Projects Systems Biology of Microbial Communities
Previous work from the lab has demonstrated the possibility of inferring the impact of genetic variants on the phenotype of the model bacterium E. coli. There is still considerable room for the improvement of these so-called genotype-to-phenotype models: for instance, variants can affect gene regulation processes such as transcription, which in turn affect observable phenotypes such as antimicrobial resistance. We are therefore interested in developing models for the impact of genetic variants on gene regulation and how those ultimately affect growth phenotypes. We are achieving this goal by collecting previously generated data, as well as measuring the transcriptome of an E. coli natural isolates collection. We are using a combination of associations and mechanistic models to develop and test a general predictive model for the impact of genetic variants on transcription.
- Galardini M, Koumoutsi A, Herrera-Dominguez L, Cordero Varela JA, 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
- Lees JA, Galardini M, Bentley SD, Weiser JN, Corander J (2018) pyseer: a comprehensive tool for microbial pangenome-wide association studies. Bioinformatics 34(24): 4310-4312.
- Galardini M, Clermont O, Baron A, Busby B, Dion S, Schubert S, Beltrao P, Denamur E (2020) Major role of iron uptake systems in the intrinsic extra-intestinal virulence of the genus Escherichia revealed by a genome-wide association study. PLoS Genet 16(10): e1009065.
Population genetics studies have increasingly shown the influence of genetic background on mutations’ fitness effects, which implies that different adaptation trajectories might be accessible across strains belonging to the same species. This large genetic variability across strains is likely to affect the ability to more or less readily develop antimicrobial resistance (AMR). In addition, the interaction between genetic variants (i.e. epistatic effects) are known to influence adaptation, leading to a “rugged” fitness landscape that is highly specific to each genetic background. As a result, certain adaptation trajectories might not be accessible to a strain, while others may be favoured. We are using Automated Laboratory Evolution (ALE) across E. coli natural isolates to understand the interaction between genetic backgrounds and evolution of AMR.
- Galardini M, Busby BP, Vieitez C, Dunham AS, Typas A, Beltrao P (2019) The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae. Mol Syst Biol 15(12): e8831.
The advent of high-throughput sequencing has resulted in the possibility to obtain the genome sequence of hundreds of bacterial isolates with limited cost. We now know that for species such as E. coli individual strains differ for up to 60% in their gene content. Those genes with low conservation, also called accessory genes, are known to contribute to survival in specialized niches; even a broad functional characterization is however not available for many of them, with an even worse outlook for members of the human microbiome. Chemical genomics approaches can be used to reconstruct the functions of these genes, but are limited to a few tens of species because of cost and labour constraints. We are using computational approaches such as machine learning trained on the wealth of data available for model organisms and using features extracted from nucleotide sequences to improve the current function prediction methods.