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• W2160508724 abstract "An early gene cDNA microarray was developed to study genes that are regulated immediately following gonadotropin-releasing hormone (GnRH) receptor activation. 956 selected candidate genes were printed in triplicate, a tstatistic-based regulation algorithm was used for data analysis, and the response to GnRH in a time course from 1 to 6 h was determined. Measurements were highly reproducible within arrays, between arrays, and between experiments. Accuracy and algorithm reliability were established by real-time polymerase chain reaction assays of 60 genes. Gene changes ranging from 1.3- to 31-fold on the microarray were confirmed by real-time polymerase chain reaction. Many of the genes were found to be highly regulated. The regulated genes identified were all elevated at 1 h of treatment and returned nearly or completely to baseline levels of expression by 3 h of treatment. This broad, robust, and transient transcriptional response to constant GnRH exposure includes modulators of signal transduction (e.g. Rgs2 and IκB), cytoskeletal proteins (e.g. γ-actin), and transcription factors (e.g. c-Fos, Egr1, and LRG21). The interplay of the activators, repressors, and feedback inhibitors identified embodies a combinatorial code to direct the activity of specific downstream secondary genes. An early gene cDNA microarray was developed to study genes that are regulated immediately following gonadotropin-releasing hormone (GnRH) receptor activation. 956 selected candidate genes were printed in triplicate, a tstatistic-based regulation algorithm was used for data analysis, and the response to GnRH in a time course from 1 to 6 h was determined. Measurements were highly reproducible within arrays, between arrays, and between experiments. Accuracy and algorithm reliability were established by real-time polymerase chain reaction assays of 60 genes. Gene changes ranging from 1.3- to 31-fold on the microarray were confirmed by real-time polymerase chain reaction. Many of the genes were found to be highly regulated. The regulated genes identified were all elevated at 1 h of treatment and returned nearly or completely to baseline levels of expression by 3 h of treatment. This broad, robust, and transient transcriptional response to constant GnRH exposure includes modulators of signal transduction (e.g. Rgs2 and IκB), cytoskeletal proteins (e.g. γ-actin), and transcription factors (e.g. c-Fos, Egr1, and LRG21). The interplay of the activators, repressors, and feedback inhibitors identified embodies a combinatorial code to direct the activity of specific downstream secondary genes. gonadotropin-releasing hormone receptor gonadotropin releasing hormone polymerase chain reaction luteinizing hormone beta subunit focused microarray analysis The mechanisms underlying the specificity of the transcriptional response to the activation of cell surface receptors are not well understood. The pituitary gonadotropin-releasing hormone receptor (GnRHR),1 which mediates the biosynthesis of the gonadotropin luteinizing hormone and follicle-stimulating hormone, provides a salient example of the exquisite requirements for signaling specificity between the membrane and the genome. The pattern of downstream gene responses depends on the frequency of receptor stimulation. Specific patterns of GnRHR stimulation lead to the generation of distinct transcriptional programs. For example, prolonged GnRH stimulation favors induction of the common α-gonadotropin subunit. In contrast, a specific physiologically relevant frequency range of receptor stimulation on the order of 1 pulse/h preferentially induces the luteinizing hormone beta subunit (LHβ) gene (1Dalkin A.C. Haisenleder D.J. Ortolano G.A. Ellis T.R. Marshall J.C. Endocrinology. 1989; 125: 917-924Crossref PubMed Scopus (269) Google Scholar, 2Weiss J. Jameson J.L. Burrin J.M. Crowley W.F. Mol. Endocrinol. 1990; 4: 557-564Crossref PubMed Scopus (119) Google Scholar). Although downstream signal transduction mediators (such as JNK and ERK) and a number of transcription factors (including Egr1, SF1, and NAB1) have been implicated in modulation of the LHβ promoter (3Tremblay J.J. Drouin J. Mol. Cell. Biol. 1999; 19: 2567-2576Crossref PubMed Google Scholar, 4Dorn C. Ou Q. Svaren J. Crawford P.A. Sadovsky Y. J. Biol. Chem. 1999; 274: 13870-13876Abstract Full Text Full Text PDF PubMed Scopus (156) Google Scholar, 5Kaiser U.B. Halvorson L.M. Chen M.T. Mol. Endocrinol. 