Underlying Mechanisms and Candidate Drugs for COVID-19 Based on the Connectivity Map Database

Exploring the Therapeutic Potential of Petiveria alliacea L. Phytochemicals: A Computational Study on Inhibiting SARS-CoV-2's Main Protease (Mpro)

The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of -8.9, -8.7 and -8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials.

Can Nitazoxanide and/or other anti-viral medications be a solution to long COVID? Case report with a brief literature review

Key Clinical Message

Findings here imply lingering of virus, SARS-CoV-2, in the body for months. Thus, Nitazoxanide and/or other anti-viral medications might be potential options to combat long COVID. This could transform treatment for long COVID patients globally.

Abstract

Long COVID or post-acute sequelae of COVID-19 (PASC) continues to affect many people even after a relatively mild acute illness. Underlying causes of PASC are poorly understood. There is no particular treatment or management program developed yet. Thus, the possibility of well-known, safe anti-viral medications use against PASC is proposed here.

A self-training subspace clustering algorithm based on adaptive confidence for gene expression data

Gene clustering is one of the important techniques to identify co-expressed gene groups from gene expression data, which provides a powerful tool for investigating functional relationships of genes in biological process. Self-training is a kind of important semi-supervised learning method and has exhibited good performance on gene clustering problem. However, the self-training process inevitably suffers from mislabeling, the accumulation of which will lead to the degradation of semi-supervised learning performance of gene expression data. To solve the problem, this paper proposes a self-training subspace clustering algorithm based on adaptive confidence for gene expression data (SSCAC), which combines the low-rank representation of gene expression data and adaptive adjustment of label confidence to better guide the partition of unlabeled data. The superiority of the proposed SSCAC algorithm is mainly reflected in the following aspects. 1) In order to improve the discriminative property of gene expression data, the low-rank representation with distance penalty is used to mine the potential subspace structure of data. 2) Considering the problem of mislabeling in self-training, a semi-supervised clustering objective function with label confidence is proposed, and a self-training subspace clustering framework is constructed on this basis. 3) In order to mitigate the negative impact of mislabeled data, an adaptive adjustment strategy based on gravitational search algorithm is proposed for label confidence. Compared with a variety of state-of-the-art unsupervised and semi-supervised learning algorithms, the SSCAC algorithm has demonstrated its superiority through extensive experiments on two benchmark gene expression datasets.

Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.

Diterpenoid Vinigrol specifically activates ATF4/DDIT3-mediated PERK arm of unfolded protein response to drive non-apoptotic death of breast cancer cells

Vinigrol is a natural diterpenoid with unprecedented chemical structure, driving great efforts into its total synthesis in the past decades. Despite anti-hypertension and anti-clot ever reported, comprehensive investigations on bioactions and molecular mechanisms of Vinigrol are entirely missing. Here we firstly carried out a complete functional prediction of Vinigrol using a transcriptome-based strategy coupled with multiple bioinformatic analyses and identified "anti-cancer" as the most prominent biofunction ahead of anti-hypertension and anti-depression/psychosis. Broad cytotoxicity was subsequently confirmed on multiple cancer types. Further mechanistic investigation on several breast cancer cells revealed that its anti-cancer effect was mainly through activating PERK/eIF2α arm of unfolded protein response (UPR) and subsequent non-apoptotic cell death independent of caspase activities. The other two branches of UPR, IRE1α and ATF6, were functionally irrelevant to Vinigrol-induced cell death. Using CRISPR/Cas9-based gene activation, repression, and knockout systems, we identified the essential contribution of ATF4 and DDIT3, not ATF6, to the death process. This study unraveled a broad anti-cancer function of Vinigrol and its underlying targets and regulatory mechanisms. It paved the way for further inspection on the structure-efficacy relationship of the whole compound family, making them a novel cluster of PERK-specific stress activators for experimental and clinical uses.

Machine learning and network medicine approaches for drug repositioning for COVID-19

We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.

Relationship between COVID-19 and movement disorders: A narrative review

BACKGROUND AND PURPOSE

The scientific literature on COVID-19 is increasingly growing.

METHODS

In this paper, we review the literature on movement disorders in the context of the COVID-19 pandemic.

RESULTS

First, there are a variety of transient movement disorders that may manifest in the acute phase of COVID-19, most often myoclonus, with more than 50 patients described in the literature. New onset parkinsonism, chorea, and tic-like behaviours have also been reported. Movement disorders as a side effect after COVID-19 vaccination are rare, occurring with a frequency of 0.00002-0.0002 depending on the product used, mostly manifesting with tremor. Current evidence for potential long-term manifestations, for example, long COVID parkinsonism, is separately discussed. Second, the pandemic has also had an impact on patients with pre-existing movement disorder syndromes, with negative effects on clinical status and overall well-being, and reduced access to medication and health care. In many parts, the pandemic has led to reorganization of the medical system, including the development of new digital solutions. The movement disorder-related evidence for this is reviewed and discussed.

