Pheromone sensing in Drosophila requires support cell-expressed Osiris 8.

BACKGROUND: The nose of most animals comprises multiple sensory subsystems, which are defined by the expression of different olfactory receptor families. Drosophila melanogaster antennae contain two morphologically and functionally distinct subsystems that express odorant receptors (Ors) or ionotropic receptors (Irs). Although these receptors have been thoroughly characterized in this species, the subsystem-specific expression and roles of other genes are much less well-understood. RESULTS: Here we generate subsystem-specific transcriptomic datasets to identify hundreds of genes, encoding diverse protein classes, that are selectively enriched in either Or or Ir subsystems. Using single-cell antennal transcriptomic data and RNA in situ hybridization, we find that most neuronal genes-other than sensory receptor genes-are broadly expressed within the subsystems. By contrast, we identify many non-neuronal genes that exhibit highly selective expression, revealing substantial molecular heterogeneity in the non-neuronal cellular components of the olfactory subsystems. We characterize one Or subsystem-specific non-neuronal molecule, Osiris 8 (Osi8), a conserved member of a large, insect-specific family of transmembrane proteins. Osi8 is expressed in the membranes of tormogen support cells of pheromone-sensing trichoid sensilla. Loss of Osi8 does not have obvious impact on trichoid sensillar development or basal neuronal activity, but abolishes high sensitivity responses to pheromone ligands. CONCLUSIONS: This work identifies a new protein required for insect pheromone detection, emphasizes the importance of support cells in neuronal sensory functions, and provides a resource for future characterization of other olfactory subsystem-specific genes.

BOOGIE: Predicting Blood Groups from High Throughput Sequencing Data.

Over the last decade, we have witnessed an incredible growth in the amount of available genotype data due to high throughput sequencing (HTS) techniques. This information may be used to predict phenotypes of medical relevance, and pave the way towards personalized medicine. Blood phenotypes (e.g. ABO and Rh) are a purely genetic trait that has been extensively studied for decades, with currently over thirty known blood groups. Given the public availability of blood group data, it is of interest to predict these phenotypes from HTS data which may translate into more accurate blood typing in clinical practice. Here we propose BOOGIE, a fast predictor for the inference of blood groups from single nucleotide variant (SNV) databases. We focus on the prediction of thirty blood groups ranging from the well known ABO and Rh, to the less studied Junior or Diego. BOOGIE correctly predicted the blood group with 94% accuracy for the Personal Genome Project whole genome profiles where good quality SNV annotation was available. Additionally, our tool produces a high quality haplotype phase, which is of interest in the context of ethnicity-specific polymorphisms or traits. The versatility and simplicity of the analysis make it easily interpretable and allow easy extension of the protocol towards other phenotypes. BOOGIE can be downloaded from URL http://protein.bio.unipd.it/download/.