Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS-CoV-2 infections and drug repurposing.

The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein-protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.

An epigenome-wide DNA methylation study of patients with COVID-19.

In the early 2000s, emerging SARS-CoV-2, which is highly pathogenic, posed a great threat to public health. During COVID-19, epigenetic regulation is deemed to be an important part of the pathophysiology and illness severity. Using the Illumina Infinium Methylation EPIC BeadChip (850 K), we investigated genome-wide differences in DNA methylation between healthy subjects and COVID-19 patients with different disease severities. We conducted a combined analysis and selected 35 "marker" genes that could indicate a SARS-CoV-2 infection, including 12 (ATHL1, CHN2, CHST15, CPLX2, CRHR2, DCAKD, GNAI2, HECW1, HYAL1, MIR510, PDE11A, and SMG6) situated in the promoter region. The functions and pathways of differentially methylated genes were enriched in biological processes, signal transduction, and the immune system. In the "Severe versus Mild" group, differentially methylated genes, after eliminating duplicates, were used for PPI analyses. The four hub genes (GNG7, GNAS, PRKCZ, and PRKAG2) that had the highest degree of nodes were identified and among them, GNG7 and GNAS genes expressions were also downregulated in the severe group in sequencing results. Above all, the results suggest that GNG7 and GNAS may play a non-ignorable role in the progression of COVID-19. In conclusion, the identified key genes and related pathways in the current study can be used to study the molecular mechanisms of COVID-19 and may provide possibilities for specific treatments.

Comprehensive comparative genomic and microsatellite analysis of SARS, MERS, BAT-SARS, and COVID-19 coronaviruses.

The coronavirus disease 2019 (COVID-19) pandemic has spread around the globe very rapidly. Previously, the evolution pattern and similarity among the COVID-19 causative organism severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and causative organisms of other similar infections have been determined using a single type of genetic marker in different studies. Herein, the SARS-CoV-2 and related ß coronaviruses Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV,  bat coronavirus (BAT-CoV) were comprehensively analyzed using a custom-built pipeline that employed phylogenetic approaches based on multiple types of genetic markers including the whole genome sequences, mutations in nucleotide sequences, mutations in protein sequences, and microsatellites. The whole-genome sequence-based phylogeny revealed that the strains of SARS-CoV-2 are more similar to the BAT-CoV strains. The mutational analysis showed that on average MERS-CoV and BAT-CoV genomes differed at 134.21 and 136.72 sites, respectively, whereas the SARS-CoV genome differed at 26.64 sites from the reference genome of SARS-CoV-2. Furthermore, the microsatellite analysis highlighted a relatively higher number of average microsatellites for MERS-CoV and SARS-CoV-2 (106.8 and 107, respectively), and a lower number for SARS-CoV and BAT-CoV (95.8 and 98.5, respectively). Collectively, the analysis of multiple genetic markers of selected ß viral genomes revealed that the newly born SARS-COV-2 is closely related to BAT-CoV, whereas, MERS-CoV is more distinct from the SARS-CoV-2 than BAT-CoV and SARS-CoV.

A single transcript for the prognosis of disease severity in COVID-19 patients.

With many countries strapped for medical resources due to the COVID-19 pandemic, it is highly desirable to allocate the precious resources to those who need them the most. Several markers have been found to be associated with the disease severity in COVID-19 patients. However, the established markers only display modest prognostic power individually and better markers are urgently needed. The aim of this study is to investigate the potential of S100A12, a prominent marker gene for bacterial infection, in the prognosis of disease severity in COVID-19 patients. To ensure the robustness of the association, a total of 1695 samples from 14 independent transcriptome datasets on sepsis, influenza infection and COVID-19 infection were examined. First, it was demonstrated that S100A12 was a marker for sepsis and severity of sepsis. Then, S100A12 was found to be a marker for severe influenza infection, and there was an upward trend of S100A12 expression as the severity level of influenza infection increased. As for COVID-19 infection, it was found that S100A12 expression was elevated in patients with severe and critical COVID-19 infection. More importantly, S100A12 expression at hospital admission was robustly correlated with future quantitative indexes of disease severity and outcome in COVID-19 patients, superior to established prognostic markers including CRP, PCT, d-dimer, ferritin, LDH and fibrinogen. Thus, S100A12 is a valuable novel prognostic marker for COVID-19 severity and deserves more attention.

Long non-coding RNAs: Promising new targets in pulmonary fibrosis.

Pulmonary fibrosis is characterized by progressive and irreversible scarring in the lungs with poor prognosis and treatment. It is caused by various factors, including environmental and occupational exposures, and some rheumatic immune diseases. Even the rapid global spread of the COVID-19 pandemic can also cause pulmonary fibrosis with a high probability. Functions attributed to long non-coding RNAs (lncRNAs) make them highly attractive diagnostic and therapeutic targets in fibroproliferative diseases. Therefore, an understanding of the specific mechanisms by which lncRNAs regulate pulmonary fibrotic pathogenesis is urgently needed to identify new possibilities for therapy. In this review, we focus on the molecular mechanisms and implications of lncRNAs targeted protein-coding and non-coding genes during pulmonary fibrogenesis, and systematically analyze the communication of lncRNAs with various types of RNAs, including microRNA, circular RNA and mRNA. Finally, we propose the potential approach of lncRNA-based diagnosis and therapy for pulmonary fibrosis. We hope that understanding these interactions between protein-coding and non-coding genes will contribute to the development of lncRNA-based clinical applications for pulmonary fibrosis.

