A yeast living ancestor reveals the origin of genomic introgressions.

Genome introgressions drive evolution across the animal1, plant2 and fungal3 kingdoms. Introgressions initiate from archaic admixtures followed by repeated backcrossing to one parental species. However, how introgressions arise in reproductively isolated species, such as yeast4, has remained unclear. Here we identify a clonal descendant of the ancestral yeast hybrid that founded the extant Saccharomyces cerevisiae Alpechin lineage5, which carries abundant Saccharomyces paradoxus introgressions. We show that this clonal descendant, hereafter defined as a 'living ancestor', retained the ancestral genome structure of the first-generation hybrid with contiguous S. cerevisiae and S. paradoxus subgenomes. The ancestral first-generation hybrid underwent catastrophic genomic instability through more than a hundred mitotic recombination events, mainly manifesting as homozygous genome blocks generated by loss of heterozygosity. These homozygous sequence blocks rescue hybrid fertility by restoring meiotic recombination and are the direct origins of the introgressions present in the Alpechin lineage. We suggest a plausible route for introgression evolution through the reconstruction of extinct stages and propose that genome instability allows hybrids to overcome reproductive isolation and enables introgressions to emerge.

Genome instability footprint under rapamycin and hydroxyurea treatments.

The mutational processes dictating the accumulation of mutations in genomes are shaped by genetic background, environment and their interactions. Accurate quantification of mutation rates and spectra under drugs has important implications in disease treatment. Here, we used whole-genome sequencing and time-resolved growth phenotyping of yeast mutation accumulation lines to give a detailed view of the mutagenic effects of rapamycin and hydroxyurea on the genome and cell growth. Mutation rates depended on the genetic backgrounds but were only marginally affected by rapamycin. As a remarkable exception, rapamycin treatment was associated with frequent chromosome XII amplifications, which compensated for rapamycin induced rDNA repeat contraction on this chromosome and served to maintain rDNA content homeostasis and fitness. In hydroxyurea, a wide range of mutation rates were elevated regardless of the genetic backgrounds, with a particularly high occurrence of aneuploidy that associated with dramatic fitness loss. Hydroxyurea also induced a high T-to-G and low C-to-A transversion rate that reversed the common G/C-to-A/T bias in yeast and gave rise to a broad range of structural variants, including mtDNA deletions. The hydroxyurea mutation footprint was consistent with the activation of error-prone DNA polymerase activities and non-homologues end joining repair pathways. Taken together, our study provides an in-depth view of mutation rates and signatures in rapamycin and hydroxyurea and their impact on cell fitness, which brings insights for assessing their chronic effects on genome integrity.

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes.

Adaptive Laboratory Evolution (ALE) of microorganisms can improve the efficiency of sustainable industrial processes important to the global economy. However, stochasticity and genetic background effects often lead to suboptimal outcomes during laboratory evolution. Here we report an ALE platform to circumvent these shortcomings through parallelized clonal evolution at an unprecedented scale. Using this platform, we evolved 104 yeast populations in parallel from many strains for eight desired wine fermentation-related traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum leading to improved wine yeasts unencumbered by unwanted side effects. At the genomic level, evolutionary gains in metabolic characteristics often coincided with distinct chromosome amplifications and the emergence of side-effect syndromes that were characteristic of each selection niche. Several high-performing ALE strains exhibited desired wine fermentation kinetics when tested in larger liquid cultures, supporting their suitability for application. More broadly, our high-throughput ALE platform opens opportunities for rapid optimization of microbes which otherwise could take many years to accomplish.

Quantifying massively parallel microbial growth with spatially mediated interactions.

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterised at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to the initial physiological states of the populations, the interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments. We further show how the causes of this variability change throughout the growth of a population. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low-parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses.

Intragenic repeat expansion in the cell wall protein gene HPF1 controls yeast chronological aging.

