A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis.

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

Use of Arabidopsis thaliana as a model to understand specific carcinogenic events: Comparison of the molecular machinery associated with cancer-hallmarks in plants and humans.

Model organisms are fundamental in cancer research given that they rise the possibility to characterize in a quantitative-objective fashion the organisms as a whole in ways that are infeasible in humans. From this perspective, model organisms with short generation times and established protocols for genetic manipulation allow the understanding of basic biology principles that might guide carcinogenic onset. The cancer-hallmarks (CHs) approach, a modular perspective for cancer understanding, stands that underlying the variability among different cancer types, critical events support the carcinogenic origin and progression. Thus, CHs as interconnected genetic circuitry, have a causal effect over cancer biogenesis and might represent a comparison scaffold among model organisms to identify and characterize evolutionarily conserved modules to understand cancer. Nevertheless, the identification of novel cancer regulators by comparative genomics approaches relies on selecting specific biological processes or related signaling cascades that limit the type of detected regulators, even more, holistic analysis from a systemic perspective is absent. Similarly, although the plant Arabidopsis thaliana has been used as a model organism to dissect specific disease-associated mechanisms, given the evolutionary distance between plants and humans, a general concern about the utility of using A. thaliana as a cancer model persists. In the present research, we take advantage of the CHs paradigm as a framework to establish a functional systemic comparison between plants and humans, that allowed the identification not only of specific novel key genetic regulators, but also, biological processes, metabolic systems, and genetic modules that might contribute to the neoplastic transformation. We propose five cancer-hallmarks that overlapped in conserved mechanisms and processes between Arabidopsis and human and thus, represent mechanisms which study can be prioritized in A. thaliana as an alternative model for cancer research. Additionally, derived from network analyses and machine learning strategies, a new set of potential candidate genes that might contribute to neoplastic transformation is described. These findings postulate A. thaliana as a suitable model to dissect, not all, but specific cancer properties, highlighting the importance of using alternative complementary models to understand carcinogenesis.

GOCompare: An R package to compare functional enrichment analysis between two species.

Functional enrichment analysis is a cornerstone in bioinformatics as it makes possible to identify functional information by using a gene list as source. Different tools are available to compare gene ontology (GO) terms, based on a directed acyclic graph structure or content-based algorithms which are time-consuming and require a priori information of GO terms. Nevertheless, quantitative procedures to compare GO terms among gene lists and species are not available. Here we present a computational procedure, implemented in R, to infer functional information derived from comparative strategies. GOCompare provides a framework for functional comparative genomics starting from comparable lists from GO terms. The program uses functional enrichment analysis (FEA) results and implement graph theory to identify statistically relevant GO terms for both, GO categories and analyzed species. Thus, GOCompare allows finding new functional information complementing current FEA approaches and extending their use to a comparative perspective. To test our approach GO terms were obtained for a list of aluminum tolerance-associated genes in Oryza sativa subsp. japonica and their orthologues in Arabidopsis thaliana. GOCompare was able to detect functional similarities for reactive oxygen species and ion binding capabilities which are common in plants as molecular mechanisms to tolerate aluminum toxicity. Consequently, the R package exhibited a good performance when implemented in complex datasets, allowing to establish hypothesis that might explain a biological process from a functional perspective, and narrowing down the possible landscapes to design wet lab experiments.

Métodos semiempíricos para la evaluación rápida de orbitales frontera en la clasificación de agonistas y antagonistas de la subunidad NR1 de los receptores iGluR-NMDA / Semiempirical methods for the rapid evaluation of frontier orbitals in the classification of agonists and antagonists of the NR1 subunit of the iGluR-NMDA receptors / Métodos semi-empíricos de avaliação rápida de orbitais fronteira na classificação dos agonistas e antagonistas da subunidade NR1 dos receptores iGluRNMDA

