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The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these two parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use the observed CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based Bayesian likelihood-free inference approaches. We tested the suitability of two evolutionary models: a standard Wright-Fisher model and a chemostat growth model. We evaluated two likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in yeast as 10−4.7 -10−4 per cell division, and a selection coefficient of 0.04 - 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our estimates using barcode lineage tracking and pairwise fitness assays. Our results are consistent with a high beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining their outsized importance in rapid adaptive evolution. More generally, our study demonstrates the utility of novel simulation-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data.

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10−4.7 to 10−4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

Summary Due to limitations on high-resolution strain tracking, selection dynamics during gut microbiota colonization and transmission between hosts remain mostly mysterious. Here, we introduced hundreds of barcoded Escherichia coli strains into germ-free mice and quantified strain-level dynamics and metagenomic changes. Mutations in genes involved in motility and metabolite utilization are reproducibly selected within days. Even with rapid selection, coprophagy enforced similar barcode distributions across co-housed mice. Whole-genome sequencing of hundreds of isolates revealed linked alleles that demonstrate between-host transmission. A population-genetics model predicts substantial fitness advantages for certain mutants and that migration accounted for ∼10% of the resident microbiota each day. Treatment with ciprofloxacin suggests interplay between selection and transmission. While initial colonization was mostly uniform, in two mice a bottleneck reduced diversity and selected for ciprofloxacin resistance in the absence of drug. These findings highlight the interplay between environmental transmission and rapid, deterministic selection during evolution of the intestinal microbiota.

SummaryDue to limitations on high-resolution strain tracking, selection dynamics during gut-microbiota colonization and transmission between hosts remain mostly mysterious. Here, we introduced hundreds of barcoded Escherichia coli strains into germ-free mice and quantified strain-level dynamics and metagenomic changes. Mutants involved in motility and utilization of abundant metabolites were reproducibly selected within days. Even with rapid selection, coprophagy enforced similar barcode distributions across co-housed mice. Whole-genome sequencing of hundreds of isolates quantified evolutionary dynamics and revealed linked alleles. A population-genetics model predicted substantial fitness advantages for certain mutants and that migration accounted for ~10% of the resident microbiota each day. Treatment with ciprofloxacin demonstrated the interplay between selection and transmission. While initial colonization was mostly uniform, in two mice a bottleneck reduced diversity and selected for ciprofloxacin resistance in the absence of drug. These findings highlight the interplay between environmental transmission and rapid, deterministic selection during evolution of the intestinal microbiota.

Trade-offs constrain the improvement of performance of multiple traits simultaneously. Such trade-offs define Pareto fronts, which represent a set of optimal individuals that cannot be improved in any one trait without reducing performance in another. Surprisingly, experimental evolution often yields genotypes with improved performance in all measured traits, perhaps indicating an absence of trade-offs at least in the short term. Here we densely sample adaptive mutations in Saccharomyces cerevisiae to ask whether first-step adaptive mutations result in trade-offs during the growth cycle. We isolated thousands of adaptive clones evolved under carefully chosen conditions and quantified their performances in each part of the growth cycle. We too find that some first-step adaptive mutations can improve all traits to a modest extent. However, our dense sampling allowed us to identify trade-offs and establish the existence of Pareto fronts between fermentation and respiration, and between respiration and stationary phases. Moreover, we establish that no single mutation in the ancestral genome can circumvent the detected trade-offs. Finally, we sequenced hundreds of these adaptive clones, revealing new targets of adaptation and defining the genetic basis of the identified trade-offs.

Large-scale genetic interaction (GI) screens in yeast have been invaluable for our understanding of molecular systems biology and for characterizing novel gene function. Owing in part to the high costs and long experiment times required, a preponderance of GI data has been generated in a single environmental condition. However, an unknown fraction of GIs may be specific to other conditions. Here, we developed a pooled-growth CRISPRi-based sequencing assay for GIs, CRISPRiSeq, which increases throughput such that GIs can be easily assayed across multiple growth conditions. We assayed the fitness of approximately 17,000 strains encompassing approximately 7700 pairwise interactions in five conditions and found that the additional conditions increased the number of GIs detected nearly threefold over the number detected in rich media alone. In addition, we found that condition-specific GIs are prevalent and improved the power to functionally classify genes. Finally, we found new links during respiratory growth between members of the Ras nutrient–sensing pathway and both the COG complex and a gene of unknown function. Our results highlight the potential of conditional GI screens to improve our understanding of cellular genetic networks.

