Parechovirus infection in human brain organoids: host innate inflammatory response and not neuro-infectivity correlates to neurologic disease.

Picornaviruses are a leading cause of central nervous system (CNS) infections. While genotypes such as parechovirus A3 (PeV-A3) and echovirus 11 (E11) can elicit severe neurological disease, the highly prevalent PeV-A1 is not associated with CNS disease. Here, we expand our current understanding of these differences in PeV-A CNS disease using human brain organoids and clinical isolates of the two PeV-A genotypes. Our data indicate that PeV-A1 and A3 specific differences in neurological disease are not due to infectivity of CNS cells as both viruses productively infect brain organoids with a similar cell tropism. Proteomic analysis shows that PeV-A infection significantly alters the host cell metabolism. The inflammatory response following PeV-A3 (and E11 infection) is significantly more potent than that upon PeV-A1 infection. Collectively, our findings align with clinical observations and suggest a role for neuroinflammation, rather than viral replication, in PeV-A3 (and E11) infection.

RNA-Small-Molecule Interaction: Challenging the "Undruggable" Tag.

RNA targeting, specifically with small molecules, is a relatively new and rapidly emerging avenue with the promise to expand the target space in the drug discovery field. From being "disregarded" as an "undruggable" messenger molecule to FDA approval of an RNA-targeting small-molecule drug Risdiplam, a radical change in perspective toward RNA has been observed in the past decade. RNAs serve important regulatory functions beyond canonical protein synthesis, and their dysregulation has been reported in many diseases. A deeper understanding of RNA biology reveals that RNA molecules can adopt a variety of structures, carrying defined binding pockets that can accommodate small-molecule drugs. Due to its functional diversity and structural complexity, RNA can be perceived as a prospective target for therapeutic intervention. This perspective highlights the proof of concept of RNA-small-molecule interactions, exemplified by targeting of various transcripts with functional modulators. The advent of RNA-oriented knowledge would help expedite drug discovery.

Temporal cytokine storm dynamics in dengue infection predicts severity.

The immunopathogenesis of dengue severity is convoluted. The primary objective of the research was to examine the dynamics of cytokine storm and its correlation with disease development in individuals affected by DENV infection. Additionally, the study aimed to discover potential biomarkers that could indicate severe dengue infection and determine the most suitable timeframe for predicting the severity of these biomarkers during the acute stage of dengue infections. We conducted a temporal analysis of the daily viral load and cytokine levels in 60 hospitalized dengue patients until discharge. Our findings reveal a distinct cytokine profile (elevated IL-8, IL-10, IL-6, GM-CSF, MCP-1, IL-13, and IL-4 and decreased IL-12, MIP-1ß) on the third day after symptom onset is predictive of severe dengue in secondary dengue infection. The imbalanced cytokine signature may inform clinical decision-making in treating severe dengue infections.

QUFIND: tool for comparative prediction and mining of G4 quadruplexes overlapping with CpG islands.

G-quadruplexes (G4s) are secondary structures in DNA that have been shown to be involved in gene regulation. They play a vital role in the cellular processes and several pathogens including bacteria, fungi, and viruses have also been shown to possess G4s that help them in their pathogenesis. Additionally, cross-talk among the CpG islands and G4s has been shown to influence biological processes. The virus-encoded G4s are affected by the mutational landscape leading to the formation/deletion of these G4s. Therefore, understanding and predicting these multivariate effects on traditional and non-traditional quadruplexes forms an important area of research, that is, yet to be investigated. We have designed a user-friendly webserver QUFIND (http://soodlab.com/qufinder/) that can predict traditional as well as non-traditional quadruplexes in a given sequence. QUFIND is connected with ENSEMBL and NCBI so that the sequences can be fetched in a real-time manner. The algorithm is designed in such a way that the user is provided with multiple options to customize the base (A, T, G, or C), size of the stem (2-5), loop length (1-30), number of bulges (1-5) as well as the number of mismatches (0-2) enabling the identification of any of the secondary structure as per their interest. QUFIND is designed to predict both CpG islands as well as G4s in a given sequence. Since G4s are very short as compared to the CpG islands, hence, QUFIND can also predict the overlapping G4s within CpG islands. Therefore, the user has the flexibility to identify either overlapping or non-overlapping G4s along with the CpG islands. Additionally, one section of QUFIND is dedicated to comparing the G4s in two viral sequences. The visualization is designed in such a manner that the user is able to see the unique quadruplexes in both the input sequences. The efficiency of QUFIND is calculated on G4s obtained from G4 high throughput sequencing data (n = 1000) or experimentally validated G4s (n = 329). Our results revealed that QUFIND is able to predict G4-quadruplexes obtained from G4-sequencing data with 90.06% prediction accuracy whereas experimentally validated quadruplexes were predicted with 97.26% prediction accuracy.

