Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction

<p>Abstract</p> <p>Background</p> <p><it>Burkholderia cenocepacia </it>is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF) or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain <it>B. cenocepacia </it>J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of <it>B. cenocepacia </it>J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets.</p> <p>Results</p> <p>We reconstructed the genome-scale metabolic network of <it>B. cenocepacia </it>J2315. An iterative reconstruction process led to the establishment of a robust model, <it>i</it>KF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model <it>i</it>KF1028 captures important metabolic capabilities of <it>B. cenocepacia </it>J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of <it>B. cenocepacia </it>J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets.</p> <p>Conclusions</p> <p>As the first genome-scale metabolic network of <it>B. cenocepacia </it>J2315, <it>i</it>KF1028 allows a systematic study of the metabolic properties of <it>B. cenocepacia </it>and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.</p

Predicting new molecular targets for rhein using network pharmacology

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.</p> <p>Methods</p> <p>In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.</p> <p>Results</p> <p>Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.</p> <p>Conclusions</p> <p>Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.</p

A Systems Biology Approach to Drug Targets in Pseudomonas aeruginosa Biofilm

Antibiotic resistance is an increasing problem in the health care system and we are in a constant race with evolving bacteria. Biofilm-associated growth is thought to play a key role in bacterial adaptability and antibiotic resistance. We employed a systems biology approach to identify candidate drug targets for biofilm-associated bacteria by imitating specific microenvironments found in microbial communities associated with biofilm formation. A previously reconstructed metabolic model of Pseudomonas aeruginosa (PA) was used to study the effect of gene deletion on bacterial growth in planktonic and biofilm-like environmental conditions. A set of 26 genes essential in both conditions was identified. Moreover, these genes have no homology with any human gene. While none of these genes were essential in only one of the conditions, we found condition-dependent genes, which could be used to slow growth specifically in biofilm-associated PA. Furthermore, we performed a double gene deletion study and obtained 17 combinations consisting of 21 different genes, which were conditionally essential. While most of the difference in double essential gene sets could be explained by different medium composition found in biofilm-like and planktonic conditions, we observed a clear effect of changes in oxygen availability on the growth performance. Eight gene pairs were found to be synthetic lethal in oxygen-limited conditions. These gene sets may serve as novel metabolic drug targets to combat particularly biofilm-associated PA. Taken together, this study demonstrates that metabolic modeling of human pathogens can be used to identify oxygen-sensitive drug targets and thus, that this systems biology approach represents a powerful tool to identify novel candidate antibiotic targets

Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction

Background: Burkholderia cenocepacia is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF) or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain B. cenocepacia J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of B. cenocepacia J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets

Desarrollo y análisis de algoritmos probabilísticos para la reconstrucción de modelos metabólicos a escala genómica

This doctoral project is focused on the development and analysis of algorithms for the reconstruction of genome-scale metabolic models, such algorithms include decision-making based on probabilistic criteria. As a fundamental result of the doctoral research, the web application Computational Platform to Access Biological Information (COPABI), which can reconstruct genome-scale metabolic models of biological systems, has been developed. During its computational implementation, it was followed the methodology used for the reconstruction of the first genome-scale metabolic model of a photosynthetic microorganism, the Synechocystis sp. PCC6803. Different mathematical algorithms were applied to compare the models that were automatically generated by COPABI with those published in the literature for different species.El presente proyecto doctoral se ha centrado en el desarrollo y análisis de algoritmos para la reconstrucción de modelos metabólicos a escala genómica; tales algoritmos incluyen la toma de decisiones a partir de criterios probabilísticos. Como resultado fundamental de la investigación doctoral cabe destacar que se ha desarrollado la aplicación web Computational Platform to Access Biological Information (COPABI) que permite reconstruir modelos metabólicos a escala genómica de sistemas biológicos. Durante su implementación computacional, se ha seguido la metodología usada para la reconstrucción del primer modelo metabólico a escala genómica de un microorganismo fotosintético, la Synechocystis sp. PCC6803. Se aplicaron diferentes algoritmos matemáticos para comparar los modelos generados automáticamente por COPABI con los publicados en la literatura para diferentes especies.Reyes Chirino, R. (2013). Desarrollo y análisis de algoritmos probabilísticos para la reconstrucción de modelos metabólicos a escala genómica [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34344TESI

