Conserved Genomic Terminals of SARS-CoV-2 as Coevolving Functional Elements and Potential Therapeutic Targets

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 40 million people worldwide, with over 1 million deaths as of October 2020 and with multiple efforts in the development and testing of antiviral drugs and vaccines under way. In order to gain insights into SARS-CoV-2 evolution and drug targets, we investigated how and to what extent the SARS-CoV-2 genome sequence differs from those of other well-characterized human and animal coronavirus genomes, as well as how polymorphic SARS-CoV-2 genomes are generally. We ultimately sought to identify features in the SARS-CoV-2 genome that may contribute to its viral replication, host pathogenicity, and vulnerabilities. Our analyses suggest the presence of unique sequence signatures in the 3′ untranslated region (3′-UTR) of betacoronavirus lineage B, which phylogenetically encompasses SARS-CoV-2 and SARS-CoV as well as multiple groups of bat and animal coronaviruses. In addition, we identified genome-wide patterns of variation across different SARS-CoV-2 strains that likely reflect the effects of selection. Finally, we provide evidence for a possible host-microRNA-mediated interaction between the 3′-UTR and human microRNA hsa-miR-1307-3p based on the results of multiple computational target prediction analyses and an assessment of similar interactions involving the influenza A H1N1 virus. This interaction also suggests a possible survival mechanism, whereby a mutation in the SARS-CoV-2 3′-UTR leads to a weakened host immune response. The potential roles of host microRNAs in SARS-CoV-2 replication and infection and the exploitation of conserved features in the 3′-UTR as therapeutic targets warrant further investigation. IMPORTANCE The coronavirus disease 2019 (COVID-19) outbreak is having a dramatic global effect on public health and the economy. As of October 2020, SARS-CoV-2 has been detected in over 189 countries, has infected over 40 million people, and is responsible for more than 1 million deaths. The genome of SARS-CoV-2 is small but complex, and its functions and interactions with human host factors are being studied extensively. The significance of our study is that, using extensive SARS-CoV-2 genome analysis techniques, we identified potential interacting human host microRNA targets that share similarity with those of influenza A virus H1N1. Our study results will allow the development of virus-host interaction models that will enhance our understanding of SARS-CoV-2 pathogenesis and motivate the exploitation of both the interacting viral and host factors as therapeutic targets.

Cluster Analysis of SARS-CoV-2 Gene using Deep Learning Autoencoder: Gene Profiling for Mutations and Transitions

“…The time series of the type C obtained by autoencoder analysis seems to be consistent with the emergence and disappearance and geo location of coevolving variant group 4 (CEVg4) reported by Chan et al (Chan et al 2020). Based on genome frequencies and geo locations, our classification of types A1, A2, B1, and D seemed to correspond to the wild type, CEVg3, CEVg1, and CEVg6, respectively.…”

“…We found the eight clusters using over 30,000 SARS-CoV-2 ORF1ab genes in the NCBI Virus database, whereas Chan et al identified nine CEVg using 86,450 genomes in the GISAID database (Chan et al 2020). Yet there is not enough data to rigorously compare the differences between the ABCDE and CEVg classifications.…”

The Role of miRNAs in COVID-19 Disease

“…Indeed, increased expression of miR-1307-3p leads to a reduction in SARS-CoV-2 replication [ 5 ]. Also, Chen et al reported that mutations in SARS-CoV-2 3′-UTR lead to virus escape from the host immune system [ 16 ]. On the other hand, miR-1307-3p can affect anti-apoptotic proteins like BCL2 to induce apoptosis and inhibit proliferation.…”

Sequencing the pandemic: rapid and high-throughput processing and analysis of COVID-19 clinical samples for 21st century public health

“…To answer the latter, a database of all SNPs in the GISAID global collection 16 was generated and periodically updated by downloading all the genomes and filtering them for quality and completeness, then aligning to the WuHan-Hu-1 reference to identify any variants, as described 30 . The list of all variants and metadata for each sample were then put into a relational database for fast querying.…”