Tradeoffs constrain the improvement of performance of multiple traits simultaneously. Such tradeoffs define Pareto fronts, which represent a set of optimal individuals that cannot be improved in any one trait without reducing performance in another. Surprisingly, experimental evolution often yields genotypes with improved performance in all measured traits, perhaps indicating an absence of tradeoffs at least in the short-term. Here we densely sample adaptive mutations inS. cerevisiaeto ask whether first-step adaptive mutations result in tradeoffs during the growth cycle. We isolated thousands of adaptive clones evolved under carefully chosen conditions and quantified their performances in each part of the growth cycle. We too find that some first-step adaptive mutations can improve all traits to a modest extent. However, our dense sampling allowed us to identify tradeoffs and establish the existence of Pareto fronts between fermentation and respiration, and between respiration and stationary phases. Moreover, we establish that no single mutation in the ancestral genome can circumvent the detected tradeoffs. Finally, we sequenced hundreds of these adaptive clones, revealing novel targets of adaptation and defining the genetic basis of the identified tradeoffs.

A large, asexual population founded by a single clone evolves into a population teeming with many, whether or not its environment is structured, and whether or not resource levels are constant or fluctuating. The maintenance of genetic complexity in such populations has been attributed to balancing selection, or to either clonal interference or clonal reinforcement, arising from antagonistic or synergistic interactions, respectively. To distinguish among these possibilities, to identify targets of selection and establish when and how often they are hit, as well as to gain insight into howde novomutations interact, we carried out 300-500 generation glucose-limited chemostat experiments founded by anE. colimutator. To discover allde novomutations reaching ≥1% frequency, we performed whole-genome, whole-population sequencing at ∼1000X-coverage every 50 generations. To establish linkage relationships among these mutations and depict the dynamics of evolving lineages we sequenced the genomes of 96 clones from each population when allelic diversity was greatest. Operon-specific mutations that enhance glucose uptake arose to high frequency first, followed by global regulatory mutations. Late-arising mutations were related to energy conservation as well as to mitigating pleiotropic effects wrought by earlier regulatory changes. We discovered extensive polymorphism at relatively few loci, with identical mutations arising independently in different lineages, both between and within replicate populations. Out of more than 3,000 SNPs detected in nearly 1,800 genes or intergenic regions, only 17 reached a frequency ≥ 98%, indicating that the evolutionary dynamics of adaptive lineages was dominated by clonal interference. Finally, our data show that even when mutational input is increased by an ancestral defect in DNA repair, the spectrum of beneficial mutations that reach high frequency in a simple, constant resource-limited environment is narrow, resulting in extreme parallelism where many adaptive mutations arise but few ever go to fixation.Author SummaryMicrobial evolution experiments open a window on the tempo and dynamics of evolutionary change in asexual populations. High-throughput sequencing can be used to catalogde novomutations, determine in which lineages they arise, and assess allelic interactions by tracking the fate of those lineages. Thisadaptive geneticsapproach makes it possible to discover whether clonal interactions are antagonistic or synergistic, and complements genetic screens of induced deleterious/loss-of-function mutants. We carried out glucose-limited chemostat experiments founded by anE. colimutator and performed whole-genome, whole-population sequencing on 300-500 generation evolutions, cataloging 3,346de novomutations that reached ≥1% frequency. Mutations enhancing glucose uptake rose to high frequency first, followed by global regulatory changes that modulate growth rate and limiting resource assimilation, then by mutations that favor energy conservation or mitigate pleiotropic effects of earlier regulatory changes. We discovered that a few loci were highly polymorphic, with identical mutations arising independently in different lineages, both between and within replicate populations. Thus, when mutational input is increased by an ancestral defect in DNA repair, the spectrum of beneficial mutations that arises under constant resource-limitation is narrow, resulting in extreme parallelism where many adaptive mutations arise but few ever become fixed.

CrAss-like phages are double-stranded DNA viruses that are prevalent in human gut microbiomes. Here, we analyze gut metagenomic data from mother-infant pairs and patients undergoing fecal microbiota transplantation to evaluate the patterns of acquisition, transmission and strain diversity of crAss-like phages. We find that crAss-like phages are rarely detected at birth but are increasingly prevalent in the infant microbiome after one month of life. We observe nearly identical genomes in 50% of cases where the same crAss-like clade is detected in both the mother and the infant, suggesting vertical transmission. In cases of putative transmission of prototypical crAssphage (p-crAssphage), we find that a subset of strains present in the mother are detected in the infant, and that strain diversity in infants increases with time. Putative tail fiber proteins are enriched for nonsynonymous strain variation compared to other genes, suggesting a potential evolutionary benefit to maintaining strain diversity in specific genes. Finally, we show that p-crAssphage can be acquired through fecal microbiota transplantation.

Elucidating population structure and levels of genetic diversity and recombination is necessary to understand the evolution and adaptation of species. Candida albicans is the second most frequent agent of human fungal infections worldwide, causing high-mortality rates. Here we present the genomic sequences of 182 C. albicans isolates collected worldwide, including commensal isolates, as well as ones responsible for superficial and invasive infections, constituting the largest dataset to date for this major fungal pathogen. Although, C. albicans shows a predominantly clonal population structure, we find evidence of gene flow between previously known and newly identified genetic clusters, supporting the occurrence of (para)sexuality in nature. A highly clonal lineage, which experimentally shows reduced fitness, has undergone pseudogenization in genes required for virulence and morphogenesis, which may explain its niche restriction. Candida albicans thus takes advantage of both clonality and gene flow to diversify., The fungal pathogen Candida albicans can undergo a parasexual process that may contribute to genetic diversity, but its actual relevance is unclear. Here, Ropars et al. analyse the genomic sequences of 182 C. albicans isolates collected worldwide and find evidence of gene flow and thus parasexuality in nature.

CrAss-like phages are double-stranded DNA viruses that are prevalent in human gut microbiomes. Here, we analyze gut metagenomic data from mother-infant pairs and patients undergoing fecal microbiota transplantation to evaluate the patterns of acquisition, transmission and strain diversity of crAss-like phages. We find that crAss-like phages are rarely detected at birth but are increasingly prevalent in the infant microbiome after one month of life. We observe nearly identical genomes in 50% of cases where the same crAss-like clade is detected in both the mother and the infant, suggesting vertical transmission. In cases of putative transmission of prototypical crAssphage (p-crAssphage), we find that a subset of strains present in the mother are detected in the infant, and that strain diversity in infants increases with time. Putative tail fiber proteins are enriched for nonsynonymous strain variation compared to other genes, suggesting a potential evolutionary benefit to maintaining strain diversity in specific genes. Finally, we show that p-crAssphage can be acquired through fecal microbiota transplantation., CrAss-like phages are bacterial viruses often found in the human gut. Here, Siranosian et al. analyze gut metagenomic data to evaluate the patterns of acquisition, transmission and strain diversity of these phages in mother-infant pairs and in patients undergoing fecal microbiota transplantation.

Studying Candida biology requires access to genomic sequence data in conjunction with experimental information that together provide functional context to genes and proteins, and aid in interpreting newly generated experimental data. The Candida Genome Database (CGD) curates the Candida literature, and integrates functional information about Candida genes and their products with a set of analysis tools that facilitate searching for sets of genes and exploring their biological roles. This chapter describes how the various types of information available at CGD can be searched, retrieved, and analyzed. Starting with the guided tour of the CGD Home page and Locus Summary page, this unit shows how to navigate the various assemblies of the C. albicans genome, how to use Gene Ontology tools to make sense of large-scale data, and how to access the microarray data archived at CGD, as well as visualize high-throughput sequencing data through the use of JBrowse.

