A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

SARS-CoV-2 remodels the landscape of small non-coding RNAs with infection time and symptom severity

The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 has significantly impacted global health, stressing the necessity of basic understanding of the host response to this viral infection. In this study, we investigated how SARS-CoV-2 remodels the landscape of small non-coding RNAs (sncRNA) from a large collection of nasopharyngeal swab samples taken at various time points from patients with distinct symptom severity. High-throughput RNA sequencing analysis revealed a global alteration of the sncRNA landscape, with abundance peaks related to species of 21-23 and 32-33 nucleotides. Host-derived sncRNAs, including microRNAs (miRNAs), transfer RNA-derived small RNAs (tsRNAs), and small nucleolar RNA-derived small RNAs (sdRNAs) exhibited significant differential expression in infected patients compared to controls. Importantly, miRNA expression was predominantly down-regulated in response to SARS-CoV-2 infection, especially in patients with severe symptoms. Furthermore, we identified specific tsRNAs derived from Glu- and Gly-tRNAs as major altered elements upon infection, with 5' tRNA halves being the most abundant species and suggesting their potential as biomarkers for viral presence and disease severity prediction. Additionally, down-regulation of C/D-box sdRNAs and altered expression of tinyRNAs (tyRNAs) were observed in infected patients. These findings provide valuable insights into the host sncRNA response to SARS-CoV-2 infection and may contribute to the development of further diagnostic and therapeutic strategies in the clinic.

A porcine kidney-derived clonal cell line with clear genetic annotation is highly susceptible to African swine fever virus

African Swine Fever virus (ASFV), the causative agent of African swine fever, is a highly lethal hemorrhagic virus affecting domestic pigs and wild boars. The primary target cells for ASFV infection are porcine alveolar macrophages (PAMs), which are difficult to obtain and maintain in vitro, and less subjective to genetic editing. To overcome these issues and facilitate ASFV research, we obtained a subclonal cell line PK1-C5 by subcloning LLC-PK1 cells that support stable ASFV proliferation. This consequential cell line exhibited high ASFV infection levels and similar viral growth characteristics to PAMs, while also allowing high-efficiency genomic editing through transfection or lentivirus transduction of Cas9. Taken together, our study provided a valuable tool for research aspects including ASFV-host interactions, pathogenicity, and vaccine development.

Human-gut bacterial protein-protein interactions: understudied but impactful to human health

The human gut microbiome is associated with a wide range of diseases; yet, the mechanisms these microbes use to influence human health are not fully understood. Protein-protein interactions (PPIs) are increasingly identified as a potential mechanism by which gut microbiota influence their human hosts. Similar to some PPIs observed in pathogens, many disease-relevant human-gut bacterial PPIs function by interacting with components of the immune system or the gut barrier. Here, we highlight recent advances in these two areas. It is our opinion that there is a vastly unexplored network of human-gut bacterial PPIs that contribute to the prevention or pathogenesis of various diseases and that future research is warranted to expand PPI discovery.

Precision nutrition to reset virus-induced human metabolic reprogramming and dysregulation (HMRD) in long-COVID

SARS-CoV-2, the etiological agent of COVID-19, is devoid of any metabolic capacity; therefore, it is critical for the viral pathogen to hijack host cellular metabolic machinery for its replication and propagation. This single-stranded RNA virus with a 29.9 kb genome encodes 14 open reading frames (ORFs) and initiates a plethora of virus-host protein-protein interactions in the human body. These extensive viral protein interactions with host-specific cellular targets could trigger severe human metabolic reprogramming/dysregulation (HMRD), a rewiring of sugar-, amino acid-, lipid-, and nucleotide-metabolism(s), as well as altered or impaired bioenergetics, immune dysfunction, and redox imbalance in the body. In the infectious process, the viral pathogen hijacks two major human receptors, angiotensin-converting enzyme (ACE)-2 and/or neuropilin (NRP)-1, for initial adhesion to cell surface; then utilizes two major host proteases, TMPRSS2 and/or furin, to gain cellular entry; and finally employs an endosomal enzyme, cathepsin L (CTSL) for fusogenic release of its viral genome. The virus-induced HMRD results in 5 possible infectious outcomes: asymptomatic, mild, moderate, severe to fatal episodes; while the symptomatic acute COVID-19 condition could manifest into 3 clinical phases: (i) hypoxia and hypoxemia (Warburg effect), (ii) hyperferritinemia ('cytokine storm'), and (iii) thrombocytosis (coagulopathy). The mean incubation period for COVID-19 onset was estimated to be 5.1 days, and most cases develop symptoms after 14 days. The mean viral clearance times were 24, 30, and 39 days for acute, severe, and ICU-admitted COVID-19 patients, respectively. However, about 25-70% of virus-free COVID-19 survivors continue to sustain virus-induced HMRD and exhibit a wide range of symptoms that are persistent, exacerbated, or new 'onset' clinical incidents, collectively termed as post-acute sequelae of COVID-19 (PASC) or long COVID. PASC patients experience several debilitating clinical condition(s) with >200 different and overlapping symptoms that may last for weeks to months. Chronic PASC is a cumulative outcome of at least 10 different HMRD-related pathophysiological mechanisms involving both virus-derived virulence factors and a multitude of innate host responses. Based on HMRD and virus-free clinical impairments of different human organs/systems, PASC patients can be categorized into 4 different clusters or sub-phenotypes: sub-phenotype-1 (33.8%) with cardiac and renal manifestations; sub-phenotype-2 (32.8%) with respiratory, sleep and anxiety disorders; sub-phenotype-3 (23.4%) with skeleto-muscular and nervous disorders; and sub-phenotype-4 (10.1%) with digestive and pulmonary dysfunctions. This narrative review elucidates the effects of viral hijack on host cellular machinery during SARS-CoV-2 infection, ensuing detrimental effect(s) of virus-induced HMRD on human metabolism, consequential symptomatic clinical implications, and damage to multiple organ systems; as well as chronic pathophysiological sequelae in virus-free PASC patients. We have also provided a few evidence-based, human randomized controlled trial (RCT)-tested, precision nutrients to reset HMRD for health recovery of PASC patients.

