There are several forces that shape nucleotide usage in viral genomes.
For instance, some motifs are more likely to be recognized by the host's immune system, and are therefore generically avoided by viruses.
Moreover, the codon usage of viruses is determined by the availability of tRNAs in the host cell, which in turn is influenced by the host's own codon usage.
This interplay between host and virus codon usage and the avoidance of motifs recognized by the host's immune system are a key factors in the adaptation of viruses to their hosts.
By studying the patterns of nucleotide and codon usage in viral genomes, we can gain insights into the evolutionary processes that shape these genomes, and use this knowledge to develop new tools for tracking emerging pathogens, developing vaccines, and designing antiviral drugs.
The aim of this project was to use methods from statistical physics to build new tools to investigate the role of nucleotide and codon usage in the adaptation of human-infecting viruses to their hosts. The project was divided into two main objectives: (1) to develop a method to identify the pressures that each host exerts on the nucleotide and codon usage of its viruses, and (2) to investigate the role of nucleotide and codon usage in host adaptation of human-infecting viruses following host jumps.
In addition to the main scientific objectives, the project was also aimed at developing the skills of the researcher, in particular by providing training in the use of statistical physics methods to study biological systems, and in the use of machine learning techniques to analyze genomic data.
Both the scientific and the training objectives of the project were successfully achieved, and the fellowship has been instrumental in the researcher's career development: immediately after the end of the project, the fellow joined a startup company as a Senior Data Scientist to perform research on machine-learning-guided development of phage cocktails for the treatment of bacterial infections.
