Adaptive landscapes of antibiotic resistance: population size and 'survival-of-the-flattest'.

Lead Research Organisation: Keele University
Department Name: Faculty of Natural Sciences


Spontaneous mutation altering genetic sequences is a key engine of evolution. However, if the rate of mutations exceeds a 'critical mutation rate', changes occur too frequently for natural selection to maintain the population's genetic makeup. For example, one can think of mutation as moving offspring away from their parents in a 'fitness landscape' where peaks are genetic sequences with high fitness. If there is too much mutation (above a critical mutation rate), selection may be able to keep a population on a broad fitness peak, but not on a narrow one, so-called 'survival-of-the-flattest'. Such critical mutation rates are generally believed to be much higher than those seen in typical biological organisms. However, we have recently discovered that, in computer simulations, critical mutation rates are much lower in very small populations (<100 individuals). This suggests that survival-of-the-flattest could be occurring within the normal range of biological mutation rates.

This proposal therefore aims to test whether survival-of-the-flattest really does occur in biology, using antibiotic resistance evolution as a test case. This requires an integrated, multi-disciplinary approach, conducting decisive wet-lab experiments, aligning computer simulations with biology and developing a rigorous and predictive theoretical framework that relates findings to mathematical understanding. Our first objective will be to characterise the fitness landscapes of bacteria resistant to different antibiotics to determine whether appropriate distinctions exist in practice between narrow and broad fitness peaks. Here we will focus on environments containing antibiotics, but will use experimental evolution (at high and minimal population sizes) with complete genome sequencing to consider mutations across the genome. Our second Objective is to develop computer simulations and theory to align with the biology. Specifically we need to relate our findings about critical mutation rates in simulations to existing theory. We then need to move both theory and simulation towards more biologically realistic assumptions. For instance we shall test more realistic fitness landscapes, particularly those determined empirically through our first Objective. Our final Objective is rigorously to test the coherence of the theory, simulation and biological experiment with each other. In particular we shall use experimental evolution at small population sizes to test whether we can detect the effect of critical mutation rates as predicted and further characterise the experimentally evolved strains to test the consistency of biological mechanisms involved.

Rigorously unpicking this novel population size effect in the evolution of antibiotic resistance could have broad impacts. Antibiotic resistant bacteria inevitably appear by mutation with a small population size and bacteria resistant to one antibiotic will be knocked down to small population sizes by another. The hypotheses we shall test here will determine whether the maintenance of particular antibiotic resistances at such small population sizes, could depend on the details of the different fitness landscapes imposed by different antibiotics in a theoretically predictable way - potentially crucial information in combatting antimicrobial resistance.

In sum this work will closely link experimental approaches to evolution in biology, simulation and theory to determine if when and how survival-of-the-flattest impinges on the increasingly critical issue of antibiotic resistance evolution.

Technical Summary

We have recently identified a dramatic drop, at low population sizes, in critical mutation rates. Above these rates a narrow fitness peak may be lost while a broad fitness peak is maintained in a population i.e. 'survival-of-the-flattest'. This brings such thresholds into the range of actual biological mutation rates. This effect could be important in determining evolutionary trajectories, e.g. in the evolution of antibiotic resistance. We therefore propose to test if such effects occur in practice. This requires a multi-disciplinary approach to develop and integrate wet-lab experimental evolution, computer simulation and theory. In the wet-lab we shall evolve Escherichia coli resistant to different antibiotics. Evolution at high population sizes will climb adaptive peaks, at minimal population sizes (mutation accumulation) will move down them and complete genome sequencing will determine the numbers of peaks involved. In simulations we shall develop state-of-the-art GPGPU implementations to relax various biologically unrealistic assumptions in current models. Thus we shall test e.g. the population size dependence in biologically realistic fitness landscapes. At the same time we shall develop existing theory of error thresholds to address critical mutation rates, finite, small populations and more biologically realistic fitness landscapes. We shall test the integration of theory, simulation and biology through close feedback between theory and simulation, experimental evolution at small but not minimal population sizes and detailed tests on specific mutations to identify pleiotropic effects. Together this will test the potential role of the relationship between population size and mutation rate thresholds in biology. This has the potential to link a well-established area of theory and simulation much more closely with biology and to bring timely, critical and novel insights into the crucial area of anti-microbial resistance evolution.

