Background: Plate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis. Results: Growthcurver summarizes the growth characteristics of microbial growth curve experiments conducted in a plate reader. The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate. Conclusions: Growthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN). The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

Prokaryotic genomes are small and compact. Either this feature is caused by neutral evolution or by natural selection favoring small genomes—genome streamlining. Three separate prior lines of evidence argue against streamlining for most prokaryotes. We find that the same three lines of evidence argue for streamlining in the genomes of thermophile bacteria. Specifically, with increasing habitat temperature and decreasing genome size, the proportion of genomic DNA in intergenic regions decreases. Furthermore, with increasing habitat temperature, generation time decreases. Genome-wide selective constraints do not decrease as in the reduced genomes of host-associated species. Reduced habitat variability is not a likely explanation for the smaller genomes of thermophiles. Genome size may be an indirect target of selection due to its association with cell volume. We use metabolic modeling to demonstrate that known changes in cell structure and physiology at high temperature can provide a selective advantage to reduce cell volume at high temperatures.

Robust to Change: The variation in genetic sequences determining the binding of transcription factors is believed to be an important facet of evolution. However, the degree to which a genome is robust, that is, able to withstand changes and how robustness affects evolution is unclear. Payne and Wagner (p. 875 ) investigated the empirical support for mutational robustness by examining transcription factor (TF) binding in the mouse and yeast genomes. A network analysis of the degree of variation revealed that the sites with the highest affinity for TF binding exhibit the greatest tolerance for nucleotide substitutions, whereas low-affinity sites exhibit greater sensitivity to mutation. Thus, while mutational robustness and evolvability are antagonistic at the genotypic level, they are synergistic at the phenotypic level.

Most laboratory evolution studies that characterize evolutionary adaptation genomically focus on genetically simple traits that can be altered by one or few mutations. Such traits are important, but they are few compared with complex, polygenic traits influenced by many genes. We know much less about complex traits, and about the changes that occur in the genome and in gene expression during their evolutionary adaptation. Salt stress tolerance is such a trait. It is especially attractive for evolutionary studies, because the physiological response to salt stress is well‐characterized on the molecular and transcriptome level. This provides a unique opportunity to compare evolutionary adaptation and physiological adaptation to salt stress. The yeastSaccharomyces cerevisiaeis a good model system to study salt stress tolerance, because it contains several highly conserved pathways that mediate the salt stress response. We evolved three replicate lines of yeast under continuous salt (NaCl) stress for 300 generations. All three lines evolved faster growth rate in high salt conditions than their ancestor. In these lines, we studied gene expression changes through microarray analysis and genetic changes through next generation population sequencing. We found two principal kinds of gene expression changes, changes in basal expression (82 genes) and changes in regulation (62 genes). The genes that change their expression involve several well‐known physiological stress‐response genes, includingCTT1, MSN4andHLR1. Next generation sequencing revealed only one high‐frequency single‐nucleotide change, in the geneMOT2, that caused increased fitness when introduced into the ancestral strain. Analysis of DNA content per cell revealed ploidy increases in all the three lines. Our observations suggest that evolutionary adaptation of yeast to salt stress is associated with genome size increase and modest expression changes in several genes.