Nitrospirillum viridazoti sp. nov., an Efficient Nitrogen-Fixing Species Isolated from Grasses.

A group of Gram-negative plant-associated diazotrophic bacteria belonging to the genus Nitrospirillum was investigated, including both previously characterized and newly isolated strains from diverse regions and biomes, predominantly in Brazil. Phylogenetic analysis of 16S rRNA and recA genes revealed the formation of a distinct clade consisting of thirteen strains, separate from the formally recognized species N. amazonense (the closest species) and N. iridis. Comprehensive taxonomic analyses using the whole genomes of four strains (BR 11140T = AM 18T = Y-2T = DSM 2788T = ATCC 35120T, BR 11142T = AM 14T = Y-1T = DSM 2787T = ATCC 35119T, BR 11145 = CBAmC, and BR 12005) supported the division of these strains into two species: N. amazonense (BR 11142 T and BR 12005) and a newly proposed species (BR 11140 T and BR 11145), distinct from N. iridis. The phylogenomic analysis further confirmed the presence of the new Nitrospirillum species. Additionally, MALDI-TOF MS analysis of whole-cell mass spectra provided further evidence for the differentiation of the proposed Nitrospirillum species, separate from N. amazonense. Analysis of chemotaxonomy markers (i.e., genes involved in fatty acid synthesis, metabolism and elongation, phospholipid synthesis, and quinone synthesis) revealed that the new species highlights high similarity and evolutionary convergence with other Nitrospirillum species. This new species exhibited nitrogen fixation ability in vitro, it has similar NifHDK protein phylogeny position with the closest species, lacked denitrification capability, but possessed the nosZ gene, enabling N2O reduction, distinguishing it from the closest species. Despite being isolated from diverse geographic regions, soil types, and ecological niches, no significant phenotypic or physiological differences were observed between the proposed new species and N. amazonense. Based on these findings, a new species, Nitrospirillum viridazoti sp. nov., was classified, with the strain BR 11140T (DSM 2788T, ATCC 35120T) designated as the type strain.

Use of Wild Type or Recombinant Lactic Acid Bacteria as an Alternative Treatment for Gastrointestinal Inflammatory Diseases: A Focus on Inflammatory Bowel Diseases and Mucositis.

The human gastrointestinal tract (GIT) is highly colonized by bacterial communities, which live in a symbiotic relationship with the host in normal conditions. It has been shown that a dysfunctional interaction between the intestinal microbiota and the host immune system, known as dysbiosis, is a very important factor responsible for the development of different inflammatory conditions of the GIT, such as the idiopathic inflammatory bowel diseases (IBD), a complex and multifactorial disorder of the GIT. Dysbiosis has also been implicated in the pathogenesis of other GIT inflammatory diseases such as mucositis usually caused as an adverse effect of chemotherapy. As both diseases have become a great clinical problem, many research groups have been focusing on developing new strategies for the treatment of IBD and mucositis. In this review, we show that lactic acid bacteria (LAB) have been capable in preventing and treating both disorders in animal models, suggesting they may be ready for clinical trials. In addition, we present the most current studies on the use of wild type or genetically engineered LAB strains designed to express anti-inflammatory proteins as a promising strategy in the treatment of IBD and mucositis.

In Silico Protein-Protein Interactions: Avoiding Data and Method Biases Over Sensitivity and Specificity.

The study of protein-protein interactions (PPIs) can help researchers raise new hypotheses about an organism or disease and guide new experiments. Various methods for the identification and analysis of PPIs have been discussed in the literature. These methods are generally categorized as experimental or computational - each having its own advantages and disadvantages. Experimental methods provide insights into the real state of biological interactions but tend to be time-consuming and costly. Computational methods, on the other hand, can study thousands of PPIs at a very low cost and in much less time; however, the accuracy of such in silico prediction results heavily depends on the specific computational approach used. Furthermore, there is no gold standard for these computational methods; a method that works well for predicting one PPI may perform poorly (by generating false positives and false negatives) for a different PPI. Therefore, all such predictions must be carefully validated, preferably with experimental data. In this paper, we review the existing computational approaches and emphasize the use of biological data as inputs for accurate predictions of PPIs. We also discuss how such input datasets and approaches may influence the sensitivity and specificity of the predicted PPI networks.