2000; 14: 1235-1245Crossref PubMed Scopus (115) Google Scholar, 6Sevetson B.R. Svaren J. Milbrandt J. J. Biol. Chem. 2000; 275: 9749-9757Abstract Full Text Full Text PDF PubMed Scopus (84) Google Scholar), the available data do not explain why the induction of LHβ requires specific patterns of GnRHR activation. The activation of specific secondary genes depends on the pattern of induction of primary genes, which can encode proteins involved in signal feedback and in modulating the transcription of downstream targets. To better understand this process, we have developed a microarray to identify the early gene program induced by GnRHR activation. Microarray techniques have emerged as important approaches for the simultaneous analysis of multiple gene transcripts. These methods have proven valuable in refining cancer classification (7Golub T.R. Slonim D.K. Tamayo P. Huard C. Gaasenbeek M. Mesirov J.P. Coller H. Loh M.L. Downing J.R. Caligiuri M.A. Bloomfield C.D. Lander E.S. Science. 1999; 286: 531-537Crossref PubMed Scopus (9189) Google Scholar, 8Alizadeh A.A. Eisen M.B. Davis R.E. Ma C. Lossos I.S. Rosenwald A. Boldrick J.C. Sabet H. Tran T., Yu, X. Powell J.I. Yang L. Marti G.E. Moore T. Hudson Jr., J. Lu L. Lewis D.B. Tibshirani R. Sherlock G. Chan W.C. Greiner T.C. Weisenburger D.D. Armitage J.O. Warnke R. Levy R. Wilson W. Grek M.R. Byrd J.C. Botstein D. Brown P.O. Staudt L.M. Nature. 2000; 403: 503-511Crossref PubMed Scopus (7995) Google Scholar) and for providing qualitative assessment of the global gene programs that accompany cell division, development, and the responses to specific stimuli (9Lashkari D.A. DeRisi J.L. McCusker J.H. Namath A.F. Gentile C. Hwang S.Y. Brown P.O. Davis R.W. Proc. Natl. Acad. Sci. U. S. A. 1997; 94: 13057-13062Crossref PubMed Scopus (530) Google Scholar, 10Iyer V.R. Eisen M.B. Ross D.T. Schuler G. Moore T. Lee J.C.F. Trent J.M. Staudt L.M. Hudson Jr., J. Boguski M.S. Lashkari D. Shalon D. Botstein D. Brown P.O. Science. 1999; 283: 83-87Crossref PubMed Scopus (1724) Google Scholar). However, data obtained using both commercial and custom global microarrays have been limited by the expense of the assays and by problems in quality control (11Knight J. Nature. 2001; 410: 860-861Crossref PubMed Scopus (134) Google Scholar). To overcome the potential limitations of global microarrays, we have integrated microarray technology with a massively parallel candidate gene approach that we refer to as focused microarray analysis (FMA). This approach has been used to develop an early gene microarray for the study of GnRHR responses. 956 cDNAs were carefully selected for inclusion on this microarray, including many early response genes identified in various experimental systems. The size of this array facilitates high quality array production, validation, and data generation. Several novel aspects of the approaches described could be generally useful. We have established array quality benchmarks and empirical data analysis algorithms that are fully supported by extensive independent gene measurement. We also report the capacity for FMA to quantify the level of gene regulation. We have utilized FMA to characterize the time course from 1 to 6 h of gene responses occurring following activation of the GnRHR in the mouse LβT2 gonadotrope cell line (12Turgeon J.L. Kimura Y. Waring D.W. Mellon P.L. Mol. Endocrinol. 1996; 10: 439-450Crossref PubMed Google Scholar). This study reveals a highly structured response induced in the genetic signaling network. LβT2 cells were obtained from Pamela Mellon (University of California, San Diego) and were maintained at 37 °C in 5% CO2 in humidified air in Dulbecco's modified Eagle's medium (Mediatech) containing 10% fetal bovine serum (Gemini). For experiments, 40–50 × 106cells were seeded in 15-cm dishes. The medium was replaced 24 h later with Dulbecco's modified Eagle's medium containing 25 mm HEPES (Mediatech) and glutamine. 