Periodic Reporting for period 2 - EvoComBac (The evolutionary epidemiology of commensal bacteria: the case of Escherichia coli from 1980 to 2025)
Reporting period: 2022-12-01 to 2024-05-31
Understanding the rapid adaptation of infectious pathogens is crucial to design better management policies and anticipate future changes. Yet, existing models often fail to explain these dynamics. We address this major challenge of evolutionary biology in the bacterial species Escherichia coli. E. coli is a commensal of the human gut and an opportunistic pathogen causing infections responsible for more than a million deaths worldwide per year. It may currently be the most deadly infectious pathogen on the planet, and causing considerable harm through relatively common infections such as urinary tract infections. E. coli has rapidly evolved over the last four decades. From the 1980s, starting from an almost fully sensitive population, multiple antibiotic resistances have emerged and stabilized at an intermediate frequency. Concomitantly, virulence, the propensity to cause infections, increased. The evolutionary processes causing these changes are largely unknown, but this evolution has an impact on human health.
To elucidate the drivers of the evolution of commensal E. coli, we develop a prospective cohort of 200 longitudinally followed healthy volunteers—the largest cohort of its kind. We analyse these data in the light of an integrative statistical and mathematical framework describing the ecology of E. coli from the within-host to the population level. These models will generate testable predictions on the evolution of genomic variants determining virulence, resistance, and colonisation ability. These predictions will be tested on an exceptional dataset composed of 1000 existing and new bacterial genomes sampled from healthy human hosts from 1980 to 2025, encompassing around 100,000 generations of bacterial evolution.
This original interdisciplinary project draws from epidemiology, evolutionary biology and genomics for a better understanding of the evolution of bacteria. This project is a step towards better predictions of evolutionary dynamics and better stewardship policies for infectious pathogens.
To elucidate the drivers of the evolution of commensal E. coli, we develop a prospective cohort of 200 longitudinally followed healthy volunteers—the largest cohort of its kind. We analyse these data in the light of an integrative statistical and mathematical framework describing the ecology of E. coli from the within-host to the population level. These models will generate testable predictions on the evolution of genomic variants determining virulence, resistance, and colonisation ability. These predictions will be tested on an exceptional dataset composed of 1000 existing and new bacterial genomes sampled from healthy human hosts from 1980 to 2025, encompassing around 100,000 generations of bacterial evolution.
This original interdisciplinary project draws from epidemiology, evolutionary biology and genomics for a better understanding of the evolution of bacteria. This project is a step towards better predictions of evolutionary dynamics and better stewardship policies for infectious pathogens.
We made significant progress in understanding Escherichia coli dynamics and pathogenicity. Our work focused on three key scientific objectives—unravelling colonisation dynamics, quantifying the selective forces acting on virulence genes, and modelling the evolution of antibiotic resistance—with the major transverse objective of generating new cohorts to study E. coli dynamics in healthy adults.
The pilot (50 healthy volunteers followed every two weeks for 4 months in Paris) was completed. For each sample, five randomly chosen E. coli colonies were typed and will be whole-genome-sequenced in early 2024. This dataset, including extensive host metadata, is already one of the most beautiful of its kind. The main cohort (200 volunteers planned, in Paris) is currently underway. An additional cohort (100 volunteers sampled every month in the North of Vietnam) with samples already collected by my collaborators in past work has been analyzed in a similar way, and will lead to similar insights in the very different context of Vietnam (middle income country, distinct diet, high prevalence of antibioresistances).
We quantified the dynamics of antibiotic resistance in healthy untreated individuals. We showed that contrary to assumptions, resistant strains coexisted with sensitive strains in untreated hosts for extended periods of time, before undergoing abrupt clearance. We currently explore the broader implications of these within-host dynamics on antibiotic resistance evolution at the between-host dynamics.
