Soon Gang Choi

G Proteins and Autocrine Signaling Differentially Regulate Gonadotropin Subunit Expression in Pituitary Gonadotrope

Background: The mechanism for differential control of gonadotropin gene induction by GnRH is not established. Results: GnRH activates Gαs and Gαq/11, which modulate LH and FSH synthesis, respectively, by a mechanism including secreted factors. Conclusion: Different G proteins and autocrine signaling regulate the pattern of FSH and LH expression by GnRH. Significance: A novel G protein and autocrine signaling mechanism has been identified. Gonadotropin-releasing hormone (GnRH) acts at gonadotropes to direct the synthesis of the gonadotropins, follicle-stimulating hormone (FSH), and luteinizing hormone (LH). The frequency of GnRH pulses determines the pattern of gonadotropin synthesis. Several hypotheses for how the gonadotrope decodes GnRH frequency to regulate gonadotropin subunit genes differentially have been proposed. However, key regulators and underlying mechanisms remain uncertain. We investigated the role of individual G proteins by perturbations using siRNA or bacterial toxins. In LβT2 gonadotrope cells, FSHβ gene induction depended predominantly on Gαq/11, whereas LHβ expression depended on Gαs. Specifically reducing Gαs signaling also disinhibited FSHβ expression, suggesting the presence of a Gαs-dependent signal that suppressed FSH biosynthesis. The presence of secreted factors influencing FSHβ expression levels was tested by studying the effects of conditioned media from Gαs knockdown and cholera toxin-treated cells on FSHβ expression. These studies and related Transwell culture experiments implicate Gαs-dependent secreted factors in regulating both FSHβ and LHβ gene expression. siRNA studies identify inhibinα as a Gαs-dependent GnRH-induced autocrine regulatory factor that contributes to feedback suppression of FSHβ expression. These results uncover differential regulation of the gonadotropin genes by Gαq/11 and by Gαs and implicate autocrine and gonadotrope-gonadotrope paracrine regulatory loops in the differential induction of gonadotropin genes.

Outside the box signaling: Secreted factors modulate GnRH receptor-mediated gonadotropin regulation

Control of gene expression following activation of membrane receptors results from the regulation of intracellular signaling pathways and transcription factors. Accordingly, research to elucidate the regulatory control circuits and cellular data processing mechanisms focuses on intracellular mechanisms. While autocrine and paracrine signaling are acknowledged in endocrinology, secreted factors are not typically recognized as fundamental components of the pathways connecting cell surface receptors to gene control in the nucleus. Studies of the gonadotrope suggest that extracellular regulatory loops may play a central role in the regulation of gonadotropin gene expression by gonadotropin-releasing hormone (GnRH) receptor activation. We review emerging evidence for this phenomenon, which we refer to as exosignaling, in gonadotropin gene control and in other receptor-mediated signaling systems. We propose that basic signaling circuit modules controlling gene expression can be seamlessly distributed across intracellular and exosignaling components that together orchestrate the precise physiological control of gene expression.

Differential gene expression in patients with amyotrophic lateral sclerosis.

Abstract Our objective was to analyze gene expression pattern in muscles from patients with amyotrophic lateral sclerosis (ALS) and multifocal motor neuropathy (MMN) compared to controls. Biopsied skeletal muscles from three ALS, three MMN and three control subjects had total RNA extracted and subjected to genome-wide gene expression analysis using Affymetrix GeneChip Exon 1.0 ST array. The most significant expression pattern differences were confirmed with RT-PCR in four additional ALS patients. Results showed that over 3000 genes were identified across the groups using q < 10%. Among 50 genes that were overexpressed only in the ALS group were: leucine-rich repeat kinase-2, follistatin, collagen type XIX alpha-1, ceramide kinase-like, sestrin-3 and CXorf64. No genes were significantly overexpressed in MMN alone. Underexpressed genes only in ALS included actinin α3, fructose-1,6-bisphosphatase-2 and homeobox C10; whereas only in MMN: hemoglobin A1 and CXorf64. Ankyrin repeat domain-1 was overexpressed in both groups. Underexpressed genes in both groups included myosin light chain kinase-2, enolase-3 and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-1. Validation analysis using RT-PCR confirmed the data for leucine-rich repeat kinase-2, follistatin, collagen type XIX alpha-1, ceramide kinase-like, sestrin-3 and CXorf64. In conclusion, there is differential tissue-specific gene expression in patients with ALS relative to MMN and controls. Further studies are necessary to evaluate the identified genes in larger patient groups and different tissues.