Interaction between host G3BP and viral nucleocapsid protein regulates SARS-CoV-2 replication and pathogenicity

G3BP1/2 are paralogous proteins that promote stress granule formation in response to cellular stresses, including viral infection. The nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inhibits stress granule assembly and interacts with G3BP1/2 via an ITFG motif, including residue F17, in the N protein. Prior studies examining the impact of the G3PB1-N interaction on SARS-CoV-2 replication have produced inconsistent findings, and the role of this interaction in pathogenesis is unknown. Here, we use structural and biochemical analyses to define the residues required for G3BP1-N interaction and structure-guided mutagenesis to selectively disrupt this interaction. We find that N-F17A mutation causes highly specific loss of interaction with G3BP1/2. SARS-CoV-2 N-F17A fails to inhibit stress granule assembly in cells, has decreased viral replication, and causes decreased pathology in vivo. Further mechanistic studies indicate that the N-F17-mediated G3BP1-N interaction promotes infection by limiting sequestration of viral genomic RNA (gRNA) into stress granules.

Systems-wide view of host-pathogen interactions across COVID-19 severities using integrated omics analysis

The mechanisms explaining the variability in COVID-19 clinical manifestations (mild, moderate, and severe) are not fully understood. To identify key gene expression markers linked to disease severity, we employed an integrated approach, combining host-pathogen protein-protein interaction data and viral-induced host gene expression data. We analyzed an RNA-seq dataset from peripheral blood mononuclear cells across 12 projects representing the spectrum of disease severity. We identified genes showing differential expression across mild, moderate, and severe conditions. Enrichment analysis of the pathways in host proteins targeted by each of the SARS-CoV-2 proteins revealed a strong association with processes related to ribosomal biogenesis, translation, and translocation. Interestingly, most of these pathways and associated cellular machinery, including ribosomal biogenesis, ribosomal proteins, and translation, were upregulated in mild conditions but downregulated in severe cases. This suggests that COVID-19 exhibits a paradoxical host response, boosting host/viral translation in mild cases but slowing it in severe cases.

Targeting G9a translational mechanism of SARS-CoV-2 pathogenesis for multifaceted therapeutics of COVID-19 and its sequalae

By largely unknown mechanism(s), SARS-CoV-2 hijacks the host translation apparatus to promote COVID-19 pathogenesis. We report that the histone methyltransferase G9a noncanonically regulates viral hijacking of the translation machinery to bring about COVID-19 symptoms of hyperinflammation, lymphopenia, and blood coagulation. Chemoproteomic analysis of COVID-19 patient peripheral mononuclear blood cells (PBMC) identified enhanced interactions between SARS-CoV-2-upregulated G9a and distinct translation regulators, particularly the N 6 -methyladenosine (m 6 A) RNA methylase METTL3. These interactions with translation regulators implicated G9a in translational regulation of COVID-19. Inhibition of G9a activity suppressed SARS-CoV-2 replication in human alveolar epithelial cells. Accordingly, multi-omics analysis of the same alveolar cells identified SARS-CoV-2-induced changes at the transcriptional, m 6 A-epitranscriptional, translational, and post-translational (phosphorylation or secretion) levels that were reversed by inhibitor treatment. As suggested by the aforesaid chemoproteomic analysis, these multi-omics-correlated changes revealed a G9a-regulated translational mechanism of COVID-19 pathogenesis in which G9a directs translation of viral and host proteins associated with SARS-CoV-2 replication and with dysregulation of host response. Comparison of proteomic analyses of G9a inhibitor-treated, SARS-CoV-2 infected cells, or ex vivo culture of patient PBMCs, with COVID-19 patient data revealed that G9a inhibition reversed the patient proteomic landscape that correlated with COVID-19 pathology/symptoms. These data also indicated that the G9a-regulated, inhibitor-reversed, translational mechanism outperformed G9a-transcriptional suppression to ultimately determine COVID-19 pathogenesis and to define the inhibitor action, from which biomarkers of serve symptom vulnerability were mechanistically derived. This cell line-to-patient conservation of G9a-translated, COVID-19 proteome suggests that G9a inhibitors can be used to treat patients with COVID-19, particularly patients with long-lasting COVID-19 sequelae.

Cytoskeletal β-tubulin and cysteine cathepsin L deregulation by SARS-CoV-2 spike protein interaction with the neuronal model cell line SH-SY5Y

SARS-CoV-2 mainly infects the respiratory tract but can also target other organs, including the central nervous system. While it was recently shown that cells of the blood-brain-barrier are permissive to SARS-CoV-2 infection in vitro, it remains debated whether neurons can be infected. In this study, we demonstrate that vesicular stomatitis virus particles pseudotyped with the spike protein of SARS-CoV-2 variants WT, Alpha, Delta and Omicron enter the neuronal model cell line SH-SY5Y. Cell biological analyses of the pseudo-virus treated cultures showed marked alterations in microtubules of SH-SY5Y cells. Because the changes in β-tubulin occurred in most cells, but only few were infected, we further asked whether interaction of the cells with spike protein might be sufficient to cause molecular and structural changes. For this, SH-SY5Y cells were incubated with trimeric spike proteins for time intervals of up to 24 h. CellProfiler™-based image analyses revealed changes in the intensities of microtubule staining in spike protein-incubated cells. Furthermore, expression of the spike protein-processing protease cathepsin L was found to be up-regulated by wild type, Alpha and Delta spike protein pseudotypes and cathepsin L was found to be secreted from spike protein-treated cells. We conclude that the mere interaction of the SARS-CoV-2 with neuronal cells can affect cellular architecture and proteolytic capacities. The molecular mechanisms underlying SARS-CoV-2 spike protein induced cytoskeletal changes in neuronal cells remain elusive and require future studies.

