Anyou Wang

Title : Evolutionary trajectory and origin of SARS-CoV-2

Abstract:

Alignment-based phylogenetics fails to uncover the evolutionary trajectory and origin of SARS-CoV-2. This study develops a novel alignment-free system based on Fréchet distance and artificial recurrent neural network to reveal the evolutionary trajectory and origin of SARS-CoV-2 from more than two millions of genome sequences. SARS-CoV-2 gradually deletes its genome during its evolution, but the mutation rate varies with genome features. Among single nucleotides, C mutates fast but T changes slowly. C-prefix dinucleotide (e.g. CG and CT) loses dramatically fast during COVID-19 outbreaks. Similarly, the virus genome also deletes several trinucleotides prefixed by C (e.g.CCT). Interestingly, trinucleotide CCT and CT centrally control the entire SARS-CoV-2 genome, and their evolutionary trajectories fit COVID-19 case spike. Therefore C-prefixed feature deletions primarily contribute to
COVID-19 pandemic. SARS-CoV-2 variants undergo both vertical and horizontal deletions during their evolutionary trajectories to infect humans. Gradual vertical mutations help variants to increase their infection, but over-mutations reduce their infection potential. Horizontal mutations make variants gain diverse mutation markers, which dramatically increase virus infection capacity, leading to COVID-19 pandemic. Mink coronavirus variants mutate 56 genome features similar to SARS-CoV-2 wild type and they are the most likely origin of SARS-CoV-2. This origin trajectory follows the order: mink, cat, tiger, mouse, hamster, dog, lion, gorilla, leopard, bat, and pangolin. Therefore, the C-feature dynamic pattern serves as the key mark of the evolutionary trajectory SARS-CoV-2 genome, and mink-origin SARS-CoV-2 gradually deletes its genome to carry diverse mutations, causing COVID-19 pandemic

Biography:

Anyou Wang received his PhD from University of California, Riverside. His research interest is in computational biology, artificial intelligence and big data. Dr. Wang develops computational algorithms to catch up the big pictures from massive data and to understand the fundamental principles of biology (combai.org). He computed petabyte level data and revealed the distinctive functional regime of endogenous lncRNAs in dark regions of human genome and unearthed that noncoding RNAs endogenously rule the cancerous regulatory realm while proteins govern the normal. Recently, Dr. Wang also developed a novel alignment-free system integrating Fréchet distance and artificial recurrent neural network to reveal the evolutionary trajectory and origin of SARS-CoV-2 from more than two millions of genome sequences.