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ABSTRACT: 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 Gα(q/11), whereas LHβ expression depended on Gα(s). Specifically reducing Gα(s) signaling also disinhibited FSHβ expression, suggesting the presence of a Gα(s)-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 Gα(s) knockdown and cholera toxin-treated cells on FSHβ expression. These studies and related Transwell culture experiments implicate Gα(s)-dependent secreted factors in regulating both FSHβ and LHβ gene expression. siRNA studies identify inhibinα as a Gα(s)-dependent GnRH-induced autocrine regulatory factor that contributes to feedback suppression of FSHβ expression. These results uncover differential regulation of the gonadotropin genes by Gα(q/11) and by Gα(s) and implicate autocrine and gonadotrope-gonadotrope paracrine regulatory loops in the differential induction of gonadotropin genes.
Journal of Biological Chemistry 05/2012; 287(25):21550-60. · 4.77 Impact Factor
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ABSTRACT: 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.
Molecular and Cellular Endocrinology 11/2011; 350(1):10-9. · 4.19 Impact Factor
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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.
Amyotrophic Lateral Sclerosis 03/2011; 12(4):250-6. · 3.40 Impact Factor
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ABSTRACT: 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.
Molecular Endocrinology 03/2011; 25(6):1027-39. · 4.54 Impact Factor
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ABSTRACT: 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.
Molecular Endocrinology 09/2010; 24(9):1863-71. · 4.54 Impact Factor
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ABSTRACT: 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 cell's 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 (Plato's 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.
PLoS Computational Biology 06/2010; 6(6):e1000828. · 5.22 Impact Factor
