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

SARS-CoV-2 NSP2 specifically interacts with cellular protein SmgGDS

The novel coronavirus, SARS-CoV-2, is responsible for the ongoing global pandemic of Coronavirus disease 2019 (COVID-19). SARS-CoV-2 belongs to the Coronaviridae family, which also includes the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). Recent studies using affinity purification mass spectrometry analysis have revealed that SARS-CoV-2 NSP2 may interact with cellular protein Small G-protein dissociation stimulator (SmgGDS), a guanine nucleotide exchange factor (GEF) that specifically regulates RhoA and RhoC proteins, which are involved in a range of cellular activities, including actin reorganization, cell motility and adhesion. Biochemical experiments have confirmed that NSP2 binds directly to SmgGDS and that this interaction requires the full-length NSP2. Given the low sequence conservation compared to other coronaviruses, this interaction with SmgGDS appears specific to SARS-CoV-2, with similar proteins in other coronaviruses unable to bind SmgGDS. Further studies have revealed that the binding of SARS-CoV-2 NSP2 to SmgGDS has a significant inhibitory effect on the GEF activity of SmgGDS. This inhibition disrupts the nucleotide exchange process on RhoA, impairing its function and potentially contributing to the pathogenic mechanisms of SARS-CoV-2. These findings highlight a novel pathway through which SARS-CoV-2 may influence host cellular processes, providing insights into the unique impact of coronaviruses on cellular regulation.

Decoding potential host protein targets against Flaviviridae using protein-protein interaction network

Flaviviridae family comprises some of the most vulnerable viruses known for causing widespread outbreaks, high mortality rates, and severe long-term health complications in humans. Viruses like Dengue (DENV), Zika (ZIKV) and Hepatitis C (HCV) are endemic across the globe, especially in tropical and subtropical regions, infecting multiple tissues and leading to significant health crises. Investigating virus-host interactions across tissues can reveal tissue-specific drug targets and aid antiviral drug repurposing. In this study, we employed a multi-step computational approach to construct a comprehensive virus-human interactome by integrating virus-host protein-protein interactions (PPIs) with tissue-specific gene expression profiles to study key protein targets associated with Flaviviridae infections. Mapping drug-target predictions revealed druggable proteins - CCNA2 in peripheral blood mononuclear cells (PBMC) and EIF2S2, CDK7 and CARS in the liver, with Tamoxifen, Sirolimus, Entrectinib and L-cysteine as potential repurposable drugs, respectively. Further protein-ligand docking and molecular dynamics (MD) simulations were conducted to assess the stability of the complexes. These findings highlight common druggable human targets exploited by DENV, ZIKV and HCV, providing a foundation for broad-spectrum antiviral therapies. By focusing on shared host pathways and targets in viral replication, we propose promising drug candidates, supporting the development of unified therapeutic strategies against Flaviviridae viruses.

Proteomic Analysis of 442 Clinical Plasma Samples From Individuals With Symptom Records Revealed Subtypes of Convalescent Patients Who Had COVID-19

After the coronavirus disease 2019 (COVID-19) pandemic, the postacute effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have gradually attracted attention. To precisely evaluate the health status of convalescent patients with COVID-19, we analyzed symptom and proteome data of 442 plasma samples from healthy controls, hospitalized patients, and convalescent patients 6 or 12 months after SARS-CoV-2 infection. Symptoms analysis revealed distinct relationships in convalescent patients. Results of plasma protein expression levels showed that C1QA, C1QB, C2, CFH, CFHR1, and F10, which regulate the complement system and coagulation, remained highly expressed even at the 12-month follow-up compared with their levels in healthy individuals. By combining symptom and proteome data, 442 plasma samples were categorized into three subtypes: S1 (metabolism-healthy), S2 (COVID-19 retention), and S3 (long COVID). We speculated that convalescent patients reporting hair loss could have a better health status than those experiencing headaches and dyspnea. Compared to other convalescent patients, those reporting sleep disorders, appetite decrease, and muscle weakness may need more attention because they were classified into the S2 subtype, which had the most samples from hospitalized patients with COVID-19. Subtyping convalescent patients with COVID-19 may enable personalized treatments tailored to individual needs. This study provides valuable plasma proteomic datasets for further studies associated with long COVID.

Multimodal SARS-CoV-2 interactome sketches the virus-host spatial organization

An accurate spatial representation of protein-protein interaction networks is needed to achieve a realistic and biologically relevant representation of interactomes. Here, we leveraged the spatial information included in Proximity-Dependent Biotin Identification (BioID) interactomes of SARS-CoV-2 proteins to calculate weighted distances and model the organization of the SARS-CoV-2-human interactome in three dimensions (3D) within a cell-like volume. Cell regions with viral occupancy were highlighted, along with the coordination of viral proteins exploiting the cellular machinery. Profiling physical intra-virus and virus-host contacts enabled us to demonstrate both the accuracy and the predictive value of our 3D map for direct interactions, meaning that proteins in closer proximity tend to interact physically. Several functionally important virus-host complexes were detected, and robust structural models were obtained, opening the way to structure-directed drug discovery screens. This PPI discovery pipeline approach brings us closer to a realistic spatial representation of interactomes, which, when applied to viruses or other pathogens, can provide significant information for infection. Thus, it represents a promising tool for coping with emerging infectious diseases.

