Predicting Human Olfactory Perception from Activities of Odorant Receptors

Kowalewski, Joel; Ray, Anandasankar

doi:10.1016/j.isci.2020.101361

Cited by 31 publications

(24 citation statements)

References 55 publications

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“…The essential CmaxORs and relevant key amino acid residues provided in this work can be prioritized in future functional demonstrations. Because more promising results were achieved using in silico approaches, we may save a lot of time finding precise insect regulators in the future 58 . The concept of reverse chemical ecology can be expanded by adding in silico methods in the future, and experiments can be designed more specifically to evaluate the functions of certain amino acid residues 59,60 .…”

Section: Discussionmentioning

confidence: 99%

Behavioural regulator and molecular reception of a double‐edge‐sword hunter beetle

Chen

Huang

et al. 2022

Pest Management Science

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BACKGROUND: The black carabid beetle Calosoma maximoviczi is a successful predator that serves as both a beneficial insect and a severe threat to economic herbivores. Its hunting technique relies heavily on olfaction, but the underlying mechanism has not been studied. Here, we report the electrophysiological, ecological and molecular traits of bioactive components identified from a comprehensive panel of natural odorants in the beetle-prey-plant system. The aim of this work was to investigate olfactory perceptions and their influence on the behaviours of C. maximoviczi.RESULTS: Among the 200 identified volatiles, 18 were concentrated in beetle and prey samples, and 14 were concentrated in plants. Insect feeding damage to plants led to a shift in the emission fingerprint. Twelve volatiles were selected using successive electrophysiological tests. Field trials showed that significant sex differences existed when trapping with a single chemical or chemical mixture. Expression profiles indicated that sex-biased catches were related to the expression of 15 annotated CmaxOBPs and 40 CmaxORs across 12 chemosensory organs. In silico evaluations were conducted with 16 CmaxORs using modelling and docking. Better recognition was predicted for the pairs CmaxOR5-(Z)-3-hexenyl acetate, CmaxOR6-⊎-caryophyllene, CmaxOR18-(E)-⊎-ocimene and CmaxOR18-tetradecane, with higher binding affinity and a suitable binding pocket. Lastly, 168Y in CmaxOR6 and 142Y in CmaxOR18 were predicted as key amino acid residues for binding ⊎-caryophyllene and tetradecane, respectively. CONCLUSION: This work provides an example pipeline for de novo investigation in C. maximoviczi baits and the underlying olfactory perceptions. The results will benefit the future development of trapping-based integrated pest management strategies and the deorphanization of odorant receptors in ground beetles.

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Section: Discussionmentioning

confidence: 99%

Behavioural regulator and molecular reception of a double‐edge‐sword hunter beetle

Chen

Huang

et al. 2022

Pest Management Science

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“…In order to improve upon and further validate the analysis, experimental methods involving a standardised chemotaxis assay can be employed for a set of chemicals, and numerical chemotactic index values can be calculated and fed into machine learning models in place of the current categorical binaries. It has been shown that the incorporation of odorant receptor data increases the accuracy of prediction in humans [76]. However, there is only a limited set of olfactory receptor data available for C. elegans due to the complexity arising from their co-expression on neurons and the presence of a large number(≈1200) of GPCR genes [30, 77].…”

Section: Discussionmentioning

confidence: 99%

A machine learning based analysis to probe the relationship between odorant structure and olfactory behaviour inC. elegans

Vishnoi

Sharma

2021

Preprint

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The chemical basis of smell remains an unsolved problem, with ongoing studies mapping perceptual descriptor data from human participants to the chemical structures using computational methods. These approaches are, however, limited by linguistic capabilities and inter-individual differences in participants. We use olfactory behaviour data from the nematode C. elegans, which has isogenic populations in a laboratory setting, and employ machine learning approaches for a binary classification task predicting whether or not the worm will be attracted to a given monomolecular odorant. Among others, we use architectures based on Natural Language Processing methods on the SMILES representation of chemicals for molecular descriptor generation and show that machine learning algorithms trained on the descriptors give robust prediction results. We further show, by data augmentation, that increasing the number of samples increases the accuracy of the models. From this detailed analysis, we are able to achieve accuracies comparable to that in human studies and infer that there exists a non trivial relationship between the features of chemical structures and the nematode's behaviour.

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“…Machine learning algorithms for feature selection are from the caret [32] and kernlab [33] packages in the R programming language and similar to the way it has been used for ligand prediction of human odorant neurons [34].…”

Section: Selecting Important Chemical Featuresmentioning

confidence: 99%

Prolonged activation of carbon dioxide-sensitive neurons in mosquitoes

2021

Self Cite

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Many insects can detect carbon dioxide (CO 2 ) plumes using a conserved receptor made up of members of the gustatory receptor (Gr) family Gr1, Gr2 and Gr3. Mosquitoes are attracted to host animals for blood meals using plumes of CO 2 in the exhaled breath using the receptor expressed in the A neuron of the capitate peg sensilla type on the maxillary palps. The receptor is known to also detect several other classes of odorants, including ones emitted from human skin. Here, we discover that a common skin odorant, butyric acid, can cause a phasic activation followed by an unusually prolonged tonic activity after the stimulus is over in the CO 2 neurons of mosquitoes. The effect is conserved in both Aedes aegypti and Anopheles gambiae mosquitoes. This raises a question about its role in a mosquito's preference for the skin odour of different individuals. Butyric acid belongs to a small number of odorants known to cause the prolonged activation of the CO 2 receptor. A chemical informatic analysis identifies a specific set of physico-chemical features that can be used in a machine learning predictive model for the prolonged activators. Interestingly, this set is different from physico-chemical features selected for activators or inhibitors, indicating that each has a distinct structural basis. The structural understanding opens up an opportunity to find novel ligands to manipulate the CO 2 receptor and mosquito behaviour.

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Predicting Human Olfactory Perception from Activities of Odorant Receptors

Cited by 31 publications

References 55 publications

Behavioural regulator and molecular reception of a double‐edge‐sword hunter beetle

Behavioural regulator and molecular reception of a double‐edge‐sword hunter beetle

A machine learning based analysis to probe the relationship between odorant structure and olfactory behaviour inC. elegans

Prolonged activation of carbon dioxide-sensitive neurons in mosquitoes

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