Incompetent memory immune response in severe COVID-19 patients under treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) associated coronavirus disease 2019 (COVID-19) pandemic has affected millions of people worldwide and declared a Public Health Emergency by the World Health Organization (WHO) on January 30, 2020. Albeit, unprecedented efforts have been made from the scientific community to understand the pathophysiology of COVID-19 disease, the host immune and inflammatory responses are not explored well in the Indian population. Continuous arrival of new variants fascinated the scientists to understand the host immune processes and to eradicate this deadly virus. The aim of this study was to see the helper and cellular host immune responses including memory and activated cell subsets of COVID-19 patients admitted to the intensive care unit (ICU) at different time intervals during the treatment. PBMCs separated from nine patients with SARS-CoV-2 infection were incubated with fluorescent conjugated antibodies and acquired on flow cytometer machine to analyze the T and B cell subsets. The results in COVID-19 patients versus healthy volunteers were as follows: elevated helper T cells (57.4% vs 44.9%); low cytotoxic T cells (42.8% vs 55.6%), and activated T (17.7% vs 21.2%) subsets. Both, TREG (40.15% vs 51.7%) and TH17 (13.2% vs 24.6%) responses were substantially decreased and high expression of TREG markers was observed in these patients compared with controls.

Screening of potential inhibitors against structural proteins from Monkeypox and related viruses of Poxviridae family via docking and molecular dynamics simulation.

Monkeypox virus (MPXV) is an orthopoxvirus which causes zoonotic infection in humans. Even though sporadic cases of this infection are limited to the African continent, but if the infection continues to increase unabated, it can be a cause of serious concern for the human populace. Smallpox vaccination has been in use against monkeypox infection but it only provides mild protection. In the current study, we have screened novel small molecules (estrone fused heterocycles (EH1-EH7)) exhibiting good binding with monkeypox virus protein and related proteins from Poxviridae family of viruses via computational approaches. EH1-7 series of small molecules selected for the work have been synthesized via cycloaddition methodology. Docking and Molecular Dynamics (MD) results highlight EH4 compound to have strong binding affinity towards monkeypox and other related viral proteins selected for the study. Thus, computational outcomes suggest EH4 as a good candidate against monkeypox. Currently, no antiviral medication has been approved against monkeypox and the treatment is only via therapeutics available for smallpox and related conditions that may be helpful against monkeypox. Our study is thus an attempt to screen novel compounds against monkeypox infection, which would, in turn, facilitate development of novel therapeutics against Poxviridae family. HIGHLIGHTSMonkeypox infection is a public health emergency and necessitates immediate drug discovery.Molecular docking study to screen estrone-fused heterocycles compounds against Monkeypox and other orthopoxviruses.Molecular dynamics simulations revealed interaction/high binding affinities among EH4 heterocyclic compound and profilin-like protein from the monkeypox virus.Estrone-fused heterocycles compounds are promising anti-viral agents as per our in silico analysis.Our study provides evidence for investigating estrone-fused heterocycles compounds for further pharmacological interventions.Communicated by Ramaswamy H. Sarma.

