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New Insights into the Mysteries of Smell

Researchers at the University of Pittsburgh School of Medicine have uncovered the mechanism underlying a phenomenon in how we smell that has puzzled researchers for decades. In an article appearing online today in the Proceedings of the National Academy of Sciences, the team reports that, surprisingly, the mechanism follows a simple physics principle called cooperativity.

Inhalation of a scent sends a complex mixture of odor molecules swirling toward the back of the nose, where they bind to specialized receptors that are located on millions of olfactory neurons. Activation of these receptors sends signals from the olfactory neurons to the brain, where the smell is deciphered.

Individual neurons have only a single type of receptor and, therefore, recognize only specific odor molecules. However, the hundreds of different types of olfactory receptors are found, or expressed, in approximately equal numbers across the entire population of neurons, which allows a person to detect a wide variety of smells, explained senior investigator Jianhua Xing, Ph.D., associate professor of computational and systems biology, Pitt School of Medicine. Richard Axel, Columbia University, and Linda Buck, now at the Fred Hutchinson Cancer Research Center, received the 2004 Nobel Prize in Physiology or Medicine for discovering the receptors and making these observations.

“Over the past decades, neuroscientists have been trying to uncover how nature accomplishes these two goals: selecting one, and only one, type of olfactory receptor for each neuron, while at the same time ensuring that all receptor types are represented in the whole population of neurons,” said Dr. Xing.

The mysteries of how we smell have generated many experimental observations about how olfactory receptors actually work. In the new study, Dr. Xing and colleagues used these existing experimental data to create a computational model of how olfactory receptor expression can be both uniform across a single neuron, yet very diverse across the entire population of neurons. They then used this model to correctly predict several additional findings that have been demonstrated by other research groups, demonstrating that their model is valid.

Surprisingly, the model suggested a three-pronged regulation of olfactory receptor gene expression that follows a basic physics principle called cooperativity, in which elements in a system influence the behavior of one another rather than function independently. Cooperativity can explain many phenomena, such as the transition between liquid and vapor states, why oil and water do not mix, and even other biological processes such as how a protein folds.

Image shows the olfactory bulb of a mouse.

The mysteries of how we smell have generated many experimental observations about how olfactory receptors actually work. In the new study, Dr. Xing and colleagues used these existing experimental data to create a computational model of how olfactory receptor expression can be both uniform across a single neuron, yet very diverse across the entire population of neurons. Image is for illustrative purposes only.

“We are amazed that nature has solved the seemingly daunting engineering process of olfactory receptor expression in such a simple way,” said Dr. Xing.

The findings pave the way for new predictions about how olfactory receptors function that can be tested in future experiments, the results of which will help the team refine their model and make even more predictions.

About this neuroscience research

The research team also included Xiao-Jun Tian, Ph.D., of Pitt; Jens Sannerud , former Pitt undergraduate summer research fellow, currently of Brown University; and Hang Zhang, Ph.D., of Virginia Polytechnic Institute and State University.

Funding: This research was funded by National Science Foundation awards DMS-1545771 and DMS-1462049.

Source: Gloria Kreps – University of Pittsburgh
Image Source: The image is in the public domain.
Original Research: Abstract for “Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design” by Xiao-Jun Tian, Hang Zhang, Jens Sannerud, and Jianhua Xing in PNAS. Published online May 9 2016 doi:10.1073/pnas.1601722113
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Abstract

Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design

Multiple-objective optimization is common in biological systems. In the mammalian olfactory system, each sensory neuron stochastically expresses only one out of up to thousands of olfactory receptor (OR) gene alleles; at the organism level, the types of expressed ORs need to be maximized. Existing models focus only on monoallele activation, and cannot explain recent observations in mutants, especially the reduced global diversity of expressed ORs in G9a/GLP knockouts. In this work we integrated existing information on OR expression, and constructed a comprehensive model that has all its components based on physical interactions. Analyzing the model reveals an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a previously unidentified enhancer competition step. This model not only recapitulates monoallelic OR expression, but also elucidates how the olfactory system maximizes and maintains the diversity of OR expression, and has multiple predictions validated by existing experimental results. Through making an analogy to a physical system with thermally activated barrier crossing and comparative reverse engineering analyses, the study reveals that the olfactory receptor selection system is optimally designed, and particularly underscores cooperativity and synergy as a general design principle for multiobjective optimization in biology.

“Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design” by Xiao-Jun Tian, Hang Zhang, Jens Sannerud, and Jianhua Xing in PNAS. Published online May 9 2016 doi:10.1073/pnas.1601722113

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