Evolutionary principles of modular gene regulation in yeasts

Species-aware DNA language models capture regulatory elements and their evolution

BACKGROUND

The rise of large-scale multi-species genome sequencing projects promises to shed new light on how genomes encode gene regulatory instructions. To this end, new algorithms are needed that can leverage conservation to capture regulatory elements while accounting for their evolution.

RESULTS

Here, we introduce species-aware DNA language models, which we trained on more than 800 species spanning over 500 million years of evolution. Investigating their ability to predict masked nucleotides from context, we show that DNA language models distinguish transcription factor and RNA-binding protein motifs from background non-coding sequence. Owing to their flexibility, DNA language models capture conserved regulatory elements over much further evolutionary distances than sequence alignment would allow. Remarkably, DNA language models reconstruct motif instances bound in vivo better than unbound ones and account for the evolution of motif sequences and their positional constraints, showing that these models capture functional high-order sequence and evolutionary context. We further show that species-aware training yields improved sequence representations for endogenous and MPRA-based gene expression prediction, as well as motif discovery.

CONCLUSIONS

Collectively, these results demonstrate that species-aware DNA language models are a powerful, flexible, and scalable tool to integrate information from large compendia of highly diverged genomes.

Natural trait variation across Saccharomycotina species

Among molecular biologists, the group of fungi called Saccharomycotina is famous for its yeasts. These yeasts in turn are famous for what they have in common-genetic, biochemical, and cell-biological characteristics that serve as models for plants and animals. But behind the apparent homogeneity of Saccharomycotina species lie a wealth of differences. In this review, we discuss traits that vary across the Saccharomycotina subphylum. We describe cases of bright pigmentation; a zoo of cell shapes; metabolic specialties; and species with unique rules of gene regulation. We discuss the genetics of this diversity and why it matters, including insights into basic evolutionary principles with relevance across Eukarya.

The ensemble of gene regulatory networks at mutation-selection balance

The evolution of diverse phenotypes both involves and is constrained by molecular interaction networks. When these networks influence patterns of expression, we refer to them as gene regulatory networks (GRNs). Here, we develop a model of GRN evolution analogous to work from quasi-species theory, which is itself essentially the mutation-selection balance model from classical population genetics extended to multiple loci. With this GRN model, we prove that-across a broad spectrum of selection pressures-the dynamics converge to a stationary distribution over GRNs. Next, we show from first principles how the frequency of GRNs at equilibrium is related to the topology of the genotype network, in particular, via a specific network centrality measure termed the eigenvector centrality. Finally, we determine the structural characteristics of GRNs that are favoured in response to a range of selective environments and mutational constraints. Our work connects GRN evolution to quasi-species theory-and thus to classical populations genetics-providing a mechanistic explanation for the observed distribution of GRNs evolving in response to various evolutionary forces, and shows how complex fitness landscapes can emerge from simple evolutionary rules.

Functional Divergence in a Multi-gene Family Is a Key Evolutionary Innovation for Anaerobic Growth in Saccharomyces cerevisiae

The amplification and diversification of genes into large multi-gene families often mark key evolutionary innovations, but this process often creates genetic redundancy that hinders functional investigations. When the model budding yeast Saccharomyces cerevisiae transitions to anaerobic growth conditions, the cell massively induces the expression of seven serine/threonine-rich anaerobically-induced cell wall mannoproteins (anCWMPs): TIP1, TIR1, TIR2, TIR3, TIR4, DAN1, and DAN4. Here, we show that these genes likely derive evolutionarily from a single ancestral anCWMP locus, which was duplicated and translocated to new genomic contexts several times both prior to and following the budding yeast whole genome duplication (WGD) event. Based on synteny and their phylogeny, we separate the anCWMPs into four gene subfamilies. To resolve prior inconclusive genetic investigations of these genes, we constructed a set of combinatorial deletion mutants to determine their contributions toward anaerobic growth in S. cerevisiae. We found that two genes, TIR1 and TIR3, were together necessary and sufficient for the anCWMP contribution to anaerobic growth. Overexpressing either gene alone was insufficient for anaerobic growth, implying that they encode non-overlapping functional roles in the cell during anaerobic growth. We infer from the phylogeny of the anCWMP genes that these two important genes derive from an ancient duplication that predates the WGD event, whereas the TIR1 subfamily experienced gene family amplification after the WGD event. Taken together, the genetic and molecular evidence suggests that one key anCWMP gene duplication event, several auxiliary gene duplication events, and functional divergence underpin the evolution of anaerobic growth in budding yeasts.

Genome doubling enabled the expansion of yeast vesicle traffic pathways

Vesicle budding and fusion in eukaryotes depend on a suite of protein types, such as Arfs, Rabs, coats and SNAREs. Distinct paralogs of these proteins act at distinct intracellular locations, suggesting a link between gene duplication and the expansion of vesicle traffic pathways. Genome doubling, a common source of paralogous genes in fungi, provides an ideal setting in which to explore this link. Here we trace the fates of paralog doublets derived from the 100-Ma-old hybridization event that gave rise to the whole genome duplication clade of budding yeast. We find that paralog doublets involved in specific vesicle traffic functions and pathways are convergently retained across the entire clade. Vesicle coats and adaptors involved in secretory and early-endocytic pathways are retained as doublets, at rates several-fold higher than expected by chance. Proteins involved in later endocytic steps and intra-Golgi traffic, including the entire set of multi-subunit and coiled-coil tethers, have reverted to singletons. These patterns demonstrate that selection has acted to expand and diversify the yeast vesicle traffic apparatus, across species and time.

Evolution of miRNA-Binding Sites and Regulatory Networks in Cichlids

The divergence of regulatory regions and gene regulatory network (GRN) rewiring is a key driver of cichlid phenotypic diversity. However, the contribution of miRNA-binding site turnover has yet to be linked to GRN evolution across cichlids. Here, we extend our previous studies by analyzing the selective constraints driving evolution of miRNA and transcription factor (TF)-binding sites of target genes, to infer instances of cichlid GRN rewiring associated with regulatory binding site turnover. Comparative analyses identified increased species-specific networks that are functionally associated to traits of cichlid phenotypic diversity. The evolutionary rewiring is associated with differential models of miRNA- and TF-binding site turnover, driven by a high proportion of fast-evolving polymorphic sites in adaptive trait genes compared with subsets of random genes. Positive selection acting upon discrete mutations in these regulatory regions is likely to be an important mechanism in rewiring GRNs in rapidly radiating cichlids. Regulatory variants of functionally associated miRNA- and TF-binding sites of visual opsin genes differentially segregate according to phylogeny and ecology of Lake Malawi species, identifying both rewired, for example, clade-specific and conserved network motifs of adaptive trait associated GRNs. Our approach revealed several novel candidate regulators, regulatory regions, and three-node motifs across cichlid genomes with previously reported associations to known adaptive evolutionary traits.