We investigated E. coli colonisation dynamics in healthy hosts using several datasets including our own pilot study. We reveal a remarkable and robust trade-off between colonisation and persistence. Major clones exhibiting a diversity of strategies organized along a trade-off from clones with long persistent times, to clones with large capacity to colonise new hosts. This trade-off together with niche differentiation explain the diversity of phylogroups circulating in the population, the more frequent occurrence of resistance in persister clones, and the coincidental evolution of virulence.
We also investigated similar trade-off at the level of individual virulence genes, confirming this trade-off and shedding light on how differentiation along the persistence-colonisation trade-off can emerge over evolutionary timescales.
Lastly, we conducted a systematic comparison of E. coli strains from infections and stool samples. We revealed significant genomic differences at all levels. A machine learning algorithm identified new genetic variants associated with pathogenicity, emphasizing the large heritability of this trait and predicting that pathogenicity has evolved upwards over 1980-2010 in France.
Together our results give a systematic picture of the determinants of E. coli pathogenicity, demonstrating that it is a highly heritable trait that may evolve (and has indeed evolved in the past) with important public health implications. Our examination of colonisation dynamics reveals intriguing patterns for antibiotic resistance, and general organizing principles at the level of major clones and virulence genes—such principles will be explored further in the coming years.
The pilot (50 healthy volunteers followed every two weeks for 4 months in Paris) was completed. For each sample, five randomly chosen E. coli colonies were typed and will be whole-genome-sequenced in early 2024. This dataset, including extensive host metadata, is already one of the most beautiful of its kind. The main cohort (200 volunteers planned, in Paris) is currently underway. An additional cohort (100 volunteers sampled every month in the North of Vietnam) with samples already collected by my collaborators in past work has been analyzed in a similar way, and will lead to similar insights in the very different context of Vietnam (middle income country, distinct diet, high prevalence of antibioresistances).
We quantified the dynamics of antibiotic resistance in healthy untreated individuals. We showed that contrary to assumptions, resistant strains coexisted with sensitive strains in untreated hosts for extended periods of time, before undergoing abrupt clearance. We currently explore the broader implications of these within-host dynamics on antibiotic resistance evolution at the between-host dynamics.
We investigated E. coli colonisation dynamics in healthy hosts using several datasets including our own pilot study. We reveal a remarkable and robust trade-off between colonisation and persistence. Major clones exhibiting a diversity of strategies organized along a trade-off from clones with long persistent times, to clones with large capacity to colonise new hosts. This trade-off together with niche differentiation explain the diversity of phylogroups circulating in the population, the more frequent occurrence of resistance in persister clones, and the coincidental evolution of virulence.
We also investigated similar trade-off at the level of individual virulence genes, confirming this trade-off and shedding light on how differentiation along the persistence-colonisation trade-off can emerge over evolutionary timescales.
Lastly, we conducted a systematic comparison of E. coli strains from infections and stool samples. We revealed significant genomic differences at all levels. A machine learning algorithm identified new genetic variants associated with pathogenicity, emphasizing the large heritability of this trait and predicting that pathogenicity has evolved upwards over 1980-2010 in France.
Together our results give a systematic picture of the determinants of E. coli pathogenicity, demonstrating that it is a highly heritable trait that may evolve (and has indeed evolved in the past) with important public health implications. Our examination of colonisation dynamics reveals intriguing patterns for antibiotic resistance, and general organizing principles at the level of major clones and virulence genes—such principles will be explored further in the coming years.
We anticipate major results to emerge in the second half of the project, with the complete analysis of the new cohorts generated over the course of the project. We will quantify further the colonisation dynamics at the level of sequence types, individual virulence genes, and explore other genetic determinants of colonisation. We hope to quantify the evolution of resistance genes in commensalism during and after treatment, especially thanks to the Vietnamese cohort (where antibiotic treatment is more frequent). We will complete the phylogenomic analysis of long-term evolution of E. coli in France with the data from 2021-2024, giving a picture of commensal evolution over almost five decades.