Tuesday, 23 March 2021

Viral variants: new algorithmic approaches to study how they arise and spread

Two new studies published by the Bicocca-IBFM-CNR Research Team in Patterns and iScience journals use data science techniques to analyze sequencing data of SARS-CoV-2 virus
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Two new studies were published by the Bicocca -Institute of Bioimaging and Molecular Physiology (IBFM-CNR) research team,

composed by Alex Graudenzi, IBFM-CNR researcher, by professors Rocco Piazza, Marco Antoniotti, Carlo Gambacorti-Passerini,

by research fellows Daniele Ramazzotti, Fabrizio Angaroni and by IBFM-CNR research associate Davide Maspero.

First article

The study published in Patterns introduces an innovative data science approach named VERSO (Viral Evolution ReconStructiOn) for the analysis of sequencing data of viral genomes.

The new approach allows one to identify a high-resolution model of the evolution of a pathogen through the tracing of genomic mutations, allowing one to reconstruct epidemiological links between infected people, i.e., a potential infectious contact between individuals, as well as to intercept possibly hazardous variants before these spread to the population.

The application of VERSO to SARS-CoV-2 6726 samples made it possible to reconstruct the evolution of the virus and to reconstruct in great detail the epidemiological contacts between individuals. Furthermore, the analysis of the mutational landscape of the virus revealed a significant increase in the overall number of mutations in the population, also allowing the identification of potentially dangerous variants, some of which are located on the "spike" gene.

Study Link "VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples", Patterns (2021)

Second Article

The study published in iScience aims at identifying and quantifying the mechanisms responsible for the generation of variants, again starting from the analysis of sequencing data of viral samples.

The study is focused on the analysis of the processes that determine the onset and spread of viral variants, and which derive from the complex combination of mutational processes induced by the interaction between the virus and its host and the dynamics of transmission of the virus among infected individuals. Data science techniques already applied in the study of cancer have proved effective in identifying different "mutational signatures" in different groups of patients.

Starting from the analysis of the sequencing data of a large number of SARS-CoV-2 samples, the study allowed us to identify some human enzymes (APOBEC and ADAR) as potentially responsible for the generation of specific mutation types observed on the viral genome. Furthermore, the analysis showed that the intensity of such mutational processes appears to be extremely heterogeneous in patients, suggesting the possibility that they may be related to the distinct outcomes of the disease. Both studies provide important new tools to researchers who study viral sequences to better understand the properties and modification of the virus over time, allowing to frame this evolution and the appearance of new mutations in the context of precise molecular mechanisms.

Study Link "Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity", iScience (2021)