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Fig. 4 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 4. The occurrence of 3 different antennal trichoid sensilla in relation to their widths (A, B and C) in male and female diamondback moth, Plutella xylostella. D shows the widths of the sensilla plotted against their lengths for the 3 trichoid sensilla types (Tr I, Tr II and Tr III). Data obtained from 3 females and 5 males.

Fig. 3 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 3. The number of each of 4 types of sensilla along various antennal segments (1–10 = proximal end, 11–20 = middle, 21–30 = distal end) of female (A, C) and male (B, D) diamondback moths, Plutella xylostella. The estimated number of sensilla (C, D) was calculated based on the surface area of each segment section, and by assuming that sensilla were present on 2/3 of the surface (mean ± S.E., n = 5–15 in females and 9–18 in males).

Fig. 7 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 7. Sensilla styloconica identified on the antennae of female (A, C and D) and male (B, E and F) Plutela xylostella. One sensillum styloconicum is located near the anterio-ventral end in each flagella subsegment (A, B). Usually 1 terminal sensory cone is present at the distal end in each sensillum styloconicum (C, D, E, F).

Fig. 2 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 2. Gross antennal morphology of female (A, B) and male (C) Plutella xylostella, and a part of a male antenna showing the presence of different morphological types of sensilla (D). Ac (sensilla auricillica); Cc (sensilla coeloconica); Ch (sensilla chaetica); Tr I (sensilla trichodea Type I); Tr II (sensilla trichodea Type II); Tr III (sensilla trichodea Type III).

Fig. 6 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 6. Detailed surface morphology of sensilla coeloconica (A, B and C) and sensilla auricillica (D, E and F) of the antennae of Plutela xylostella.

Fig. 8 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 8. Traces of the response spikes of the olfactory receptor neurons (ORNs) in class A trichoid sensilla of female Plutella xylostella in response to various green leaf volatiles. Each trace shows the extracellular signals for a period of 5 s. The scale bar indicates the stimulation for 0.1 s with corresponding test stimulus.

Fig. 9 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 9. Traces of the response spikes of the olfactory receptor neurons (ORNs) in class D trichoid sensilla of female Plutella xylostella in response to geraniol and (±)-linalool. Each trace shows the extracellular signals for a period of 5 s. The scale bar indicates the stimulation for 0.1 s with corresponding test stimulus.

Fig. 1 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 1. The length (triangles) and diam (squares) of each flagellar subsegment of the antennae of female (A) and male (B) diamondback moth, Plutella xylostella. Data obtained from 3 females and 5 males.

Fig. 5 in Antennal sensillum morphology and electrophysiological responses of olfactory receptor neurons in trichoid sensilla of the diamondback moth (Lepidoptera: Plutellidae)

Fig. 5. Detailed surface morphology of the sensilla trichodea (A, B, C and D) and sensilla chaetica (A, B and E) of the antennae of Plutela xylostella.

opencc-by-4.0Dec 2018View details →

Characterizing Novel Olfactory Receptors Expressed in the Murine Renal Cortex: Supplemental Table S1

Ligand screening of Olfr90, Olfr461, Olfr558, Olfr1034, and Olfr1396. The complete screening results of all compounds tested on all 5 murine ORs. Compounds were tested at 0.5 mM unless otherwise specified. “+” indicates robust statistically signification activation, while “–” indicates no response. ORs listed in the Classification column are siblings of tested ORs. The term “general” is used for compounds in our library that are commonly used to screen ORs, and “biofluids” refers to compounds listed in the Human Metabolome Database which are detected in biofluids such as blood, urine, etc.

opencc-by-4.0Apr 2019View details →

Characterizing Novel Olfactory Receptors Expressed in the Murine Renal Cortex: Supplemental Table S2

