Intramolecular energies of the cytotoxic protein CagA of Helicobacter pylori as a possible descriptor of strains' pathogenicity level.

The Helicobacter pylori cytotoxin-associated gene A (CagA) is known for causing gastroduodenal diseases, such as atrophic gastritis and peptic ulcerations. Furthermore Helicobacter pylori CagA positive strains has been reported as one of the main risk factors for gastric cancer (Parsonnet et al., 1997). Structural variations in the CagA structure can alter its affinity with the host proteins, inducing differences in the pathogenicity of H. pylori. CagA N-terminal region is characterized for be conserved among all H. pylori strains since the C-terminal region is characterized by an intrinsically disorder behavior. We generated complete structural models of CagA using different conformations of the C-terminal region for two H. pylori strains. These models contain the same EPIYA (ABC1C2) motifs but different level of pathogenicity: gastric cancer and duodenal ulcer. Using these structural models we evaluated the pathogenicity level of the H. pylori strain, based on the affinity of the interaction with SHP-2 and Grb2 receptors and on the number of interactions with the EPIYA motif. We found that the main differences in the interaction was due to the contributions of certain types of energies from each strain and not from the total energy of the molecule. Specifically, the electrostatic energy, helix dipole energy, Wander Waals clashes, torsional clash, backbone clash and cis bond energy allowed a separation between severe and mild pathology for the interaction of only CagA with SHP2.

Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils.

Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.

Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia.

BACKGROUND: Chronic lymphocytic leukemia (CLL) is an incurable malignancy of mature B-lymphocytes, characterized as being a heterogeneous disease with variable clinical manifestation and survival. Mutational statuses of rearranged immunoglobulin heavy chain variable (IGVH) genes has been consider one of the most important prognostic factors in CLL, but despite of its proven value to predict the course of the disease, the regulatory programs and biological mechanisms responsible for the differences in clinical behavior are poorly understood. METHODS: In this study, (i) we performed differential gene expression analysis between the IGVH statuses using multiple and independent CLL cohorts in microarrays platforms, based on this information, (ii) we constructed a simplified protein-protein interaction (PPI) network and (iii) investigated its structure and critical genes. This provided the basis to (iv) develop a Boolean model, (v) infer biological regulatory mechanism and (vi) performed perturbation simulations in order to analyze the network in dynamic state. RESULTS: The result of topological analysis and the Boolean model showed that the transcriptional relationships of IGVH mutational status were determined by specific regulatory proteins (PTEN, FOS, EGR1, TNF, TGFBR3, IFGR2 and LPL). The dynamics of the network was controlled by attractors whose genes were involved in multiple and diverse signaling pathways, which may suggest a variety of mechanisms related with progression occurring over time in the disease. The overexpression of FOS and TNF fixed the fate of the system as they can activate important genes implicated in the regulation of process of adhesion, apoptosis, immune response, cell proliferation and other signaling pathways related with cancer. CONCLUSION: The differences in prognosis prediction of the IGVH mutational status are related with several regulatory hubs that determine the dynamic of the system.