How fast do bacteria grow outside the laboratory? This simple question is very difficult to address directly, because it is near-impossible to track a lineage of bacterial cells, ancestor-to-decendant, inside an infected patient or through a river. Now in new work published in Proceedings B, Beth Gibson, Ed Feil, Adam Eyre-Walker and I exploit genome sequencing to try to get a handle on the problem indirectly.
We have done it by comparing two known quantities and taking the ratio: the rate at which DNA mutates in bacteria per year, and the rate it mutates per replication. This tells us in theory how many replications there are per year.
The mutation rate per replication has long been studied in the laboratory, and is around once per billion letters. Meanwhile, the recent avalanche of genomic data has allowed microbiologists to quantify the rate at which bacteria evolve over short time scales such as a year, including during outbreaks and even within individual infected patients. Most bugs mutate about once per million letters per year, with ten-fold variation above and below this not uncommon among different species.
For five species both these quantities exist. The fastest bug we looked at causes cholera and we estimate it doubles once every hour on average (give or take 30 minutes). The slowest was Salmonella, which we estimate doubles once a day on average (give or take 8 hours). In between were Staph. aureus and Pseudomonas at about two hours each, and E. coli at 15 hours. These are average over the very diverse and often hostile conditions that a bacterial cell may find itself in during the course of its natural lifecycle. To find out more about the work, please check out the paper.
Category Archives: Escherichia coli
PhD Studentship: Genomic prediction of antimicrobial resistance spread
This position is now closed
An opportunity has arisen for a D.Phil. (Ph.D.) place on the BBSRC-funded Oxford Interdisciplinary Bioscience Doctoral Training Partnership in the area of Artificial Intelligence, specifically Predicting the spread of antimicrobial resistance from genomics using machine learning.
If successful in a competitive application process, the candidate will join a cohort of students enrolled in the DTP’s one-year interdisciplinary training programme, before commencing the research project and joining my research group at the Big Data Institute.
This project addresses the BBSRC priority area “Combatting antimicrobial resistance” by using ML to predict the spread of antimicrobial resistance in human, animal and environmental bacteria exemplified by Escherichia coli. Understanding how quickly antimicrobial resistance (AMR) will spread helps plan effective prevention, improved biosecurity, and strategic investment into new measures. We will develop ML tools for large genomic datasets to predict the future spread of AMR in humans, animals and the environment. The project will create new methods based on award-winning probabilistic ML tools pioneered in my group (BASTA, SCOTTI) by training models using genomic and epidemiological data informative about past spread of AMR. We will apply the tools collaboratively to genomic studies of E. coli in Kenya, the UK and across Europe from humans, animals and the environment, Enterobacteriaceae in North-West England, and Campylobacter in Wales. Genomics has proven effective for asking “what went wrong” in the context of outbreak investigation and AMR spread; here we will address the greater challenge of repurposing such information using ML for forward prediction of future spread of AMR. Scrutiny will be intense because future predictions can and will be tested, raising the bar for the biological realism required while producing computationally efficient tools.
Attributes of suitable applicants: Understanding of genomics. Interest in infectious disease. Some numeracy, e.g. mathematics A-level, desirable. Experience of coding would help.
Funding notes: BBSRC eligibility criteria for studentship funding applies (https://www.ukri.org/files/legacy/news/training-grants-january-2018-pdf/). Successful students will receive a stipend of no less than the standard RCUK stipend rate, currently set at £14,777 per year.
How to apply: send me a CV and brief covering letter/email (no more than 1 page) explaining why you are interested and suitable by the Wednesday 11 July initial deadline. I will invite the best applicant/s to submit with me a formal application in time for the Friday 13 July second-stage deadline.
An opportunity has arisen for a D.Phil. (Ph.D.) place on the BBSRC-funded Oxford Interdisciplinary Bioscience Doctoral Training Partnership in the area of Artificial Intelligence, specifically Predicting the spread of antimicrobial resistance from genomics using machine learning.
If successful in a competitive application process, the candidate will join a cohort of students enrolled in the DTP’s one-year interdisciplinary training programme, before commencing the research project and joining my research group at the Big Data Institute.
