The metabolic domestication syndrome of budding yeast.

Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.

Principles of metabolome conservation in animals.

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

Proteome-wide landscape of solubility limits in a bacterial cell.

Proteins are prone to aggregate when expressed above their solubility limits. Aggregation may occur rapidly, potentially as early as proteins emerge from the ribosome, or slowly, following synthesis. However, in vivo data on aggregation rates are scarce. Here, we classified the Escherichia coli proteome into rapidly and slowly aggregating proteins using an in vivo image-based screen coupled with machine learning. We find that the majority (70%) of cytosolic proteins that become insoluble upon overexpression have relatively low rates of aggregation and are unlikely to aggregate co-translationally. Remarkably, such proteins exhibit higher folding rates compared to rapidly aggregating proteins, potentially implying that they aggregate after reaching their folded states. Furthermore, we find that a substantial fraction (~ 35%) of the proteome remain soluble at concentrations much higher than those found naturally, indicating a large margin of safety to tolerate gene expression changes. We show that high disorder content and low surface stickiness are major determinants of high solubility and are favored in abundant bacterial proteins. Overall, our study provides a global view of aggregation rates and hence solubility limits of proteins in a bacterial cell.

Underground metabolism as a rich reservoir for pathway engineering.

MOTIVATION: Bioproduction of value-added compounds is frequently achieved by utilizing enzymes from other species. However, expression of such heterologous enzymes can be detrimental due to unexpected interactions within the host cell. Recently, an alternative strategy emerged, which relies on recruiting side activities of host enzymes to establish new biosynthetic pathways. Although such low-level 'underground' enzyme activities are prevalent, it remains poorly explored whether they may serve as an important reservoir for pathway engineering. RESULTS: Here, we use genome-scale modeling to estimate the theoretical potential of underground reactions for engineering novel biosynthetic pathways in Escherichia coli. We found that biochemical reactions contributed by underground enzyme activities often enhance the in silico production of compounds with industrial importance, including several cases where underground activities are indispensable for production. Most of these new capabilities can be achieved by the addition of one or two underground reactions to the native network, suggesting that only a few side activities need to be enhanced during implementation. Remarkably, we find that the contribution of underground reactions to the production of value-added compounds is comparable to that of heterologous reactions, underscoring their biotechnological potential. Taken together, our genome-wide study demonstrates that exploiting underground enzyme activities could be a promising addition to the toolbox of industrial strain development. AVAILABILITY AND IMPLEMENTATION: The data and scripts underlying this article are available on GitHub at https://github.com/pappb/Kovacs-et-al-Underground-metabolism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rapid decline of bacterial drug-resistance in an antibiotic-free environment through phenotypic reversion.

Antibiotic resistance typically induces a fitness cost that shapes the fate of antibiotic-resistant bacterial populations. However, the cost of resistance can be mitigated by compensatory mutations elsewhere in the genome, and therefore the loss of resistance may proceed too slowly to be of practical importance. We present our study on the efficacy and phenotypic impact of compensatory evolution in Escherichia coli strains carrying multiple resistance mutations. We have demonstrated that drug-resistance frequently declines within 480 generations during exposure to an antibiotic-free environment. The extent of resistance loss was found to be generally antibiotic-specific, driven by mutations that reduce both resistance level and fitness costs of antibiotic-resistance mutations. We conclude that phenotypic reversion to the antibiotic-sensitive state can be mediated by the acquisition of additional mutations, while maintaining the original resistance mutations. Our study indicates that restricting antimicrobial usage could be a useful policy, but for certain antibiotics only.

High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds.

