The Airborne Metagenome in an Indoor Urban Environment

The indoor atmosphere is an ecological unit that impacts on public health. To investigate the composition of organisms in this space, we applied culture-independent approaches to microbes harvested from the air of two densely populated urban buildings, from which we analyzed 80 megabases genomic DNA sequence and 6000 16S rDNA clones. The air microbiota is primarily bacteria, including potential opportunistic pathogens commonly isolated from human-inhabited environments such as hospitals, but none of the data contain matches to virulent pathogens or bioterror agents. Comparison of air samples with each other and nearby environments suggested that the indoor air microbes are not random transients from surrounding outdoor environments, but rather originate from indoor niches. Sequence annotation by gene function revealed specific adaptive capabilities enriched in the air environment, including genes potentially involved in resistance to desiccation and oxidative damage. This baseline index of air microbiota will be valuable for improving designs of surveillance for natural or man-made release of virulent pathogens

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

Loss of C2orf69 defines a fatal autoinflammatory syndrome in humans and zebrafish that evokes a glycogen-storage-associated mitochondriopathy

Summary Human C2orf69 is an evolutionarily conserved gene whose function is unknown. Here, we report eight unrelated families from which 20 children presented with a fatal syndrome consisting of severe autoinflammation and progredient leukoencephalopathy with recurrent seizures; 12 of these subjects, whose DNA was available, segregated homozygous loss-of-function C2orf69 variants. C2ORF69 bears homology to esterase enzymes, and orthologs can be found in most eukaryotic genomes, including that of unicellular phytoplankton. We found that endogenous C2ORF69 (1) is loosely bound to mitochondria, (2) affects mitochondrial membrane potential and oxidative respiration in cultured neurons, and (3) controls the levels of the glycogen branching enzyme 1 (GBE1) consistent with a glycogen-storage-associated mitochondriopathy. We show that CRISPR-Cas9-mediated inactivation of zebrafish C2orf69 results in lethality by 8 months of age due to spontaneous epileptic seizures, which is preceded by persistent brain inflammation. Collectively, our results delineate an autoinflammatory Mendelian disorder of C2orf69 deficiency that disrupts the development/homeostasis of the immune and central nervous systems

Cap 24 The Pathology and Molecular Pathology of Hepatocellular Carcinoma

Non disponibil