This project addresses the BBSRC priority area “Combatting antimicrobial resistance” by using ML to predict the spread of antimicrobial resistance in human, animal and environmental bacteria exemplified by Escherichia coli. Understanding how quickly antimicrobial resistance (AMR) will spread helps plan effective prevention, improved biosecurity, and strategic investment into new measures. We will develop ML tools for large genomic datasets to predict the future spread of AMR in humans, animals and the environment. The project will create new methods based on award-winning probabilistic ML tools pioneered in my group (BASTA, SCOTTI) by training models using genomic and epidemiological data informative about past spread of AMR. We will apply the tools collaboratively to genomic studies of E. coli in Kenya, the UK and across Europe from humans, animals and the environment, Enterobacteriaceae in North-West England, and Campylobacter in Wales. Genomics has proven effective for asking “what went wrong” in the context of outbreak investigation and AMR spread; here we will address the greater challenge of repurposing such information using ML for forward prediction of future spread of AMR. Scrutiny will be intense because future predictions can and will be tested, raising the bar for the biological realism required while producing computationally efficient tools.
Attributes of suitable applicants: Understanding of genomics. Interest in infectious disease. Some numeracy, e.g. mathematics A-level, desirable. Experience of coding would help.
Funding notes: BBSRC eligibility criteria for studentship funding applies (https://www.ukri.org/files/legacy/news/training-grants-january-2018-pdf/). Successful students will receive a stipend of no less than the standard RCUK stipend rate, currently set at £14,777 per year.
How to apply: send me a CV and brief covering letter/email (no more than 1 page) explaining why you are interested and suitable by the Wednesday 11 July initial deadline. I will invite the best applicant/s to submit with me a formal application in time for the Friday 13 July second-stage deadline.
Collaborative PhD and postdoc positions available
Dr Nicole Stoesser, Prof. Derrick Crook, myself and colleagues in Oxford are seeking a postdoc in Microbial Genomics with statistics skills to join a new three-year project investigating antimicrobial resistance in environmental, human and animal reservoirs of E. coli and related organisms. The application deadline is noon Monday 11th July. For more details click here.
Dr Pierre Mahe of bioMérieux in Grenoble, France, is seeking to appoint an industry-linked PhD position developing statistical methods for genome-based characterization of antimicrobial resistance and virulence genes, with a focus on the opportunistic pathogen Pseudomonas aeruginosa. The position involves a secondment here in Oxford. For more details click here or contact Pierre Mahe.
Dr Pierre Mahe of bioMérieux in Grenoble, France, is seeking to appoint an industry-linked PhD position developing statistical methods for genome-based characterization of antimicrobial resistance and virulence genes, with a focus on the opportunistic pathogen Pseudomonas aeruginosa. The position involves a secondment here in Oxford. For more details click here or contact Pierre Mahe.
Making the most of bacterial GWAS: new paper in Nature Microbiology
In a new paper published this week in Nature Microbiology, we report the performance of genome wide association studies (GWAS) in bacteria to identify causal mechanisms of antibiotic resistance in four major pathogens, and introduce a new method, bugwas, to make the most of bacterial GWAS for traits under less strong selection.
As explained by Sarah Earle, joint first author with Jessie Wu and Jane Charlesworth, the problem with GWAS in bacteria is strong population structure and the consequent strong coinheritance of genetic variants throughout the genome. This phenomenon - known as genome-wide linkage disequilibrium (LD) - comes about because exchange of genes is relatively infrequent in bacteria, which reproduce clonally, compared to organisms that exchange genes every generation through sexual reproduction.
Genome-wide LD makes it difficult for GWAS to distinguish variants that causally influence a trait from other, coinherited variants that have no direct effect on the trait.
In the case of antibiotic resistance - a trait of high importance to human health - bacteria are under extraordinary selection pressures because resistance is a matter of life and death, to them as well as their human host. This helps overcome coinheritance and pinpoint causal variants because antibiotic usage selects for the independent evolution of the same resistance-causing variants in different genetic backgrounds.
