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From the * Departments of Biochemistry and
Molecular Biology, Physiology and Biophysics,
and Protein Information Resource, Georgetown
University Medical Center, Washington, DC.
| Correspondence to: V. Papadopoulos, Department of Biochemistry and Molecular Biology, Georgetown University Medical Center, Basic Sciences Bldg, 3900 Reservoir Rd, Washington, DC 20057 (e-mail: papadopv{at}georgetown.edu). |
| Received for publication April 5, 2004; accepted for publication June 20, 2004. |
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Abstract |
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Key words: Testis, gene ontology, genomics, proteomics, steroidogenesis
Steroidogenesis is regulated by trophic hormones, which bind to their specific plasma membrane receptors and activate a stimulatory guanosine triphosphate (GTP)-binding protein that, in turn, stimulates adenylate cyclase. Activation of steroidogenesis by hCG occurs by several steps involving the plasma membrane and mitochondria. The stimulation results in an increase in cAMP, which is the major second messenger of this system. cAMP may trigger 3 responses: i) changes in the state of phosphorylation of specific proteins, via the cAMP-protein kinase (PKA), ii) induction of protein synthesis, and iii) stimulation of lipid synthesis (Simpson and Waterman, 1983; Hall, 1985; Kimura, 1986; Jefcoate, 2002). One or all of these cAMP-induced changes will trigger the transport of cholesterol from sites of storage or synthesis to the inner mitochondrial membrane, where C27 side-chain cleavage takes place via an enzymatic reaction. The P450scc enzyme, dependent on an electron-transport system comprising a ferredoxin and a flavoprotein, catalyzes this reaction. Detailed studies have shown that the reaction catalyzed by P-450scc is not the rate-limiting step in the synthesis of steroid hormones, but rather, it is the transport of the precursor, cholesterol, from intracellular sources to the inner mitochondrial membrane and the subsequent loading of cholesterol in the P450scc active site (Simpson and Waterman, 1983; Jefcoate, 2002). This hormone-dependent transport mechanism was shown to be mediated by cAMP and to be localized in the mitochondrion (Simpson and Waterman, 1983; Hall, 1985; Jefcoate, 2002). Pregnenolone formed then leaves the mitochondrion to undergo enzymatic transformation in the endoplasmic reticulum that will give raise to the final steroid products. Two proteins, steroidogenic acute regulatory protein (StAR, actually a family of proteins), a cholesterol-transfer protein (Clark et al, 1994; Stocco, 2001), and the peripheral benzodiazepine receptor (PBR), an 18-kDa high-affinity drug ligand and cholesterol-binding protein (Papadopoulos et al 1990; Lacapere and Papadopoulos, 2003) are involved in this process. Cholesterol transfer across the membranes is dependent on de novo protein synthesis, which is the regulated step in the process (Simpson and Waterman, 1983; Jefcoate, 2002). It is also well established that steroidogenesis in Leydig cells is regulated by LH/hCG via a signal transduction pathway involving the second messenger cAMP. In addition to the identified proteins mediating this hormonal signal, recent data have indicated that many signal transduction pathways are activated and that complex protein-protein and protein-lipid interactions might also be involved in this process (Finkielstein et al, 1998; Seger et al, 2001; Hirakawa et al, 2002; Li et al, 2001).
In the present study, MA-10 Leydig tumor cells were used in an effort to identify new response targets of hCG and to gain further insight into the changes in gene expression that might underlie the regulation of steroidogenesis. We measured gene expression profiles using the Murine Genome U74A v2 GeneChip (Thimmulappa et al, 2002), which permits the screening of changes occurring in about 8000 combined genes and expressed sequence tag (EST) clusters, and the BD PowerBlot (Castedo et al, 2001; Malakhov et al, 2003), a high-throughput immunoblotting technique that permits analysis of the protein expression of about 800 gene products. We also used the real-time quantitative polymerase chain reaction (Q-PCR) and Western blotting to confirm findings obtained with the above-mentioned techniques. The data provided herein indicate that posttranscriptional events, such as regulated translation, protein stability, and posttranslational modifications, likely contribute to the acute stimulation of Leydig cell steroid formation by hCG.
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Materials and Methods |
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Preparation of Samples for Oligonucleotide Microarray Analysis
After incubation of MA-10 cells for 2 hours in the presence or absence of
50 ng/mL hCG, the cells were collected and RNA was isolated. The experiment
was repeated 3 times, and total RNA collected from the replicates was pooled.
