What influence does genetic diversity have on the bacterial phenotype?

What is this research project about?

Assigning a putative function to all the genes encoded in bacterial pangenomes.

What is this research project about?

We usually think of genetic differences in the context of human genetics. And so we are familiar with the fact that two people differ on average at about five million sites in the genome – that is, at only about 0.8 per cent of the entire genome. But genetic variation in bacteria has a completely different dimension: For example, two strains of Escherichia coli can differ in up to 60 percent of their genetic content.

This means that each bacterial species has not only a single genome, but also a diverse ensemble of gene combinations called a pangenome. We want to investigate the influence of this great genetic diversity on bacterial phenotypes.

What’s the current status?

Understanding how variations in genotype lead to variations in phenotype is an old topic in molecular biology. We are exploring how to better predict the phenotypic consequences of genetic variants in bacteria. This can be done with a combination of “forward-looking” mechanistic models, and “backward-looking” statistical genomics models.
Certain bacterial species, such as E.coli, have so-called open pangenomes. This means that new genes (“accessory genes”) are discovered when the genomes of new isolates are sequenced. Current functional genomics techniques cannot scale up fast enough to keep up with the flood of genes with unknown function that are being discovered every day.

Studying how different bacterial strains adapt to the same selective pressure (e.g. antimicrobials).

What are the project goals?

Our goal is to develop methods to assign functions to accessory genes by computer. The wealth of data available for different types of bacteria, as well as advances in machine learning, are one possible way to solve this problem. Most importantly, it helps that our lab has access to a large collection of E.coli strains.

How do we get there?

It has been shown that the genetic variability between bacterial strains also affects genes that are indispensable for growth and reproduction. We suspect that these differences could also influence the ability to adapt to selection pressures, such as the use of antimicrobials. As we explore the influence of bacterial pangenomes on clinically relevant phenotypes, we are also investigating whether different genetic backgrounds make people more or less susceptible to developing antibiotic resistance.

Computational and experimental approaches to link genotypic to phenotypic variation.

Projectleader

Project title: Influence of genetic diversity on the bacterial phenotype

Prof. Dr. Marco Galardini

CV & VIDEO

Project C4 Publications

Publications 

Genome wide association study of Escherichia coli bloodstream infection isolates identifies genetic determinants for the portal of entry but not fatal outcome. Denamur E, Condamine B, Esposito-Farèse M, Royer G, Clermont O, Laouenan C, Lefort A, de Lastours V, Galardini M; COLIBAFI; SEPTICOLI groups. PLoS Genet. 2022 Mar 24

Genome wide association study of human bacteremia Escherichia coli isolates identifies genetic determinants for the portal of entry but not fatal outcome. Denamur, E., Condamine, B., Esposito-Farèse, M., Royer, G., Clermont, O., Laouenan, C., Galardini, M. (2021). medRxiv

Major role of iron uptake systems in the intrinsic extra-intestinal virulence of the genus Escherichia revealed by a genome-wide association study. Galardini M., Clermont O., Baron A., Busby B., Dion S., Schubert S., Beltrao P. & Denamur E. (2020). PLOS Genetics 16(10): e1009065.

The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae. Galardini, M., Busby, B. P., Vieitez, C., Dunham, A. S., Typas, A., & Beltrao, P. (2019). The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae. Molecular Systems Biology, 15 (12)

pyseer: a comprehensive tool for microbial pangenome-wide association studies. Lees, J. A., Galardini, M., Bentley, S. D., Weiser, J. N., & Corander, J. (2018). Bioinformatics, 34(24), 4310-4312.

Phenotype inference in an Escherichia coli strain panel, Galardini, M., Koumoutsi, A., Herrera, C. M., Cordero Varela, J. A., Telzerow, A., Wagih O., Wartel M., Clermont O., Denamur E., Typas, A., Beltrao, P. (2017). Phenotype inference in an Escherichia coli strain panel. eLife, 6.

Publications of the Project C4