Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast populations

Johnson, Milo S.; Gopalakrishnan,; Goyal,; Dillingham, Megan E.; Bakerlee, Christopher W; Humphrey, Tim; Jagdish,; Jerison,; Kosheleva,; Lawrence,; Min, MIN; Moulana,; Phillips, Nicholas; Piper, Matthew D. W.; Purkanti, Ramya; Rego-Costa,; McDonald, Vanessa M.; Ba, Nguyen Van; Desai,

doi:10.1101/2020.10.09.330191

Cited by 19 publications

(47 citation statements)

References 60 publications

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“…After evolving for 500 generations, we evaluated the ploidy status of each population by staining for DNA content using a procedure previously described (Jerison et al 2020; Johnson et al 2020), with slight modifications. Briefly, 6μL of saturated culture from each population was added to 120μL water in a fresh 96-well plate and centrifuged (2,000 rcf, 2 minutes).…”

Section: Methodsmentioning

confidence: 99%

“…Extracted genomic DNA was then subjected to Nextera tagmentation (following Baym et al 2015) in preparation for multiplexed Illumina sequencing. Tagmented PCR products were then purified using PCRcleanDX magnetic beads (Aline Biosciences) through a two-sided size selection procedure with 0.5/0.75X or 0.5/0.8X bead buffer ratios (Johnson et al 2020). Quality of the multiplexed libraries was verified by estimating their fragment-size distributions using the Agilent 4200 TapeStation system and sequenced with 2×150bp paired-end chemistry on Illumina NextSeq 500 and Illumina NovaSeq platforms.…”

Section: Methodsmentioning

confidence: 99%

“…To prepare sequencing libraries for all samples in parallel, we used a BiomekFXP liquid handling robot (Beckman Coulter) to extract total genomic DNA from ~500 µL saturated cultures of all samples, following a previously described procedure (Johnson et al 2020). A high-throughput Bio-On-Magnetic-Beads (BOMB) protocol with paramagnetic beads and GITC lysis buffer (Oberacker et al 2019) was used for this step, followed by DNA quantification using the AccuGreen™ High Sensitivity dsDNA Quantitation kit ( Biotium, 31066) on clear flat-bottom 96-well polystyrene plates (Corning®, VWR Life Science, 25381-056).…”

Section: Genotyping With Whole-genome Sequencingmentioning

confidence: 99%

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The Genetic Basis of Differential Autodiploidization in Evolving Yeast Populations

Tung

Bakerlee

Phillips

et al. 2021

Preprint

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Spontaneous whole-genome duplication, or autodiploidization, is a common route to adaptation in experimental evolution of haploid budding yeast populations. The rate at which autodiploids fix in these populations appears to vary across strain backgrounds, but the genetic basis of these differences remains poorly characterized. Here we show that the frequency of autodiploidization differs dramatically between two closely related laboratory strains of Saccharomyces cerevisiae, BY4741 and W303. To investigate the genetic basis of this difference, we crossed these strains to generate hundreds of unique F1 segregants and tested the tendency of each segregant to autodiplodize across hundreds of generations of laboratory evolution. We find that variants in the SSD1 gene are the primary genetic determinant of differences in autodiploidization. We then used multiple laboratory and wild strains of S. cerevisiae to show that clonal populations of strains with a functional copy of SSD1 autodiploidize more frequently in evolution experiments, while knocking out this gene or replacing it with the W303 allele reduces autodiploidization propensity across all genetic backgrounds tested. These results suggest a potential strategy for modifying rates of spontaneous whole-genome duplications in laboratory evolution experiments in haploid budding yeast. They may also have relevance to other settings in which eukaryotic genome stability plays an important role, such as biomanufacturing and the treatment of pathogenic fungal diseases and cancers.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Genotyping With Whole-genome Sequencingmentioning

confidence: 99%

See 1 more Smart Citation

The Genetic Basis of Differential Autodiploidization in Evolving Yeast Populations