Transcription factors canonically bind nucleosome-free DNA, making the positioning of nucleosomes within regulatory regions crucial to the regulation of gene expression. Using the assay of transposase accessible chromatin (ATAC-seq), we observe a highly structured pattern of DNA fragment lengths and positions around nucleosomes in Saccharomyces cerevisiae, and use this distinctive two-dimensional nucleosomal “fingerprint” as the basis for a new nucleosome-positioning algorithm called NucleoATAC. We show that NucleoATAC can identify the rotational and translational positions of nucleosomes with up to base-pair resolution and provide quantitative measures of nucleosome occupancy in S. cerevisiae, Schizosaccharomyces pombe, and human cells. We demonstrate the application of NucleoATAC to a number of outstanding problems in chromatin biology, including analysis of sequence features underlying nucleosome positioning, promoter chromatin architecture across species, identification of transient changes in nucleosome occupancy and positioning during a dynamic cellular response, and integrated analysis of nucleosome occupancy and transcription factor binding.

Evolution of large asexual cell populations underlies ∼30% of deaths worldwide, including those caused by bacteria, fungi, parasites, and cancer. However, the dynamics underlying these evolutionary processes remain poorly understood because they involve many competing beneficial lineages, most of which never rise above extremely low frequencies in the population. To observe these normally hidden evolutionary dynamics, we constructed a sequencing-based ultra high-resolution lineage tracking system in Saccharomyces cerevisiae that allowed us to monitor the relative frequencies of ∼500,000 lineages simultaneously. In contrast to some expectations, we found that the spectrum of fitness effects of beneficial mutations is neither exponential nor monotonic. Early adaptation is a predictable consequence of this spectrum and is strikingly reproducible, but the initial small-effect mutations are soon outcompeted by rarer large-effect mutations that result in variability between replicates. These results suggest that early evolutionary dynamics may be deterministic for a period of time before stochastic effects become important.

The diploid genome of the yeast Candida albicans is highly plastic, exhibiting frequent loss-of-heterozygosity (LOH) events. To provide a deeper understanding of the mechanisms leading to LOH, we investigated the repair of a unique DNA double-strand break (DSB) in the laboratory C. albicans SC5314 strain using the I-SceI meganuclease. Upon I-SceI induction, we detected a strong increase in the frequency of LOH events at an I-SceI target locus positioned on chromosome 4 (Chr4), including events spreading from this locus to the proximal telomere. Characterization of the repair events by single nucleotide polymorphism (SNP) typing and whole-genome sequencing revealed a predominance of gene conversions, but we also observed mitotic crossover or break-induced replication events, as well as combinations of independent events. Importantly, progeny that had undergone homozygosis of part or all of Chr4 haplotype B (Chr4B) were inviable. Mining of genome sequencing data for 155 C. albicans isolates allowed the identification of a recessive lethal allele in the GPI16 gene on Chr4B unique to C. albicans strain SC5314 which is responsible for this inviability. Additional recessive lethal or deleterious alleles were identified in the genomes of strain SC5314 and two clinical isolates. Our results demonstrate that recessive lethal alleles in the genomes of C. albicans isolates prevent the occurrence of specific extended LOH events. While these and other recessive lethal and deleterious alleles are likely to accumulate in C. albicans due to clonal reproduction, their occurrence may in turn promote the maintenance of corresponding nondeleterious alleles and, consequently, heterozygosity in the C. albicans species., IMPORTANCE Recessive lethal alleles impose significant constraints on the biology of diploid organisms. Using a combination of an I-SceI meganuclease-mediated DNA DSB, a fluorescence-activated cell sorter (FACS)-optimized reporter of LOH, and a compendium of 155 genome sequences, we were able to unmask and identify recessive lethal and deleterious alleles in isolates of Candida albicans, a diploid yeast and the major fungal pathogen of humans. Accumulation of recessive deleterious mutations upon clonal reproduction of C. albicans could contribute to the maintenance of heterozygosity despite the high frequency of LOH events in this species.

