Predicting C4 photosynthesis evolution: modular, individually adaptive steps on a Mount Fuji fitness landscape.

An ultimate goal of evolutionary biology is the prediction and experimental verification of adaptive trajectories on macroevolutionary timescales. This aim has rarely been achieved for complex biological systems, as models usually lack clear correlates of organismal fitness. Here, we simulate the fitness landscape connecting two carbon fixation systems: C3 photosynthesis, used by most plant species, and the C4 system, which is more efficient at ambient CO2 levels and elevated temperatures and which repeatedly evolved from C3. Despite extensive sign epistasis, C4 photosynthesis is evolutionarily accessible through individually adaptive steps from any intermediate state. Simulations show that biochemical subtraits evolve in modules; the order and constitution of modules confirm and extend previous hypotheses based on species comparisons. Plant-species-designated C3-C4 intermediates lie on predicted evolutionary trajectories, indicating that they indeed represent transitory states. Contrary to expectations, we find no slowdown of adaptation and no diminishing fitness gains along evolutionary trajectories.

SPOT: A machine learning model that predicts specific substrates for transport proteins.

Transport proteins play a crucial role in cellular metabolism and are central to many aspects of molecular biology and medicine. Determining the function of transport proteins experimentally is challenging, as they become unstable when isolated from cell membranes. Machine learning-based predictions could provide an efficient alternative. However, existing methods are limited to predicting a small number of specific substrates or broad transporter classes. These limitations stem partly from using small data sets for model training and a choice of input features that lack sufficient information about the prediction problem. Here, we present SPOT, the first general machine learning model that can successfully predict specific substrates for arbitrary transport proteins, achieving an accuracy above 92% on independent and diverse test data covering widely different transporters and a broad range of metabolites. SPOT uses Transformer Networks to represent transporters and substrates numerically. To overcome the problem of missing negative data for training, it augments a large data set of known transporter-substrate pairs with carefully sampled random molecules as non-substrates. SPOT not only predicts specific transporter-substrate pairs, but also outperforms previously published models designed to predict broad substrate classes for individual transport proteins. We provide a web server and Python function that allows users to explore the substrate scope of arbitrary transporters.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

Viridiplantae-specific GLXI and GLXII isoforms co-evolved and detoxify glucosone in planta.

Reactive carbonyl species (RCS) such as methylglyoxal (MGO) and glyoxal (GO) are highly reactive, unwanted side-products of cellular metabolism maintained at harmless intracellular levels by specific scavenging mechanisms.MGO and GO are metabolized through the glyoxalase (GLX) system, which consists of two enzymes acting in sequence, GLXI and GLXII. While plant genomes encode a number of different GLX isoforms, their specific functions and how they arose during evolution are unclear. Here, we used Arabidopsis (Arabidopsis thaliana) as a model species to investigate the evolutionary history of GLXI and GLXII in plants and whether the GLX system can protect plant cells from the toxicity of RCS other than MGO and GO. We show that plants possess two GLX systems of different evolutionary origins and with distinct structural and functional properties. The first system is shared by all eukaryotes, scavenges MGO and GO, especially during seedling establishment, and features Zn2+-type GLXI proteins with a metal cofactor preference that were present in the last eukaryotic common ancestor. GLXI and GLXII of the second system, featuring Ni2+-type GLXI, were acquired by the last common ancestor of Viridiplantae through horizontal gene transfer from proteobacteria and can together metabolize keto-D-glucose (KDG, glucosone), a glucose-derived RCS, to D-gluconate. When plants displaying loss-of-function of a Viridiplantae-specific GLXI were grown in KDG, D-gluconate levels were reduced to 10%-15% of those in the wild type, while KDG levels showed an increase of 48%-67%. In contrast to bacterial GLXI homologs, which are active as dimers, plant Ni2+-type GLXI proteins contain a domain duplication, are active as monomers, and have a modified second active site. The acquisition and neofunctionalization of a structurally, biochemically, and functionally distinct GLX system indicates that Viridiplantae are under strong selection to detoxify diverse RCS.