Aging varies among individuals due to both genetics and environment, but the underlying molecular mechanisms remain largely unknown. Using a highly recombined Saccharomyces cerevisiae population, we found 30 distinct quantitative trait loci (QTLs) that control chronological life span (CLS) in calorie-rich and calorie-restricted environments and under rapamycin exposure. Calorie restriction and rapamycin extended life span in virtually all genotypes but through different genetic variants. We tracked the two major QTLs to the cell wall glycoprotein genes FLO11 and HPF1 We found that massive expansion of intragenic tandem repeats within the N-terminal domain of HPF1 was sufficient to cause pronounced life span shortening. Life span impairment by HPF1 was buffered by rapamycin but not by calorie restriction. The HPF1 repeat expansion shifted yeast cells from a sedentary to a buoyant state, thereby increasing their exposure to surrounding oxygen. The higher oxygenation altered methionine, lipid, and purine metabolism, and inhibited quiescence, which explains the life span shortening. We conclude that fast-evolving intragenic repeat expansions can fundamentally change the relationship between cells and their environment with profound effects on cellular lifestyle and longevity.

Predictive evolution of metabolic phenotypes using model-designed environments.

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

Mutagenic mechanisms of cancer-associated DNA polymerase ϵ alleles.

A single amino acid residue change in the exonuclease domain of human DNA polymerase ϵ, P286R, is associated with the development of colorectal cancers, and has been shown to impart a mutator phenotype. The corresponding Pol ϵ allele in the yeast Saccharomyces cerevisiae (pol2-P301R), was found to drive greater mutagenesis than an entirely exonuclease-deficient Pol ϵ (pol2-4), an unexpected phenotype of ultra-mutagenesis. By studying the impact on mutation frequency, type, replication-strand bias, and sequence context, we show that ultra-mutagenesis is commonly observed in yeast cells carrying a range of cancer-associated Pol ϵ exonuclease domain alleles. Similarities between mutations generated by these alleles and those generated in pol2-4 cells indicate a shared mechanism of mutagenesis that yields a mutation pattern similar to cancer Signature 14. Comparison of POL2 ultra-mutator with pol2-M644G, a mutant in the polymerase domain decreasing Pol ϵ fidelity, revealed unexpected analogies in the sequence context and strand bias of mutations. Analysis of mutational patterns unique to exonuclease domain mutant cells suggests that backtracking of the polymerase, when the mismatched primer end cannot be accommodated in the proofreading domain, results in the observed insertions and T>A mutations in specific sequence contexts.

Protein kinase A controls yeast growth in visible light.

BACKGROUND: A wide variety of photosynthetic and non-photosynthetic species sense and respond to light, having developed protective mechanisms to adapt to damaging effects on DNA and proteins. While the biology of UV light-induced damage has been well studied, cellular responses to stress from visible light (400-700 nm) remain poorly understood despite being a regular part of the life cycle of many organisms. Here, we developed a high-throughput method for measuring growth under visible light stress and used it to screen for light sensitivity in the yeast gene deletion collection. RESULTS: We found genes involved in HOG pathway signaling, RNA polymerase II transcription, translation, diphthamide modifications of the translational elongation factor eEF2, and the oxidative stress response to be required for light resistance. Reduced nuclear localization of the transcription factor Msn2 and lower glycogen accumulation indicated higher protein kinase A (cAMP-dependent protein kinase, PKA) activity in many light-sensitive gene deletion strains. We therefore used an ectopic fluorescent PKA reporter and mutants with constitutively altered PKA activity to show that repression of PKA is essential for resistance to visible light. CONCLUSION: We conclude that yeast photobiology is multifaceted and that protein kinase A plays a key role in the ability of cells to grow upon visible light exposure. We propose that visible light impacts on the biology and evolution of many non-photosynthetic organisms and have practical implications for how organisms are studied in the laboratory, with or without illumination.

Shared Molecular Targets Confer Resistance over Short and Long Evolutionary Timescales.

