Reconciliation of Genome-Scale Metabolic Reconstructions for Comparative Systems Analysis

In the past decade, over 50 genome-scale metabolic reconstructions have been built for a variety of single- and multi- cellular organisms. These reconstructions have enabled a host of computational methods to be leveraged for systems-analysis of metabolism, leading to greater understanding of observed phenotypes. These methods have been sparsely applied to comparisons between multiple organisms, however, due mainly to the existence of differences between reconstructions that are inherited from the respective reconstruction processes of the organisms to be compared. To circumvent this obstacle, we developed a novel process, termed metabolic network reconciliation, whereby non-biological differences are removed from genome-scale reconstructions while keeping the reconstructions as true as possible to the underlying biological data on which they are based. This process was applied to two organisms of great importance to disease and biotechnological applications, Pseudomonas aeruginosa and Pseudomonas putida, respectively. The result is a pair of revised genome-scale reconstructions for these organisms that can be analyzed at a systems level with confidence that differences are indicative of true biological differences (to the degree that is currently known), rather than artifacts of the reconstruction process. The reconstructions were re-validated with various experimental data after reconciliation. With the reconciled and validated reconstructions, we performed a genome-wide comparison of metabolic flexibility between P. aeruginosa and P. putida that generated significant new insight into the underlying biology of these important organisms. Through this work, we provide a novel methodology for reconciling models, present new genome-scale reconstructions of P. aeruginosa and P. putida that can be directly compared at a network level, and perform a network-wide comparison of the two species. These reconstructions provide fresh insights into the metabolic similarities and differences between these important Pseudomonads, and pave the way towards full comparative analysis of genome-scale metabolic reconstructions of multiple species

mir-181A/B-1 controls thymic selection of treg cells and tunes their suppressive capacity

The interdependence of selective cues during development of regulatory T cells (Treg cells) in the thymus and their suppressive function remains incompletely understood. Here, we analyzed this interdependence by taking advantage of highly dynamic changes in expression of microRNA 181 family members miR-181a-1 and miR-181b-1 (miR-181a/b-1) during late T-cell development with very high levels of expression during thymocyte selection, followed by massive down-regulation in the periphery. Loss of miR-181a/b-1 resulted in inefficient de novo generation of Treg cells in the thymus but simultaneously permitted homeostatic expansion in the periphery in the absence of competition. Modulation of T-cell receptor (TCR) signal strength in vivo indicated that miR-181a/b-1 controlled Treg-cell formation via establishing adequate signaling thresholds. Unexpectedly, miR-181a/b-1–deficient Treg cells displayed elevated suppressive capacity in vivo, in line with elevated levels of cytotoxic T-lymphocyte–associated 4 (CTLA-4) protein, but not mRNA, in thymic and peripheral Treg cells. Therefore, we propose that intrathymic miR-181a/b-1 controls development of Treg cells and imposes a developmental legacy on their peripheral function

In-Vivo Expression Profiling of Pseudomonas aeruginosa Infections Reveals Niche-Specific and Strain-Independent Transcriptional Programs

Pseudomonas aeruginosa is a threatening, opportunistic pathogen causing disease in immunocompromised individuals. The hallmark of P. aeruginosa virulence is its multi-factorial and combinatorial nature. It renders such bacteria infectious for many organisms and it is often resistant to antibiotics. To gain insights into the physiology of P. aeruginosa during infection, we assessed the transcriptional programs of three different P. aeruginosa strains directly after isolation from burn wounds of humans. We compared the programs to those of the same strains using two infection models: a plant model, which consisted of the infection of the midrib of lettuce leaves, and a murine tumor model, which was obtained by infection of mice with an induced tumor in the abdomen. All control conditions of P. aeruginosa cells growing in suspension and as a biofilm were added to the analysis. We found that these different P. aeruginosa strains express a pool of distinct genetic traits that are activated under particular infection conditions regardless of their genetic variability. The knowledge herein generated will advance our understanding of P. aeruginosa virulence and provide valuable cues for the definition of prospective targets to develop novel intervention strategies

Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential

Erkundung der metabolischen Spielräume der Gattung Pseudomonas durch genomweite Constraint-basierte Modellierung

