DOI: 10.5061/dryad.3xsj3txs6
title: Metabolic remodeling and de novo mutations transcend cryptic variation as drivers of adaptation in yeast

description: Many organisms live in predictable environments with periodic variations
 in growth conditions. Adaptation to these conditions can lead to loss of
 nonessential functions, which could be maladaptive in new environments.
 Alternatively, living in a predictable environment can allow populations
 to accumulate cryptic genetic variation that may have no fitness benefit
 in that condition, but can facilitate adaptation to new environments.
 However, how these processes together shape the fitness of populations
 growing in predictable environments remains unclear. Through laboratory
 evolution experiments in yeast, we show that populations grown in a
 nutrient-rich environment for 1000 generations generally have reduced
 fitness and lower adaptability to novel stressful environments. These
 populations showed metabolic remodeling and increased lipid accumulation
 in rich medium which seemed to provide osmotic protection in salt stress.
 Subsequent adaptation to stressors was primarily driven by de novo
 mutations, with very little contribution from the mutations accumulated
 prior to the exposure. Thus, our work suggests that without exposure to
 new environments, populations might lose their ability to respond
 effectively to these environments.  Further, our findings
 highlight a major role of exaptation and de novo mutations in adaptation
 to new environments but do not reveal a significant contribution of
 cryptic variation in this process.
description: Cells were washed with PBS and resuspended in PBS. Data were
 collected using a BD LSR Fortessa Cell Analyzer machine in appropriate
 channels (FITC or PE-Texas Red or both). The R package ‘flowCore’ was used
 to read FCS files and to convert them to text format. The data points with
 FSC-A values more than 2000 and SSC-A values more than were considered
 yeast cells. The points that showed lower values than these thresholds
 represented cell debris or noise and hence, were discarded.  In addition,
 a filtering for cell aggregates was performed, since aggregates can
 generate stronger signals than single cells. To filter out cell
 aggregates, the ratio of FSC-A and FSC-H values was calculated, and data
 points showed two clusters. The data points with FSC-A/FSC-H values lower
 than 1.9 were considered as single cells and thus, were included in the
 quantification of the fluorescence signal.
description: # Metabolic remodeling and de novo mutations transcend cryptic variation
 as drivers of adaptation in yeast
 [https://doi.org/10.5061/dryad.3xsj3txs6](https://doi.org/10.5061/dryad.3xsj3txs6) ## Description of the data and file structure ### Files and variables #### File: ICL1-GFP.zip **Description:** Flow cytometry data for the ICL1-GFP tagged strains  #### File: Rh123.zip **Description:** Flow cytometry data of the ancestral and evolved lines stained with the Rh123 stain for measurement of mitochondrial membrane potential  #### File: NileRed.zip **Description:** Flow cytometry data of the ancestral and evolved lines stained with the Nile red stain for measurement of intracellular lipid content #### File: SOD1-GFP.zip **Description:** Flow cytometry data for the SOD1-GFP tagged strains  #### File: PDC1-GFP.zip **Description:** Flow cytometry data for the PDC1-GFP tagged strains  ## Code/software Programs and codes used for processing of data generated in yeast experimental evolution in a single environment FlowDataProcessing folder:  - contains code for processing of flow cytometry data of GFP-tagged strains and from cell staining experiments GenomeDataAnalysis folder: - contains programs used for analysis for processing of genome sequencing data and identification of SNPs and CNVs GrowthcurveAnalysis folder:  - contains code and an example data file for the processing of growth curve experiment data