18 h later, the cells were treated with 100 nm GnRH or vehicle and were returned to the CO2 incubator for 1, 3, or 6 h. The incubation was stopped by aspirating the incubation medium and adding 10 ml of lysis buffer (4 m guanidinium thiocyanate, 25 mm sodium citrate, pH 7.0, 0.5%N-laurylsarcosine, and 0.1 m 2-mercaptoethanol). Total RNA was isolated according to the method of Chomczynski and Sacchi (13Chomczynski P. Sacchi N. Anal. Biochem. 1987; 162: 156-159Crossref PubMed Scopus (63145) Google Scholar). Approximately 400 μg of total RNA was obtained from each plate. Plasmids were purified using the Qiaprep 96 Turbo Miniprep kit. Following insert amplification by PCR, products were confirmed by agarose gel electrophoresis and purified with Qiaquick 96 kit (Qiagen). The product was dried, dissolved in either 18 μl of H2O, 50% Me2SO, or 3× SSC, and spotted (3 hits/feature) with a GMS 417 Arrayer (Affymetrix) on CMT-GAPS-coated glass slides (Corning). DNA was fixed either by incubating the slide for 3.5 h at 40 °C followed by 10 min at 100 °C or for 2 h at 85 °C or by UV cross-linking with 90 mJ (Stratalinker, Stratagene). The suspension solutions and fixing protocols were compared in a 3×3 design because feature morphology may be influenced by surface tension effects arising from surface and spotting solution chemistry and by attachment efficiency. We evaluated feature morphology and attachment by hybridizing a small test array with a Cy5-labeled oligonucleotide complementary to one of the amplification primers (5′-CGT TTT ACA ACG TCG TGA CTG GG-3′). The optimum morphology and intensity were reproducibly observed, independently of insert sequence or size, with the PCR product dissolved in 50% Me2SO and fixed for 2 h at 85 °C to CMT-GAPS-coated slides, which was used for subsequent array production (Fig. 1). The 956 clones were selected from a NIA 15K library (14Tanaka T.S. Jaradat S.A. Lim M.K. Kargul G.J. Wang X. Grahovac M.J. Pantano S. Sano Y. Piao Y. Nagaraja R. Doi H. Wood III, W.H. Becker K.G. Ko M.S. Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 9127-9132Crossref PubMed Scopus (364) Google Scholar) or purchased from Research Genetics. The gene selection was based on literature searches, input from collaborating laboratories, and unpublished data. A large number of genes known to be induced at early time points in other experimental systems were included as well as 67 putative “housekeeping” genes. The quality of the libraries utilized and the reliability of the clone picking and isolation were evaluated by sequencing 119 clones on the array, including randomly selected clones on each 96-well plate and all genes that were subsequently identified as regulated. 92% of the clones picked from the NIA library and 82% of the clones purchased from Research Genetics had been correctly identified. Based on the distribution of clone sources, we estimate that clone identification of the unsequenced clones on the array was 91% accurate. 98.7% of the clones generated sufficient PCR product to be detectable by gel electrophoresis. In order to distinguish signals arising from surface artifacts and facilitate analysis, the selected genes were spotted in triplicate. Arrays were stored light-protected at room temperature until use. A test hybridization using the Cy5-labeled oligomer confirmed the presence of all spotted genes at similar concentrations (see supplementary data, Fig. 7). 20 μg of total RNA from each sample was labeled with either Cy3 or Cy5 using the Atlas indirect labeling kit (CLONTECH) as indicated by the manufacturer. Following array prehybridization in 6× SSC, 0.5% SDS, and 1% bovine serum albumin at 42 °C for 45 min, the probe was denatured and hybridized in 24 μl of 50% formamid, 6× SSC, 0.5% SDS, and 5× Denhardt's with 2.4 μg of salmon sperm DNA and 10 μg of poly(dA) at 42 °C (room temperature for test oligomer hybridization) for 16 h. Following 10-min washes in 0.1× SSC, 0.1% SDS, and twice in 0.1× SSC, the slide was scanned using the GMS 418 Scanner (Affymetrix). We used a previously described protocol (15Yuen T. Ebersole B.J. Zhang W. Sealfon S.C. Methods Enzymol. 2001; 345: 556-569Crossref Scopus (46) Google Scholar). Briefly, 5 μg of total RNA was converted into cDNA, and 1:400 (∼250 pg) was utilized for 40-cycle three-step PCR in an ABI Prism 7700 in 20 mm Tris, pH 8.4, 50 mm KCl, 3 mm MgCl2, 200 μm dNTPs, 0.5× SYBR green (Molecular Probes), 200 nm each primer, and 0.25 units of Platinum Taq (Life Technologies). Amplicon size and reaction specificity were confirmed by agarose gel electrophoresis. The number of target copies in each sample was interpolated from its detection threshold (CT) value using a plasmid or purified PCR product standard curve included on each plate. The sequence of the 60 primer sets utilized can be found in the supplementary material. Each transcript in each sample was assayed five times, and the median CT values were used for analysis. Scanned microarray data were exported as TIFF files to Genepix (Axon Instruments), and spot registration was manually optimized as suggested by the developer. The median background-subtracted feature intensity was utilized for further analysis. Overall differences in the signal intensity of the two wavelengths measured on each slide (λ=532 nm and λ=635 nm) were corrected using the locally linear robust scatter plot smoother implemented in the loess function in S Plus Professional (Insightful Corporation). Predictors were generated using a symmetric distribution, span = 0.75 (16Venables W.N. Ripley B.D. Modern Applied Statistics with S-PLUS. 3rd Ed. Statistics and computing, Springer-Verlag, New York1999Crossref Google Scholar). The data quality from each array was estimated by determining the median coefficient of variation within each array for each gene, which indicates the overall variation of triplicate measurements on a given array, cv=100R¯∑i=1n(Ri−R¯)2n−1Equation 1 where R̄ is the geometric mean ratio for that gene on each slide, R i is each ratio, andn is the number of measurements. The sources of variation in these experiments were explored using analysis of variance for each gene measured (17Box G.E.P. Hunter W.G. Hunter J.S. Statistics for Experimenters: an Introduction to Design, Data Analysis, and Model Building. John Wiley & Sons, Inc., New York1978: 165-207Google Scholar). Variance within and between experiments were determined. Variance within, SR2=∑t=1k∑i=1nt(yti−y¯t)2N−kEquation 2 where N is the total number of measurements of each gene, k is the number of arrays, nt is the number of measurements of each gene on each array,y ti is the individual ratio, andȳ t is the average ratio for that gene on each array. Variance between, ST2=∑t=1knt(y¯t−y¯)2k−1Equation 3 where k is the number of arrays,nt is the number of measurements, andȳ is the average ratio for that gene from all experiments. In order to select genes for further study, t values for the log transform ratios (r) were determined for data from each slide. t=r¯ns,where s=∑i=1n(ri−r¯)2n−1Equation 4 Genes were selected according to the following algorithm: (i) ± fold-change of >1.3, (ii) ∣t∣ >3, (iii) signal intensity >1% of the median signal intensity value in at least one channel, and (iv) criteria i–iii observed in at least two of three experiments. The early transcriptional response to GnRH was studied in the GnRHR-expressing LβT2 gonadotrope cell line (12Turgeon J.L. Kimura Y. Waring D.W. Mellon P.L. Mol. Endocrinol. 1996; 10: 439-450Crossref PubMed Google Scholar). Three separate experiments comparing the effects of a 1-h exposure to GnRH or vehicle were performed. Hybridization was uniform across all arrays, and background signal was low (Fig. 2). Data reproducibility and labeling bias were assessed by repeating the labeling and hybridization for one pair of samples two additional times, once with the control (Cy3) and treated (Cy5) labels reversed. The reliability of the data generated from these five microarray experiments was substantiated by the reproducibility of measurements within arrays, between arrays, and between experiments. The pattern of hybridization observed was qualitatively similar for all experiments (Fig. 2). Because the triplicate measurements within each array were nearly identical for each of the five hybridizations (median cv, 8–13%), regulated genes could reliably be distinguished from surface artifacts (Fig. 2 D). The reproducibility of the three measurements of each gene on each slide was utilized in our data analysis algorithm (see below). When the identical two RNA samples (control and treated) were labeled repeatedly, a high linear correlation was obtained for the ratios of the regulated genes (r=0.985). Similar analysis of a dye swap experiment also showed a very high correlation, r = 0.975. These data indicate that the relative expression obtained from these comparisons are reproducible and do not show sequence bias, as has been reported with direct labeling protocols (18Taniguchi M. Miura K. Iwao H. Yamanaka S. Genomics. 