On the optimality of the enzyme-substrate relationship in bacteria.

Much recent progress has been made to understand the impact of proteome allocation on bacterial growth; much less is known about the relationship between the abundances of the enzymes and their substrates, which jointly determine metabolic fluxes. Here, we report a correlation between the concentrations of enzymes and their substrates in Escherichia coli. We suggest this relationship to be a consequence of optimal resource allocation, subject to an overall constraint on the biomass density: For a cellular reaction network composed of effectively irreversible reactions, maximal reaction flux is achieved when the dry mass allocated to each substrate is equal to the dry mass of the unsaturated (or "free") enzymes waiting to consume it. Calculations based on this optimality principle successfully predict the quantitative relationship between the observed enzyme and metabolite abundances, parameterized only by molecular masses and enzyme-substrate dissociation constants (Km). The corresponding organizing principle provides a fundamental rationale for cellular investment into different types of molecules, which may aid in the design of more efficient synthetic cellular systems.

Deep learning allows genome-scale prediction of Michaelis constants from structural features.

The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme-substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme-substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme's amino acid sequence. We provide genome-scale KM predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism.

Optimal density of bacterial cells.

A substantial fraction of the bacterial cytosol is occupied by catalysts and their substrates. While a higher volume density of catalysts and substrates might boost biochemical fluxes, the resulting molecular crowding can slow down diffusion, perturb the reactions' Gibbs free energies, and reduce the catalytic efficiency of proteins. Due to these tradeoffs, dry mass density likely possesses an optimum that facilitates maximal cellular growth and that is interdependent on the cytosolic molecule size distribution. Here, we analyze the balanced growth of a model cell, accounting systematically for crowding effects on reaction kinetics. Its optimal cytosolic volume occupancy depends on the nutrient-dependent resource allocation into large ribosomal vs. small metabolic macromolecules, reflecting a tradeoff between the saturation of metabolic enzymes, favoring larger occupancies with higher encounter rates, and the inhibition of the ribosomes, favoring lower occupancies with unhindered diffusion of tRNAs. Our predictions across growth rates are quantitatively consistent with the experimentally observed reduction in volume occupancy on rich media compared to minimal media in E. coli. Strong deviations from optimal cytosolic occupancy only lead to minute reductions in growth rate, which are nevertheless evolutionarily relevant due to large bacterial population sizes. In sum, cytosolic density variation in bacterial cells appears to be consistent with an optimality principle of cellular efficiency.

Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth.

The physiology of biological cells evolved under physical and chemical constraints, such as mass conservation across the network of biochemical reactions, nonlinear reaction kinetics, and limits on cell density. For unicellular organisms, the fitness that governs this evolution is mainly determined by the balanced cellular growth rate. We previously introduced growth balance analysis (GBA) as a general framework to model and analyze such nonlinear systems, revealing important analytical properties of optimal balanced growth states. It has been shown that at optimality, only a minimal subset of reactions can have nonzero flux. However, no general principles have been established to determine if a specific reaction is active at optimality. Here, we extend the GBA framework to study the optimality of each biochemical reaction, and we identify the mathematical conditions determining whether a reaction is active or not at optimal growth in a given environment. We reformulate the mathematical problem in terms of a minimal number of dimensionless variables and use the Karush-Kuhn-Tucker (KKT) conditions to identify fundamental principles of optimal resource allocation in GBA models of any size and complexity. Our approach helps to identify from first principles the economic values of biochemical reactions, expressed as marginal changes in cellular growth rate; these economic values can be related to the costs and benefits of proteome allocation into the reactions' catalysts. Our formulation also generalizes the concepts of Metabolic Control Analysis to models of growing cells. We show how the extended GBA framework unifies and extends previous approaches of cellular modeling and analysis, putting forward a program to analyze cellular growth through the stationarity conditions of a Lagrangian function. GBA thereby provides a general theoretical toolbox for the study of fundamental mathematical properties of balanced cellular growth.

