The microbiome-gut-brain axis in acute and chronic brain diseases

The gut microbiome — the largest reservoir of microorganisms of the human body — is emerging as an important player in neurodevelopment and ageing as well as in brain diseases including stroke, Alzheimer’s disease and Parkinson’s disease. The growing knowledge on mediators and triggered pathways has advanced our understanding of the interactions along the gut-brain axis. Gut bacteria produce neuroactive compounds and can modulate neuronal function, plasticity and behavior. Furthermore, intestinal microorganisms impact the host’s metabolism and immune status which in turn affect neuronal pathways in the enteric and central nervous systems. Here, we discuss the recent insights from human studies and animal models on the bi-directional communication along the microbiome-gut-brain axis in both acute and chronic brain diseases

Sample preservation and storage significantly impact taxonomic and functional profiles in metaproteomics studies of the human gut microbiome

With the technological advances of the last decade, it is now feasible to analyze microbiome samples, such as human stool specimens, using multi-omic techniques. Given the inherent sample complexity, there exists a need for sample methods which preserve as much information as possible about the biological system at the time of sampling. Here, we analyzed human stool samples preserved and stored using different methods, applying metagenomics as well as metaproteomics. Our results demonstrate that sample preservation and storage have a significant effect on the taxonomic composition of identified proteins. The overall identification rates, as well as the proportion of proteins from were much higher when samples were flash frozen. Preservation in RNAlater overall led to fewer protein identifications and a considerable increase in the share of , as well as . Additionally, a decrease in the share of metabolism-related proteins and an increase of the relative amount of proteins involved in the processing of genetic information was observed for RNAlater-stored samples. This suggests that great care should be taken in choosing methods for the preservation and storage of microbiome samples, as well as in comparing the results of analyses using different sampling and storage methods. Flash freezing and subsequent storage at -80 °C should be chosen wherever possible

Comparison of low--energy resonances in 15N(alpha,gamma)19F and 15O(alpha,gamma)19Ne and related uncertainties

A disagreement between two determinations of Gamma_alpha of the astro- physically relevant level at E_x=4.378 MeV in 19F has been stated in two recent papers by Wilmes et al. and de Oliveira et al. In this work the uncertainties of both papers are discussed in detail, and we adopt the value Gamma_alpha=(1.5^{+1.5}_{-0.8})10^-9eV for the 4.378 MeV state. In addition, the validity and the uncertainties of the usual approximations for mirror nuclei Gamma_gamma(19F) approx Gamma_gamma(19Ne), theta^2_alpha(19F) approx theta^2_alpha(19Ne) are discussed, together with the resulting uncertainties on the resonance strengths in 19Ne and on the 15O(alpha,gamma)19Ne rate.Comment: 9 pages, Latex, To appear in Phys. Rev.

A Semi-Quantitative, Synteny-Based Method to Improve Functional Predictions for Hypothetical and Poorly Annotated Bacterial and Archaeal Genes

During microbial evolution, genome rearrangement increases with increasing sequence divergence. If the relationship between synteny and sequence divergence can be modeled, gene clusters in genomes of distantly related organisms exhibiting anomalous synteny can be identified and used to infer functional conservation. We applied the phylogenetic pairwise comparison method to establish and model a strong correlation between synteny and sequence divergence in all 634 available Archaeal and Bacterial genomes from the NCBI database and four newly assembled genomes of uncultivated Archaea from an acid mine drainage (AMD) community. In parallel, we established and modeled the trend between synteny and functional relatedness in the 118 genomes available in the STRING database. By combining these models, we developed a gene functional annotation method that weights evolutionary distance to estimate the probability of functional associations of syntenous proteins between genome pairs. The method was applied to the hypothetical proteins and poorly annotated genes in newly assembled acid mine drainage Archaeal genomes to add or improve gene annotations. This is the first method to assign possible functions to poorly annotated genes through quantification of the probability of gene functional relationships based on synteny at a significant evolutionary distance, and has the potential for broad application