The evolution, evolvability and engineering of gene regulatory DNA

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness^1-3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces^4-6. Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution.

Enabling Studies of Genome-Scale Regulatory Network Evolution in Large Phylogenies with MRTLE

Transcriptional regulatory networks specify context-specific patterns of genes and play a central role in how species evolve and adapt. Inferring genome-scale regulatory networks in non-model species is the first step for examining patterns of conservation and divergence of regulatory networks. Transcriptomic data obtained under varying environmental stimuli in multiple species are becoming increasingly available, which can be used to infer regulatory networks. However, inference and analysis of multiple gene regulatory networks in a phylogenetic setting remains challenging. We developed an algorithm, Multi-species Regulatory neTwork LEarning (MRTLE), to facilitate such studies of regulatory network evolution. MRTLE is a probabilistic graphical model-based algorithm that uses phylogenetic structure, transcriptomic data for multiple species, and sequence-specific motifs in each species to simultaneously infer genome-scale regulatory networks across multiple species. We applied MRTLE to study regulatory network evolution across six ascomycete yeasts using transcriptomic measurements collected across different stress conditions. MRTLE networks recapitulated experimentally derived interactions in the model organism S. cerevisiae as well as non-model species, and it was more beneficial for network inference than methods that do not use phylogenetic information. We examined the regulatory networks across species and found that regulators associated with significant expression and network changes are involved in stress-related processes. MTRLE and its associated downstream analysis provide a scalable and principled framework to examine evolutionary dynamics of transcriptional regulatory networks across multiple species in a large phylogeny.

Single cell gene regulatory networks in plants: Opportunities for enhancing climate change stress resilience

Global warming poses major challenges for plant survival and agricultural productivity. Thus, efforts to enhance stress resilience in plants are key strategies for protecting food security. Gene regulatory networks (GRNs) are a critical mechanism conferring stress resilience. Until recently, predicting GRNs of the individual cells that make up plants and other multicellular organisms was impeded by aggregate population scale measurements of transcriptome and other genome-scale features. With the advancement of high-throughput single cell RNA-seq and other single cell assays, learning GRNs for individual cells is now possible, in principle. In this article, we report on recent advances in experimental and analytical methodologies for single cell sequencing assays especially as they have been applied to the study of plants. We highlight recent advances and ongoing challenges for scGRN prediction, and finally, we highlight the opportunity to use scGRN discovery for studying and ultimately enhancing abiotic stress resilience in plants.

Paralogs in the PKA Regulon Traveled Different Evolutionary Routes to Divergent Expression in Budding Yeast

Functional divergence of duplicate genes, or paralogs, is an important driver of novelty in evolution. In the model yeast Saccharomyces cerevisiae, there are 547 paralog gene pairs that survive from an interspecies Whole Genome Hybridization (WGH) that occurred ~100MYA. In this work, we report that ~1/6th (110) of these WGH paralogs pairs (or ohnologs) are differentially expressed with a striking pattern upon Protein Kinase A (PKA) inhibition. One member of each pair in this group has low basal expression that increases upon PKA inhibition, while the other has moderate and unchanging expression. For these genes, expression of orthologs upon PKA inhibition in the non-WGH species Kluyveromyces lactis and for PKA-related stresses in other budding yeasts shows unchanging expression, suggesting that lack of responsiveness to PKA was likely the typical ancestral phenotype prior to duplication. Promoter sequence analysis across related budding yeast species further revealed that the subsequent emergence of PKA-dependence took different evolutionary routes. In some examples, regulation by PKA and differential expression appears to have arisen following the WGH, while in others, regulation by PKA appears to have arisen in one of the two parental lineages prior to the WGH. More broadly, our results illustrate the unique opportunities presented by a WGH event for generating functional divergence by bringing together two parental lineages with separately evolved regulation into one species. We propose that functional divergence of two ohnologs can be facilitated through such regulatory divergence.

A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies

Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association.

Evolution of regulatory networks associated with traits under selection in cichlids

BACKGROUND

Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically diverse species. Here we use a novel approach for comparative GRN analysis in vertebrate species to study GRN evolution in representative species of the most striking examples of adaptive radiations, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids can be attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence.

RESULTS

To investigate these mechanisms across species at a genome-wide scale, we develop a novel computational pipeline that predicts regulators for co-extant and ancestral co-expression modules along a phylogeny, and candidate regulatory regions associated with traits under selection in cichlids. As a case study, we apply our approach to a well-studied adaptive trait-the visual system-for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids.

CONCLUSIONS

Our approach reveals numerous novel potential candidate regulators and regulatory regions across cichlid genomes, including some novel and some previously reported associations to known adaptive evolutionary traits.

Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

Functional Constitutional Dynamic Networks Revealing Evolutionary Reproduction/Variation/Selection Principles

Within the broad research efforts to engineer chemical pathways to yield high-throughput evolutionary synthesis of genes and their screening for dictated functionalities, we introduce the evolution of nucleic-acid-based constitutional dynamic networks (CDNs) that follow reproduction/variation/selection principles. These fundamental principles are demonstrated by assembling a library of nucleic-acid strands and hairpins as functional modules for evolving networks. Primary T1-initiated selection of components from the library assembles a parent CDN X, where the evolved constituents exhibit catalytic properties to cleave the hairpins in the library. Cleavage of the hairpins yields fragments, which reproduces T1 to replicate CDN X, whereas the other fragments T2 and T3 select other components to evolve two other CDNs, Y and Z (variation). By applying appropriate counter triggers, we demonstrate the guided selection of networks from the evolved CDNs. By integrating additional hairpin substrates into the system, CDN-dictated emergent catalytic transformations are accomplished. The study provides pathways to construct evolutionary dynamic networks revealing enhanced gated and cascaded functions.

Independent evolution of transcript abundance and gene regulatory dynamics

Changes in gene expression drive novel phenotypes, raising interest in how gene expression evolves. In contrast to the static genome, cells modulate gene expression in response to changing environments. Previous comparative studies focused on specific conditions, describing interspecies variation in expression levels, but providing limited information about variation across different conditions. To close this gap, we profiled mRNA levels of two related yeast species in hundreds of conditions and used coexpression analysis to distinguish variation in the dynamic pattern of gene expression from variation in expression levels. The majority of genes whose expression varied between the species maintained a conserved dynamic pattern. Cases of diverged dynamic pattern correspond to genes that were induced under distinct subsets of conditions in the two species. Profiling the interspecific hybrid allowed us to distinguish between genes with predominantly cis- or trans-regulatory variation. We find that trans-varying alleles are dominantly inherited, and that cis-variations are often complemented by variations in trans Based on these results, we suggest that gene expression diverges primarily through changes in expression levels, but does not alter the pattern by which these levels are dynamically regulated.

Gene Repositioning Is Under Constraints After Evolutionary Conserved Gene Neighborhood Separate

Genes are not randomly distributed on eukaryotic chromosomes. Some neighboring genes show order conservation among species, while some neighboring genes separate during evolution even though their neighborhoods are conserved in some species. Here, I investigated whether after-separation gene repositioning is under natural selection for evolutionary conserved gene neighborhoods compared with nonconserved neighborhoods. After separation, genes with conserved neighborhoods show low-expression divergence between the after-separation species and the before-separation species. After genes separate from their conserved gene neighbors, their after-separation gene neighbors tend to show coexpression and coprotein complex with their before-separation gene neighbors. These results indicate evolutionary constraints on the selection of neighboring genes after evolutionary conserved gene neighborhoods separate.

The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs

Gene duplication is a driver of the evolution of new functions. The duplication of genes encoding homomeric proteins leads to the formation of homomers and heteromers of paralogs, creating new complexes after a single duplication event. The loss of these heteromers may be required for the two paralogs to evolve independent functions. Using yeast as a model, we find that heteromerization is frequent among duplicated homomers and correlates with functional similarity between paralogs. Using in silico evolution, we show that for homomers and heteromers sharing binding interfaces, mutations in one paralog can have structural pleiotropic effects on both interactions, resulting in highly correlated responses of the complexes to selection. Therefore, heteromerization could be preserved indirectly due to selection for the maintenance of homomers, thus slowing down functional divergence between paralogs. We suggest that paralogs can overcome the obstacle of structural pleiotropy by regulatory evolution at the transcriptional and post-translational levels.

Comparative Transcriptome Analysis Shows Conserved Metabolic Regulation during Production of Secondary Metabolites in Filamentous Fungi

Filamentous fungi possess great potential as sources of medicinal bioactive compounds, such as antibiotics, but efficient production is hampered by a limited understanding of how their metabolism is regulated. We investigated the metabolism of six secondary metabolite-producing fungi of the Penicillium genus during nutrient depletion in the stationary phase of batch fermentations and assessed conserved metabolic responses across species using genome-wide transcriptional profiling. A coexpression analysis revealed that expression of biosynthetic genes correlates with expression of genes associated with pathways responsible for the generation of precursor metabolites for secondary metabolism. Our results highlight the main metabolic routes for the supply of precursors for secondary metabolism and suggest that the regulation of fungal metabolism is tailored to meet the demands for secondary metabolite production. These findings can aid in identifying fungal species that are optimized for the production of specific secondary metabolites and in designing metabolic engineering strategies to develop high-yielding fungal cell factories for production of secondary metabolites. IMPORTANCE Secondary metabolites are a major source of pharmaceuticals, especially antibiotics. However, the development of efficient processes of production of secondary metabolites has proved troublesome due to a limited understanding of the metabolic regulations governing secondary metabolism. By analyzing the conservation in gene expression across secondary metabolite-producing fungal species, we identified a metabolic signature that links primary and secondary metabolism and that demonstrates that fungal metabolism is tailored for the efficient production of secondary metabolites. The insight that we provide can be used to develop high-yielding fungal cell factories that are optimized for the production of specific secondary metabolites of pharmaceutical interest.

Design, execution, and analysis of CRISPR-Cas9-based deletions and genetic interaction networks in the fungal pathogen Candida albicans

The study of fungal pathogens is of immediate importance, yet progress is hindered by the technical challenges of genetic manipulation. For Candida species, their inability to maintain plasmids, unusual codon usage, and inefficient homologous recombination are among the obstacles limiting efficient genetic manipulation. New advances in genomic biotechnologies-particularly CRISPR-based tools-have revolutionized genome editing for many fungal species. Here, we present a protocol for CRISPR-Cas9-based manipulation in Candida albicans using a modified gene-drive-based strategy that takes ~1 month to complete. We detail the generation of Candida-optimized Cas9-based plasmids for gene deletion, an efficient transformation protocol using C. albicans haploids, and an optimized mating strategy to generate homozygous single- and double-gene diploid mutants. We further describe protocols for quantifying cell growth and analysis pipelines to calculate fitness and genetic interaction scores for genetic mutants. This protocol overcomes previous limitations associated with genetic manipulation in C. albicans and advances researchers' ability to perform genetic analysis in this pathogen; the protocol also has broad applicability to other mating-competent microorganisms.

Comparative expression profiling reveals widespread coordinated evolution of gene expression across eukaryotes

Comparative studies of gene expression across species have revealed many important insights, but have also been limited by the number of species represented. Here we develop an approach to identify orthologs between highly diverged transcriptome assemblies, and apply this to 657 RNA-seq gene expression profiles from 309 diverse unicellular eukaryotes. We analyzed the resulting data for coevolutionary patterns, and identify several hundred protein complexes and pathways whose expression levels have evolved in a coordinated fashion across the trillions of generations separating these species, including many gene sets with little or no within-species co-expression across environmental or genetic perturbations. We also detect examples of adaptive evolution, for example of tRNA ligase levels to match genome-wide codon usage. In sum, we find that comparative studies from extremely diverse organisms can reveal new insights into the evolution of gene expression, including coordinated evolution of some of the most conserved protein complexes in eukaryotes.

Gene pairs that are coexpressed across various environmental conditions in multiple species suggest functional similarity. Here the authors analyze patterns of gene expression co-evolution across diverse eukaryotes, and identify hundreds of protein complexes and pathways whose gene expression levels have co-evolved since their ancient divergence.

A prior-based integrative framework for functional transcriptional regulatory network inference

Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization.

Comparative Transcriptomics Highlights New Features of the Iron Starvation Response in the Human Pathogen Candida glabrata

In this work, we used comparative transcriptomics to identify regulatory outliers (ROs) in the human pathogen Candida glabrata. ROs are genes that have very different expression patterns compared to their orthologs in other species. From comparative transcriptome analyses of the response of eight yeast species to toxic doses of selenite, a pleiotropic stress inducer, we identified 38 ROs in C. glabrata. Using transcriptome analyses of C. glabrata response to five different stresses, we pointed out five ROs which were more particularly responsive to iron starvation, a process which is very important for C. glabrata virulence. Global chromatin Immunoprecipitation and gene profiling analyses showed that four of these genes are actually new targets of the iron starvation responsive Aft2 transcription factor in C. glabrata. Two of them (HBS1 and DOM34b) are required for C. glabrata optimal growth in iron limited conditions. In S. cerevisiae, the orthologs of these two genes are involved in ribosome rescue by the NO GO decay (NGD) pathway. Hence, our results suggest a specific contribution of NGD co-factors to the C. glabrata adaptation to iron starvation.

