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Highlights • RNA-dependent RNA polymerase (RdRp), Spike (S) and glycoprotein (M) were selected. • Both S and M were chosen for the development of a chimeric vaccine. • siRNAs were also designed for S and M gene silencing. • Six new drug candidates were suggested that might inhibit the activity of RdRp., Coronavirus disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has become an immense threat to global public health. In this study, we performed complete genome sequencing of a SARS-CoV-2 isolate. More than 67,000 genome sequences were further inspected from Global Initiative on Sharing All Influenza Data (GISAID). Using several in silico techniques, we proposed prospective therapeutics against this virus. Through meticulous analysis, several conserved and therapeutically suitable regions of SARS-CoV-2 such as RNA-dependent RNA polymerase (RdRp), Spike (S) and Membrane glycoprotein (M) coding genes were selected. Both S and M were chosen for the development of a chimeric vaccine that can generate memory B and T cells. siRNAs were also designed for S and M gene silencing. Moreover, six new drug candidates were suggested that might inhibit the activity of RdRp. Since SARS-CoV-2 and SARS-CoV-1 have 82.30% sequence identity, a Gene Expression Omnibus (GEO) dataset of Severe Acute Respiratory Syndrome (SARS) patients were analyzed. In this analysis, 13 immunoregulatory genes were found that can be used to develop type 1 interferon (IFN) based therapy. The proposed vaccine, siRNAs, drugs and IFN based analysis of this study will accelerate the development of new treatments.

A novel coronavirus related to severe acute respiratory syndrome virus, (SARS-CoV-2) is the causal agent of the COVID-19 pandemic. Despite the genetic mutations across the SARS-CoV-2 genome being recently investigated, its transcriptomic genetic polymorphisms at inter-host level and the viral gene expression level based on each Open Reading Frame (ORF) remains unclear. Using available High Throughput Sequencing (HTS) data and based on SARS-CoV-2 infected human transcriptomic data, this study presents a high-resolution map of SARS-CoV-2 single nucleotide polymorphism (SNP) hotspots in a viral population at inter-host level. Four throat swab samples from COVID-19 infected patients were pooled, with RNA-Seq read retrieved from SRA NCBI to detect 21 SNPs and a replacement across the SARS-CoV-2 genomic population. Twenty-two RNA modification sites on viral transcripts were identified that may cause inter-host genetic diversity of this virus. In addition, the canonical genomic RNAs of N ORF showed higher expression in transcriptomic data and reverse transcriptase quantitative PCR compared to other SARS-CoV-2 ORFs, indicating the importance of this ORF in virus replication or other major functions in virus cycle. Phylogenetic and ancestral sequence analyses based on the entire genome revealed that SARS-CoV-2 is possibly derived from a recombination event between SARS-CoV and Bat SARS-like CoV. Ancestor analysis of the isolates from different locations including Iran suggest shared Chinese ancestry. These results propose the importance of potential inter-host level genetic variations to the evolution of SARS-COV-2, and the formation of viral quasi-species. The RNA modifications discovered in this study may cause amino acid sequence changes in polyprotein, spike protein, product of ORF8 and nucleocapsid (N) protein, suggesting further insights to understanding the functional impacts of mutations in the life cycle and pathogenicity of SARS-CoV-2., Highlights • RdRp, S proteins, ORF8, and nucleoprotein (N) of SARS-CoV-2 have undergone mutations at the inter-host level. • SARS-CoV-2 N gene is expressed abundantly during infection comparing to the 5′ terminal genes. • Single nucleotide polymorphisms in SARS-CoV-2 at Inter-host level cause amino acid changes in the virus genome. • Ancestor analysis of the SARS-CoV-2 isolates from different locations reveals shared Chinese ancestry.

Coronaviruses (CoVs) consist of six strains, and the severe acute respiratory syndrome coronavirus (SARS-CoV), newly found coronavirus (SARS-CoV-2) has rapidly spread leading to a global outbreak. The ferret (Mustela putorius furo) serves as a useful animal model for studying SARS-CoV/SARS-CoV-2 infection and developing therapeutic strategies. A holistic approach for distinguishing differences in gene signatures during disease progression is lacking. The present study discovered gene expression profiles of short-term (3 days) and long-term (14 days) ferret models after SARS-CoV/SARS-CoV-2 infection using a bioinformatics approach. Through Gene Ontology (GO) and MetaCore analyses, we found that the development of stemness signaling was related to short-term SARS-CoV/SARS-CoV-2 infection. In contrast, pathways involving extracellular matrix and immune responses were associated with long-term SARS-CoV/SARS-CoV-2 infection. Some highly expressed genes in both short- and long-term models played a crucial role in the progression of SARS-CoV/SARS-CoV-2 infection, including DPP4, BMP2, NFIA, AXIN2, DAAM1, ZNF608, ME1, MGLL, LGR4, ABHD6, and ACADM. Meanwhile, we revealed that metabolic, glucocorticoid, and reactive oxygen species-associated networks were enriched in both short- and long-term infection models. The present study showed alterations in gene expressions from short-term to long-term SARS-CoV/SARS-CoV-2 infection. The current result provides an explanation of the pathophysiology for post-infectious sequelae and potential targets for treatment., Highlights • The development of Wnt and stemness signaling were related to short-term SARS-CoV/SARS-CoV-2 infection. • The extracellular matrix and immune response were associated with long-term SARS-CoV/SARS-CoV-2 infection. • Metabolic, glucocorticoid, and reactive oxygen species-associated networks were enriched in both short- and long-term SARS-CoV/SARS-CoV-2 infected models.

Background Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets. Large single-cell datasets like Human Cell Atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics, and vaccine targets, as the world struggles to cope with the on-going spread of COVID-19 infections. Main body In this manuscript we describe REVEAL: SingleCell, which enables storage, retrieval, and rapid query of single-cell datasets inclusive of millions of cells. The array native database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypotheses. One such example includes identifying the most prevalent cell type annotation tag on returned cells. We used REVEAL: SingleCell to evaluate the expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in Conclusion In this paper, we introduce the REVEAL: SingleCell database that addresses immediate needs for SARS-CoV-2 research and has the potential to be used more broadly for many precision medicine applications. We used the REVEAL: SingleCell database as a reference to ask questions relevant to drug development and precision medicine regarding cell type and co-expression for genes that encode proteins necessary for SARS-CoV-2 to enter and reproduce in cells.