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eQuilibrator (equilibrator.weizmann.ac.il) is a database of biochemical equilibrium constants and Gibbs free energies, originally designed as a web-based interface. While the website now counts around 1,000 distinct monthly users, its design could not accommodate larger compound databases and it lacked a scalable Application Programming Interface (API) for integration into other tools developed by the systems biology community. Here, we report on the recent updates to the database as well as the addition of a new Python-based interface to eQuilibrator that adds many new features such as a 100-fold larger compound database, the ability to add novel compounds, improvements in speed and memory use, and correction for Mg2+ ion concentrations. Moreover, the new interface can compute the covariance matrix of the uncertainty between estimates, for which we show the advantages and describe the application in metabolic modelling. We foresee that these improvements will make thermodynamic modelling more accessible and facilitate the integration of eQuilibrator into other software platforms., Nucleic Acids Research, 50 (D1), ISSN:1362-4962, ISSN:0301-5610

Viral genome sequencing has guided our understanding of the spread and extent of genetic diversity of SARS-CoV-2 during the COVID-19 pandemic. SARS-CoV-2 viral genomes are usually sequenced from nasopharyngeal swabs of individual patients to track viral spread., Viral genome sequencing has guided our understanding of the spread and extent of genetic diversity of SARS-CoV-2 during the COVID-19 pandemic. SARS-CoV-2 viral genomes are usually sequenced from nasopharyngeal swabs of individual patients to track viral spread. Recently, RT-qPCR of municipal wastewater has been used to quantify the abundance of SARS-CoV-2 in several regions globally. However, metatranscriptomic sequencing of wastewater can be used to profile the viral genetic diversity across infected communities. Here, we sequenced RNA directly from sewage collected by municipal utility districts in the San Francisco Bay Area to generate complete and nearly complete SARS-CoV-2 genomes. The major consensus SARS-CoV-2 genotypes detected in the sewage were identical to clinical genomes from the region. Using a pipeline for single nucleotide variant calling in a metagenomic context, we characterized minor SARS-CoV-2 alleles in the wastewater and detected viral genotypes which were also found within clinical genomes throughout California. Observed wastewater variants were more similar to local California patient-derived genotypes than they were to those from other regions within the United States or globally. Additional variants detected in wastewater have only been identified in genomes from patients sampled outside California, indicating that wastewater sequencing can provide evidence for recent introductions of viral lineages before they are detected by local clinical sequencing. These results demonstrate that epidemiological surveillance through wastewater sequencing can aid in tracking exact viral strains in an epidemic context.

Bacterial autotrophs often rely on CO2 concentrating mechanisms (CCMs) to assimilate carbon. Although many CCM proteins have been identified, a systematic screen of the components of CCMs is lacking. Here, we performed a genome-wide barcoded transposon screen to identify essential and CCM-related genes in the γ-proteobacterium Halothiobacillus neapolitanus. Screening revealed that the CCM comprises at least 17 and probably no more than 25 genes, most of which are encoded in 3 operons. Two of these operons (DAB1 and DAB2) contain a two-gene locus that encodes a domain of unknown function (Pfam: PF10070) and a putative cation transporter (Pfam: PF00361). Physiological and biochemical assays demonstrated that these proteins-which we name DabA and DabB, for DABs accumulate bicarbonate-assemble into a heterodimeric complex, which contains a putative β-carbonic anhydrase-like active site and functions as an energy-coupled inorganic carbon (Ci) pump. Interestingly, DAB operons are found in a diverse range of bacteria and archaea. We demonstrate that functional DABs are present in the human pathogens Bacillus anthracis and Vibrio cholerae. On the basis of these results, we propose that DABs constitute a class of energized Ci pumps and play a critical role in the metabolism of Ci throughout prokaryotic phyla.

Rubisco is the primary carboxylase of the Calvin cycle, the most abundant enzyme in the biosphere, and one of the best-characterized enzymes. On the basis of correlations between Rubisco kinetic parameters, it is widely posited that constraints embedded in the catalytic mechanism enforce trade-offs between CO2 specificity, SC/O, and maximum carboxylation rate, kcat,C. However, the reasoning that established this view was based on data from ≈20 organisms. Here, we re-examine models of trade-offs in Rubisco catalysis using a data set from ≈300 organisms. Correlations between kinetic parameters are substantially attenuated in this larger data set, with the inverse relationship between kcat,C and SC/O being a key example. Nonetheless, measured kinetic parameters display extremely limited variation, consistent with a view of Rubisco as a highly constrained enzyme. More than 95% of kcat,C values are between 1 and 10 s-1, and no measured kcat,C exceeds 15 s-1. Similarly, SC/O varies by only 30% among Form I Rubiscos and

Many bacterial autotrophs rely on CO2concentrating mechanisms (CCMs) to assimilate carbon. Although many CCM proteins have been identified, including a 200+ MDa protein organelle called the carboxysome, a systematic screen of CCM components has not been carried out. Here, we performed a genome-wide barcoded transposon screen to identify essential and CCM-related genes in the ɣ-proteobacteriumH. neapolitanus. Our screen revealed an operon critical for CCM function which encodes a domain of unknown function (PFAM:PF10070) and putative cation transporter subunit (PFAM:PF00361). These two proteins, which we name DabA and DabB for “DABs accumulate bicarbonate,” function as a heterodimeric, energy-coupled inorganic carbon pump inE. coli. Furthermore, DabA binds zinc and has a an active site homologous to a β-carbonic anhydrase. Based on these results, we propose that DABs function as vectorial CAs coupled to cation gradients and serve as inorganic carbon pumps throughout prokaryotic phyla.

Electrosynthesis is a promising approach that enables the biological production of commodities, like fuels and fine chemicals, using renewably produced electricity. Several techniques have been proposed to mediate the transfer of electrons from the cathode to living cells. Of these, the electroproduction of formate as a mediator seems especially promising: formate is readily soluble, of low toxicity and can be produced at relatively high efficiency and at reasonable current density. While organisms that are capable of formatotrophic growth, i.e. growth on formate, exist naturally, they are generally less suitable for bulk cultivation and industrial needs. Hence, it may be helpful to engineer a model organism of industrial relevance, such as E. coli, for growth on formate. There are numerous metabolic pathways that can potentially support formatotrophic growth. Here we analyze these diverse pathways according to various criteria including biomass yield, thermodynamic favorability, chemical motive force, kinetics and the practical challenges posed by their expression. We find that the reductive glycine pathway, composed of the tetrahydrofolate system, the glycine cleavage system, serine hydroxymethyltransferase and serine deaminase, is a promising candidate to support electrosynthesis in E. coli. The approach presented here exemplifies how combining different computational approaches into a systematic analysis methodology provides assistance in redesigning metabolism. This article is part of a Special Issue entitled: Metals in Bioenergetics and Biomimetics Systems.

Thermodynamics impose a major constraint on the structure of metabolic pathways. Here, we use carbon fixation pathways to demonstrate how thermodynamics shape the structure of pathways and determine the cellular resources they consume. We analyze the energetic profile of prototypical reactions and show that each reaction type displays a characteristic change in Gibbs energy. Specifically, although carbon fixation pathways display a considerable structural variability, they are all energetically constrained by two types of reactions: carboxylation and carboxyl reduction. In fact, all adenosine triphosphate (ATP) molecules consumed by carbon fixation pathways – with a single exception – are used, directly or indirectly, to power one of these unfavorable reactions. When an indirect coupling is employed, the energy released by ATP hydrolysis is used to establish another chemical bond with high energy of hydrolysis, e.g. a thioester. This bond is cleaved by a downstream enzyme to energize an unfavorable reaction. Notably, many pathways exhibit reduced ATP requirement as they couple unfavorable carboxylation or carboxyl reduction reactions to exergonic reactions other than ATP hydrolysis. In the most extreme example, the reductive acetyl coenzyme A (acetyl-CoA) pathway bypasses almost all ATP-consuming reactions. On the other hand, the reductive pentose phosphate pathway appears to be the least ATP-efficient because it is the only carbon fixation pathway that invests ATP in metabolic aims other than carboxylation and carboxyl reduction. Altogether, our analysis indicates that basic thermodynamic considerations accurately predict the resource investment required to support a metabolic pathway and further identifies biochemical mechanisms that can decrease this requirement.

