TY - JOUR
T1 - Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design
AU - Tian, Xiao Jun
AU - Zhang, Hang
AU - Sannerud, Jens
AU - Xing, Jianhua
N1 - Funding Information:
We thank Drs. David Lyons, Andrew Chess, Jing Chen, and Ken Kim for many helpful discussions. The research was supported by the US National Science Foundation Awards (DMS-1545771 and DMS-1462049). J.S. was supported by NSF/Department of Defense Grant DBI-1263020, which funds the TECBio Research Experiences for Undergraduates (REU) program.
PY - 2016/5/24
Y1 - 2016/5/24
N2 - 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.
AB - 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.
KW - Barrier crossing
KW - Cooperativity
KW - Dual-objective optimization
KW - Enhancer competition
KW - Epigenetics
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U2 - 10.1073/pnas.1601722113
DO - 10.1073/pnas.1601722113
M3 - Article
C2 - 27162367
AN - SCOPUS:84969776741
SN - 0027-8424
VL - 113
SP - E2889-E2898
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 21
ER -