Desarrollo de métodos de simulación aplicados a la optimización de funciones objetivo biológicas

[ES] La Biología de Sistemas es un campo de la investigación en el que confluyen varias disciplinas de conocimiento como la Física, Matemática, Química y Biología, donde las interacciones de los elementos internos de un microorganismo y el medio ambiente influyen en el desarrollo de procesos que se representan mediante un modelo matemático. Este enfoque permite comprender el funcionamiento de los sistemas biológicos y profundizar en el entendimiento de cómo sus interacciones conllevan a la aparición de nuevas propiedades y procesos. En el estudio de los procesos biológicos, se realiza la confirmación o refutación de una teoría que se confronta con resultados experimentales. La Biología de Sistemas utiliza una hipótesis basada en el estudio de los procesos mediante una modelización matemática de los mismos. Uno de los elementos principales de análisis en Biología de Sistemas es la reconstrucción de modelos metabólicos determinante a la hora de poder modificar el funcionamiento de un organismo determinado. Este trabajo se aborda la automatización de esta actividad, así como los fundamentos esenciales de la Herramienta COPABI, como paso fundamental para una buena reconstrucción antes de aplicar diferentes métodos de optimización a un modelo metabólico a escala genómica. Esta investigación se basa en métodos no tradicionales que permiten ofrecer mejoras en los resultados de las simulaciones, con un mejor acercamiento a la realidad en el contexto de la ingeniería metabólica. Presentando PyNetMet, una librería de Python, como herramienta para trabajar con redes y modelos metabólicos. Con el fin de ilustrar las características más importantes y algunos de sus usos, se muestran resultados de la herramienta como el cálculo de la agrupación media de las redes que representan a cada uno de los modelos metabólicos, el número de metabolitos desconectados en cada modelo y la distancia media entre dos metabolitos cualesquiera de la red. Analizar los modelos metabólicos partiendo de la optimización monobjetivo no siempre se acerca todo lo deseado a la realidad, puesto que uno o más objetivos pueden entrar en conflicto porque tienen como denominador común la necesidad de elegir entre diferentes alternativas que han de evaluarse en base a diversos criterios. Para ello, se presentó un algoritmo de optimización multiobjetivo basado en algoritmos evolutivos que consiste en una adaptación del algoritmo sp-MODE implementado en la herramienta bioinformática BioMOE, que considera de manera simultánea la optimización de dos o más objetivos, a menudo en conflicto, dando como soluciones diferentes distribuciones de flujo en la que una no es mejor que la otra. En el área de la comparación de modelos metabólicos se muestra una herramienta bioinformática llamada CompNet, basada en conceptos de teoría de grafos como las Redes de Petri, para poder establecer una comparación entre modelos metabólicos, determinando qué cambios serían necesarios para modificar determinadas funciones en uno de los modelos con respecto al otro, a través de la métrica Distancia de Edición. Mediante las métricas de Baláž y Bunke se muestra el grado de semejanza que existe entre dos modelos mediante un valor cuantitativo que indica las semejanzas y diferencias ellos.[EN] Systems Biology is a field of research in which several disciplines of knowledge converge such as Physics, Mathematics, Chemistry and Biology, where the interactions of the internal elements of a microorganism and the environment influence the development of processes that are represented by a mathematical model. This approach allows us to understand how biological systems work and to deepen our understanding of how their interactions lead to the emergence of new properties and processes. In the study of biological processes, the confirmation or refutation of a theory that is confronted with experimental results is performed. Systems Biology uses a hypothesis based on the study of processes by means of a mathematical modeling of them. One of the main elements of analysis in Systems Biology is the reconstruction of metabolic models, which is decisive when it comes to modifying the functioning of a given organism. This work addresses the automation of this activity, as well as the essential fundamentals of the COPABI Tool, as a fundamental step for a good reconstruction before applying different optimization methods to a metabolic model at genomic scale. This research is based on non-traditional methods that allow us to offer improvements in simulation results, with a better approach to reality in the context of metabolic engineering. Introducing PyNetMet, a Python library, as a tool for working with metabolic networks and models. In order to illustrate the most important characteristics