Trans-ethnic gut microbial signatures of prediabetic subjects from India and Denmark.

Background: Recent studies have indicated an association of gut microbiota and microbial metabolites with type 2 diabetes mellitus (T2D). However, large-scale investigation of the gut microbiota of "prediabetic" (PD) subjects has not been reported. Identifying robust gut microbiome signatures of prediabetes and characterizing early prediabetic stages is important for the understanding of disease development and could be crucial in early diagnosis and prevention.

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).

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.

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.