SARS-CoV-2 transcriptome analysis and molecular cataloguing of immunodominant epitopes for multi-epitope based vaccine design

Point-of-care detection of Japanese encephalitis virus biomarker in clinical samples using a portable smartphone-enabled electrochemical "Sensit" device

Japanese encephalitis (JE), a neglected tropical zoonotic disease prevalent in south-east Asian and western pacific countries, caused by the flavivirus JE virus (JEV), has a dearth of electrochemical point-of-care (PoC) diagnostic tools available to manage endemic breakouts. To overcome this, we have developed a screen-printed carbon electrode (SPCE) immunosensor for rapid PoC detection of JEV nonstructural 1 (NS1) antigen (Ag), found circulating in serum of infected individuals using a smartphone based portable "Sensit" device. The modification of SPCE surface with JEV NS1 antibody (Ab) was confirmed via observation of globular protein structures via scanning electron microscopy (SEM), increase in electrode surface hydrophilicity via contact angle measurement and decrease in current via differential pulse voltammetry (DPV). The fabrication and testing parameters were optimized based on highest current output obtained using DPV. The SPCE was tested for detection limit of target JEV NS1 Ag ranging from 1 fM to 1 μM, which was determined as 0.45 fM in spiked serum. The disposable immunosensor was also found to be highly specific in detecting JEV NS1 Ag over other flaviviral NS1 Ag. Finally, the modified SPCE was clinically validated by testing 62 clinical JEV samples using both a portable miniaturized electrochemical "Sensit" device coupled with a smartphone and a laboratory-based potentiostat. The results were corroborated with gold-standard RT-PCR and showed 96.77% accuracy, 96.15% sensitivity, and 97.22% specificity. Hence, this technique may further be developed into a one-step rapid diagnostic tool for JEV, especially in rural areas.

New Short RNA Motifs Potentially Relevant in the SARS-CoV-2 Genome

Background

The coronavirus disease has led to an exhaustive exploration of the SARS-CoV-2 genome. Despite the amount of information accumulated, the prediction of short RNA motifs encoding peptides mediating protein-protein or protein-drug interactions has received limited attention.

Objective

The study aims to predict short RNA motifs that are interspersed in the SARS-CoV-2 genome.

Methods

A method in which 14 trinucleotide families, each characterized by being composed of triplets with identical nucleotides in all possible configurations, was used to find short peptides with biological relevance. The novelty of the approach lies in using these families to search how they are distributed across genomes of different CoV genera and then to compare the distributions of these families with each other.

Results

We identified distributions of trinucleotide families in different CoV genera and also how they are related, using a selection criterion that identified short RNA motifs. The motifs were reported to be conserved in SARS-CoVs; in the remaining CoV genomes analysed, motifs contained, exclusively, different configurations of the trinucleotides A, T, G and A, C, G. Eighty-eight short RNA motifs, ranging in length from 12 to 49 nucleotides, were found: 50 motifs in the 1a polyprotein-encoding orf, 27 in the 1b polyprotein-encoding orf, 5 in the spike-encoding orf, and 6 in the nucleocapsid-encoding orf. Although some motifs (~27%) were found to be intercalated or attached to functional peptides, most of them have not yet been associated with any known functions.

Conclusion

Some of the trinucleotide family distributions in different CoV genera are not random; they are present in short peptides that, in many cases, are intercalated or attached to functional sites of the proteome.

Gold nanoparticle conjugate-based lateral flow immunoassay (LFIA) for rapid detection of RBD antigen of SARS-CoV-2 in clinical samples using a smartphone-based application

The coronavirus disease 2019 (COVID-19) pandemic has emphasized the need for development of a rapid diagnostic device for the effective treatment and its mitigation. Lateral flow immunoassay (LFIA) belongs to a class of diagnostic devices, which has the benefit of providing quick results, easy to handle, low cost, and on-site applicable. So far, several LFIA has been developed for the detection of infectious severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), however, only a few of them are antigen (Ag)-based. Here, we describe an antibody (Ab)-labeled gold nanoparticles (AuNPs)-based LFIA (AuNPs-LFIA) for the detection of Receptor-Binding Domain (RBD) of SARS-CoV-2. For this, RBD Ab of SARS-CoV-2 was conjugated with the AuNPs, which served as a detecting probe. The fabricated LFIA strip was optimized for different parameters such as membrane pore size, blocking conditions, Ab coating concentration, and conjugate incubation. The optimized LFIA strips were validated in spiked buffer samples and the optimal limit of detection was found to be 1 ng/ml, which was confirmed by a smartphone-based application. Moreover, the developed AuNPs-LFIA strips effectively detected RBD Ag in 100 clinical samples with 94.3% sensitivity and 90.9% specificity in clinical samples when compared with the gold standard (RT-PCR). The fabricated LFIAs are reported to have storage stability of up to 21 days at 4°C and room temperature (RT). Hence, the developed LFIA can be used as a portable, cost-effective diagnostic device for rapid detection of SARS-CoV-2.

