(623ac) Modeling of the Transcriptional Regulation of Tryptophan Hydroxylase In C. Elegans | AIChE

(623ac) Modeling of the Transcriptional Regulation of Tryptophan Hydroxylase In C. Elegans


Lee, H. - Presenter, Georgia Institute of Technology

Organisms respond to environmental conditions and modulate their physiological outcomes.  These responses vary from individual to individual even in isogenic populations.  Many previous studies described physiological consequences such as alternative cell fate decision as a result of gene regulatory networks.  However, it is still not well understood how genes integrate intrinsic and environmental signals.  Here, we analyzed transcriptional regulation of tryptophan hydroxylase (tph-1) in C. elegans using mathematical modeling.  We are interested in tph-1 because it encodes a key enzyme for serotonin synthesis and its expression is regulated by several environmental factors including food and temperature.  Furthermore, changes in tph-1 expression level can modulate physiological consequences like aging, fat storage, and egg laying.  We hypothesize that regulatory mechanism from environmental perception to physiology can be revealed by analyzing transcriptional regulation of tph-1.  In the model, tph-1 expression of wild-type and that of unc-43 gain-of-function mutant were compared.  CaMKII encoded by unc-43 is one of the major effectors of tph-1 expression in ADF neurons.  It has been known that unc-43(gf) increased the level of tph-1 expression but the detailed mechanism of its regulation is yet to be clarified.  The promoter activity of tph-1 was analyzed by characterizing transcriptional activities.  There are two known transcriptional factors in tph-1 regulation: one inhibitor and one activator.  These transcriptional factor activities are regulated by phosphorylation from signal transduction network (kinase cascade).  Based on modeling results, we can confirm the fact that the promoter of tph-1 has high activity in stressful condition.  After developing a deterministic model, we applied the Gillespie algorithm to include stochastic effects on tph-1 transcription.  The distribution of tph-1 expression of wild-type and that of unc-43 (gf) mutant were compared.  Simulation results agree with experimental results, showing that mutant population has a large deviation compared to wild-type even considered elevated tph-1 expression on average.  This result may reflect higher variability under stressful condition in environment.