Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes

Brettner, Leandra; Ho, Wei-Chin; Schmidlin, Kara; Apodaca, Sam; Eder, Rachel; Geiler-Samerotte, Kerry

doi:10.1016/j.gde.2022.101951

Cited by 10 publications

(8 citation statements)

References 87 publications

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“…But previous work suggests that the phenotypic basis of adaptation is less diverse than the genotypic basis. This is important because it means that evolutionary outcomes are more predictable at the level of phenotype (Kinsler et al 2020; Brettner et al 2022b; Iwasawa et al 2022). Even so, every mutant could have a slightly different fitness profile if each affects the same handful of molecular-level phenotypes but to relatively different degrees.…”

Section: Resultsmentioning

confidence: 99%

“…Instead, many studies focus on identifying the genetic basis of adaptation in order to glean insights about the underlying mechanisms (Tenaillon et al 2012; Cowen et al 2014; Venkataram et al 2016). However, genotyping lineages from barcoded pools is technically challenging (Venkataram et al 2016), and further, genotype does not necessarily predict phenotype (Eguchi et al 2019; Brettner et al 2022b). For example, previous work using the same massively-replicate evolution platform that we use here discovered that many of mutations that provide an advantage in glucose-limited conditions are in genes comprising a canonical glucose-sensing pathway (Venkataram et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Schmidlin,

Apodaca,

Newell

et al. 2023

Preprint

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There is growing interest in designing multidrug therapies that leverage tradeoffs to combat drug resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into six classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different molecular mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, and some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments that thwart resistance. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a relatively smaller number of groups, our findings suggest that resistance evolves through a relatively small number of mechanisms such that multidrug strategies that hinder resistance may be possible. Finally, by grouping mutants that likely affect fitness through similar underlying mechanisms, our findings also guide efforts to map the phenotypic impacts of mutation and predict the impacts of some mutations from others.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Schmidlin,

Apodaca,

Newell

et al. 2023

Preprint

Self Cite

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“…Experimental evolution has also begun to answer questions related to pleiotropy by measuring the fitness of mutants evolved in one environment in another (see Cooper, 2018; Long et al, 2015; Jerison and Desai, 2015; Brettner et al, 2022; Jagdish and Nguyen Ba, 2022; McDonald, 2019 for reviews). Early work (Cooper and Lenski, 2000) showed that E. coli evolved for 20,000 generations often lost various catabolic functions, though it was unclear whether such loss was due to pleiotropy of adaptive mutations, or instead was the result of additional non-adaptive mutations due to mutation accumulation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Far From Home: Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments

Chen

Johnson

Hérissant

et al. 2023

Preprint

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Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in “non-home” environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.

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“…While application of barcoding initially focussed on adaptive evolution in single populations of model microbes, particularly yeast ( Saccharomyces cerevisiae ) (Blundell and Levy 2014; Cvijović et al 2018) and Escherichia coli (Jasinska et al 2020), strategies have since been devised for studying the evolution of cancer using barcoded lung cancer cells in mice (Rogers et al 2018) and quantification of eco-evolutionary dynamics in microbial communities (Venkataram et al 2022). The potential for extending strategies and applications is considerable, ranging from tracing object provenance (Nguyen Ba et al 2022), enhancing experimental power through mutant-multiplexing (Jackson et al 2020), extending understanding of microbiome transmission (Vasquez et al 2021), to the study of demographic changes during colonisation of new environments (Brettner et al 2022). _ However one impediment to progress is the effort required to develop the initial barcoded library, especially in non-model systems.…”

Section: Introductionmentioning

confidence: 99%

Barcoding populations ofPseudomonas fluorescensSBW25

Theodosiou

Farr

Rainey

2022

Preprint

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In recent years evolutionary biologists have developed increasing interest in the use of barcoding strategies to study eco-evolutionary dynamics of lineages within evolving populations and communities. Although barcoded populations can deliver unprecedented insight into evolutionary change, barcoding microbes presents specific technical challenges. Here, strategies are described for barcoding populations of the model bacterium Pseudomonas fluorescens SBW25, including the design and cloning of barcoded regions, preparation of libraries for amplicon sequencing, and quantification of resulting barcoded lineages. In so doing, we hope to aid the design and implementation of barcoding methodologies in a broad range of model and non-model organisms.

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Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes

Cited by 10 publications

References 87 publications

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Far From Home: Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments

Barcoding populations ofPseudomonas fluorescensSBW25

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