Decoding gonadotropin-releasing hormone (GnRH) pulse frequency

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Within the body, cells communicate with one another using chemical signals such as hormones and neurotransmitters. These are often secreted in pulses and their effects are dependent upon pulse frequency so understanding how cells decode pulse frequency is fundamental to understanding how information is conveyed between (and within) cells. The brain's control of reproduction provides an excellent example and model for scientific exploration. Here, a neurohormone called GnRH (gonadotropin-releasing hormone) acts on cells in the pituitary gland to stimulate the synthesis and release of two other hormones (LH and FSH) that, in turn control the production of eggs and sex steroids in the gonads. A fundamental feature of this system is that GnRH secretion is pulsatile. Pulses of GnRH can be used to stimulate LH and FSH secretion and this is exploited in assisted reproduction. In contrast, sustained stimulation with GnRH ultimately reduces LH and FSH secretion. This, in turn reduces synthesis of sex steroids enabling treatment of hormone-dependent cancers (i.e. breast, ovary and prostate cancers). Thus, there is a "bell-shaped" frequency-response relationship (where sub-maximal GnRH pulse frequency elicits maximal responses) that underlies exploitation of the system, but remarkably little is known about the cellular, molecular or mathematical basis of this relationship. To explore this we have recently developed novel methods for monitoring effects of GnRH pulses on two intracellular biochemical pathways that mediate GnRH effects on gene expression (ERK and NFAT pathways). Using automated fluorescence microscopy to monitor these pathways in live cells we found that they are not GnRH frequency decoders (because they do not exhibit the negative feedback previously thought to underlie the bell-shaped frequency response relationship). However, we used this experimental data to develop and validate a sophisticated mathematical model for the mechanisms of GnRH action at the cellular level, and this model predicts that frequency decoding actually reflects the convergence of these pathways on the DNA elements that mediate GnRH effects on gene expression. Our unique wet-lab data and mathematical modelling has generated a novel theoretical frame-work that we believe represents a major breakthrough in understanding pulsatile GnRH signalling. In essence we are proposing that GnRH pulse frequency decoding is an emergent feature of the GnRH cell signalling network (rather than a characteristic of a single protein or pathway within the network) but we are still at a very early stage, as the mathematical model has not yet been tested experimentally. One of the most intriguing aspects of the modelling is the prediction that GnRH frequency-response relationships will be regulable rather than fixed (i.e. that the optimal pulse frequency for GnRH effects could differ before and after puberty, or could vary through the menstrual cycle) and this application aims to explore this possibility. Using the mathematical model for hypothesis generation, we now plan to define how some of the key model variables (such as GnRH receptor number and exposure to sex steroids) influence GnRH frequency-response relationships. We also plan to use the wet-lab data to refine the model, and to use a more formal mathematical approach for development and extension of the model. The direct importance of the planned work lies in the potential for greater understanding of GnRH signalling with physiologically relevant stimulation and for identifying novel targets for manipulation in the context in human and veterinary medicine as well as agriculture and aquaculture. The work is also likely to have widespread application because the structures and mechanisms considered are widespread in biological systems.