Isotopic constraints on nitrogen transformation rates in the deep sedimentary marine biosphere

Author Posting. © American Geophysical Union,2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Isotopic constraints on nitrogen transformation rates in the deep sedimentary marine biosphere. Global Biogeochemical Cycles, 32, (2018):1688–1702., doi: 10.1029/2018GB005948.Little is known about the nature of microbial community activity contributing to the cycling of nitrogen in organic-poor sediments underlying the expansive oligotrophic ocean gyres. Here we use pore water concentrations and stable N and O isotope measurements of nitrate and nitrite to constrain rates of nitrogen cycling processes over a 34-m profile from the deep North Atlantic spanning fully oxic to anoxic conditions. Using a 1-D reaction-diffusion model to predict the distribution of nitrogen cycling rates, results converge on two distinct scenarios: (1) an exceptionally high degree of coupling between nitrite oxidation and nitrate reduction near the top of the anoxic zone or (2) an unusually large N isotope effect (~60‰) for nitrate reduction that is decoupled from the corresponding O isotope effect, which is possibly explained by enzyme-level interconversion between nitrite and nitrate.Samples analyzed for this study were collected during the final expedition of the RV Knorr, KN223. The expedition would not have been possible without the captain and crew of the RV Knorr and the efforts of the shipboard science party. We would like to acknowledge Robert Pockalny for planning and facilitating the expedition. Inorganic geochemistry sample collection, processing, and analysis were performed shipboard by Arthur Spivack,Dennis Graham, Chloe Anderson, Emily Estes, Kira Homola, Claire McKinley, Theodore Present, and Justine Sauvage. Coring capabilities were provided by the Oregon State University and Woods Hole Oceanographic Institute Coring Facilities, directed and funded by the U. S. National Science Foundation (NSF) Ship Facilities Program. The cored materials and discrete samples from the expedition are curated and stored by the Marine Geological Samples Laboratory at the University of Rhode Island, codirected by Rebecca Robinson and Katherine Kelly and funded by the NSF Ocean Sciences Division. The nutrient and isotope data from pore waters in this study will be available at The Biological and Chemical Data Management Office (https://www.bcodmo.org/project/567401). This project was partially funded by an NSF CDEBI postdoctoral fellowship to C. Buchwald. Portions of this material are based upon work supported while R. W. M. was serving at the National Science Foundation.2019-04-1

Consistent Estimation of Gibbs Energy Using Component Contributions

Standard Gibbs energies of reactions are increasingly being used in metabolic modeling for applying thermodynamic constraints on reaction rates, metabolite concentrations and kinetic parameters. The increasing scope and diversity of metabolic models has led scientists to look for genome-scale solutions that can estimate the standard Gibbs energy of all the reactions in metabolism. Group contribution methods greatly increase coverage, albeit at the price of decreased precision. We present here a way to combine the estimations of group contribution with the more accurate reactant contributions by decomposing each reaction into two parts and applying one of the methods on each of them. This method gives priority to the reactant contributions over group contributions while guaranteeing that all estimations will be consistent, i.e. will not violate the first law of thermodynamics. We show that there is a significant increase in the accuracy of our estimations compared to standard group contribution. Specifically, our cross-validation results show an 80% reduction in the median absolute residual for reactions that can be derived by reactant contributions only. We provide the full framework and source code for deriving estimates of standard reaction Gibbs energy, as well as confidence intervals, and believe this will facilitate the wide use of thermodynamic data for a better understanding of metabolism

