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

Host Genetic Risk Factors Associated with COVID-19 Susceptibility and Severity in Vietnamese

Since the emergence and rapid transmission of SARS-CoV-2, numerous scientific reports have searched for the association of host genetic variants with COVID-19, but the data are mostly acquired from Europe. In the current work, we explored the link between host genes (SARS-CoV-2 entry and immune system related to COVID-19 sensitivity/severity) and ABO blood types with COVID-19 from whole-exome data of 200 COVID-19 patients and 100 controls in Vietnam. The O blood type was found to be a protective factor that weakens the worst outcomes of infected individuals. For SARS-CoV-2 susceptibility, rs2229207 (TC genotype, allele C) and rs17860118 (allele T) of IFNAR2 increased the risk of infection, but rs139940581 (CT genotype, allele T) of SLC6A20 reduced virus sensitivity. For COVID-19 progress, the frequencies of rs4622692 (TG genotype) and rs1048610 (TC genotype) of ADAM17 were significantly higher in the moderate group than in the severe/fatal group. The variant rs12329760 (AA genotype) of TMPRSS2 was significantly associated with asymptomatic/mild symptoms. Additionally, rs2304255 (CT genotype, allele T) of TYK2 and rs2277735 (AG genotype) of DPP9 were associated with severe/fatal outcomes. Studies on different populations will give better insights into the pathogenesis, which is ethnic-dependent, and thus decipher the genetic factor's contribution to mechanisms that predispose people to being more vulnerable to COVID-19.

Towards robust diagnosis of COVID-19 using vision self-attention transformer

The outbreak of COVID-19, since its appearance, has affected about 200 countries and endangered millions of lives. COVID-19 is extremely contagious disease, and it can quickly incapacitate the healthcare systems if infected cases are not handled timely. Several Conventional Neural Networks (CNN) based techniques have been developed to diagnose the COVID-19. These techniques require a large, labelled dataset to train the algorithm fully, but there are not too many labelled datasets. To mitigate this problem and facilitate the diagnosis of COVID-19, we developed a self-attention transformer-based approach having self-attention mechanism using CT slices. The architecture of transformer can exploit the ample unlabelled datasets using pre-training. The paper aims to compare the performances of self-attention transformer-based approach with CNN and Ensemble classifiers for diagnosis of COVID-19 using binary Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and multi-class Hybrid-learning for UnbiaSed predicTion of COVID-19 (HUST-19) CT scan dataset. To perform this comparison, we have tested Deep learning-based classifiers and ensemble classifiers with proposed approach using CT scan images. Proposed approach is more effective in detection of COVID-19 with an accuracy of 99.7% on multi-class HUST-19, whereas 98% on binary class SARS-CoV-2 dataset. Cross corpus evaluation achieves accuracy of 93% by training the model with Hust19 dataset and testing using Brazilian COVID dataset.

Revisiting the standard for modeling the spread of infectious diseases

The COVID-19 epidemic brought to the forefront the value of mathematical modelling for infectious diseases as a guide to help manage a formidable challenge for human health. A standard dynamic model widely used for a spreading epidemic separates a population into compartments-each comprising individuals at a similar stage before, during, or after infection-and keeps track of the population fraction in each compartment over time, by balancing compartment loading, discharge, and accumulation rates. The standard model provides valuable insight into when an epidemic spreads or what fraction of a population will have been infected by the epidemic's end. A subtle issue, however, with that model, is that it may misrepresent the peak of the infectious fraction of a population, the time to reach that peak, or the rate at which an epidemic spreads. This may compromise the model's usability for tasks such as "Flattening the Curve" or other interventions for epidemic management. Here we develop an extension of the standard model's structure, which retains the simplicity and insights of the standard model while avoiding the misrepresentation issues mentioned above. The proposed model relies on replacing a module of the standard model by a module resulting from Padé approximation in the Laplace domain. The Padé-approximation module would also be suitable for incorporation in the wide array of standard model variants used in epidemiology. This warrants a re-examination of the subject and could potentially impact model-based management of epidemics, development of software tools for practicing epidemiologists, and related educational resources.

