Background

A novel Gram-negative, non-haemolytic, non-motile, rod-shaped bacterium was discovered in the lungs of a dead parakeet (Melopsittacus undulatus) that was kept in captivity in a petshop in Basel, Switzerland. The organism is described with a chemotaxonomic profile and the nearly complete genome sequence obtained through the assembly of short sequence reads.

Results

Genome sequence analysis and characterization of respiratory quinones, fatty acids, polar lipids, and biochemical phenotype is presented here. Comparison of gene sequences revealed that the most similar species is Pelistega europaea, with BLAST identities of only 93% to the 16S rDNA gene, 76% identity to the rpoB gene, and a similar GC content (~43%) as the organism isolated from the parakeet, DSM 24701 (40%). The closest full genome sequences are those of Bordetella spp. and Taylorella spp. High-throughput sequencing reads from the Illumina-Solexa platform were assembled with the Edena de novo assembler to form 195 contigs comprising the ~2 Mb genome. Genome annotation with RAST, construction of phylogenetic trees with the 16S rDNA (rrs) gene sequence and the rpoB gene, and phylogenetic placement using other highly conserved marker genes with ML Tree all suggest that the bacterial species belongs to the Alcaligenaceae family. Analysis of samples from cages with healthy parakeets suggested that the newly discovered bacterial species is not widespread in parakeet living quarters.

Conclusions

Classification of this organism in the current taxonomy system requires the formation of a new genus and species. We designate the new genus Basilea and the new species psittacipulmonis. The type strain of Basilea psittacipulmonis is DSM 24701 (= CIP 110308 T, 16S rDNA gene sequence Genbank accession number JX412111 and GI 406042063).

Background

The salivary microbiota is a potential diagnostic indicator of several diseases. Culture-independent techniques are required to study the salivary microbial community since many of its members have not been cultivated.

Methods

We explored the bacterial community composition in the saliva sample using metagenomic whole genome shotgun (WGS) sequencing, the extraction of 16S rRNA gene fragments from metagenomic sequences (16S-WGS) and high-throughput sequencing of PCR-amplified bacterial 16S rDNA gene (16S-HTS) regions V1 and V3.

Results

The hierarchical clustering of data based on the relative abundance of bacterial genera revealed that distances between 16S-HTS datasets for V1 and V3 regions were greater than those obtained for the same V region with different numbers of PCR cycles. Datasets generated by 16S-HTS and 16S-WGS were even more distant. Finally, comparison of WGS and 16S-based datasets revealed the highest dissimilarity.The analysis of the 16S-HTS, WGS and 16S-WGS datasets revealed 206, 56 and 39 bacterial genera, respectively, 124 of which have not been previously identified in salivary microbiomes. A large fraction of DNA extracted from saliva corresponded to human DNA. Based on sequence similarity search against completely sequenced genomes, bacterial and viral sequences represented 0.73% and 0.0036% of the salivary metagenome, respectively. Several sequence reads were identified as parts of the human herpesvirus 7.

Conclusions

Analysis of the salivary metagenome may have implications in diagnostics e.g. in detection of microorganisms and viruses without designing specific tests for each pathogen.

Arbuscular mycorrhizal fungi (AMF) are a group of soil microorganisms that establish symbioses with the vast majority of land plants. To date, generation of AMF coding information has been limited to model genera that grow well axenically; Rhizoglomus and Gigaspora. Meanwhile, data on the functional gene repertoire of most AMF families is non-existent. Here, we provide primary large-scale transcriptome data from eight poorly studied AMF species (Acaulospora morrowiae, Diversispora versiforme, Scutellospora calospora, Racocetra castanea, Paraglomus brasilianum, Ambispora leptoticha, Claroideoglomus claroideum and Funneliformis mosseae) using ultra-low input ribonucleic acid (RNA)-seq approaches. Our analyses reveals that quiescent spores of many AMF species harbour a diverse functional diversity and solidify known evolutionary relationships within the group. Our findings demonstrate that RNA-seq data obtained from low-input RNA are reliable in comparison to conventional RNA-seq experiments. Thus, our methodology can potentially be used to deepen our understanding of fungal microbial function and phylogeny using minute amounts of RNA material.

As pollinators, bees are cornerstones for terrestrial ecosystem stability and key components in agricultural productivity. All animals, including bees, are associated with a diverse community of microbes, commonly referred to as the microbiome. The bee microbiome is likely to be a crucial factor affecting host health. However, with the exception of a few pathogens, the impacts of most members of the bee microbiome on host health are poorly understood. Further, the evolutionary and ecological forces that shape and change the microbiome are unclear. Here, we discuss recent progress in our understanding of the bee microbiome, and we present challenges associated with its investigation. We conclude that global coordination of research efforts is needed to fully understand the complex and highly dynamic nature of the interplay between the bee microbiome, its host, and the environment. High-throughput sequencing technologies are ideal for exploring complex biological systems, including host-microbe interactions. To maximize their value and to improve assessment of the factors affecting bee health, sequence data should be archived, curated, and analyzed in ways that promote the synthesis of different studies. To this end, the BeeBiome consortium aims to develop an online database which would provide reference sequences, archive metadata, and host analytical resources. The goal would be to support applied and fundamental research on bees and their associated microbes and to provide a collaborative framework for sharing primary data from different research programs, thus furthering our understanding of the bee microbiome and its impact on pollinator health.