An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes.

The distribution of cellular resources across bacterial proteins has been quantified through phenomenological growth laws. Here, we describe a complementary bacterial growth law for RNA composition, emerging from optimal cellular resource allocation into ribosomes and ternary complexes. The predicted decline of the tRNA/rRNA ratio with growth rate agrees quantitatively with experimental data. Its regulation appears to be implemented in part through chromosomal localization, as rRNA genes are typically closer to the origin of replication than tRNA genes and thus have increasingly higher gene dosage at faster growth. At the highest growth rates in E. coli, the tRNA/rRNA gene dosage ratio based on chromosomal positions is almost identical to the observed and theoretically optimal tRNA/rRNA expression ratio, indicating that the chromosomal arrangement has evolved to favor maximal transcription of both types of genes at this condition.

Growth-mediated negative feedback shapes quantitative antibiotic response.

Dose-response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose-response curves. The shape of the dose-response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose-response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose-response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose-response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.

Each of 3,323 metabolic innovations in the evolution of E. coli arose through the horizontal transfer of a single DNA segment.

Even closely related prokaryotes often show an astounding diversity in their ability to grow in different nutritional environments. It has been hypothesized that complex metabolic adaptations-those requiring the independent acquisition of multiple new genes-can evolve via selectively neutral intermediates. However, it is unclear whether this neutral exploration of phenotype space occurs in nature, or what fraction of metabolic adaptations is indeed complex. Here, we reconstruct metabolic models for the ancestors of a phylogeny of 53 Escherichia coli strains, linking genotypes to phenotypes on a genome-wide, macroevolutionary scale. Based on the ancestral and extant metabolic models, we identify 3,323 phenotypic innovations in the history of the E. coli clade that arose through changes in accessory genome content. Of these innovations, 1,998 allow growth in previously inaccessible environments, while 1,325 increase biomass yield. Strikingly, every observed innovation arose through the horizontal acquisition of a single DNA segment less than 30 kb long. Although we found no evidence for the contribution of selectively neutral processes, 10.6% of metabolic innovations were facilitated by horizontal gene transfers on earlier phylogenetic branches, consistent with a stepwise adaptation to successive environments. Ninety-eight percent of metabolic phenotypes accessible to the combined E. coli pangenome can be bestowed on any individual strain by transferring a single DNA segment from one of the extant strains. These results demonstrate an amazing ability of the E. coli lineage to adapt to novel environments through single horizontal gene transfers (followed by regulatory adaptations), an ability likely mirrored in other clades of generalist bacteria.

Network reduction methods for genome-scale metabolic models.

Genome-scale metabolic models (GSMs) provide a comprehensive representation of cellular metabolism. GSMs provide a mechanistic link between cellular genotypes and metabolic phenotypes, and are thus widely used to analyze metabolism at the systems level. GSMs consist of hundreds or thousands of reactions. They have thus largely been analyzed with computationally efficient constraint-based methods such as flux-balance analysis, limiting their scope and phenotype prediction accuracy. Computationally more demanding but potentially more informative methods, such as kinetic and dynamic modeling, are currently limited to small or medium-sized models. Thus, it is desirable to achieve unbiased stoichiometric reductions of large-scale metabolic models to small, coarse-grained model representations that capture significant metabolic modules. Here, we review published automated and semiautomated methods used for large-scale metabolic model reduction. The top-down methods discussed provide minimal networks that retain a set of user-protected phenotypes, but may reduce the model's metabolic and phenotypic versatility. In contrast, the two bottom-up approaches reviewed retain a more unbiased set of phenotypes; at the same time, these methods require the partitioning of the GSM into metabolic subsystems by the user, and make strong assumptions on the subsystems' connections and their states, respectively.

Evolview v3: a webserver for visualization, annotation, and management of phylogenetic trees.

