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

Abstract

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 β subunits (FSHβ/LHβ). Contrary to , 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 inhibitory factors ( inhibin α and VGF nerve growth factor-inducible), 2) activation of a signaling component with a refractory period ( G protein), and 3) inactivation of a factor needed for induction ( growth differentiation factor 9). Simulations with all three models recapitulated the 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 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 , 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 and genes responded only to variations in average GnRH concentration, 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 gene activity.

Regulatory Architecture of the LβT2 Gonadotrope Cell Underlying the Response to Gonadotropin-Releasing Hormone

“…Protocols for GnRH stimulation were selected to provide well-characterized response patterns. Hence, early gene responses in LβT2 cells are sensitive to GnRH concentration, while Fshb transcript levels respond to variations in both GnRH concentration and pulse patterns ( 9 , 13 ). Because GnRH stimulation of LβT2 cells was previously shown to induce an early gene program, whose transcripts are upregulated within an hour of GnRH exposure ( 12 , 13 ), a 40- to 45-min GnRH treatment was used in this study to analyze the transcriptome response to GnRH.…”

“…Because GnRH stimulation of LβT2 cells was previously shown to induce an early gene program, whose transcripts are upregulated within an hour of GnRH exposure ( 12 , 13 ), a 40- to 45-min GnRH treatment was used in this study to analyze the transcriptome response to GnRH. For the analysis of chromatin accessibility by ATAC-seq, a pattern of pulsatile GnRH exposure was employed to capture the chromatin state that accompanies maximal Fshb induction ( 9 , 18 ).…”

“…In the presence of steroid hormones, LβT2 cells further increase the LH secretory response to GnRH pulses as well as the levels of Lhb and Gnrhr mRNAs ( 6 ). In addition, LβT2 cells induce Fshb under either activin A ( 7 , 8 ) or GnRH pulse stimulation ( 3 ), with the level of Fshb being influenced by both pulse frequency and average concentration of GnRH ( 9 ). While LβT2 cells exhibit an increase in intracellular calcium and exocytosis in response to GnRH stimulation ( 5 , 6 ), they differ from mature anterior pituitary cells in that they lack a characteristic large-amplitude calcium oscillatory response to GnRH ( 10 ).…”

“…Following treatment with pulses of gonadotropin-releasing hormone (GnRH), LβT2 cells cultured in coverslips placed in racks [see in Ref. ( 9 )] were harvested by trypsinization and resuspended in medium, as indicated. Cell samples were lysed, generating crude nuclei preparations.…”

Advances in computational modeling approaches of pituitary gonadotropin signaling

“…These approaches raise a generic theoretical question: which network motifs are able to decode and discriminate different pulse frequency-coded signals and/or to preserve the frequency information downstream in the signaling cascades? Some steps forward have been made to answer this question through the careful analysis of low-dimensional ODE models corresponding to small network motifs [105,106]. Information theory and stochastic modeling have also been used to assess how reliable a signaling pathway can be in transmitting information from a given input into a given output [107].…”