Monkeypox: This orthopoxvirus leads to mpox (monkeypox) disease which shows symptoms similar to that smallpox, however to less severe extent.Poxviridae family: This is commonly a family of double-stranded DNA viruses. The natural hosts for these viruses are arthropods and Vertebrates.Molecular Dynamic simulation: MD simulation is crucial for determining the ligand's stability and revealing the duration of its interaction with the respective macromolecular structure.Molecular Docking: Molecular docking aids in determining specific sites where the ligand binds with the macromolecule as well as its binding affinity. Bioinformatics tools such as docking have been widely employed for aiding drug discovery efforts.Protein binding energy: On docking protein with the ligand, the binding energy shows the free energy change during binding process between protein-ligand.

Systems-level temporal immune-metabolic profile in Crimean-Congo hemorrhagic fever virus infection.

Crimean-Congo hemorrhagic fever (CCHF) caused by CCHF virus (CCHFV) is one of the epidemic-prone diseases prioritized by the World Health Organisation as public health emergency with an urgent need for accelerated research. The trajectory of host response against CCHFV is multifarious and remains unknown. Here, we reported the temporal spectrum of pathogenesis following the CCHFV infection using genome-wide blood transcriptomics analysis followed by advanced systems biology analysis, temporal immune-pathogenic alterations, and context-specific progressive and postinfection genome-scale metabolic models (GSMM) on samples collected during the acute (T0), early convalescent (T1), and convalescent-phase (T2). The interplay between the retinoic acid-inducible gene-I-like/nucleotide-binding oligomerization domain-like receptor and tumor necrosis factor signaling governed the trajectory of antiviral immune responses. The rearrangement of intracellular metabolic fluxes toward the amino acid metabolism and metabolic shift toward oxidative phosphorylation and fatty acid oxidation during acute CCHFV infection determine the pathogenicity. The upregulation of the tricarboxylic acid cycle during CCHFV infection, compared to the noninfected healthy control and between the severity groups, indicated an increased energy demand and cellular stress. The upregulation of glycolysis and pyruvate metabolism potentiated energy generation through alternative pathways associated with the severity of the infection. The downregulation of metabolic processes at the convalescent phase identified by blood cell transcriptomics and single-cell type proteomics of five immune cells (CD4+ and CD8+ T cells, CD14+ monocytes, B cells, and NK cells) potentially leads to metabolic rewiring through the recovery due to hyperactivity during the acute phase leading to post-viral fatigue syndrome.

iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers.

BACKGROUND: Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimmune diseases. Interestingly, the recent association of IL-13 with COVID-19 severity has sparked interest in this cytokine. Therefore characterization of new molecules which can regulate IL-13 induction might lead to novel therapeutics. RESULTS: Here, we present an improved prediction of IL-13-inducing peptides. The positive and negative datasets were obtained from a recent study (IL13Pred) and the Pfeature algorithm was used to compute features for the peptides. As compared to the state-of-the-art which used the regularization based feature selection technique (linear support vector classifier with the L1 penalty), we used a multivariate feature selection technique (minimum redundancy maximum relevance) to obtain non-redundant and highly relevant features. In the proposed study (improved IL-13 prediction (iIL13Pred)), the use of the mRMR feature selection method is instrumental in choosing the most discriminatory features of IL-13-inducing peptides with improved performance. We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. We report improved AUC, and MCC scores of 0.83 and 0.33 on validation data as compared to the current method. CONCLUSIONS: Extensive benchmarking experiments suggest that the proposed method (iIL13Pred) could provide improved performance metrics in terms of sensitivity, specificity, accuracy, the area under the curve - receiver operating characteristics (AUCROC) and Matthews correlation coefficient (MCC) than the existing state-of-the-art approach (IL13Pred) on the validation dataset and an external dataset comprising of experimentally validated IL-13-inducing peptides. Additionally, the experiments were performed with an increased number of experimentally validated training datasets to obtain a more robust model. A user-friendly web server ( www.soodlab.com/iil13pred ) is also designed to facilitate rapid screening of IL-13-inducing peptides.

Immune Control of Avian Influenza Virus Infection and Its Vaccine Development.

