Trade-offs between microbial growth phases lead to frequency-dependent and non-transitive selection

Manhart, Michael; Adkar, Bharat V.; Shakhnovich, E. I.

doi:10.1098/rspb.2017.2459

Cited by 35 publications

(54 citation statements)

References 47 publications

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“…The most frequent example of exponential statistics is perhaps found in the logistic growth of microorganisms, where the exponential face is usually preceded by a lag phase (Madigan et al , ). Interestingly, the expression that relates the lag time is remarkably similar to our equation (Manhart et al , ), which highlights a parallel between microorganismal growth and host decay (Wang & Goldenfeld, ). In this respect, our pathogen lethality δ plays the analog of the bacterial growth rate, which has recently shown to be one of the few key fundamental parameters determining the state of the cell (Scott et al , ).…”

Section: Discussionsupporting

confidence: 74%

Disentangling bacterial invasiveness from lethality in an experimental host‐pathogen system

Biancalani

Gore

2019

Molecular Systems Biology

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Quantifying virulence remains a central problem in human health, pest control, disease ecology, and evolutionary biology. Bacterial virulence is typically quantified by the LT 50 (i.e., the time taken to kill 50% of infected hosts); however, such an indicator cannot account for the full complexity of the infection process, such as distinguishing between the pathogen's ability to colonize versus kill the hosts. Indeed, the pathogen needs to breach the primary defenses in order to colonize, find a suitable environment to replicate, and finally express the virulence factors that cause disease. Here, we show that two virulence attributes, namely pathogen lethality and invasiveness, can be disentangled from the survival curves of a laboratory population of Caenorhabditis elegans nematodes exposed to three bacterial pathogens: Pseudomonas aeruginosa , Serratia marcescens, and Salmonella enterica . We first show that the host population eventually experiences a constant mortality rate, which quantifies the lethality of the pathogen. We then show that the time necessary to reach this constant mortality rate regime depends on the pathogen growth rate and colonization rate, and thus determines the pathogen invasiveness. Our framework reveals that Serratia marcescens is particularly good at the initial colonization of the host, whereas Salmonella enterica is a poor colonizer yet just as lethal once established. Pseudomonas aeruginosa , on the other hand, is both a good colonizer and highly lethal after becoming established. The ability to quantitatively characterize the ability of different pathogens to perform each of these steps has implications for treatment and prevention of disease and for the evolution and ecology of pathogens.

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Section: Discussionsupporting

confidence: 74%

Disentangling bacterial invasiveness from lethality in an experimental host‐pathogen system

Biancalani

Gore

2019

Molecular Systems Biology

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“…In addition, our study considers the fitness effects in a single environment and a single phase of growth. Fitness effects of mutations (even the sign of the effect) can vary greatly with environment (18) and may be growth phase dependent, which has important consequences for selection (66). A key assumption of our study is that TEM-1's known catalytic activity (or any unknown secondary activity) does not affect any biological process in the cell that impacts fitness.…”

Section: Discussionmentioning

confidence: 99%

Collateral fitness effects of mutations

Mehlhoff

Stearns

Rohm

et al. 2019

Preprint

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AbstractThe distribution of fitness effects (DFE) of mutation plays a central role in constraining protein evolution. The underlying mechanisms by which mutations lead to fitness effects are typically attributed to changes in protein specific activity or abundance. Here, we reveal the importance of a mutation’s collateral fitness effects, which we define as effects that do not derive from changes in the protein’s ability to perform its physiological function. We comprehensively measured the collateral fitness effects of missense mutations in the E. coli TEM-1 β-lactamase antibiotic resistance gene using growth competition experiments in the absence of antibiotic. At least 42% of missense mutations in TEM-1 were deleterious, indicating that for some proteins, collateral fitness effects occur as frequently as effects on protein activity and abundance. Deleterious mutations caused improper post-translational processing, incorrect disulfide-bond formation, protein aggregation, changes in gene expression, and pleiotropic effects on cell phenotype. Deleterious collateral fitness effects occurred more frequently in TEM-1 than deleterious effects on antibiotic resistance in environments with low concentrations of the antibiotic. The surprising prevalence of deleterious collateral fitness effects suggests they may play a role in constraining protein evolution, particularly for highly-expressed proteins, for proteins under intermittent selection for their physiological function, and for proteins whose contribution to fitness is buffered against mutations with deleterious effects on protein activity and protein abundance.Significance StatementMutations provide the source of genetic variability upon which evolution acts. Deleterious protein mutations are commonly thought of in terms of how they compromise the protein’s ability to perform its physiological function. However, mutations might also be deleterious if they cause negative effects on one of the countless other cellular processes. The frequency and magnitude of such collateral fitness effects is unknown. Our systematic study of mutations in a bacterial protein finds widespread collateral fitness effects that were associated with protein aggregation, improper protein processing, incomplete protein transport across membranes, incorrect disulfide-bond formation, induction of stress-response pathways, and unexpected changes in cell properties. Our results suggest that deleterious collateral fitness effects may be an important constraint on protein evolution.

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“…Furthermore, in monocultures the slow growers exhibited higher lag times than the fast growers (Fig. S5), which would seem to be disadvantageous in low-dilution, high-density conditions where resources could be quickly consumed by a competitor with a shorter lag 36 . The frequency of the tradeoff in other systems is a question worthy of further investigation, in particular because natural microbial systems, such as soil communities or the gut microbiome, are better represented with a low dilution rate than a high dilution rate 37,38 .…”

Section: Discussionmentioning

confidence: 99%

Mortality causes universal changes in microbial community composition

Abreu

Friedman

Woltz

et al. 2018

Preprint

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6All organisms are sensitive to the abiotic environment, and a deteriorating 7 environment can lead to extinction. However, survival in a multispecies 8 community also depends upon inter-species interactions, and some species may 9 even be favored by a harsh environment that impairs competitors. A 10 deteriorating environment can thus cause surprising transitions in community 11 composition. Here, we combine theory and laboratory microcosms to develop a 12 predictive understanding of how simple multispecies communities change under 13 added mortality, a parameter that represents environmental harshness. In order 14to explain changes in a multispecies microbial system across a mortality gradient, 15we examine its members' pairwise interactions. We find that increasing mortality 16 favors the faster grower, confirming a prediction of simple models. Furthermore, 17if the slower grower outcompetes the faster grower in environments with low or 18 no added mortality, the competitive outcome can reverse as mortality increases. 19We find that this tradeoff between growth rate and competitive ability is indeed 20 prevalent in our system, allowing for striking pairwise outcome changes that 21 propagate up to multispecies communities. These results argue that a bottom-up 22 approach can provide insight into how communities will change under stress. 23

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Trade-offs between microbial growth phases lead to frequency-dependent and non-transitive selection

Cited by 35 publications

References 47 publications

Disentangling bacterial invasiveness from lethality in an experimental host‐pathogen system

Disentangling bacterial invasiveness from lethality in an experimental host‐pathogen system

Collateral fitness effects of mutations

Mortality causes universal changes in microbial community composition

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