Assessment of the preventive effect of knockdown of cellular genes NXF1, PRPS1PRPS1 and NAA10 in influenza infection in an in vitro model

INTRODUCTION

Influenza is an acute respiratory viral infectious disease caused by the influenza viruses. Current preventive and therapeutic approaches are of great anti-epidemic importance, but there are a number of problems, such as the rapid emergence of resistant strains, the lack of cross-immunity and the effectiveness of vaccines. One of the approaches to the development of anti-influenza agents is the use of RNA interference and small interfering RNAs complementary to the mRNA target of viral and cellular genes. Aim ‒ to evaluate the prophylactic anti-influenza effect of siRNAs directed to the cellular genes NXF1, PRPS1 and NAA10 in an in vitro model.

MATERIALS AND METHODS

Antigenic variants of influenza A virus: A/California/7/09 (H1N1), A/WSN/33 (H1N1) and A/Brisbane/59/07 (H1N1); cell cultures A549 and MDCK. The study was performed using molecular genetic (transfection, NC isolation, RT-PCR-RV) and virological (cell culture infection, titration by visual CPE, viral titer assessment using the Ramakrishnan method) methods.

RESULTS

It was shown that siRNAs targeting the cellular genes NXF1, PRPS1 and NAA10, when used prophylactically in cell culture at a concentration of 0.25 μg per well, during infection with influenza virus strains A/California/7/09 (H1N1), A/WSN/33 (H1N1) and A/Brisbane/59/07 (H1N1) at a multiplicity of infection of 0.01, reduced viral replication to a level of 220 TCID50 per 1 ml of cell medium, whereas in control untreated cells the viral yield was ~106 TCID50 per 1 ml of medium.

CONCLUSIONS

Reproduction of influenza A viruses directly depends on the protein products of the NXF1, PRPS1, and NAA10 genes. Reduced expression of these genes disrupts the life cycle and activity of influenza viruses. Such an approach can potentially be studied and used for closely and distantly related representatives of other virus families.

Disruption of Molecular Interactions between the G3BP1 Stress Granule Host Protein and the Nucleocapsid (NTD-N) Protein Impedes SARS-CoV-2 Virus Replication

The Ras GTPase-activating protein SH3-domain-binding protein 1 (G3BP1) serves as a formidable barrier to viral replication by generating stress granules (SGs) in response to viral infections. Interestingly, viruses, including SARS-CoV-2, have evolved defensive mechanisms to hijack SG proteins like G3BP1 for the dissipation of SGs that lead to the evasion of the host's immune responses. Previous research has demonstrated that the interaction between the NTF2-like domain of G3BP1 (G3BP1NTF-2) and the intrinsically disordered N-terminal domain (NTD-N1-25) of the N-protein plays a crucial role in regulating viral replication and pathogenicity. Interestingly, the current study identified an additional upstream stretch of residues (128KDGIIWVATEG138) (N128-138) within the N-terminal domain of the N-protein (NTD-N41-174) that also forms molecular contacts with the G3BP1 protein, as revealed through in silico analysis, site-directed mutagenesis, and biochemical analysis. Remarkably, WIN-62577, and fluspirilene, the small molecules targeting the conserved peptide-binding pocket in G3BP1NTF-2, not only disrupted the protein-protein interactions (PPIs) between NTD-N41-174 and G3BP1NTF-2 but also exhibited significant antiviral efficacy against SARS-CoV-2 replication with EC50 values of ∼1.8 and ∼1.3 μM, respectively. The findings of this study, validated by biophysical thermodynamics and biochemical investigations, advance the potential of developing therapeutics targeting the SG host protein against SARS-CoV-2, which may also serve as a broad-spectrum antiviral target.

4D-DIA Proteomics Uncovers New Insights into Host Salivary Response Following SARS-CoV-2 Omicron Infection

Since late 2021, Omicron variants have dominated the epidemiological scenario as the most successful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sublineages, driving new and breakthrough infections globally over the past two years. In this study, we investigated for the first time the host salivary response of COVID-19 patients infected with Omicron variants (BA.1, BA.2, and BA.4/5) by using an untargeted four-dimensional data-independent acquisition (4D-DIA)-based proteomics approach. We identified 137 proteins whose abundance levels differed between the COVID-19 positive and negative groups. Salivary signatures were mainly enriched in ribosomal proteins, linked to mRNAviral translation, protein synthesis and processing, immune innate, and antiapoptotic signaling. The higher abundance of 14-3-3 proteins (YWHAG, YWHAQ, YWHAE, and SFN) in saliva, first reported here, may be associated with increased infectivity and improved viral replicative fitness. We also identified seven proteins (ACTN1, H2AC2, GSN, NDKA, CD109, GGH, and PCYOX) that yielded comprehension into Omicron infection and performed outstandingly in screening patients with COVID-19 in a hospital setting. This panel also presented an enhanced anti-COVID-19 and anti-inflammatory signature, providing insights into disease severity, supported by comparisons with other proteome data sets. The salivary signature provided valuable insights into the host's response to SARS-CoV-2 Omicron infection, shedding light on the pathophysiology of COVID-19, particularly in cases associated with mild disease. It also underscores the potential clinical applications of saliva for disease screening in hospital settings. Data are available via ProteomeXchange with the identifier PXD054133.

