The COVID-19 Drug and Gene Set Library

Bioinformatics and systems biology analysis revealed PMID26394986-Compound-10 as potential repurposable drug against covid-19

The global health pandemic known as COVID-19, which stems from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a significant concern worldwide. Several treatment methods exist for COVID-19; however, there is an urgent demand for previously established drugs and vaccines to effectively combat the disease. Since, discovering new drugs poses a significant challenge, making the repurposing of existing drugs can potentially reduce time and costs compared to developing entirely new drugs from scratch. The objective of this study is to identify hub genes and associated repurposed drugs targeting them. We analyzed differentially expressed genes (DEGs) by analyzing RNA-seq transcriptomic datasets and integrated with genes associated with COVID-19 present in different databases. We detected 173 Covid-19 associated genes for the construction of a protein-protein interaction (PPI) network which resulted in the identification of the top 10 hub genes/proteins (STAT1, IRF7, MX1, IRF9, ISG15, OAS3, OAS2, OAS1, IRF3, and IRF1). Hub genes were subjected to GO functional and KEGG pathway enrichment analyses, which indicated some key roles and signaling pathways that were strongly related to SARS-CoV-2 infections. We conducted drug repurposing analysis using CMap, TTD, and DrugBank databases with these 10 hub genes, leading to the identification of Piceatannol, CKD-712, and PMID26394986-Compound-10 as top-ranked candidate drugs. Finally, drug-gene interactions analysis through molecular docking and validated via molecular dynamic simulation for 80 ns suggests PMID26394986-Compound-10 as the only potential drug. Our research demonstrates how in silico analysis might produce repurposing candidates to help respond faster to new disease outbreaks.Communicated by Ramaswamy H. Sarma.

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.

Antiviral responses in a Jamaican fruit bat intestinal organoid model of SARS-CoV-2 infection

Bats are natural reservoirs for several zoonotic viruses, potentially due to an enhanced capacity to control viral infection. However, the mechanisms of antiviral responses in bats are poorly defined. Here we established a Jamaican fruit bat (JFB, Artibeus jamaicensis) intestinal organoid model of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Upon infection with SARS-CoV-2, increased viral RNA and subgenomic RNA was detected, but no infectious virus was released, indicating that JFB organoids support only limited viral replication but not viral reproduction. SARS-CoV-2 replication was associated with significantly increased gene expression of type I interferons and inflammatory cytokines. Interestingly, SARS-CoV-2 also caused enhanced formation and growth of JFB organoids. Proteomics revealed an increase in inflammatory signaling, cell turnover, cell repair, and SARS-CoV-2 infection pathways. Collectively, our findings suggest that primary JFB intestinal epithelial cells mount successful antiviral interferon responses and that SARS-CoV-2 infection in JFB cells induces protective regenerative pathways.

DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing

BACKGROUND

Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.

RESULTS

This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%.

CONCLUSION

We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

Sleep and circadian rhythm disruption alters the lung transcriptome to predispose to viral infection

Sleep and circadian rhythm disruption (SCRD), as encountered during shift work, increases the risk of respiratory viral infection including SARS-CoV-2. However, the mechanism(s) underpinning higher rates of respiratory viral infection following SCRD remain poorly characterized. To address this, we investigated the effects of acute sleep deprivation on the mouse lung transcriptome. Here we show that sleep deprivation profoundly alters the transcriptional landscape of the lung, causing the suppression of both innate and adaptive immune systems, disrupting the circadian clock, and activating genes implicated in SARS-CoV-2 replication, thereby generating a lung environment that could promote viral infection and associated disease pathogenesis. Our study provides a mechanistic explanation of how SCRD increases the risk of respiratory viral infections including SARS-CoV-2 and highlights possible therapeutic avenues for the prevention and treatment of respiratory viral infection.