An Updated Review on the Role of Single Nucleotide Polymorphisms in COVID-19 Disease Severity: A Global Aspect

Abstract:

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and recently has become a serious global pandemic. Age, gender, and comorbidities are known to be common risk factors for severe COVID-19 but are not enough to fully explain the magnitude of their effect on the risk of severity of the disease. Single nucleotide polymorphisms (SNPs) in several genes have been reported as a genetic factor contributing to COVID-19 severity. This comprehensive review focuses on the association between SNPs in four important genes and COVID-19 severity in a global aspect. We discuss a total of 39 SNPs in this review: five SNPs in the ABO gene, nine SNPs in the angiotensin-converting enzyme 2 (ACE2) gene, 19 SNPs in the transmembrane protease serine 2 (TMPRSS2) gene, and six SNPs in the toll-like receptor 7 (TLR7) gene. These SNPs data could assist in monitoring an individual's risk of severe COVID-19 disease, and therefore personalized management and pharmaceutical treatment could be planned in COVID-19 patients.

Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes

Gene-based tests are valuable techniques for identifying genetic factors in complex traits. Here, we propose a gene-based testing framework that incorporates data on long-range chromatin interactions, several recent technical advances for region-based tests, and leverages the knockoff framework for synthetic genotype generation for improved gene discovery. Through simulations and applications to genome-wide association studies (GWAS) and whole-genome sequencing data for multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests in the literature, identifies genes that replicate in larger studies, and can provide a more narrow focus on the possible causal genes at a locus by reducing the confounding effect of linkage disequilibrium. Furthermore, our results show that incorporating genetic variation in distal regulatory elements tends to improve power over conventional tests. Results for UK Biobank and BioBank Japan traits are also available in a publicly accessible database that allows researchers to query gene-based results in an easy fashion.

SARS-CoV-2 infection in patients with primary central nervous system lymphoma

BACKGROUND

Cancer patients may be at higher risk for severe coronavirus infectious disease-19 (COVID-19); however, the outcome of Primary Central Nervous System Lymphoma (PCNSL) patients with SARS-CoV-2 infection has not been described yet.

METHODS

We conducted a retrospective study within the Lymphomes Oculo-Cérébraux national network (LOC) to assess the clinical characteristics and outcome of SARS-CoV-2 infection in PCNSL patients (positive real-time polymerase chain reaction of nasopharyngeal swab or evocative lung computed tomography scan). We compared clinical characteristics between patients with severe (death and/or intensive care unit admission) and mild disease.

RESULTS

Between March and May 2020, 13 PCNSL patients were diagnosed with SARS-CoV-2 infection, 11 (85%) of whom were undergoing chemotherapy at the time of infection. The mortality rate was 23% (3/13), and two additional patients (15%) required mechanical ventilation. Two patients (15%) had no COVID-19 symptoms. History of diabetes mellitus was more common in severe patients (3/5 vs 0/8, p = 0.03). Two patients recovered from COVID-19 after mechanical ventilation during more than two weeks and resumed chemotherapy. In all, chemotherapy was resumed after COVID-19 recovery in nine patients (69%) after a median delay of 16 days (range 3-32), none of whom developed unusual chemotherapy complication nor SARS-Cov2 reactivation.

CONCLUSION

This preliminary analysis suggests that, while being at higher risk be for severe illness, PCNSL patients with COVID-19 might be treated maximally especially if they achieved oncological response at the time of SARS-CoV-2 infection. Chemotherapy might be resumed without prolonged delay in PCNSL patients with COVID-19.

Inflammatory biomarkers as independent prognosticators of 28-day mortality for COVID-19 patients admitted to general medicine or ICU wards: a retrospective cohort study

Inflammatory biomarkers may be associated with disease severity and increased mortality in COVID-19 patients but have not been studied in North American populations. We sought to determine whether a set of commonly ordered inflammatory biomarkers can predict 28-day mortality. We analyzed a multi-centered (four) COVID-19 registry cohort from March 4th to December 7th, 2020. This cohort included COVID-19-positive patients admitted to medical wards or intensive care units. Patients presenting to the emergency department for COVID-19 symptoms and then subsequently discharged were also included. We performed Cox-regression analysis to measure whether commonly used biomarkers were associated with an increased 28-day mortality. Of 336 COVID-19-positive patients, 267 required hospital admission, and 69 were seen in the emergency room and discharged. The median age was 63 years (IQR 80-50) and the female-to-male ratio was 49:51. Derivation of internally validated cut-offs suggested that C-reactive protein ≥ 78.4 mg/L, neutrophil-to-lymphocyte ratio ≥ 6.1, lymphocyte-to-white blood cell ratio < 0.127, and a modified Glasgow prognostic score equal to 2 vs. 1 or 0 were associated with the highest increased risk of 28-day mortality. We provide early estimates of cut-off values for inflammatory biomarkers and indices measured at the time of admission that may be useful to clinicians for predicting 28-day mortality in North American COVID-19 patients.

