Source–sink dynamics shape the evolution of antibiotic resistance and its pleiotropic fitness cost by Perron
Read on in Winston-Salem, NC, rated 5/5Summary
In this article Perron and colleagues experimentally tested the impact of immigration and environment harshness on Pseudomonas aeruginosa. In their full factoral experiment they tested how the immigration rate of the bacteria as well as the treatment environment could impact the fitness exploring the idea behind source-sink dynamics. They found that:
- Increasing immigration rates had a positive impact on the density of Pseudomonas in all environments (in alignment with source-sink dynamics theory)
- Antibiotic therapy introduced mutations with fitness costs (i.e., fitness costs were introduced in order for the bacteria to reproduce within harsh environment)
- So called bi-therapy (or simultaneous multi-drug therapy) induced higher fitness costs
- Cycling of different antibiotics imposed fitness costs, but less so than the bi-therapy.
This study is important for a few reasons:
- Experimental support for source-sink dynamics (and associated immigration) on introducing more rapid evolution of resistance
- Important implications for the control of nosocomial infections as immigration of pathogens (especially under monotherapy)
this means that in important nosocomial infections, such as those by P. aeruginosa, the immigration of susceptible bacteria established in an antibiotic-free reservoir (e.g. contaminated water; Trautmann et al. 2005) into transient secondary niches supplemented with antibiotic (e.g. respiratory tract of treated patient; Festini et al. 2006) can not only foster the rapid evolution of antibiotic resistance, but can also create resistant mutants with little or no fitness cost.
Main Points
The evolution and maintenance of antibiotic resistance depend on both the probability of resistance mutations and the pleiotropic fitness cost associated with resistance (Andersson 2006); the latter often manifested as slower growth rate or a reduced competitive ability in the absence of antibiotics (Andersson 2003)
There are some concerns regarding epistasis in that the harsher environments will result in fixed mutations or evolutionary pathways in which a local, but suboptimal, evolutionary equilibrium could exist.
As with the probability of resistance evolution, the fitness of a resistant mutant that goes to fixation will be a positive function of the mutation supply (Levin et al. 2000) rate, for which migration is likely to be a key determinant as higher migration brings more mutants. Under higher migration rates, many resistance mutations will arise simultaneously, allowing selection to fix the mutation with the lowest fitness cost. By contrast, if mutation supply rate is low, the less fit resistant mutation is likely to reach fixation before a better mutation appears.
Source-sink models
These models have been applied more recently to micro-organisms to explain the evolution of virulence (Sokurenko et al. 2006; Chattopadhyay et al. 2007)
source - population is found in its “fundamental niche” which is a set of environment al conditions and resources that permit a population to persist, grow (i.e., birth rate larger than death rate over some range of densities), and produce immigrants (Hutchinson 1978)
sink- a population fundamental niche (a harsh environment) has a mean fitness lower than 1 (e.g. death rate exceeds the birth rate) and cannot be sustained without passive (Holt 1985/10) or active (Pulliam 1988) immigration and is therefore referred to as a ‘sink’ population
With sustained immigration, there should be adaption to the sinks and that adaptation will occur quicker in less harsh environment. Adaptation will then look often like punctuated and rapid growth from this interplay of immigration and selection.
Conversely, immigration can also constrain adaptive evolution, because gene flow can swamp locally favoured variants, and because immigrants can compete with better-adapted residents. This effect hinders adaptation by maintaining the population away from the local fitness optimum.
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Questions
The statistics were a bit wild and I wish we had access to the original experimental data. We could likely calculate growth rates under the different conditions rather than including a second order polynomial as they have described in their methods.