The dynamics of genetic diversity in large clonally evolving cell populations are poorly understood, despite having implications for the treatment of cancer and microbial infections. Here, we combine barcode lineage tracking, sequencing of adaptive clones and mathematical modelling of mutational dynamics to understand adaptive diversity changes during experimental evolution of Saccharomyces cerevisiae under nitrogen and carbon limitation. We find that, despite differences in beneficial mutational mechanisms and fitness effects, early adaptive genetic diversity increases predictably, driven by the expansion of many single-mutant lineages. However, a crash in adaptive diversity follows, caused by highly fit double-mutant 'jackpot' clones that are fed from exponentially growing single mutants, a process closely related to the classic Luria-Delbruck experiment. The diversity crash is likely to be a general feature of asexual evolution with clonal interference; however, both its timing and magnitude are stochastic and depend on the population size, the distribution of beneficial fitness effects and patterns of epistasis.

[Background] The fungal genus Aspergillus is of critical importance to humankind. Species include those with industrial applications, important pathogens of humans, animals and crops, a source of potent carcinogenic contaminants of food, and an important genetic model. The genome sequences of eight aspergilli have already been explored to investigate aspects of fungal biology, raising questions about evolution and specialization within this genus., [Results] We have generated genome sequences for ten novel, highly diverse Aspergillus species and compared these in detail to sister and more distant genera. Comparative studies of key aspects of fungal biology, including primary and secondary metabolism, stress response, biomass degradation, and signal transduction, revealed both conservation and diversity among the species. Observed genomic differences were validated with experimental studies. This revealed several highlights, such as the potential for sex in asexual species, organic acid production genes being a key feature of black aspergilli, alternative approaches for degrading plant biomass, and indications for the genetic basis of stress response. A genome-wide phylogenetic analysis demonstrated in detail the relationship of the newly genome sequenced species with other aspergilli., [Conclusions] Many aspects of biological differences between fungal species cannot be explained by current knowledge obtained from genome sequences. The comparative genomics and experimental study, presented here, allows for the first time a genus-wide view of the biological diversity of the aspergilli and in many, but not all, cases linked genome differences to phenotype. Insights gained could be exploited for biotechnological and medical applications of fungi., The sugar transporter analysis was supported by grants AGL2011-29925 and AGL2015-66131- AGL2015-66131-C2-2-R (MINECO/FEDER).

The characterization of mutational spectra is usually carried out in one of three ways–by direct observation through mutation accumulation (MA) experiments, through parent-offspring sequencing, or by indirect inference from sequence data. Direct observations of spontaneous mutations with MA experiments are limited, given (i) the rarity of spontaneous mutations, (ii) applicability only to laboratory model species with short generation times, and (iii) the possibility that mutational spectra under lab conditions might be different from those observed in nature. Trio sequencing is an elegant solution, but it is not applicable in all organisms. Indirect inference, usually from divergence data, faces no such technical limitations, but rely upon critical assumptions regarding the strength of natural selection that are likely to be violated. Ideally, new mutational events would be directly observed before the biased filter of selection, and without the technical limitations common to lab experiments. One approach is to identify very young mutations from population sequencing data. Here we do so by leveraging two characteristics common to all new mutations—new mutations are necessarily rare in the population, and absent in the genomes of immediate relatives. From 132 clinical yeast strains, we were able to identify 1,425 putatively new mutations and show that they exhibit extremely low signatures of selection, as well as display a mutational spectrum that is similar to that identified by a large scale MA experiment. We verify that population sequencing data are a potential wealth of information for inferring mutational spectra, and should be considered for analysis where MA experiments are infeasible or especially tedious., Author Summary The mutational spectrum is central to our understanding of molecular evolution. However, mutational spectra are difficult to study because spontaneous mutations are rare, difficult to observe, and a large number of events is required to detect subtle differences between mutational bias, selection and selection like forces. The possibility of estimating mutational spectra from population polymorphism data, with neither the need for tedious experiments nor the restrictions and biases of lab conditions, is a crucial step in overcoming such difficulties. We show that with sufficiently broad population sequencing and proper identification of young polymorphisms, it is possible to recapitulate the experimental yeast mutation spectrum. This holds implications for future applications to all species where population sequencing is possible.

