Modeling and high-throughput experimental data uncover the mechanisms underlying Fshb gene sensitivity to gonadotropin-releasing hormone pulse frequency.

Neuroendocrine control of reproduction by brain-secreted pulses of gonadotropin-releasing hormone (GnRH) represents a longstanding puzzle about extracellular signal decoding mechanisms. GnRH regulates the pituitary gonadotropin's follicle-stimulating hormone (FSH) and luteinizing hormone (LH), both of which are heterodimers specified by unique ß subunits (FSHß/LHß). Contrary to Lhb, Fshb gene induction has a preference for low-frequency GnRH pulses. To clarify the underlying regulatory mechanisms, we developed three biologically anchored mathematical models: 1) parallel activation of Fshb inhibitory factors (e.g. inhibin α and VGF nerve growth factor-inducible), 2) activation of a signaling component with a refractory period (e.g. G protein), and 3) inactivation of a factor needed for Fshb induction (e.g. growth differentiation factor 9). Simulations with all three models recapitulated the Fshb expression levels obtained in pituitary gonadotrope cells perifused with varying GnRH pulse frequencies. Notably, simulations altering average concentration, pulse duration, and pulse frequency revealed that the apparent frequency-dependent pattern of Fshb expression in model 1 actually resulted from variations in average GnRH concentration. In contrast, models 2 and 3 showed "true" pulse frequency sensing. To resolve which components of this GnRH signal induce Fshb, we developed a high-throughput parallel experimental system. We analyzed over 4,000 samples in experiments with varying near-physiological GnRH concentrations and pulse patterns. Whereas Egr1 and Fos genes responded only to variations in average GnRH concentration, Fshb levels were sensitive to both average concentration and true pulse frequency. These results provide a foundation for understanding the role of multiple regulatory factors in modulating Fshb gene activity.

Feedback from peripheral musculature to central pattern generator in the neurogenic heart of the crab Callinectes sapidus: role of mechanosensitive dendrites.

The neurogenic heart of decapod crustaceans is a very simple, self-contained, model central pattern generator (CPG)-effector system. The CPG, the nine-neuron cardiac ganglion (CG), is embedded in the myocardium itself; it generates bursts of spikes that are transmitted by the CG's five motor neurons to the periphery of the system, the myocardium, to produce its contractions. Considerable evidence suggests that a CPG-peripheral loop is completed by a return feedback pathway through which the contractions modify, in turn, the CG motor pattern. One likely pathway is provided by dendrites, presumably mechanosensitive, that the CG neurons project into the adjacent myocardial muscle. Here we have tested the role of this pathway in the heart of the blue crab, Callinectes sapidus. We performed "de-efferentation" experiments in which we cut the motor neuron axons to the myocardium and "de-afferentation" experiments in which we cut or ligated the dendrites. In the isolated CG, these manipulations had no effect on the CG motor pattern. When the CG remained embedded in the myocardium, however, these manipulations, interrupting either the efferent or afferent limb of the CPG-peripheral loop, decreased contraction amplitude, increased the frequency of the CG motor neuron spike bursts, and decreased the number of spikes per burst and burst duration. Finally, passive stretches of the myocardium likewise modulated the spike bursts, an effect that disappeared when the dendrites were cut. We conclude that feedback through the dendrites indeed operates in this system and suggest that it completes a loop through which the system self-regulates its activity.

Decoding modulation of the neuromuscular transform.

When modulators of neuromuscular function alter the motor neuron spike patterns that elicit muscle contractions, it is predicted that they will also retune correspondingly the connecting processes of the neuromuscular transform. Here we confirm this prediction by analyzing data from the cardiac neuromuscular system of the blue crab. We apply a method that decodes the contraction response to the spike pattern in terms of three elementary building-block functions that completely characterize the neuromuscular transform. This method allows us to dissociate modulator-induced changes in the neuromuscular transform from changes in the spike pattern in the normally operating, essentially unperturbed neuromuscular system.

A method for decoding the neurophysiological spike-response transform.

Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.