Linear interaction between replication and transcription shapes DNA break dynamics at recurrent DNA break Clusters.

Recurrent DNA break clusters (RDCs) are replication-transcription collision hotspots; many are unique to neural progenitor cells. Through high-resolution replication sequencing and a capture-ligation assay in mouse neural progenitor cells experiencing replication stress, we unravel the replication features dictating RDC location and orientation. Most RDCs occur at the replication forks traversing timing transition regions (TTRs), where sparse replication origins connect unidirectional forks. Leftward-moving forks generate telomere-connected DNA double-strand breaks (DSBs), while rightward-moving forks lead to centromere-connected DSBs. Strand-specific mapping for DNA-bound RNA reveals co-transcriptional dual-strand DNA:RNA hybrids present at a higher density in RDC than in other actively transcribed long genes. In addition, mapping RNA polymerase activity uncovers that head-to-head interactions between replication and transcription machinery result in 60% DSB contribution to the head-on compared to 40% for co-directional. Taken together we reveal TTR as a fragile class and show how the linear interaction between transcription and replication impacts genome stability.

Linear Interaction Between Replication and Transcription Shapes DNA Break Dynamics at Recurrent DNA Break Clusters.

Recurrent DNA break clusters (RDCs) are replication-transcription collision hotspots; many are unique to neural progenitor cells. Through high-resolution replication sequencing and a capture-ligation assay in mouse neural progenitor cells experiencing replication stress, we unraveled the replication features dictating RDC location and orientation. Most RDCs occur at the replication forks traversing timing transition regions (TTRs), where sparse replication origins connect unidirectional forks. Leftward-moving forks generate telomere-connected DNA double-strand breaks (DSBs), while rightward-moving forks lead to centromere-connected DSBs. Strand-specific mapping for DNA-bound RNA revealed co-transcriptional dual-strand DNA:RNA hybrids present at a higher density in RDC than in other actively transcribed long genes. In addition, mapping RNA polymerase activity revealed that head-to-head interactions between replication and transcription machinery resulted in 60% DSB contribution to the head-on compared to 40% for co-directional. Our findings revealed TTR as a novel fragile class and highlighted how the linear interaction between transcription and replication impacts genome stability.

Predictive evolution of metabolic phenotypes using model-designed environments.

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

Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.

Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair's activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.

Bioaccumulation of therapeutic drugs by human gut bacteria.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

Polarization of microbial communities between competitive and cooperative metabolism.

Resource competition and metabolic cross-feeding are among the main drivers of microbial community assembly. Yet the degree to which these two conflicting forces are reflected in the composition of natural communities has not been systematically investigated. Here, we use genome-scale metabolic modelling to assess the potential for resource competition and metabolic cooperation in large co-occurring groups (up to 40 members) across thousands of habitats. Our analysis reveals two distinct community types, which are clustered at opposite ends of a spectrum in a trade-off between competition and cooperation. At one end are highly cooperative communities, characterized by smaller genomes and multiple auxotrophies. At the other end are highly competitive communities, which feature larger genomes and overlapping nutritional requirements, and harbour more genes related to antimicrobial activity. The latter are mainly present in soils, whereas the former are found in both free-living and host-associated habitats. Community-scale flux simulations show that, whereas competitive communities can better resist species invasion but not nutrient shift, cooperative communities are susceptible to species invasion but resilient to nutrient change. We also show, by analysing an additional data set, that colonization by probiotic species is positively associated with the presence of cooperative species in the recipient microbiome. Together, our results highlight the bifurcation between competitive and cooperative metabolism in the assembly of natural communities and its implications for community modulation.

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities.

Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies.

Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.

Yeast Creates a Niche for Symbiotic Lactic Acid Bacteria through Nitrogen Overflow.

Many microorganisms live in communities and depend on metabolites secreted by fellow community members for survival. Yet our knowledge of interspecies metabolic dependencies is limited to few communities with small number of exchanged metabolites, and even less is known about cellular regulation facilitating metabolic exchange. Here we show how yeast enables growth of lactic acid bacteria through endogenous, multi-component, cross-feeding in a readily established community. In nitrogen-rich environments, Saccharomyces cerevisiae adjusts its metabolism by secreting a pool of metabolites, especially amino acids, and thereby enables survival of Lactobacillus plantarum and Lactococcus lactis. Quantity of the available nitrogen sources and the status of nitrogen catabolite repression pathways jointly modulate this niche creation. We demonstrate how nitrogen overflow by yeast benefits L. plantarum in grape juice, and contributes to emergence of mutualism with L. lactis in a medium with lactose. Our results illustrate how metabolic decisions of an individual species can benefit others.

Yeast metabolic chassis designs for diverse biotechnological products.

The diversity of industrially important molecules for which microbial production routes have been experimentally demonstrated is rapidly increasing. The development of economically viable producer cells is, however, lagging behind, as it requires substantial engineering of the host metabolism. A chassis strain suitable for production of a range of molecules is therefore highly sought after but remains elusive. Here, we propose a genome-scale metabolic modeling approach to design chassis strains of Saccharomyces cerevisiae - a widely used microbial cell factory. For a group of 29 products covering a broad range of biochemistry and applications, we identified modular metabolic engineering strategies for re-routing carbon flux towards the desired product. We find distinct product families with shared targets forming the basis for the corresponding chassis cells. The design strategies include overexpression targets that group products by similarity in precursor and cofactor requirements, as well as gene deletion strategies for growth-product coupling that lead to non-intuitive product groups. Our results reveal the extent and the nature of flux re-routing necessary for producing a diverse range of products in a widely used cell factory and provide blueprints for constructing pre-optimized chassis strains.

Metabolic dependencies drive species co-occurrence in diverse microbial communities.

Microbial communities populate most environments on earth and play a critical role in ecology and human health. Their composition is thought to be largely shaped by interspecies competition for the available resources, but cooperative interactions, such as metabolite exchanges, have also been implicated in community assembly. The prevalence of metabolic interactions in microbial communities, however, has remained largely unknown. Here, we systematically survey, by using a genome-scale metabolic modeling approach, the extent of resource competition and metabolic exchanges in over 800 communities. We find that, despite marked resource competition at the level of whole assemblies, microbial communities harbor metabolically interdependent groups that recur across diverse habitats. By enumerating flux-balanced metabolic exchanges in these co-occurring subcommunities we also predict the likely exchanged metabolites, such as amino acids and sugars, that can promote group survival under nutritionally challenging conditions. Our results highlight metabolic dependencies as a major driver of species co-occurrence and hint at cooperative groups as recurring modules of microbial community architecture.

Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.

Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae.