Nitric oxide activates diverse signaling pathways to regulate gene expression.

Nitric oxide signaling is crucial for effecting long lasting changes in cells, including gene expression, cell cycle arrest, apoptosis, and differentiation. We have determined the temporal order of gene activation induced by NO in mammalian cells and have examined the signaling pathways that mediate the action of NO. Using microarrays to study the kinetics of gene activation by NO, we have determined that NO induces three distinct waves of gene activity. The first wave is induced within 30 min of exposure to NO and represents the primary gene targets of NO. It is followed by subsequent waves of gene activity that may reflect further cascades of NO-induced gene expression. We verified our results using quantitative real time PCR and further validated our conclusions about the effects of NO by using cytokines to induce endogenous NO production. We next applied pharmacological and genetic approaches to determine the signaling pathways that are used by NO to regulate gene expression. We used inhibitors of particular signaling pathways, as well as cells from animals with a deleted p53 gene, to define groups of genes that require phosphatidylinositol 3-kinase, protein kinase C, NF-kappaB, p53, or combinations thereof for activation by NO. Our results demonstrate that NO utilizes several independent signaling pathways to induce gene expression.

Nitric oxide regulates a wide range of physiological responses (1). Most of the studies of NO action in animals have focused on its activity as an effector of rapid responses, such as regulation of blood pressure, smooth muscle contraction, and neurotransmission in the central and peripheral nervous systems. In these contexts, NO action is mediated by fast acting signaling cascades, which rapidly subside after the disappearance of the original stimulus (e.g. calcium influx).
NO can also stimulate signaling pathways that elicit long lasting changes in the cell, and it can also reset genetic programs. This function of NO is manifested in its ability to regulate gene expression (2), to mediate immune response and inflammation (3), and to act as an antiproliferative factor to mediate tissue differentiation and organ development (4,5). The molecular basis of these long lasting effects of NO is still unclear. To reveal the targets of NO in the cell, it will be important to understand the signaling pathways that are activated in response to NO.
To investigate mechanisms underlying the long term action of NO, we used DNA microarrays to determine the temporal expression profiles of genes that respond to NO. We identified genes that are affected by NO and analyzed the temporal patterns of their expression to reveal cascades of NO-dependent gene regulation events. These results were verified by using quantitative real time PCR and validated by demonstrating that similar genes are activated in an NO-dependent manner in response to cytokines. We next used chemical inhibitors of specific signaling molecules and a mutant cell line to determine the signaling pathways that lead to the activation of specific gene targets of NO. Our results show that NO employs a variety of signaling pathways to induce gene expression; these pathways may mediate the long lasting effects of NO during cell division, tissue differentiation, response to pathogens, and disease.
RNA Extraction and Probe Generation-Total RNA was isolated using Trizol (Invitrogen), and poly(A) ϩ RNA was selected with Fast-Track 2.0 (Invitrogen) according to the manufacturer's protocols. A common reference was generated by combining equal portions of poly(A) ϩ RNA from all of the untreated control samples obtained with each time point analyzed. The probes were synthesized from poly(A) ϩ RNA using Superscript II RT (Invitrogen) to incorporate amine-modified nucleotides (amino allyl-dUTP; Sigma). cDNA samples were purified and concentrated using Microcon 30 spin columns (Millipore, Bedford, MA), dried, and stored at Ϫ20°C until hybridization.
Microarray Hybridization-Gene expression changes were detected by using custom printed slide arrays produced by the Cold Spring Harbor Laboratory Genome Center (intron.cshl.org/msr/). Each slide was comprised of 9,600 features, encompassing both expressed sequence tags (ϳ47%) and known genes (ϳ53%) (acquired from the NIA, National Institutes of Health, Bethesda, MD; Igsun.grc.nia.nih.gov/ cDNA/cDNA.html), along with appropriate controls, such as housekeeping genes and marker genes to monitor by us predicted gene expression profiles. cDNAs of NO-inducible genes identified by us previously were incorporated into the array. For hybridization, the slides were hydrated by exposure to 1ϫ saline sodium citrate for 1.5 min at 30°C in a humid chamber, snap-dried on a 100°C heating plate, rinsed in 0.1% SDS for 30 s, rinsed in distilled water for 30 s, boiled in distilled water for 5 min, then rinsed in Ϫ20°C benzene-free ethanol, and spun dry at 2,000 ϫ g for 5 min. cDNA samples were resuspended in buffer (0.3 M sodium bicarbonate, pH 9.0), coupled to monofunctional NHS-Cy5 or Cy3 dye (Amersham Biosciences) for 15 min at 25°C, purified using Microcon 30 columns, and combined with mouse Cot-1 DNA (20 g), poly(A) ϩ RNA (2 g), and tRNA (2 g). The microarrays were hybridized using GlassHyb hybridization solution (Clontech Laboratories) and washed according to the manufacturer's instructions. Hybridizations for the complete time course were performed in triplicate with color reversals for each individual time point, resulting in a total of six replicates/time point. To minimize variability, all of the samples from each experimental treatment were simultaneously hybridized, washed, and scanned.
Microarray Image Analysis-The microarray slides were scanned using GenePix scanner and software (version 3.0; Axon Instruments, Inc., Foster City, CA). Fluorescence intensities for all spots were exported to the data analysis software, GeneSpring 4.2 (Silicon Genetics, Redwood City, CA) and normalized by the "per chip normalization" method. Expression ratio values obtained from the six independent replicates were averaged for each experimental time point and filtered for changes that were statistically significant (p Ͻ 0.05, compared with reference by Student's t test for each time point) and either up-regulated or down-regulated 1.3-fold. Expression profiles of the filtered data set were further analyzed for coordinated expression patterns and functional information by using the hierarchical clustering program in the GeneSpring program suite. Functional annotation was performed by searching NCBI Protein Database and SOURCE database (source.stanford.edu).
Real Time Quantitative PCR-Total RNA (2 g) was reverse transcribed into cDNA using the Taqman probe kit (Applied Biosystems) following the manufacturer's instructions. Primers for selected genes were designed via the Primer Express software (version 1.0; PE Applied Biosystems) and are listed in Table 1 of the supplemental material. Quantitative real time PCR (Q-PCR) included the following: diluted cDNA sample, 0.5 mol/liter primers, nucleotides, Taq DNA polymerase, and buffer included in the SYBR Green I Mastermix (PE Applied Biosystems). Using the ABI Prism 7700 sequence detection system (PE Applied Biosystems), PCR cycling conditions were as follows: 50°C for 2 min, 95°C for 10 min, 40 cycles at 94°C for 15 s, and 60°C for 1 min. Sequence Detector Software (version 1.6.3; PE Applied Biosystems) was used to extract the PCR data, which were then exported to Excel (Microsoft, Redmond, WA) for further analysis. Expression of target genes were measured in triplicate and were normalized to ␤-actin expression levels.
Western Blot Analysis-Following exposure to a cytokine mixture, the cells were harvested on ice and resuspended in 0.2 ml of lysis buffer (10 mM Tris-HCl, pH 7.5, 1 mM EDTA, 400 mM NaCl, 10% glycerol, 0.5% Nonidet P-40, 1 mM dithiothreitol, 1 mM phenylmethylsulfonyl fluoride, 1 g/ml aprotinin, 1 g/ml leupeptin, and 1 g/ml pepstatin) for 25 min. Following a 30-min spin at 14,000 rpm, the supernatant was collected. Protein concentration was determined using the BCA protein assay reagent (Pierce), and all of the samples were normalized by diluting with the lysis buffer. The protein samples were mixed with the sample buffer (125 mM Tris-HCl, pH 6.8, 4% SDS, 20% glycerol, 0.01% bromphenol blue, and 100 mM 2-mercaptoethanol), boiled for 5 min, and loaded onto a 7.5% SDS-polyacrylamide gel. Separated proteins were transferred from the gel to Immobilon P membrane (Millipore) by semi-dry blotting. The membrane was washed with phosphate-buffered saline and blocked overnight in phosphate-buffered saline with 5% dry milk. The membranes were incubated with antibodies to iNOS (1:2000; N32030; Transduction Labs) and ␤-actin (1:2000; A-5441; Sigma) followed by anti-mouse antibody conjugated to horseradish peroxidase and the signal was detected using chemiluminescent substrate for horseradish peroxidase (Supersignal West Femto Maximum Sensitivity Substrate; Pierce) and Hyperfilm (Amersham Biosciences).
Measurements of NOS Activity in Cellular Extracts-The cell ex-tracts were prepared from NIH3T3 fibroblasts at 4, 12, 16, 24, and 30 h after the addition of the cytokines, as well as from untreated cells. The extracts were prepared as follows. The cells were washed with phosphate-buffered saline, pH 7.4, resuspended in extraction buffer (20 mM HEPES, pH 7.4, 1 g/ml aprotinin, 1 g/ml leupeptin, 1 g/ml pepstatin, and 1 mM phenylmethylsulfonyl fluoride), and subjected to six rounds of freezing and thawing. After centrifugation for 15 min at 14,000 rpm, the supernatants were collected and used for the NOS assay as described (6). NOS assay reactions contained 50 mM HEPES, pH 7.4, 2 mM CaCl 2 , 2.5 M L-arginine, 1 mM NADPH, 20 M tetrahydro-L-biopterin, 10 g/ml calmodulin, 2 l of L-[ 3 H]arginine (2.29 terabecquerel/mmol, 62.0 Ci/mmol) (Amersham Biosciences) and 50 l (100 -150 g/ml) of a soluble protein extract in a 150-l reaction mixture. All of the reactions were incubated at 25°C for 30 min and then processed to assess the amount of [ 3 H]arginine converted to [ 3 H]citrulline. The BCA reagent system (Pierce) was used to determine the protein concentration in the extracts, and the results were used to normalize the arginine-citrulline conversion assays.

RESULTS
Temporal Patterns of Gene Activation in Response to NO-To identify the gene targets of NO, we determined the temporal gene expression profile of cells exposed to NO by using cDNA microarrays. We used NIH3T3 cells as a test cell line; these are well characterized cells that have served for many years as a standard for biochemical and genetic experiments. To produce NO, we used an NO donor compound SNAP whose chemical and pharmacological properties have been thoroughly described (1, 7). An additional consideration for choosing a well characterized fibroblast line and a well studied donor of NO was to establish a "base-line" data base that will assist in future studies describing gene activation by NO and dissecting the signaling pathways in specialized cell types.
We added NO donor SNAP (250 M) to exponentially growing NIH3T3 cells, harvested the cells at 0.5, 0.75, 1, 2, 4, 8, 12, 16, 24, and 48 h, and isolated the RNA (Fig. 1A). This concentration of SNAP was determined to induce cell cycle arrest; this arrest was followed by renewed cell division ϳ40 h after the addition of the chemical (data not shown). Importantly, even higher concentrations of SNAP (Ͼ500 M) did not induce detectable cell death (data not shown). The time points were chosen to identify (a) the very early responses of cells to NO, which reflect activation of immediate gene targets; (b) the intermediate and long term responses that may reflect multiple cascades of gene activation triggered by NO; and (c) the late responses when the levels of NO have decreased enough for the cells to resume division. cDNAs prepared from SNAP-treated samples and the corresponding controls were labeled with either Cy5 or Cy3 and hybridized to microarrays containing ϳ10,000 cDNA probes, (further supplemented with NO-induced target genes that we had previously identified in independent experiments (8). 2 Each comparison was performed in triplicate with reciprocal labeling (color reversals); thus, each time point is a result of at least six independent measurements.
After normalizing and averaging the replicate hybridizations for each time point, the data set was analyzed for genes that were up-regulated and down-regulated more than 1.3-fold in a statistically significant manner (p Ͻ 0.05). Approximately 560 genes were selected (gene identities and associated changes in mRNA levels are provided in Table 2 of the supplemental material) and further analyzed by hierarchical clustering to reveal sets of genes that share expression patterns over the examined time points (Fig. 1B). The dendrogram reveals clusters of genes whose expression patterns changed in a similar manner after exposure to NO.