The rewiring of transcription circuits in evolution

The binding of transcription regulators to cis-regulatory sequences is a key step through which all cells regulate expression of their genes. Due to gains and losses of cis-regulatory sequences and changes in the transcription regulators themselves, the binding connections between regulators and their target genes rapidly change over evolutionary time and constitute a major source of biological novelty. This review covers recent work, carried out in a wide range of species, that addresses the overall extent of these evolutionary changes, their consequences, and some of the molecular mechanisms that lie behind them.

Tracking the evolution of 3D gene organization demonstrates its connection to phenotypic divergence

It has recently been shown that the organization of genes in eukaryotic genomes, and specifically in 3D, is strongly related to gene expression and function and partially conserved between organisms. However, previous studies of 3D genomic organization analyzed each organism independently from others. Here, we propose an approach for unified inter-organismal analysis of gene organization based on a network representation of Hi-C data. We define and detect four classes of spatially co-evolving orthologous modules (SCOMs), i.e. gene families that co-evolve in their 3D organization, based on patterns of divergence and conservation of distances. We demonstrate our methodology on Hi-C data from Saccharomyces cerevisiae and Schizosaccharomyces pombe, and identify, among others, modules relating to RNA splicing machinery and chromatin silencing by small RNA which are central to S. pombe's lifestyle. Our results emphasize the importance of 3D genomic organization in eukaryotes and suggest that the evolutionary mechanisms that shape gene organization affect the organism fitness and phenotypes. The proposed algorithms can be utilized in future studies of genome evolution and comparative analysis of spatial genomic organization in different tissues, conditions and single cells.

The ancestral levels of transcription and the evolution of sexual phenotypes in filamentous fungi

Changes in gene expression have been hypothesized to play an important role in the evolution of divergent morphologies. To test this hypothesis in a model system, we examined differences in fruiting body morphology of five filamentous fungi in the Sordariomycetes, culturing them in a common garden environment and profiling genome-wide gene expression at five developmental stages. We reconstructed ancestral gene expression phenotypes, identifying genes with the largest evolved increases in gene expression across development. Conducting knockouts and performing phenotypic analysis in two divergent species typically demonstrated altered fruiting body development in the species that had evolved increased expression. Our evolutionary approach to finding relevant genes proved far more efficient than other gene deletion studies targeting whole genomes or gene families. Combining gene expression measurements with knockout phenotypes facilitated the refinement of Bayesian networks of the genes underlying fruiting body development, regulation of which is one of the least understood processes of multicellular development.

Evolution of reduced co-activator dependence led to target expansion of a starvation response pathway

Although combinatorial regulation is a common feature in gene regulatory networks, how it evolves and affects network structure and function is not well understood. In S. cerevisiae, the phosphate starvation (PHO) responsive transcription factors Pho4 and Pho2 are required for gene induction and survival during phosphate starvation. In the related human commensal C. glabrata, Pho4 is required but Pho2 is dispensable for survival in phosphate starvation and is only partially required for inducing PHO genes. Phylogenetic survey suggests that reduced dependence on Pho2 evolved in C. glabrata and closely related species. In S. cerevisiae, less Pho2-dependent Pho4 orthologs induce more genes. In C. glabrata, its Pho4 binds to more locations and induces three times as many genes as Pho4 in S. cerevisiae does. Our work shows how evolution of combinatorial regulation allows for rapid expansion of a gene regulatory network’s targets, possibly extending its physiological functions.

The diversity of life on Earth has intrigued generations of scientists and nature lovers alike. Research over recent decades has revealed that much of the diversity we can see did not require the invention of new genes. Instead, living forms diversified mostly by using old genes in new ways – for example, by changing when or where an existing gene became active. This kind of change is referred to as “regulatory evolution”.

A class of proteins called transcription factors are hot spots in regulatory evolution. These proteins recognize specific sequences of DNA to control the activity of other genes, and so represent the “readers” of the genetic information. Small changes to how a transcription factor is regulated, or the genes it targets, can lead to dramatic changes in an organism. Before we can understand how life on Earth evolved to be so diverse, scientists must first answer how transcription factors evolve and what consequences this has on their target genes.

So far, most studies of regulatory evolution have focused on networks of transcription factors and genes that control how an organism develops. He et al. have now studied a regulatory network that is behind a different process, namely how an organism responds to stress or starvation. These two types of regulatory networks are structured differently and work in different ways. These differences made He et al. wonder if the networks evolved differently too.

The chemical phosphate is an essential nutrient for all living things, and He et al. compared how two different species of yeast responded to a lack of phosphate. The key difference was how much a major transcription factor known as Pho4 depended on a so-called co-activator protein named Pho2 to carry out its role. Baker’s yeast (Saccharomyces cerevisiae), which is commonly used in laboratory experiments, requires both Pho4 and Pho2 to activate about 20 genes when inorganic phosphate is not available in its environment. However, in a related yeast species called Candida glabrata, Pho4 has evolved to depend less on Pho2. He et al. went on to show that, as well as being less dependent on Pho2, Pho4 in C. glabrata activates more than three times as many genes as Pho4 in S. cerevisiae does in the absence of phosphate. These additional gene targets for Pho4 in C. glabrata are predicted to extend the network’s activities, and allow it to regulate new process including the yeast’s responses to other types of stress and the building of the yeast’s cell wall.

Together these findings show a new way that regulatory networks can evolve, that is, by reducing its dependence on the co-activator, a transcription factor can expand the number of genes it targets. This has not been seen for regulatory networks related to development, suggesting that different networks can indeed evolve in different ways. Lastly, because disease-causing microbes are often stressed inside their hosts and C. glabrata sometimes infects humans, understanding how this yeast’s response to stress has evolved may lead to new ways to prevent and treat this infection.

Glycerol stress in Saccharomyces cerevisiae: Cellular responses and evolved adaptations

Glycerol synthesis is key to central metabolism and stress biology in Saccharomyces cerevisiae, yet the cellular adjustments needed to respond and adapt to glycerol stress are little understood. Here, we determined impacts of acute and chronic exposures to glycerol stress in S. cerevisiae. Glycerol stress can result from an increase of glycerol concentration in the medium due to the S. cerevisiae fermenting activity or other metabolic activities. Acute glycerol-stress led to a 50% decline in growth rate and altered transcription of more than 40% of genes. The increased genetic diversity in S. cerevisiae population, which had evolved in the standard nutrient medium for hundreds of generations, led to an increase in growth rate and altered transcriptome when such population was transferred to stressful media containing a high concentration of glycerol; 0.41 M (0.990 water activity). Evolution of S. cerevisiae populations during a 10-day period in the glycerol-containing medium led to transcriptome changes and readjustments to improve control of glycerol flux across the membrane, regulation of cell cycle, and more robust stress response; and a remarkable increase of growth rate under glycerol stress. Most of the observed regulatory changes arose in duplicated genes. These findings elucidate the physiological mechanisms, which underlie glycerol-stress response, and longer-term adaptations, in S. cerevisiae; they also have implications for enigmatic aspects of the ecology of this otherwise well-characterized yeast.