Motivation: The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions. Results: Both methods account for changes in cellular conditions (pH, ionic strength, etc.) by using linear regression over the ΔG○ of pseudoisomers and reactions. The Pseudoisomeric Reactant Contribution method systematically infers compound formation energies using measured K′ and pKa data. The Pseudoisomeric Group Contribution method extends the group contribution method and achieves a high coverage of unmeasured reactions. We define a continuous index that predicts the reversibility of a reaction under a given physiological concentration range. In the characteristic physiological range 3μM–3mM, we find that roughly half of the reactions in Escherichia coli's metabolism are reversible. These new tools can increase the accuracy of thermodynamic-based models, especially in non-standard pH and ionic strengths. The reversibility index can help modelers decide which reactions are reversible in physiological conditions. Availability: Freely available on the web at: http://equilibrator.weizmann.ac.il. Website implemented in Python, MySQL, Apache and Django, with all major browsers supported. The framework is open-source (code.google.com/p/milo-lab), implemented in pure Python and tested mainly on Linux. Contact: ron.milo@weizmann.ac.il Supplementary Information: Supplementary data are available at Bioinformatics online.

The laws of thermodynamics constrain the action of biochemical systems. However, thermodynamic data on biochemical compounds can be difficult to find and is cumbersome to perform calculations with manually. Even simple thermodynamic questions like 'how much Gibbs energy is released by ATP hydrolysis at pH 5?' are complicated excessively by the search for accurate data. To address this problem, eQuilibrator couples a comprehensive and accurate database of thermodynamic properties of biochemical compounds and reactions with a simple and powerful online search and calculation interface. The web interface to eQuilibrator (http://equilibrator.weizmann.ac.il) enables easy calculation of Gibbs energies of compounds and reactions given arbitrary pH, ionic strength and metabolite concentrations. The eQuilibrator code is open-source and all thermodynamic source data are freely downloadable in standard formats. Here we describe the database characteristics and implementation and demonstrate its use.

Michaelis and Menten’s mechanism for enzymatic catalysis is remarkable both in its simplicity and its wide applicability. The extension for reversible processes, as done by Haldane, makes it even more relevant as most enzymes catalyze reactions that are reversible in nature and carry in vivo flux in both directions. Here, we decompose the reversible Michaelis–Menten equation into three terms, each with a clear physical meaning: catalytic capacity, substrate saturation and thermodynamic driving force. This decomposition facilitates a better understanding of enzyme kinetics and highlights the relationship between thermodynamics and kinetics, a relationship which is often neglected. We further demonstrate how our separable rate law can be understood from different points of view, shedding light on factors shaping enzyme catalysis.

Contrary to the textbook portrayal of glycolysis as a single pathway conserved across all domains of life, not all sugar-consuming organisms use the canonical Embden–Meyerhoff–Parnass (EMP) glycolytic pathway. Prokaryotic glucose metabolism is particularly diverse, including several alternative glycolytic pathways, the most common of which is the Entner–Doudoroff (ED) pathway. The prevalence of the ED pathway is puzzling as it produces only one ATP per glucose—half as much as the EMP pathway. We argue that the diversity of prokaryotic glucose metabolism may reflect a tradeoff between a pathway’s energy (ATP) yield and the amount of enzymatic protein required to catalyze pathway flux. We introduce methods for analyzing pathways in terms of thermodynamics and kinetics and show that the ED pathway is expected to require several-fold less enzymatic protein to achieve the same glucose conversion rate as the EMP pathway. Through genomic analysis, we further show that prokaryotes use different glycolytic pathways depending on their energy supply. Specifically, energy-deprived anaerobes overwhelmingly rely upon the higher ATP yield of the EMP pathway, whereas the ED pathway is common among facultative anaerobes and even more common among aerobes. In addition to demonstrating how protein costs can explain the use of alternative metabolic strategies, this study illustrates a direct connection between an organism’s environment and the thermodynamic and biochemical properties of the metabolic pathways it employs.