Because of the variability of the cell response to hCG, RNA was collected only
from cells showing a 50-fold increase in progesterone formation in response to
hCG. Total RNA was amplified and biotinylated using the protocol detailed by
Affymetrix (Santa Clara, Calif). Briefly, double-stranded cDNA was synthesized
from 15 µg of total RNA using the SuperScript Choice System (InVitrogen
Life Technologies, Carlsbad, Calif) and a T7-(dT)24 primer (Genset Corp, San
Diego, Calif). In vitro transcription using double-stranded cDNA as a template
in the presence of biotinylated UTP and CTP was carried out using the Enzo
BioArray High Yield RNA Transcript Labeling Kit (Affymetrix). Biotinylated
cRNA was then purified, fragmented, and hybridized to the arrays. After
washing, the arrays were stained with streptavidin-phycoerythrin (Molecular
Probes, Inc, Eugene, Ore) and enhanced with anti-streptavidin antibody (Vector
Laboratories, Burlingame, Calif) using a fluidics system and then scanned with
a specifically designed confocal scanner (Affymetrix). Image data were
analyzed using the MicroArray Suite v.5 Gene Expression analysis program
(Affymetrix). The data were normalized, filtered, and subjected to cluster
analysis.
Oligonucleotide Microarray Analysis
The Affymetrix Murine Genome U74A v2 GeneChip Expression probe assay, which
represents all gene sequences (
6000) in the mouse UniGene database (Build
74) and about 2000 EST clusters, was used to profile changes in gene
expression and to characterize targets that respond to acute treatment with
hCG. For control and treated cells, 3 separate preparations of RNA samples
were pooled and submitted to array hybridization. This process was repeated
twice, resulting in qualitatively identical results. hCG-treated samples and
untreated control samples were compared using Affymetrix Microarray Suite
software. This software, which is based on a decision matrix that includes the
net change in intensity values, fold of change, and other parameters, enables
one to determine whether a given gene is differently expressed. Genes for
which expression changed twofold or more than twofold were considered to be
hCG acute-response genes. The choice of a twofold difference limit was based
on our previous experience with microarray data analysis and is also in
general agreement with the limit used in other reported array experiments.
High-Throughput Western Blot Screening and Data Analysis
Primary screening was performed by BD Biosciences Transduction Laboratories
(Lexington, Ky) using the PowerBlotTM assay
(Castedo et al, 2001; Malakhov et al, 2003), which
determined the expression level of 860 different signal-transducing proteins.
Briefly, extracts containing total cellular protein from 3 separate
hCG-treated or control MA-10 cells, obtained from 3 independent experiments,
were separated on 5%–15% sodium dodecyl sulfate-polyacrylamide gel
electrophoresis (SDS-PAGE) linear gradients and transferred onto
nitrocellulose membranes that were then divided into 40 lanes using a
chamber-forming grid. Each individual lane was incubated with a blend of
several specifically selected monoclonal antibodies (mAbs) that recognize
proteins of nonoverlapping molecular weights. A total of 860 individual mAbs
are used in the assay, some of which are different clones recognizing the same
protein or phosphorylated derivatives of the same protein. Immunoblotting with
specific mAbs was revealed by a secondary goat anti-mouse horseradish
peroxidase followed by the capture of chemiluminescence data by a
charge-coupled device camera-based system, and densitometric data by
computerized processing. Data were normalized to the total intensity value of
all pixels in an image multiplied by 1 000 000. Ratios were calculated in
order to express increases or decreases in protein expression. Each sample was
submitted 3 times in PowerBlot analysis.
Real-Time Quantitative PCR (Q-PCR)
Following incubation of MA-10 cells in the presence or absence of hCG, the
cells were washed 3 times with 1x PBS (pH 7.4), and then total RNA was
isolated using the Qiagen RNeasy Mini kit (Qiagen, Valencia, Calif) according
to the manufacturer's specifications. Total RNA was submitted to On-Column
DNase I digestion with RNase-free DNase to remove genomic DNA contamination.
Q-PCR was performed using the ABI PRISM 7700 Sequence Detector (Applied
Biosystems, Foster City, Calif) as previously described
(Li et al, 2002). Briefly,
total RNA was reverse-transcribed into cDNA, and the resulting cDNAs were then
processed for amplification of the genes of interest using specific primers.