The Phenotypic Plasticity of Duplicated Genes in Saccharomyces cerevisiae and the Origin of Adaptations

Gene and genome duplication are the major sources of biological innovations in plants and animals. Functional and transcriptional divergence between the copies after gene duplication has been considered the main driver of innovations . However, here we show that increased phenotypic plasticity after duplication plays a more major role than thought before in the origin of adaptations. We perform an exhaustive analysis of the transcriptional alterations of duplicated genes in the unicellular eukaryote Saccharomyces cerevisiae when challenged with five different environmental stresses. Analysis of the transcriptomes of yeast shows that gene duplication increases the transcriptional response to environmental changes, with duplicated genes exhibiting signatures of adaptive transcriptional patterns in response to stress. The mechanism of duplication matters, with whole-genome duplicates being more transcriptionally altered than small-scale duplicates. The predominant transcriptional pattern follows the classic theory of evolution by gene duplication; with one gene copy remaining unaltered under stress, while its sister copy presents large transcriptional plasticity and a prominent role in adaptation. Moreover, we find additional transcriptional profiles that are suggestive of neo- and subfunctionalization of duplicate gene copies. These patterns are strongly correlated with the functional dependencies and sequence divergence profiles of gene copies. We show that, unlike singletons, duplicates respond more specifically to stress, supporting the role of natural selection in the transcriptional plasticity of duplicates. Our results reveal the underlying transcriptional complexity of duplicated genes and its role in the origin of adaptations.

Zinc Cluster Transcription Factors Alter Virulence in Candida albicans

Almost all humans are colonized with Candida albicans However, in immunocompromised individuals, this benign commensal organism becomes a serious, life-threatening pathogen. Here, we describe and analyze the regulatory networks that modulate innate responses in the host niches. We identified Zcf15 and Zcf29, two Zinc Cluster transcription Factors (ZCF) that are required for C. albicans virulence. Previous sequence analysis of clinical C. albicans isolates from immunocompromised patients indicates that both ZCF genes diverged during clonal evolution. Using in vivo animal models, ex vivo cell culture methods, and in vitro sensitivity assays, we demonstrate that knockout mutants of both ZCF15 and ZCF29 are hypersensitive to reactive oxygen species (ROS), suggesting they help neutralize the host-derived ROS produced by phagocytes, as well as establish a sustained infection in vivo Transcriptomic analysis of mutants under resting conditions where cells were not experiencing oxidative stress revealed a large network that control macro and micronutrient homeostasis, which likely contributes to overall pathogen fitness in host niches. Under oxidative stress, both transcription factors regulate a separate set of genes involved in detoxification of ROS and down-regulating ribosome biogenesis. ChIP-seq analysis, which reveals vastly different binding partners for each transcription factor (TF) before and after oxidative stress, further confirms these results. Furthermore, the absence of a dominant binding motif likely facilitates their mobility, and supports the notion that they represent a recent expansion of the ZCF family in the pathogenic Candida species. Our analyses provide a framework for understanding new aspects of the interface between C. albicans and host defense response, and extends our understanding of how complex cell behaviors are linked to the evolution of TFs.

A Network of Paralogous Stress Response Transcription Factors in the Human Pathogen Candida glabrata

The yeast Candida glabrata has become the second cause of systemic candidemia in humans. However, relatively few genome-wide studies have been conducted in this organism and our knowledge of its transcriptional regulatory network is quite limited. In the present work, we combined genome-wide chromatin immunoprecipitation (ChIP-seq), transcriptome analyses, and DNA binding motif predictions to describe the regulatory interactions of the seven Yap (Yeast AP1) transcription factors of C. glabrata. We described a transcriptional network containing 255 regulatory interactions and 309 potential target genes. We predicted with high confidence the preferred DNA binding sites for 5 of the 7 CgYaps and showed a strong conservation of the Yap DNA binding properties between S. cerevisiae and C. glabrata. We provided reliable functional annotation for 3 of the 7 Yaps and identified for Yap1 and Yap5 a core regulon which is conserved in S. cerevisiae, C. glabrata, and C. albicans. We uncovered new roles for CgYap7 in the regulation of iron-sulfur cluster biogenesis, for CgYap1 in the regulation of heme biosynthesis and for CgYap5 in the repression of GRX4 in response to iron starvation. These transcription factors define an interconnected transcriptional network at the cross-roads between redox homeostasis, oxygen consumption, and iron metabolism.

Expansion of antisense lncRNA transcriptomes in budding yeast species since the loss of RNAi

Antisense long noncoding RNAs (ASlncRNAs) have been implicated in regulating gene expression in response to physiological cues. However, little is known about the evolutionary dynamics of ASlncRNA and what underlies the evolution of their expression. Here, using budding yeast Saccharomyces spp. and Naumovozyma castellii as models, we show that ASlncRNA repertoires have expanded since the loss of RNA interference (RNAi), in terms of their expression levels, their lengths and their degree of overlap with coding genes. Furthermore, we show that RNAi is inhibitory to ASlncRNA transcriptomes and that increased expression of ASlncRNAs in the presence of RNAi is deleterious to N. castellii, which has retained RNAi. Together, our results suggest that the loss of RNAi had substantial effects on the genome-wide increase in expression of ASlncRNAs during the evolution of budding yeasts.

Divergent MLS1 Promoters Lie on a Fitness Plateau for Gene Expression

Qualitative patterns of gene activation and repression are often conserved despite an abundance of quantitative variation in expression levels within and between species. A major challenge to interpreting patterns of expression divergence is knowing which changes in gene expression affect fitness. To characterize the fitness effects of gene expression divergence, we placed orthologous promoters from eight yeast species upstream of malate synthase (MLS1) in Saccharomyces cerevisiae. As expected, we found these promoters varied in their expression level under activated and repressed conditions as well as in their dynamic response following loss of glucose repression. Despite these differences, only a single promoter driving near basal levels of expression caused a detectable loss of fitness. We conclude that the MLS1 promoter lies on a fitness plateau whereby even large changes in gene expression can be tolerated without a substantial loss of fitness.