The avian influenza A virus (AIV) is naturally prevalent in aquatic birds, infecting different avian species and transmitting from birds to humans. Both AIVs, the H5N1 and H7N9 viruses, have the potential to infect humans, causing an acute influenza disease syndrome in humans, and are a possible pandemic threat. AIV H5N1 is highly pathogenic, whereas AIV H7N9 has comparatively low pathogenicity. A clear insight into the disease pathogenesis is significant to understand the host's immunological response, which in turn facilitates the design of the control and prevention strategies. In this review, we aim to provide comprehensive details on the pathogenesis and clinical features of the disease. Moreover, the innate and adaptive immunological responses to AIV and the recent studies conducted on the CD8+ T cell immunity against AIVs are detailed upon. Further, the current status and advancement in the development of AIV vaccines, along with the challenges, are also discussed. The information provided will be helpful in combating the transmission of AIV from birds to humans and, thus, preventing severe outbreaks leading to pandemics worldwide.

COVID Vaccines vs. Viral Evolution: Who Will Win the Battle?

The emergence of new variants seems to perpetually prolong the COVID-19 pandemic. On top of that, the waning of vaccine protection is further thwarting our efforts to end it. Our article discusses the incessant question raging in everybody's mind: Are we ever going to win?

In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs.

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 has resulted in enormous deaths around the world. Clues from genomic sequences of parent and their mutants can be obtained to understand the evolving pathogenesis of this virus. Apart from the viral proteins, virus-encoded microRNAs (miRNAs) have been shown to play a vital role in regulating viral pathogenesis. Thus we sought to investigate the miRNAs encoded by SARS-CoV-2, its mutants, and the host. Here, we present the results obtained using a dual approach i.e (i) identifying host-encoded miRNAs that might regulate viral pathogenesis and (ii) identifying viral-encoded miRNAs that might regulate host cell signaling pathways and aid in viral pathogenesis. Analysis utilizing the first approach resulted in the identification of ten host-encoded miRNAs that could target the SARS, SARS-CoV-2, and its mutants. Interestingly our analysis revealed that there is a significantly higher number of host miRNAs that could target the SARS-CoV-2 genome as compared to the SARS reference genome. Results from the second approach resulted in the identification of a set of virus-encoded miRNAs which might regulate host signaling pathways. Our analysis further identified a similar "GA" rich motif in the SARS-CoV-2 and its mutant genomes that was shown to play a vital role in lung pathogenesis during severe SARS infections. In summary, we have identified human and virus-encoded miRNAs that might regulate the pathogenesis of SARS coronaviruses and describe similar non-coding RNA sequences in SARS-CoV-2 that were shown to regulate SARS-induced lung pathology in mice.

Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations.

The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C>T in the 5' untranslated region (UTR), 3037C>T in nsp3, 14408C>T in the RNA-dependent RNA polymerase (RdRp), and 23403A>G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C>T), P323L (14408C>T), and Q57H (25563G>T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.

Novel insights into host responses to Japanese Encephalitis Virus infection: Reanalysis of public transcriptome and microRNAome datasets.

PURPOSE: Japanese encephalitis (JE), caused by the Japanese encephalitis virus (JEV), is the principal cause of viral encephalitis in South-East Asian and Western Pacific countries; accounting for 68,000 cases, and up to 20,400 fatalities, annually across the world. Despite being a high-risk condition, there is no specific treatment for JE. Given rapid additions in genomics databases and the power of data reanalysis in addressing critical medical questions, the present study was designed to identify novel host factors that might have potential roles in JEV infection. METHODS: We extracted microarray and RNA-Seq data sets from NCBI-GEO and compared mock and JEV-infected samples. Raw data from all the studies were re-analyzed to identify host factors associated with JEV replication. RESULTS: We identified several coding and non-coding host factors that had no prior known role in viral infections. Of these, the coding transcripts: Myosin Heavy Chain 10 (MYH10), Progestin and AdipoQ Receptor Family Member 8 (PAQR8), and the microRNAs: hsa-miR-193b-5p, hsa-miR-3714 and hsa-miR-513a-5p were found to be novel host factors deregulated during JEV infection. MYH10 encodes a conventional non-muscle myosin, and mutations in MYH10 have been shown to cause neurological defects. PAQR8 has been associated with epilepsy, which exhibits symptoms similar to JEV infection. JE is a neuro-degenerative disease, and the known involvement of MYH10 and PAQR8 in neurological disorders strongly indicates potential roles of these host factors in JEV infection. Additionally, we observed that MYH10 and PAQR8 had a significant negative correlation with Activating transcription factor 3 (ATF3), which is a previously validated modulator of JEV infection. ATF3 is a transcription factor that binds to the promotors of genes encoding other transcription factors or interferon-stimulated genes and negatively regulates host antiviral responses during JE. CONCLUSION: Our findings demonstrate the significance of data reanalysis in the identification of novel host factors that may become targets for diagnosis/ therapy against viral diseases of major concern, such as, JE. The deregulated coding and non-coding transcripts identified in this study need further experimental analysis for validation.