Research Resource: Gonadotropin-Releasing Hormone Receptor-Mediated Signaling Network in LβT2 Cells: A Pathway-Based Web-Accessible Knowledgebase

The GnRH receptor (GnRHR), expressed at the cell surface of the anterior pituitary gonadotrope, is critical for normal secretion of gonadotropins LH and FSH, pubertal development, and reproduction. The signaling network downstream of the GnRHR and the molecular bases of the regulation of gonadotropin expression have been the subject of intense research. The murine LbetaT2 cell line represents a mature gonadotrope and therefore is an important model for the study of GnRHR-signaling pathways and modulation of the gonadotrope cell by physiological regulators. In order to facilitate access to the information contained in this complex and evolving literature, we have developed a pathway-based knowledgebase that is web hosted. At present, using 106 relevant primary publications, we curated a comprehensive knowledgebase of the GnRHR signaling in the LbetaT2 cell in the form of a process diagram. Positive and negative controls of gonadotropin gene expression, which included GnRH itself, hypothalamic factors, gonadal steroids and peptides, as well as other hormones, were illustrated. The knowledgebase contains 187 entities and 206 reactions. It was assembled using CellDesigner software, which provides an annotated graphic representation of interactions, stored in Systems Biology Mark-up Language. We then utilized Biological Pathway Publisher, a software suite previously developed in our laboratory, to host the knowledgebase in a web-accessible format as a public resource. In addition, the network entities were linked to a public wiki, providing a forum for discussion, updating, and error correction. The GnRHR-signaling network is openly accessible at http://tsb.mssm.edu/pathwayPublisher/GnRHR_Pathway/GnRHR_Pathway_ index.html.

Growth Differentiation Factor 9 (GDF9) Forms an Incoherent Feed-forward Loop Modulating Follicle-stimulating Hormone β-Subunit (FSHβ) Gene Expression

Background: The mechanisms underlying differential regulation of gonadotropin subunit genes are not fully elucidated. Results: Gonadotrope growth differentiation factor 9 (GDF9) expression, which is suppressed by GnRH, stimulates FSHβ expression. Conclusion: Autocrine secretion of GDF9 contributes to FSH biosynthesis. Significance: Regulation of FSH by GDF9 may contribute to gonadotrope function. Gonadotropin-releasing hormone (GnRH) is secreted in brief pulses from the hypothalamus and regulates follicle-stimulating hormone β-subunit (FSHβ) gene expression in pituitary gonadotropes in a frequency-sensitive manner. The mechanisms underlying its preferential and paradoxical induction of FSHβ by low frequency GnRH pulses are incompletely understood. Here, we identify growth differentiation factor 9 (GDF9) as a GnRH-suppressed autocrine inducer of FSHβ gene expression. GDF9 gene transcription and expression were preferentially decreased by high frequency GnRH pulses. GnRH regulation of GDF9 was concentration-dependent and involved ERK and PKA. GDF9 knockdown or immunoneutralization reduced FSHβ mRNA expression. Conversely, exogenous GDF9 induced FSHβ expression in immortalized gonadotropes and in mouse primary pituitary cells. GDF9 exposure increased FSH secretion in rat primary pituitary cells. GDF9 induced Smad2/3 phosphorylation, which was impeded by ALK5 knockdown and by activin receptor-like kinase (ALK) receptor inhibitor SB-505124, which also suppressed FSHβ expression. Smad2/3 knockdown indicated that FSHβ induction by GDF9 involved Smad2 and Smad3. FSHβ mRNA induction by GDF9 and GnRH was synergistic. We hypothesized that GDF9 contributes to a regulatory loop that tunes the GnRH frequency-response characteristics of the FSHβ gene. To test this, we determined the effects of GDF9 knockdown on FSHβ induction at different GnRH pulse frequencies using a parallel perifusion system. Reduction of GDF9 shifted the characteristic pattern of GnRH pulse frequency sensitivity. These results identify GDF9 as contributing to an incoherent feed-forward loop, comprising both intracellular and secreted components, that regulates FSHβ expression in response to activation of cell surface GnRH receptors.