Global fungal-host interactome mapping identifies host targets of candidalysin

Candidalysin, a cytolytic peptide toxin secreted by the human fungal pathogen Candida albicans, is critical for fungal pathogenesis. Yet, its intracellular targets have not been extensively mapped. Here, we performed a high-throughput enhanced yeast two-hybrid (HT-eY2H) screen to map the interactome of all eight Ece1 peptides with their direct human protein targets and identified a list of potential interacting proteins, some of which were shared between the peptides. CCNH, a regulatory subunit of the CDK-activating kinase (CAK) complex involved in DNA damage repair, was identified as one of the host targets of candidalysin. Mechanistic studies revealed that candidalysin triggers a significantly increased double-strand DNA breaks (DSBs), as evidenced by the formation of γ-H2AX foci and colocalization of CCNH and γ-H2AX. Importantly, candidalysin binds directly to CCNH to activate CAK to inhibit DNA damage repair pathway. Loss of CCNH alleviates DSBs formation under candidalysin treatment. Depletion of candidalysin-encoding gene fails to induce DSBs and stimulates CCNH upregulation in a murine model of oropharyngeal candidiasis. Collectively, our study reveals that a secreted fungal toxin acts to hijack the canonical DNA damage repair pathway by targeting CCNH and to promote fungal infection.

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease

The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

Identifying diseases associated with Post-COVID syndrome through an integrated network biology approach

A growing body of research shows that COVID-19 is now recognized as a multi-organ disease with a wide range of manifestations that can have long-lasting repercussions, referred to as post-COVID-19 syndrome. It is unknown why the vast majority of COVID-19 patients develop post-COVID-19 syndrome, or why patients with pre-existing disorders are more likely to experience severe COVID-19. This study used an integrated network biology approach to obtain a comprehensive understanding of the relationship between COVID-19 and other disorders. The approach involved building a PPI network with COVID-19 genes and identifying highly interconnected regions. The molecular information contained within these subnetworks, as well as the pathway annotations, were used to reveal the link between COVID-19 and other disorders. Using Fisher's exact test and disease-specific gene information, significant COVID-19-disease associations were discovered. The study discovered diseases that affect multiple organs and organ systems, thus proving the theory of multiple organ damage caused by COVID-19. Cancers, neurological disorders, hepatic diseases, cardiac disorders, pulmonary diseases, and hypertensive diseases are just a few of the conditions linked to COVID-19. Pathway enrichment analysis of shared proteins revealed the shared molecular mechanism of COVID-19 and these diseases. The findings of the study shed new light on the major COVID-19-associated disease conditions and how their molecular mechanisms interact with COVID-19. The novelty of studying disease associations in the context of COVID-19 provides new insights into the management of rapidly evolving long-COVID and post-COVID syndromes, which have significant global implications.Communicated by Ramaswamy H. Sarma.

New Insights into the Link between SARS-CoV-2 Infection and Renal Cancer

Cancer has been described as a risk factor for greater susceptibility to SARS-CoV-2 infection and severe COVID-19, mainly for patients with metastatic disease. Conversely, to that reported for most solid and hematological malignancies, the few available clinical studies reported that the infection did not increase the risk of death in renal cancer patients. The expression on proximal tubular renal cells of the key players in cellular viral uptake, ACE2, TMPRSS2, and NRP1, seems to be the mechanism for the direct kidney injury seen in patients with COVID-19. Interestingly, data from The Cancer Genome Atlas and experimental analyses on various renal cancer cell lines demonstrated that the above-reported receptors/cofactors are maintained by renal cancer cells. However, whether SARS-CoV-2 infection directly kills renal cancer cells or generates enhanced immunogenicity is a question worth investigating. In addition, some researchers have further addressed the topic by studying the expression and prognostic significance of gene signatures related to SARS-CoV-2 infection in renal cancer patients. The emerging data highlights the importance of better understanding the existence of a link between renal cancer and COVID-19 since it could lead to the identification of new prognostic factors and the development of new therapeutic targets in the management of renal cancer patients.

Differences in syncytia formation by SARS-CoV-2 variants modify host chromatin accessibility and cellular senescence via TP53

Coronavirus disease 2019 (COVID-19) remains a significant public health threat due to the ability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants to evade the immune system and cause breakthrough infections. Although pathogenic coronaviruses such as SARS-CoV-2 and Middle East respiratory syndrome (MERS)-CoV lead to severe respiratory infections, how these viruses affect the chromatin proteomic composition upon infection remains largely uncharacterized. Here, we use our recently developed integrative DNA And Protein Tagging methodology to identify changes in host chromatin accessibility states and chromatin proteomic composition upon infection with pathogenic coronaviruses. SARS-CoV-2 infection induces TP53 stabilization on chromatin, which contributes to its host cytopathic effect. We mapped this TP53 stabilization to the SARS-CoV-2 spike and its propensity to form syncytia, a consequence of cell-cell fusion. Differences in SARS-CoV-2 spike variant-induced syncytia formation modify chromatin accessibility, cellular senescence, and inflammatory cytokine release via TP53. Our findings suggest that differences in syncytia formation alter senescence-associated inflammation, which varies among SARS-CoV-2 variants.

Aminoacyl-tRNA synthetase interactions in SARS-CoV-2 infection

Aminoacyl-tRNA synthetases (aaRSs) are ancient enzymes that serve a foundational role in the efficient and accurate translation of genetic information from messenger RNA to proteins. These proteins play critical, non-canonical functions in a multitude of cellular processes. Multiple viruses are known to hijack the functions of aaRSs for proviral outcomes, while cells modify antiviral responses through non-canonical functions of certain synthetases. Recent findings have revealed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of coronaviral disease 19 (COVID-19), utilizes canonical and non-canonical functions of aaRSs, establishing a complex interplay of viral proteins, cellular factors and host aaRSs. In a striking example, an unconventional multi-aaRS complex consisting of glutamyl-prolyl-, lysyl-, arginyl- and methionyl-tRNA synthetases interact with a previously unknown RNA-element in the 3'-end of SARS-CoV-2 genomic and subgenomic RNAs. This review aims to highlight the aaRS-SARS-CoV-2 interactions identified to date, with possible implications for the biology of host aaRSs in SARS-CoV-2 infection.