A summary of the changes evoked by NO is presented by 2 N. Nakaya, J. Hemish, and G. Enikolopov, unpublished data. histograms ( Fig. 1C), which show the number of genes activated by NO at each time point. Three distinct waves of gene induction were observed (see Table 2 of the supplemental material for genes included in groups I, II, and III). The first group of genes (group I) is induced very rapidly, as early as 30 min after exposure to the NO donor; these genes may belong to the class of immediate-early genes that do not require protein synthesis for activation. The second group (group II) is induced 4 h following the addition of the NO donor. This group may include genes that require production of new proteins for their activation. Because some of the genes in group I code for transcription factors (e.g. c-fos and egr-1), it is likely that some of the genes in group II correspond to the transcriptional targets of the factors belonging to group I. Finally, a large number of genes (group III) is activated 12 h after the addition of SNAP. These genes may include the targets of group II genes as well as genes whose expression reflects the cell cycle arrest at that time point. Interestingly, not only the number of up-regulated genes but also the magnitude of change in the expression levels was greater for genes in group III compared with genes in groups I and II. Together, these data clearly show that there are distinct temporal cascades of gene activation events induced by NO. A large number of the NO-regulated genes were binned into categories based on functional annotation (as determined by the NCBI Protein Database and SOURCE). These genes are involved in signaling (3% of the analyzed genes), metabolism (7%), cell cycle (3%), stress response (1%), transcription (6%), protein degradation (3%), iron homeostasis (1%), adhesion (2%), as well as transport, apoptosis, and formation of the cytoskeleton ( Fig. 2A). The remaining genes are present as expressed sequence tags in the collection of the tested genes (54% of the genes analyzed by temporal clustering). Examples of genes assigned to the functional categories are provided in Fig. 1 of the supplemental material. A wide range of functional groups revealed by this classification suggests that NO has a broad impact on cell physiology, affecting genes involved in a variety of biochemical pathways and diverse cellular functions.
We next examined the temporal profile of the changes within the functional categories ( Fig. 1 of the supplemental material and Fig. 2B). Although in some functional groups the majority of genes changed their expression in a coordinated manner (e.g. stress response genes), genes in other groups showed few signs of coordinated changes (e.g. genes related to cell cycle or iron regulation).
Our results show that NO-inducible genes can be grouped into distinct clusters based on similar changes in expression pattern; these clusters may represent targets of the same NOactivated signaling pathways. At the same time, the diversity of the expression patterns indicates that NO activates a complex cascade of signaling events.
Verification of the Identified Changes-We sought to confirm the changes in selected candidate transcripts levels identified by the micorarray experiments by using Q-PCR. The same RNA samples used for the microarray experiments were used in Q-PCR to analyze three groups of genes: those induced early Approximately 560 genes are clustered hierarchically on the basis of the similarity of their expression profiles using GeneSpring software. Averaged experimental time points are ordered along the y axis, and the genes are ordered along the x axis. Expression relative to reference is shown colorimetrically on the right. Green represents a decreased expression level, red represents increased expression, and yellow represents no change. C, distribution of NO-induced gene targets as a function of time. The number of genes whose expression changes were statistically significant (p Ͻ 0.05; Student's t test) and up-regulated Ն1.3-fold is presented as gray bars; the smooth line represents changes for each time point that were different from the previous time point (see Table 2 of the supplemental material, where the genes in each group are listed as color-coded entries).
Interestingly, Q-PCR also revealed some subtle features of the response patterns that were not apparent from the microarray analysis. For instance, a number of the immediate-early genes (egr-1, gly96, c-myc, and c-fos) showed a biphasic pattern of expression; a very rapid response (within 30 min after addition of the NO donor) was later accompanied by a second wave of increase in RNA levels. This second increase started 2-4 h after the initial exposure to NO and may represent protein synthesis-dependent changes in the expression of these genes.
Validation of the Identified Changes-We have demonstrated that the NO-induced changes identified using microarrays can be confirmed using an alternative approach. We next sought to validate the identified targets of NO by comparing the changes generated by exposure to an external source of NO (SNAP) with the changes induced by NO that is enzymatically produced by NO synthases in the cells in vivo. To this end, we stimulated expression of the iNOS and production of endogenous NO by treating NIH3T3 cells with a mixture of cytokines (tumor necrosis factor-␣, interferon-␥, and interleukin-1␤). This mixture is known as a potent inducer of iNOS gene ex-pression in a variety of cells (1,9,10). Expression of iNOS protein was clearly seen on the Western blots 12 h after the addition of the cytokines (Fig. 4A), with or without a specific NOS inhibitor L-NAME. Analysis of iNOS RNA using Q-PCR confirmed that there was a strong response to the addition of the cytokine mixture (Fig. 4B). Finally, we confirmed the induction of iNOS by measuring its enzymatic activity, which strongly increased over time from the undetectable levels in unstimulated cells (Fig. 4C).