Integrated Data Analysis Uncovers New COVID-19 Related Genes and Potential Drug Re-Purposing Candidates

The COVID-19 pandemic is an acute and rapidly evolving global health crisis. To better understand this disease's molecular basis and design therapeutic strategies, we built upon the recently proposed concept of an integrated cell, iCell, fusing three omics, tissue-specific human molecular interaction networks. We applied this methodology to construct infected and control iCells using gene expression data from patient samples and three cell lines. We found large differences between patient-based and cell line-based iCells (both infected and control), suggesting that cell lines are ill-suited to studying this disease. We compared patient-based infected and control iCells and uncovered genes whose functioning (wiring patterns in iCells) is altered by the disease. We validated in the literature that 18 out of the top 20 of the most rewired genes are indeed COVID-19-related. Since only three of these genes are targets of approved drugs, we applied another data fusion step to predict drugs for re-purposing. We confirmed with molecular docking that the predicted drugs can bind to their predicted targets. Our most interesting prediction is artenimol, an antimalarial agent targeting ZFP62, one of our newly identified COVID-19-related genes. This drug is a derivative of artemisinin drugs that are already under clinical investigation for their potential role in the treatment of COVID-19. Our results demonstrate further applicability of the iCell framework for integrative comparative studies of human diseases.

Antiviral response mechanisms in a Jamaican Fruit Bat intestinal organoid model of SARS-CoV-2 infection

Bats are natural reservoirs for several zoonotic viruses, potentially due to an enhanced capacity to control viral infection. However, the mechanisms of antiviral responses in bats are poorly defined. Here we established a Jamaican fruit bat (JFB) intestinal organoid model of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. JFB organoids were susceptible to SARS-CoV-2 infection, with increased viral RNA and subgenomic RNA detected in cell lysates and supernatants. Gene expression of type I interferons and inflammatory cytokines was induced in response to SARS-CoV-2 but not in response to TLR agonists. Interestingly, SARS-CoV-2 did not lead to cytopathic effects in JFB organoids but caused enhanced organoid growth. Proteomic analyses revealed an increase in inflammatory signaling, cell turnover, cell repair, and SARS-CoV-2 infection pathways. Collectively, our findings suggest that primary JFB intestinal epithelial cells can mount a successful antiviral interferon response and that SARS-CoV-2 infection in JFB cells induces protective regenerative pathways.

COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data

Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks.

A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility

Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes that reduce COVID-19 host susceptibility is a critical next step. Using a translational genomics approach that integrates COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX), and perturbagen signatures, we identified IL10RB as the top candidate gene target for COVID-19 host susceptibility. In a series of validation steps, we show that predicted GReX upregulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes and that in vitro IL10RB overexpression is associated with increased viral load and activation of disease-relevant molecular pathways.

Human milk inhibits some enveloped virus infections, including SARS-CoV-2, in an intestinal model

Human milk is important for antimicrobial defense in infants and has well demonstrated antiviral activity. We evaluated the protective ability of human milk against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a human fetal intestinal cell culture model. We found that, in this model, human milk blocks SARS-CoV-2 replication, irrespective of the presence of SARS-CoV-2 spike-specific antibodies. Complete inhibition of both enveloped Middle East respiratory syndrome coronavirus and human respiratory syncytial virus infections was also observed, whereas no inhibition of non-enveloped enterovirus A71 infection was seen. Transcriptome analysis after 24 h of the intestinal monolayers treated with human milk showed large transcriptomic changes from human milk treatment, and subsequent analysis suggested that <i>ATP1A1</i> down-regulation by milk might be of importance. Inhibition of ATP1A1 blocked SARS-CoV-2 infection in our intestinal model, whereas no effect on EV-A71 infection was seen. Our data indicate that human milk has potent antiviral activity against particular (enveloped) viruses by potentially blocking the ATP1A1-mediated endocytic process.

Cytopathic SARS-CoV-2 screening on VERO-E6 cells in a large-scale repurposing effort

Worldwide, there are intensive efforts to identify repurposed drugs as potential therapies against SARS-CoV-2 infection and the associated COVID-19 disease. To date, the anti-inflammatory drug dexamethasone and (to a lesser extent) the RNA-polymerase inhibitor remdesivir have been shown to be effective in reducing mortality and patient time to recovery, respectively, in patients. Here, we report the results of a phenotypic screening campaign within an EU-funded project (H2020-EXSCALATE4COV) aimed at extending the repertoire of anti-COVID therapeutics through repurposing of available compounds and highlighting compounds with new mechanisms of action against viral infection. We screened 8702 molecules from different repurposing libraries, to reveal 110 compounds with an anti-cytopathic IC50 < 20 µM. From this group, 18 with a safety index greater than 2 are also marketed drugs, making them suitable for further study as potential therapies against COVID-19. Our result supports the idea that a systematic approach to repurposing is a valid strategy to accelerate the necessary drug discovery process.

A Cell-Free Assay for Rapid Screening of Inhibitors of hACE2-Receptor-SARS-CoV-2-Spike Binding

We present a cell-free assay for rapid screening of candidate inhibitors of protein binding, focusing on inhibition of the interaction between the SARS-CoV-2 Spike receptor binding domain (RBD) and human angiotensin-converting enzyme 2 (hACE2). The assay has two components: fluorescent polystyrene particles covalently coated with RBD, termed virion-particles (v-particles), and fluorescently labeled hACE2 (hACE2F) that binds the v-particles. When incubated with an inhibitor, v-particle-hACE2F binding is diminished, resulting in a reduction in the fluorescent signal of bound hACE2F relative to the noninhibitor control, which can be measured via flow cytometry or fluorescence microscopy. We determine the amount of RBD needed for v-particle preparation, v-particle incubation time with hACE2F, hACE2F detection limit, and specificity of v-particle binding to hACE2F. We measure the dose response of the v-particles to known inhibitors. Finally, utilizing an RNA-binding protein tdPP7 incorporated into hACE2F, we demonstrate that RNA-hACE2F granules trap v-particles effectively, providing a basis for potential RNA-hACE2F therapeutics.

In silico analysis of SARS-CoV-2 proteins as targets for clinically available drugs

The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires treatments with rapid clinical translatability. Here we develop a multi-target and multi-ligand virtual screening method to identify FDA-approved drugs with potential activity against SARS-CoV-2 at traditional and understudied viral targets. 1,268 FDA-approved small molecule drugs were docked to 47 putative binding sites across 23 SARS-CoV-2 proteins. We compared drugs between binding sites and filtered out compounds that had no reported activity in an in vitro screen against SARS-CoV-2 infection of human liver (Huh-7) cells. This identified 17 "high-confidence", and 97 "medium-confidence" drug-site pairs. The "high-confidence" group was subjected to molecular dynamics simulations to yield six compounds with stable binding poses at their optimal target proteins. Three drugs-amprenavir, levomefolic acid, and calcipotriol-were predicted to bind to 3 different sites on the spike protein, domperidone to the Mac1 domain of the non-structural protein (Nsp) 3, avanafil to Nsp15, and nintedanib to the nucleocapsid protein involved in packaging the viral RNA. Our "two-way" virtual docking screen also provides a framework to prioritize drugs for testing in future emergencies requiring rapidly available clinical drugs and/or treating diseases where a moderate number of targets are known.

Gene and drug landing page aggregator

Motivation

Many biological and biomedical researchers commonly search for information about genes and drugs to gather knowledge from these resources. For the most part, such information is served as landing pages in disparate data repositories and web portals.

Results

The Gene and Drug Landing Page Aggregator (GDLPA) provides users with access to 50 gene-centric and 19 drug-centric repositories, enabling them to retrieve landing pages corresponding to their gene and drug queries. Bringing these resources together into one dashboard that directs users to the landing pages across many resources can help centralize gene- and drug-centric knowledge, as well as raise awareness of available resources that may be missed when using standard search engines. To demonstrate the utility of GDLPA, case studies for the gene klotho and the drug remdesivir were developed. The first case study highlights the potential role of klotho as a drug target for aging and kidney disease, while the second study gathers knowledge regarding approval, usage, and safety for remdesivir, the first approved coronavirus disease 2019 therapeutic. Finally, based on our experience, we provide guidelines for developing effective landing pages for genes and drugs.

Availability and implementation

GDLPA is open source and is available from: https://cfde-gene-pages.cloud/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

Day-night and seasonal variation of human gene expression across tissues

Circadian and circannual cycles trigger physiological changes whose reflection on human transcriptomes remains largely uncharted. We used the time and season of death of 932 individuals from GTEx to jointly investigate transcriptomic changes associated with those cycles across multiple tissues. Overall, most variation across tissues during day-night and among seasons was unique to each cycle. Although all tissues remodeled their transcriptomes, brain and gonadal tissues exhibited the highest seasonality, whereas those in the thoracic cavity showed stronger day-night regulation. Core clock genes displayed marked day-night differences across multiple tissues, which were largely conserved in baboon and mouse, but adapted to their nocturnal or diurnal habits. Seasonal variation of expression affected multiple pathways and it was enriched among genes associated with the immune response, consistent with the seasonality of viral infections. Furthermore, they unveiled cytoarchitectural changes in brain regions. Altogether, our results provide the first combined atlas of how transcriptomes from human tissues adapt to major cycling environmental conditions.