Human herpesvirus-associated transposable element activation in human aging brains with Alzheimer's disease

INTRODUCTION

Human herpesvirus (HHV) has been linked to Alzheimer's disease (AD), but the underlying mechanisms remain unknown.

METHODS

We leveraged functional genomics data from Religious Orders Study or the Rush Memory and Aging Project (ROS/MAP) and Mount Sinai Brain Bank (MSBB) brain biobanks and single-cell RNA-sequencing data from HHV-infected forebrain organoids to investigate HHV-infection-associated transposable element (TE) dysregulation underlying AD etiologies.

RESULTS

We identified widespread TE dysregulation in HHV-positive human AD brains, including an astrocyte-specific upregulation of LINE1 subfamily TEs in HHV-positive human AD brains. We further pinpointed astrocyte-specific LINE1 upregulation that could potentially regulate target gene NEAT1 expression via long-range enhancer-promoter chromatin interactions. This LINE1 dysregulation can be partially reversed by the usage of anti-HHV drugs (valacyclovir and acyclovir) in a virus-infected human brain organoid model. Finally, we demonstrated that valacyclovir rescued tau-associated neuropathology and alleviated LINE1 activation in an experimental tau aggregation model.

DISCUSSION

Our analysis provides associations linking molecular, clinical, and neuropathological AD features with HHV infection, which warrants future clinical validation.

HIGHLIGHTS

Via analysis of bulk RNA-seq data in two large-scale human brain biobanks, ROS/MAP (n = 109 pathologically confirmed AD and n = 44 cognitively healthy controls) and MSBB (n = 284 AD and n = 150 cognitively healthy controls), we identified widespread TE activation in HHV-positive human AD brains and significantly positive associations of HHV RNA abundance with APOE4 genotype, Braak staging score, and CERAD score. We identified cell type-specific LINE1 upregulation in both microglia and astrocytes of human AD brains via long-range enhancer-promoter chromatin interactions on lncRNA nuclear enriched abundant transcript 1 (NEAT1). We determined that usage of valacyclovir and acyclovir was significantly associated with reduced incidence of AD in a large real-world patient database. Using the HEK293 tau P301S model and U2OS mt-Keima cell model, we determined that valacyclovir treatment rescued tau-associated neuropathology and alleviated activation of LINE1 with increased cellular autophagy-level mechanistically supported clinical benefits of valacyclovir in real-world patient data.

Pressure to evade cell-autonomous innate sensing reveals interplay between mitophagy, IFN signaling, and SARS-CoV-2 evolution

SARS-CoV-2 emerged, and continues to evolve, to efficiently infect humans worldwide. SARS-CoV-2 evades early innate recognition, interferon signaling occurring only in bystander cells. How the virus continues to evolve in the face of innate responses 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 (mitophagy) 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, loss of autophagy increased MAVS and overcomes ORF9b-mediated antagonism. This has driven the evolution of SARS-CoV-2 to express more ORF9b, allowing SARS-CoV-2 to replicate under conditions of increased MAVS signaling. Altogether, we find a critical role of mitophagy in the regulation of innate immunity and uncover an evolutionary trajectory of SARS-CoV-2 ORF9b to overcome host defenses.

Dynamic Cellular Proteome Remodeling during SARS-CoV-2 Infection. Identification of Plasma Protein Readouts

The outbreak of COVID-19, led to an ongoing pandemic with devastating consequences for the global economy and human health. With the global spread of SARS-CoV-2, multidisciplinary initiatives were launched to explore new diagnostic, therapeutic, and vaccination strategies. From this perspective, proteomics could help to understand the mechanisms associated with SARS-CoV-2 infection and to identify new therapeutic options. A TMT-based quantitative proteomics and phosphoproteomics analysis was performed to study the proteome remodeling of human lung alveolar cells expressing human ACE2 (A549-ACE2) after infection with SARS-CoV-2. Detectability and the prognostic value of selected proteins was analyzed by targeted PRM. A total of 6802 proteins and 6428 phospho-sites were identified in A549-ACE2 cells after infection with SARS-CoV-2. The differential proteins here identified revealed that A549-ACE2 cells undergo a time-dependent regulation of essential processes, delineating the precise intervention of the cellular machinery by the viral proteins. From this mechanistic background and by applying machine learning modeling, 29 differential proteins were selected and detected in the serum of COVID-19 patients, 14 of which showed promising prognostic capacity. Targeting these proteins and the protein kinases responsible for the reported phosphorylation changes may provide efficient alternative strategies for the clinical management of COVID-19.

