High-Resolution Mutation Mapping Reveals Parallel Experimental Evolution in Yeast

Understanding the genetic basis of evolutionary adaptation is limited by our ability to efficiently identify the genomic locations of adaptive mutations. Here we describe a method that can quickly and precisely map the genetic basis of naturally and experimentally evolved complex traits using linkage analysis. A yeast strain that expresses the evolved trait is crossed to a distinct strain background and DNA from a large pool of progeny that express the trait of interest is hybridized to oligonucleotide microarrays that detect thousands of polymorphisms between the two strains. Adaptive mutations are detected by linkage to the polymorphisms from the evolved parent. We successfully tested our method by mapping five known genes to a precision of 0.2–24 kb (0.1–10 cM), and developed computer simulations to test the effect of different factors on mapping precision. We then applied this method to four yeast strains that had independently adapted to a fluctuating glucose–galactose environment. All four strains had acquired one or more missense mutations in GAL80, the repressor of the galactose utilization pathway. When transferred into the ancestral strain, the gal80 mutations conferred the fitness advantage that the evolved strains show in the transition from glucose to galactose. Our results show an example of parallel adaptation caused by mutations in the same gene

Comparison of time-gated surface-enhanced raman spectroscopy (TG-SERS) and classical SERS based monitoring of Escherichia coli cultivation samples

The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time-gated Raman (TG-Raman)-, continuous wave NIR-process Raman (NIR-Raman), and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis. (c) 2018 American Institute of Chemical EngineersPeer reviewe

Known mutator alleles do not markedly increase mutation rate in clinical Saccharomyces cerevisiae strains

Natural selection has the potential to act on all phenotypes, including genomic mutation rate. Classic evolutionary theory predicts that in asexual populations, mutator alleles, which cause high mutation rates, can fix due to linkage with beneficial mutations. This phenomenon has been demonstrated experimentally and may explain the frequency of mutators found in bacterial pathogens. By contrast, in sexual populations, recombination decouples mutator alleles from beneficial mutations, preventing mutator fixation. In the facultatively sexual yeast Saccharomyces cerevisiae, segregating alleles of MLH1 and PMS1 have been shown to be incompatible, causing a high mutation rate when combined. These alleles had never been found together naturally, but were recently discovered in a cluster of clinical isolates. Here we report that the incompatible mutator allele combination only marginally elevates mutation rate in these clinical strains. Genomic and phylogenetic analyses provide no evidence of a historically elevated mutation rate. We conclude that the effect of the mutator alleles is dampened by background genetic modifiers. Thus, the relationship between mutation rate and microbial pathogenicity may be more complex than once thought. Our findings provide rare observational evidence that supports evolutionary theory suggesting that sexual organisms are unlikely to harbour alleles that increase their genomic mutation rate

Divergence Involving Global Regulatory Gene Mutations in an Escherichia coli Population Evolving under Phosphate Limitation

Many of the important changes in evolution are regulatory in nature. Sequenced bacterial genomes point to flexibility in regulatory circuits but we do not know how regulation is remodeled in evolving bacteria. Here, we study the regulatory changes that emerge in populations evolving under controlled conditions during experimental evolution of Escherichia coli in a phosphate-limited chemostat culture. Genomes were sequenced from five clones with different combinations of phenotypic properties that coexisted in a population after 37 days. Each of the distinct isolates contained a different mutation in 1 of 3 highly pleiotropic regulatory genes (hfq, spoT, or rpoS). The mutations resulted in dissimilar proteomic changes, consistent with the documented effects of hfq, spoT, and rpoS mutations. The different mutations do share a common benefit, however, in that the mutations each redirect cellular resources away from stress responses that are redundant in a constant selection environment. The hfq mutation lowers several individual stress responses as well the small RNA–dependent activation of rpoS translation and hence general stress resistance. The spoT mutation reduces ppGpp levels, decreasing the stringent response as well as rpoS expression. The mutations in and upstream of rpoS resulted in partial or complete loss of general stress resistance. Our observations suggest that the degeneracy at the core of bacterial stress regulation provides alternative solutions to a common evolutionary challenge. These results can explain phenotypic divergence in a constant environment and also how evolutionary jumps and adaptive radiations involve altered gene regulation

Severe zinc depletion of escherichia coli: roles for high affinity zinc binding by ZinT, zinc transport and zinc-independent proteins