Human Immunodeficiency Virus Type 1 Vif Up-Regulates the Expression of Tat via AKT Signaling Pathway: Role of Ubiquitin Specific Protease 17.

Human immunodeficiency virus type 1 (HIV-1) has RNA genome and depends on host cellular machinery for most of its activities. Host cellular proteins modulate the expression and activity of viral proteins to combat the virus. HIV-1 proteins are known to regulate each other for the benefit of virus by exploiting these modulations. Here, we report that HIV-1 Vif increases the levels of Tat via AKT signaling pathway. We show that HIV-1 Vif activates AKT signaling pathway by inducing phosphorylation of AKT. Mdm2, downstream target of AKT signaling, increases the levels of Tat protein in ubiquitin-dependent manner by inducing Ubiquitin Specific Protease 17 (USP17), which is a deubiquitinase and stabilizes Tat protein. Thus, HIV-1 proteins exploit AKT signaling pathway to promote viral replication.

A novel binary k-mer approach for classification of coding and non-coding RNAs across diverse species.

Classification among coding sequences (CDS) and non-coding RNA (ncRNA) sequences is a challenge and several machine learning models have been developed for the same. Since the frequency of curated CDS is many-folds as compared to that of the ncRNAs, we devised a novel approach to work with the complete datasets from fifteen diverse species. In our proposed binary approach, we replaced all the 'A's and 'T's with '0's and 'G's and 'C's with '1's to obtain a binary form of CDS and ncRNAs. The k-mer analysis of these binary sequences revealed that the frequency of binary patterns among the CDS and ncRNAs can be used as features to distinguish among them. Using insights from these distinguishing frequencies, we used k-nearest neighbor classifier to classify among them. Our strategy is not only time-efficient but leads to significantly increased performance metrics in terms of Matthews Correlation Coefficient (MCC), Accuracy, F1 score, Precision, Recall and AUC-ROC, for species like P. paniscus, M. mulatta, M. lucifugus, G. gallus, C. japonica, C. abingdonii, A. carolinensis, D. melanogaster and C. elegans when compared with the conventional ATGC approach. Additionally, we also show that the performance obtained for diverse species tested on the model based on H. sapiens, correlated with the geological evolutionary timeline, thereby further strengthening our approach. Therefore, we propose that CDS and ncRNAs can be efficiently classified using "2-character" binary frequency as compared to "4-character" frequency of ATGC approach. Thus, our highly efficient binary approach can replace the more complex ATGC approach successfully.

RNA thermometers and other regulatory elements: Diversity and importance in bacterial pathogenesis.

Survival of microorganisms depends to a large extent on environmental conditions and the occupied host. By adopting specific strategies, microorganisms can thrive in the surrounding environment and, at the same time, preserve their viability. Evading the host defenses requires several mechanisms compatible with the host survival which include the production of RNA thermometers to regulate the expression of genes responsible for heat or cold shock as well as of those involved in virulence. Microorganisms have developed a variety of molecules in response to the environmental changes in temperature and even more specifically to the host they invade. Among all, RNA-based regulatory mechanisms are the most common ones, highlighting the importance of such molecules in gene expression control and novel drug development by suitable structure-based alterations. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA in Disease and Development > RNA in Disease RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

In silico characterization of mutations circulating in SARS-CoV-2 structural proteins.