Neuropathophysiology of coronavirus disease 2019: neuroinflammation and blood brain barrier disruption are critical pathophysiological processes that contribute to the clinical symptoms of SARS-CoV-2 infection

Coronavirus disease 2019 (COVID-19) is caused by the novel SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) first discovered in Wuhan, Hubei province, China in December 2019. SARS-CoV-2 has infected several millions of people, resulting in a huge socioeconomic cost and over 2.5 million deaths worldwide. Though the pathogenesis of COVID-19 is not fully understood, data have consistently shown that SARS-CoV-2 mainly affects the respiratory and gastrointestinal tracts. Nevertheless, accumulating evidence has implicated the central nervous system in the pathogenesis of SARS-CoV-2 infection. Unfortunately, however, the mechanisms of SARS-CoV-2 induced impairment of the central nervous system are not completely known. Here, we review the literature on possible neuropathogenic mechanisms of SARS-CoV-2 induced cerebral damage. The results suggest that downregulation of angiotensin converting enzyme 2 (ACE2) with increased activity of the transmembrane protease serine 2 (TMPRSS2) and cathepsin L in SARS-CoV-2 neuroinvasion may result in upregulation of proinflammatory mediators and reactive species that trigger neuroinflammatory response and blood brain barrier disruption. Furthermore, dysregulation of hormone and neurotransmitter signalling may constitute a fundamental mechanism involved in the neuropathogenic sequelae of SARS-CoV-2 infection. The viral RNA or antigenic peptides also activate or interact with molecular signalling pathways mediated by pattern recognition receptors (e.g., toll-like receptors), nuclear factor kappa B, Janus kinase/signal transducer and activator of transcription, complement cascades, and cell suicide molecules. Potential molecular targets and therapeutics of SARS-CoV-2 induced neurologic damage are also discussed.

Host factors facilitating SARS-CoV-2 virus infection and replication in the lungs

SARS-CoV-2 is the virus causing the major pandemic facing the world today. Although, SARS-CoV-2 primarily causes lung infection, a variety of symptoms have proven a systemic impact on the body. SARS-CoV-2 has spread in the community quickly infecting humans from all age, ethnicities and gender. However, fatal outcomes have been linked to specific host factors and co-morbidities such as age, hypertension, immuno-deficiencies, chronic lung diseases or metabolic disorders. A major shift in the microbiome of patients suffering of the coronavirus disease 2019 (COVID-19) have also been observed and is linked to a worst outcome of the disease. As many co-morbidities are already known to be associated with a dysbiosis of the microbiome such as hypertension, diabetes and metabolic disorders. Host factors and microbiome changes are believed to be involved as a network in the acquisition of the infection and the development of the diseases. We will review in detail in this manuscript, the immune response toward SARS-CoV-2 infection as well as the host factors involved in the facilitation and worsening of the infection. We will also address the impact of COVID-19 on the host's microbiome and secondary infection which also worsen the disease.

Distinctive features of severe SARS-CoV-2 pneumonia

The coronavirus disease 2019 (COVID-19) pandemic is among the most important public health crises of our generation. Despite the promise of prevention offered by effective vaccines, patients with severe COVID-19 will continue to populate hospitals and intensive care units for the foreseeable future. The most common clinical presentation of severe COVID-19 is hypoxemia and respiratory failure, typical of the acute respiratory distress syndrome (ARDS). Whether the clinical features and pathobiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia differ from those of pneumonia secondary to other pathogens is unclear. This uncertainty has created variability in the application of historically proven therapies for ARDS to patients with COVID-19. We review the available literature and find many similarities between patients with ARDS from pneumonia attributable to SARS-CoV-2 versus other respiratory pathogens. A notable exception is the long duration of illness among patients with COVID-19, which could result from its unique pathobiology. Available data support the use of care pathways and therapies proven effective for patients with ARDS, while pointing to unique features that might be therapeutically targeted for patients with severe SARS-CoV-2 pneumonia.

SARS-CoV-2 therapeutics: how far do we stand from a remedy?

The SARS-CoV-2 has affected millions worldwide and has posed an immediate need for effective pharmacological interventions. Ever since the outbreak was declared, the medical fraternity across the world is facing a unique situation of offering assistance and simultaneously generating reliable data with high-quality evidence to extend the scope of finding a treatment. With no proven vaccine or other interventions available hitherto, there is a frenzied urgency of sharing preliminary data from laboratories and trials to shape a global response against the virus. Several clinical trials with investigational and approved repurposed therapeutics have shown promising results. This review aims to compile the information of the reported molecules approved for emergency use and those under clinical trials and still others with good results in the studies conducted so far. Being an RNA virus, SARS-CoV-2 is prone to mutation; thus, the possibility of gaining resistance to available drugs is high. Consequently, a cocktail therapy based on drug interaction with different stages of its replicative cycle is desirable to reduce the chances of evolving drug resistance. Since this virus encodes several proteins, including 16 nonstructural and 4 structural proteins, this review also offers an insight into potential drug targets within SARS-CoV-2.