Selecting Superior De Novo Transcriptome Assemblies: Lessons Learned by Leveraging the Best Plant Genome

Whereas de novo assemblies of RNA-Seq data are being published for a growing number of species across the tree of life, there are currently no broadly accepted methods for evaluating such assemblies. Here we present a detailed comparison of 99 transcriptome assemblies, generated with 6 de novo assemblers including CLC, Trinity, SOAP, Oases, ABySS and NextGENe. Controlled analyses of de novo assemblies for Arabidopsis thaliana and Oryza sativa transcriptomes provide new insights into the strengths and limitations of transcriptome assembly strategies. We find that the leading assemblers generate reassuringly accurate assemblies for the majority of transcripts. At the same time, we find a propensity for assemblers to fail to fully assemble highly expressed genes. Surprisingly, the instance of true chimeric assemblies is very low for all assemblers. Normalized libraries are reduced in highly abundant transcripts, but they also lack 1000s of low abundance transcripts. We conclude that the quality of de novo transcriptome assemblies is best assessed through consideration of a combination of metrics: 1) proportion of reads mapping to an assembly 2) recovery of conserved, widely expressed genes, 3) N50 length statistics, and 4) the total number of unigenes. We provide benchmark Illumina transcriptome data and introduce SCERNA, a broadly applicable modular protocol for de novo assembly improvement. Finally, our de novo assembly of the Arabidopsis leaf transcriptome revealed ~20 putative Arabidopsis genes lacking in the current annotation.

Reconstruction and Analysis of the Evolution of Modular Transcriptional Regulatory Programs Using Arboretum

Comparative functional genomics aims to measure and compare genome-wide functional data such as transcriptomes, proteomes, and epigenomes across multiple species to study the conservation and divergence patterns of such quantitative measurements. However, computational methods to systematically compare these quantitative genomic profiles across multiple species are in their infancy. We developed Arboretum, a novel algorithm to identify modules of co-expressed genes and trace their evolutionary history across multiple species from a complex phylogeny. To interpret the results from Arboretum we developed several measures to examine the extent of conservation and divergence in modules and their relationship to species lifestyle, cis-regulatory elements, and gene duplication. We applied Arboretum to study the evolution of modular transcriptional regulatory programs controlling transcriptional response to different environmental stresses in the yeast Ascomycota phylogeny. We found that modules of similar patterns of expression captured the transcriptional responses to different stresses across species; however, the genes exhibiting these patterns were not the same. Divergence in module membership was associated with changes in lifestyle and specific clades and that gene duplication was a major factor contributing to the divergence of module membership.

Heat shock at higher cell densities improves measles hemagglutinin translocation and human GRP78/BiP secretion in Saccharomyces cerevisiae

The yield of heterologous proteins is often limited by several bottlenecks in the secretory pathway of yeast Saccharomyces cerevisiae. It was shown earlier that synthesis of measles virus hemagglutinin (MeH) is inefficient mostly due to a bottleneck in the translocation of viral protein precursors into the endoplasmic reticulum (ER) of yeast cells. Here we report that heat shock with subsequent induction of MeH expression at 37°C improved translocation of MeH precursors when applied at higher cell densities. The amount of MeH glycoprotein increased by about 3-fold after heat shock in the late-log phases of both glucose and ethanol growth. The same temperature conditions increased both secretion titer and yield of another heterologous protein human GRP78/BiP by about 50%. Furthermore, heat shock at the late-log glucose growth phase also improved endogenous invertase yield by approximately 2.7-fold. In contrast, a transfer of yeast culture to lower temperature at diauxic shift followed by protein expression at 20°C almost totally inhibited translocation of MeH precursors. The difference in amounts of MeH glycoprotein under expression at 37°C and 20°C was about 80-fold, while amounts of unglycosylated MeH polypeptides were similar under both conditions. Comparative proteomic analysis revealed that besides over-expressed ER-resident chaperone Kar2, an increased expression of several cytosolic proteins (such as Hsp104, Hsp90 and eEF1A) may contribute to improved translocation of MeH.

Genomics and the making of yeast biodiversity

Yeasts are unicellular fungi that do not form fruiting bodies. Although the yeast lifestyle has evolved multiple times, most known species belong to the subphylum Saccharomycotina (syn. Hemiascomycota, hereafter yeasts). This diverse group includes the premier eukaryotic model system, Saccharomyces cerevisiae; the common human commensal and opportunistic pathogen, Candida albicans; and over 1000 other known species (with more continuing to be discovered). Yeasts are found in every biome and continent and are more genetically diverse than angiosperms or chordates. Ease of culture, simple life cycles, and small genomes (∼10-20Mbp) have made yeasts exceptional models for molecular genetics, biotechnology, and evolutionary genomics. Here we discuss recent developments in understanding the genomic underpinnings of the making of yeast biodiversity, comparing and contrasting natural and human-associated evolutionary processes. Only a tiny fraction of yeast biodiversity and metabolic capabilities has been tapped by industry and science. Expanding the taxonomic breadth of deep genomic investigations will further illuminate how genome function evolves to encode their diverse metabolisms and ecologies.

Evolution of regulatory networks in Candida glabrata: learning to live with the human host

The opportunistic human fungal pathogen Candida glabrata is second only to C. albicans as the cause of Candida infections and yet is more closely related to Saccharomyces cerevisiae. Recent advances in functional genomics technologies and computational approaches to decipher regulatory networks, and the comparison of these networks among these and other Ascomycete species, have revealed both unique and shared strategies in adaptation to a human commensal/opportunistic pathogen lifestyle and antifungal drug resistance in C. glabrata. Recently, several C. glabrata sister species in the Nakeseomyces clade representing both human associated (commensal) and environmental isolates have had their genomes sequenced and analyzed. This has paved the way for comparative functional genomics studies to characterize the regulatory networks in these species to identify informative patterns of conservation and divergence linked to phenotypic evolution in the Nakaseomyces lineage.

Divergent Evolution of the Transcriptional Network Controlled by Snf1-Interacting Protein Sip4 in Budding Yeasts

Cellular responses to starvation are of ancient origin since nutrient limitation has always been a common challenge to the stability of living systems. Hence, signaling molecules involved in sensing or transducing information about limiting metabolites are highly conserved, whereas transcription factors and the genes they regulate have diverged. In eukaryotes the AMP-activated protein kinase (AMPK) functions as a central regulator of cellular energy homeostasis. The yeast AMPK ortholog SNF1 controls the transcriptional network that counteracts carbon starvation conditions by regulating a set of transcription factors. Among those Cat8 and Sip4 have overlapping DNA-binding specificity for so-called carbon source responsive elements and induce target genes upon SNF1 activation. To analyze the evolution of the Cat8-Sip4 controlled transcriptional network we have compared the response to carbon limitation of Saccharomyces cerevisiae to that of Kluyveromyces lactis. In high glucose, S. cerevisiae displays tumor cell-like aerobic fermentation and repression of respiration (Crabtree-positive) while K. lactis has a respiratory-fermentative life-style, respiration being regulated by oxygen availability (Crabtree-negative), which is typical for many yeasts and for differentiated higher cells. We demonstrate divergent evolution of the Cat8-Sip4 network and present evidence that a role of Sip4 in controlling anabolic metabolism has been lost in the Saccharomyces lineage. We find that in K. lactis, but not in S. cerevisiae, the Sip4 protein plays an essential role in C2 carbon assimilation including induction of the glyoxylate cycle and the carnitine shuttle genes. Induction of KlSIP4 gene expression by KlCat8 is essential under these growth conditions and a primary function of KlCat8. Both KlCat8 and KlSip4 are involved in the regulation of lactose metabolism in K. lactis. In chromatin-immunoprecipitation experiments we demonstrate binding of both, KlSip4 and KlCat8, to selected CSREs and provide evidence that KlSip4 counteracts KlCat8-mediated transcription activation by competing for binding to some but not all CSREs. The finding that the hierarchical relationship of these transcription factors differs between K. lactis and S. cerevisiae and that the sets of target genes have diverged contributes to explaining the phenotypic differences in metabolic life-style.