The Nucleocapsid (N) Proteins of Different Human Coronaviruses Demonstrate a Variable Capacity to Induce the Formation of Cytoplasmic Condensates

To date, seven human coronaviruses (HCoVs) have been identified. Four of these viruses typically manifest as a mild respiratory disease, whereas the remaining three can cause severe conditions that often result in death. The reasons for these differences remain poorly understood, but they may be related to the properties of individual viral proteins. The nucleocapsid (N) protein plays a crucial role in the packaging of viral genomic RNA and the modification of host cells during infection, in part due to its capacity to form dynamic biological condensates via liquid-liquid phase separation (LLPS). In this study, we investigated the capacity of N proteins derived from all HCoVs to form condensates when transiently expressed in cultured human cells. Some of the transfected cells were observed to contain cytoplasmic granules in which most of the N proteins were accumulated. Notably, the N proteins of SARS-CoV and SARS-CoV-2 showed a significantly reduced tendency to form cytoplasmic condensates. The condensate formation was not a consequence of overexpression of N proteins, as is typical for LLPS-inducing proteins. These condensates contained components of stress granules (SGs), indicating that the expression of N proteins caused the formation of SGs, which integrate N proteins. Thus, the N proteins of two closely related viruses, SARS-CoV and SARS-CoV-2, have the capacity to antagonize SG induction and/or assembly, in contrast to all other known HCoVs.

Protein-Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species, where databases are incomplete. Here, the relationship extraction performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools was compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist between the two other approaches, with variable performances. The selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes

Historically, there have been many outbreaks of viral diseases that have continued to claim millions of lives. Research on human-virus protein-protein interactions (PPIs) is vital to understanding the principles of human-virus relationships, providing an essential foundation for developing virus control strategies to combat diseases. The rapidly accumulating data on human-virus PPIs offer unprecedented opportunities for bioinformatics research around human-virus PPIs. However, available detailed analyses and summaries to help use these resources systematically and efficiently are lacking. Here, we comprehensively review the bioinformatic resources used in human-virus PPI research, and discuss and compare their functions, performance, and limitations. This review aims to provide researchers with a bioinformatic toolbox that will hopefully better facilitate the exploration of human-virus PPIs based on binding modes.

Prediction of protein interactions between pine and pine wood nematode using deep learning and multi-dimensional feature fusion

Pine Wilt Disease (PWD) is a devastating forest disease that has a serious impact on ecological balance ecological. Since the identification of plant-pathogen protein interactions (PPIs) is a critical step in understanding the pathogenic system of the pine wilt disease, this study proposes a Multi-feature Fusion Graph Attention Convolution (MFGAC-PPI) for predicting plant-pathogen PPIs based on deep learning. Compared with methods based on single-feature information, MFGAC-PPI obtains more 3D characterization information by utilizing AlphaFold and combining protein sequence features to extract multi-dimensional features via Transform with improved GCN. The performance of MFGAC-PPI was compared with the current representative methods of sequence-based, structure-based and hybrid characterization, demonstrating its superiority across all metrics. The experiments showed that learning multi-dimensional feature information effectively improved the ability of MFGAC-PPI in plant and pathogen PPI prediction tasks. Meanwhile, a pine wilt disease PPI network consisting of 2,688 interacting protein pairs was constructed based on MFGAC-PPI, which made it possible to systematically discover new disease resistance genes in pine trees and promoted the understanding of plant-pathogen interactions.

Productive infection of the retinal pigment epithelium by SARS-CoV-2: Initial effects and consideration of long-term consequences

As the SARS-CoV-2 coronavirus continues to evolve and infect the global population, many individuals are likely to suffer from post-acute sequelae of SARS-CoV-2 infection (PASC). Manifestations of PASC include vision symptoms, but little is known about the ability of SARS-CoV-2 to infect and impact the retinal cells. Here, we demonstrate that SARS-CoV-2 can infect and perturb the retinal pigment epithelium (RPE) in vivo, after intranasal inoculation of a transgenic mouse model of SARS-CoV-2 infection, and in cell culture. Separate lentiviral studies showed that SARS-CoV-2 Spike protein mediates viral entry and replication in RPE cells, while the Envelope and ORF3a proteins induce morphological changes. Infection with major variants of SARS-CoV-2 compromised the RPE barrier function and phagocytic capacity. It also caused complement activation and production of cytokines and chemokines, resulting in an inflammatory response that spread across the RPE layer. This inflammatory signature has similarities to that associated with the onset of age-related macular degeneration (AMD), a major cause of human blindness, resulting from RPE pathology that eventually leads to photoreceptor cell loss. Thus, our findings suggest that post-acute sequelae of SARS-CoV-2 infection of the RPE may have long-term implications for vision, perhaps comparable to the increased occurrence of AMD found among individuals infected by HIV, but with greater public health consequences due to the much larger number of SARS-CoV-2 infections.

