruses. TLR4 can sense both bacterial lipopolysaccharides (LPS) and endogenous oxidized phospholipids triggered by Covid-19 infection. Two TLR4 single nucleotide polymorphisms (SNPs), Asp299Gly and Thr399Ile have been linked to infectious diseases. No studies have focused on these SNPs in association with Covid-19. This study aims to reveal the association between Covid-19 infection with these SNPs by comparing a group of patients and a general population. Restriction fragment length polymorphisms (RFLP) were used to identify the TLR4 SNPs in both the general population (n = 114) and Covid-19 patient groups (n = 125). The results found no association between the TLR4 polymorphisms and Covid-19 infections as the data showed no statistically significant difference between the compared groups. This suggested that these TLR4 SNPs may not be associated with Covid-19 infections.

An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences

Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classifiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.

Coronavirus Host Genomics Study: South Africa (COVIGen-SA)

Host genetic factors are known to modify the susceptibility, severity, and outcomes of COVID-19 and vary across populations. However, continental Africans are yet to be adequately represented in such studies despite the importance of genetic factors in understanding Africa's response to the pandemic. We describe the development of a research resource for coronavirus host genomics studies in South Africa known as COVIGen-SA-a multicollaborator strategic partnership designed to provide harmonised demographic, clinical, and genetic information specific to Black South Africans with COVID-19. Over 2,000 participants have been recruited to date. Preliminary results on 1,354 SARS-CoV-2 positive participants from four participating studies showed that 64.7% were female, 333 had severe disease, and 329 were people living with HIV. Through this resource, we aim to provide insights into host genetic factors relevant to African-ancestry populations, using both genome-wide association testing and targeted sequencing of important genomic loci. This project will promote and enhance partnerships, build skills, and develop resources needed to address the COVID-19 burden and associated risk factors in South African communities.

Allelic Variations in the Human Genes TMPRSS2 and CCR5, and the Resistance to Viral Infection by SARS-CoV-2

During the first wave of COVID-19 infection in Italy, the number of cases and the mortality rates were among the highest compared to the rest of Europe and the world. Several studies demonstrated a severe clinical course of COVID-19 associated with old age, comorbidities, and male gender. However, there are cases of virus infection resistance in subjects living in close contact with infected subjects. Thus, to explain the predisposition to virus infection and to COVID-19 disease progression, we must consider, in addition to the genetic variability of the virus and other environmental or comorbidity conditions, the allelic variants of specific human genes, directly or indirectly related to the life cycle of the virus. Here, we analyzed three human genetic polymorphisms belonging to the TMPRSS2 and CCR5 genes in a sample population from Sicily (Italy) to investigate possible correlations with the resistance to viral infection and/or to COVID-19 disease progression as recently described in other human populations. Our results did not show any correlations of the rs35074065, rs12329760, and rs333 polymorphisms with SARS-CoV-2 infection or with COVID-19 disease severity. Further studies on other human genetic polymorphisms should be performed to identify the major human determinants of SARS-CoV-2 viral resistance.

Up-regulated serum levels of interleukin (IL)-17A and IL-22 in Egyptian pediatric patients with COVID-19 and MIS-C: Relation to the disease outcome

Both IL-17A and IL-22 share cellular sources and signaling pathways. They have synergistic action on epithelial cells to stimulate their production of antimicrobial peptides which are protective against infections. However, both interleukins may contribute to ARDS pathology if their production is not controlled. This study aimed to investigate serum levels of IL-17A and IL-22 in relation to the disease outcome in patients with SARS-CoV-2. Serum IL-17A and IL-22 were measured by ELISA in 40 patients with SARS-CoV-2, aged between 2 months and 16 years, (18 had COVID-19 and 22 had multisystem inflammatory syndrome in children "MIS-C") in comparison to 48 age- and sex-matched healthy control children. Patients with COVID-19 and MIS-C had significantly higher serum IL-17A and IL-22 levels than healthy control children (P < 0.001). Increased serum IL-17A and IL-22 levels were found in all patients. Elevated CRP and serum ferritin levels were found in 90% of these patients. Lymphopenia, neutrophilia, neutropenia, thrombocytopenia and elevated ALT, LDH and D-dimer were found in 45%, 42.5 %, 2.5%, 30%, 32.5%, 82.5%, and 65%, respectively of these patients. There were non-significant differences between patients who recovered and those who died or had a residual illness in serum levels of IL-17A, IL-22 and the routine inflammatory markers of COVID-19. In conclusions, serum IL-17A and IL-22 levels were up-regulated in all patients with COVID-19 and MIS-C. Levels of serum IL-17A, IL-22 and the routine inflammatory markers of COVID-19 were not correlated with the disease outcome. Our conclusions are limited by the sample size.

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-sex unbiased meta-clustering technique

Background

The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes—the division of populations of patients into more meaningful subgroups driven by clinical features—and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources.

Objective

We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes.

Methods

We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance.

Results

In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities.

Conclusions

The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.

Long COVID in Hospitalized COVID-19 Patients: A Retrospective Cohort Study

Background:

We aimed to evaluate the Long COVID frequency, and related factors in patients followed up after hospitalization.

Methods:

This retrospective cohort study included 133 inpatients with COVID-19 PCR test positivity from Nigde Province, Turkey between 01.01.2021 and 28.02.2021. The characteristics of the patients were recorded by examining the files, and the symptom questioning was made by telephone interviewing with the patients approximately four months after the date of diagnosis. The presence of at least one symptom lasting more than four weeks was described as Long COVID.

Results:

The frequency of Long COVID was 64.7%. The most common Long COVID symptoms were fatigue (45.9%), respiratory distress (25.6%), and muscle / joint pain (24.8%), respectively. In comparison analysis to identify factors associated with Long COVID; Long COVID was found to be more frequent among women (P=0.04); patients with severe COVID-19 (P<0.01), patients with prolonged hospital stay (P=0.03), patients with the comorbid disease (P=0.03), and Diabetes Mellitus patients (P=0.02). Additionally, the frequency of Long COVID increased as the depression score stated by the person increased after COVID-19 disease (P=0.02).

Conclusion:

The treatment of COVID-19 patients should not end when they are discharged from the hospital. On the contrary, these patients, especially high-risk patients, should be followed up in post-COVID clinics and rehabilitated physically and psychosocially with a multidisciplinary approach following the recovery period of the acute illness.

TaqI polymorphism of the VDR gene: aspects related to the clinical behavior of COVID-19 in Cuban patients

Purpose

To determine the relationship between the genotypes of the TaqI polymorphism of VDR gene and the clinical forms of COVID-19 in Cuban patients.

Methods

TaqI polymorphism was determined by the PCR in 104 Cuban patients, who suffered different clinical forms of COVID-19.

Results

There was a greater possibility of presenting symptomatic forms [OR = 2.081, 95% CI: 0.243-17.842], even severe [OR = 1.200, 95% CI: 0.217-6.638], related to the tt genotype.

Conclusion

There are signs of association between the risk of developing COVID-19 and the genotypes of the TaqI polymorphism of the VDR gene in the studied Cuban patients.

Prospective epidemiological, molecular, and genetic characterization of a novel coronavirus disease in the Val Venosta/Vinschgau: the CHRIS COVID-19 study protocol

The COVID-19 pandemic has been threatening the healthcare and socioeconomic systems of entire nations. While population-based surveys to assess the distribution of SARS-CoV-2 infection have become a priority, pre-existing longitudinal studies are ideally suited to assess the determinants of COVID-19 onset and severity.The Cooperative Health Research In South Tyrol (CHRIS) study completed the baseline recruitment of 13,393 adults from the Venosta/Vinschgau rural district in 2018, collecting extensive phenotypic and biomarker data, metabolomic data, densely imputed genotype and whole-exome sequencing data.Based on CHRIS, we designed a prospective study, called CHRIS COVID-19, aimed at: 1) estimating the incidence of SARS-CoV-2 infections; 2) screening for and investigating the determinants of incident infection among CHRIS participants and their household members; 3) monitoring the immune response of infected participants prospectively.An online screening questionnaire was sent to all CHRIS participants and their household members. A random sample of 1450 participants representative of the district population was invited to assess active (nasopharyngeal swab) or past (serum antibody test) infections. We prospectively invited for complete SARS-CoV-2 testing all questionnaire completers gauged as possible cases of past infection and their household members. In positive tested individuals, antibody response is monitored quarterly for one year. Untested and negative participants receive the screening questionnaire every four weeks until gauged as possible incident cases or till the study end.Originated from a collaboration between researchers and community stakeholders, the CHRIS COVID-19 study aims at generating knowledge about the epidemiological, molecular, and genetic characterization of COVID-19 and its long-term sequelae.

Immunophenotyping assessment in a COVID-19 cohort (IMPACC): A prospective longitudinal study

The Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective longitudinal study designed to enroll 1000 hospitalized patients with COVID-19 (NCT04378777). IMPACC collects detailed clinical, laboratory, and radiographic data along with longitudinal biologic sampling of blood and respiratory secretions for in-depth testing. Clinical and laboratory data are integrated to identify immunologic, virologic, proteomic, metabolomic, and genomic features of COVID-19–related susceptibility, severity, and disease progression. The goals of IMPACC are to better understand the contributions of pathogen dynamics and host immune responses to the severity and course of COVID-19 and to generate hypotheses for identification of biomarkers and effective therapeutics, including optimal timing of such interventions. In this report, we summarize the IMPACC study design and protocols including clinical criteria and recruitment, multisite standardized sample collection and processing, virologic and immunologic assays, harmonization of assay protocols, high-level analyses, and the data sharing plans.

Predictability in Contemporary Medicine

Medical practice is increasingly coming under the guidance of statistical-mathematical models that are, undoubtedly, valuable tools but are also only a partial representation of reality. Indeed, given that statistics may be more or less adequate, a model is still a subjective interpretation of the researcher and is also influenced by the historical context in which it operates. From this opinion, I will provide a short historical excursus that retraces the advent of probabilistic medicine as a long process that has a beginning that should be sought in the discovery of the complexity of disease. By supporting the belonging of this evolution to the scientific domain it is also acknowledged that the underlying model can be imperfect or fallible and, therefore, confutable as any product of science. Indeed, it seems non-trivial here to recover these concepts, especially today where clinical decisions are entrusted to practical guidelines, which are a hybrid product resulting from the aggregation of multiple perspectives, including the probabilistic approach, to disease. Finally, before the advent of precision medicine, by limiting the use of guidelines to the original consultative context, an aged approach is supported, namely, a relationship with the individual patient.

N-Glycosylation Network Construction and Analysis to Modify Glycans on the Spike (S) Glycoprotein of SARS-CoV-2

Background: The N-glycan structure and composition of the spike (S) protein of SARS-CoV-2 are pertinent to vaccine development and efficacy.

Methods: We reconstructed the glycosylation network based on previously published mass spectrometry data using GNAT, a glycosylation network analysis tool. Our compilation of the network tool had 26 glycosyltransferase and glucosidase enzymes and could infer the pathway of glycosylation machinery based on glycans in the virus spike protein. Once the glycan biosynthesis pathway was generated, we simulated the effect of blocking specific enzymes—swainsonine or deoxynojirimycin for blocking mannosidase-II and indolizidine for blocking alpha-1,6-fucosyltransferase—to see how they would affect the biosynthesis network and the glycans that were synthesized.

Results: The N-glycan biosynthesis network of SARS-CoV-2 spike protein shows an elaborate enzymatic pathway with several intermediate glycans, along with the ones identified by mass spectrometric studies. Of the 26 enzymes, the following were involved—Man-Ia, MGAT1, MGAT2, MGAT4, MGAT5, B3GalT, B4GalT, Man-II, SiaT, ST3GalI, ST3GalVI, and FucT8. Blocking specific enzymes resulted in a substantially modified glycan profile of SARS-CoV-2.

Conclusion: Variations in the final N-glycan profile of the virus, given its site-specific microheterogeneity, are factors in the host response to the infection, vaccines, and antibodies. Heterogeneity in the N-glycan profile of the spike (S) protein and its potential effect on vaccine efficacy or adverse reactions to the vaccines remain unexplored. Here, we provide all the resources we generated—the glycans in the glycoCT xml format and the biosynthesis network for future work.

Beyond virology: environmental constraints of the first wave of COVID-19 cases in Italy

Global warming and air pollution affect the transmission pathway and the survival of viruses, altering the human immune system as well. The first wave of the COVID-19 pandemic dramatically highlights the key roles of climate and air chemistry in viral epidemics. The elongated form of the Italian peninsula and the two major islands (the largest in Europe) is a perfect case study to assess some of these key roles, as the fate of the virus is mirroring the industrialization in the continental part of our country. Fine particulate matter (PM2.5), geography, and climate explain what is happening in Italy and support cleaner air actions to address efficiently other outbreaks. Besides the environmental factors, future works should also address the genetic difference among individuals to explain the spatial variability of the human response to viral infections.