Each sample was run in triplicate. PCR products were detected directly by
measuring the increase in fluorescence caused by the binding of SYBR®
Green I Dye to double-stranded DNA. The Comparative CT Method was
used to analyze the data. mRNA expression corresponding to the target gene was
normalized to the endogenous reference (18S rRNA). Target genes and endogenous
reference genes were run separately. Results shown are means ± standard
error of the mean (SEM) of four independent experiments carried out in
triplicates.
Immunoblot (Western) Analysis
In order to validate the results of high-throughput Western blot analysis,
we subjected proteins of interest, obtained from MA-10 cells, to
immunoblotting using mAbs specific for StAR, IKK
(inhibitor of kappaB
kinase gamma), HSP 70 (heat-shock proteins of average weight 70 kD),
PKARIIß (cAMP-dependent protein kinase type II-beta regulatory chain),
ChK2 (serine/threonine-protein kinase Chk2), ERK1 (extracellular
signal-regulated kinase 1; also termed mitogen-activated protein kinase 3;
MAPK3), and LCB1 (a component of serine palmitoyltransferase 1, which
catalyzes the initial step in the biosynthesis of the long-chain base
component of sphingolipids). Briefly, after exposing the cells to the
above-described treatment protocol, whole-cell extracts were prepared in lysis
buffer consisting of 100 mM dithiothreitol, 2% sodium dodecyl sulfate (SDS),
50 mM Tris-HCl (pH 6.8), 50% glycerol, and 0.1% bromophenol blue. Protein
samples were resolved over 4%–20% Tris-Glycine Gel (InVitrogen) and
transferred to a nitrocellulose membrane. Western blot analyses were performed
according to the manufacturer's protocol. Proteins were visualized using an
ECL kit (Amersham Health, North Arlington Heights, Ill) and analyzed using
Fujifilm LAS-1000 (FUJIFILM Medical Systems USA, Inc, Stamford, Conn). For
each antibody, Western blot was performed 3 times from 3 independent
experimental treatments, and only 1 experiment is shown here.
Radioimmunoassay (RIA)
When MA-10 mouse Leydig tumor cells are stimulated by exposure to the
trophic hormones LH and hCG, they respond by producing progesterone rather
than testosterone as the major steroid
(Ascoli, 1981). Therefore, we
measured progesterone synthesis by RIA to provide a steroidogenic index of
MA-10 Leydig cells, as previously described
(Papadopoulos et al,
1990).
Bioinformatics and Functional Analysis of Differentially Expressed Genes and Proteins
Bioinformatic analysis of the Affymetrix gene expression results was
conducted using the annotation information and Gene Ontology (GO) mining tools
of the Affymetrix NetAffx Analysis Center
(Liu et al, 2003). Annotation
information for the probe sets includes gene chip and probe design
information, gene and protein references (such as GenBank), and functional
annotation (such as GO) (Ashburner et al,
2000). GO is a widely accepted standard vocabulary for annotating
genes, consisting of terms for biological process, molecular function, and
cellular component.
The BD PowerBlot protein expression results were analyzed using an integrated knowledge-base system developed at the Protein Information Resource (Wu et al, 2003). A protein information matrix was compiled for proteins affected by the hCG treatment, summarizing both known annotations and homology-based functional inference. Rich protein annotations were retrieved from iProClass, a value-added database of protein family, structure, and function information with links and summaries for over 50 molecular databases (Huang et al, 2003). Annotations were also inferred based on fully characterized protein homologs, with an evidence attribution indicating the sources and strength of the inference. The major function and pathway groups of differentially expressed proteins were identified using the mappings of the GO and KEGG (Kanehisa et al, 2002) and BioCarta (http://www.biocarta.com/) pathways. Specifically, the network structure of GO molecular function terms provided the basis for functional classification, while the metabolic, regulatory, or signal transduction pathway categories in KEGG and BioCarta were used for pathway categorization.
Statistics
Multiple means were compared using InStat's one-way analysis of variance
(ANOVA) (Prism v. 3.0; GraphPad Inc, San Diego, Calif). All P values
are provided in the text or figure legends.
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Results |
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High-Throughput Immunoblot Identification of hCG-Responsive Genes by BD PowerBlot
A semiquantitative value representing the general trend of the changes
observed in protein detected by the BD PowerBlot analysis was used for
assessing protein levels in hCG-treated cells relative to control cells. Based
on the confidence level at which the identity of the protein could be deduced,
we separated the bands into 5 groups (Table
2). As shown in the table, 9 of the 860 gene products examined by
PowerBlot were differentially expressed in a significant manner (greater than
twofold in triplicate from good-quality signals, confidence level 5), 10 genes
were changed 1.5- to 1.9-fold in triplicate from good-quality signals (level
4), 8 genes were changed greater than twofold in triplicate from low signals
(level 3), 28 genes were changed 1.25- to 1.5-fold in triplicate (level 2),
and finally 38 genes were changed greater than twofold in duplicate from
good-quality signals (level 1); proteins having multiple bands have observed
molecular weights included in their names.