and some of its uses, results of the tool are shown, such as the calculation of the mean grouping of the networks representing each of the metabolic models, the number of metabolites disconnected in each model and the mean distance between any two metabolites in the network. Analyzing metabolic models on the basis of monobjective optimization does not always bring the desired closer to reality, since one or more objectives may come into conflict because their common denominator is the need to choose between different alternatives to be evaluated on the basis of different criteria. To this end, a multi-target optimization algorithm based on evolutionary algorithms was presented, consisting of an adaptation of the sp-MODE algorithm implemented in the bioinformatics tool BioMOE, which simultaneously considers the optimization of two or more objectives, often in conflict, giving as solutions different flow distributions in which one is not better than the other. In the area of the comparison of metabolic models, a bioinformatics tool called Network-Compare is shown, based on concepts of graph theory such as Petri dishes, in order to establish a comparison between metabolic models, determining what changes would be necessary to modify certain functions in one of the models with respect to the other, through the Editing Distance metric. By means of the Baláž and Bunke metrics, the degree of similarity between two models is shown by means of a quantitative value that indicates the similarities and differences between them.[CA] La Biologia de Sistemes és un camp de la recerca en què conflueixen diverses disciplines de coneixement com la Física, Matemàtica, Química i Biologia, on les interaccions dels elements interns d'un microorganisme i el medi ambient influeixen en el desenvolupament de processos que es representen mitjançant un model matemàtic. Aquesta perspectiva permet entendre el funcionament dels sistemes biològics i aprofundir en la comprensió de com les seves interaccions generen noves propietats i processos. En l'estudi dels processos biològics, es realitza la confirmació o refutació d'una teoria que es confronta amb resultats experimentals. La Biologia de Sistemes utilitza una hipòtesi basada en l'estudi dels processos mitjançant una modelització matemàtica dels mateixos. Un dels elements principals d'anàlisi en Biologia de Sistemes és la reconstrucció de models metabòlics determinants a l'hora de poder modificar el funcionament d'un organisme determinat. En aquest treball s'aborda l'automatització d'aquesta activitat, així com els fonaments essencials de l'Eina COPABI, com a pas fonamental per a una bona reconstrucció abans d'aplicar diferents mètodes d'optimització a un model metabòlic a escala genòmica. Aquesta investigació es basa en mètodes no tradicionals que permeten oferir millores en els resultats de les simulacions, amb una millor aproximació a la realitat en el context de l'enginyeria metabòlica. Es presenta PyNetMet, una llibreria de Python, com a eina per treballar amb xarxes i models metabòlics. Per tal d'il¿lustrar les característiques més importants i alguns dels seus usos, es mostren resultats de l'eina com el càlcul de l'agrupació mitjana de les xarxes que representen a cada un dels models metabòlics, el nombre de metabòlits desconnectats en cada model i la distància mitjana entre dos metabòlits qualssevol de la xarxa. Analitzar els models metabòlics partint de l'optimització mono-objectiu no sempre s'acosta tot el desitjat a la realitat, ja que un o més objectius poden entrar en conflicte perquè tenen com a denominador comú la necessitat de triar entre diferents alternatives que han d'avaluar-se sobre la base de diversos criteris. Per a això, es va presentar un algoritme d'optimització multi-objectiu basat en algoritmes evolutius que consisteix en una adaptació de l'algoritme sp-MODE implementat en l'eina bioinformàtica BioMOE, que considera de manera simultània l'optimització de dos o més objectius, sovint en conflicte, donant com solucions diferents distribucions de flux en la qual una no és millor que l'altra. En l'àrea de la comparació de models metabòlics es mostra una eina bioinformàtica anomenada CompNet, basada en conceptes de teoria de grafs com les Xarxes de Petri, per poder establir una comparació entre models metabòlics, determinant quins canvis serien necessaris per a modificar determinades funcions en un dels models respecte a l'altre, a través de la mètrica Distància d'Edició. Mitjançant les mètriques de Balaz i Bunke es mostra el grau de semblança que hi ha entre dos models a través d'un valor quantitatiu que indica les semblances i diferències entre ells.Jaime Infante, RA. (2020). Desarrollo de métodos de simulación aplicados a la optimización de funciones objetivo biológicas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/147112TESI