Role of the Neanderthal Genome in Genetic Susceptibility to COVID-19: 3p21.31 Locus in the Spotlight

Since the outbreak of COVID-19, genome-wide association studies have tried to discover the role of genetic predisposition in the clinical variability of this viral infection. The findings of various investigations have led to several loci for COVID-19 genetic susceptibility. Among candidate regions, the 3p21.31 locus has been in the spotlight among scientists, as it can increase the risk of severe COVID-19 by almost two fold. In addition to its substantial association with COVID-19 severity, this locus is related to some common diseases, such as diabetes, malignancies, and coronary artery disease. This locus also harbors evolutionary traces of Neanderthal genomes, which is believed to be the underlying reason for its association with COVID-19 severity. Additionally, the inheritance of this locus from Neanderthals seems to be under positive selection. This review aims to summarize a collection of evidence on the 3p21.31 locus and its impact on COVID-19 outcomes by focusing on the risk variants originated from the Neanderthal genome. Moreover, we discuss candidate genes at this locus and the possible mechanisms by which they influence the progression of COVID-19 symptoms. Better insights into human genetic susceptibility to newly emerging diseases such as COVID-19 and its evolutionary origin can provide fundamentals for risk assessment of different populations as well as the development of personalized prevention and treatments based on genomic medicine.

Control of nuclear localization of the nucleocapsid protein of SARS-CoV-2

The nucleocapsid (N) protein of coronaviruses is a structural protein that binds viral RNA for assembly into the mature virion, a process that occurs in the cytoplasm. Several coronavirus N proteins also localize to the nucleus. Herein, we identify that two sequences (NLSs) are required for nuclear localization of the SARS-CoV-2 N protein. Deletion or mutation of these two sequences creates an N protein that does not localize to the nucleus in HEK293T cells. Overexpression of both wild-type and NLS-mutated N proteins dysregulate a largely overlapping set of mRNAs in HEK293T cells, suggesting that these N proteins do not have direct nuclear effects on transcription. Consistent with that hypothesis, both N proteins induce nuclear localization of NF-κB p65 and dysregulate a set of previously identified NF-κB-dependent genes. The effects of N on nuclear properties are proposed to alter host cell functions that contribute to viral pathogenesis or replication.

Backstage Heroes-Yeast in COVID-19 Research

The extremely rapid development of understanding and technology that led to the containment of the COVID-19 pandemic resulted from collaborative efforts in the fields of Betacoronavirus pandemicum (SARS-CoV-2) biology, pharmacology, vaccinology, and medicine. Perhaps surprisingly, much of the research was conducted using simple and efficient yeast models. In this manuscript, we describe how yeast, eukaryotic microorganisms, have been used to research this global challenge, focusing on the therapeutic potential of the studies discussed herein. Thus, we outline the role of yeast in studying viral protein interactions with the host cell proteome, including the binding of the SARS-CoV-2 virus spike protein to the human ACE2 receptor and its modulation. The production and exploration of viral antigens in yeast systems, which led to the development of two approved COVID-19 vaccines, are also detailed. Moreover, yeast platforms facilitating the discovery and production of single-domain antibodies (nanobodies) against SARS-CoV-2 are described. Methods guiding modern and efficient drug discovery are explained at length. In particular, we focus on studies designed to search for inhibitors of the main protease (Mpro), a unique target for anti-coronaviral therapies. We highlight the adaptability of the techniques used, providing opportunities for rapid modification and implementation alongside the evolution of the SARS-CoV-2 virus. Approaches introduced in yeast systems that may have universal potential application in studies of emerging viral diseases are also described.

KVX-053, a protein tyrosine phosphatase 4A3 inhibitor, ameliorates SARS-CoV-2 spike protein subunit 1-induced acute lung injury in mice

Acute respiratory distress syndrome (ARDS), often preceded by acute lung injury (ALI), is characterized by the accumulation of inflammatory fluid in the lung alveoli, leaky alveolar epithelium and endothelium, and overexpression of proinflammatory cytokines. This progression from ALI to ARDS is a major contributor to the high mortality observed in patients with COVID-19. The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to lung angiotensin-converting enzyme 2 (ACE2), and in addition to facilitating viral cell entry, it plays an important role in the development of ALI and ARDS, especially in the later phases of COVID-19 as well as long-COVID. Protein tyrosine phosphatase (PTP) 4A3 is a key mediator of ARDS pathology. This study tested the hypothesis that targeting PTP4A3 would prevent COVID-19-associated ALI. Intratracheal administration of SARS-CoV-2 spike protein subunit 1 to K18-hACE2 transgenic mice expressing human ACE2 elicited pulmonary and systemic inflammation, leaky alveoli, overexpression of cytokines, structural lung injury, and lung dysfunction; all these symptoms were ameliorated by the selective, allosteric inhibitor of PTP4A3, KVX-053. These findings provide the first evidence supporting a role for PTP4A3 in the development of SARS-CoV-2-mediated ALI. SIGNIFICANCE STATEMENT: This study tested the hypothesis that targeting PTP4A3 would prevent COVID-19-associated ALI/ARDS. Intratracheal administration of SARS-CoV-2 spike protein subunit 1 to K18-hACE2 transgenic mice expressing human ACE2 elicited pulmonary and systemic inflammation, leaky alveoli, overexpression of cytokines and chemokines, structural lung injury, and lung dysfunction; all these symptoms were ameliorated by the selective, allosteric inhibitor of PTP4A3, KVX-053. These findings suggest that this novel PTP4A3 inhibitor may be useful against COVID-19 and potentially other viral-induced ARDS.