2001; 71: 34-39Crossref PubMed Scopus (159) Google Scholar). To determine the relative sources of variability in the microarray experiments, an analysis of variance was performed on the data from these five hybridizations. There are several sources of variation in these experiments: (i) the variation between the triplicate measurements for each gene on each array, (ii) the variation between the measurement of each gene obtained by repeated labeling and hybridization, and (iii) the variation between the levels of each mRNA that occur in independent, replicated experiments. The median variance for the triplicate measurements within each array, which represents the variation of the three repeated measurements, was sR2 = 0.0198 for all arrays, indicating a very low level of variability. The two potential sources of variability between replicated experiments can be distinguished by comparing the analysis of replicate hybridizations of the same samples (n=3) and of repeated, independent experiments (n=3). The median sample variances between the ratios obtained with repeated labeling/hybridization of the same samples and between RNA samples obtained from independent experiments were sT2 = 0.073 and sT2 = 0.071, respectively. These data reveal similar levels of variation between the measurements obtained from repeated assays of the same samples and of samples from independent experiments. Thus the level of variation between the regulation of mRNAs in replicate experiments was so low that it does not add appreciably to the modest measurement variation resulting from the hybridization procedure. Scatter graphs of data from the three independent stimulation experiments, normalized using the robust locally linear loess function, are shown in Fig. 3. We preferred this normalization to an overall linear correction because it compensates for variation of the correction factor with signal intensity and is largely unaffected by outliers, which include the regulated transcripts. Most transcripts are not regulated, and the normalized data are tightly grouped along y = x. The triplets corresponding to several regulated genes are evident and show similar regulation and scatter graph location in all three independent experiments. These strikingly similar results obtained in independent experiments reflect a high level of reliability and reproducibility in all aspects of these studies, including array production, cell culture and treatment, RNA extraction, labeling, hybridization, and data acquisition. In order to identify candidate genes whose regulation is less marked than the visually obvious triplets indicated in Fig. 3, an empirical selection algorithm was implemented. The presence of triplicate features for each cDNA allowed calculation of a t statistic for each gene on each array (see “Experimental Procedures”). The criteria utilized to select the regulated gene candidates were based on fold-change (>±1.3), the t statistic (∣t∣>3), and signal intensity (>1% of median signal intensity in at least one channel) within each experiment (see “Experimental Procedures”). Genes that met these criteria in at least two independent experiments were selected for further study. This selection strategy identified 31 candidate regulated genes, 28 of which increased and 3 of which decreased (see below). Four of these genes (Rgs2, TSC22, PRL1, andNrf2) were represented by two different clones and spotted in different locations on the arrays. In all four cases, regulation was detected by our criteria, and the degree of change was similar (Rgs2, 4.0 ± 1.3, 2.9 ± 1.2;TSC22, 3.4 ± 0.4, 3.3 ± 0.6; PRL1, 2.0 ± 0.4, 1.5 ± 0.4; Nrf2, 1.3 ± 0.2, 1.3 ± 0.1). These data indicate that the observed changes were independent of position on the array. The measurement accuracy of the microarray and the biological variability of the transcriptional program identified were evaluated through a validation study comprising real-time PCR assays of 60 transcripts, including 26 genes that met the threshold for regulation in the microarray assay. To generate accurate and precise reference measurements of these genes, five measurements were made of each gene in each RNA sample, and the measurements were calibrated with a standard curve included on each PCR plate (Fig. 4). Nine experimental pairs of vehicle and GnRH-treated cultures were tested, including the microarray assayed samples. 