Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2

© 2024 Wiley Periodicals LLC..

A genome, composed of a precisely ordered sequence of four nucleotides (ATCG), encompasses a multitude of specific genome features like AAA motif. Mutations occurring within a genome disrupt the sequential order and composition of these features, thereby influencing the evolutionary trajectories and yielding variants. The evolutionary relatedness between a variant and its ancestor can be estimated by assessing evolutionary distances across a spectrum of genome features. This study develops a novel, alignment-free algorithm that considers both the sequential order and composition of genome features, enabling computation of the Fréchet distance (Fr) across multiple genome features to quantify the evolutionary status of a variant. Integrating this algorithm with an artificial recurrent neural network (RNN) reveals the quantitative evolutionary trajectory and origin of SARS-CoV-2, a puzzle unsolved by alignment-based phylogenetics. The RNN generates the evolutionary trajectory from Fr data at two levels: genome sequence mutations and organism variants. At the genome sequence level, SARS-CoV-2 evolutionarily shortens its genome to enhance its infectious capacity. Mutating signature features, such as TTA and GCT, increases its infectious potential and drives its evolution. At the organism level, variants mutating a single biomarker possess low infectious potential. However, mutating multiple markers dramatically increases their infectious capacity, propelling the COVID-19 pandemic. SARS-CoV-2 likely originates from mink coronavirus variants, with its origin trajectory traced as follows: mink, cat, tiger, mouse, hamster, dog, lion, gorilla, leopard, bat, and pangolin. Together, mutating multiple signature features and biomarkers delineates the evolutionary trajectory of mink-origin SARS-CoV-2, leading to the COVID-19 pandemic.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:96

Enthalten in:

Journal of medical virology - 96(2024), 3 vom: 08. März, Seite e29557

Sprache:

Englisch

Beteiligte Personen:

Wang, Anyou [VerfasserIn]

Links:

Volltext

Themen:

Biostatistics and bioinformatics
Evolution
Journal Article
SARS coronavirus
Virus classification

Anmerkungen:

Date Completed 21.03.2024

Date Revised 21.03.2024

published: Print

Citation Status MEDLINE

doi:

10.1002/jmv.29557

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM369957962