Identification and Development of Therapeutics for COVID-19

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.

Advances in the computational landscape for repurposed drugs against COVID-19

The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.

Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology

Genetic variants showing associations with specific biological traits and diseases detected by genome-wide association studies (GWAS) commonly map to non-coding DNA regulatory regions. Many of these regions are located considerable distances away from the genes they regulate and come into their proximity through 3D chromosomal interactions. We previously developed COGS, a statistical pipeline for linking GWAS variants with their putative target genes based on 3D chromosomal interaction data arising from high-resolution assays such as Promoter Capture Hi-C (PCHi-C). Here, we applied COGS to COVID-19 Host Genetic Consortium (HGI) GWAS meta-analysis data on COVID-19 susceptibility and severity using our previously generated PCHi-C results in 17 human primary cell types and SARS-CoV-2-infected lung carcinoma cells. We prioritise 251 genes putatively associated with these traits, including 16 out of 47 genes highlighted by the GWAS meta-analysis authors. The prioritised genes are expressed in a broad array of tissues, including, but not limited to, blood and brain cells, and are enriched for genes involved in the inflammatory response to viral infection. Our prioritised genes and pathways, in conjunction with results from other prioritisation approaches and targeted validation experiments, will aid in the understanding of COVID-19 pathology, paving the way for novel treatments.

Dendronization: A practical strategy to improve the performance of molecular systems used in biomedical applications

Nanomedicine is an emerging area that largely influences the efficacy of various therapies through the rational design of new materials exhibiting more targeted behavior. The synthetic effort, the amount of used material, and the cost are critical parameters to bear in mind if the production of the designed material is intended to be scaled for their widespread use. Even though materials science offers diverse options for different types of therapies, it is a difficult task to meet all the parameters mentioned above. The dendronization appears as an insightful approach to incorporate all the known benefits of the dendritic architecture by the attachment of dendrons to therapeutic agents, but in a much more affordable manner in terms of synthetic effort, amount of material, and cost. As will be presented, the most common dendrons used for biomedical applications are polyamide, polyester, carbosilane, polyether, and glycol-type, which are bonded to biological active molecules (BAMs), or molecular nanoplatforms (MPs) by hydrolysable bonds. Also relevant is the fact that the incorporation of dendrons not larger than third generation (G3) is sufficient to improve essential properties of these molecular systems, such as aqueous solubility, stability, and cellular internalization, among others. The type of dendron and its location on the BAMs or MPs, similar to placing a Lego piece on a model, will be decisive for obtaining the desired properties.

HumanNet v3: an improved database of human gene networks for disease research

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein–protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene–phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein–protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries

To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.

COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19

The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet ( http://compmedchem.unibo.it/covidrugnet ), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies.

Morphological cell profiling of SARS-CoV-2 infection identifies drug repurposing candidates for COVID-19

Since its emergence in China in December 2019, SARS-CoV-2 has caused a global pandemic. Repurposing of FDA-approved drugs is a promising strategy for identifying rapidly deployable treatments for COVID-19. Herein, we developed a pipeline for quantitative, high-throughput, image-based screening of SARS-CoV-2 infection in human cells that led to the identification of several FDA-approved drugs and clinical candidates with in vitro antiviral activity.

The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly identified and translated to clinical care. Traditional drug discovery methods have a >90% failure rate and can take 10 to 15 y from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious agents against SARS-CoV-2. From a library of 1,425 US Food and Drug Administration (FDA)-approved compounds and clinical candidates, we identified 17 hits that inhibited SARS-CoV-2 infection and analyzed their antiviral activity across multiple cell lines, including lymph node carcinoma of the prostate (LNCaP) cells and a physiologically relevant model of alveolar epithelial type 2 cells (iAEC2s). Additionally, we found that inhibitors of the Ras/Raf/MEK/ERK signaling pathway exacerbate SARS-CoV-2 infection in vitro. Notably, we discovered that lactoferrin, a glycoprotein found in secretory fluids including mammalian milk, inhibits SARS-CoV-2 infection in the nanomolar range in all cell models with multiple modes of action, including blockage of virus attachment to cellular heparan sulfate and enhancement of interferon responses. Given its safety profile, lactoferrin is a readily translatable therapeutic option for the management of COVID-19.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism-targeting as a promising antiviral strategy.