Clinical characteristics and respiratory support of 310 COVID-19 patients, diagnosed at the emergency room: a single-center retrospective study

An ongoing outbreak of pneumonia associated with severe acute respiratory coronavirus 2 (SARS-CoV-2) occurred at the end of February 2020 in Lombardy, Italy. We analyzed data from a retrospective, single-center case series of 310 consecutive patients, with confirmed SARS-CoV-2 infection, admitted to the emergency room. We aimed to describe the clinical course, treatment and outcome of a cohort of patients with COVID-19 pneumonia, with special attention to oxygen delivery and ventilator support. Throughout the study period, 310 consecutive patients, with confirmed SARS-CoV-2 infection, attended the Emergency Room (ER), of these, 34 were discharged home directly from the ER. Of the remaining 276 patients, the overall mortality was 30.4%: 7 patients died in the ER and 77 during hospitalization. With respect to oxygen delivery: 22 patients did not need any oxygen support (8.0%), 151 patients were treated with oxygen only (54.7%), and 49 (17.8%) were intubated. 90 patients (32.6%) were treated with CPAP (Continuous Positive Airway Pressure) or NIV (Non Invasive Ventilation); in this group, 27 patients had a Do Not Intubate (DNI) order and were treated with CPAP/NIV as an upper threshold therapy, showing high mortality rate (88.9%). Among the 63 patients treated with CPAP/NIV without DNI, NIV failure occurred in 36 patients (57.1%), with mortality rate of 47.2%. Twenty-seven (27) patients were treated with CPAP/NIV without needing mechanical ventilation and 26 were discharged alive (96.3%). The study documents the poor prognosis of patients with severe respiratory failure, although a considerable minority of patients treated with CPAP/NIV had a positive outcome.

DPP9: Comprehensive In Silico Analyses of Loss of Function Gene Variants and Associated Gene Expression Signatures in Human Hepatocellular Carcinoma

Dipeptidyl peptidase (DPP) 9, DPP8, DPP4 and fibroblast activation protein (FAP) are the four enzymatically active members of the S9b protease family. Associations of DPP9 with human liver cancer, exonic single nucleotide polymorphisms (SNPs) in DPP9 and loss of function (LoF) variants have not been explored. Human genomic databases, including The Cancer Genome Atlas (TCGA), were interrogated to identify DPP9 LoF variants and associated cancers. Survival and gene signature analyses were performed on hepatocellular carcinoma (HCC) data. We found that DPP9 and DPP8 are intolerant to LoF variants. DPP9 exonic LoF variants were most often associated with uterine carcinoma and lung carcinoma. All four DPP4-like genes were overexpressed in liver tumors and their joint high expression was associated with poor survival in HCC. Increased DPP9 expression was associated with obesity in HCC patients. High expression of genes that positively correlated with overexpression of DPP4, DPP8, and DPP9 were associated with very poor survival in HCC. Enriched pathways analysis of these positively correlated genes featured Toll-like receptor and SUMOylation pathways. This comprehensive data mining suggests that DPP9 is important for survival and that the DPP4 protease family, particularly DPP9, is important in the pathogenesis of human HCC.

Cancer vs. SARS-CoV-2 induced inflammation, overlapping functions, and pharmacological targeting

Inflammation is an intrinsic defence mechanism triggered by the immune system against infection or injury. Chronic inflammation allows the host to recover or adapt through cellular and humoral responses, whereas acute inflammation leads to cytokine storms resulting in tissue damage. In this review, we present the overlapping outcomes of cancer inflammation with virus-induced inflammation. The study emphasises how anti-inflammatory drugs that work against cancer inflammation may work against the inflammation caused by the viral infection. It is established that the cytokine storm induced in response to SARS-CoV-2 infection contributes to disease-associated mortality. While cancer remains the second among the diseases associated with mortality worldwide, cancer patients' mortality rates are often observed upon extended periods after illness, usually ranging from months to years. However, the mortality rates associated with COVID-19 disease are robust. The cytokine storm induced by SARS-CoV-2 infection appeared to be responsible for the multi-organ failure and increased mortality rates. Since both cancer and COVID-19 disease share overlapping inflammatory mechanisms, repurposing some anticancer and anti-inflammatory drugs for COVID-19 may lower mortality rates. Here, we review some of these inflammatory mechanisms and propose some potential chemotherapeutic agents to intervene in them. We also discuss the repercussions of anti-inflammatory drugs such as glucocorticoids and hydroxychloroquine with zinc or antiviral drugs such as ivermectin and remdesivir against SARS-CoV-2 induced cytokine storm. In this review, we emphasise on various possibilities to reduce SARS-CoV-2 induced cytokine storm.