Analysis of the structure and interactions of the SARS-CoV-2 ORF7b accessory protein

SARS-CoV-2 carries a sizeable number of proteins that are accessory to replication but may be essential for virus-host interactions and modulation of the host immune response. Here, we investigated the structure and interactions of the largely unknown ORF7b, a small membranous accessory membrane protein of SARS-CoV-2. We show that structural predictions indicate a transmembrane (TM) leucine zipper for ORF7b, and experimentally confirm the predominantly α-helical secondary structure within a phospholipid membrane mimetic by solid-state NMR. We also show that ORF7b forms heterogeneous higher-order multimers. We determined ORF7b interactions with cellular TM leucine zipper proteins using both biochemical and NMR approaches, providing evidence for ORF7b interaction with the TM domains of E-cadherin, as well as phospholamban. Our results place ORF7b as a hypothetical interferer in cellular processes that utilize leucine zipper motifs in transmembrane multimerization domains.

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.

Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions

Assessing mutation impact on the binding affinity change (ΔΔG) of protein-protein interactions (PPIs) plays a crucial role in unraveling structural-functional intricacies of proteins and developing innovative protein designs. In this study, we present a deep learning framework, PIANO, for improved prediction of ΔΔG in PPIs. The PIANO framework leverages a graph masked self-distillation scheme for protein structural geometric representation pre-training, which effectively captures the structural context representations surrounding mutation sites, and makes predictions using a multi-branch network consisting of multiple encoders for amino acids, atoms, and protein sequences. Extensive experiments demonstrated its superior prediction performance and the capability of pre-trained encoder in capturing meaningful representations. Compared to previous methods, PIANO can be widely applied on both holo complex structures and apo monomer structures. Moreover, we illustrated the practical applicability of PIANO in highlighting pathogenic mutations and crucial proteins, and distinguishing de novo mutations in disease cases and controls in PPI systems. Overall, PIANO offers a powerful deep learning tool, which may provide valuable insights into the study of drug design, therapeutic intervention, and protein engineering.

SARS-CoV-2 Assembly: Gaining Infectivity and Beyond

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was responsible for causing the COVID-19 pandemic. Intensive research has illuminated the complex biology of SARS-CoV-2 and its continuous evolution during and after the COVID-19 pandemic. While much attention has been paid to the structure and functions of the viral spike protein and the entry step of viral infection, partly because these are targets for neutralizing antibodies and COVID-19 vaccines, the later stages of SARS-CoV-2 replication, including the assembly and egress of viral progenies, remain poorly characterized. This includes insight into how the activities of the viral structural proteins are orchestrated spatially and temporally, which cellular proteins are assimilated by the virus to assist viral assembly, and how SARS-CoV-2 counters and evades the cellular mechanisms antagonizing virus assembly. In addition to becoming infectious, SARS-CoV-2 progenies also need to survive the hostile innate and adaptive immune mechanisms, such as recognition by neutralizing antibodies. This review offers an updated summary of the roles of SARS-CoV-2 structural proteins in viral assembly, the regulation of assembly by viral and cellular factors, and the cellular mechanisms that restrict this process. Knowledge of these key events often reveals the vulnerabilities of SARS-CoV-2 and aids in the development of effective antiviral therapeutics.

Valuable Contributions and Lessons Learned from Proteomics and Metabolomics Studies of COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus infected more than 775,686,716 humans and was responsible for the death of more than 7,054,093 individuals. COVID-19 has taught us that the development of vaccines, repurposing of drugs, and understanding the mechanism of a disease can be done within a short time. The COVID-19 proteomics and metabolomics has contributed to its diagnosis, understanding of its progression, host-virus interaction, disease mechanism, and also in the search of suitable anti-COVID therapeutics. Mass spectrometry based proteomics was used to find the potential biomarkers of different stages of COVID-19 including severe and nonsevere cases in the blood serum. Notably, protein-protein interaction techniques to understand host-virus interactions were also significantly useful. The single-cell proteomics studies were carried out to ascertain the changes in immune cell composition and its activation in mild COVID-19 patients versus severe COVID-19 patients using whole-blood and peripheral-blood mononuclear cells. Modern technologies were helpful to deal with the pandemic; however, there is still scope for further development. Further, attempts were made to understand the protein-protein, metabolite-metabolite, and protein-metabolite interactomes, derived from proteins and metabolite fingerprints of COVID-19 patients by reanalysis of COVID-19 public mass spectrometry based proteomics and metabolomics studies. Further, some of these interactions were supported by the literature as validations in the COVID-19 studies.