Zinc ions play indispensable roles in biological chemistry. However, bacteria have an impressive ability to acquire Zn2+ from the environment, making it exceptionally difficult to achieve Zn2+ deficiency, and so a comprehensive understanding of the importance of Zn2+ has not been attained. Reduction of the Zn2+ content of Escherichia coli growth medium to 60 nM or less is reported here for the first time, without recourse to chelators of poor specificity. Cells grown in Zn2+-deficient medium had a reduced growth rate and contained up to five times less cellular Zn2+. To understand global responses to Zn2+ deficiency, microarray analysis was conducted of cells grown under Zn2+-replete and Zn2+-depleted conditions in chemostat cultures. Nine genes were up-regulated more than 2-fold (p<0.05) in cells from Zn2+-deficient chemostats, including zinT (yodA). zinT is shown to be regulated by Zur ( zinc uptake regulator). A mutant lacking zinT displayed a growth defect and a 3-fold lowered cellular Zn2+ level under Zn2+ limitation. The purified ZinT protein possessed a single, high affinity metal-binding site that can accommodate Zn2+ or Cd2+. A further up-regulated gene, ykgM, is believed to encode a non-Zn2+ finger-containing paralogue of the Zn2+ finger ribosomal protein L31. The gene encoding the periplasmic Zn2+- binding protein znuA showed increased expression. During both batch and chemostat growth, cells "found" more Zn2+ than was originally added to the culture, presumably because of leaching from the culture vessel. Zn2+ elimination is shown to be a more precise method of depleting Zn2+ than by using the chelator N,N,N',N'-tetrakis(2-pyridylmethyl)ethylenediamine

Mutations in two global regulators lower individual mortality in Escherichia coli

There has been considerable investigation into the survival of bacterial cells under stress conditions, but little is known about the causes of mortality in the absence of exogenous stress. That there is a basal frequency of cell death in such populations may reflect that it is either impossible to avoid all lethal events, or alternatively, that it is too costly. Here, through a genetic screen in the model organism Escherichia coli, we identify two mutants with lower frequencies of mortality: rssB and fliA. Intriguingly, these two genes both affect the levels of different sigma factors within the cell. The rssB mutant displays enhanced resistance to multiple external stresses, possibly indicating that the cell gains its increased vitality through elevated resistance to spontaneous, endogenous stresses. The loss of fliA does not result in elevated stress resistance; rather, its survival is apparently due to a decreased physical stress linked to the insertion of the flagellum through the membrane and energy saved through the loss of the motor proteins. The identification of these two mutants implies that reducing mortality is not impossible; rather, due to its cost, it is subject to trade-offs with other traits that contribute to the competitive success of the organism

Repeated, Selection-Driven Genome Reduction of Accessory Genes in Experimental Populations

Genome reduction has been observed in many bacterial lineages that have adapted to specialized environments. The extreme genome degradation seen for obligate pathogens and symbionts appears to be dominated by genetic drift. In contrast, for free-living organisms with reduced genomes, the dominant force is proposed to be direct selection for smaller, streamlined genomes. Most variation in gene content for these free-living species is of “accessory” genes, which are commonly gained as large chromosomal islands that are adaptive for specialized traits such as pathogenicity. It is generally unclear, however, whether the process of accessory gene loss is largely driven by drift or selection. Here we demonstrate that selection for gene loss, and not a shortened genome, per se, drove massive, rapid reduction of accessory genes. In just 1,500 generations of experimental evolution, 80% of populations of Methylobacterium extorquens AM1 experienced nearly parallel deletions removing up to 10% of the genome from a megaplasmid present in this strain. The absence of these deletion events in a mutation accumulation experiment suggested that selection, rather than drift, has dominated the process. Reconstructing these deletions confirmed that they were beneficial in their selective regimes, but led to decreased performance in alternative environments. These results indicate that selection can be crucial in eliminating unnecessary genes during the early stages of adaptation to a specialized environment

Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments

Analysis of genetic systems using experimental evolution and whole-genome sequencing

The application of whole-genome sequencing to the study of microbial evolution promises to reveal the complex functional networks of mutations that underlie adaptation. A recent study of parallel evolution in populations of Escherichia coli shows how adaptation involves both functional changes to specific proteins as well as global changes in regulation