Rapidly spreading antibiotic resistance and the low discovery rate of new antimicrobial compounds demand more effective strategies for early drug discovery. One bottleneck in the drug discovery pipeline is the identification of the modes of action (MoAs) of new compounds. We have developed a rapid systematic metabolome profiling strategy to classify the MoAs of bioactive compounds. The method predicted MoA-specific metabolic responses in the nonpathogenic bacterium Mycobacterium smegmatis after treatment with 62 reference compounds with known MoAs and different metabolic and nonmetabolic targets. We then analyzed a library of 212 new antimycobacterial compounds with unknown MoAs from a drug discovery effort by the pharmaceutical company GlaxoSmithKline (GSK). More than 70% of these new compounds induced metabolic responses in M. smegmatis indicative of known MoAs, seven of which were experimentally validated. Only 8% (16) of the compounds appeared to target unconventional cellular processes, illustrating the difficulty in discovering new antibiotics with different MoAs among compounds used as monotherapies. For six of the GSK compounds with potentially new MoAs, the metabolome profiles suggested their ability to interfere with trehalose and lipid metabolism. This was supported by whole-genome sequencing of spontaneous drug-resistant mutants of the pathogen Mycobacterium tuberculosis and in vitro compound-proteome interaction analysis for one of these compounds. Our compendium of drug-metabolome profiles can be used to rapidly query the MoAs of uncharacterized antimicrobial compounds and should be a useful resource for the drug discovery community.

Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal.

Energy metabolism is central to cellular biology. Thus, genome-scale models of heterotrophic unicellular species must account appropriately for the utilization of external nutrients to synthesize energy metabolites such as ATP. However, metabolic models designed for flux-balance analysis (FBA) may contain thermodynamically impossible energy-generating cycles: without nutrient consumption, these models are still capable of charging energy metabolites (such as ADP→ATP or NADP+→NADPH). Here, we show that energy-generating cycles occur in over 85% of metabolic models without extensive manual curation, such as those contained in the ModelSEED and MetaNetX databases; in contrast, such cycles are rare in the manually curated models of the BiGG database. Energy generating cycles may represent model errors, e.g., erroneous assumptions on reaction reversibilities. Alternatively, part of the cycle may be thermodynamically feasible in one environment, while the remainder is thermodynamically feasible in another environment; as standard FBA does not account for thermodynamics, combining these into an FBA model allows erroneous energy generation. The presence of energy-generating cycles typically inflates maximal biomass production rates by 25%, and may lead to biases in evolutionary simulations. We present efficient computational methods (i) to identify energy generating cycles, using FBA, and (ii) to identify minimal sets of model changes that eliminate them, using a variant of the GlobalFit algorithm.

Adaptive evolution of complex innovations through stepwise metabolic niche expansion.

A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes.

Indispensability of Horizontally Transferred Genes and Its Impact on Bacterial Genome Streamlining.

Why are certain bacterial genomes so small and compact? The adaptive genome streamlining hypothesis posits that selection acts to reduce genome size because of the metabolic burden of replicating DNA. To reveal the impact of genome streamlining on cellular traits, we reduced the Escherichia coli genome by up to 20% by deleting regions which have been repeatedly subjects of horizontal transfer in nature. Unexpectedly, horizontally transferred genes not only confer utilization of specific nutrients and elevate tolerance to stresses, but also allow efficient usage of resources to build new cells, and hence influence fitness in routine and stressful environments alike. Genome reduction affected fitness not only by gene loss, but also by induction of a general stress response. Finally, we failed to find evidence that the advantage of smaller genomes would be due to a reduced metabolic burden of replicating DNA or a link with smaller cell size. We conclude that as the potential energetic benefit gained by deletion of short genomic segments is vanishingly small compared with the deleterious side effects of these deletions, selection for reduced DNA synthesis costs is unlikely to shape the evolution of small genomes.

Network-level architecture and the evolutionary potential of underground metabolism.

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network.

Understanding how evolution of antimicrobial resistance increases resistance to other drugs is a challenge of profound importance. By combining experimental evolution and genome sequencing of 63 laboratory-evolved lines, we charted a map of cross-resistance interactions between antibiotics in Escherichia coli, and explored the driving evolutionary principles. Here, we show that (1) convergent molecular evolution is prevalent across antibiotic treatments, (2) resistance conferring mutations simultaneously enhance sensitivity to many other drugs and (3) 27% of the accumulated mutations generate proteins with compromised activities, suggesting that antibiotic adaptation can partly be achieved without gain of novel function. By using knowledge on antibiotic properties, we examined the determinants of cross-resistance and identified chemogenomic profile similarity between antibiotics as the strongest predictor. In contrast, cross-resistance between two antibiotics is independent of whether they show synergistic effects in combination. These results have important implications on the development of novel antimicrobial strategies.

Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection.

BACKGROUND: Genome-wide sensitivity screens in yeast have been immensely popular following the construction of a collection of deletion mutants of non-essential genes. However, the auxotrophic markers in this collection preclude experiments on minimal growth medium, one of the most informative metabolic environments. Here we present quantitative growth analysis for mutants in all 4,772 non-essential genes from our prototrophic deletion collection across a large set of metabolic conditions. RESULTS: The complete collection was grown in environments consisting of one of four possible carbon sources paired with one of seven nitrogen sources, for a total of 28 different well-defined metabolic environments. The relative contributions to mutants' fitness of each carbon and nitrogen source were determined using multivariate statistical methods. The mutant profiling recovered known and novel genes specific to the processing of nutrients and accurately predicted functional relationships, especially for metabolic functions. A benchmark of genome-scale metabolic network modeling is also given to demonstrate the level of agreement between current in silico predictions and hitherto unavailable experimental data. CONCLUSIONS: These data address a fundamental deficiency in our understanding of the model eukaryote Saccharomyces cerevisiae and its response to the most basic of environments. While choice of carbon source has the greatest impact on cell growth, specific effects due to nitrogen source and interactions between the nutrients are frequent. We demonstrate utility in characterizing genes of unknown function and illustrate how these data can be integrated with other whole-genome screens to interpret similarities between seemingly diverse perturbation types.

Bacterial evolution of antibiotic hypersensitivity.

The evolution of resistance to a single antibiotic is frequently accompanied by increased resistance to multiple other antimicrobial agents. In sharp contrast, very little is known about the frequency and mechanisms underlying collateral sensitivity. In this case, genetic adaptation under antibiotic stress yields enhanced sensitivity to other antibiotics. Using large-scale laboratory evolutionary experiments with Escherichia coli, we demonstrate that collateral sensitivity occurs frequently during the evolution of antibiotic resistance. Specifically, populations adapted to aminoglycosides have an especially low fitness in the presence of several other antibiotics. Whole-genome sequencing of laboratory-evolved strains revealed multiple mechanisms underlying aminoglycoside resistance, including a reduction in the proton-motive force (PMF) across the inner membrane. We propose that as a side effect, these mutations diminish the activity of PMF-dependent major efflux pumps (including the AcrAB transporter), leading to hypersensitivity to several other antibiotics. More generally, our work offers an insight into the mechanisms that drive the evolution of negative trade-offs under antibiotic selection.

ModuLand plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality.

UNLABELLED: The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux or Mac OS. We demonstrate its use on protein structure and metabolic networks. AVAILABILITY: The plug-in and its user guide can be downloaded freely from: http://www.linkgroup.hu/modules.php.

Marked genetic differences between BRAF and NRAS mutated primary melanomas as revealed by array comparative genomic hybridization.

Somatic mutations of BRAF and NRAS oncogenes are thought to be among the first steps in melanoma initiation, but these mutations alone are insufficient to cause tumor progression. Our group studied the distinct genomic imbalances of primary melanomas harboring different BRAF or NRAS genotypes. We also aimed to highlight regions of change commonly seen together in different melanoma subgroups. Array comparative genomic hybridization was performed to assess copy number changes in 47 primary melanomas. BRAF and NRAS were screened for mutations by melting curve analysis. Reverse transcription PCR and fluorescence in-situ hybridization were performed to confirm the array comparative genomic hybridization results. Pairwise comparisons revealed distinct genomic profiles between melanomas harboring different mutations. Primary melanomas with the BRAF mutation exhibited more frequent losses on 10q23-q26 and gains on chromosome 7 and 1q23-q25 compared with melanomas with the NRAS mutation. Loss on the 11q23-q25 sequence was found mainly in conjunction with the NRAS mutation. Primary melanomas without the BRAF or the NRAS mutation showed frequent alterations in chromosomes 17 and 4. Correlation analysis revealed chromosomal alterations that coexist more often in these tumor subgroups. To find classifiers for BRAF mutation, random forest analysis was used. Fifteen candidates emerged with 87% prediction accuracy. Signaling interactions between the EGF/MAPK-JAK pathways were observed to be extensively altered in melanomas with the BRAF mutation. We found marked differences in the genetic pattern of the BRAF and NRAS mutated melanoma subgroups that might suggest that these mutations contribute to malignant melanoma in conjunction with distinct cooperating oncogenic events.