Molecular signatures in the progression of COVID-19 severity

SARS-CoV-2 is the causative agent of COVID-19 that has infected over 642 million and killed over 6.6 million people around the globe. Underlying a wide range of clinical manifestations of this disease, from moderate to extremely severe systemic conditions, could be genes or pathways differentially expressing in the hosts. It is therefore important to gain insights into pathways involved in COVID-19 pathogenesis and host defense and thus understand the host response to this pathogen at the physiological and molecular level. To uncover genes and pathways involved in the differential clinical manifestations of this disease, we developed a novel gene co-expression network based pipeline that uses gene expression obtained from different SARS-CoV-2 infected human tissues. We leveraged the network to identify novel genes or pathways that likely differentially express and could be physiologically significant in the COVID-19 pathogenesis and progression but were deemed statistically non-significant and therefore not further investigated in the original studies. Our network-based approach aided in the identification of co-expression modules enriched in differentially expressing genes (DEGs) during different stages of COVID-19 and enabled discovery of novel genes involved in the COVID-19 pathogenesis, by virtue of their transcript abundance and association with genes expressing differentially in modules enriched in DEGs. We further prioritized by considering only those enriched gene modules that have most of their genes differentially expressed, inferred by the original studies or this study, and document here 7 novel genes potentially involved in moderate, 2 in severe, 48 in extremely severe COVID-19, and 96 novel genes involved in the progression of COVID-19 from severe to extremely severe conditions. Our study shines a new light on genes and their networks (modules) that drive the progression of COVID-19 from moderate to extremely severe condition. These findings could aid development of new therapeutics to combat COVID-19.

Probing the Immune System Dynamics of the COVID-19 Disease for Vaccine Designing and Drug Repurposing Using Bioinformatics Tools

The pathogenesis of COVID-19 is complicated by immune dysfunction. The impact of immune-based therapy in COVID-19 patients has been well documented, with some notable studies on the use of anti-cytokine medicines. However, the complexity of disease phenotypes, patient heterogeneity and the varying quality of evidence from immunotherapy studies provide problems in clinical decision-making. This review seeks to aid therapeutic decision-making by giving an overview of the immunological responses against COVID-19 disease that may contribute to the severity of the disease. We have extensively discussed theranostic methods for COVID-19 detection. With advancements in technology, bioinformatics has taken studies to a higher level. The paper also discusses the application of bioinformatics and machine learning tools for the diagnosis, vaccine design and drug repurposing against SARS-CoV-2.

Identification of Unique Peptides for SARS-CoV-2 Diagnostics and Vaccine Development by an In Silico Proteomics Approach

Ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus strains is posing new COVID-19 diagnosis and treatment challenges. To help efforts to meet these challenges we examined data acquired from proteomic analyses of human SARS-CoV-2-infected cell lines and samples from COVID-19 patients. Initially, 129 unique peptides were identified, which were rigorously evaluated for repeats, disorders, polymorphisms, antigenicity, immunogenicity, toxicity, allergens, sequence similarity to human proteins, and contributions from other potential cross-reacting pathogenic species or the human saliva microbiome. We also screened SARS-CoV-2-infected NBHE and A549 cell lines for presence of antigenic peptides, and identified paratope peptides from crystal structures of SARS-CoV-2 antigen-antibody complexes. We then selected four antigen peptides for docking with known viral unbound T-cell receptor (TCR), class I and II peptide major histocompatibility complex (pMHC), and identified paratope sequences. We also tested the paratope binding affinity of SARS-CoV T- and B-cell peptides that had been previously experimentally validated. The resultant antigenic peptides have high potential for generating SARS-CoV-2-specific antibodies, and the paratope peptides can be directly used to develop a COVID-19 diagnostics assay. The presented genomics and proteomics-based in-silico approaches have apparent utility for identifying new diagnostic peptides that could be used to fight SARS-CoV-2.

Immune Responses to Orally Administered Recombinant Lactococcus lactis Expressing Multi-Epitope Proteins Targeting M Cells of Foot-and-Mouth Disease Virus

Foot and mouth disease virus (FMDV), whose transmission occurs through mucosal surfaces, can also be transmitted through aerosols, direct contact, and pollutants. Therefore, mucosal immunity can efficiently inhibit viral colonization. Since vaccine material delivery into immune sites is important for efficient oral mucosal vaccination, the M cell-targeting approach is important for effective vaccination given M cells are vital for luminal antigen influx into the mucosal lymph tissues. In this study, we coupled M cell-targeting ligand Co1 to multi-epitope TB1 of FMDV to obtain TB1-Co1 in order to improve delivery efficiency of the multi-epitope protein antigen TB1. Lactococcus lactis (L. lactis) was engineered to express heterologous antigens for applications as vaccine vehicles with the ability to elicit mucosal as well as systemic immune responses. We successfully constructed L. lactis (recombinant) with the ability to express multi-epitope antigen proteins (TB1 and TB1-Co1) of the FMDV serotype A (named L. lactis-TB1 and L. lactis-TB1-Co1). Then, we investigated the immunogenic potential of the constructed recombinant L. lactis in mice and guinea pigs. Orally administered L. lactis-TB1 as well as L. lactis-TB1-Co1 in mice effectively induced mucosal secretory IgA (SIgA) and IgG secretion, development of a strong cell-mediated immune reactions, substantial T lymphocyte proliferation in the spleen, and upregulated IL-2, IFN-γ, IL-10, and IL-5 levels. Orally administered ligand-conjugated TB1 promoted specific IgG as well as SIgA responses in systemic and mucosal surfaces, respectively, when compared to orally administered TB1 alone. Then, guinea pigs were orally vaccinated with L. lactis-TB1-Co1 plus adjuvant CpG-ODN at three different doses, L. lactis-TB1-Co1, and PBS. Animals that had been immunized with L. lactis-TB1-Co1 plus adjuvant CpG-ODN and L. lactis-TB1-Co1 developed elevated antigen-specific serum IgG, IgA, neutralizing antibody, and mucosal SIgA levels, when compared to control groups. Particularly, in mice, L. lactis-TB1-Co1 exhibited excellent immune effects than L. lactis-TB1. Therefore, L. lactis-TB1-Co1 can induce elevations in mucosal as well as systemic immune reactions, and to a certain extent, provide protection against FMDV. In conclusion, M cell-targeting approaches can be employed in the development of effective oral mucosa vaccines for FMDV.

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

MOTIVATION

The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data.

RESULTS

Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions.

CONTACT

hguzzi@unicz.it, sroy01@cus.ac.in.