Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning.

Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein-protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.

Discovery of putative inhibitors against main drivers of SARS-CoV-2 infection: Insight from quantum mechanical evaluation and molecular modeling.

SARS-CoV-2 triggered a worldwide medical crisis, affecting the world's social, emotional, physical, and economic equilibrium. However, treatment choices and targets for finding a solution to COVID-19's threat are becoming limited. A viable approach to combating the threat of COVID-19 is by unraveling newer pharmacological and therapeutic targets pertinent in the viral survival and adaptive mechanisms within the host biological milieu which in turn provides the opportunity to discover promising inhibitors against COVID-19. Therefore, using high-throughput virtual screening, manually curated compounds library from some medicinal plants were screened against four main drivers of SARS-CoV-2 (spike glycoprotein, PLpro, 3CLpro, and RdRp). In addition, molecular docking, Prime MM/GBSA (molecular mechanics/generalized Born surface area) analysis, molecular dynamics (MD) simulation, and drug-likeness screening were performed to identify potential phytodrugs candidates for COVID-19 treatment. In support of these approaches, we used a series of computational modeling approaches to develop therapeutic agents against COVID-19. Out of the screened compounds against the selected SARS-CoV-2 therapeutic targets, only compounds with no violations of Lipinski's rule of five and high binding affinity were considered as potential anti-COVID-19 drugs. However, lonchocarpol A, diplacol, and broussonol E (lead compounds) were recorded as the best compounds that satisfied this requirement, and they demonstrated their highest binding affinity against 3CLpro. Therefore, the 3CLpro target and the three lead compounds were selected for further analysis. Through protein-ligand mapping and interaction profiling, the three lead compounds formed essential interactions such as hydrogen bonds and hydrophobic interactions with amino acid residues at the binding pocket of 3CLpro. The key amino acid residues at the 3CLpro active site participating in the hydrophobic and polar inter/intra molecular interaction were TYR54, PRO52, CYS44, MET49, MET165, CYS145, HIS41, THR26, THR25, GLN189, and THR190. The compounds demonstrated stable protein-ligand complexes in the active site of the target (3CLpro) over a 100 ns simulation period with stable protein-ligand trajectories. Drug-likeness screening shows that the compounds are druggable molecules, and the toxicity descriptors established that the compounds demonstrated a good biosafety profile. Furthermore, the compounds were chemically reactive with promising molecular electron potential properties. Collectively, we propose that the discovered lead compounds may open the way for establishing phytodrugs to manage COVID-19 pandemics and new chemical libraries to prevent COVID-19 entry into the host based on the findings of this computational investigation.

Clinical Characteristics, Outcomes and Prognostic Factors for Critical Illness in Hospitalized COVID-19 Patients in Saudi Arabia: A Retrospective Cohort Study.

Background: A good understanding of the possible risk factors for coronavirus disease 19 (COVID-19) severity could help clinicians in identifying patients who need prioritized treatment to prevent disease progression and adverse outcome. In the present study, we aimed to correlate clinical and laboratory characteristics of hospitalized COVID-19 patients to disease outcome in Saudi Arabia. Materials and Methods: The present study included 199 COVID-19 patients admitted to King Fahd Specialist Hospital, Buraydah, Qassim, Saudi Arabia, from April to December 2020. Patients were followed-up until discharge either for recovery or death. Demographic data, clinical data and laboratory results were retrieved from electronic patient records. Results: Critical COVID-19 cases showed higher mean of age and higher prevalence of co-morbid conditions. Fifty-five patients died during the observation period. Risk factors for in hospital death for COVID 19 patients were leukocytosis (OR 1.89, 95% CI 1.008-3.548, p = 0.081), lymphocytopenia (OR 2.152, 95% CI 1.079-4.295, p = 0.020), neutrophilia (OR 1.839, 95% CI 0.951-3.55, p = 0.047), thrombocytopenia (OR 2.152, 95% CI 0.852-5.430, p = 0.085), liver injury (OR 2.689, 95% CI 1.373-4.944, p = 0.003), acute kidney injury (OR 1.248, 95% CI 0.631-2.467 p = 0.319), pancreatic injury (OR 1.973, 95% CI 0.939-4.144, p = 0.056) and high D dimer (OR 2.635, 95% CI 0.747-9.287, p = 0.091). Conclusion: Clinical and laboratory data of COVID-19 patients may help understanding the pathogenesis of the disease and subsequently improve of the outcome of patients by determination of the associated risk factors and recognition of high risk group who are more liable for complications and in hospital death. The present study put an eye on some parameters (laboratory and clinical) that should be alarming signs that the patient is at high risk bad prognosis.