Adaptation of olfactory receptor abundances for efficient coding

Abstract
Data availability
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Abstract

Olfactory receptor usage is highly heterogeneous, with some receptor types being orders of magnitude more abundant than others. We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a regime of efficient coding that is sensitive to the global context of correlated sensor responses. This model predicts that in mammals, where olfactory sensory neurons are replaced regularly, receptor abundances should continuously adapt to odor statistics. Experimentally, increased exposure to odorants leads variously, but reproducibly, to increased, decreased, or unchanged abundances of different activated receptors. We demonstrate that this diversity of effects is required for efficient coding when sensors are broadly correlated, and provide an algorithm for predicting which olfactory receptors should increase or decrease in abundance following specific environmental changes. Finally, we give simple dynamical rules for neural birth and death processes that might underlie this adaptation.

Data availability

All the code necessary to reproduce our results and the figures from the paper is available on GitHub, at https://github.com/ttesileanu/OlfactoryReceptorDistribution. The olfactory receptor affinity data were originally published in Hallem et al. (2006) and Saito et al. (2009), and the olfactory receptor expression levels in mouse were originally published in Ibarra-Soria et al. (2017).

The following previously published data sets were used

1. Hallem EA
2. Carlson JR
(2006) Coding of odors by a receptor repertoire
doi:10.1016/j.cell.2006.01.050.

https://www.sciencedirect.com/science/article/pii/S0092867406003631
1. Saito H
2. Chi Q
3. Zhuang H
4. Matsunami H
5. Mainland JD
(2009) Odor Coding by a Mammalian Receptor Repertoire
doi:10.1126/scisignal.2000016.

http://stke.sciencemag.org/content/2/60/ra9
1. Ibarra-Soria X
2. Nakahara TS
3. Lilue J
4. Jiang Y
5. Trimmer C
6. Souza MAA
7. Netto PHM
8. Ikegami K
9. Murphy NR
10. Kusma M
11. Kirton A
12. Saraiva LR
13. Keane TM
14. Matsunami H
15. Mainland J
16. Papes F
17. Logan DW
(2017) Variation in olfactory neuron repertoires is genetically controlled and environmentally modulated
doi:10.1101/074872.

https://elifesciences.org/articles/21476

Article and author information

Author details

Tiberiu Tesileanu

Center for Computational Biology, Flatiron Institute, New York, United States

For correspondence
ttesileanu@gmail.com

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-3107-3088
Simona Cocco

Laboratoire de Physique Statistique, École Normale Supérieure, Paris, France

Competing interests
The authors declare that no competing interests exist.
Remi Monasson

Laboratoire de Physique Théorique, École Normale Supérieure, Paris, France

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-4459-0204
Vijay Balasubramanian

Department of Physics, University of Pennsylvania, Philadelphia, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-6497-3819

Funding

Simons Foundation (400425)

Vijay Balasubramanian

Aspen Center for Physics (PHY-160761)

Vijay Balasubramanian

Swartz Foundation

Tiberiu Tesileanu

National Science Foundation (PHY-1734030)

Tiberiu Tesileanu
Vijay Balasubramanian

US-Israel Binational Science Foundation (2011058)

Vijay Balasubramanian

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.