Olfactory receptors selected for study. Olfactory receptors (ORs) selected for study based on mapped reads in at least 7 out of 8 murine renal cortex samples. Murine samples are listed as A - M. Samples A - G were fed high fat diet, while samples I - M were fed control diet. (mm10) FPKM counts for ORs selected for study based on the GRCm30/mm10 genome build using previously published OR coordinates. ORs are listed using the "Olfr" gene names, as well as the "CUFFOR" names as determined by Ibarra-Soria X et al. (mm9) FPKM counts for ORs selected for study based on the NCBI37/mm9 genome build using established coordinates. ORs listed in green were identified and cloned from kidney RNA previously.   Data accessible at NCBI GEO database, accession number GSE117249 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117249

dryad40/100

Network of artificial olfactory receptors for spatiotemporal monitoring of toxic gas

Open the record for dataset details and reuse information.

publicSep 2024View details →

opencc-zeroJan 2024View details →

Data from: Does the number of functional olfactory receptor genes predict olfactory sensitivity and discrimination performance in mammals?

The number of functional genes coding for olfactory receptors differs markedly between species and has repeatedly been suggested to be predictive of a species' olfactory capabilities. To test this assumption, we compiled a database of all published olfactory detection threshold values in mammals and used three sets of data on olfactory discrimination performance that employed the same structurally related monomolecular odor pairs with different mammal species. We extracted the number of functional olfactory receptor genes of the 20 mammal species for which we found data on olfactory sensitivity and/or olfactory discrimination performance from the Chordata Olfactory Receptor Database. We found that the overall olfactory detection thresholds significantly correlates with the number of functional olfactory receptor genes. Similarly, the overall proportion of successfully discriminated monomolecular odor pairs significantly correlates with the number of functional olfactory receptor genes. These results provide the first statistically robust evidence for the relation between olfactory capabilities and their genomics correlates. However, when analysed individually, of the 44 monomolecular odorants for which data on olfactory sensitivity from at least five mammal species are available, only five yielded a significant correlation between olfactory detection thresholds and the number of functional olfactory receptors genes. Also, for the olfactory discrimination performance, no significant correlation was found for any of the 74 relationships between the proportion of successfully discriminated monomolecular odor pairs and the number of functional olfactory receptor genes. While only a rather limited amount of data on olfactory detection thresholds and olfactory discrimination scores in a rather limited number of mammal species is available so far, we conclude that the number of functional olfactory receptor genes may be a predictor of olfactory sensitivity and discrimination performance in mammals.

opencc-zeroDec 2017View details →

Data from: Olfaction written in bone: cribriform plate size parallels olfactory receptor gene repertoires in Mammalia

The evolution of mammalian olfaction is manifested in a remarkable diversity of gene repertoires, neuroanatomy, and skull morphology across living species. Olfactory receptor genes (ORG), which initiate the conversion of odorant molecules into odor perceptions and help an animal resolve the olfactory world, range in number from a mere handful to several thousand genes across species. Within the snout, each of these ORGs is exclusively expressed by a discrete population of olfactory sensory neurons (OSN), suggesting that newly evolved ORGs may be coupled with new OSN populations in the nasal epithelium. Because OSNs axon bundles leave high-fidelity perforations (foramina) in the bone as they traverse the cribriform plate (CP) to reach the brain, we predicted that taxa with larger ORG repertoires would have proportionately expanded footprints in the CP foramina. Previous work found a correlation between ORG number and absolute CP size that disappeared when body size effects were accounted for. Using updated, digital measurement data from high-resolution CT scans and reexamining the relationship between CP and body size, we report a striking linear correlation between relative CP area and number of functional ORGs across species from all mammalian superorders. This correlation suggests strong developmental links in the olfactory pathway between genes, neurons, and skull morphology. Furthermore, because ORG number is linked to olfactory discriminatory function, this correlation supports relative CP size as a viable metric for inferring olfactory capacity across modern and extinct species. By quantifying CP area from a fossil sabertooth cat (Smilodon fatalis) we predicted a likely ORG repertoire for this extinct felid.

opencc-zeroJul 2022View details →

Olfactory receptor alignments for: Ecological constraints on highly evolvable olfactory receptor genes and morphology in neotropical bats