100% (17 of 17) of the gene changes showing >1.6-fold change on the microarray and 66% (6 of 9) of gene changes showing between 1.3- and 1.6-fold changes were confirmed by PCR. All of the confirmed genes were up-regulated (Table I). Three genes, which showed a low level of regulation on the microarray, had less than 1.3-fold regulation (below the threshold for regulation) with the real-time PCR, but the actual fold-change measurements were actually relatively close (Table I). Three of 32 genes that appeared to be unregulated on the microarray were found to be up-regulated by real-time PCR (Table I). Thus the algorithm utilized to identify regulated genes was able to correctly identify regulated transcripts showing changes as low as 1.3-fold regulation on the microarray.Table IFold-changes ± S.E. from the early gene microarray compared to real-time PCRValues meeting the criteria for up- or down-regulation are indicated by red or green, respectively. The identification of all clones listed was confirmed by sequencing. Open table in a new tab Values meeting the criteria for up- or down-regulation are indicated by red or green, respectively. The identification of all clones listed was confirmed by sequencing. This data set of 5400 PCR assays provides a reference standard that allows assessment of the accuracy of the microarray and reliable quantification of the degree of regulation of these transcripts following GnRH exposure. The accuracy of the fold-change determinations obtained from the microarray was poor for transcripts showing high degrees of regulation (Table I), but the fold-change was meaningful for transcripts showing less extreme levels of induction. The power function correlation for the fold-change measurements by microarray and real-time PCR for genes showing <20-fold regulation by PCR (n=24) was r = 0.87 (Table I and Fig. 5). These data indicate that the microarray measurements can be calibrated to provide a quantitative estimate of the degree of gene regulation. Our data reveal that GnRHR activation modulates the expression of a large number of genes. The regulated genes identified include transcription factors (e.g. Klf4, Egr1, andEgr2), cell signaling modulators (e.g. Rgs2 and IκB), channel regulators (gem), and proteins contributing to cytoskeletal dynamics (γ-actin and transgelin). More than half of the induced genes are transcription factors, including both activators and repressors, with the major structural motifs encoded being leucine zipper factors (c-jun, Nrf2, LRG21, andTSC22) and zinc finger proteins (Egr1,Egr2, Klf4, Klf-like EST, andNr4a1) (Table I and Fig. 6). Many immediate early genes are known to be only transiently induced by various stimuli. Analysis of samples obtained from cells exposed to GnRH for 3 and 6 h reveals that nearly all of the induced genes return to baseline levels of expression by 3 h (Table I). A commonality of many induced transcripts is that the proteins encoded, after synthesis, would contribute to subsequent down-regulation of receptor-activated signaling. This category includes Fos, Rgs2, IκB, MKP1, PRL1, and gem (see “Discussion”). We have identified a broad, robust, and transient transcriptional response to the activation of the GnRH receptor. The proteins encoded by the induced genes represent five categories: transcription factors, cytoskeletal proteins, signaling mediators, channel regulators, and proteins of miscellaneous or unknown function (Fig. 6). Several of the genes that we have newly discovered to be regulated by GnRH have obvious implications for the function of the GnRH signaling system. For example, Klf4 is a zinc-finger protein in the Sp/XKLF family (19Philipsen S. Suske G. Nucleic Acids Res. 1999; 27: 2991-3000Crossref PubMed Scopus (533) Google Scholar). Sp1 sites in the LHβ promoter have been implicated in the response to pulsatile GnRH (20Weck J. Anderson A.C. Jenkins S. Fallest P.C. Shupnik M.A. Mol. Endocrinol. 2000; 14: 472-485Crossref PubMed Google Scholar). Therefore, our data raise the possibility that Klf4 may bind to the LHβ promoter and contribute to its GnRH-mediated activation. Notably, many induced transcripts can be recognized as encoding proteins that would down-regulate or suppress GnRHR-activated signaling. Fos is known to transrepress the induction of many genes (21Ofir R. Dwarki V.J. Rashid D. Verma I.M. Nature. 1990; 348: 80-82Crossref PubMed Scopus (137) Google Scholar). Rgs2 is a GTPase-activating protein that down-regulates the activity of Gαq (22De Vries L. Gist Farquhar M. Trends Cell Biol. 1999; 9: 138-144Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar), the major G-protein utilized by the GnRH receptor (23Grosse R. Schmid A. Schoneberg T. Herrlich A. Muhn P. Schultz G. Gudermann T. J. Biol. Chem. 2000; 275: 9193-9200Abstract Full Text Full Text PDF PubMed Scopus (128) Google Scholar). The small G-protein gem (kir) has recently been reported to down-regulate L-type calcium channel function (24Beguin P. Nagashima K. Gonoi T. Shibasaki T. Takahashi K. Kashima Y. Ozaki N. Geering K. Iwanaga T. Seino S. Nature. 2001; 411: 701-706Crossref PubMed Scopus (244) Google Scholar). The induction of gem by GnRH could contribute to the modulation of GnRH-induced gonadotropin release and MAP kinase activation, which both depend on L-channel activation (25Mulvaney J.M. Zhang T. Fewtrell C. Roberson M.S. J. Biol. Chem. 1999; 274: 29796-29804Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar). IκB inhibits NFκB signaling. MKP1 and PRL1 are both phosphatases that could attenuate GnRHR signaling mediated by kinase activation. The pattern of gene induction observed reveals that the first wave of genes induced by GnRH represents a seamless transition between the cellular signaling network and the downstream gene responses representing the ultimate targets of this signaling. The components of this gene network we have identified that (after synthesis) can suppress more proximal GnRH signaling intermediates are likely to play a significant role in modulating the responses to receptor activation. For example, our data reveal the rapid induction of Rgs2 by GnRH, which would likely attenuate GnRH receptor signaling. We speculate that a sinusoidal induction of Rgs2 by pulsatile receptor stimulation could contribute to the well known frequency dependence of downstream secondary genes. The activation or suppression of specific secondary gene targets is dictated by a combinatorial code. Induction of a specific secondary gene requires the presence of a particular combination of induced and constitutive factors and a relative absence of repressors for that promoter. Because the genetic network activates transcripts that encode proteins for activators and repressors as well as feedback inhibitors to signaling, it is ideally structured to generate different patterns of co-expressed activating and repressing factors in response to different stimuli. This formulation is consonant with the known control of secondary targets, such as the LHβ gene. The LHβ promoter binds transcription factors relatively weakly and requires the presence of multiple distinct activating proteins for efficient transcription (3Tremblay J.J. Drouin J. Mol. Cell. Biol. 1999; 19: 2567-2576Crossref PubMed Google Scholar, 4Dorn C. Ou Q. Svaren J. Crawford P.A. Sadovsky Y. J. Biol. Chem. 1999; 274: 13870-13876Abstract Full Text Full Text PDF PubMed Scopus (156) Google Scholar, 5Kaiser U.B. Halvorson L.M. Chen M.T. Mol. Endocrinol. 2000; 14: 1235-1245Crossref PubMed Scopus (115) Google Scholar, 6Sevetson B.R. Svaren J. Milbrandt J. J. Biol. Chem. 2000; 275: 9749-9757Abstract Full Text Full Text PDF PubMed Scopus (84) Google Scholar, 20Weck J. Anderson A.C. Jenkins S. Fallest P.C. Shupnik M.A. Mol. Endocrinol. 2000; 14: 472-485Crossref PubMed Google Scholar, 26Yokoi T. Ohmichi M. Tasaka K. Kimura A. Kanda Y. Hayakawa J. Tahara M. Hisamoto K. Kurachi H. Murata Y. J. Biol. Chem. 2000; 275: 21639-21647Abstract Full Text Full Text PDF P" @default.
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• W2160508724 date "2001-12-01" @default.
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• W2160508724 title "Gonadotropin-releasing Hormone Receptor-coupled Gene Network Organization" @default.
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