Formulación de un modelo metabólicamente estructurado para optimizar la producción de Polihidroxialcanoatos (PHA) a partir de Burkholderia cepacia.

El mejoramiento de procesos debe ser soportado en análisis cuantitativos que permitan tomar decisiones, reduciendo la incertidumbre. Los modelos matemáticos de procesos fermentativos permiten predecir su comportamiento, tanto en estado estacionario como en condiciones dinámicas, por lo que son una herramienta clave en el mejoramiento de estos procesos. El grupo de Bioprocesos de la Universidad Nacional de Colombia ha estado estudiando la producción de Polihidroxialcanoatos desde el aislamiento de cepas promisorias hasta su producción en planta piloto (100 L) empleando una cepa hiperproductora de Burkholderia cepacia y como fuente de carbono ácidos grasos; sin embargo, se han presentado desafíos para aumentar la concentración de producto y la productividad del proceso. El objetivo de este trabajo es desarrollar un modelo matemático que simule el crecimiento y producción de PHB en la cepa B cepacia B27, considerando los cambios metabólicos que ocurren entre las fases de feast and famine, y que permita la optimización del proceso. Para lograr este objetivo fue necesario: i) identificar el comportamiento de la cepa mediante fermentaciones batch, ii) caracterizar el metabolismo en varias etapas del cultivo mediante MFA lineal, iii) identificar condiciones ambientales clave que definen el cambio entre las fases feast and famine, y la activación del nuevo comportamiento metabólico, iv) ajustar modelos semi-empíricos para cada fase y, v) validar el modelo con la estrategia operacional óptima (lote alimentado). Los resultados muestran que los flujos metabólicos experimentan cambios significativos entre las dos fases: las vías metabólicas se ajustan para dirigir el carbono, en mayor proporción, hacia el crecimiento en la fase de feast, y a la producción de PHB en la fase famine. Además, la relación C/N fue identificada como el parámetro de correlación clave para la transición entre las dos fases. Adicionalmente, para la fase famine se detectó un periodo de adaptación, este periodo es el tiempo que requiere el ajuste de la maquinaria metabólica para el nuevo objetivo. El lote alimentado para optimizar el proceso emplea pulsos para la adición de las fuentes de carbono y nitrógeno, por separado. La alimentación permite extender la fase feast y obtener mayores concentraciones de biomasa, lo que genera mayores concentraciones de PHB al final del proceso.Abstract: Process improvement must be supported in quantitative analyzes that allow decisions making with reduced uncertainty. Mathematical models of fermentative processes allow to predict their behavior, both in steady state and in dynamic conditions, so they are a key tool in the improvement of these processes. The Bioprocess group of Universidad Nacional de Colombia has been studying the production of Polyhydroxyalkanoates from the isolation of promising strains to its production in a pilot plant (100 L) using a hyper-producing strain of Burkholderia cepacia and fatty acid as carbon source carbon; however, there are challenges for increasing product concentration and process productivity. The objective of this work is to develop a mathematical model that simulates the growth and production of PHB in B. cepacia B27, considering metabolic changes that occur between the phases of feast and famine, and that allows the process optimization. To achieve this objective, it was necessary: i) to identify the behavior of the strain by batch fermentation, ii) to characterize the metabolism at several culture stages by linear MFA, iii) to identify key environmental conditions that define the change between the F and F phases and the activation of a new metabolic behavior, iv) adjust semi-empirical models for each phase and, v) validate the model with the optimal operational strategy (fed batch). Results show that metabolic fluxes undergo significant changes between the two phases: metabolic pathways are adjusted to lead carbon, in a greater extent, towards growth in feast phase, and towards PHB production in famine phase. In addition, the C/N ratio was identified as the key correlation parameter for the transition between the two phases. Additionally, for the famine phase an adaptation period was detected, this period is the time required to adjust the metabolic machinery for the new objective. Fed batch for optimal operation employs pulses for adding carbon and nitrogen sources, separately. The feed allows to extend the feast phase and obtain higher biomass concentrations, which generates higher concentrations of PHB at the end of the process.Doctorad

