The increasing number of OMICs studies demands bioinformatic tools that aid in the analysis of large sets of genes or proteins to understand their roles in the cell and establish functional networks and pathways. In the last decade, over-representation or enrichment tools have played a successful role in the functional analysis of large gene/protein lists, which is evidenced by thousands of publications citing these tools. However, in most cases the results of these analyses are long lists of biological terms associated to proteins that are difficult to digest and interpret. Here we present NeVOmics, Network-based Visualization for Omics, a functional enrichment analysis tool that identifies statistically over-represented biological terms within a given gene/protein set. This tool provides a hypergeometric distribution test to calculate significantly enriched biological terms, and facilitates analysis on cluster distribution and relationship of proteins to processes and pathways. NeVOmics is adapted to use updated information from the two main annotation databases: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). NeVOmics compares favorably to other Gene Ontology and enrichment tools regarding coverage in the identification of biological terms. NeVOmics can also build different network-based graphical representations from the enrichment results, which makes it an integrative tool that greatly facilitates interpretation of results obtained by OMICs approaches. NeVOmics is freely accessible at .

BackgroundThe heterotrimeric Gα protein Pga1-mediated signaling pathway regulates the entire developmental program in Penicillium chrysogenum, from spore germination to the formation of conidia. In addition it participates in the regulation of penicillin biosynthesis. We aimed to advance the understanding of this key signaling pathway using a proteomics approach, a powerful tool to identify effectors participating in signal transduction pathways.Results Penicillium chrysogenum mutants with different levels of activity of the Pga1-mediated signaling pathway were used to perform comparative proteomic analyses by 2D-DIGE and LC–MS/MS. Thirty proteins were identified which showed differences in abundance dependent on Pga1 activity level. By modifying the intracellular levels of cAMP we could establish cAMP-dependent and cAMP-independent pathways in Pga1-mediated signaling. Pga1 was shown to regulate abundance of enzymes in primary metabolic pathways involved in ATP, NADPH and cysteine biosynthesis, compounds that are needed for high levels of penicillin production. An in vivo phosphorylated protein containing a pleckstrin homology domain was identified; this protein is a candidate for signal transduction activity. Proteins with possible roles in purine metabolism, protein folding, stress response and morphogenesis were also identified whose abundance was regulated by Pga1 signaling.ConclusionsThirty proteins whose abundance was regulated by the Pga1-mediated signaling pathway were identified. These proteins are involved in primary metabolism, stress response, development and signal transduction. A model describing the pathways through which Pga1 signaling regulates different cellular processes is proposed.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-016-0564-x) contains supplementary material, which is available to authorized users.

Bifidobacteria have been investigated due to their mutualistic microbe–host interaction with humans throughout their life. This work aims to make a biochemical and genomic characterization of Bifidobacterium pseudocatenulatum JCLA3. By multilocus analysis, the species of B. pseudocatenulatum JCLA3 was established as pseudocatenulatum. It contains one circular genome of 2,369,863 bp with G + C content of 56.6%, no plasmids, 1937 CDSs, 54 tRNAs, 16 rRNAs, 1 tmRNA, 1 CRISPR region, and 401 operons predicted, including a CRISPR-Cas operon; it encodes an extensive number of enzymes, which allows it to utilize different carbohydrates. The ack gene was found as part of an operon formed by xfp and pta genes. Two genes of ldh were found at different positions. Chromosomally encoded resistance to ampicillin and cephalothin, non-hemolytic activity, and moderate inhibition of Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 6538 were demonstrated by B. pseudocatenulatum JCLA3; it can survive 100% in simulated saliva, can tolerate primary and secondary glyco- or tauro-conjugated bile salts but not in a mix of bile; the strain did not survive at pH 1.5–5. The cbh gene coding to choloylglycine hydrolase was identified in its genome, which could be related to the ability to deconjugate secondary bile salts. Intact cells showed twice as much antioxidant activity than debris. B. pseudocatenulatum JCLA3 showed 49% of adhesion to Caco-2 cells. The genome and biochemical analysis help to elucidate further possible biotechnological applications of B. pseudocatenulatum JCLA3.

Lactic acid bacteria (LAB) resist sodium selenite of concentrations greater than 100 mg/L in fermentation media. Selenium affects the growth rate, but once the microorganism absorbs selenium, this element is converted through a complex mechanism into selenocysteine and then into a selenoprotein structure. This study verified the presence of selenocysteine in Enterococcus faecium ABMC-05. The microorganism was cultivated in a medium enriched with a minimum inhibitory concentration of sodium selenite (184 mg/L). The concentration of selenium absorbed and the bioconversion into selenocysteine were determined by inductively coupled plasma optical emission spectrometry (ICP-OES) and reverse-phase high-performance chromatography (RP-HPLC), respectively. The presence of the selD, selA, and cysK genes was determined by amplifying the 16S rDNA through polymerase chain reaction (PCR). The microorganism accumulated inorganic selenium, and part was transformed into selenocysteine. The growth curves were atypical for a lactic acid bacterium with a stationary phase greater than 70 h. Determining the genetic expression showed only the presence of the cysK gene and the absence of the selD and the selA genes. The results demonstrate that this microorganism produces selenocysteine through a mechanism independent of the SelA and SelD pathways in contrast to other LAB.

Background Obesity, a public health problem, is a state of metainflammation that influences the development of chronic degenerative diseases, particularly in patients with severe obesity. Objective The objective of this study was to evidence immunometabolic differences in patients with different degrees of obesity, including severe obesity, by determining correlations between lymphocyte subpopulations and metabolic, body composition, and clinical variables. Methods Peripheral blood immune cells (CD4+, CD8+ memory and effector T lymphocytes) were analyzed, and measures of body composition, blood pressure, and biochemical composition (glucose, glycated hemoglobin (HbA1c), insulin, C-reactive protein (CRP), and the lipid profile) were carried out in patients with different degrees of obesity. Results The patients were classified according to total body fat (TBF) percentage as normal body fat, class 1 and 2 obesity, class 3 obesity, and class 4 obesity. The greater the TBF percentage, the more pronounced the differences in body composition (such as a decrease in the fat-free mass (FFM) that is defined as sarcopenic obesity) and the immunometabolic profile. There was an increase of CD3+ T lymphocytes (mainly CD4+, CD4+CD62-, and CD8+CD45RO+ T lymphocytes) and an increase in the TBF percentage (severity of obesity). Conclusions The correlations between lymphocyte subpopulations and metabolic, body composition, and clinical variables demonstrated the existence of a chronic, low-intensity inflammatory process in obesity. Therefore, measuring the immunometabolic profile by means of lymphocyte subpopulations in patients with severe obesity could be useful to determine the severity of the disease and the increased risk of presenting obesity-associated chronic degenerative diseases.