Evolview is an interactive tree visualization tool designed to help researchers in visualizing phylogenetic trees and in annotating these with additional information. It offers the user with a platform to upload trees in most common tree formats, such as Newick/Phylip, Nexus, Nhx and PhyloXML, and provides a range of visualization options, using fifteen types of custom annotation datasets. The new version of Evolview was designed to provide simple tree uploads, manipulation and viewing options with additional annotation types. The 'dataset system' used for visualizing tree information has evolved substantially from the previous version, and the user can draw on a wide range of additional example visualizations. Developments since the last public release include a complete redesign of the user interface, new annotation dataset types, additional tree visualization styles, full-text search of the documentation, and some backend updates. The project management aspect of Evolview was also updated, with a unified approach to tree and project management and sharing. Evolview is freely available at: https://www.evolgenius.info/evolview/.

MVP: a microbe-phage interaction database.

Phages invade microbes, accomplish host lysis and are of vital importance in shaping the community structure of environmental microbiota. More importantly, most phages have very specific hosts; they are thus ideal tools to manipulate environmental microbiota at species-resolution. The main purpose of MVP (Microbe Versus Phage) is to provide a comprehensive catalog of phage-microbe interactions and assist users to select phage(s) that can target (and potentially to manipulate) specific microbes of interest. We first collected 50 782 viral sequences from various sources and clustered them into 33 097 unique viral clusters based on sequence similarity. We then identified 26 572 interactions between 18 608 viral clusters and 9245 prokaryotes (i.e. bacteria and archaea); we established these interactions based on 30 321 evidence entries that we collected from published datasets, public databases and re-analysis of genomic and metagenomic sequences. Based on these interactions, we calculated the host range for each of the phage clusters and accordingly grouped them into subgroups such as 'species-', 'genus-' and 'family-' specific phage clusters. MVP is equipped with a modern, responsive and intuitive interface, and is freely available at: http://mvp.medgenius.info.

BlockFeST: Bayesian calculation of region-specific FST to detect local adaptation.

Summary: The fixation index FST can be used to identify non-neutrally evolving loci from genome-scale SNP data across two or more populations. Recent years have seen the development of sophisticated approaches to estimate FST based on Markov-Chain Monte-Carlo simulations. Here, we present a vectorized R implementation of an extension of the widely used BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing SNPs that evolved under directional selection. Availability and implementation: The R implementation of our method, which builds on the powerful population genetics and genomics software PopGenome, is available freely from CRAN. Supplementary information: Supplementary data are available at Bioinformatics online.

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.

Evolview v2: an online visualization and management tool for customized and annotated phylogenetic trees.

Evolview is an online visualization and management tool for customized and annotated phylogenetic trees. It allows users to visualize phylogenetic trees in various formats, customize the trees through built-in functions and user-supplied datasets and export the customization results to publication-ready figures. Its 'dataset system' contains not only the data to be visualized on the tree, but also 'modifiers' that control various aspects of the graphical annotation. Evolview is a single-page application (like Gmail); its carefully designed interface allows users to upload, visualize, manipulate and manage trees and datasets all in a single webpage. Developments since the last public release include a modern dataset editor with keyword highlighting functionality, seven newly added types of annotation datasets, collaboration support that allows users to share their trees and datasets and various improvements of the web interface and performance. In addition, we included eleven new 'Demo' trees to demonstrate the basic functionalities of Evolview, and five new 'Showcase' trees inspired by publications to showcase the power of Evolview in producing publication-ready figures. Evolview is freely available at: http://www.evolgenius.info/evolview/.

The ancestors of diatoms evolved a unique mitochondrial dehydrogenase to oxidize photorespiratory glycolate.