The first 12 months of COVID-19: a timeline of immunological insights

Since the initial reports of a cluster of pneumonia cases of unidentified origin in Wuhan, China, in December 2019, the novel coronavirus that causes this disease - severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) - has spread throughout the world, igniting the twenty-first century's deadliest pandemic. Over the past 12 months, a dizzying array of information has emerged from numerous laboratories, covering everything from the putative origin of SARS-CoV-2 to the development of numerous candidate vaccines. Many immunologists quickly pivoted from their existing research to focus on coronavirus disease 2019 (COVID-19) and, owing to this unprecedented convergence of efforts on one viral infection, a remarkable body of work has been produced and disseminated, through both preprint servers and peer-reviewed journals. Here, we take readers through the timeline of key discoveries during the first year of the pandemic, which showcases the extraordinary leaps in our understanding of the immune response to SARS-CoV-2 and highlights gaps in our knowledge as well as areas for future investigations.

Disease spectrum of gastric cancer susceptibility genes

Pathogenic variants in germline cancer susceptibility genes can increase the risk of a large number of diseases. Our study aims to assess the disease spectrum of gastric cancer susceptibility genes and to develop a comprehensive resource of gene-disease associations for clinicians. Twenty-seven potential germline gastric cancer susceptibility genes were identified from three review articles and from six commonly used genetic information resources. The diseases associated with each gene were evaluated via a semi-structured review of six genetic resources and an additional literature review using a natural language processing (NLP)-based procedure. Out of 27 candidate genes, 13 were identified as gastric cancer susceptibility genes (APC, ATM, BMPR1A, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, MUTYH-Biallelic, PALB2, SMAD4, and STK11). A total of 145 gene-disease associations (with 45 unique diseases) were found to be associated with these 13 genes. Other gastrointestinal cancers were prominent among identified associations, with 11 of 13 gastric cancer susceptibility genes also associated with colorectal cancer, eight genes associated with pancreatic cancer, and seven genes associated with small intestine cancer. Gastric cancer susceptibility genes are frequently associated with other diseases as well as gastric cancer, with potential implications for how carriers of these genes are screened and managed. Unfortunately, commonly used genetic resources provide heterogeneous information with regard to these genes and their associated diseases, highlighting the importance of developing guides for clinicians that integrate data across available resources and the medical literature.

A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions

Assessing pulmonary lesions using computed tomography (CT) images is of great significance to the severity diagnosis and treatment of coronavirus disease 2019 (COVID-19)-infected patients. Such assessment mainly depends on radiologists' subjective judgment, which is inefficient and presents difficulty for those with low levels of experience, especially in rural areas. This work focuses on developing a radiomics signature to quantitatively analyze whether COVID-19-infected pulmonary lesions are mild (Grade I) or moderate/severe (Grade II). We retrospectively analyzed 1160 COVID-19-infected pulmonary lesions from 16 hospitals. First, texture features were extracted from the pulmonary lesion regions of CT images. Then, feature preselection was performed and a radiomics signature was built using a stepwise logistic regression. The stepwise logistic regression also calculated the correlation between the radiomics signature and the grade of a pulmonary lesion. Finally, a logistic regression model was trained to classify the grades of pulmonary lesions. Given a significance level of α = 0.001, the stepwise logistic regression achieved an R (multiple correlation coefficient) of 0.70, which is much larger than Rα = 0.18 (the critical value of R). In the classification, the logistic regression model achieved an AUC of 0.87 on an independent test set. Overall, the radiomics signature is significantly correlated with the grade of a pulmonary lesion in COVID-19 infection. The classification model is interpretable and can assist radiologists in quickly and efficiently diagnosing pulmonary lesions. This work aims to develop a CT-based radiomics signature to quantitatively analyze whether COVID-19-infected pulmonary lesions are mild (Grade I) or moderate/severe (Grade II). The logistic regression model established based on this radiomics signature can assist radiologists to quickly and efficiently diagnose the grades of pulmonary lesions. The model calculates a radiomics score for a lesion and is interpretable and appropriate for clinical use.