Modern views of ancient metabolic networks

Metabolism is a molecular, cellular, ecological and planetary phenomenon, whose fundamental principles are likely at the heart of what makes living matter different from inanimate one. Systems biology approaches developed for the quantitative analysis of metabolism at multiple scales can help understand metabolism's ancient history. In this review, we highlight work that uses network-level approaches to shed light on key innovations in ancient life, including the emergence of proto-metabolic networks, collective autocatalysis and bioenergetics coupling. Recent experiments and computational analyses have revealed new aspects of this ancient history, paving the way for the use of large datasets to further improve our understanding of life's principles and abiogenesis.https://www.sciencedirect.com/science/article/pii/S2452310017302196Published versio

Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism

Published in final edited form as: Nat Ecol Evol. 2019 December ; 3(12): 1715–1724. doi:10.1038/s41559-019-1018-8.It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals before genetically encoded enzymes. A major challenge in unravelling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms with physico-chemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fuelled by a thioester- and redox-driven variant of the reductive tricarboxylic acid cycle that is capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors are not necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a protocell, and find that different combinations of carbon sources and electron donors can support the continuous production of a minimal ancient 'biomass' composed of putative early biopolymers and fatty acids.80NSSC17K0295 - Intramural NASA; 80NSSC17K0296 - Intramural NASA; T32 GM100842 - NIGMS NIH HHSAccepted manuscrip

Remnants of an ancient metabolism without phosphate

Phosphate is essential for all living systems, serving as a building block of genetic and metabolic machinery. However, it is unclear how phosphate could have assumed these central roles on primordial Earth, given its poor geochemical accessibility. We used systems biology approaches to explore the alternative hypothesis that a protometabolism could have emerged prior to the incorporation of phosphate. Surprisingly, we identified a cryptic phosphate-independent core metabolism producible from simple prebiotic compounds. This network is predicted to support the biosynthesis of a broad category of key biomolecules. Its enrichment for enzymes utilizing iron-sulfur clusters, and the fact that thermodynamic bottlenecks are more readily overcome by thioester rather than phosphate couplings, suggest that this network may constitute a "metabolic fossil" of an early phosphate-free nonenzymatic biochemistry. Our results corroborate and expand previous proposals that a putative thioester-based metabolism could have predated the incorporation of phosphate and an RNA-based genetic system. PAPERCLIP

Genome-scale architecture of small molecule regulatory networks and the fundamental trade-off between regulation and enzymatic activity

Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.P30 CA008748 - NCI NIH HHS; R01 GM121950 - NIGMS NIH HH

Thermodynamics drives coenzyme redundancy in metabolism

Coenzymes redistribute a variety of resources (e.g., electrons, phosphate groups, methyl groups) throughout cellular metabolism. For a variety of reactions requiring acceptors or donors of specific resources, there often exist degenerate sets of molecules (e.g., NAD(H) and NADP(H)) that carry out similar functions. Several hypotheses can explain the persistence of coenzyme degeneracy, but none have been tested quantitatively. Here, we use genome-wide metabolic modeling approaches to decompose the selective pressures driving enzymatic specificity for coenzymes in the metabolic network of Escherichia coli. Flux balance modeling predicts that only two enzymes (encoded by leuB and pdxB) are thermodynamically constrained to use NAD(H) over NADP(H). In contrast, structural and sequence analyses reveal widespread conservation of residues that retain selectivity for NAD(H), suggesting that additional forces drive enzyme specificity. Using a model accounting for the cost of oxidoreductase enzyme expression, we find that coenzyme redundancy universally reduces the minimal amount of protein required to catalyze coenzyme-coupled reactions, inducing individual reactions to strongly prefer one coenzyme over another when reactions are near thermodynamic equilibrium. We propose that partitioning of flux across multiple coenzyme pools could be a generic phenomenon of cellular metabolism, and hypothesize that coenzymes typically thought to exist in a single pool (e.g., CoA) may exist in more than one form (e.g., dephospho-CoA)

Emergent simplicity in microbial community assembly

Published in final edited form as: Science. 2018 August 03; 361(6401): 469–474. doi:10.1126/science.aat1168.A major unresolved question in microbiome research is whether the complex taxonomic architectures observed in surveys of natural communities can be explained and predicted by fundamental, quantitative principles. Bridging theory and experiment is hampered by the multiplicity of ecological processes that simultaneously affect community assembly in natural ecosystems. We addressed this challenge by monitoring the assembly of hundreds of soil- and plant-derived microbiomes in well-controlled minimal synthetic media. Both the community-level function and the coarse-grained taxonomy of the resulting communities are highly predictable and governed by nutrient availability, despite substantial species variability. By generalizing classical ecological models to include widespread nonspecific cross-feeding, we show that these features are all emergent properties of the assembly of large microbial communities, explaining their ubiquity in natural microbiomes.The funding for this work partly results from a Scialog Program sponsored jointly by the Research Corporation, for Science Advancement and. the Gordon and Betty Moore Foundation through grants to Yale University and Boston University by the Research Corporation and by the Simons Foundation. This work was also supported by a young; investigator award from the Human Frontier Science Program to A.S. (RGY0077/2016) and by NIH NIGMS grant 1R35GM119461 and a Simons Investigator as in the Mathematical Modeling of Living Systems (MMLS) to P.M.; D.S. and J.E.G. additionally acknowledge funding from the Defense Advanced Research Projects Agency (purchase request no. HR0011515303, contract no.. HR0011-15-0-0091), the U.S. Department of Energy (DE-SC0012627), the NIH (T32GM100842, 5R01DE024468, R01GM121950, and Sub_P30DK036836_P&F), the National Science Foundation (1457695), the Human Frontier Science Program (RGP0020/2016) and the Boston University Interdisciplinary Biomedical Research Office. (Research Corporation, for Science Advancement; Gordon and Betty Moore Foundation; Boston University by the Research Corporation; Simons Foundation.; RGY0077/2016 - uman Frontier Science Program; 1R35GM119461 - NIH NIGMS grant; Simons Investigator as in the Mathematical Modeling of Living Systems (MMLS); HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-0-0091 - Defense Advanced Research Projects Agency; T32GM100842 - NIH; 5R01DE024468 - NIH; R01GM121950 - NIH; ub_P30DK036836 - NIH; 1457695 - National Science Foundation; RGP0020/2016 - Human Frontier Science Program; Boston University Interdisciplinary Biomedical Research Office)Accepted manuscrip

Unsupervised Identification of Isotope-Labeled Peptides

<i>In vivo</i> isotopic labeling coupled with high-resolution proteomics is used to investigate primary metabolism in techniques such as stable isotope probing (protein-SIP) and peptide-based metabolic flux analysis (PMFA). Isotopic enrichment of carbon substrates and intracellular metabolism determine the distribution of isotopes within amino acids. The resulting amino acid mass distributions (AMDs) are convoluted into peptide mass distributions (PMDs) during protein synthesis. With no <i>a priori</i> knowledge on metabolic fluxes, the PMDs are therefore unknown. This complicates labeled peptide identification because prior knowledge on PMDs is used in all available peptide identification software. An automated framework for the identification and quantification of PMDs for nonuniformly labeled samples is therefore lacking. To unlock the potential of peptide labeling experiments for high-throughput flux analysis and other complex labeling experiments, an unsupervised peptide identification and quantification method was developed that uses discrete deconvolution of mass distributions of identified peptides to inform on the mass distributions of otherwise unidentifiable peptides. Uniformly ¹³C-labeled <i>Escherichia coli</i> protein was used to test the developed feature reconstruction and deconvolution algorithms. The peptide identification was validated by comparing MS²-identified peptides to peptides identified from PMDs using unlabeled <i>E. coli</i> protein. Nonuniformly labeled <i>Glycine max</i> protein was used to demonstrate the technology on a representative sample suitable for flux analysis. Overall, automatic peptide identification and quantification were comparable or superior to manual extraction, enabling proteomics-based technology for high-throughput flux analysis studies

A thermodynamic atlas of carbon redox chemical space

Redox biochemistry plays a key role in the transduction of chemical energy in living systems. However, the compounds observed in metabolic redox reactions are a minuscule fraction of chemical space. It is not clear whether compounds that ended up being selected as metabolites display specific properties that distinguish them from nonbiological compounds. Here, we introduce a systematic approach for comparing the chemical space of all possible redox states of linear-chain carbon molecules to the corresponding metabolites that appear in biology. Using cheminformatics and quantum chemistry, we analyze the physicochemical and thermodynamic properties of the biological and nonbiological compounds. We find that, among all compounds, aldose sugars have the highest possible number of redox connections to other molecules. Metabolites are enriched in carboxylic acid functional groups and depleted of ketones and aldehydes and have higher solubility than nonbiological compounds. Upon constructing the energy landscape for the full chemical space as a function of pH and electron-donor potential, we find that metabolites tend to have lower Gibbs energies than nonbiological molecules. Finally, we generate Pourbaix phase diagrams that serve as a thermodynamic atlas to indicate which compounds are energy minima in redox chemical space across a set of pH values and electron-donor potentials. While escape from thermodynamic equilibrium toward kinetically driven states is a hallmark of life and its origin, we envision that a deeper quantitative understanding of the environment-dependent thermodynamic landscape of putative prebiotic molecules will provide a crucial reference for future origins-of-life models.ISSN:0027-8424ISSN:1091-649