Network design and analysis for multi-enzyme biocatalysis

In vitro synthesis is a biotechnological alternative to classic chemical catalysts. However, the manual design of multi-step biosynthesis routes is very challenging, especially when enzymes from different organisms are involved. There is therefore a demand for in silico tools to guide the design of such synthesis routes using computational methods for the path-finding, as well as the reconstruction of suitable genome-scale metabolic networks that are able to harness the growing amount of biological data available. This work presents an algorithm for finding pathways from arbitrary metabolites to a target product of interest. The algorithm is based on a mixed-integer linear program (MILP) and combines graph topology and reaction stoichiometry. The pathway candidates are ranked using different ranking criteria to help finding the best suited synthesis pathway candidates. Additionally, a comprehensive workflow for the reconstruction of metabolic networks based on data of the Kyoto Encyclopedia of Genes and Genomes (KEGG) combined with thermodynamic data for the determination of reaction directions is presented. The workflow comprises a filtering scheme to remove unsuitable data. With this workflow, a panorganism network reconstruction as well as single organism network models are established. These models are analyzed with graph-theoretical methods. It is also discussed how the results can be used for the planning of biosynthetic production pathways.In vitro Synthese ist eine biotechnologische Alternative zu klassischen chemischen Katalysen. Der manuelle Entwurf von mehrstufigen Biosynthesewegen ist jedoch sehr anspruchsvoll, vor allem wenn Enzyme verschiedener Organismen beteiligt sind. Daher besteht ein Bedarf an Methoden, die helfen solche Synthesewege in silico zu entwerfen und die in der Lage sind große Mengen biologischer Daten zu bewältigen - insbesondere in Hinblick auf die Rekonstruktion genomskaliger metabolischer Netzwerkmodelle und die Pfadsuche in solchen Netzwerken. In dieser Arbeit wird ein Algorithmus zur Pfadsuche zu einem Zielprodukt ausgehend von beliebigen Substraten präsentiert. Der Algorithmus basiert auf einem gemischt-ganzzahligen linearen Programm, das Graphtopologie mit Reaktionsstöchiometrien kombiniert. Die Pfadkandidaten werden anhand verschiedener Kriterien geordnet, um die am besten geeigneten Kandidaten für die Synthese zu finden. Außerdem wird ein umfassender Workflow für die Rekonstruktion metabolischer Netzwerke basierend auf der Datenbank KEGG sowie thermodynamischen Daten vorgestellt. Dieser umfasst einen Filter, der anhand verschiedener Kriterien geeignete Reaktionen auswählt. Der Workflow wird zum Erstellen einer organismusübergreifenden Netzwerkrekonstruktion, sowie Netzwerken einzelner Organismen genutzt. Diese Modelle werden mit graphentheoretischen Methoden analysiert. Es wird diskutiert, wie die Ergebnisse für die Planung von biosynthetischen Produktionswegen genutzt werden können.BMBF; Initiative “Biotechnologie 2020+: Basistechnologien für eine nächste Generation biotechnologischer Verfahren”; Projekt MECA

Genome-scale modeling of redox metabolism and therapeutic response in radiation-resistant tumors

Despite being one of the oldest forms of cancer therapy and still a primary treatment modality, radiation therapy is not effective across all cancer types and tumor resistance to radiation is still not well understood. As our ability to characterize tumor pathophysiology increases with new -omic technologies, a broad clinical goal is prognostic indicators of therapeutic outcomes for personalizing therapeutic regimens. While redox metabolism is a known factor, methods for analyzing systems-level involvement of cellular metabolism in radiation response have not been previously developed. This dissertation presents the construction of novel genome-scale Flux Balance Analysis (FBA) models of individual radiation-sensitive and -resistant patient tumors from The Cancer Genome Atlas (TCGA) to explore the role of redox metabolism in radiation sensitivity, to identify diagnostic and therapeutic biomarkers for radiation response, and to predict response to radiation-sensitizing chemotherapies in radiation-resistant tumors. A novel bioinformatics platform was developed to integrate genomic, transcriptomic, kinetic, and thermodynamic parameters from 716 radiation-sensitive and 199 radiation-resistant TCGA tumors into personalized genome-scale FBA models. Pan-cancer model predictions identified increased mitochondrial production of redox cofactors, including NADPH and glutathione, as well as increased H2O2-scavenging fluxes in radiation-resistant tumors. Simulated gene knockout screens were utilized to discover novel targets in redox metabolism, central carbon metabolism, and folate metabolism which differentially impact antioxidant production and ROS clearance in radiation-resistant tumors; these targets were experimentally validated through siRNA gene knockdown in matched radiation-sensitive and -resistant cancer cell lines among multiple cancer types. Finally, personalized metabolic flux profiles were generated for individual radiation-resistant cancer patients to identify optimal targets for radiation sensitization. This work not only improved upon methodological shortcomings of previous FBA models of cancer metabolism, but is the first to utilize genome-scale modeling for identifying metabolic differences between radiation-sensitive and -resistant tumors that could be exploited for improving radiation sensitivity. Machine learning classifiers were developed which integrate multi-omic data from TCGA patients and novel metabolic outcomes from personalized FBA models to predict radiation sensitivity. A dataset- independent ensemble architecture with gradient boosting models and Bayesian optimization yielded improved predictive accuracy and biomarker detection compared to previously-developed classifiers for radiation response. Experimentally-validated predictions of metabolite production from radiation- sensitive and -resistant FBA tumor models were integrated into multi-omic classifiers; metabolites involved in lipid metabolism, nucleotide metabolism, and immune modulation were identified as having significant associations with radiation response. Subgroups of patients with differing utilities of clinical versus metabolomic datasets for radiation response prediction were discovered, and personalized panels of multi-omic and non-invasive biomarkers with optimal diagnostic utility were developed. This work made significant advancements by being the first to integrate FBA model predictions into machine learning classifiers for cancer treatment outcomes. Finally, FBA models of radiation-resistant TCGA tumors were used to predict response to radiation-sensitizing chemotherapies and investigate their effects on tumor redox metabolism. A novel multi-feature FBA objective function screen was developed, resulting in significant improvements in model predictions of treatment response, as well as identification of redox cofactors directly involved in drug metabolism. The radiation-sensitizing effect of chemotherapeutic treatment was predicted in radiation-resistant tumors by assessing drug-associated decreases in antioxidant levels, and machine learning regressors were utilized to identify multi-omic biomarkers from patient tumors which are associated with increased radiation sensitization. This work was the first to utilize genome-scale modeling to assess the role of chemotherapeutic treatment on tumor redox metabolism and radiation sensitization. In summary, a generalizable framework for creating genome-scale metabolic models of individual patient tumors was developed. The collective properties of these personalized models improved pathophysiological insights into the role of redox metabolism in the tumor responses to radiation and radiation-sensitizing chemotherapies. This framework resulted in a reduced set of clinically-useful biomarkers for both the a priori prediction of radiation response as well as targeted sensitization of radiation-resistant tumors to radiation therapy. This personalized medicine approach represents a paradigm shift in how diagnostic and treatment strategies for radiation-resistant cancer patients are developed, ultimately improving the standard of care for these patients.Ph.D

Structural modelling and robustness analysis of complex metabolic networks and signal transduction cascades

The dissertation covers the topic of structural robustness of metabolic networks on the basis of the concept of elementary flux modes (EFMs). It is shown that the number of EFMs does not reflect the topology of a network sufficiently. Thus, new methods are developed to determine the structural robustness of metabolic networks. These methods are based on systematic in-silico knockouts and the subsequent calculation of dropped out EFMs. Thereby, together with single knockouts also double and multiple knockouts can be used. After evaluation of these methods they are applied to metabolic networks of human erythrocyte and hepatocyte as well as to a metabolic network of Escherichia coli (E. coli). It is found that the erythrocyte has the lowest structural robustness, followed by the hepatocyte and E. coli. These results coincide very well with the circumstance that human erythrocyte and hepatocyte and E. coli are able to adapt to conditions with increasing diversity. In a further part of the dissertation the concept of EFMs is expanded to signal transduction pathways consisting of kinase cascades. The concept of EFMs is based on the steady-state condition for metabolic pathways. It is shown that under certain circumstances this steady-state condition also holds for signalling cascades. Furthermore, it is shown that it is possible to deduce minimal conditions for signal transduction without knowledge about the kinetics involved. On the basis of these assumptions it is possible to calculate EFMs for signalling cascades. But due to the fact that these EFMs do no longer just have mass flux but also information flux, they are now called elementary signalling modes (ESMs).Die Dissertation behandelt die strukturelle Robustheit von metabolischen Netzwerken auf der Basis des Konzepts der elementaren Flussmoden (EFMen). Es wird gezeigt, dass die Anzahl der EFMen die Topologie eines metabolischen Netzes nicht ausreichend widerspiegelt. Darauf aufbauend werden neue Methoden entwickelt, um die strukturelle Robustheit metabolischer Netze zu bestimmen. Diese Methoden beruhen auf systematischen in-silico-Knockouts und der anschließenden Bestimmung des Anteils an weggefallenen EFMen. Dabei können neben Einfach-Knockouts auch Doppel- oder Mehrfach-Knockouts verwendet werden. Nach der Evaluierung werden diese Methoden auf metabolische Netzwerke des menschlichen Erythrozyten und Hepatozyten, sowie des Bakteriums Escherichia coli (E. coli) angewendet. Es zeigt sich, dass der Erythrozyt die im Vergleich geringste strukturelle Robustheit besitzt, gefolgt vom Hepatozyten und E. coli. Diese Ergebnisse stimmen sehr gut mit der Beobachtung überein, dass sich die menschlichen Erythrozyten und Hepatozyten, sowie E. coli an zunehmend verschiedene Bedingungen anpassen können. In einem weiteren Teil der Dissertation wird das Konzept der EFMen auf Signaltransduktionswege bestehend aus Kinase-Kaskaden erweitert. Das Konzept der EFMen beruht auf der Annahme eines quasi-stationären Zustands für metabolische Netzwerke. Es wird gezeigt, dass dieser quasi-stationäre Zustand unter bestimmten Bedingungen auch in Signal-Kaskaden angenommen werden kann. Weiterhin wird gezeigt, dass man ohne Kenntnis der beteiligten Kinetiken Minimalbedingungen für die Signalweiterleitung ableiten kann. Auf Basis dieser Annahmen lassen sich für Signal-Kaskaden EFMen berechnen. Aber aufgrund der Tatsache, dass sie nicht mehr nur Masse-, sondern auch Informationsfluss beschreiben, werden sie nun als elementare Signalmoden (ESMen) bezeichnet

