Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).Copyright © 2021 Rando et al.

Clustering Based Identification of SARS-CoV-2 Subtypes

With the availability of more than half a million SARS-CoV-2 sequences and counting, many approaches have recently appeared which aim to leverage this information towards understanding the genomic diversity and dynamics of this virus. Early approaches involved building transmission networks or phylogenetic trees, the latter for which scalability becomes more of an issue with each day, due to its high computational complexity. In this work, we propose an alternative approach based on clustering sequences to identify novel subtypes of SARS-CoV-2 using methods designed for haplotyping intra-host viral populations. We assess this approach using cluster entropy, a notion which very naturally captures the underlying process of viral mutation-the first time entropy was used in this context. Using our approach, we were able to identify the well-known B.1.1.7 subtype from the sequences of the EMBL-EBI (UK) database, and also show that the associated cluster is consistent with a measure of fitness. This demonstrates that our approach as a viable and scalable alternative to unveiling trends in the spread of SARS-CoV-2.

A Novel Network Representation of SARS-CoV-2 Sequencing Data

The unprecedented level of genome sequencing during the SARS-CoV-2 pandemic brought about the challenge of processing this genomic data. However, the state-of-the-art phylogenetic methods were mostly designed for analyzing data that are significantly sparser and require extensive subsampling of strains. We present (ε, τ) -MSN, a novel tool that reconstructs a viral genetic relatedness network based on genetic distances, that can process hundreds of thousands of sequences in under several hours. We applied (ε, τ) -MSN to the global COVID-19 outbreak data and were able to build a genetic network on more than 100,000 SARS-CoV-2 sequences. We show that (ε, τ) -MSN can accurately detect transmission events and build a genetic network with significantly higher assortativity with respect to continent and country attributes of SARS-CoV-2 samples. The source code for this software suite is available at https://github.com/Sergey-Knyazev/eMST. © 2021, Springer Nature Switzerland AG.