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A protein information matrix categorizing the features of hCG acute-response genes identified by BD PowerBlot analysis into 8 Process categories, 11 pathway clusters, and 38 functional categories is provided in Table 3. Prominent groups of gene products that responded to hCG are involved in apoptosis (6 up-regulated, 7 down-regulated), cell-cycle regulation (3 up-regulated, 3 down-regulated), cell signaling (11 up-regulated, 10 down-regulated), cytokines/chemokines (6 up-regulated, 6 down-regulated), immunology (7 up-regulated, 3 down-regulated), and metabolism (10 up-regulated, 4 down-regulated) pathway. And prominent groups of gene products that responded to hCG are involved in hydrolase activity (6 up-regulated, 3 down-regulated), kinase activity (10 up-regulated, 5 down-regulated), nucleic acid binding (9 up-regulated, 8 down-regulated), nucleotide binding (15 up-regulated, 9 down-regulated), protein binding (5 up-regulated, 9 down-regulated), transferase activity (12 up-regulated, 5 down-regulated).
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A comparison of the changes in gene expression shown by the Affymetrix array and Protein PowerBlot is summarized in Table 4. Affymetrix Murine Genome U74A v2 GeneChip permits the screening of changes occurring in about 8000 combined genes and EST clusters, and the BD PowerBlot Western Array only permits the identification and quantitation of proteins recognized by 860 distinct monoclonal antibodies. The lack of complete concordance between these 2 methods could be attributable either to false-positive signals in the array data or to discrepancies between transcript and protein expression.
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Confirmation of Array Data by Q-PCR
Q-PCR was used to confirm changes of interest that were shown using the
Affymetrix array. Results shown are means ± SEM of 4 independent
experiments carried out in triplicate. As shown in
Figure 1 and
Table 1, changes in the
expression of 3 genes that were detected by Q-PCR (ie, NR4a1, StAR and JunB),
correlated well with the array data. Results obtained with the array showed
that NR4a1 increased by about 17-fold, StAR increased by about 2- to 4-fold,
and JunB increased by nearly 12-fold after hCG treatment. Results obtained
with Q-PCR showed that NR4a1 was increased by 11.52- ± 1.43-fold, StAR
increased by 5.41- ± 0.47-fold, and JunB increased by 1.98- ±
0.21-fold at the 50 ng/mL concentration of hCG. The expression levels of genes
encoding NR4a1, StAR, and JunB all increased in a hCG concentration-dependent
manner (Figure 1). Statistical
analyses indicated that all of these values differed significantly from
control values (see legend to Figure
1). Q-PCR results also indicated that the expression levels of
NR4a1, StAR, and JunB increased with different time courses
(Figure 2) and that these
effects were statistically significant (see legend to
Figure 2).
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Verification of BD PowerBlot Data and Oligonucleotide Array Data Using Individual Antibodies
Western immunoblot analyses were performed with antibodies specific for
proteins of interest that were obtained from hCG-treated MA-10 cells (ie,
LCB1, ChK2, PKARIIß, ERK1, HSP 70, and IKK; see
Figure 3). LCB1 was increased
1.5- to 1.9-fold in triplicate from good-quality signals (confidence level 4)
in PowerBlot (Table 2). LCB1
was confirmed increased 1.17- to 1.2-fold in cells treated with 50 and 100
ng/mL hCG (Figure 3). ChK2 and
PKARIIß were changed 1.25- to 1.5-fold in triplicate (confidence level 2)
in PowerBlot (Table 2),
PKARIIß was confirmed increased by hCG treatment, but ChK2 was not
confirmed to be by Western immunoblot
(Figure 3). ERK1, HSP 70, and
IKK were changed greater than twofold in duplicate from good-quality
signals (confidence level 1) in PowerBlot
(Table 2), and only IKK
was confirmed to be decreased in Western immunoblot
(Figure 3), while ERK1 and HSP
70 showed no significant change by hCG stimulation. Western immunoblot was
also performed with antibody specific for StAR
(Figure 3), which confirmed
again the oligonucleotide array data (Table
1). Compared with control, StAR protein level was increased by
1.29-, 1.87-, and 2.29-fold in cells treated with 10, 50, and 100 ng/mL hCG
for 2 hours, respectively. The graph represents 1 of the 3 independent
experiments.