Mapping, modeling, and characterization of protein–protein interactions on a proteomic scale

Proteins effect a number of biological functions, from cellular signaling, organization, mobility, and transport to catalyzing biochemical reactions and coordinating an immune response. These varied functions are often dependent upon macromolecular interactions, particularly with other proteins. Small-scale studies in the scientific literature report protein-protein interactions (PPIs), but slowly and with bias towards well-studied proteins. In an era where genomic sequence is readily available, deducing genotype-phenotype relationships requires an understanding of protein connectivity at proteome-scale. A proteome-scale map of the protein-protein interaction network provides a global view of cellular organization and function. Here, we discuss a summary of methods for building proteome-scale interactome maps and the current status and implications of mapping achievements. Not only do interactome maps serve as a reference, detailing global cellular function and organization patterns, but they can also reveal the mechanisms altered by disease alleles, highlight the patterns of interaction rewiring across evolution, and help pinpoint biologically and therapeutically relevant proteins. Despite the considerable strides made in proteome-wide mapping, several technical challenges persist. Therefore, future considerations that impact current mapping efforts are also discussed.

Characterization of a MAPK Scaffolding Protein Logic Gate in Gonadotropes

In the pituitary gonadotropes, both protein kinase C (PKC) and MAPK/ERK signaling cascades are activated by GnRH. Phosphoprotein-enriched in astrocytes 15 (PEA-15) is a cytosolic ERK scaffolding protein, which is expressed in LβT2 gonadotrope cells. Pharmacological inhibition of PKC and small interfering RNA-mediated silencing of Gαq/11 revealed that GnRH induces accumulation of phosphorylated PEA-15 in a PKC-dependent manner. To investigate the potential role of PEA-15 in GnRH signaling, we examined the regulation of ERK subcellular localization and the activation of ribosomal S6 kinase, a substrate of ERK. Results obtained by cellular fractionation/Western blot analysis and immunohistochemistry revealed that GnRH-induced accumulation of phosphorylated ERK in the nucleus was attenuated when PEA-15 expression was reduced. Conversely, in the absence of GnRH stimulation, PEA-15 anchors ERK in the cytosol. Our data suggest that GnRH-induced nuclear translocation of ERK requires its release from PEA-15, which occurs upon PEA-15 phosphorylation by PKC. Additional gene-silencing experiments in GnRH-stimulated cells demonstrated that ribosomal S6 kinase activation was dependent on both PEA-15 and PKC. Furthermore, small interfering RNA-mediated knockdown of PEA-15 caused a reduction in GnRH-stimulated expression of early response genes Egr2 and c-Jun, as well as gonadotropin FSHβ-subunit gene expression. PEA-15 knockdown increased LHβ and common α-glycoprotein subunit mRNAs, suggesting a possible role in differential regulation of gonadotropin subunit gene expression. We propose that PEA-15 represents a novel point of convergence of the PKC and MAPK/ERK pathways under GnRH stimulation. PKC, ERK, and PEA-15 form an AND logic gate that shapes the response of the gonadotrope cell to GnRH.

Optimized amplification and single-cell analysis identify GnRH-mediated activation of Rap1b in primary rat gonadotropes

Identifying the early gene program induced by GnRH would help understand how GnRH-activated signaling pathways modulate gonadotrope secretory response. We previously analyzed GnRH-induced early genes in LβT2 cells, however these lack GnRH self-potentiation, a physiological attribute of gonadotropes. To minimize cellular heterogeneity, rat primary pituitary cultures were enriched for gonadotropes by 40-60% using a sedimentation gradient. Given the limited number of gonadotropes, RNA was amplified prior to microarray analysis. Thirty-three genes were up-regulated 40 min after GnRH stimulation. Real-time PCR confirmed regulation of several transcripts including fosB, c-fos, egr-2 and rap1b, a small GTPase and member of the Ras family. GnRH stimulated rap1b gene expression in gonadotropes, measured by a sensitive single cell assay. Immunocytochemistry revealed increased Rap1 protein in GnRH-stimulated gonadotropes. These data establish rap1b as a novel gene rapidly induced by GnRH and a candidate to modulate gonadotropin secretion in rat gonadotropes.

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 gonadotropins follicle-stimulating hormone (FSH) and luteinizing hormone (LH), both of which are heterodimers specified by unique β subunits (FSHβ/LHβ). 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.

Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cells state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Platos Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.