Cryptococcus , a major cause of disseminated infections in immunocompromised patients, kills over 600,000 people per year worldwide. Genes involved in the virulence of the meningitis-causing fungus are being characterized at an increasing rate, and to date, at least 648 Cryptococcus gene names have been published. However, these data are scattered throughout the literature and are challenging to find. Furthermore, conflicts in locus identification exist, so that named genes have been subsequently published under new names or names associated with one locus have been used for another locus. To avoid these conflicts and to provide a central source of Cryptococcus gene information, we have collected all published Cryptococcus gene names from the scientific literature and associated them with standard Cryptococcus locus identifiers and have incorporated them into FungiDB ( www.fungidb.org ). FungiDB is a panfungal genome database that collects gene information and functional data and provides search tools for 61 species of fungi and oomycetes. We applied these published names to a manually curated ortholog set of all Cryptococcus species currently in FungiDB, including Cryptococcus neoformans var. neoformans strains JEC21 and B-3501A, C. neoformans var. grubii strain H99, and Cryptococcus gattii strains R265 and WM276, and have written brief descriptions of their functions. We also compiled a protocol for gene naming that summarizes guidelines proposed by members of the Cryptococcus research community. The centralization of genomic and literature-based information for Cryptococcus at FungiDB will help researchers communicate about genes of interest, such as those related to virulence, and will further facilitate research on the pathogen.

Different organisms have independently and recurrently evolved similar phenotypic traits at different points throughout history. This phenotypic convergence may be caused by genotypic convergence and constrained by historical contingency. To investigate how convergence may be driven by selection in a particular environment and constrained by history, we analyzed nine life-history traits and four metabolic traits during an experimental evolution of six yeast strains in four different environments. In each of the environments, the population converged towards a different life-history strategy. However, phenotypic convergence was partly associated with the selection of mutations in genes involved in the same pathway. In a fifth of our evolution experiments, mutations in the same gene, BMH1, were selected, in three out of the six ancestral genotypes. Two types of BMH1 mutation with opposite phenotypic effects on several traits were found. The evolution of most traits, as well as the occurrence of BMH1 mutations, was significantly influenced by the ancestral strain. However, this effect could not be easily predicted from ancestors’ phylogeny or past-selection. All together, our data demonstrate that phenotypic and its underlying genotypic convergence depends on a complex interplay between the evolutionary environment, pleiotropy and the ancestor genetic background but are not straightforwardly predicable.

The Aspergillus Genome Database (AspGD; http://www.aspgd.org) is a freely available web-based resource that was designed for Aspergillus researchers and is also a valuable source of information for the entire fungal research community. In addition to being a repository and central point of access to genome, transcriptome and polymorphism data, AspGD hosts a comprehensive comparative genomics toolbox that facilitates the exploration of precomputed orthologs among the 20 currently available Aspergillus genomes. AspGD curators perform gene product annotation based on review of the literature for four key Aspergillus species: Aspergillus nidulans, Aspergillus oryzae, Aspergillus fumigatus and Aspergillus niger. We have iteratively improved the structural annotation of Aspergillus genomes through the analysis of publicly available transcription data, mostly expressed sequenced tags, as described in a previous NAR Database article (Arnaud et al. 2012). In this update, we report substantive structural annotation improvements for A. nidulans, A. oryzae and A. fumigatus genomes based on recently available RNA-Seq data. Over 26 000 loci were updated across these species; although those primarily comprise the addition and extension of untranslated regions (UTRs), the new analysis also enabled over 1000 modifications affecting the coding sequence of genes in each target genome.

PortEco (http://porteco.org) aims to collect, curate and provide data and analysis tools to support basic biological research in Escherichia coli (and eventually other bacterial systems). PortEco is implemented as a ‘virtual’ model organism database that provides a single unified interface to the user, while integrating information from a variety of sources. The main focus of PortEco is to enable broad use of the growing number of high-throughput experiments available for E. coli, and to leverage community annotation through the EcoliWiki and GONUTS systems. Currently, PortEco includes curated data from hundreds of genome-wide RNA expression studies, from high-throughput phenotyping of single-gene knockouts under hundreds of annotated conditions, from chromatin immunoprecipitation experiments for tens of different DNA-binding factors and from ribosome profiling experiments that yield insights into protein expression. Conditions have been annotated with a consistent vocabulary, and data have been consistently normalized to enable users to find, compare and interpret relevant experiments. PortEco includes tools for data analysis, including clustering, enrichment analysis and exploration via genome browsers. PortEco search and data analysis tools are extensively linked to the curated gene, metabolic pathway and regulation content at its sister site, EcoCyc.