PROTEIN SUPPRESSION OF FLAVIN SEMIQUINONE AS A MECHANISTICALLY IMPORTANT CONTROL OF REACTIVITY: A STUDY COMPARING FLAVOENZYMES WHICH DIFFER IN REDOX PROPERTIES, SUBSTRATES, AND ABILITY TO BIFURCATE ELECTRONS

A growing number of flavoprotein systems have been observed to bifurcate pairs of electrons. Flavin-based electron bifurcation (FBEB) results in products with greater reducing power than that of the reactants with less reducing power. Highly reducing electrons at low reduction midpoint potential are required for life processes of both aerobic and anaerobic metabolic processes. For electron bifurcation to function, the semiquinone (SQ) redox intermediate needs to be destabilized in the protein to suppress its ability to trap electrons. This dissertation examines SQ suppression across a number of flavin systems for the purpose of better understanding the nature of SQ suppression within FBEB and elucidates potential mechanistic roles of SQ. The major achievement of this work is advancing the understanding of SQ suppression and its utility in flavoproteins with the capacity to bifurcate pairs of electrons. Much of these achievements are highlighted in Chapter 6. To contextualize these mechanistic studies, we examined the kinetic and thermodynamic properties of non-bifurcating flavoproteins (Chapters 2 and 3) as well as bifurcating flavoproteins (Chapters 4 and 5). Proteins were selected as models for SQ suppression with the aim of elucidating the role of an intermediate SQ in bifurcation. The chemical reactions of flavins and those mediated by flavoproteins play critical roles in the bioenergetics of all lifeforms, both aerobic and anaerobic. We highlight our findings in the context of electron bifurcation, the recently discovered third form of biological energy conservation. Bifurcating NADH-dependent ferredoxin-NADP+ oxidoreductase I (Nfn) and the non-bifurcating flavoproteins nitroreductase, NADH oxidase, and flavodoxin were studied by transient absorption spectroscopy to compare electron transfer rates and mechanisms in the picosecond range. Different mechanisms were found to dominate SQ decay in the different proteins, producing lifetimes ranging over 3 orders of magnitude. The presence of a short-lived SQ alone was found to be insufficient to infer bifurcating activity. We established a model wherein the short SQ lifetime in Nfn results from efficient electron propagation. Such mechanisms of SQ decay may be a general feature of redox active site ensembles able to carry out bifurcation. We also investigated the proposed bifurcating electron transfer flavoprotein (Etf) from Pyrobaculum aerophilum (Pae), a hyperthermophilic archaeon. Unlike other Etfs, we observed a stable and strong charge transfer band (λmax= 724 nm) for Pae’s Etf upon reduction by NADH. Using a series of reductive titrations to probe bounds for the reduction midpoint potential of the two flavins, we argue that the heterodimer alone could participate in a bifurcation mechanism

The compositional and evolutionary logic of metabolism

Metabolism displays striking and robust regularities in the forms of modularity and hierarchy, whose composition may be compactly described. This renders metabolic architecture comprehensible as a system, and suggests the order in which layers of that system emerged. Metabolism also serves as the foundation in other hierarchies, at least up to cellular integration including bioenergetics and molecular replication, and trophic ecology. The recapitulation of patterns first seen in metabolism, in these higher levels, suggests metabolism as a source of causation or constraint on many forms of organization in the biosphere. We identify as modules widely reused subsets of chemicals, reactions, or functions, each with a conserved internal structure. At the small molecule substrate level, module boundaries are generally associated with the most complex reaction mechanisms and the most conserved enzymes. Cofactors form a structurally and functionally distinctive control layer over the small-molecule substrate. Complex cofactors are often used at module boundaries of the substrate level, while simpler ones participate in widely used reactions. Cofactor functions thus act as "keys" that incorporate classes of organic reactions within biochemistry. The same modules that organize the compositional diversity of metabolism are argued to have governed long-term evolution. Early evolution of core metabolism, especially carbon-fixation, appears to have required few innovations among a small number of conserved modules, to produce adaptations to simple biogeochemical changes of environment. We demonstrate these features of metabolism at several levels of hierarchy, beginning with the small-molecule substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells.Comment: 56 pages, 28 figure