Genetic control of encoding strategy in a food-sensing neural circuit
Giovanni Diana,
Dhaval S Patel,
Eugeni V Entchev,
Mei Zhan,
Hang Lu,
QueeLim Ch'ng
Affiliations
Giovanni Diana
Centre for Developmental Neurobiology, King's College London, London, United Kingdom
Dhaval S Patel
Centre for Developmental Neurobiology, King's College London, London, United Kingdom
Eugeni V Entchev
Centre for Developmental Neurobiology, King's College London, London, United Kingdom
Mei Zhan
Interdisciplinary Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, United States; Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, United States; School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States
Hang Lu
Interdisciplinary Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, United States; Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, United States; School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States
Neuroendocrine circuits encode environmental information via changes in gene expression and other biochemical activities to regulate physiological responses. Previously, we showed that daf-7 TGF[Formula: see text] and tph-1 tryptophan hydroxylase expression in specific neurons encode food abundance to modulate lifespan in Caenorhabditis elegans, and uncovered cross- and self-regulation among these genes (Entchev et al., 2015). Here, we now extend these findings by showing that these interactions between daf-7 and tph-1 regulate redundancy and synergy among neurons in food encoding through coordinated control of circuit-level signal and noise properties. Our analysis further shows that daf-7 and tph-1 contribute to most of the food-responsiveness in the modulation of lifespan. We applied a computational model to capture the general coding features of this system. This model agrees with our previous genetic analysis and highlights the consequences of redundancy and synergy during information transmission, suggesting a rationale for the regulation of these information processing features.