Comprehensive Consensus Analysis of SARS-CoV-2 Drug Repurposing Campaigns

The current COVID-19 pandemic has elicited extensive repurposing efforts (both small and large scale) to rapidly identify COVID-19 treatments among approved drugs. Herein, we provide a literature review of large-scale SARS-CoV-2 antiviral drug repurposing efforts and highlight a marked lack of consistent potency reporting. This variability indicates the importance of standardizing best practices-including the use of relevant cell lines, viral isolates, and validated screening protocols. We further surveyed available biochemical and virtual screening studies against SARS-CoV-2 targets (Spike, ACE2, RdRp, PLpro, and Mpro) and discuss repurposing candidates exhibiting consistent activity across diverse, triaging assays and predictive models. Moreover, we examine repurposed drugs and their efficacy against COVID-19 and the outcomes of representative repurposed drugs in clinical trials. Finally, we propose a drug repurposing pipeline to encourage the implementation of standard methods to fast-track the discovery of candidates and to ensure reproducible results.

A critical overview of computational approaches employed for COVID-19 drug discovery

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

CovidExpress: an interactive portal for intuitive investigation on SARS-CoV-2 related transcriptomes

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans could cause coronavirus disease 2019 (COVID-19). Since its first discovery in Dec 2019, SARS-CoV-2 has become a global pandemic and caused 3.3 million direct/indirect deaths (2021 May). Amongst the scientific community's response to COVID-19, data sharing has emerged as an essential aspect of the combat against SARS-CoV-2. Despite the ever-growing studies about SARS-CoV-2 and COVID-19, to date, only a few databases were curated to enable access to gene expression data. Furthermore, these databases curated only a small set of data and do not provide easy access for investigators without computational skills to perform analyses. To fill this gap and advance open-access to the growing gene expression data on this deadly virus, we collected about 1,500 human bulk RNA-seq datasets from publicly available resources, developed a database and visualization tool, named CovidExpress (https://stjudecab.github.io/covidexpress). This open access database will allow research investigators to examine the gene expression in various tissues, cell lines, and their response to SARS-CoV-2 under different experimental conditions, accelerating the understanding of the etiology of this disease to inform the drug and vaccine development. Our integrative analysis of this big dataset highlights a set of commonly regulated genes in SARS-CoV-2 infected lung and Rhinovirus infected nasal tissues, including OASL that were under-studied in COVID-19 related reports. Our results also suggested a potential FURIN positive feedback loop that might explain the evolutional advantage of SARS-CoV-2.

Drug-induced phospholipidosis confounds drug repurposing for SARS-CoV-2

Repurposing drugs as treatments for COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has drawn much attention. Beginning with sigma receptor ligands and expanding to other drugs from screening in the field, we became concerned that phospholipidosis was a shared mechanism underlying the antiviral activity of many repurposed drugs. For all of the 23 cationic amphiphilic drugs we tested, including hydroxychloroquine, azithromycin, amiodarone, and four others already in clinical trials, phospholipidosis was monotonically correlated with antiviral efficacy. Conversely, drugs active against the same targets that did not induce phospholipidosis were not antiviral. Phospholipidosis depends on the physicochemical properties of drugs and does not reflect specific target-based activities-rather, it may be considered a toxic confound in early drug discovery. Early detection of phospholipidosis could eliminate these artifacts, enabling a focus on molecules with therapeutic potential.