Pre-existing and de novo genetic variants can both drive adaptation to environmental changes, but their relative contributions and interplay remain poorly understood. Here we investigated the evolutionary dynamics in drug-treated yeast populations with different levels of pre-existing variation by experimental evolution coupled with time-resolved sequencing and phenotyping. We found a doubling of pre-existing variation alone boosts the adaptation by 64.1% and 51.5% in hydroxyurea and rapamycin, respectively. The causative pre-existing and de novo variants were selected on shared targets: RNR4 in hydroxyurea and TOR1, TOR2 in rapamycin. Interestingly, the pre-existing and de novo TOR variants map to different functional domains and act via distinct mechanisms. The pre-existing TOR variants from two domesticated strains exhibited opposite rapamycin resistance effects, reflecting lineage-specific functional divergence. This study provides a dynamic view on how pre-existing and de novo variants interactively drive adaptation and deepens our understanding of clonally evolving populations.

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data.

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81-0.97). While the best models most frequently employed gene content, an average accuracy score of 0.79 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.

Inhibiting conjugation as a tool in the fight against antibiotic resistance.

Antibiotic resistance, especially in gram-negative bacteria, is spreading globally and rapidly. Development of new antibiotics lags behind; therefore, novel approaches to the problem of antibiotic resistance are sorely needed and this commentary highlights one relatively unexplored target for drug development: conjugation. Conjugation is a common mechanism of horizontal gene transfer in bacteria that is instrumental in the spread of antibiotic resistance among bacteria. Most resistance genes are found on mobile genetic elements and primarily spread by conjugation. Furthermore, conjugative elements can act as a reservoir to maintain antibiotic resistance in the bacterial population even in the absence of antibiotic selection. Thus, conjugation can spread antibiotic resistance quickly between bacteria of the microbiome and pathogens when selective pressure (antibiotics) is introduced. Potential drug targets include the plasmid-encoded conjugation system and the host-encoded proteins important for conjugation. Ideally, a conjugation inhibitor will be used alongside antibiotics to prevent the spread of resistance to or within pathogens while not acting as a growth inhibitor itself. Inhibiting conjugation will be an important addition to our arsenal of strategies to combat the antibiotic resistance crisis, allowing us to extend the usefulness of antibiotics.

The cellular growth rate controls overall mRNA turnover, and modulates either transcription or degradation rates of particular gene regulons.

We analyzed 80 different genomic experiments, and found a positive correlation between both RNA polymerase II transcription and mRNA degradation with growth rates in yeast. Thus, in spite of the marked variation in mRNA turnover, the total mRNA concentration remained approximately constant. Some genes, however, regulated their mRNA concentration by uncoupling mRNA stability from the transcription rate. Ribosome-related genes modulated their transcription rates to increase mRNA levels under fast growth. In contrast, mitochondria-related and stress-induced genes lowered mRNA levels by reducing mRNA stability or the transcription rate, respectively. We also detected these regulations within the heterogeneity of a wild-type cell population growing in optimal conditions. The transcriptomic analysis of sorted microcolonies confirmed that the growth rate dictates alternative expression programs by modulating transcription and mRNA decay.The regulation of overall mRNA turnover keeps a constant ratio between mRNA decay and the dilution of [mRNA] caused by cellular growth. This regulation minimizes the indiscriminate transmission of mRNAs from mother to daughter cells, and favors the response capacity of the latter to physiological signals and environmental changes. We also conclude that, by uncoupling mRNA synthesis from decay, cells control the mRNA abundance of those gene regulons that characterize fast and slow growth.

Disentangling genetic and epigenetic determinants of ultrafast adaptation.