Association between Interferon-Lambda-3 rs12979860, TLL1 rs17047200 and DDR1 rs4618569 Variant Polymorphisms with the Course and Outcome of SARS-CoV-2 Patients

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection provides a critical host-immunological challenge.

AIM

We explore the effect of host-genetic variation in interferon-lambda-3 rs12979860, Tolloid Like-1 (TLL1) rs17047200 and Discoidin domain receptor 1(DDR1) rs4618569 on host response to respiratory viral infections and disease severity that may probe the mechanistic approach of allelic variation in virus-induced inflammatory responses.

METHODS

141 COVID-19 positive patients and 100 healthy controls were tested for interferon-lambda-3 rs12979860, TLL1 rs17047200 and DDR1 rs4618569 polymorphism by TaqMan probe-based genotyping. Different genotypes were assessed regarding the COVID-19 severity and prognosis.

RESULTS

There were statistically significant differences between the studied cases and control group with regard to the presence of comorbidities, total leucocytic count, lymphocytic count, CRP, serum LDH, ferritin and D-dimer (p < 0.01). The CC genotype of rs12979860 cytokine, the AA genotype of TLL1 rs17047200 and the AA genotype of the rs4618569 variant of DDR1 showed a higher incidence of COVID-19 compared to the others. There were significant differences between the rs4618569 variant of DDR and the outcome of the disease, with the highest mortality in AG genotype 29 (60.4%) in comparison to 16 (33.3%) and 3 (6.2%) in the AA and GG genotypes, respectively (p = 0.007*), suggesting that the A allele is associated with a poor outcome in the disease.

CONCLUSION

Among people who carry C and A alleles of SNPs IFN-λ rs12979860 and TLL1 rs17047200, respectively, the AG genotype of the DDR1 rs4618569 variant is correlated with a COVID-19 poor outcome. In those patients, the use of anti-IFN-λ 3, TLL1 and DDR1 therapy may be promising for personalized translational clinical practice.

Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2

The outbreak of existing public health distress is threatening the entire world with emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel coronavirus disease 2019 (COVID-19) is mild in most people. However, in some elderly people with co-morbid conditions, it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction leading to death. COVID-19 has caused global panic in the healthcare sector and has become one of the biggest threats to the global economy. Drug discovery researchers are expected to contribute rapidly than ever before. The complete genome sequence of coronavirus had been reported barely a month after the identification of first patient. Potential drug targets to combat and treat the coronavirus infection have also been explored. The iterative structure-based drug design (SBDD) approach could significantly contribute towards the discovery of new drug like molecules for the treatment of COVID-19. The existing antivirals and experiences gained from SARS and MERS outbreaks may pave way for identification of potential drug molecules using the approach. SBDD has gained momentum as the essential tool for faster and costeffective lead discovery of antivirals in the past. The discovery of FDA approved human immunodeficiency virus type 1 (HIV-1) inhibitors represent the foremost success of SBDD. This systematic review provides an overview of the novel coronavirus, its pathology of replication, role of structure based drug design, available drug targets and recent advances in in-silico drug discovery for the prevention of COVID-19. SARSCoV- 2 main protease, RNA dependent RNA polymerase (RdRp) and spike (S) protein are the potential targets, which are currently explored for the drug development.

Genetic variation and function: revealing potential factors associated with microbial phenotypes

Innovations in sequencing technology have generated voluminous microbial and host genomic data, making it possible to detect these genetic variations and analyze the function influenced by them. Recently, many studies have linked such genetic variations to phenotypes through association or comparative analysis, which have further advanced our understanding of multiple microbial functions. In this review, we summarized the application of association analysis in microbes like Mycobacterium tuberculosis, focusing on screening of microbial genetic variants potentially associated with phenotypes such as drug resistance, pathogenesis and novel drug targets etc.; reviewed the application of additional comparative genomic or transcriptomic methods to identify genetic factors associated with functions in microbes; expanded the scope of our study to focus on host genetic factors associated with certain microbes or microbiome and summarized the recent host genetic variations associated with microbial phenotypes, including susceptibility and load after infection of HIV, presence/absence of different taxa, and quantitative traits of microbiome, and lastly, discussed the challenges that may be encountered and the apparent or potential viable solutions. Gene-function analysis of microbe and microbiome is still in its infancy, and in order to unleash its full potential, it is necessary to understand its history, current status, and the challenges hindering its development.