SARS-CoV-2 has recently emerged as a pandemic that has caused more than 2.4 million deaths worldwide. Since the onset of infections, several full-length sequences of viral genome have been made available which have been used to gain insights into viral dynamics. We utilised a meta-data driven comparative analysis tool for sequences (Meta-CATS) algorithm to identify mutations in 829 SARS-CoV-2 genomes from around the world. The algorithm predicted sixty-one mutations among SARS-CoV-2 genomes. We observed that most of the mutations were concentrated around three protein coding genes viz nsp3 (non-structural protein 3), RdRp (RNA-directed RNA polymerase) and Nucleocapsid (N) proteins of SARS-CoV-2. We used various computational tools including normal mode analysis (NMA), C-α discrete molecular dynamics (DMD) and all-atom molecular dynamic simulations (MD) to study the effect of mutations on functionality, stability and flexibility of SARS-CoV-2 structural proteins including envelope (E), N and spike (S) proteins. PredictSNP predictor suggested that four mutations (L37H in E, R203K and P344S in N and D614G in S) out of seven were predicted to be neutral whilst the remaining ones (P13L, S197L and G204R in N) were predicted to be deleterious in nature thereby impacting protein functionality. NMA, C-α DMD and all-atom MD suggested some mutations to have stabilizing roles (P13L, S197L and R203K in N protein) where remaining ones were predicted to destabilize mutant protein. In summary, we identified significant mutations in SARS-CoV-2 genomes as well as used computational approaches to further characterize the possible effect of highly significant mutations on SARS-CoV-2 structural proteins.Communicated by Ramaswamy H. Sarma.

Where all the Roads Meet? A Crossover Perspective on Host Factors Regulating SARS-CoV-2 infection.

COVID-19 caused by SARS-CoV-2 is the latest pandemic which has thrown the world into an unprecedented social and economic uncertainties along with huge loss to humanity. Identification of the host factors regulating the replication of SARS-CoV-2 in human host may help in the development of novel anti-viral therapies to combat the viral infection and spread. Recently, some research groups used genome-wide CRISPR/Cas screening to identify the host factors critical for the SARS-CoV-2 replication and infection. A comparative analysis of these significant host factors (p < 0.05) identified fifteen proteins common in these studies. Apart from ACE2 (receptor for SARS-CoV-2 attachment), other common host factors were CSNK2B, GDI2, SLC35B2, DDX51, VPS26A, ARPP-19, C1QTNF7, ALG6, LIMA1, COG3, COG8, BCOR, LRRN2 and TLR9. Additionally, viral interactome of these host factors revealed that many of them were associated with several SARS-CoV-2 proteins as well. Interestingly, some of these host factors have already been shown to be critical for the pathogenesis of other viruses suggesting their crucial role in virus-host interactions. Here, we review the functions of these host factors and their role in other diseases with special emphasis on viral diseases.

Clinical Predictors of COVID-19 Severity and Mortality: A Perspective.

The COVID-19 pandemic has caused huge socio-economic losses and continues to threat humans worldwide. With more than 4.5 million deaths and more than 221 million confirmed COVID-19 cases, the impact on physical, mental, social and economic resources is immeasurable. During any novel disease outbreak, one of the primary requirements for effective mitigation is the knowledge of clinical manifestations of the disease. However, in absence of any unique identifying characteristics, diagnosis/prognosis becomes difficult. It intensifies misperception and leads to delay in containment of disease spread. Numerous clinical research studies, systematic reviews and meta-analyses have generated considerable data on the same. However, identification of some of the distinct clinical signs and symptoms, disease progression biomarkers and the risk factors leading to adverse COVID-19 outcomes warrant in-depth understanding. In view of this, we assessed 20 systematic reviews and meta-analyses with an intent to understand some of the potential independent predictors/biomarkers/risk factors of COVID-19 severity and mortality.