High-Throughput Identification of Cis-Regulatory Rewiring Events in Yeast

A coregulated module of genes ("regulon") can have evolutionarily conserved expression patterns and yet have diverged upstream regulators across species. For instance, the ribosomal genes regulon is regulated by the transcription factor (TF) TBF1 in Candida albicans, while in Saccharomyces cerevisiae it is regulated by RAP1. Only a handful of such rewiring events have been established, and the prevalence or conditions conducive to such events are not well known. Here, we develop a novel probabilistic scoring method to comprehensively screen for regulatory rewiring within regulons across 23 yeast species. Investigation of 1,713 regulons and 176 TFs yielded 5,353 significant rewiring events at 5% false discovery rate (FDR). Besides successfully recapitulating known rewiring events, our analyses also suggest TF candidates for certain processes reported to be under distinct regulatory controls in S. cerevisiae and C. albicans, for which the implied regulators are not known: 1) Oxidative stress response (Sc-MSN2 to Ca-FKH2) and 2) nutrient modulation (Sc-RTG1 to Ca-GCN4/Ca-UME6). Furthermore, a stringent screen to detect TF rewiring at individual genes identified 1,446 events at 10% FDR. Overall, these events are supported by strong coexpression between the predicted regulator and its target gene(s) in a species-specific fashion (>50-fold). Independent functional analyses of rewiring TF pairs revealed greater functional interactions and shared biological processes between them (P = 1 × 10(-3)).Our study represents the first comprehensive assessment of regulatory rewiring; with a novel approach that has generated a unique high-confidence resource of several specific events, suggesting that evolutionary rewiring is relatively frequent and may be a significant mechanism of regulatory innovation.

Comparative analysis of gene regulatory networks: from network reconstruction to evolution

Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data

Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.

Experimental evolution of the model eukaryote Saccharomyces cerevisiae yields insight into the molecular mechanisms underlying adaptation

Understanding how changes in DNA drive the emergence of new phenotypes and fuel evolution remains a major challenge. One major hurdle is the lack of a fossil record of DNA that allows linking mutations to phenotypic changes. However, the emergence of high-throughput sequencing technologies now allows sequencing genomes of natural and experimentally evolved microbial populations to study how mutations arise and spread through a population, how new phenotypes arise and how this ultimately leads to adaptation. Here, we highlight key studies that have increased our mechanistic understanding of evolution. We specifically focus on the model eukaryote Saccharomyces cerevisiae because its relatively short replication time, much-studied biology and available molecular toolbox have made it a prime model for molecular evolution studies.

Evolution of intraspecific transcriptomic landscapes in yeasts

Variations in gene expression have been widely explored in order to obtain an accurate overview of the changes in regulatory networks that underlie phenotypic diversity. Numerous studies have characterized differences in genomic expression between large numbers of individuals of model organisms such as Saccharomyces cerevisiae. To more broadly survey the evolution of the transcriptomic landscape across species, we measured whole-genome expression in a large collection of another yeast species: Lachancea kluyveri (formerly Saccharomyces kluyveri), using RNAseq. Interestingly, this species diverged from the S. cerevisiae lineage prior to its ancestral whole genome duplication. Moreover, L. kluyveri harbors a chromosome-scale compositional heterogeneity due to a 1-Mb ancestral introgressed region as well as a large set of unique unannotated genes. In this context, our comparative transcriptomic analysis clearly showed a link between gene evolutionary history and expression behavior. Indeed, genes that have been recently acquired or under function relaxation tend to be less transcribed show a higher intraspecific variation (plasticity) and are less involved in network (connectivity). Moreover, utilizing this approach in L. kluyveri also highlighted specific regulatory network signatures in aerobic respiration, amino-acid biosynthesis and glycosylation, presumably due to its different lifestyle. Our data set sheds an important light on the evolution of intraspecific transcriptomic variation across distant species.

The fascinating and secret wild life of the budding yeast S. cerevisiae

The budding yeast Saccharomyces cerevisiae has been used in laboratory experiments for over a century and has been instrumental in understanding virtually every aspect of molecular biology and genetics. However, it wasn't until a decade ago that the scientific community started to realise how little was known about this yeast's ecology and natural history, and how this information was vitally important for interpreting its biology. Recent large-scale population genomics studies coupled with intensive field surveys have revealed a previously unappreciated wild lifestyle of S. cerevisiae outside the restrictions of human environments and laboratories. The recent discovery that Chinese isolates harbour almost twice as much genetic variation as isolates from the rest of the world combined suggests that Asia is the likely origin of the modern budding yeast.

DOI:http://dx.doi.org/10.7554/eLife.05835.001

Examining the evolution of the regulatory circuit controlling secondary metabolism and development in the fungal genus Aspergillus

Filamentous fungi produce diverse secondary metabolites (SMs) essential to their ecology and adaptation. Although each SM is typically produced by only a handful of species, global SM production is governed by widely conserved transcriptional regulators in conjunction with other cellular processes, such as development. We examined the interplay between the taxonomic narrowness of SM distribution and the broad conservation of global regulation of SM and development in Aspergillus, a diverse fungal genus whose members produce well-known SMs such as penicillin and gliotoxin. Evolutionary analysis of the 2,124 genes comprising the 262 SM pathways in four Aspergillus species showed that most SM pathways were species-specific, that the number of SM gene orthologs was significantly lower than that of orthologs in primary metabolism, and that the few conserved SM orthologs typically belonged to non-homologous SM pathways. RNA sequencing of two master transcriptional regulators of SM and development, veA and mtfA, showed that the effects of deletion of each gene, especially veA, on SM pathway regulation were similar in A. fumigatus and A. nidulans, even though the underlying genes and pathways regulated in each species differed. In contrast, examination of the role of these two regulators in development, where 94% of the underlying genes are conserved in both species showed that whereas the role of veA is conserved, mtfA regulates development in the homothallic A. nidulans but not in the heterothallic A. fumigatus. Thus, the regulation of these highly conserved developmental genes is divergent, whereas-despite minimal conservation of target genes and pathways-the global regulation of SM production is largely conserved. We suggest that the evolution of the transcriptional regulation of secondary metabolism in Aspergillus represents a novel type of regulatory circuit rewiring and hypothesize that it has been largely driven by the dramatic turnover of the target genes involved in the process.