A review on viral data sources and search systems for perspective mitigation of COVID-19

With the outbreak of the COVID-19 disease, the research community is producing unprecedented efforts dedicated to better understand and mitigate the effects of the pandemic. In this context, we review the data integration efforts required for accessing and searching genome sequences and metadata of SARS-CoV2, the virus responsible for the COVID-19 disease, which have been deposited into the most important repositories of viral sequences. Organizations that were already present in the virus domain are now dedicating special interest to the emergence of COVID-19 pandemics, by emphasizing specific SARS-CoV2 data and services. At the same time, novel organizations and resources were born in this critical period to serve specifically the purposes of COVID-19 mitigation while setting the research ground for contrasting possible future pandemics. Accessibility and integration of viral sequence data, possibly in conjunction with the human host genotype and clinical data, are paramount to better understand the COVID-19 disease and mitigate its effects. Few examples of host-pathogen integrated datasets exist so far, but we expect them to grow together with the knowledge of COVID-19 disease; once such datasets will be available, useful integrative surveillance mechanisms can be put in place by observing how common variants distribute in time and space, relating them to the phenotypic impact evidenced in the literature.

Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

BACKGROUND

The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules.

RESULTS

Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19.

CONCLUSIONS

Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

Projected Utility of Pharmacogenomic Testing Among Individuals Hospitalized With COVID-19: A Retrospective Multicenter Study in the United States

Many academic institutions are collecting blood samples from patients seeking treatment for coronavirus disease 2019 (COVID-19) to build research biorepositories. It may be feasible to extract pharmacogenomic (PGx) information from biorepositories for clinical use. We sought to characterize the potential value of multigene PGx testing among individuals hospitalized with COVID-19 in the United States. We performed a cross-sectional analysis of electronic health records from consecutive individuals hospitalized with COVID-19 at a large, urban academic health system. We characterized medication orders, focusing on medications with actionable PGx guidance related to 14 commonly assayed genes (CYP2C19, CYP2C9, CYP2D6, CYP3A5, DPYD, G6PD, HLA-A, HLA-B, IFNL3, NUDT15, SLCO1B1, TPMT, UGT1A1, and VKORC1). A simulation analysis combined medication data with population phenotype frequencies to estimate how many treatment modifications would be enabled if multigene PGx results were available. Sixty-four unique medications with PGx guidance were ordered at least once in the cohort (n = 1,852, mean age 60.1 years). Nearly nine in 10 individuals (89.7%) had at least one order for a medication with PGx guidance and 427 patients (23.1%) had orders for 4 or more actionable medications. Using a simulation, we estimated that 17 treatment modifications per 100 patients would be enabled if PGx results were available. The genes CYP2D6 and CYP2C19 were responsible for the majority of treatment modifications, and the medications most often affected were ondansetron, oxycodone, and clopidogrel. PGx results would be relevant for nearly all individuals hospitalized with COVID-19 and would provide the opportunity to improve clinical care.

Low-Cost and Scalable Platform with Multiplexed Microwell Array Biochip for Rapid Diagnosis of COVID-19

Sensitive detection of SARS-CoV-2 is of great importance for inhibiting the current pandemic of COVID-19. Here, we report a simple yet efficient platform integrating a portable and low-cost custom-made detector and a novel microwell array biochip for rapid and accurate detection of SARS-CoV-2. The instrument exhibits expedited amplification speed that enables colorimetric read-out within 25 minutes. A polymeric chip with a laser-engraved microwell array was developed to process the reaction between the primers and the respiratory swab RNA extracts, based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP). To achieve clinically acceptable performance, we synthesized a group of six primers to identify the conserved regions of the ORF1ab gene of SARS-CoV-2. Clinical trials were conducted with 87 PCR-positive and 43 PCR-negative patient samples. The platform demonstrated both high sensitivity (95.40%) and high specificity (95.35%), showing potentials for rapid and user-friendly diagnosis of COVID-19 among many other infectious pathogens.

Cognitive disorders associated with hospitalization of COVID-19: Results from an observational cohort study

INTRODUCTION

Our understanding of risk factors for COVID‑19, including pre-existing medical conditions and genetic variations, is limited. To what extent the pre-existing clinical condition and genetic background have implications for COVID-19 still needs to be explored.

METHODS

Our study included 389,620 participants of European descent from the UK Biobank, of whom 3,884 received the COVID-19 test and 1,091 were tested positive for COVID-19. We examined the association of COVID-19 status with an extensive list of 974 medical conditions and 30 blood biomarkers. Additionally, we tested the association of genetic variants in two key genes related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2), with COVID-19 or any other phenotypes.