Co-evolution of SARS-CoV-2 variants and host immune response trajectories underlie COVID-19 pandemic to epidemic transition

COVID-19 pandemic saw emergence of multiple SAR-CoV-2 variants. Exacerbated risk of severe outcome and hospital admissions led us to comprehend differential host-immune kinetics associated with SARS-CoV-2 variants. Longitudinal investigation was conducted through different time periods of Pre-VOC and VOCs (Delta & Omicron) mapping host transcriptome features. Robust antiviral type-1 interferon response marked Omicron infection, which was largely missing during Pre-VOC and Delta waves. SARS-CoV-2-host protein-protein interactions and docking complexes highlighted N protein to interact with HNRNPA1 in Pre-VOC, demonstrating its functional role for enhanced viral replication. Omicron revealed enhanced binding efficiency of LARP1 to N protein, probably potentiating antiviral effects of LARP1. Differential expression of zinc finger protein genes, especially in Omicron, mechanistically support induction of strong IFN (Interferon) response, thereby strengthening early viral clearance. Study highlights eventual adaptation of host to immune activation patterns that interrupt virus evolution with enhanced immune-evasion mutations and counteraction mechanisms, delimiting the next phase of COVID-19 pandemic.

In silico drug repurposing carvedilol and its metabolites against SARS-CoV-2 infection using molecular docking and molecular dynamic simulation approaches

The pandemic of coronavirus disease 2019 (COVID-19) caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a significant impact on the economy and public health worldwide. Therapeutic options such as drugs and vaccines for this newly emerged disease are eagerly desired due to the high mortality. Using the U.S. Food and Drug Administration (FDA) approved drugs to treat a new disease or entirely different diseases, in terms of drug repurposing, minimizes the time and cost of drug development compared to the de novo design of a new drug. Drug repurposing also has some other advantages such as reducing safety evaluation to accelerate drug application on time. Carvedilol, a non-selective beta-adrenergic blocker originally designed to treat high blood pressure and manage heart disease, has been shown to impact SARS-CoV-2 infection in clinical observation and basic studies. Here, we applied computer-aided approaches to investigate the possibility of repurposing carvedilol to combat SARS-CoV-2 infection. The molecular mechanisms and potential molecular targets of carvedilol were identified by evaluating the interactions of carvedilol with viral proteins. Additionally, the binding affinities of in vivo metabolites of carvedilol with selected targets were evaluated. The docking scores for carvedilol and its metabolites with RdRp were - 10.0 kcal/mol, - 9.8 kcal/mol (1-hydroxyl carvedilol), - 9.7 kcal/mol (3-hydroxyl carvedilol), - 9.8 kcal/mol (4-hydroxyl carvedilol), - 9.7 kcal/mol (5-hydroxyl carvedilol), - 10.0 kcal/mol (8-hydroxyl carvedilol), and - 10.1 kcal/mol (O-desmethyl carvedilol), respectively. Using the molecular dynamics simulation (100 ns) method, we further confirmed the stability of formed complexes of RNA-dependent RNA polymerase (RdRp) and carvedilol or its metabolites. Finally, the drug-target interaction mechanisms that contribute to the complex were investigated. Overall, this study provides the molecular targets and mechanisms of carvedilol and its metabolites as repurposed drugs to fight against SARS-CoV-2 infection.

In silico protein function prediction: the rise of machine learning-based approaches

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors

The integration of artificial intelligence (AI) into drug discovery has markedly advanced the search for effective therapeutics. In our study, we employed a comprehensive computational-experimental approach to identify potential anti-SARS-CoV-2 compounds. We developed a predictive model to assess the activities of compounds based on their structural features. This model screened a library of approximately 700,000 compounds, culminating in the selection of the top 100 candidates for experimental validation. In vitro assays on human intestinal epithelial cells (Caco-2) revealed that 19 of these compounds exhibited inhibitory activity. Notably, eight compounds demonstrated dose-dependent activity in Vero cell lines, with half-maximal effective concentration (EC50) values ranging from 1 μM to 7 μM. Furthermore, we utilized a clustering approach to pinpoint potential nucleoside analog inhibitors, leading to the discovery of two promising candidates: azathioprine and its metabolite, thioinosinic acid. Both compounds showed in vitro activity against SARS-CoV-2, with thioinosinic acid also significantly reducing viral loads in mouse lungs. These findings underscore the utility of AI in accelerating drug discovery processes.

Evolutionary arms race between SARS-CoV-2 and interferon signaling via dynamic interaction with autophagy

SARS-CoV-2 emerged, and is evolving to efficiently infect humans worldwide. SARS-CoV-2 evades early innate recognition, interferon signaling activated only in bystander cells. This balance of innate activation and viral evasion has important consequences, but the pathways involved are incompletely understood. Here we find that autophagy genes regulate innate immune signaling, impacting the basal set point of interferons, and thus permissivity to infection. Mechanistically, autophagy genes negatively regulate MAVS, and this low basal level of MAVS is efficiently antagonized by SARS-CoV-2 ORF9b, blocking interferon activation in infected cells. However, upon loss of autophagy increased MAVS overcomes ORF9b-mediated antagonism suppressing infection. This has led to the evolution of SARS-CoV-2 variants to express higher levels of ORF9b, allowing SARS-CoV-2 to replicate under conditions of increased MAVS signaling. Altogether, we find a critical role of autophagy in the regulation of innate immunity and uncover an evolutionary trajectory of SARS-CoV-2 ORF9b to overcome host defenses.

SARS-CoV-2 and the DNA damage response

The recent coronavirus disease 2019 (COVID-19) pandemic was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is characterized by respiratory distress, multiorgan dysfunction and, in some cases, death. The virus is also responsible for post-COVID-19 condition (commonly referred to as 'long COVID'). SARS-CoV-2 is a single-stranded, positive-sense RNA virus with a genome of approximately 30 kb, which encodes 26 proteins. It has been reported to affect multiple pathways in infected cells, resulting, in many cases, in the induction of a 'cytokine storm' and cellular senescence. Perhaps because it is an RNA virus, replicating largely in the cytoplasm, the effect of SARS-Cov-2 on genome stability and DNA damage responses (DDRs) has received relatively little attention. However, it is now becoming clear that the virus causes damage to cellular DNA, as shown by the presence of micronuclei, DNA repair foci and increased comet tails in infected cells. This review considers recent evidence indicating how SARS-CoV-2 causes genome instability, deregulates the cell cycle and targets specific components of DDR pathways. The significance of the virus's ability to cause cellular senescence is also considered, as are the implications of genome instability for patients suffering from long COVID.

Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

Nanoscopic Elucidation of Spontaneous Self-Assembly of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Open Reading Frame 6 (ORF6) Protein

Open reading frame 6 (ORF6), the accessory protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that suppresses host type-I interferon signaling, possesses amyloidogenic sequences. ORF6 amyloidogenic peptides self-assemble to produce cytotoxic amyloid fibrils. Currently, the molecular properties of the ORF6 remain elusive. Here, we investigate the structural dynamics of the full-length ORF6 protein in a near-physiological environment using high-speed atomic force microscopy. ORF6 oligomers were ellipsoidal and readily assembled into ORF6 protofilaments in either a circular or a linear pattern. The formation of ORF6 protofilaments was enhanced at higher temperatures or on a lipid substrate. ORF6 filaments were sensitive to aliphatic alcohols, urea, and SDS, indicating that the filaments were predominantly maintained by hydrophobic interactions. In summary, ORF6 self-assembly could be necessary to sequester host factors and causes collateral damage to cells via amyloid aggregates. Nanoscopic imaging unveiled the innate molecular behavior of ORF6 and provides insight into drug repurposing to treat amyloid-related coronavirus disease 2019 complications.

HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.

Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19. Materials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis .

Identification of motif-based interactions between SARS-CoV-2 protein domains and human peptide ligands pinpoint antiviral targets

The virus life cycle depends on host-virus protein-protein interactions, which often involve a disordered protein region binding to a folded protein domain. Here, we used proteomic peptide phage display (ProP-PD) to identify peptides from the intrinsically disordered regions of the human proteome that bind to folded protein domains encoded by the SARS-CoV-2 genome. Eleven folded domains of SARS-CoV-2 proteins were found to bind 281 peptides from human proteins, and affinities of 31 interactions involving eight SARS-CoV-2 protein domains were determined (KD ∼ 7-300 μM). Key specificity residues of the peptides were established for six of the interactions. Two of the peptides, binding Nsp9 and Nsp16, respectively, inhibited viral replication. Our findings demonstrate how high-throughput peptide binding screens simultaneously identify potential host-virus interactions and peptides with antiviral properties. Furthermore, the high number of low-affinity interactions suggest that overexpression of viral proteins during infection may perturb multiple cellular pathways.

An Overview on Anti-COVID-19 Drug Achievements and Challenges Ahead

The appearance of several coronavirus pandemics/epidemics during the last two decades (SARS-CoV-1 in 2002, MERS-CoV in 2012, and SARS-CoV-2 in 2019) indicates that humanity will face increasing challenges from coronaviruses in the future. The emergence of new strains with similar transmission characteristics as SARS-CoV-2 and mortality rates similar to SARS-CoV-1 (∼10% mortality) or MERS-CoV (∼35% mortality) in the future is a terrifying possibility. Therefore, getting enough preparations to face such risks is an inevitable necessity. The present study aims to review the drug achievements and challenges in the fight against SARS-CoV-2 with a combined perspective derived from pharmacology, pharmacotherapy, and medicinal chemistry insights. Appreciating all the efforts made during the past few years, there is strong evidence that the desired results have not yet been achieved and research in this area should still be pursued seriously. By expressing some pessimistic possibilities and concluding that the drug discovery and pharmacotherapy of COVID-19 have not been successful so far, this short essay tries to draw the attention of responsible authorities to be more prepared against future coronavirus epidemics/pandemics.

Differences in syncytia formation by SARS-CoV-2 variants modify host chromatin accessibility and cellular senescence via TP53

COVID-19 remains a significant public health threat due to the ability of SARS-CoV-2 variants to evade the immune system and cause breakthrough infections. Although pathogenic coronaviruses such as SARS-CoV-2 and MERS-CoV lead to severe respiratory infections, how these viruses affect the chromatin proteomic composition upon infection remains largely uncharacterized. Here we used our recently developed integrative DNA And Protein Tagging (iDAPT) methodology to identify changes in host chromatin accessibility states and chromatin proteomic composition upon infection with pathogenic coronaviruses. SARS-CoV-2 infection induces TP53 stabilization on chromatin, which contributes to its host cytopathic effect. We mapped this TP53 stabilization to the SARS-CoV-2 spike and its propensity to form syncytia, a consequence of cell-cell fusion. Differences in SARS-CoV-2 spike variant-induced syncytia formation modify chromatin accessibility, cellular senescence, and inflammatory cytokine release via TP53. Our findings suggest that differences in syncytia formation alter senescence-associated inflammation, which varies among SARS-CoV-2 variants.

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.

A comprehensive Drosophila resource to identify key functional interactions between SARS-CoV-2 factors and host proteins

Development of effective therapies against SARS-CoV-2 infections relies on mechanistic knowledge of virus-host interface. Abundant physical interactions between viral and host proteins have been identified, but few have been functionally characterized. Harnessing the power of fly genetics, we develop a comprehensive Drosophila COVID-19 resource (DCR) consisting of publicly available strains for conditional tissue-specific expression of all SARS-CoV-2 encoded proteins, UAS-human cDNA transgenic lines encoding established host-viral interacting factors, and GAL4 insertion lines disrupting fly homologs of SARS-CoV-2 human interacting proteins. We demonstrate the utility of the DCR to functionally assess SARS-CoV-2 genes and candidate human binding partners. We show that NSP8 engages in strong genetic interactions with several human candidates, most prominently with the ATE1 arginyltransferase to induce actin arginylation and cytoskeletal disorganization, and that two ATE1 inhibitors can reverse NSP8 phenotypes. The DCR enables parallel global-scale functional analysis of SARS-CoV-2 components in a prime genetic model system.