What governs the concentrations of metabolites within living cells? Beyond specific metabolic and enzymatic considerations, are there global trends that affect their values? We hypothesize that the physico-chemical properties of metabolites considerably affect their in-vivo concentrations. The recently achieved experimental capability to measure the concentrations of many metabolites simultaneously has made the testing of this hypothesis possible. Here, we analyze such recently available data sets of metabolite concentrations within E. coli, S. cerevisiae, B. subtilis and human. Overall, these data sets encompass more than twenty conditions, each containing dozens (28-108) of simultaneously measured metabolites. We test for correlations with various physico-chemical properties and find that the number of charged atoms, non-polar surface area, lipophilicity and solubility consistently correlate with concentration. In most data sets, a change in one of these properties elicits a ∼100 fold increase in metabolite concentrations. We find that the non-polar surface area and number of charged atoms account for almost half of the variation in concentrations in the most reliable and comprehensive data set. Analyzing specific groups of metabolites, such as amino-acids or phosphorylated nucleotides, reveals even a higher dependence of concentration on hydrophobicity. We suggest that these findings can be explained by evolutionary constraints imposed on metabolite concentrations and discuss possible selective pressures that can account for them. These include the reduction of solute leakage through the lipid membrane, avoidance of deleterious aggregates and reduction of non-specific hydrophobic binding. By highlighting the global constraints imposed on metabolic pathways, future research could shed light onto aspects of biochemical evolution and the chemical constraints that bound metabolic engineering efforts., Author Summary What governs the identity and concentrations of metabolites within living cells? The first part of this question has received much attention. Organisms were found to qualitatively prefer hydrophilic and charged metabolites, a phenomenon that was explained to be a result of constraints imposed by contemporary as well as archaic metabolism. However, among the metabolites that are used, a quantitative preference has never been analyzed systematically. Here we use the most comprehensive data sets of metabolite concentrations available to explore such trends. We find that in various organisms and growth conditions, living cells minimize the concentrations of non-polar, un-charged metabolites. More specifically, metabolites' hydrophobicity alters concentrations by two orders of magnitudes on average and explains up to half of the variation of metabolite concentrations within cells. We suggest that this can be attributed to an evolutionary pressure to avoid an unspecific hydrophobic effect: the preference of hydrophobic surfaces in an aqueous environment to adhere to other hydrophobic surfaces. Our findings shed light on the evolution of the internal makeup of living cells and can assist in establishing metabolic models that support synthetic biology and metabolic engineering efforts.

Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes). Results We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