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Effect of hCG on Progesterone Production by MA-10 Cells
As shown in Figure 4,
incubation of MA-10 Leydig cells for 2 hours with increasing concentrations of
hCG (1.0–100 ng/mL) led to a highly significant concentration-dependent
increase in progesterone production. Progesterone production was increased
even at the low concentration of 0.5 ng/mL hCG.
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Use of the Gene Ontology Mining Tool
As mentioned above, results obtained with the Affymetrix Array MG-U74A v2
showed that the expression of 79 genes was altered by twofold or more than
twofold by hCG treatment, as compared with controls (see also
Table 1). Up-loading of these
79 probe sets (Genes) in the GO Mining Tool in the Affymetrix NetAffx Analysis
Center (Liu et al, 2003)
showed that 48 probe sets (genes) have annotations for GO biological process,
42 probe sets (genes) have annotations for GO cellular component, and 53 probe
sets (genes) have annotations for GO molecular function. Each gene may belong
to more than one of the following categories. In GO biological processes, many
of the genes are involved in cellular processes (n = 22) and physiological
processes (n = 47), which include cell communication (n = 5), death (n = 5),
cell growth and/or maintenance (n = 16), metabolism (n = 34), response to
external stimuli (n = 5), and response to stress (n = 4). In the GO cellular
component, genes belong to the cell (n = 41) component and extracellular (n =
2) components. For the 41 genes belonging to cell, 37 genes are intracellular
and 8 genes are located in the membrane. GO molecular function shows genes
involved in enzyme regulator activity (n = 4), structural molecule activity (n
= 3), binding (n = 37), catalytic activity (21), signal transducer activity (n
= 5), transporter activity (n = 4), translation regulator activity (n = 6),
and transcription regulator activity (n = 11).
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Discussion |
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Results obtained with the GO method and other methods indicated that a prominent group of genes, the expression of which was altered by hCG treatment, is implicated in regulating transcription as well as the cell cycle, cell growth, and cell maintenance. Many positive cell-cycle regulators were induced by hCG, among which were JunB (Andrecht et al, 2002; Finch et al, 2002; Passegue et al, 2002) and cyclin C (Tassan et al, 1995; Liu et al, 1998). Changes in the expression of these genes could modify cell-cycle progression. The expression of certain genes that are involved in steroid biosynthesis, general metabolism, and protein biosynthesis was also altered by exposure of MA-10 cells to hCG. In addition, hCG up-regulated the expression of StAR, transferase, nucleic acid-binding protein, and heat-shock protein. The results provided in Table 1 show that some signal transduction genes were also responsive to hCG. Also, hCG modulated a large group of transcription factors (trans-acting factors), in addition to JunB and NR4a1, which play critical roles in regulating cell-cycle progression and of which altered expression could lead to modifications in the transcription of other genes.
Some of the observed changes seem deserving of special comment because they relate to the primary purpose of the experiments that were conducted. First, our results showed that hCG can increase the steady-state level of StAR transcripts and protein in MA-10 cells in a time- and concentration-dependent manner, which confirms previous results (Clark et al, 1994, 1995; Stocco, 2001; Jefcoate, 2002). StAR, which is synthesized in response to LH stimulation and the expression of which, in the absence of hormone stimulation, is sufficient to induce steroid production (Bose et al, 2002), is a candidate for the role of a newly synthesized, acute regulatory protein involved in regulating cholesterol transfer across mitochondrial membranes. StAR plays a crucial role in regulating steroid hormone biosynthesis in the gonads and adrenal glands. Two hours of exposure to hCG did not significantly affect the 18-kDa transcript and monomeric PBR protein levels (data not shown), in agreement with previous reports (Boujrad et al, 1994).
The up-regulation of JunB by exposure of MA-10 cells to hCG is also of special interest. It is known that JunB controls cyclin A during cell-cycle regulation in mouse and that its antagonism of the action of c-Jun in transcriptional regulation indicates that it might be a negative regulator of cell proliferation. Cyclin A is a direct transcriptional target of JunB in relation to cell proliferation (Andrecht et al, 2002). At the clinical level, it is of interest that the expression of JunB protein has a negative effect on malignant tumor cell proliferation in part through its ability to inhibit AP-1 transactivation (Finch et al, 2002).