Super-resolution proximity labeling reveals anti-viral protein network and its structural changes against SARS-CoV-2 viral proteins

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replicates in human cells by interacting with host factors following infection. To understand the virus and host interactome proximity, we introduce a super-resolution proximity labeling (SR-PL) method with a "plug-and-playable" PL enzyme, TurboID-GBP (GFP-binding nanobody protein), and we apply it for interactome mapping of SARS-CoV-2 ORF3a and membrane protein (M), which generates highly perturbed endoplasmic reticulum (ER) structures. Through SR-PL analysis of the biotinylated interactome, 224 and 272 peptides are robustly identified as ORF3a and M interactomes, respectively. Within the ORF3a interactome, RNF5 co-localizes with ORF3a and generates ubiquitin modifications of ORF3a that can be involved in protein degradation. We also observe that the SARS-CoV-2 infection rate is efficiently reduced by the overexpression of RNF5 in host cells. The interactome data obtained using the SR-PL method are presented at https://sarscov2.spatiomics.org. We hope that our method will contribute to revealing virus-host interactions of other viruses in an efficient manner.

Coronaviral Main Protease Induces LPCAT3 Cleavage and Endoplasmic Reticulum (ER) Stress

Zoonotic coronaviruses infect mammals and birds, causing pulmonary and gastrointestinal infections. Some animal coronaviruses, such as the porcine epidemic diarrhea virus (PEDV) and transmissible gastroenteritis virus (TGEV), lead to severe diarrhea and animal deaths. Gastrointestinal symptoms were also found in COVID-19 and SARS patients. However, the pathogenesis of gastrointestinal symptoms in coronavirus diseases remains elusive. In this study, the main protease-induced LPCAT3 cleavage was monitored by exogenous gene expression and protease inhibitors, and the related regulation of gene expression was confirmed by qRT-PCR and gene knockdown. Interestingly, LPCAT3 plays an important role in lipid absorption in the intestines. The Mpro of coronaviruses causing diarrhea, such as PEDV and MERS-CoV, but not the Mpro of HCoV-OC43 and HCoV-HKU1, which could induce LPCAT3 cleavage. Mutagenesis analysis and inhibitor experiments indicated that LPCAT3 cleavage was independent of the catalytic activity of Mpro. Moreover, LPCAT3 cleavage in cells boosted CHOP and GRP78 expression, which were biomarkers of ER stress. Since LPCAT3 is critical for lipid absorption in the intestines and malabsorption may lead to diarrhea in coronavirus diseases, Mpro-induced LPCAT3 cleavage might trigger gastrointestinal symptoms during coronavirus infection.

Integrative analysis of functional genomic screening and clinical data identifies a protective role for spironolactone in severe COVID-19

We demonstrate that integrative analysis of CRISPR screening datasets enables network-based prioritization of prescription drugs modulating viral entry in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by developing a network-based approach called Rapid proXimity Guidance for Repurposing Investigational Drugs (RxGRID). We use our results to guide a propensity-score-matched, retrospective cohort study of 64,349 COVID-19 patients, showing that a top candidate drug, spironolactone, is associated with improved clinical prognosis, measured by intensive care unit (ICU) admission and mechanical ventilation rates. Finally, we show that spironolactone exerts a dose-dependent inhibitory effect on viral entry in human lung epithelial cells. Our RxGRID method presents a computational framework, implemented as an open-source software package, enabling genomics researchers to identify drugs likely to modulate a molecular phenotype of interest based on high-throughput screening data. Our results, derived from this method and supported by experimental and clinical analysis, add additional supporting evidence for a potential protective role of the potassium-sparing diuretic spironolactone in severe COVID-19.

In Vitro Reconstitution and Analysis of SARS-CoV-2/Host Protein-Protein Interactions

The emergence of viral threats such as Ebola, ZIKA, and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requires a rapid and efficient approach for elucidating mechanisms of pathogenesis and development of therapeutics. In this context, cell-free protein synthesis (CFPS) holds a promise to resolve the bottlenecks of multiplexed protein production and interaction analysis among host and pathogen proteins. Here, we applied a eukaryotic CFPS system based on Leishmania tarentolae extract (LTE) protein expression in combination with AlphaLISA proximity-based protein interaction technology to identify intraviral and viral-human protein interactions of SARS-CoV-2 virus that can potentially be targeted by the existing or novel antiviral therapeutics. We produced and tested 54 putative human-viral protein pairs in vitro and identified 45 direct binary protein interactions. As a casing example of the assay's suitability for drug development applications, we analyzed the effect of a putative biologic on the human angiotensin-converting enzyme 2/receptor-binding domain (hACE2/RBD) interaction. This suggests that the presented pathogen characterization platform can facilitate the development of new therapeutic agents.

Interaction between host G3BP and viral nucleocapsid protein regulates SARS-CoV-2 replication

G3BP1/2 are paralogous proteins that promote stress granule formation in response to cellular stresses, including viral infection. G3BP1/2 are prominent interactors of the nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the functional consequences of the G3BP1-N interaction in the context of viral infection remain unclear. Here we used structural and biochemical analyses to define the residues required for G3BP1-N interaction, followed by structure-guided mutagenesis of G3BP1 and N to selectively and reciprocally disrupt their interaction. We found that mutation of F17 within the N protein led to selective loss of interaction with G3BP1 and consequent failure of the N protein to disrupt stress granule assembly. Introduction of SARS-CoV-2 bearing an F17A mutation resulted in a significant decrease in viral replication and pathogenesis in vivo, indicating that the G3BP1-N interaction promotes infection by suppressing the ability of G3BP1 to form stress granules.