How do regulatory networks evolve and expand throughout evolution?

Throughout evolution, regulatory networks need to expand and adapt to accommodate novel genes and gene functions. However, the molecular details explaining how gene networks evolve remain largely unknown. Recent studies demonstrate that changes in transcription factors contribute to the evolution of regulatory networks. In particular, duplication of transcription factors followed by specific mutations in their DNA-binding or interaction domains propels the divergence and emergence of new networks. The innate promiscuity and modularity of regulatory networks contributes to their evolvability: duplicated promiscuous regulators and their target promoters can acquire mutations that lead to gradual increases in specificity, allowing neofunctionalization or subfunctionalization.

Metagenomic chromosome conformation capture (meta3C) unveils the diversity of chromosome organization in microorganisms

Genomic analyses of microbial populations in their natural environment remain limited by the difficulty to assemble full genomes of individual species. Consequently, the chromosome organization of microorganisms has been investigated in a few model species, but the extent to which the features described can be generalized to other taxa remains unknown. Using controlled mixes of bacterial and yeast species, we developed meta3C, a metagenomic chromosome conformation capture approach that allows characterizing individual genomes and their average organization within a mix of organisms. Not only can meta3C be applied to species already sequenced, but a single meta3C library can be used for assembling, scaffolding and characterizing the tridimensional organization of unknown genomes. By applying meta3C to a semi-complex environmental sample, we confirmed its promising potential. Overall, this first meta3C study highlights the remarkable diversity of microorganisms chromosome organization, while providing an elegant and integrated approach to metagenomic analysis.

DOI:http://dx.doi.org/10.7554/eLife.03318.001

Microbial communities play vital roles in the environment and sustain animal and plant life. Marine microbes are part of the ocean's food chain; soil microbes support the turnover of major nutrients and facilitate plant growth; and the microbial communities residing in the human gut support digestion and the immune system, among other roles. These communities are very complex systems, often containing 1000s of different species engaged in co-dependent relationships, and are therefore very difficult to study.

The entire DNA sequence of an organism constitutes its genome, and much of this genetic information is stored in large structures called chromosomes. Examining the genome of a species can provide important clues about its lifestyle and how it evolved. To do this, DNA is extracted from cells and is then usually cut into smaller fragments, amplified, and sequenced. The small stretches of sequence obtained, called reads, are finally assembled, yielding ideally the complete genome of the organism under study.

Metagenomics attempts to interpret the combined genome of all the different species in a microbial community and has been instrumental in deciphering how the different species interact with each other. Metagenomics involves sequencing stretches of the community's DNA and matching these pieces to individual species to ultimately assemble whole genomes. While this may be a relatively straightforward task for communities that contain only a handful of members, the metagenomes derived from complex microbial communities are huge, fragmented, and incomplete. This often makes it very difficult or even nearly impossible to match the inferred DNA stretches to individual species.

A method called chromosome conformation capture (or ‘3C’ for short) can reveal the physical contacts between different regions of a chromosome and between the different chromosomes of a cell. How often each of these chromosomal contacts occurs provides a kind of physical signature to each genome and each individual chromosome within it.

Marbouty et al. took advantage of these interactions to develop a technique that combines metagenomics and chromosome conformation capture—called meta3C—that can analyze the DNA of many different species mixed together. Testing meta3C on artificial mixtures of a few species of yeast or bacteria showed that meta3C can separate the genomes of the different species without any prior knowledge of the composition of the mix. In a single experiment, meta3C can identify individual chromosomes, match each of them to its species of origin, and reveal the three-dimensional structure of each genome in the mix. Further tests showed that meta3C can also interpret more complex communities where the number and types of the species present are not known.

Meta3C holds great promise for understanding how microbial communities work and how the genomes of the species within a community are organized. However, further developments of the technique will be required to investigate communities as diverse as those present in most natural environments.

DOI:http://dx.doi.org/10.7554/eLife.03318.002

Global proteome turnover analyses of the Yeasts S. cerevisiae and S. pombe

How cells maintain specific levels of each protein and whether that control is evolutionarily conserved are key questions. Here, we report proteome-wide steady-state protein turnover rate measurements for the evolutionarily distant but ecologically similar yeasts, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We find that the half-life of most proteins is much longer than currently thought and determined to a large degree by protein synthesis and dilution due to cell division. However, we detect a significant subset of proteins (∼15%) in both yeasts that are turned over rapidly. In addition, the relative abundances of orthologous proteins between the two yeasts are highly conserved across the 400 million years of evolution. In contrast, their respective turnover rates differ considerably. Our data provide a high-confidence resource for studying protein degradation in common yeast model systems.

Duplication of a promiscuous transcription factor drives the emergence of a new regulatory network

The emergence of new genes throughout evolution requires rewiring and extension of regulatory networks. However, the molecular details of how the transcriptional regulation of new gene copies evolves remain largely unexplored. Here we show how duplication of a transcription factor gene allowed the emergence of two independent regulatory circuits. Interestingly, the ancestral transcription factor was promiscuous and could bind different motifs in its target promoters. After duplication, one paralogue evolved increased binding specificity so that it only binds one type of motif, whereas the other copy evolved a decreased activity so that it only activates promoters that contain multiple binding sites. Interestingly, only a few mutations in both the DNA-binding domains and in the promoter binding sites were required to gradually disentangle the two networks. These results reveal how duplication of a promiscuous transcription factor followed by concerted cis and trans mutations allows expansion of a regulatory network.

Genomic evidence for adaptation by gene duplication

Gene duplication is widely believed to facilitate adaptation, but unambiguous evidence for this hypothesis has been found in only a small number of cases. Although gene duplication may increase the fitness of the involved organisms by doubling gene dosage or neofunctionalization, it may also result in a simple division of ancestral functions into daughter genes, which need not promote adaptation. Hence, the general validity of the adaptation by gene duplication hypothesis remains uncertain. Indeed, a genome-scale experiment found similar fitness effects of deleting pairs of duplicate genes and deleting individual singleton genes from the yeast genome, leading to the conclusion that duplication rarely results in adaptation. Here we contend that the above comparison is unfair because of a known duplication bias among genes with different fitness contributions. To rectify this problem, we compare homologous genes from the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. We discover that simultaneously deleting a duplicate gene pair in S. cerevisiae reduces fitness significantly more than deleting their singleton counterpart in S. pombe, revealing post-duplication adaptation. The duplicates-singleton difference in fitness effect is not attributable to a potential increase in gene dose after duplication, suggesting that the adaptation is owing to neofunctionalization, which we find to be explicable by acquisitions of binary protein-protein interactions rather than gene expression changes. These results provide genomic evidence for the role of gene duplication in organismal adaptation and are important for understanding the genetic mechanisms of evolutionary innovation.