Deleterious Effects of SARS-CoV-2 Infection on Human Pancreatic Cells

COVID-19 pandemic has infected more than 154 million people worldwide and caused more than 3.2 million deaths. It is transmitted by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and affects the respiratory tract as well as extra-pulmonary systems, including the pancreas, that express the virus entry receptor, Angiotensin-Converting Enzyme 2 (ACE2) receptor. Importantly, the endocrine and exocrine pancreas, the latter composed of ductal and acinar cells, express high levels of ACE2, which correlates to impaired functionality characterized as acute pancreatitis observed in some cases presenting with COVID-19. Since acute pancreatitis is already one of the most frequent gastrointestinal causes of hospitalization in the U.S. and the majority of studies investigating the effects of SARS-CoV-2 on the pancreas are clinical and observational, we utilized human iPSC technology to investigate the potential deleterious effects of SARS-CoV-2 infection on iPSC-derived pancreatic cultures containing endocrine and exocrine cells. Interestingly, iPSC-derived pancreatic cultures allow SARS-CoV-2 entry and establish infection, thus perturbing their normal molecular and cellular phenotypes. The infection increased a key cytokine, CXCL12, known to be involved in inflammatory responses in the pancreas. Transcriptome analysis of infected pancreatic cultures confirmed that SARS-CoV-2 hijacks the ribosomal machinery in these cells. Notably, the SARS-CoV-2 infectivity of the pancreas was confirmed in post-mortem tissues from COVID-19 patients, which showed co-localization of SARS-CoV-2 in pancreatic endocrine and exocrine cells and increased the expression of some pancreatic ductal stress response genes. Thus, we demonstrate that SARS-CoV-2 can directly infect human iPSC-derived pancreatic cells with strong supporting evidence of presence of the virus in post-mortem pancreatic tissue of confirmed COVID-19 human cases. This novel model of iPSC-derived pancreatic cultures will open new avenues for the comprehension of the SARS-CoV-2 infection and potentially establish a platform for endocrine and exocrine pancreas-specific antiviral drug screening.

IL10RB as a key regulator of COVID-19 host susceptibility and severity

BACKGROUND

Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes and readily available compounds that reduce COVID-19 host susceptibility is a critical next step.

METHODS

We integrate COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX) and perturbargen signatures to identify candidate genes and compounds that reverse the predicted gene expression dysregulation associated with COVID-19 susceptibility. The top candidate gene is validated by testing both its GReX and observed blood transcriptome association with COVID-19 severity, as well as by in vitro perturbation to quantify effects on viral load and molecular pathway dysregulation. We validate the in silico drug repositioning analysis by examining whether the top candidate compounds decrease COVID-19 incidence based on epidemiological evidence.

RESULTS

We identify IL10RB as the top key regulator of COVID-19 host susceptibility. Predicted GReX up-regulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes. In vitro IL10RB overexpression is associated with increased viral load and activation of immune-related molecular pathways. Azathioprine and retinol are prioritized as candidate compounds to reduce the likelihood of testing positive for COVID-19.

CONCLUSIONS

We establish an integrative data-driven approach for gene target prioritization. We identify and validate IL10RB as a suitable molecular target for modulation of COVID-19 host susceptibility. Finally, we provide evidence for a few readily available medications that would warrant further investigation as drug repositioning candidates.

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19

Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine, enable collective assessment of their contribution for disease or phenotype characterization. We show the utility of this workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identify several pathways and biological processes that could enable understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.

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Defined a consensus gene signature across three models of SARS-CoV-2 infection

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Characterized subnetworks of host proteins interacting with SARS-CoV-2 proteome

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Integrated a wide range of COVID-19 and related data to build functional modules

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Identified gene functional modules that can further the understanding of COVID-19

This study is based on the premise that combining information from multiple layers of data can result in new biologically interpretable associations in several ways. The underlying and unifying theme of this study is data integration, data mining, and meta-analysis for pattern detection that supports knowledge discovery and generation of hypotheses. The methods and the workflow used are disease agnostic and can be applied to any disease or phenotype that has multiple models and heterogeneous data elements. By integrating and joint analysis of several heterogeneous data types (multiple disease models, viral-host protein interaction data, single-cell RNA-sequencing data, protein-protein interactions, and genome-wide association study data), gene functional modules are identified that can have direct bearing on furthering the understanding of COVID-19.