Tung

Bakerlee

Phillips

et al. 2021

Preprint

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“…Avida was designed specifically to study evolution using digital organisms, and it provides many tools that enabled us to observe the process in great detail. To that end, we have constructed and analyzed a selfcontained evolving system, comparable in some ways to the E. coli Long-Term Evolution Experiment [17,[45][46][47][48][49][50][51][52][53][54] and other microbial evolution experiments [3,[55][56][57][58][59][60][61]. While it is difficult to compare evolving microbes to evolving computer programs, it is useful to recognize features of the digital world that are similar to the biological world, as well as others that are different.…”

Section: Future Directionsmentioning

confidence: 99%

How the footprint of history shapes the evolution of digital organisms

Bundy

Ofria

Lenski

2021

Preprint

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Gould had a thought experiment of replaying the tape of life that provides a conceptual framework for experiments that quantify the contributions of adaptation, chance, and history to evolutionary outcomes. For example, we can empirically measure how varying the depth of history in one environment influences subsequent evolution in a new environment. Can this footprint of history, the genomic legacy of prior adaptation- grow too deep to overcome? Can it constrain adaptation, even with intense selection in the new environment? We investigated these questions using digital organisms. Specifically, we evolved ten populations from one ancestor under identical conditions. We then replayed evolution from three time points in the history of each population (corresponding to shallow, intermediate, and deep history) in two new environments (one similar and one dissimilar to the prior environment). We measured the contributions of adaptation, chance, and history to the among-lineage variation in fitness and genome length in both new environments. In both environments, variation in genome length depended largely on history and chance, not adaptation, indicating weak selection. By contrast, adaptation, chance, and history all contributed to variation in fitness. Crucially, whether the depth of history affected adaptation depended on the environment. When the ancestral and new environments overlapped, history was as important as adaptation to the fitness achieved in the new environment for the populations with the deepest history. However, when the ancestral and novel environments favored different traits, adaptation overwhelmed even deep history. This experimental design for assessing the influence of the depth of history is promising for both biological and digital systems.

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“…The best known such study is one from the Lenski lab, where 12 populations of Escherichia coli have been cultured since 1988 in minimal media by batch culture for more than 60,000 generations (Good et al, 2017;Lenski, 2017). In another long-running experiment, Saccharomyces cerevisiae populations evolved over 3 years, for some 10,000 generations in three environments (Johnson et al, 2021;McDonald, 2019). These and other studies have given valuable insights into evolutionary processes.…”

Section: Introductionmentioning

confidence: 99%

Increased pathogenicity of the nematophagous fungus Drechmeria coniospora following long-term laboratory culture

Courtine

Zhang

Ewbank

2021

Preprint

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Domestication provides a window into adaptive change. Over the course of 2 decades of laboratory culture, a strain of the nematode-specific fungus Drechmeria coniospora became more virulent during its infection of Caenorhabditis elegans. Through a close comparative examination of the genome sequences of the original strain and its more pathogenic derivative, we identified a small number of non-synonymous mutations in protein-coding genes. In one case, the mutation was predicted to affect a gene involved in hypoxia resistance and we provide direct corroborative evidence for such an effect. The mutated genes with functional annotation were all predicted to impact the general physiology of the fungus and this was reflected in an increased in vitro growth, even in the absence of C. elegans. While most cases involved single nucleotide substitutions predicted to lead to a loss of function, we also observed a predicted restoration of gene function through deletion of an extraneous tandem repeat. This latter change affected the regulatory subunit of a cAMP-dependent protein kinase. Remarkably, we also found a mutation in a gene for a second protein of the same, protein kinase A, pathway. Together, we predict that they result in a stronger repression of the pathway for given levels of ATP and adenylate cyclase activity. Finally, we also identified mutations in a few lineage-specific genes of unknown function that are candidates for factors that influence virulence in a more direct manner.

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Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast populations

Cited by 19 publications

References 60 publications

The Genetic Basis of Differential Autodiploidization in Evolving Yeast Populations

The Genetic Basis of Differential Autodiploidization in Evolving Yeast Populations

How the footprint of history shapes the evolution of digital organisms

Increased pathogenicity of the nematophagous fungus Drechmeria coniospora following long-term laboratory culture

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