Los receptores iGluR-NMDA poseen gran interés farmacológico debido a que están implicados en desórdenes neurodegenerativos y neurosiquiátricos incluso participan en procesos como plasticidad sináptica, esencial para la formación de la memoria. La subunidad NR1 de los iGluR-NMDA es fundamental para que este tipo de receptores se activen apropiadamente, de hecho muchos de los fármacos estudiados para los desórdenes anteriormente mencionados, están dirigidos específicamente a la subunidad NR1. Estudios previos han determinado que el orbital molecular de más baja energía (LUMO), puede ser usado como parámetro para estimar la actividad agonista o antagonista en la subunidad NR1. Objetivo. Evaluar el método semiempírico CNDO para el cálculo rápido de la energía LUMO, con la finalidad de crear un modelo sencillo para el diseño in silico de nuevos fármacos. Materiales y métodos. Fueron seleccionadas 168 moléculas entre agonistas y antagonistas de la subunidad NR1. La energía de cada estructura fue optimizada y posteriormente fueron calculadas las energías de los orbitales frontera, el LogP, la energía total, la capacidad de formar puentes de hidrógeno, la energía de unión y el momento dipolar. Resultados. Se demuestra que la energía LUMO es suficiente para discriminar entre moléculas agonistas y antagonistas de esta subunidad y que el método CNDO evalúa estas propiedades de manera rápida y eficiente. Conclusión. El método CNDO permite el cálculo rápido, generando a futuro procedimientos eficaces para la caracterización de fármacos potenciales que actúen sobre este sitio en particular.

The ionotropic glutamate receptors activated by N-Methyl-D-Aspartate (iGluR-NMDA) are of great importance in pharmacology since they are involved in neurodegenerative and neuropsychiatric disorders; they even participate in processes such as synaptic plasticity that are essential for memory formation. Subunit NR1 iGluRs-NMDA is of paramount importance for the appropriate activation of this type of receptors; in fact, many of the pharmaceutical products studied for the abovementioned disorders are targeted specifically to the NR1 subunit. Previous studies have shown that the lowest energy unoccupied molecular orbital (LUMO) can be used as a parameter to estimate the agonist and antagonist activity of the NR1subunit. Objective. Evaluate the semiemprical method CNDO for the rapid calculation of the LUMO energy with the aim of preparing a simple model for the in silico design of new pharmacological substances. Materials and methods. 168 molecules with agonist and antagonist activity in the NR1 subunit were selected. Energy of each structure was optimized and then we calculated the energy of the frontier orbital, the LogP, total energy, capacity of forming hydrogen bonds, binding energy, and dipolar moment. Results. We demonstrate that LUMO energy is enough for discriminating agonist and antagonist molecules of the NR1 subunit and that the CNDO method evaluates these properties in a rapid and efficient way. Conclusions. The CNDO method facilitates a rapid calculation, enabling a future development of effective procedures for the characterization of potential pharmacological substances acting on this particular site.

Os receptores IGluR-NMDA têm grande interesse farmacológico porque estão envolvidos em desordens neurodegenerativas e neuropsiquiátricas inclusive participam em processos de plasticidade sináptica, essenciais para a formação da memória. A subunidade NR1 dos iGluR-NMDA é fundamental para que este tipo de receptores se ativem de forma adequada, de fato, muitos dos fármacos estudados para os transtornos mencionados acima, são orientados especificamente pela subunidade NR1. Estudos prévios determinaram que o orbital molecular de mais baixa energia (LUMO), pode ser usado como um parâmetro para estimar a atividade agonista ou antagonista na subunidade NR1. Objetivo. Avaliar o método semi-empírico CNDO para o cálculo rápido da energia LUMO, a fim de criar um modelo simples para o desenho in silicio de novas drogas. Materiais e métodos. Foram selecionadas 168 moléculas entre agonistas e antagonistas da subunidade NR1. A energia de cada estrutura foi otimizada e, em seguida, foram calculadas as energias de orbitais fronteira, o LogP, a energia total, a capacidade de formar pontes de hidrogênio, a energia de ligação e o momento dipolar. Resultados. Foi demonstrado que a energia LUMO é suficiente para discriminar entre moléculas agonistas e antagonistas desta subunidade e que o método CNDO avalia essas propriedades de forma rápida e eficiente. Conclusão. O método CNDO permite o cálculo rápido, gerando a futuro procedimentos eficazes para a caracterização de potenciais medicamentos que agem neste sitio em particular.