CONCLUSIONS

The pandemic and the associated preventive measures have had a negative impact on the clinical status, access to health care, and overall well-being of patients with pre-existing movement disorders.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

Potential for the Repurposing of Adamantane Antivirals for COVID-19

Several adamantanes have established actions against coronaviruses. Amantadine, rimantadine, bananins and the structurally related memantine are effective against human respiratory coronavirus HCoV-OC43, bovine coronavirus and severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) and a spiroadamantane amine is effective against the coronavirus strain 229E. Molecular docking studies suggest that amantadine may block the viral E protein channel, leading to impaired viral propagation. Additionally, amantadine analogues may inhibit entry of the virus into the host cell by increasing the pH of the endosomes and thus inhibiting the action of host cell proteases such as Cathepsin L. High-throughput drug screen gene expression analysis identified compounds able to down-regulate Cathepsin L expression where the fifth most potent agent of 466 candidates was amantadine. Amantadine inhibits severe acute respiratory syndrome coronavirus 2 replication in vitro but does not inhibit the binding of the spike protein to ACE2. Adamantanes also may act against coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via antagonism of glutamate (NMDA) and the α-7 subtype of the nicotinic acetylcholine receptor located on bronchial and alveolar epithelial cells. As an NMDA receptor antagonist, memantine has the potential to inhibit entry of SARS-CoV-2 into these cell populations. Amantadine and memantine are widely employed for the treatment of neurodegenerative diseases and a pathophysiologic link between the antiviral and anti-Parkinson actions of amantadine has been entertained. Case reports involving 23 patients with reverse transcription polymerase chain reaction-confirmed coronavirus disease 2019 (COVID-19) and a range of co-morbidities including type 2 diabetes mellitus, Parkinson's disease, multiple sclerosis and severe cognitive impairment reveal significant potential benefits of amantadine and memantine for the prevention and/or treatment of coronavirus disease 2019 and its neurological complications.

Co-crystallization and structure determination: An effective direction for anti-SARS-CoV-2 drug discovery

Safer and more-effective drugs are urgently needed to counter infections with the highly pathogenic SARS-CoV-2, cause of the COVID-19 pandemic. Identification of efficient inhibitors to treat and prevent SARS-CoV-2 infection is a predominant focus. Encouragingly, using X-ray crystal structures of therapeutically relevant drug targets (PLpro, Mpro, RdRp, and S glycoprotein) offers a valuable direction for anti-SARS-CoV-2 drug discovery and lead optimization through direct visualization of interactions. Computational analyses based primarily on MMPBSA calculations have also been proposed for assessing the binding stability of biomolecular structures involving the ligand and receptor. In this study, we focused on state-of-the-art X-ray co-crystal structures of the abovementioned targets complexed with newly identified small-molecule inhibitors (natural products, FDA-approved drugs, candidate drugs, and their analogues) with the assistance of computational analyses to support the precision design and screening of anti-SARS-CoV-2 drugs.

All-cause mortality among patients treated with repurposed antivirals and antibiotics for COVID-19 in Mexico City: A real-world observational study

The aim of our study was to evaluate all-cause mortality risk in patients with laboratory-confirmed COVID-19 in Mexico City treated with repurposed antivirals and antibiotics. This real-world retrospective cohort study contemplated 395,343 patients evaluated for suspected COVID-19 between February 24 and September 14, 2020 in 688 primary-to-tertiary medical units in Mexico City. Patients were included with a positive RT-PCR for SARS-CoV-2; those receiving unspecified antivirals, excluded; and groups of antivirals prescribed in < 30 patients, eliminated. Survival and mortality risks were determined for patients receiving antivirals, antibiotics, both, or none. We assessed the effect of early (<2 days) versus late (>2 days) use of antivirals on mortality in a sub-cohort of patients. Multivariable adjustment, propensity score matching, generalized estimating equations, and calculation of E-values were performed to limit confounding. 136,855 patients were analyzed; mean age 44.2 (SD:16.8) years; 51.3 % were men. 16.6 % received antivirals (3 %), antibiotics (10 %), or both (3.6 %). Antivirals studied were Oseltamivir (n=8414), Amantadine (n=319), Lopinavir-Ritonavir (n=100), Rimantadine (n=61), Zanamivir (n=39), and Acyclovir (n=36). Survival with antivirals (73.7 %, p<0.0001) and antibiotics (85.8 %, p<0.0001) was lower than no antiviral/antibiotic (93.6 %). After multivariable adjustment, increased risk of death occurred with antivirals (HR=1.72, 95 % CI: 1.61-1.84) in ambulatory (HR=4.7, 95 % CI: 3.94-5.62) and non-critical (HR=2.03, 95 % CI: 1.86-2.21) patients. Oseltamivir increased mortality risk in the general population (HR=1.72, 95 % CI: 1.61-1.84), ambulatory (HR=4.79, 95 % CI: 4.01-5.75), non-critical (HR=2.05, 95 % CI: 1.88-2.23), and pregnancy (HR=8.35, 95 % CI: 1.77-39.30); as well as hospitalized (HR=1.13, 95 % CI: 1.01-1.26) and critical patients (HR=1.22, 95 % CI: 1.05-1.43) after propensity score-matching. Early versus late oseltamivir did not modify the risk. Antibiotics were a risk factor in general population (HR=1.13, 95 % CI: 1.08-1.19) and pediatrics (HR=4.22, 95 % CI: 2.01-8.86), but a protective factor in hospitalized (HR=0.81, 95 % CI: 0.77-0.86) and critical patients (HR=0.67, 95 % CI: 0.63-0.72). No significant benefit for repurposed antivirals was observed; oseltamivir was associated with increased mortality. Antibiotics increased mortality risk in the general population but may increase survival in hospitalized and critical patients.

Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach

A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.