Consequently, bacterial GWAS works very efficiently for antibiotic resistance: the variants most significantly associated with antibiotic resistance in 26 out of the 27 GWAS we performed were genuine resistance-conferring mutations. In the 27th we uncovered a putative novel mechanism of resistance to cefazolin in E. coli. These results for 17 antibiotics (ampicillin, cefazolin, cefuroxime, ceftriaxone, ciprofloxacin, erythromycin, ethambutol, fusidic acid, gentamicin, isoniazid, penicillin, pyrazinamide, methicillin, rifampicin, tetracycline, tobramycin and trimethoprim) across four species (E. coli, K. pneumoniae, M. tuberculosis and S. aureus) build on earlier work investigating beta-lactam resistance in S. pneumoniae, and convincingly demonstrate the potential for bacterial GWAS to discover new genes underlying important traits under strong selection.
What about traits under less strong selection, which probably includes pretty much every other bacterial trait? We show in this context that coinheritance poses a major challenge, based on detailed simulations. Often it may not be possible to use GWAS to pinpoint individual variants responsible for different traits because they are coinherited with - possibly many - other uninvolved variants.
But all is not lost. We show that even when individual locus-level effects cannot be pinpointed, there is often excellent power to characterize lineage-level differences in phenotype between strains. This is helpful for multiple reasons: (1) we often conceptualize trait variability in bacteria at the level of strain-to-strain differences (2) these differences can be highly predictive (3) we can prioritize variants for functional follow-up based on their contribution to strain-level differences.
These concepts represent a substantial departure from regular GWAS. In the human setting for instance, lineage-level differences are usually discarded as uninteresting or artefactual, and variants are almost always prioritized based on statistical evidence for involvement over-and-above any contribution to lineage-level differences. In the bacterial setting, we are forced to depart from these conventions because a large proportion of all genetic variation is strongly strain-stratified. To find out more, see the paper and try our methods.
As explained by Sarah Earle, joint first author with Jessie Wu and Jane Charlesworth, the problem with GWAS in bacteria is strong population structure and the consequent strong coinheritance of genetic variants throughout the genome. This phenomenon - known as genome-wide linkage disequilibrium (LD) - comes about because exchange of genes is relatively infrequent in bacteria, which reproduce clonally, compared to organisms that exchange genes every generation through sexual reproduction.
Genome-wide LD makes it difficult for GWAS to distinguish variants that causally influence a trait from other, coinherited variants that have no direct effect on the trait.
In the case of antibiotic resistance - a trait of high importance to human health - bacteria are under extraordinary selection pressures because resistance is a matter of life and death, to them as well as their human host. This helps overcome coinheritance and pinpoint causal variants because antibiotic usage selects for the independent evolution of the same resistance-causing variants in different genetic backgrounds.
Consequently, bacterial GWAS works very efficiently for antibiotic resistance: the variants most significantly associated with antibiotic resistance in 26 out of the 27 GWAS we performed were genuine resistance-conferring mutations. In the 27th we uncovered a putative novel mechanism of resistance to cefazolin in E. coli. These results for 17 antibiotics (ampicillin, cefazolin, cefuroxime, ceftriaxone, ciprofloxacin, erythromycin, ethambutol, fusidic acid, gentamicin, isoniazid, penicillin, pyrazinamide, methicillin, rifampicin, tetracycline, tobramycin and trimethoprim) across four species (E. coli, K. pneumoniae, M. tuberculosis and S. aureus) build on earlier work investigating beta-lactam resistance in S. pneumoniae, and convincingly demonstrate the potential for bacterial GWAS to discover new genes underlying important traits under strong selection.
What about traits under less strong selection, which probably includes pretty much every other bacterial trait? We show in this context that coinheritance poses a major challenge, based on detailed simulations. Often it may not be possible to use GWAS to pinpoint individual variants responsible for different traits because they are coinherited with - possibly many - other uninvolved variants.
But all is not lost. We show that even when individual locus-level effects cannot be pinpointed, there is often excellent power to characterize lineage-level differences in phenotype between strains. This is helpful for multiple reasons: (1) we often conceptualize trait variability in bacteria at the level of strain-to-strain differences (2) these differences can be highly predictive (3) we can prioritize variants for functional follow-up based on their contribution to strain-level differences.
These concepts represent a substantial departure from regular GWAS. In the human setting for instance, lineage-level differences are usually discarded as uninteresting or artefactual, and variants are almost always prioritized based on statistical evidence for involvement over-and-above any contribution to lineage-level differences. In the bacterial setting, we are forced to depart from these conventions because a large proportion of all genetic variation is strongly strain-stratified. To find out more, see the paper and try our methods.