Drugmonizome and Drugmonizome-ML: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning

Understanding the underlying molecular and structural similarities between seemingly heterogeneous sets of drugs can aid in identifying drug repurposing opportunities and assist in the discovery of novel properties of preclinical small molecules. A wealth of information about drug and small molecule structure, targets, indications and side effects; induced gene expression signatures; and other attributes are publicly available through web-based tools, databases and repositories. By processing, abstracting and aggregating information from these resources into drug set libraries, knowledge about novel properties of drugs and small molecules can be systematically imputed with machine learning. In addition, drug set libraries can be used as the underlying database for drug set enrichment analysis. Here, we present Drugmonizome, a database with a search engine for querying annotated sets of drugs and small molecules for performing drug set enrichment analysis. Utilizing the data within Drugmonizome, we also developed Drugmonizome-ML. Drugmonizome-ML enables users to construct customized machine learning pipelines using the drug set libraries from Drugmonizome. To demonstrate the utility of Drugmonizome, drug sets from 12 independent SARS-CoV-2 in vitro screens were subjected to consensus enrichment analysis. Despite the low overlap among these 12 independent in vitro screens, we identified common biological processes critical for blocking viral replication. To demonstrate Drugmonizome-ML, we constructed a machine learning pipeline to predict whether approved and preclinical drugs may induce peripheral neuropathy as a potential side effect. Overall, the Drugmonizome and Drugmonizome-ML resources provide rich and diverse knowledge about drugs and small molecules for direct systems pharmacology applications. Database URL: https://maayanlab.cloud/drugmonizome/.

Phospholipidosis is a shared mechanism underlying the in vitro antiviral activity of many repurposed drugs against SARS-CoV-2

Repurposing drugs as treatments for COVID-19 has drawn much attention. A common strategy has been to screen for established drugs, typically developed for other indications, that are antiviral in cells or organisms. Intriguingly, most of the drugs that have emerged from these campaigns, though diverse in structure, share a common physical property: cationic amphiphilicity. Provoked by the similarity of these repurposed drugs to those inducing phospholipidosis, a well-known drug side effect, we investigated phospholipidosis as a mechanism for antiviral activity. We tested 23 cationic amphiphilic drugs-including those from phenotypic screens and others that we ourselves had found-for induction of phospholipidosis in cell culture. We found that most of the repurposed drugs, which included hydroxychloroquine, azithromycin, amiodarone, and four others that have already progressed to clinical trials, induced phospholipidosis in the same concentration range as their antiviral activity; indeed, there was a strong monotonic correlation between antiviral efficacy and the magnitude of the phospholipidosis. Conversely, drugs active against the same targets that did not induce phospholipidosis were not antiviral. Phospholipidosis depends on the gross physical properties of drugs, and does not reflect specific target-based activities, rather it may be considered a confound in early drug discovery. Understanding its role in infection, and detecting its effects rapidly, will allow the community to better distinguish between drugs and lead compounds that more directly impact COVID-19 from the large proportion of molecules that manifest this confounding effect, saving much time, effort and cost.

Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19

Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.

Insights into biological therapeutic strategies for COVID-19

The worldwide pandemic of novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late December 2019 requires the urgent development of therapeutic options. So far, numerous studies have investigated and uncovered the underlying epidemiology and clinical characteristics of COVID-19 infections in order to develop effective drugs. Compared with antiviral small-molecule inhibitors, biotherapeutics have unique advantages such as fewer side effects by virtue of their high specificity, and thus can be rapidly developed for promising treatments of COVID-19. Here, we summarize potential biotherapeutics and their mechanisms of action, including convalescent plasma, therapeutic antibodies, peptides, engineered ACE2, interferons, cytokine inhibitors, and RNAi-based therapeutics, and discuss in depth the advancements and precautions for each type of biotherapeutics in the treatment of COVID-19.

Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.

SARSCOVIDB-A New Platform for the Analysis of the Molecular Impact of SARS-CoV-2 Viral Infection

The COVID-19 pandemic caused by the new coronavirus (SARS-CoV-2) has become a global emergency issue for public health. This threat has led to an acceleration in related research and, consequently, an unprecedented volume of clinical and experimental data that include changes in gene expression resulting from infection. The SARS-CoV-2 infection database (SARSCOVIDB: https://sarscovidb.org/) was created to mitigate the difficulties related to this scenario. The SARSCOVIDB is an online platform that aims to integrate all differential gene expression data, at messenger RNA and protein levels, helping to speed up analysis and research on the molecular impact of COVID-19. The database can be searched from different experimental perspectives and presents all related information from published data, such as viral strains, hosts, methodological approaches (proteomics or transcriptomics), genes/proteins, and samples (clinical or experimental). All information was taken from 24 articles related to analyses of differential gene expression out of 5,554 COVID-19/SARS-CoV-2-related articles published so far. The database features 12,535 genes whose expression has been identified as altered due to SARS-CoV-2 infection. Thus, the SARSCOVIDB is a new resource to support the health workers and the scientific community in understanding the pathogenesis and molecular impact caused by SARS-CoV-2.

PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies

PAGER-CoV (http://discovery.informatics.uab.edu/PAGER-CoV/) is a new web-based database that can help biomedical researchers interpret coronavirus-related functional genomic study results in the context of curated knowledge of host viral infection, inflammatory response, organ damage, and tissue repair. The new database consists of 11 835 PAGs (Pathways, Annotated gene-lists, or Gene signatures) from 33 public data sources. Through the web user interface, users can search by a query gene or a query term and retrieve significantly matched PAGs with all the curated information. Users can navigate from a PAG of interest to other related PAGs through either shared PAG-to-PAG co-membership relationships or PAG-to-PAG regulatory relationships, totaling 19 996 993. Users can also retrieve enriched PAGs from an input list of COVID-19 functional study result genes, customize the search data sources, and export all results for subsequent offline data analysis. In a case study, we performed a gene set enrichment analysis (GSEA) of a COVID-19 RNA-seq data set from the Gene Expression Omnibus database. Compared with the results using the standard PAGER database, PAGER-CoV allows for more sensitive matching of known immune-related gene signatures. We expect PAGER-CoV to be invaluable for biomedical researchers to find molecular biology mechanisms and tailored therapeutics to treat COVID-19 patients.

Impact of obesity and SARS-CoV-2 infection: implications for host defence - a living review

The role of obesity in the pathophysiology of respiratory virus infections has become particularly apparent during the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, where obese patients are twice as likely to suffer from severe coronavirus disease 2019 (COVID-19) than healthy weight individuals. Obesity results in disruption of systemic lipid metabolism promoting a state of chronic low-grade inflammation. However, it remains unclear how these underlying metabolic and cellular processes promote severe SARS-CoV-2 infection. Emerging data in SARS-CoV-2 and Influenza A virus (IAV) infections show that viruses can further subvert the host's altered lipid metabolism and exploit obesity-induced alterations in immune cell metabolism and function to promote chronic inflammation and viral propagation. In this review, we outline the systemic metabolic and immune alterations underlying obesity and discuss how these baseline alterations impact the immune response and disease pathophysiology. A better understanding of the immunometabolic landscape of obese patients may aid better therapies and future vaccine design.

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

Virtual and In Vitro Antiviral Screening Revive Therapeutic Drugs for COVID-19

The urgent need for a cure for early phase COVID-19 infected patients critically underlines drug repositioning strategies able to efficiently identify new and reliable treatments by merging computational, experimental, and pharmacokinetic expertise. Here we report new potential therapeutics for COVID-19 identified with a combined virtual and experimental screening strategy and selected among already approved drugs. We used hydroxychloroquine (HCQ), one of the most studied drugs in current clinical trials, as a reference template to screen for structural similarity against a library of almost 4000 approved drugs. The top-ranked drugs, based on structural similarity to HCQ, were selected for in vitro antiviral assessment. Among the selected drugs, both zuclopenthixol and nebivolol efficiently block SARS-CoV-2 infection with EC50 values in the low micromolar range, as confirmed by independent experiments. The anti-SARS-CoV-2 potential of ambroxol, amodiaquine, and its active metabolite (N-monodesethyl amodiaquine) is also discussed. In trying to understand the "hydroxychloroquine" mechanism of action, both pK a and the HCQ aromatic core may play a role. Further, we show that the amodiaquine metabolite and, to a lesser extent, zuclopenthixol and nebivolol are active in a SARS-CoV-2 titer reduction assay. Given the need for improved efficacy and safety, we propose zuclopenthixol, nebivolol, and amodiaquine as potential candidates for clinical trials against the early phase of the SARS-CoV-2 infection and discuss their potential use as adjuvant to the current (i.e., remdesivir and favipiravir) COVID-19 therapeutics.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

MOTIVATION

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.

RESULTS

Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

AVAILABILITY

https://neo4covid19.ncats.io.