A major rationale for the advocacy of epigenetically mediated adaptive responses is that they facilitate faster adaptation to environmental challenges. This motivated us to develop a theoretical-experimental framework for disclosing the presence of such adaptation-speeding mechanisms in an experimental evolution setting circumventing the need for pursuing costly mutation-accumulation experiments. To this end, we exposed clonal populations of budding yeast to a whole range of stressors. By growth phenotyping, we found that almost complete adaptation to arsenic emerged after a few mitotic cell divisions without involving any phenotypic plasticity. Causative mutations were identified by deep sequencing of the arsenic-adapted populations and reconstructed for validation. Mutation effects on growth phenotypes, and the associated mutational target sizes were quantified and embedded in data-driven individual-based evolutionary population models. We found that the experimentally observed homogeneity of adaptation speed and heterogeneity of molecular solutions could only be accounted for if the mutation rate had been near estimates of the basal mutation rate. The ultrafast adaptation could be fully explained by extensive positive pleiotropy such that all beneficial mutations dramatically enhanced multiple fitness components in concert. As our approach can be exploited across a range of model organisms exposed to a variety of environmental challenges, it may be used for determining the importance of epigenetic adaptation-speeding mechanisms in general.

Kinetochore assembly and heterochromatin formation occur autonomously in Schizosaccharomyces pombe.

Kinetochores in multicellular eukaryotes are usually associated with heterochromatin. Whether this heterochromatin simply promotes the cohesion necessary for accurate chromosome segregation at cell division or whether it also has a role in kinetochore assembly is unclear. Schizosaccharomyces pombe is an important experimental system for investigating centromere function, but all of the previous work with this species has exploited a single strain or its derivatives. The laboratory strain and most other S. pombe strains contain three chromosomes, but one recently discovered strain, CBS 2777, contains four. We show that the genome of CBS 2777 is related to that of the laboratory strain by a complex chromosome rearrangement. As a result, two of the kinetochores in CBS 2777 contain the central core sequences present in the laboratory strain centromeres, but lack adjacent heterochromatin. The closest block of heterochromatin to these rearranged kinetochores is ∼100 kb away at new telomeres. Despite lacking large amounts of adjacent heterochromatin, the rearranged kinetochores bind CENP-A(Cnp1) and CENP-C(Cnp3) in similar quantities and with similar specificities as those of the laboratory strain. The simplest interpretation of this result is that constitutive kinetochore assembly and heterochromatin formation occur autonomously.

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics.

BACKGROUND: Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology. RESULTS: To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis. CONCLUSIONS: PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

Concerted evolution of life stage performances signals recent selection on yeast nitrogen use.

Exposing natural selection driving phenotypic and genotypic adaptive differentiation is an extraordinary challenge. Given that an organism's life stages are exposed to the same environmental variations, we reasoned that fitness components, such as the lag, rate, and efficiency of growth, directly reflecting performance in these life stages, should often be selected in concert. We therefore conjectured that correlations between fitness components over natural isolates, in a particular environmental context, would constitute a robust signal of recent selection. Critically, this test for selection requires fitness components to be determined by different genetic loci. To explore our conjecture, we exhaustively evaluated the lag, rate, and efficiency of asexual population growth of natural isolates of the model yeast Saccharomyces cerevisiae in a large variety of nitrogen-limited environments. Overall, fitness components were well correlated under nitrogen restriction. Yeast isolates were further crossed in all pairwise combinations and coinheritance of each fitness component and genetic markers were traced. Trait variations tended to map to quantitative trait loci (QTL) that were private to a single fitness component. We further traced QTLs down to single-nucleotide resolution and uncovered loss-of-function mutations in RIM15, PUT4, DAL1, and DAL4 as the genetic basis for nitrogen source use variations. Effects of SNPs were unique for a single fitness component, strongly arguing against pleiotropy between lag, rate, and efficiency of reproduction under nitrogen restriction. The strong correlations between life stage performances that cannot be explained by pleiotropy compellingly support adaptive differentiation of yeast nitrogen source use and suggest a generic approach for detecting selection.