In metabolism research, thermodynamics is usually used to determine the directionality of a reaction or the feasibility of a pathway. However, the relationship between thermodynamic potentials and fluxes is not limited to questions of directionality: thermodynamics also affects the kinetics of reactions through the flux-force relationship, which states that the logarithm of the ratio between the forward and reverse fluxes is directly proportional to the change in Gibbs energy due to a reaction (ΔrG′). Accordingly, if an enzyme catalyzes a reaction with a ΔrG′ of -5.7 kJ/mol then the forward flux will be roughly ten times the reverse flux. As ΔrG′ approaches equilibrium (ΔrG′ = 0 kJ/mol), exponentially more enzyme counterproductively catalyzes the reverse reaction, reducing the net rate at which the reaction proceeds. Thus, the enzyme level required to achieve a given flux increases dramatically near equilibrium. Here, we develop a framework for quantifying the degree to which pathways suffer these thermodynamic limitations on flux. For each pathway, we calculate a single thermodynamically-derived metric (the Max-min Driving Force, MDF), which enables objective ranking of pathways by the degree to which their flux is constrained by low thermodynamic driving force. Our framework accounts for the effect of pH, ionic strength and metabolite concentration ranges and allows us to quantify how alterations to the pathway structure affect the pathway's thermodynamics. Applying this methodology to pathways of central metabolism sheds light on some of their features, including metabolic bypasses (e.g., fermentation pathways bypassing substrate-level phosphorylation), substrate channeling (e.g., of oxaloacetate from malate dehydrogenase to citrate synthase), and use of alternative cofactors (e.g., quinone as an electron acceptor instead of NAD). The methods presented here place another arrow in metabolic engineers' quiver, providing a simple means of evaluating the thermodynamic and kinetic quality of different pathway chemistries that produce the same molecules., Author Summary Given data about enzyme kinetics and reaction thermodynamics, traditional metabolic control analysis (MCA) can pinpoint the enzymes whose expression will have the largest effect on steady-state flux through the pathway. These analyses can aid experimentalists in tuning enzyme expression levels along a metabolic pathway. In this work, we offer a framework that is complementary to MCA. Rather than focusing on the relationship between enzyme levels and pathway flux, we examine a pathway's stoichiometry and thermodynamics and ask whether it is likely to support high flux in cellular conditions. Our framework calculates a single thermodynamically-derived metric (the MDF) for each pathway, which is convenient for selecting the promising pathways from a large collection. This approach has several advantages. First, enzyme kinetic properties are laborious to measure and differ between organisms and isozymes, but no kinetic data is required to calculate the MDF. Second, as our framework accounts for pH, ionic strength and allowed concentration ranges, it is simple to model the effect of these parameters on the MDF. Finally, as it can be difficult to control the exact expression level of enzymes within cells, the MDF helps identify alternative pathways that are less sensitive to the levels of their constituent enzymes.

Protein levels are a dominant factor shaping natural and synthetic biological systems. Although proper functioning of metabolic pathways relies on precise control of enzyme levels, the experimental ability to balance the levels of many genes in parallel is a major outstanding challenge. Here, we introduce a rapid and modular method to span the expression space of several proteins in parallel. By combinatorially pairing genes with a compact set of ribosome-binding sites, we modulate protein abundance by several orders of magnitude. We demonstrate our strategy by using a synthetic operon containing fluorescent proteins to span a 3D color space. Using the same approach, we modulate a recombinant carotenoid biosynthesis pathway in Escherichia coli to reveal a diversity of phenotypes, each characterized by a distinct carotenoid accumulation profile. In a single combinatorial assembly, we achieve a yield of the industrially valuable compound astaxanthin 4-fold higher than previously reported. The methodology presented here provides an efficient tool for exploring a high-dimensional expression space to locate desirable phenotypes.

CO 2 is converted into biomass almost solely by the enzyme rubisco. The poor carboxylation properties of plant rubiscos have led to efforts that made it the most kinetically characterized enzyme, yet these studies focused on 100) were active in vitro, with the fastest having a turnover number of 22 ± 1 s−1—sixfold faster than the median plant rubisco and nearly twofold faster than the fastest measured rubisco to date. Unlike rubiscos from plants and cyanobacteria, the fastest variants discovered here are homodimers and exhibit a much simpler folding and activation kinetics. Our pipeline can be utilized to explore the kinetic space of other enzymes of interest, allowing us to get a better view of the biosynthetic potential of the biosphere., Metagenomic and biochemical analysis of previously uncharacterized naturally‐occurring form‐II and II/III rubiscos leads to identification of an enzyme with the fastest CO2 fixation rate described to date.