Assessing the significance of knockout cascades in metabolic networks

Complex networks have been shown to be robust against random structural perturbations, but vulnerable against targeted attacks. Robustness analysis usually simulates the removal of individual or sets of nodes, followed by the assessment of the inflicted damage. For complex metabolic networks, it has been suggested that evolutionary pressure may favor robustness against reaction removal. However, the removal of a reaction and its impact on the network may as well be interpreted as selective regulation of pathway activities, suggesting a tradeoff between the efficiency of regulation and vulnerability. Here, we employ a cascading failure algorithm to simulate the removal of single and pairs of reactions from the metabolic networks of two organisms, and estimate the significance of the results using two different null models: degree preserving and mass-balanced randomization. Our analysis suggests that evolutionary pressure promotes larger cascades of non-viable reactions, and thus favors the ability of efficient metabolic regulation at the expense of robustness

A Pseudomonas putida efflux pump acts on short-chain alcohols

Abstract Background The microbial production of biofuels is complicated by a tradeoff between yield and toxicity of many fuels. Efflux pumps enable bacteria to tolerate toxic substances by their removal from the cells while bypassing the periplasm. Their use for the microbial production of biofuels can help to improve cell survival, product recovery, and productivity. However, no native efflux pump is known to act on the class of short-chain alcohols, important next-generation biofuels, and it was considered unlikely that such an efflux pump exists. Results We report that controlled expression of the RND-type efflux pump TtgABC from Pseudomonas putida DOT-T1E strongly improved cell survival in highly toxic levels of the next-generation biofuels n-butanol, isobutanol, isoprenol, and isopentanol. GC-FID measurements indicated active efflux of n-butanol when the pump is expressed. Conversely, pump expression did not lead to faster growth in media supplemented with low concentrations of n-butanol and isopentanol. Conclusions TtgABC is the first native efflux pump shown to act on multiple short-chain alcohols. Its controlled expression can be used to improve cell survival and increase production of biofuels as an orthogonal approach to metabolic engineering. Together with the increased interest in P. putida for metabolic engineering due to its flexible metabolism, high native tolerance to toxic substances, and various applications of engineering its metabolism, our findings endorse the strain as an excellent biocatalyst for the high-yield production of next-generation biofuels

Mass-balanced randomization of metabolic networks

Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem

Evolutionary significance of metabolic network properties

Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein–protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone

Uterine scars after caesarean delivery: From histology to the molecular and ultrastructural level

Uterine rupture during a trial of labor after caesarean delivery (CD) is a serious complication for mother and fetus. The lack of knowledge on histological features and molecular pathways of uterine wound healing has hindered research in this area from evolving over time. We analysed collagen content and turnover in uterine scars on a histological, molecular and ultrastructural level. Therefore, tissue samples from the lower uterine segment were obtained during CD from 16 pregnant women with at least one previous CD, from 16 pregnant women without previous CD, and from 16 non-pregnant premenopausal women after hysterectomy for a benign disease. Histomorphometrical collagen quantification showed, that the collagen content of the scar area in uterine wall specimens after previous CD was significantly higher than in the unscarred myometrium of the same women and the control groups. Quantitative real-time PCR of uterine scar tissue from FFPE samples delineated by laser microdissection yielded a significantly higher COL3A1 expression and a significantly lower COL1A2/COL3A1 ratio in scarred uteri than in samples from unscarred uteri. Histological collagen content and the expression of COL1A2 and COL3A1 were positively correlated, while COL1A2/COL3A1 ratio was negatively correlated with the histological collagen content. Transmission electron microscopy revealed a destroyed myometrial ultrastructure in uterine scars with increased collagen density. We conclude that the high collagen content in uterine scars results from an ongoing overexpression of collagen I and III. This is a proof of concept to enable further analyses of specific factors that mediate uterine wound healing

Integrating food webs with metabolic networks: modeling contaminant degradation in marine ecosystems

Georg Basler is supported by a Marie Curie Intra European Fellowship within the 7th European Community Framework Programme, ERC grant agreement number 329682. Evangelos Simeonidis is supported by the Luxembourg Centre for Systems Biomedicine.Peer reviewedPeer Reviewe

Massebalancierte Randomisierung : ein Maß für Signifikanz in metabolischen Netzwerken

Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, including small average path length, large clustering coefficient, heavy-tail degree distribution, and hierarchical organization, viewed as requirements for efficient and robust system architectures. Existing studies estimate the significance of network properties using a generic randomization scheme - a Markov-chain switching algorithm - which generates unrealistic reactions in metabolic networks, as it does not account for the physical principles underlying metabolism. Therefore, it is unclear whether the properties identified with this generic approach are related to the functions of metabolic networks. Within this doctoral thesis, I have developed an algorithm for mass-balanced randomization of metabolic networks, which runs in polynomial time and samples networks almost uniformly at random. The properties of biological systems result from two fundamental origins: ubiquitous physical principles and a complex history of evolutionary pressure. The latter determines the cellular functions and abilities required for an organism’s survival. Consequently, the functionally important properties of biological systems result from evolutionary pressure. By employing randomization under physical constraints, the salient structural properties, i.e., the smallworld property, degree distributions, and biosynthetic capabilities of six metabolic networks from all kingdoms of life are shown to be independent of physical constraints, and thus likely to be related to evolution and functional organization of metabolism. This stands in stark contrast to the results obtained from the commonly applied switching algorithm. In addition, a novel network property is devised to quantify the importance of reactions by simulating the impact of their knockout. The relevance of the identified reactions is verified by the findings of existing experimental studies demonstrating the severity of the respective knockouts. The results suggest that the novel property may be used to determine the reactions important for viability of organisms. Next, the algorithm is employed to analyze the dependence between mass balance and thermodynamic properties of Escherichia coli metabolism. The thermodynamic landscape in the vicinity of the metabolic network reveals two regimes of randomized networks: those with thermodynamically favorable reactions, similar to the original network, and those with less favorable reactions. The results suggest that there is an intrinsic dependency between thermodynamic favorability and evolutionary optimization. The method is further extended to optimizing metabolic pathways by introducing novel chemically feasibly reactions. The results suggest that, in three organisms of biotechnological importance, introduction of the identified reactions may allow for optimizing their growth. The approach is general and allows identifying chemical reactions which modulate the performance with respect to any given objective function, such as the production of valuable compounds or the targeted suppression of pathway activity. These theoretical developments can find applications in metabolic engineering or disease treatment. The developed randomization method proposes a novel approach to measuring the significance of biological network properties, and establishes a connection between large-scale approaches and biological function. The results may provide important insights into the functional principles of metabolic networks, and open up new possibilities for their engineering.In der Systembiologie und Bioinformatik wurden in den letzten Jahren immer komplexere Netzwerke zur Beschreibung verschiedener biologischer Prozesse, wie Genregulation, Protein-Interaktionen und Stoffwechsel (Metabolismus) rekonstruiert. Ein Hauptziel der Forschung besteht darin, die strukturellen Eigenschaften von Netzwerken für Vorhersagen über deren Funktion nutzbar zu machen, also eine Verbindung zwischen Netzwerkeigenschaften und Funktion herzustellen. Die netzwerkbasierte Forschung zielte bisher vor allem darauf ab, gemeinsame Eigenschaften von Netzwerken unterschiedlichen Ursprungs zu entdecken. Dazu zählen die durchschnittliche Länge von Verbindungen im Netzwerk, die Häufigkeit redundanter Verbindungen, oder die hierarchische Organisation der Netzwerke, welche als Voraussetzungen für effiziente Kommunikationswege und Robustheit angesehen werden. Dabei muss zunächst bestimmt werden, welche Eigenschaften für die Funktion eines Netzwerks von besonderer Bedeutung (Signifikanz) sind. Die bisherigen Studien verwenden dafür eine Methode zur Erzeugung von Zufallsnetzwerken, welche bei der Anwendung auf Stoffwechselnetzwerke unrealistische chemische Reaktionen erzeugt, da sie physikalische Prinzipien missachtet. Es ist daher fraglich, ob die Eigenschaften von Stoffwechselnetzwerken, welche mit dieser generischen Methode identifiziert werden, von Bedeutung für dessen biologische Funktion sind, und somit für aussagekräftige Vorhersagen in der Biologie verwendet werden können. In meiner Dissertation habe ich eine Methode zur Erzeugung von Zufallsnetzwerken entwickelt, welche physikalische Grundprinzipien berücksichtigt, und somit eine realistische Bewertung der Signifikanz von Netzwerkeigenschaften ermöglicht. Die Ergebnisse zeigen anhand der Stoffwechselnetzwerke von sechs Organismen, dass viele der meistuntersuchten Netzwerkeigenschaften, wie das Kleine-Welt-Phänomen und die Vorhersage der Biosynthese von Stoffwechselprodukten, von herausragender Bedeutung für deren biologische Funktion sind, und somit für Vorhersagen und Modellierung verwendet werden können. Die Methode ermöglicht die Identifikation von chemischen Reaktionen, welche wahrscheinlich von lebenswichtiger Bedeutung für den Organismus sind. Weiterhin erlaubt die Methode die Vorhersage von bisher unbekannten, aber physikalisch möglichen Reaktionen, welche spezifische Zellfunktionen, wie erhöhtes Wachstum in Mikroorganismen, ermöglichen könnten. Die Methode bietet einen neuartigen Ansatz zur Bestimmung der funktional relevanten Eigenschaften biologischer Netzwerke, und eröffnet neue Möglichkeiten für deren Manipulation

Optimizing metabolic pathways by screening for feasible synthetic reactions

AbstractBackgroundReconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze.ResultsHere, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coli, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis.ConclusionsWhile many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering