Epistatic interactions between the high pathogenicity island and other iron uptake systems shape Escherichia coli extra-intestinal virulence.

The intrinsic virulence of extra-intestinal pathogenic Escherichia coli is associated with numerous chromosomal and/or plasmid-borne genes, encoding diverse functions such as adhesins, toxins, and iron capture systems. However, the respective contribution to virulence of those genes seems to depend on the genetic background and is poorly understood. Here, we analyze genomes of 232 strains of sequence type complex STc58 and show that virulence (quantified in a mouse model of sepsis) emerged in a sub-group of STc58 due to the presence of the siderophore-encoding high-pathogenicity island (HPI). When extending our genome-wide association study to 370 Escherichia strains, we show that full virulence is associated with the presence of the aer or sit operons, in addition to the HPI. The prevalence of these operons, their co-occurrence and their genomic location depend on strain phylogeny. Thus, selection of lineage-dependent specific associations of virulence-associated genes argues for strong epistatic interactions shaping the emergence of virulence in E. coli.

Time-shift experiments and patterns of adaptation across time and space.

Time-shift experiments provide measures of the mean fitness of a population in environments of different points in time. Here, we show how to use this type of data to decompose mean fitness into (1) the effect of the environment in which the population is transplanted, (2) the effect of the genetic composition of the population and (3) 'temporal adaptation', which measures how the population fits the environment at that time. We derive analytical results for the pattern of 'temporal adaptation' and show that it is in general maximal in the recent past. The link between 'temporal adaptation' and 'local adaptation' is discussed, and we show when patterns of adaptation in time and space are expected to be similar. Finally, we illustrate the potential use of this approach using a data set measuring the adaptation of HIV to the immune response of several recently infected patients.