The Candida Genome Database (CGD, http://www.candidagenome.org/) is a freely available online resource that provides gene, protein and sequence information for multiple Candida species, along with web-based tools for accessing, analyzing and exploring these data. The goal of CGD is to facilitate and accelerate research into Candida pathogenesis and biology. The CGD Web site is organized around Locus pages, which display information collected about individual genes. Locus pages have multiple tabs for accessing different types of information; the default Summary tab provides an overview of the gene name, aliases, phenotype and Gene Ontology curation, whereas other tabs display more in-depth information, including protein product details for coding genes, notes on changes to the sequence or structure of the gene and a comprehensive reference list. Here, in this update to previous NAR Database articles featuring CGD, we describe a new tab that we have added to the Locus page, entitled the Homology Information tab, which displays phylogeny and gene similarity information for each locus.

Candida albicans is an opportunistic fungal pathogen that can cause disseminated infection in patients with indwelling catheters or other implanted medical devices. A common resident of the human microbiome, C. albicans responds to environmental signals, such as cell contact with catheter materials and exposure to serum or CO 2 , by triggering the expression of a variety of traits, some of which are known to contribute to its pathogenic lifestyle. Such traits include adhesion, biofilm formation, filamentation, white-to-opaque (W-O) switching, and two recently described phenotypes, finger and tentacle formation. Under distinct sets of environmental conditions and in specific cell types (mating type-like a [MTL a ]/alpha cells, MTL homozygotes, or daughter cells), C. albicans utilizes (or reutilizes) a single signal transduction pathway—the Ras pathway—to affect these phenotypes. Ras1, Cyr1, Tpk2, and Pde2, the proteins of the Ras signaling pathway, are the only nontranscriptional regulatory proteins that are known to be essential for regulating all of these processes. How does C. albicans utilize this one pathway to regulate all of these phenotypes? The regulation of distinct and yet related processes by a single, evolutionarily conserved pathway is accomplished through the use of downstream transcription factors that are active under specific environmental conditions and in different cell types. In this minireview, we discuss the role of Ras signaling pathway components and Ras pathway-regulated transcription factors as well as the transcriptional regulatory networks that fine-tune gene expression in diverse biological contexts to generate specific phenotypes that impact the virulence of C. albicans .

Characterization of the cell cycle–regulated transcripts in U2OS cells yielded 1871 unique genes. FOXM1 targets were identified via ChIP-seq, and novel targets in G2/M and S phases were verified using a real-time luciferase assay. ChIP-seq data were used to map cell cycle transcriptional regulators of cell cycle–regulated gene expression in U2OS cells., We identify the cell cycle–regulated mRNA transcripts genome-wide in the osteosarcoma-derived U2OS cell line. This results in 2140 transcripts mapping to 1871 unique cell cycle–regulated genes that show periodic oscillations across multiple synchronous cell cycles. We identify genomic loci bound by the G2/M transcription factor FOXM1 by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) and associate these with cell cycle–regulated genes. FOXM1 is bound to cell cycle–regulated genes with peak expression in both S phase and G2/M phases. We show that ChIP-seq genomic loci are responsive to FOXM1 using a real-time luciferase assay in live cells, showing that FOXM1 strongly activates promoters of G2/M phase genes and weakly activates those induced in S phase. Analysis of ChIP-seq data from a panel of cell cycle transcription factors (E2F1, E2F4, E2F6, and GABPA) from the Encyclopedia of DNA Elements and ChIP-seq data for the DREAM complex finds that a set of core cell cycle genes regulated in both U2OS and HeLa cells are bound by multiple cell cycle transcription factors. These data identify the cell cycle–regulated genes in a second cancer-derived cell line and provide a comprehensive picture of the transcriptional regulatory systems controlling periodic gene expression in the human cell division cycle.