The genus Pseudomonas is a versatile group of bacteria of high biological relevance. The species of Pseudomonas aeruginosa and Pseudomonas putida are two representatives of this genus and both extremely high biological interest, albeit for different reasons. P. aeruginosa is an opportunistic human pathogen, whereas P. putida is a non-pathogenic bacterium renowned, among others, for its vast potential for environmental and industrial applications. To elucidate the metabolic wiring of both bacteria, to compare their metabolic spaces, as well as to devise possible intervention strategies that could either counteract the pathogenicity of P. aeruginosa or to improve biocatalytic properties of P. putida, metabolic reconstructions of P. aeruginosa PAO1 and P. putida KT2440 were created using the framework of constraint-based modeling. Network reconstruction and Flux Balance Analysis (FBA) enabled to define the structure of the metabolic network, to identify knowledge gaps and to pinpoint essential metabolic functions. FBA and Flux Variability Analysis were used to analyze the properties, potential and limits of the models. These analyses enabled to identify key features of the metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The models were validated against multiple sets of experimental data. These included continuous cell cultures, high-throughput phenotyping, 13C-measurement of internal flux distributions, and genome-wide gene-essentiality assays. Finally, a new computational method was developed to direct product optimization of compounds of which the production is not coupled to the reactions needed for survival of the cell, which was applied to polyhydroxyalkanoate production in P. putida. The solidly validated models yield valuable insights into genotype-phenotype relationships and provide a sound framework to explore these interesting bacteria, to compare their metabolic spaces and thus identify factors that determine such different properties.Die Gattung Pseudomonas ist eine vielfältige Gruppe von Bakterien von großer biologischer Bedeutung. Pseudomonas aeruginosa und Pseudomonas putida sind zwei aus unterschiedlichen Gründen sehr interessante Vertreter dieser Gattung. P. aeruginosa ist ein opportunistischer menschlicher Erreger,dahingegen ist P. putida ein nicht pathogenes großes industrielles Potential besitzendes Bakterium. Um sowohl das Verbindungsnetzwerk innerhalb des Stoffwechsels der beiden Bakterien aufzuklären als auch mögliche Eingriffe herauszufinden, die entweder gegen die Pathogenität der P. aeruginosa wirken oder zur Verbesserung der biokatalytischen Merkmale der P. putida führen, wurden mittels Constraint-basierter Modellierung die Stoffwechselwege von P. aeruginosa PAO1 und P. putida KT2440 computergestützt rekonstruiert. Zusammen mit einer Flussbilanzanalyse (FBA) ermöglichten diesen Rekonstruktionen, unter anderen, die Ermittlung der Struktur der Stoffwechselwege der Bakterienund die Bestimmung der wesentlichen metabolischen Aufgaben. FBA und Flussvariabilitätanalyse wurden verwendet, um Eigenschaften, Leistungsvermögen und Beschränkungen der Modelle zu analysieren. Die Modelle wurden gegen verschiedene Typen von experimentellen Daten validiert, wie zum Beispiel kontinuierlichen Zellkulturen, Hochdurchsatz-Phänotypisierung, 13C-basierten Messungen von inneren Flussverteilungen, und Genomweite Genunentbehrlichkeitsuntersuchung. Schließlich wurde eine neue computergestützte Methode entwickelt, die Produktionsoptimierung von chemischen Verbindungen leitet, deren Produktion nicht an der für das Überleben der Zelle wesentlichen Reaktionen gekoppelt ist, und für die Produktion von polyhydroxyalkanoaten in P. putida angewandt. Die solide validierten Modelle erbringen wertvolle Einblicke in die Genotyp-Phänotyp Verhältnisse und bieten einen soliden Rahmen für Erkundung dieser interessanten Bakterien und für einen Vergleich der Spielräume ihrer Stoffwechsel, um damit die Faktoren zu identifiezieren, die unterschiedliche Eigenschaften bestimmen

Bridging the Gap between Stochastic and Deterministic Regimes in the Kinetic Simulations of the Biochemical Reaction Networks

The biochemical reaction networks include elementary reactions differing by many orders of magnitude in the numbers of molecules involved. The kinetics of reactions involving small numbers of molecules can be studied by exact stochastic simulation. This approach is not practical for the simulation of metabolic processes because of the computational cost of accounting for individual molecular collisions. We present the “maximal time step method,” a novel approach combining the Gibson and Bruck algorithm with the Gillespie τ-leap method. This algorithm allows stochastic simulation of systems composed of both intensive metabolic reactions and regulatory processes involving small numbers of molecules. The method is applied to the simulation of glucose, lactose, and glycerol metabolism in Escherichia coli. The gene expression, signal transduction, transport, and enzymatic activities are modeled simultaneously. We show that random fluctuations in gene expression can propagate to the level of metabolic processes. In the cells switching from glucose to a mixture of lactose and glycerol, random delays in transcription initiation determine whether lactose or glycerol operon is induced. In a small fraction of cells severe decrease in metabolic activity may also occur. Both effects are epigenetically inherited by the progeny of the cell in which the random delay in transcription initiation occurred