The evolution of defense mechanisms correlate with the explosive diversification of autodigesting Coprinellus mushrooms (Agaricales, Fungi).

Bursts of diversification are known to have contributed significantly to the extant morphological and species diversity, but evidence for many of the theoretical predictions about adaptive radiations have remained contentious. Despite their tremendous diversity, patterns of evolutionary diversification and the contribution of explosive episodes in fungi are largely unknown. Here, using the genus Coprinellus (Psathyrellaceae, Agaricales) as a model, we report the first explosive fungal radiation and infer that the onset of the radiation correlates with a change from a multilayered to a much simpler defense structure on the fruiting bodies. We hypothesize that this change constitutes a key innovation, probably relaxing constraints on diversification imposed by nutritional investment into the development of protective tissues of fruiting bodies. Fossil calibration suggests that Coprinellus mushrooms radiated during the Miocene coinciding with global radiation of large grazing mammals following expansion of dry open grasslands. In addition to diversification rate-based methods, we test the hard polytomy hypothesis, by analyzing the resolvability of internal nodes of the backbone of the putative radiation using Reversible-Jump MCMC. We discuss potential applications and pitfalls of this approach as well as how biologically meaningful polytomies can be distinguished from alignment shortcomings. Our data provide insights into the nature of adaptive radiations in general by revealing a deceleration of morphological diversification through time. The dynamics of morphological diversification was approximated by obtaining the temporal distribution of state changes in discrete traits along the trees and comparing it with the tempo of lineage accumulation. We found that the number of state changes correlate with the number of lineages, even in parts of the tree with short internal branches, and peaks around the onset of the explosive radiation followed by a slowdown, most likely because of the decrease in available niches.

Use of genome-scale metabolic models in evolutionary systems biology.

One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology.

An integrated approach to characterize genetic interaction networks in yeast metabolism.

Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ∼185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

CoNSEnsX: an ensemble view of protein structures and NMR-derived experimental data.

BACKGROUND: In conjunction with the recognition of the functional role of internal dynamics of proteins at various timescales, there is an emerging use of dynamic structural ensembles instead of individual conformers. These ensembles are usually substantially more diverse than conventional NMR ensembles and eliminate the expectation that a single conformer should fulfill all NMR parameters originating from 10(16) - 10(17) molecules in the sample tube. Thus, the accuracy of dynamic conformational ensembles should be evaluated differently to that of single conformers. RESULTS: We constructed the web application CoNSEnsX (Consistency of NMR-derived Structural Ensembles with eXperimental data) allowing fast, simple and convenient assessment of the correspondence of the ensemble as a whole with diverse independent NMR parameters available. We have chosen different ensembles of three proteins, human ubiquitin, a small protease inhibitor and a disordered subunit of cGMP phosphodiesterase 5/6 for detailed evaluation and demonstration of the capabilities of the CoNSEnsX approach. CONCLUSIONS: Our results present a new conceptual method for the evaluation of dynamic conformational ensembles resulting from NMR structure determination. The designed CoNSEnsX approach gives a complete evaluation of these ensembles and is freely available as a web service at http://consensx.chem.elte.hu.

Folded-unfolded cross-predictions and protein evolution: the case study of coiled-coils.

Here we report a thorough analysis of cross-predictions between coiled-coil and disordered protein segments using various prediction algorithms for both sequence classes. Coiled-coils are often predicted to be unstructured, consistent with their obligate multimeric nature, whereas reverse cross-predictions are rare due to the regularity of coiled-coil sequences. We propose the simultaneous use of the programs Coils and IUPred to achieve acceptable prediction accuracy and minimize the extent of cross-predictions. The relevance of observed cross-predictions might be that disordered sequences can adopt coiled-coil conformation relatively easily during protein evolution.