SARS-CoV-2: analysis of the effects of mutations in non-structural proteins

A worldwide pandemic that started in China in late 2019 was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a single-stranded RNA virus belonging to the family Coronaviridae. Due to its structural variability and mutability, this virus continues to evolve and pose a major health threat around the world. Its characteristics, such as transmissibility, antigenicity, and resistance to drugs and vaccines, are continually altered through mutations. Examining mutational hotspots and their structural repercussions can thus aid in the development of more-effective vaccinations and treatment plans. In this study, we used full genome sequences of SARS-CoV-2 variants to predict structural changes in viral proteins. These sequences were obtained from the Global Initiative on Sharing Avian Influenza Data (GISAID), and a set of significant mutations were identified in each of the non-structural proteins (NSP1-16) and structural proteins, including the envelope, nucleocapsid, membrane, and spike proteins. The mutations were characterized as stabilizing or destabilizing based on their effect on protein dynamics and stability, and their impact on structure and function was evaluated. Among all of the proteins, NSP6 stands out as especially variable. The results of this study augment our understanding of how mutational events influence virus pathogenicity and evolution.

Proteomic applications in identifying protein-protein interactions

There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.

Repurposing clinically available drugs and therapies for pathogenic targets to combat SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic has affected a large portion of the global population, both physically and mentally. Current evidence suggests that the rapidly evolving coronavirus subvariants risk rendering vaccines and antibodies ineffective due to their potential to evade existing immunity, with enhanced transmission activity and higher reinfection rates that could lead to new outbreaks across the globe. The goal of viral management is to disrupt the viral life cycle as well as to relieve severe symptoms such as lung damage, cytokine storm, and organ failure. In the fight against viruses, the combination of viral genome sequencing, elucidation of the structure of viral proteins, and identifying proteins that are highly conserved across multiple coronaviruses has revealed many potential molecular targets. In addition, the time- and cost-effective repurposing of preexisting antiviral drugs or approved/clinical drugs for these targets offers considerable clinical advantages for COVID-19 patients. This review provides a comprehensive overview of various identified pathogenic targets and pathways as well as corresponding repurposed approved/clinical drugs and their potential against COVID-19. These findings provide new insight into the discovery of novel therapeutic strategies that could be applied to the control of disease symptoms emanating from evolving SARS-CoV-2 variants.

Shaping Nanobodies and Intrabodies against Proteoforms

Proteoforms expand genomic diversity and direct developmental processes. While high-resolution mass spectrometry has accelerated characterization of proteoforms, molecular techniques working to bind and disrupt the function of specific proteoforms have lagged behind. In this study, we worked to develop intrabodies capable of binding specific proteoforms. We employed a synthetic camelid nanobody library expressed in yeast to identify nanobody binders of different SARS-CoV-2 receptor binding domain (RBD) proteoforms. Importantly, employment of the positive and negative selection mechanisms inherent to the synthetic system allowed for amplification of nanobody-expressing yeast that bind to the original (Wuhan strain RBD) but not the E484 K (Beta variant) mutation. Nanobodies raised against specific RBD proteoforms were validated by yeast-2-hybrid analysis and sequence comparisons. These results provide a framework for development of nanobodies and intrabodies that target proteoforms.

A Novel Viral Assembly Inhibitor Blocks SARS-CoV-2 Replication in Airway Epithelial Cells

The ongoing evolution of SARS-CoV-2 to evade vaccines and therapeutics underlines the need for novel therapies with high genetic barriers to resistance. The small molecule PAV-104, identified through a cell-free protein synthesis and assembly screen, was recently shown to target host protein assembly machinery in a manner specific to viral assembly. Here, we investigated the capacity of PAV-104 to inhibit SARS-CoV-2 replication in human airway epithelial cells (AECs). Our data demonstrate that PAV-104 inhibited > 99% of infection with diverse SARS-CoV-2 variants in primary and immortalized human AECs. PAV-104 suppressed SARS-CoV-2 production without affecting viral entry or protein synthesis. PAV-104 interacted with SARS-CoV-2 nucleocapsid (N) and interfered with its oligomerization, blocking particle assembly. Transcriptomic analysis revealed that PAV-104 reversed SARS-CoV-2 induction of the Type-I interferon response and the 'maturation of nucleoprotein' signaling pathway known to support coronavirus replication. Our findings suggest that PAV-104 is a promising therapeutic candidate for COVID-19.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

Large-scale phage-based screening reveals extensive pan-viral mimicry of host short linear motifs

Viruses mimic host short linear motifs (SLiMs) to hijack and deregulate cellular functions. Studies of motif-mediated interactions therefore provide insight into virus-host dependencies, and reveal targets for therapeutic intervention. Here, we describe the pan-viral discovery of 1712 SLiM-based virus-host interactions using a phage peptidome tiling the intrinsically disordered protein regions of 229 RNA viruses. We find mimicry of host SLiMs to be a ubiquitous viral strategy, reveal novel host proteins hijacked by viruses, and identify cellular pathways frequently deregulated by viral motif mimicry. Using structural and biophysical analyses, we show that viral mimicry-based interactions have similar binding strength and bound conformations as endogenous interactions. Finally, we establish polyadenylate-binding protein 1 as a potential target for broad-spectrum antiviral agent development. Our platform enables rapid discovery of mechanisms of viral interference and the identification of potential therapeutic targets which can aid in combating future epidemics and pandemics.

The Archer and the Prey: The Duality of PAF1C in Antiviral Immunity

In the ongoing arms race between virus and host, fine-tuned gene expression plays a critical role in antiviral signaling. However, viruses have evolved to disrupt this process and promote their own replication by targeting host restriction factors. Polymerase-associated factor 1 complex (PAF1C) is a key player in this relationship, recruiting other host factors to regulate transcription and modulate innate immune gene expression. Consequently, PAF1C is consistently targeted by a diverse range of viruses, either to suppress its antiviral functions or co-opt them for their own benefit. In this review, we delve into the current mechanisms through which PAF1C restricts viruses by activating interferon and inflammatory responses at the transcriptional level. We also highlight how the ubiquity of these mechanisms makes PAF1C especially vulnerable to viral hijacking and antagonism. Indeed, as often as PAF1C is revealed to be a restriction factor, viruses are found to have targeted the complex in reply.