Planned Impact

Who will benefit from this research?
Beneficiaries will include:

a) The wider scientific community and those who benefit from their research. This applies particularly to those with interests in antibiotics, healthcare policy, population genetics and evolutionary dynamics of small populations, conservation biology, evolutionary algorithms, genetic search, problem solving using biologically inspired genetic algorithms, and interdisciplinary approaches to biology.

b) The researcher Co-I and PDRA employed on the project.

c) Members of the wider public with interests in antibiotics, healthcare, medicine, evolution and conservation.

d) All those involved in dealing with the current crises in antibiotic resistance (ultimately all who will benefit from the use of antibiotics which are currently becoming unusable due to the evolution of antibiotic resistant microbes).

e) Charities and organisations with an interest in developing conservation strategies and understanding the threats to populations at risk of extinction.

How will they benefit?

a) The wider scientific community will benefit as outlined in the Academic Beneficiaries section.

b) The researcher Co-I and PDRA will benefit from training, both in the general practicalities of cross-disciplinary research and, in the case of the researcher-Co-I, specific training in a next-generation sequence analysis (see Pathways to Impact).

c) As we have discovered through our existing work on antibiotic resistance, the wider public have strong interests in issues surrounding antibiotic resistant microbes and their evolution. This research will shed new light on this issue in terms of the role of mutation, population size and the effects of different fitness landscapes. They will benefit by gaining a deepening of understanding of the fundamental science behind the current crisis. Beyond that, this relevance offers a route in to inspiring interest in a range of STEM subjects and the interaction between them, providing a real world case study emphasising their importance to society

d) In the long-term this research could impact healthcare strategy through drug regime policy. Specifically, the use of combinations of antibiotics is becoming increasingly important and, depending on the results of this project, it may become clear that mutation rates and available evolutionary trajectories, in combination with population size, will result in predictably different outcomes if antibiotics are applied in different regimes.

e) Here we investigate fundamental theoretical issues of small population size and whether they are truly relevant to biological systems. While we use antibiotic resistant microbes as a tractable system, if we discover biological relevance, this will be applicable to animals as much as microbes. Therefore all those concerned with organisms at small population sizes, e.g. conservation organisations, could ultimately benefit from this work in uncovering the effect of mutation that could be cryptically causing endangered organisms to lose fit alleles, thereby influencing both conservation practice and policy.
Description The critical mutation rate (CMR) determines the shift between survival-of-the-fittest and the survival of individuals with greater mutational robustness (the "flattest"). Small populations are more likely to exceed the CMR and become less well adapted; understanding the CMR is crucial to understanding the potential fate of small populations under threat of extinction. We have developed and published a simulation model capable of utilising input parameter values within a biologically relevant range. A previous study identified an exponential fall in CMR with decreasing population size, but the parameters and output were not directly relevant outside artificial systems. The first key contribution of our published work is the identification of an inverse relationship between CMR and gene length when the gene length is comparable to that found in biological populations. The exponential relationship is maintained, and the CMR is lowered to between two to five orders of magnitude above existing estimates of per base mutation rate for a variety of organisms. The second key contribution is the identification of an inverse relationship between CMR and the number of genes. Using a gene number in the range for Arabidopsis thaliana produces a CMR close to its known mutation rate; per base mutation rates for other organisms are also within one order of magnitude. This is the third key contribution, as it represents the first time such a simulation model has used input and produced output both within range for a given biological organism. This novel convergence of CMR model with biological reality is of particular relevance to populations undergoing a bottleneck, under stress, and subsequent conservation strategy for populations on the brink of extinction. In a fourth key contribution, we showed that CMR can be lowered by Horizontal Gene Transfer (HGT), in both clonal and non-clonal populations, and that a population reproducing clonally has a higher CMR than one in which individuals undergo crossover. In all HGT cases the change in CMR with population size is greater for populations with 100 individuals or less. This represents a significant stage in bacterial evolution; smaller populations will exist when a population is founded or near to extinction. This will also be the case if a subset of the population is considered as a population in its own right, for example, the sub population of resistant bacteria that emerges due to the introduction of antibiotic resistance genes. Understanding the effect of mutation at such a critical stage is key to predicting the likely fate of a population.
Exploitation Route The above mentioned novel convergence of our CMR model with biological reality (regarding mutation rate) is of particular relevance to populations undergoing a bottleneck, under stress, and subsequent conservation strategy for populations on the brink of extinction. This knowledge will be taken forward within the current project, future projects we plan to propose, and will also be of use to other researchers interested in conservation strategy and preventing extinctions. This knowledge will also be of use to the evolutionary algorithms community.
Sectors Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Environment,Healthcare,Pharmaceuticals and Medical Biotechnology