2009 Abstracts - Association for Chemoreception Sciences

#48 Workshop: Computational problemsin sequential stages of odor processingExploring the Interaction between Odorants and OdorantReceptors using Functional and Computational MethodsCharles W. Luetje, Sarah E. Repicky, Tatjana AbaffyMolecular and Cellular Pharmacology, University of MiamiMiami, FL, USAThe mammalian olfactory system uses a vast array of odorantreceptors to detect and distinguish among an enormous numberof odorant molecules. However, odorant ligand specificity hasbeen determined for only a handful of odorant receptors and inonly a few cases have the details of ligand recognition beenexamined at the molecular level. Expanding our knowledge of themolecular basis for ligand recognition among odorant receptors ischallenging task. We are using functional analysis and site-directedmutagenesis, in combination with computational homologymodeling and virtual ligand docking, to examine the interactionbetween odorant ligands and mammalian odorant receptors. Wehave developed a functional assay for ORs using the Xenopusoocyte expression system and robotic electrophysiology. Thisassay allows us to use both conventional mutagenesis and theSubstituted Cysteine Accessibility Method to test and refine ourinitial receptor models. The assay also allows functional testing ofligand predictions derived from virtual docking. By iteratingbetween computational and functional approaches, we hope toimprove our understanding of the interaction between odorantsand odorant receptors, with a particular emphasis onunderstanding ligand selectivity.#49 Workshop: Computational problems insequential stages of odor processingModeling Diversity in the Signal Transduction of theMouse Olfactory Receptor NeuronDaniel P. DoughertyMichigan State University, Lyman Briggs College of Science andDept. of Statistics and Probability East Lansing, MI, USAWe use various statistical parameterization techniques to drive thedevelopment of a physiologically-based computational model ofthe slow (transduction) and fast (action potential) currents of theolfactory receptor neuron in mouse. The response to an odorantplume can elicit a diversity of responses across a population ofORN. We hope that by bridging various statistical andcomputational approaches an improved understanding of themolecular basis of this diversity can be obtained.#50 Workshop: Computational problems insequential stages of odor processingOdorant mixture interactions in rat olfactory receptorneurons: models and experimentsJean-Pierre Rospars 1 , Petr Lansky 2 , Michel Chaput 3 , PatriciaDuchamp-Viret 31UMR 1272 Physiologie de l’Insecte, INRA, Versailles, France ,2Institute of Physiology, Academy of Sciences, Prague, CzechRepublic , 3 UMR 5020 Neurosciences Sensorielles, Comportement,Cognition, Lyon, FranceAn odor perception is the brain’s translation of the activation byodorant molecules of olfactory receptor neurons (ORNs) locatedin the nasal cavity. Most ORNs express a single type of olfactoryreceptor that is differentially sensitive to a wide variety of odorantmolecules. Many kinds of odorant-receptor interactions can takeplace, especially with mixtures of many odorants which make upthe vast majority of real world odors. The simplest kind ofinteraction is pure competition when two or more agonists canbind to the receptor site which triggers receptor activation,although only one can be bound at a time. Noncompetitive effectsmay result from various mechanisms, as for example agonistbinding to another site of the receptor (allostery). Then twomolecules can bind at the same time and binding at the second sitemay modify the properties of the main binding site. We developeda quantitative model based on odorant-receptor interaction,relating the stimulus concentration to the ORN firing frequency.We extended this model to predict the ORN response in thesimple case of pure competitive interaction between odorants.To test the model we recorded the electrophysiological responsesof rat ORNs in vivo to odorant agonists and their binary mixturesat various concentrations. We interpreted the resultingconcentration-response curves in the framework of our model.We found that it accounts for all curves obtained with singleodorants and for about half of those obtained with binarymixtures. In the other half, the experimental curves weresignificantly different from those predicted by the model,indicating the occurrence of noncompetitive interactions.These noncompetitive interactions raise challenging problemsfor predicting the perception elicited by most odors, which likeperfumes, wine bouquet, food aroma, etc. are mixtures of dozens,even hundreds, of odorants because they imply that the brain’scode for mixtures of odorants will not be easily predicted fromthe known codes of their individual components.#51 Workshop: Computational problems insequential stages of odor processingOdor Maps in the Mouse Olfactory BulbVenkatesh N Murthy 1 , Dinu F Albeanu 2 , Edward R Soucy 1 ,Markus Meister 11Harvard University Cambridge, MA, USA, 2 Cold Spring HarborLaboratory Cold Spring Harbor, NY, USAMice and rats use ~1,000 olfactory receptor types to probechemical space. Each glomerulus in the olfactory bulb receivesinput from a single receptor type, whose ligand binding propertiesdetermine the spectrum of odor responses. In this way, the layoutof glomeruli on the bulb forms a two-dimensional map of odors.We recorded the odor responses of dorsal glomeruli in miceexpressing synaptopHluorin in sensory nerve terminals. By usinga chemically-diverse battery of several hundred odors, we couldassign unique functional identities to several dozen glomeruli.This allowed us to determine that the position of a givenAbstracts | 19