Replenishment and mobilization of intracellular nitrogen pools decouples wine yeast nitrogen uptake from growth.

Wine yeast capacity to take up nitrogen from the environment and catabolize it to support population growth, fermentation, and aroma production is critical to wine production. Under nitrogen restriction, yeast nitrogen uptake is believed to be intimately coupled to reproduction with nitrogen catabolite repression (NCR) suggested mediating this link. We provide a time- and strain-resolved view of nitrogen uptake, population growth, and NCR activity in wine yeasts. Nitrogen uptake was found to be decoupled from growth due to early assimilated nitrogen being used to replenish intracellular nitrogen pools rather than being channeled directly into reproduction. Internally accumulated nitrogen was later mobilized to support substantial population expansion after external nitrogen was depleted. On good nitrogen sources, the decoupling between nitrogen uptake and growth correlated well with relaxation of NCR repression, raising the potential that the latter may be triggered by intracellular build-up of nitrogen. No link between NCR activity and nitrogen assimilation or growth on poor nitrogen sources was found. The decoupling between nitrogen uptake and growth and its influence on NCR activity is of relevance for both wine production and our general understanding of nitrogen use.

Ancient evolutionary trade-offs between yeast ploidy states.

The number of chromosome sets contained within the nucleus of eukaryotic organisms is a fundamental yet evolutionarily poorly characterized genetic variable of life. Here, we mapped the impact of ploidy on the mitotic fitness of baker's yeast and its never domesticated relative Saccharomyces paradoxus across wide swaths of their natural genotypic and phenotypic space. Surprisingly, environment-specific influences of ploidy on reproduction were found to be the rule rather than the exception. These ploidy-environment interactions were well conserved across the 2 billion generations separating the two species, suggesting that they are the products of strong selection. Previous hypotheses of generalizable advantages of haploidy or diploidy in ecological contexts imposing nutrient restriction, toxin exposure, and elevated mutational loads were rejected in favor of more fine-grained models of the interplay between ecology and ploidy. On a molecular level, cell size and mating type locus composition had equal, but limited, explanatory power, each explaining 12.5%-17% of ploidy-environment interactions. The mechanism of the cell size-based superior reproductive efficiency of haploids during Li(+) exposure was traced to the Li(+) exporter ENA. Removal of the Ena transporters, forcing dependence on the Nha1 extrusion system, completely altered the effects of ploidy on Li(+) tolerance and evoked a strong diploid superiority, demonstrating how genetic variation at a single locus can completely reverse the relative merits of haploidy and diploidy. Taken together, our findings unmasked a dynamic interplay between ploidy and ecology that was of unpredicted evolutionary importance and had multiple molecular roots.

A high-definition view of functional genetic variation from natural yeast genomes.

The question of how genetic variation in a population influences phenotypic variation and evolution is of major importance in modern biology. Yet much is still unknown about the relative functional importance of different forms of genome variation and how they are shaped by evolutionary processes. Here we address these questions by population level sequencing of 42 strains from the budding yeast Saccharomyces cerevisiae and its closest relative S. paradoxus. We find that genome content variation, in the form of presence or absence as well as copy number of genetic material, is higher within S. cerevisiae than within S. paradoxus, despite genetic distances as measured in single-nucleotide polymorphisms being vastly smaller within the former species. This genome content variation, as well as loss-of-function variation in the form of premature stop codons and frameshifting indels, is heavily enriched in the subtelomeres, strongly reinforcing the relevance of these regions to functional evolution. Genes affected by these likely functional forms of variation are enriched for functions mediating interaction with the external environment (sugar transport and metabolism, flocculation, metal transport, and metabolism). Our results and analyses provide a comprehensive view of genomic diversity in budding yeast and expose surprising and pronounced differences between the variation within S. cerevisiae and that within S. paradoxus. We also believe that the sequence data and de novo assemblies will constitute a useful resource for further evolutionary and population genomics studies.