Emerging roles of SARS-CoV-2 Spike-ACE2 in immune evasion and pathogenesis

The COVID-19 pandemic, caused by SARS-CoV-2, has caused an estimated 5 billion infections and 20 million deaths by respiratory failure. In addition to the respiratory disease, SARS-CoV-2 infection has been associated with many extrapulmonary complications not easily explainable by the respiratory infection. A recent study showed that the SARS-CoV-2 spike protein, which mediates cell entry by binding to the angiotensin-converting enzyme 2 (ACE2) receptor, signals through ACE2 to change host cell behavior. In CD8⁺ T cells, spike-dependent ACE2-mediated signaling suppresses immunological synapse (IS) formation and impairs their killing ability, leading to immune escape of virus-infected cells. In this opinion article, we discuss the consequences of ACE2 signaling on the immune response and propose that it contributes to the extrapulmonary manifestations of COVID-19.

Targeting viral proteins for restraining SARS-CoV-2: focusing lens on viral proteins beyond spike for discovering new drug targets

INTRODUCTION

Emergence of highly infectious SARS-CoV-2 variants are reducing protection provided by current vaccines, requiring constant updates in antiviral approaches. The virus encodes four structural and sixteen nonstructural proteins which play important roles in viral genome replication and transcription, virion assembly, release , entry into cells, and compromising host cellular defenses. As alien proteins to host cells, many viral proteins represent potential targets for combating the SARS-CoV-2.

AREAS COVERED

Based on literature from PubMed and Web of Science databases, the authors summarize the typical characteristics of SARS-CoV-2 from the whole viral particle to the individual viral proteins and their corresponding functions in virus life cycle. The authors also discuss the potential and emerging targeted interventions to curb virus replication and spread in detail to provide unique insights into SARS-CoV-2 infection and countermeasures against it.

EXPERT OPINION

Our comprehensive analysis highlights the rationale to focus on non-spike viral proteins that are less mutated but have important functions. Examples of this include: structural proteins (e.g. nucleocapsid protein, envelope protein) and extensively-concerned nonstructural proteins (e.g. NSP3, NSP5, NSP12) along with the ones with relatively less attention (e.g. NSP1, NSP10, NSP14 and NSP16), for developing novel drugs to overcome resistance of SARS-CoV-2 variants to preexisting vaccines and antibody-based treatments.

HuCoPIA: An Atlas of Human vs. SARS-CoV-2 Interactome and the Comparative Analysis with Other Coronaviridae Family Viruses

SARS-CoV-2, a novel betacoronavirus strain, has caused a pandemic that has claimed the lives of nearly 6.7M people worldwide. Vaccines and medicines are being developed around the world to reduce the disease spread, fatality rates, and control the new variants. Understanding the protein-protein interaction mechanism of SARS-CoV-2 in humans, and their comparison with the previous SARS-CoV and MERS strains, is crucial for these efforts. These interactions might be used to assess vaccination effectiveness, diagnose exposure, and produce effective biotherapeutics. Here, we present the HuCoPIA database, which contains approximately 100,000 protein-protein interactions between humans and three strains (SARS-CoV-2, SARS-CoV, and MERS) of betacoronavirus. The interactions in the database are divided into common interactions between all three strains and those unique to each strain. It also contains relevant functional annotation information of human proteins. The HuCoPIA database contains SARS-CoV-2 (41,173), SARS-CoV (31,997), and MERS (26,862) interactions, with functional annotation of human proteins like subcellular localization, tissue-expression, KEGG pathways, and Gene ontology information. We believe HuCoPIA will serve as an invaluable resource to diverse experimental biologists, and will help to advance the research in better understanding the mechanism of betacoronaviruses.

Selenoprotein S Interacts with the Replication and Transcription Complex of SARS-CoV-2 by Binding nsp7

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replicates and evades detection using ER membranes and their associated protein machinery. Among these hijacked human proteins is selenoprotein S (selenos). This selenoprotein takes part in the protein quality control, signaling, and the regulation of cytokine secretion. While the role of selenos in the viral life cycle is not yet known, it has been reported to interact with SARS-CoV-2 nonstructural protein 7 (nsp7), a viral protein essential for the replication of the virus. We set to study whether selenos and nsp7 interact directly and if they can still bind when nsp7 is bound to the replication and transcription complex of the virus. Using biochemical assays, we show that selenos binds directly to nsp7. In addition, we found that selenos can bind to nsp7 when it is in a complex with the coronavirus's minimal replication and transcription complex, comprised of nsp7, nsp8, and the RNA-dependent RNA polymerase nsp12. In addition, through crosslinking experiments, we mapped the interaction sites of selenos and nsp7 in the replication complex and showed that the hydrophobic segment of selenos is essential for binding to nsp7. This arrangement leaves an extended helix and the intrinsically disordered segment of selenos-including the reactive selenocysteine-exposed and free to potentially recruit additional proteins to the replication and transcription complex.

Applications of nanobodies in the prevention, detection, and treatment of the evolving SARS-CoV-2

Global health and economy are deeply influenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its newly emerging variants. Nanobodies with nanometer-scale size are promising for the detection and treatment of SARS-CoV-2 and its variants because they are superior to conventional antibodies in terms of cryptic epitope accessibility, tissue penetration, cost, formatting adaptability, and especially protein stability, which enables their aerosolized specific delivery to lung tissues. This review summarizes the progress in the prevention, detection, and treatment of SARS-CoV-2 using nanobodies, as well as strategies to combat the evolving SARS-CoV-2 variants. Generally, highly efficient generation of potent broad-spectrum nanobodies targeting conserved epitopes or further construction of multivalent formats targeting non-overlapping epitopes can promote neutralizing activity against SARS-CoV-2 variants and suppress immune escape.

Bioinformatics approaches for unveiling virus-host interactions

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.