Predictive immunogenetic markers in COVID-19.

Immunorelevant genes are among the most probable modulators of coronavirus disease 2019 (COVID-19) progression and prognosis. However, in the few months of the pandemic, data generated on host genetics has been scarce. The present study retrieved data sets of HLA-B alleles, KIR genes and functional single nucleotide polymorphisms (SNPs) in cytokines related to COVID-19 cytokine storm from two publicly available databases: Allele Frequency Net Database and Ensembl, and correlated these frequency data with Case Fatality Rate (CFR) and Daily Death Rates (DDR) across countries. Correlations of eight HLA-B alleles and polymorphisms in three cytokine genes (IL6, IL10, and IL12B) were observed and were mainly associated with DDR. Additionally, HLA-B correlations suggest that differences in allele affinities to SARS-CoV-2 peptides are also associated with DDR. These results may provide rationale for future host genetic marker surveys on COVID-19.

A Biomarker-Centric Approach to Drug Discovery and Development: Lessons Learned from the Coronavirus Disease 2019 Pandemic.

Faced with the health and economic consequences of the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the biomedical community came together to identify, diagnose, prevent, and treat the novel disease at breathtaking speeds. The field advanced from a publicly available viral genome to a commercialized globally scalable diagnostic biomarker test in less than 2 months, and first-in-human dosing with vaccines and repurposed antivirals followed shortly thereafter. This unprecedented efficiency was driven by three key factors: 1) international multistakeholder collaborations, 2) widespread data sharing, and 3) flexible regulatory standards tailored to meet the urgency of the situation. Learning from the remarkable success achieved during this public health crisis, we are proposing a biomarker-centric approach throughout the drug development pipeline. Although all therapeutic areas would benefit from end-to-end biomarker science, efforts should be prioritized to areas with the greatest unmet medical needs, including neurodegenerative diseases, chronic lower respiratory diseases, metabolic disorders, and malignant neoplasms. SIGNIFICANCE STATEMENT: Faced with the unprecedented threat of the severe acute respiratory syndrome coronavirus 2 pandemic, the biomedical community collaborated to develop a globally scalable diagnostic biomarker (viral DNA) that catalyzed therapeutic development at breathtaking speeds. Learning from this remarkable efficiency, we propose a multistakeholder biomarker-centric approach to drug development across therapeutic areas with unmet medical needs.

Expression Analyses of MicroRNAs in Hamster Lung Tissues Infected by SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an infectious disease with multiple severe symptoms, such as fever over 37.5°C, cough, dyspnea, and pneumonia. In our research, microRNAs (miRNAs) binding to the genome sequences of severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory-related coronavirus (MERS-CoV), and SARS-CoV-2 were identified by bioinformatic tools. Five miRNAs (hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-195-5p, hsa-miR-16-5p, and hsa-miR-196a-1-3p) were found to commonly bind to SARS-CoV, MERS-CoV, and SARS-CoV-2. We also identified miRNAs that bind to receptor proteins, such as ACE2, ADAM17, and TMPRSS2, which are important for understanding the infection mechanism of SARS-CoV-2. The expression patterns of those miRNAs were examined in hamster lung samples infected by SARS-CoV-2. Five miRNAs (hsa-miR-15b-5p, hsa-miR-195-5p, hsa-miR-221-3p, hsa-miR-140-3p, and hsa-miR-422a) showed differential expression patterns in lung tissues before and after infection. Especially, hsa-miR-15b-5p and hsa-miR-195-5p showed a large difference in expression, indicating that they may potentially be diagnostic biomarkers for SARS-CoV-2 infection.

Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization.

We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies. However, identifying viral subtypes in real-time is challenging: SARS-CoV-2 is a novel virus, and the pandemic is rapidly expanding. Viral subtypes may be difficult to detect due to rapid evolution; founder effects are more significant than selection pressure; and the clustering threshold for subtyping is not standardized. We propose to identify mutational signatures of available SARS-CoV-2 sequences using a population-based approach: an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Through ISM compression, we find that certain distant nucleotide variants covary, including non-coding and ORF1ab sites covarying with the D614G spike protein mutation which has become increasingly prevalent as the pandemic has spread. ISMs are also useful for downstream analyses, such as spatiotemporal visualization of viral dynamics. By analyzing sequence data available in the GISAID database, we validate the utility of ISM-based subtyping by comparing spatiotemporal analyses using ISMs to epidemiological studies of viral transmission in Asia, Europe, and the United States. In addition, we show the relationship of ISMs to phylogenetic reconstructions of SARS-CoV-2 evolution, and therefore, ISMs can play an important complementary role to phylogenetic tree-based analysis, such as is done in the Nextstrain project. The developed pipeline dynamically generates ISMs for newly added SARS-CoV-2 sequences and updates the visualization of pandemic spatiotemporal dynamics, and is available on Github at https://github.com/EESI/ISM (Jupyter notebook), https://github.com/EESI/ncov_ism (command line tool) and via an interactive website at https://covid19-ism.coe.drexel.edu/.

Genetics and COVID-19: How to Protect the Susceptible.

Along with the potential for breakthroughs in care and prevention, the search for genetic mechanisms underlying the spread and severity of coronavirus disease 2019 (COVID-19) introduces the risk of discrimination against those found to have markers for susceptibility. We propose new legal protections to mitigate gaps in protections under existing laws.