Like other oxygenic photosynthetic organisms, diatoms produce glycolate, a toxic intermediate, as a consequence of the oxygenase activity of Rubisco. Diatoms can remove glycolate through excretion and through oxidation as part of the photorespiratory pathway. The diatom Phaeodactylum tricornutum encodes two proteins suggested to be involved in glycolate metabolism: PtGO1 and PtGO2. We found that these proteins differ substantially from the sequences of experimentally characterized proteins responsible for glycolate oxidation in other species, glycolate oxidase (GOX) and glycolate dehydrogenase. We show that PtGO1 and PtGO2 are the only sequences of P. tricornutum homologous to GOX. Our phylogenetic analyses indicate that the ancestors of diatoms acquired PtGO1 during the proposed first secondary endosymbiosis with a chlorophyte alga, which may have previously obtained this gene from proteobacteria. In contrast, PtGO2 is orthologous to an uncharacterized protein in Galdieria sulphuraria, consistent with its acquisition during the secondary endosymbiosis with a red alga that gave rise to the current plastid. The analysis of amino acid residues at conserved positions suggests that PtGO2, which localizes to peroxisomes, may use substrates other than glycolate, explaining the lack of GOX activity we observe in vitro. Instead, PtGO1, while only very distantly related to previously characterized GOX proteins, evolved glycolate-oxidizing activity, as demonstrated by in gel activity assays and mass spectrometry analysis. PtGO1 localizes to mitochondria, consistent with previous suggestions that photorespiration in diatoms proceeds in these organelles. We conclude that the ancestors of diatoms evolved a unique alternative to oxidize photorespiratory glycolate: a mitochondrial dehydrogenase homologous to GOX able to use electron acceptors other than O2.

Combining genetic and evolutionary engineering to establish C4 metabolism in C3 plants.

To feed a world population projected to reach 9 billion people by 2050, the productivity of major crops must be increased by at least 50%. One potential route to boost the productivity of cereals is to equip them genetically with the 'supercharged' C4 type of photosynthesis; however, the necessary genetic modifications are not sufficiently understood for the corresponding genetic engineering programme. In this opinion paper, we discuss a strategy to solve this problem by developing a new paradigm for plant breeding. We propose combining the bioengineering of well-understood traits with subsequent evolutionary engineering, i.e. mutagenesis and artificial selection. An existing mathematical model of C3-C4 evolution is used to choose the most promising path towards this goal. Based on biomathematical simulations, we engineer Arabidopsis thaliana plants that express the central carbon-fixing enzyme Rubisco only in bundle sheath cells (Ru-BSC plants), the localization characteristic for C4 plants. This modification will initially be deleterious, forcing the Ru-BSC plants into a fitness valley from where previously inaccessible adaptive steps towards C4 photosynthesis become accessible through fitness-enhancing mutations. Mutagenized Ru-BSC plants are then screened for improved photosynthesis, and are expected to respond to imposed artificial selection pressures by evolving towards C4 anatomy and biochemistry.

Freeze-quenched maize mesophyll and bundle sheath separation uncovers bias in previous tissue-specific RNA-Seq data.

The high efficiency of C4 photosynthesis relies on spatial division of labor, classically with initial carbon fixation in the mesophyll and carbon reduction in the bundle sheath. By employing grinding and serial filtration over liquid nitrogen, we enriched C4 tissues along a developing leaf gradient. This method treats both C4 tissues in an integrity-preserving and consistent manner, while allowing complementary measurements of metabolite abundance and enzyme activity, thus providing a comprehensive data set. Meta-analysis of this and the previous studies highlights the strengths and weaknesses of different C4 tissue separation techniques. While the method reported here achieves the least enrichment, it is the only one that shows neither strong 3' (degradation) bias, nor different severity of 3' bias between samples. The meta-analysis highlighted previously unappreciated observations, such as an accumulation of evidence that aspartate aminotransferase is more mesophyll specific than expected from the current NADP-ME C4 cycle model, and a shift in enrichment of protein synthesis genes from bundle sheath to mesophyll during development. The full comparative dataset is available for download, and a web visualization tool (available at http://www.plant-biochemistry.hhu.de/resources.html) facilitates comparison of the the Z. mays bundle sheath and mesophyll studies, their consistencies and their conflicts.