Myelin basic protein antagonizes the SARS-CoV-2 protein ORF3a-induced autophagy inhibition

Inhibition of autophagy is one of the hallmarks of the SARS-CoV-2 infection. Recently it was reported that SARS-CoV-2 protein ORF3a inhibits fusion of autophagosomes with lysosomes via interaction with VPS39 thus preventing binding of homotypic fusion and protein sorting (HOPS) complex to RAB7 GTPase. Here we report that myelin basic protein (MBP), a major structural component of the myelin sheath, binds ORF3a and is colocalized with it in mammalian cells. Co-expression of MBP with ORF3a restores autophagy in mammalian cells, inhibited by viral protein. Our data suggest that basic charge of MBP drives suppression of ORF3a-induced autophagy inhibition as its deaminated variants lost ability to bind ORF3a and counteract autophagy blockade. These results together with our recent findings, indicating that MBP interacts with structural components of the vesicle transport machinery-synaptosomal-associated protein 23 (SNAP23), vesicle-associated membrane protein 3 (VAMP3) and Sec1/Munc18-1 family members, may suggest protective role of the MBP in terms of the maintaining of protein traffic and autophagosome-lysosome fusion machinery in oligodendrocytes during SARS-CoV-2 infection. Finally, our data may indicate that deimination of MBP observed in the patients with multiple sclerosis (MS) may contribute to the previously reported worser outcomes of COVID-19 and increase of post-COVID-19 neurologic symptoms in patients with MS.

Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research

Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.

A genome-wide arrayed CRISPR screen identifies PLSCR1 as an intrinsic barrier to SARS-CoV-2 entry that recent virus variants have evolved to resist

Interferons (IFNs) play a crucial role in the regulation and evolution of host-virus interactions. Here, we conducted a genome-wide arrayed CRISPR knockout screen in the presence and absence of IFN to identify human genes that influence Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. We then performed an integrated analysis of genes interacting with SARS-CoV-2, drawing from a selection of 67 large-scale studies, including our own. We identified 28 genes of high relevance in both human genetic studies of Coronavirus Disease 2019 (COVID-19) patients and functional genetic screens in cell culture, with many related to the IFN pathway. Among these was the IFN-stimulated gene PLSCR1. PLSCR1 did not require IFN induction to restrict SARS-CoV-2 and did not contribute to IFN signaling. Instead, PLSCR1 specifically restricted spike-mediated SARS-CoV-2 entry. The PLSCR1-mediated restriction was alleviated by TMPRSS2 overexpression, suggesting that PLSCR1 primarily restricts the endocytic entry route. In addition, recent SARS-CoV-2 variants have adapted to circumvent the PLSCR1 barrier via currently undetermined mechanisms. Finally, we investigate the functional effects of PLSCR1 variants present in humans and discuss an association between PLSCR1 and severe COVID-19 reported recently.

Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.

Interaction of Receptor-Binding Domain of the SARS-CoV-2 Omicron Variant with hACE2 and Actin

The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in 2021 as a variant with heavy amino acid mutations in the spike protein, which is targeted by most vaccines, compared to previous variants. Amino acid substitutions in the spike proteins may alter their affinity for host viral receptors and the host interactome. Here, we found that the receptor-binding domain (RBD) of the omicron variant of SARS-CoV-2 exhibited an increased affinity for human angiotensin-converting enzyme 2, a viral cell receptor, compared to the prototype RBD. Moreover, we identified β- and γ-actin as omicron-specific binding partners of RBD. Protein complex predictions revealed that many omicron-specific amino acid substitutions affected the affinity between RBD of the omicron variant and actin. Our findings indicate that proteins localized to different cellular compartments exhibit strong binding to the omicron RBD.

Understanding the molecular mechanisms of human diseases: the benefits of fission yeasts

The role of model organisms such as yeasts in life science research is crucial. Although the baker's yeast (Saccharomyces cerevisiae) is the most popular model among yeasts, the contribution of the fission yeasts (Schizosaccharomyces) to life science is also indisputable. Since both types of yeasts share several thousands of common orthologous genes with humans, they provide a simple research platform to investigate many fundamental molecular mechanisms and functions, thereby contributing to the understanding of the background of human diseases. In this review, we would like to highlight the many advantages of fission yeasts over budding yeasts. The usefulness of fission yeasts in virus research is shown as an example, presenting the most important research results related to the Human Immunodeficiency Virus Type 1 (HIV-1) Vpr protein. Besides, the potential role of fission yeasts in the study of prion biology is also discussed. Furthermore, we are keen to promote the uprising model yeast Schizosaccharomyces japonicus, which is a dimorphic species in the fission yeast genus. We propose the hyphal growth of S. japonicus as an unusual opportunity as a model to study the invadopodia of human cancer cells since the two seemingly different cell types can be compared along fundamental features. Here we also collect the latest laboratory protocols and bioinformatics tools for the fission yeasts to highlight the many possibilities available to the research community. In addition, we present several limiting factors that everyone should be aware of when working with yeast models.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Network Medicine: A Potential Approach for Virtual Drug Screening

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Metabolic and mitochondria alterations induced by SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10

Antiviral signaling, immune response and cell metabolism are dysregulated by SARS-CoV-2, the causative agent of COVID-19. Here, we show that SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10 induce a significant mitochondrial and metabolic reprogramming in A549 lung epithelial cells. While ORF9b, ORF9c and ORF10 induced largely overlapping transcriptomes, ORF3a induced a distinct transcriptome, including the downregulation of numerous genes with critical roles in mitochondrial function and morphology. On the other hand, all four ORFs altered mitochondrial dynamics and function, but only ORF3a and ORF9c induced a marked alteration in mitochondrial cristae structure. Genome-Scale Metabolic Models identified both metabolic flux reprogramming features both shared across all accessory proteins and specific for each accessory protein. Notably, a downregulated amino acid metabolism was observed in ORF9b, ORF9c and ORF10, while an upregulated lipid metabolism was distinctly induced by ORF3a. These findings reveal metabolic dependencies and vulnerabilities prompted by SARS-CoV-2 accessory proteins that may be exploited to identify new targets for intervention.

Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations

A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19.

IMPORTANCE

A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

Network-based approach for drug repurposing against mpox

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

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.

Surface plasmons-phonons for mid-infrared hyperspectral imaging

Surface plasmons have proven their ability to boost the sensitivity of mid-infrared hyperspectral imaging by enhancing light-matter interactions. Surface phonons, a counterpart technology to plasmons, present unclear contributions to hyperspectral imaging. Here, we investigate this by developing a plasmon-phonon hyperspectral imaging system that uses asymmetric cross-shaped nanoantennas composed of stacked plasmon-phonon materials. The phonon modes within this system, controlled by light polarization, capture molecular refractive index intensity and lineshape features, distinct from those observed with plasmons, enabling more precise and sensitive molecule identification. In a deep learning-assisted imaging demonstration of severe acute respiratory syndrome coronavirus (SARS-CoV), phonons exhibit enhanced identification capabilities (230,400 spectra/s), facilitating the de-overlapping and observation of the spatial distribution of two mixed SARS-CoV spike proteins. In addition, the plasmon-phonon system demonstrates increased identification accuracy (93%), heightened sensitivity, and enhanced detection limits (down to molecule monolayers). These findings extend phonon polaritonics to hyperspectral imaging, promising applications in imaging-guided molecule screening and pharmaceutical analysis.

Host factors of SARS-CoV-2 in infection, pathogenesis, and long-term effects

SARS-CoV-2 is the causative virus of the devastating COVID-19 pandemic that results in an unparalleled global health and economic crisis. Despite unprecedented scientific efforts and therapeutic interventions, the fight against COVID-19 continues as the rapid emergence of different SARS-CoV-2 variants of concern and the increasing challenge of long COVID-19, raising a vast demand to understand the pathomechanisms of COVID-19 and its long-term sequelae and develop therapeutic strategies beyond the virus per se. Notably, in addition to the virus itself, the replication cycle of SARS-CoV-2 and clinical severity of COVID-19 is also governed by host factors. In this review, we therefore comprehensively overview the replication cycle and pathogenesis of SARS-CoV-2 from the perspective of host factors and host-virus interactions. We sequentially outline the pathological implications of molecular interactions between host factors and SARS-CoV-2 in multi-organ and multi-system long COVID-19, and summarize current therapeutic strategies and agents targeting host factors for treating these diseases. This knowledge would be key for the identification of new pathophysiological aspects and mechanisms, and the development of actionable therapeutic targets and strategies for tackling COVID-19 and its sequelae.

A 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 innovative therapies with high genetic barriers to resistance. Therefore, there is pronounced interest in identifying new pharmacological targets in the SARS-CoV-2 viral life cycle. 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. In this study, we investigate the capacity of PAV-104 to inhibit SARS-CoV-2 replication in human airway epithelial cells (AECs). We show that PAV-104 inhibits >99% of infection with diverse SARS-CoV-2 variants in immortalized AECs, and in primary human AECs cultured at the air-liquid interface (ALI) to represent the lung microenvironment in vivo. Our data demonstrate that PAV-104 inhibits SARS-CoV-2 production without affecting viral entry, mRNA transcription, or protein synthesis. PAV-104 interacts with SARS-CoV-2 nucleocapsid (N) and interferes with its oligomerization, blocking particle assembly. Transcriptomic analysis reveals that PAV-104 reverses 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 with a mechanism of action that is distinct from existing clinical management approaches.

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.

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.

Predicting host-based, synthetic lethal antiviral targets from omics data

Traditional antiviral therapies often have limited effectiveness due to toxicity and the emergence of drug resistance. Host-based antivirals are an alternative, but can cause nonspecific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR knockout (KO) screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting antiviral SL drug targets. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Our comparison of SARS-CoV-2 and influenza infection data revealed potential broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.