Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses (preprint)

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learned. As a component of the Platform, the SARS-CoV-2 Data Hubs enabled the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

In-silico Analysis of SARS-Cov2 Spike Proteins of Different Field Variants (preprint)

Background: Coronaviruses belong to the group of RNA family of viruses which trigger diseases in birds, humans, and mammals, which can cause respiratory tract infections. The COVID-19 pandemic has badly affected every part of the world, and the situation in the world is getting worse with the emergence of novel variants. Our study aims to explore the genome of SARS-CoV2 followed by in silico analysis of its proteins. Methods: Different nucleotide and protein variants of SARS-Cov2 were retrieved from NCBI. Contigs & consensus sequences were developed to identify variations in these variants by using SnapGene. Data of variants that significantly differ from each other was run through Predict Protein software to understand changes produced in protein structure The SOPMA web server was used to predict the secondary structure of proteins. Tertiary structure details of selected proteins were analyzed using the online web server SWISS-MODEL. Findings: Sequencing results shows numerous single nucleotide polymorphisms in surface glycoprotein, nucleocapsid, ORF1a, and ORF1ab polyprotein. While envelope, membrane, ORF3a, ORF6, ORF7a, ORF8, and ORF10 genes have no or few SNPs. Contigs were mto identifyn of variations in Alpha & Delta Variant of SARs-CoV-2 with reference strain (Wuhan). The secondary structures of SARs-CoV-2 proteins were predicted by using sopma software & were further compared with reference strain of SARS-CoV-2 (Wuhan) proteins. The tertiary structure details of only spike proteins were analyzed through the SWISS-MODEL and Ramachandran plot. By Swiss-model, a comparison of the tertiary structure model of SARS-COV-2 spike protein of Alpha & Delta Variant was made with reference strain (Wuhan). Alpha & Delta Variant of SARs-CoV-2 isolates submitted in GISAID from Pakistan with changes in structural and nonstructural proteins were compared with reference strain & 3D structure mapping of spike glycoprotein and mutations in amino acid were seen. Conclusion: The surprising increased rate of SARS-CoV-2 transmission has forced numerous countries to impose a total lockdown due to an unusual occurrence. In this research, we employed in silico computational tools to analyze SARS-CoV-2 genomes worldwide to detect vital variations in structural proteins and dynamic changes in all SARS-CoV-2 proteins, mainly spike proteins, produced due to many mutations. Our analysis revealed substantial differences in functional, immunological, physicochemical, & structural variations in SARS-CoV-2 isolates. However real impact of these SNPs can only be determined further by experiments. Our results can aid in vivo and in vitro experiments in the future.

Pangenome analysis of SARS-CoV2 strains to Identify Potential vaccine targets by Reverse Vaccinology (preprint)

Background: Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) leads to respiratory failure and obstructive alveolar damage, which may be fatal in immunocompromised individuals. COVID-19 pandemic has badly affected every part of the world, and still, the situation in the world is getting worse with the emergence of novel variants. The aim of our study is to explore the genome of SARS-CoV2 followed by in silico reverse vaccinology analysis. This will help to identify the most putative vaccine candidate against the virus in a robust manner and enables cost-effective development of vaccines compared with traditional strategies. Methods: The genomic sequencing data is retrieved from NCBI (Reference Sequence Number NC_045512.2). The sequences are explored through comparative genomics approaches by GENOMICS to find out the core genome. A comprehensive set of proteins obtained was employed in computational vaccinology approaches (names of tools?) for the prediction of the best possible B and T cell epitopes through ABCpred and IEDB Analysis Resource, respectively. The multi-epitopes were further tested against human toll-like receptors and cloned in an E. coli plasmid vector. Findings: The designed Multiepitope Subunit Vaccine was non-allergenic, antigenic (0.6543), & non-toxic, with significant connections with the human leukocyte antigen (HLA) binding alleles, and collective global population coverage of 84.38%. It has 276 amino acids, consisting of an adjuvant with the aid of an EAAAK linker, AAY linkers used to join the 4 CTL epitopes, and GPGPG linkers used to join the 3 HTL epitopes and KK linkers used to join the 7 B-cell epitopes. MESV docking with human pathogenic toll-like receptors-3 (TLR3) exhibited a stable & high binding affinity. An in-silico codon optimization approach was used in the codon system of E. coli (strain K12) to obtain the GC-Content of Escherichia coli (strain K12): 50.7340272413779 and CAI-Value of the improved sequence: 0.9542834278823386. The multi-epitope vaccine's optimized gene sequence was cloned in-silico in E. coli plasmid vector pET-30a (+), BamHI, and HindIII restriction sites were added to the N and C-terminals of the sequence, respectively. Conclusion: There is a pressing need to combat Covid-19 and we need quick and reliable approaches to Covid-19. By using In-silico approaches, we acquire an effective vaccine that could trigger adequate immune responses at the cellular and humoral levels. The suggested sequences can be further validated through in vivo and in vitro experimentation. Keywords: Covid-19, SARS Cov-2, Pangenome Analysis, Reverse Vaccinology