While evolvability of genes and traits may promote specialization during species diversification, how ecology subsequently restricts such variation remains unclear. Chemosensation requires animals to decipher a complex chemical background to locate fitness-related resources, and thus the underlying genomic architecture and morphology must cope with constant exposure to a changing odorant landscape; detecting adaptation amidst extensive chemosensory diversity is an open challenge. In phyllostomid bats, an ecologically diverse clade that evolved plant-visiting from an insectivorous ancestor, the evolution of novel food detection mechanisms is suggested to be a key innovation, as plant-visiting species rely strongly on olfaction, supplementarily using echolocation. If this is true, exceptional variation in underlying olfactory genes and phenotypes may have preceded dietary diversification. We compared olfactory receptor (OR) genes sequenced from olfactory epithelium transcriptomes and olfactory epithelium surface area of bats with differing diets. Surprisingly, although OR evolution rates were quite variable and generally high, they are largely independent of diet. Olfactory epithelial surface area, however, is relatively larger in plant-visiting bats and there is an inverse relationship between OR evolution rates and surface area. Relatively larger surface areas suggest greater reliance on olfactory detection and stronger constraint on maintaining an already diverse OR repertoire. Instead of the typical case in which specialization and elaboration are coupled with rapid diversification of associated genes, here the relevant genes are already evolving so quickly that increased reliance on smell has led to stabilizing selection, presumably to maintain the ability to consistently discriminate among specific odorants — a potential ecological constraint on sensory evolution.

zenodo36/100

Datasets and geometries for "MORE-Q, Dataset for molecular olfactorial receptor engineering by quantum mechanics"

We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have performed accurate QM calculations in different stages of the sensor design and, accordingly, MORE-Q splits into three subsets: i) MORE-Q-G1: QM data of 18 receptors and 102 BOV molecules, ii) MORE-Q-G2: QM data of 23, 838 BOV-receptor configurations, and iii) MORE-Q-G3: QM data of 1, 836 BOV-receptor-graphene systems. Each subset involves geometries optimized using GFN2-xTB with D4 dispersion correction and up to 39 physicochemical properties, including global and local properties as well as binding features, all computed at the tightly converged PBE+D3 level of theory. By addressing BOV-receptor-graphene systems from a QM perspective, MORE-Q can serve as a benchmark dataset for state-of-the-art machine learning methods developed to predict binding features. This, in turn, can provide valuable insights for developing the next-generation mucin-derived olfactory receptor sensing devices. The dataset is provided in 3 HDF5 based files. One can also find here a README file with technical usage details and examples of how to access the information stored in the dataset (see createDF.py). We also offer a Github repository for user guide, see https://github.com/LiC1117/MORE-Q. For more details, one can refer to the manuscript doi: <a href="https://doi.org/10.1038/s41597-025-04616-6" rel="nofollow">https://doi.org/10.1038/s41597-025-04616-6</a>

opencc-by-4.0Sep 2024View details →

zenodo36/100

Data supporting "Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study"

Simulation data and input files in support of the Manuscript:"Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study". The archive is organized in 6 different folders: 1. 7x7_rmsd, which contains a tcl script (to be run in VMD) to compute the 7x7 RMSD matrix (see Wang et al. J Struct. Bio, (2017)). 2. a100_plumed, which contains PLUMED input files to compute A100 index on OR51E2 trajectories. 3. initial_structures, which contains the 6 different conformation for hOR51E2 obtained by the different predictors in the pdb format. 4. mdp_files, which contains the GROMACS mdp files to perform all the protocol described in the paper. 5. topologies, which contains the 6 different topologies (in .top format) and the initial conformation (in .gro format) for the hOR51E2 embedded in the membrane and solvated. 6. trajectories, which contains the 18 (6 systems, 3 replicas per system) different trajectories with the sodium in place close to D692.50 without solvent and ions (in .xtc format with a frame every 100 ps) and a reference conformation (in .gro format). Here we have also added the trajectory for the SwissModel-derived simulation without sodium ion in place, where we observed the ion binding. This last trajectory contains also solvent and ions, but with a lower printing frequency (1 ns).

opencc-by-4.0Feb 2023View details →