RESULTS

The most significant risk factors for COVID-19 include Alzheimer's disease (OR = 2.29, 95% CI: 1.25-4.16), dementia (OR = 2.16, 95% CI: 1.36-3.42), and the overall category of delirium, dementia, amnestic and other cognitive disorders (OR = 1.90, 95% CI: 1.24-2.90). Evidence suggesting associations of genetic variants in SARS-CoV-2 infection-related genes with COVID-19 (rs7282236, OR = 1.33, 95% CI: 1.14-1.54, p = 2.31 × 10^-4) and other phenotypes, such as an immune deficiency (p = 5.65 × 10^-5) and prostate cancer (p = 1.1 × 10^-5), was obtained.

CONCLUSIONS

Our unbiased and extensive search identified pre-existing Alzheimer's disease and dementia as top risk factors for hospital admission due to COVID-19, highlighting the importance of providing special protective care for patients with cognitive disorders during this pandemic. We also obtained evidence suggesting a direct association of genetic variants with COVID-19.

Statistical power in COVID-19 case-control host genomic study design

The identification of genetic variation that directly impacts infection susceptibility to SARS-CoV-2 and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic, and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity, and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity in published COVID-19 study designs. We demonstrate that limited phenotypic data and exposure/infection information in the early stages of the pandemic significantly impact the ability to detect most genetic variants with moderate effect sizes, especially when studying susceptibility to SARS-CoV-2 infection. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies.

Chemokine receptor gene polymorphisms and COVID-19: Could knowledge gained from HIV/AIDS be important?

Emerging results indicate that an uncontrolled host immune response, leading to a life-threatening condition called cytokine release syndrome (also termed "cytokine storm"), is the major driver of pathology in severe COVID-19. In this pandemic, considerable effort is being focused on identifying host genomic factors that increase susceptibility or resistance to the complications of COVID-19 and translating these findings to improved patient care. In this regard, the chemokine receptor-ligand nexus has been reported as potentially important in severe COVID-19 disease pathogenesis and its treatment. Valuable genomic insights into the chemokine receptor-ligand nexus have been gained from HIV infection and disease progression studies. Applying that knowledge, together with newly discovered potential host genomic factors associated with COVID-19, may lead to a more comprehensive understanding of the pathogenesis and treatment outcomes in COVID-19 patients.

The Role of Host Genetic Factors in Coronavirus Susceptibility: Review of Animal and Systematic Review of Human Literature

BACKGROUND

The recent SARS-CoV-2 pandemic raises many scientific and clinical questions. One set of questions involves host genetic factors that may affect disease susceptibility and pathogenesis. New work is emerging related to SARS-CoV-2; previous work has been conducted on other coronaviruses that affect different species.

OBJECTIVES

We aimed to review the literature on host genetic factors related to coronaviruses, with a systematic focus on human studies.

METHODS

We conducted a PubMed-based search and analysis for articles relevant to host genetic factors in coronavirus. We categorized articles, summarized themes related to animal studies, and extracted data from human studies for analyses.

RESULTS

We identified 1,187 articles of potential relevance. Forty-five studies were related to human host genetic factors related to coronavirus, of which 35 involved analysis of specific genes or loci; aside from one meta-analysis on respiratory infections, all were candidate-driven studies, typically investigating small number of research subjects and loci. Multiple significant loci were identified, including 16 related to susceptibility to coronavirus (of which 7 identified protective alleles), and 16 related to outcomes or clinical variables (of which 3 identified protective alleles). The types of cases and controls used varied considerably; four studies used traditional replication/validation cohorts. Of the other studies, 28 involved both human and non-human host genetic factors related to coronavirus, 174 involved study of non-human (animal) host genetic factors related to coronavirus, 584 involved study of non-genetic host factors related to coronavirus, including involving immunopathogenesis, 16 involved study of other pathogens (not coronavirus), 321 involved other studies of coronavirus, and 18 studies were assigned to the other categories and removed.

KEY FINDINGS

We have outlined key genes and loci from animal and human host genetic studies that may bear investigation in the nascent host genetic factor studies of COVID-19. Previous human studies to date have been limited by issues that may be less impactful on current endeavors, including relatively low numbers of eligible participants and limited availability of advanced genomic methods.