The impact of misinformation on the COVID-19 pandemic

Since the inception of the current pandemic, COVID-19 related misinformation has played a role in defaulting control of the situation. It has become evident that the internet, social media, and other communication outlets with readily available data have contributed to the dissemination and availability of misleading information. It has perpetuated beliefs that led to vaccine avoidance, mask refusal, and utilization of medications with insignificant scientific data, ultimately contributing to increased morbidity. Undoubtedly, misinformation has become a challenge and a burden to individual health, public health, and governments globally. Our review article aims at providing an overview and summary regarding the role of media, other information outlets, and their impact on the pandemic. The goal of this article is to increase awareness of the negative impact of misinformation on the pandemic. In addition, we discuss a few recommendations that could aid in decreasing this burden, as preventing the conception and dissemination of misinformation is essential.

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach.

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning's contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures -InceptionV3, ResNet50, and VGG19-on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.

Factors contributing to distress among school and college-going adolescents during COVID-19 Lockdown: A cross-sectional study conducted in Sibi Balochistan, Pakistan.

BACKGROUND: Due to the COVID-19 pandemic, many countries have implemented nationwide lockdowns. While this leads to a decrease in disease transmission, there is a concurrent increase in the levels of psychological distress. To estimate the levels of psychological distress in school- and college-going adolescents currently under lockdown and to determine the factors associated with this psychological distress. MATERIALS AND METHODS: A cross-sectional study conducted in Army Public School and College (APSAC) Sibi, Balochistan province of Pakistan between March and May 2020. Students of APSAC Sibi were enrolled in this research. Modified Kuppuswamy Socioeconomic Scale, Godin Leisure-Time Exercise Questionnaire, and Kessler-10 were used for data acquisition. Chi-square and t-tests and univariate analysis (nonparametric test) were performed using the Statistical Package for the Social Sciences (SPSS) version 23.0 (IBM, Armonk, US). RESULTS: Out of 225 participants, 57.4% were studying at school. Sixty-four percent of the participants were likely to be suffering from psychological distress. There is a significant effect of physical activity, sleep duration, bedtime at night, screen-time duration, and COVID-19 positive family member on the levels of distress. A moderate positive correlation was between psychological distress and bed-time at night (rho[223] = 0.328, P < 0.001) and screen time duration (rho[223] = 0.541, P < 0.001). A moderate negative correlation of physical activity (rho[223] = -0.340, P < 0.001) and a weak negative correlation of sleep duration hours (rho[225] = -0.158, P = 0.018) was found with psychological distress levels. CONCLUSIONS: The COVID-19 lockdown and pandemic have had a considerable psychological impact on both school-going and college-going students, showing increased level of stress. A strong public health campaign along with mental and physical and social support programs are the need of the hour.