Interactive Mechanism of Potential Inhibitors with Glycosyl for SARS-CoV-2 by Molecular Dynamics Simulation

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a type of Ribonucleic Acid (RNA) coronavirus and it has infected and killed many people around the world. It is reported that the receptor binding domain of the spike protein (S_RBD) of the SARS-CoV-2 virus is responsible for attachment to human angiotensin converting enzyme II (ACE2). Many researchers are attempting to search potential inhibitors for fighting SARS-CoV-2 infection using theoretical or experimental methods. In terms of experimental and theoretical research, Cefuroxime, Erythromycin, Lincomycin and Ofloxacin are the potential inhibitors of SARS-CoV-2. However, the interactive mechanism of the protein SARS-CoV-2 and the inhibitors are still elusive. Here, we investigated the interactions between S_RBD and the inhibitors using molecular dynamics (MD) simulations. Interestingly, we found that there are two binding sites of S_RBD for the four small molecules. In addition, our analysis also illustrated that hydrophobic and π-π stacking interactions play crucial roles in the interactions between S_RBD and the small molecules. In our work, we also found that small molecules with glycosyl group have more effect on the conformation of S_RBD than other inhibitors, and they are also potential inhibitors for the genetic variants of SARS-CoV-2. This study provides in silico-derived mechanistic insights into the interactions of S_RBD and inhibitors, which may provide new clues for fighting SARS-CoV-2 infection.

Exploring The Molecular Mechanisms of Classical Hodgkin Lymphoma Through Bioinformatics Analysis (preprint)

Background Classic Hodgkin lymphoma (CHL) is the most common HL in the modern society. Although the treatment of cHL has made great progress, its molecular mechanisms have yet to be deciphered.Objectives The purpose of this study is to find out the crucial potential genes and pathways associated with cHL.Methods We downloaded the cHL microarray dataset (GSE12453) from Gene Expression Omnibus (GEO) database and to identify the differentially expressed genes (DEGs) between cHL samples and normal samples through the limma package in R. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out. Finally, we constructed the protein-protein interaction network to screen out the hub genes using Search Tool for the Retrieval of Interacting Genes (STRING) database.Results We screened out 788 DEGs in the cHL dataset, such as BATF3, IER3, RAB13 and FCRL2. GO functional enrichment analysis indicated that the DEGs were related with regulation of lymphocyte activation, secretory granule lumen and chemokine activity. KEGG pathway analysis showed that the genes enriched in Prion disease, Complement and coagulation cascades and Parkinson disease Coronavirus disease-COVID-19 pathway. Protein-protein interaction network construction identified 10 hub genes (IL6, ITGAM, CD86, FN1, MMP9, CXCL10, CCL5, CD19, IFNG, SELL, UBB) in the network.Conclusions In the present investigation, we identified several pathways and hub genes related to the occurrence and development of cHL, which may provide an important basis for further research and novel therapeutic targets and prognostic indicators for cHL.

Early Risk Factors for Extrapulmonary Organ Injury in Adult COVID-19 Patients (preprint)

Objective COVID-19 is becoming a global pandemic and often develops extrapulmonary organ injury. However, the risk factors for extrapulmonary organ injury are still unclear. We aim to explore the risk factors for extrapulmonary organ injury for COVID-19 and the association between extrapulmonary organ injury and the prognosis of COVID-19 patients. Methods This is a single-center, retrospective, observational study and total 349 confirmed COVID-19 patients admitted to Tongji Hospital from January 25 to February 25, 2020 were enrolled. We collected demographic, clinical, laboratory and treatment data from electronic medical records. Potential risk factors for extrapulmonary organ injury of COVID-19 patients were analyzed by a multivariable binary logistic model, and multivariable COX proportional hazard regression model was used for survival analysis in the patients with extrapulmonary organ injury. Results Average age of the included patients was 61.73±14.64 years. In the final logistic model, variables including aged 60 or older (OR 1.826, 95% CI 1.060-3.142), ARDS (OR 2.748, 95% CI 1.051-7.185), lymphocytes count lower than 1.1 ×109/L (OR 0.478, 95% CI 0.240-0.949), level of IL-6 greater than 7 pg/ml (OR 1.664, 95% CI 1.005-2.751) and D-Dimer greater than 0.5 μg/ml (OR 2.190, 95% CI 1.176-4.084) were significantly associated with the extrapulmonary organ injury. Kaplan-Meier curve and log-rank test showed that the probabilities of survival for patients with extrapulmonary organ injury were significantly lower than those without extrapulmonary organ injury.between Multivariate COX proportional hazards model showed that only myocardial injury (P=0.000, HR: 5.068, 95% CI: 2.728-9.417) and circulatory system injury (P=0.000, HR: 4.076, 95% CI: 2.216-7.498) were the independent factors associated with COVID-19 patients’ poor prognosis. Conclusion Older age, lymphocytopenia, high level of D-Dimer and IL-6 and the severity of lung injury were the high-risk factors of extrapulmonary organ injury in COVID-19 patients. Myocardial and circulatory system injury were the most important risk factors related to poor outcomes of COVID-19 patients. It may help clinicians to identify extrapulmonary organ injury early and provide relevant management strategy.

The Trans-omics Landscape of COVID-19 (preprint)

The outbreak of coronavirus disease 2019 (COVID-19) has been causing a global health emergency. Although previous studies investigated COVID-19 at different omics levels, the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here, we presented a trans-omics landscape for COVID-19 based on integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles from blood samples of 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Notably, we found neutrophils heterogeneity existed between asymptomatic and critically ill patients. Expression discordance of inflammatory cytokines at mRNA and protein levels in asymptomatic patients could possibly be explained by post-transcriptional regulation by RNA binding proteins (RBPs) and microRNAs. Neutrophils over-activation, induced arginine depletion, and tryptophan metabolites accumulation contributed to T/NK cell dysfunction in critical patients. Anti-virus interferons were gradually suppressed along with disease severity. Overall, our study systematically revealed multi-omics characteristics of COVID-19, and the data we generated could hopefully help illuminate COVID-19 pathogenesis and provide valuable clues about potential therapeutic strategies for COVID-19.

The Trans-omics Landscape of COVID-19 (preprint)

System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment.

Adjuvant corticosteroid therapy for critically ill patients with COVID-19 (preprint)

Addition of adjuvant corticosteroid therapy to standard antiviral treatment of patients with coronavirus disease (COVID-19) is common in clinical practice. However, evidence is scarce regarding the efficacy of adjuvant corticosteroids in patients who are critically ill. We retrospectively evaluated the effects of adjuvant corticosteroid treatment on the outcome of 244 critically ill patients with COVID-19, using a risk stratification model that adjusts for potential differences between the steroid group (n=151) and the non-steroid group (n=93). We observed that adjuvant corticosteroid therapy was independent from 28-day mortality, either in multivariate logistic regression of the entire cohort after adjustment for major mortality-associated variables (age, SpO2/FiO2, and lymphocyte count) and individual propensity score (adjusted OR: 1.05; 95% CI: -1.92-2.01), or in propensity score-matched (1:1 without replacement) case-control analysis (62 patients in 31 pairs; log-rank test P=0.17). Additionally, subgroup analyses of 147 (60%) patients who had dyspnea and 87 (36%) patients who had ARDS revealed corticosteroid treatment was not associated with clinical outcome (both, P>0.3). However, increased corticosteroids dosage was significantly associated with elevated mortality risk after adjustment for administration duration (P=0.003); every ten-milligram increase in hydrocortisone-equivalent dosage was associated with additional 4% mortality risk (adjusted HR: 1.04, 95% CI: 1.01-1.07). Our findings indicated that limited effect of corticosteroid therapy could pose to overall survival and prudent dose within effective limits may be recommended for critically ill patients under certain circumstances.

Classification of COVID-19 in intensive care patients: towards rational and effective clinical triage (preprint)

The number of pertinent researches of COVID-19 has increased rapidly but they mainly focused on the description of general information of patients with confirmed infection. We aimed to bridge the gap between disease classification and clinical outcome in intensive care patients, data of which are scarce and such classification could help in individual evaluation and provide effective triage for treatment and management. Specifically, we collected and filtered out 151 intensive care patients with complete medical records from Tongji hospital in Wuhan, China. We constructed a fully Bayesian latent variable model for integrative clustering of six data categories, including demographic information, symptoms, original comorbidities, vital signs, blood routine tests and inflammatory marker measurements. We identified four prognostic types of COVID-19 in intensive care patients, presenting a stepwise distribution in age, respiratory condition and inflammatory markers, suggesting the prognostic efficacy of these indicators. This report, to our knowledge, is the first attempt of dealing with classification of COVID-19 in intensive care patients. We acknowledge the limitation of ignoring the effect of treatment, but we believe such classification is enlightening for better triage, allowing for a more rational allocation of scarce medical resources in a resource constrained environment.

Validation of reported risk factors for disease classification and prognosis in COVID-19: a descriptive and retrospective study (preprint)

Risk indicators viral load (ORF1ab Ct), lymphocyte percentage (LYM%), C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT) and lactic acid (LA) in COVID-19 patients have been proposed in recent studies. However, the predictive effects of those indicators on disease classification and prognosis remains largely unknown. We dynamically measured those reported indicators in 132 cases of COVID-19 patients including the moderate-cured (moderated and cured), severe-cured (severe and cured) and critically ill (died). Our data showed that CRP, PCT, IL-6, LYM%, lactic acid and viral load could predict prognosis and guide classification of COVID-19 patients in different degrees. CRP, IL-6 and LYM% were more effective than other three factors in predicting prognosis. For disease classification, CRP and LYM% were sensitive in identifying the types between critically ill and severe (or moderate). Notably, among the investigated factors, LYM% was the only one that could distinguish between the severe and moderate types. Collectively, we concluded that LYM% was the most sensitive and reliable predictor for disease typing and prognosis. During the COVID-19 pandemic, the precise classification and prognosis prediction are critical for saving the insufficient medical resources, stratified treatment and improving the survival rate of critically ill patients. We recommend that LYM% be used independently or in combination with other indicators in the management of COVID-19.