Death of a bacterium: exploring the inhibition of Staphylococcus aureus by Burkholderia cenocepacia.

Antimicrobial resistance is a phenomenon of increasing concern as antimicrobial overuse and misuse eliminate current therapeutic options, ushering society into a post-antimicrobial era. Antibiotic discovery and synthesis efforts are urgently needed to counter the increasing burden of antimicrobial resistance. Staphylococcus aureus is a causative agent of a variety of clinical manifestations including bacteremia, endocarditis, soft tissue infection, osteomyelitis, and device-related infections. S. aureus infection presents additional treatment challenges due to its capacity for biofilm formation, which is a mode of growth that confers protection from antibiotics and physical elimination, and the emergence of antibiotic resistant strains, including methicillin-resistant S. aureus and vancomycin-resistant S. aureus. Infection with antibiotic-resistant strains occurs within both nosocomial and community settings, broadening the potential impact of this organism. Bacteria within the genus Burkholderia hold vast potential as sources of antimicrobial agents. Our analysis of patient culture data, provided by the Cystic Fibrosis Foundation, suggests a negative relationship between members of the Burkholderia cepacia complex and Staphylococcus aureus. An in vitro screen for activity against S. aureus indicated several clinical strains of Burkholderia cenocepacia confer potent anti-Staphylococcus activity. This dissertation characterizes the deleterious effect of the presence of B. cenocepacia J2315 and H111, two clinical isolates from cystic fibrosis patients, against S. aureus. Co-culture biofilm-associated survival of both methicillin-sensitive and methicillin-resistant strains was, overall, decreased with both B. cenocepacia J2315 and H111. I further established the breadth of antibiotic activity of these two strains in co-culture with multiple Staphylococcus and other Gram-positive species, including Enterococcus, Bacillus and Listeria. While both B. cenocepacia strains demonstrated detrimental effects against survival of co-inoculated Staphylococcus species, the extent of inhibition of other Gram-positive species differed. Antagonistic activity against the Enterococcus and Bacillus strains assessed in co-culture with B. cenocepacia H111 was profound, with reduction of many co-cultured strains to below the limit of detection. Co-culture survival of the same Gram-positive species with B. cenocepacia J2315 indicated no significant reduction versus cognate mono-culture. Inhibition of S. aureus by both B. cenocepacia strains occurs via a secreted compound, as evidenced by reduction in survival of S. aureus when exposed to B. cenocepacia sterile biofilm supernatants. The inhibitory substance, at least for B. cenocepacia J2315 is secreted in larger quantities in response to the presence of S. aureus. Enzymatic treatment of the supernatants suggests that a protein and an RNA, or a nucleoprotein, are involved in the B. cenocepacia J2315-mediated antagonism of S. aureus, but that inhibition by B. cenocepacia H111 involves a different mechanism. The inhibitory effect is largely dependent upon culture medium, and B. cenocepacia J2315 is more sensitive to differences in nutrient composition of the growth medium than B. cenocepacia H111. Further, this decrease in viable S. aureus is not simply due to B. cenocepacia causing a release of S. aureus from biofilms but is due to killing of S. aureus. Collectively, these data confirm the biotechnological potential of two B. cenocepacia strains and serve to optimize conditions for observation and analysis of this phenomenon