Steroid or nuclear hormones receptors were also altered in the present study. These proteins constitute an important superfamily of transcription regulators that are involved in diverse physiological functions, including control of embryonic development, cell differentiation, and homeostasis. Nuclear receptors are extremely important in medical research, a large number of them being implicated in diseases such as cancer, diabetes, and hormone-resistance syndromes. More specifically, NR4A1 was up-regulated by exposure of MA-10 cells to hCG (see Table 1), in agreement with a published report (Song et al, 2001). It might be noted that NR4A1 (also termed Nur77) is an orphan member of the nuclear hormonereceptor superfamily. NR4A1 and its close relatives Nurr1 and NOR-1 bind as monomers to a consensus binding site, the nerve growth factor-induced protein I-B-binding response element. These nuclear receptors are classified as immediate early response genes, which are induced through multiple signal-transduction pathways. They have been implicated in cell proliferation, differentiation, and apoptosis (Wansa et al, 2002).
Cyclin C, which was also up-regulated by exposure of MA-10 cells to hCG, may play a dual role within the cell in that it can regulate both cell-cycle progression and gene transcription. Cyclin C associates with Cdk8 (Tassan et al, 1995), forming a complex that can induce gene transcription of Cdc2 (Cdk1) (Liu et al, 1998). Because Cdc2 kinase is important for cell entry into the mitotic phase of the cell cycle, the capacity of cyclin C to regulate cell-cycle progression may be attributed, in part, to its modulation of Cdc2 protein expression (Liu et al, 1998).
Checkpoint kinase Chk1 was increased at both the mRNA and protein levels in response to hCG treatment. Chk1 is required for normal cell division and Chk-1-dependent processes are participating in tumor suppression (Bartek and Lukas, 2003; Zachos et al, 2003), suggesting that this protein may be part of the hormone-induced mechanism reducing cell proliferation of steroidogenic tumor cells (Morera and Saez, 1980).
Among the hCG-acute response genes identified by both the genomic and proteomic analyses, the general transcription factor TFII-I and the A kinase anchor protein 121 (AKAP121) are of distinct interest for Leydig cell function: TFII-I, because it is a multifactorial transcriptional activator induced in response to various extracellular signals. TFII-I upon activation translocates to the nucleus to initiate signal-induced gene expression (Roy, 2001). AKAP121 targets the cAMP-dependent protein kinase type II to the outer mitochondrial membrane (Cardone et al, 2002). AKAP121 induction was shown to stimulate the cAMP-dependent protein kinase A-induced phosphorylation of proteins associated with the mitochondria, such as BAD (Affaitati et al, 2003), and its expression in HeLa cells induced the translocation of the manganese superoxide dismutase mRNA from the cytosol to the mitochondria, in a cAMP-dependent manner (Ginsberg et al, 2003). Translation at the mitochondrial level was suggested as a mechanism facilitating the import of mitochondrial proteins (Ginsberg et al, 2003). This may be one of the hCG and cAMP-induced mechanisms, in the first minutes upon stimulation, by which StAR mRNA may be targeted to mitochondria for local translation and import.
Akt1/PKB was increased 1.25- to 1.5-fold in triplicate (level 2) among hCG response proteins identified by BD PowerBlot (Table 2). PKB is activated in response to growth factors through the activation of PI3-kinase and Ras (Marte and Downward, 1997). Cellular stress leading to the activation of the p38 MAP kinase also induces PKB activation, indicating a multiplicity of signaling pathways that activate PKB. However, Taylor (2002) failed to show an involvement of PI3K in the hormone-induced steroid formation by MA-10 cells. In preliminary studies, we also failed to show a role of PKB in Leydig cell steroidogenesis after silencing the PKB mRNA (data not shown).
In conclusion, although this study demonstrates the power of the genomic, proteomic, and bioinformatic methodology in unveiling novel pathways activated by hCG in Leydig cells, the results presented herein raise a number of issues to be taken into consideration: i) changes at the mRNA level do not always translate into changes at the protein level, ii) changes at the protein level may occur in a manner independent of changes at the mRNA level, iii) a minor change at the mRNA level may result in major change at the protein expression level, iv) a major level at the mRNA level does not necessarily translate into a major change at the protein level, and v) hormones may induce changes at either the mRNA or protein levels of genes not linked to the steroidogenic response.
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Acknowledgments |
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Footnotes |
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