We next examined whether the cytokine-mediated induction of iNOS also induced the expression of genes that we had identified previously. We used Q-PCR to determine the changes invoked by the cytokines on NO-inducible genes identified in the microarray screen. We also evaluated the contribution of NO to cytokine action on gene expression by adding a NOS inhibitor (L-NAME) along with the cytokines. Examples of the changes induced by the cytokines are shown in Fig. 4D. Expression of HO-1 gene was strongly induced by the cytokines and gradually increased from 4 to 30 h after the addition of the cytokines. Expression of mdm2 was also strongly induced by the cytokines and was further augmented after 24 h of treatment. The addition of NOS inhibitor strongly reduced the response for both cases; this was particularly apparent at the later time points. This suggests that the largest component of the response of HO-1 and mdm2 to the cytokines is NO-dependent. However, it also suggests that a small part of the response, particularly at the earlier time points, may be independent of NO production and may reflect gene activation by NO-independent pathways. Fig. 4D also presents examples of genes (BNIP3 and gly96) whose activation by cytokines is less dependent on NO than that of HO-1 and mdm2. In this case a large part of the response cannot be eliminated by the addition of the NOS inhibitor, indicating that for these genes the NO signal mediates only a small part of the response to the cytokines. For all of the genes tested in this series of experiments, incubation with NOS inhibitors alone (in the absence of the cytokines) does not change their expression (data not shown).
Importantly, in each case tested, those genes that have been previously identified as inducible by the NO donor were also induced by the addition of the cytokines, and at least part of that response was dependent on NO. These data suggest that endogenously produced NO activates a set of genes similar to that activated by the exogenously added NO and further validates the gene targets of NO we have identified.
Signaling Pathways Employed by NO to Activate Gene Expression-We next sought to determine the signaling pathways that mediate the action of NO on gene expression by using  (TrxR1), BNIP3, and transferrin receptor (TfR). C, late induced targets include cyclin G, mdm2, nidogen 1, and p21/WAF1. The expression levels for all genes were normalized to the amount of ␤-actin mRNA and are indicated relative to their expression level in untreated NIH3T3 fibroblasts, which is taken as a reference. All of the data shown are the means Ϯ S.E. of triplicate reactions. Note that the y axes scales differ and are adjusted to optimize visualization. pharmacological and genetic approaches. In a pharmacological approach, we exposed the cells to NO in the presence of selective inhibitors of specific signaling pathways: wortmannin, which inhibits PI 3-kinase-mediated signaling; calphostin, which inhibits PKC signaling; SN50, which inhibits NF-B action; and ODQ, which inhibits soluble guanylate cyclase. Additionally, we used a genetic approach to compare the response to NO in normal cells and in cells lacking the tumor suppressor gene p53 (which has been implicated in the action of NO in several settings) (8,(11)(12)(13)(14). For this series of experiments, we used Q-PCR on a subset of NO-inducible genes, because we expected that in some cases only a part of the response may be affected by the inhibitors and because this technique is more likely to reveal subtle changes. The data for these experiments (each data point determined by at least three independent measurements) are summarized in Table I. Induction of a large number of the tested genes by NO was affected by the addition of wortmannin, indicating that PI 3-kinase activity is required for NO to activate these genes. These genes include mdm2, p21/WAF1, gadd45, transferrin receptor, mcp3, Bcl-XL, gly96, PKC-interacting protein, 14-3-3-, and two genes with unknown function, IMAGE 539200 and IMAGE 524571 (the latter of which we have recently cloned and characterized). 2 Interestingly, induction of one gene (Bcl-XL) by NO was augmented rather than suppressed after exposure to wortmannin, suggesting that PI 3-kinase exerts a negative control on NO-dependent activation of this gene. Activation by NO of another group of genes that includes p21/ WAF1, transferrin receptor, perlecan, 14-3-3-, IMAGE 524571, and IMAGE 539200 was diminished by calphostin, indicating that this activation is dependent on the action of PKC. Furthermore, the addition of SN50 suppressed the activation of ornithine decarboxylase, Bcl-XL, and gly96, indicating that NF-B mediates the NO signal when these genes are expressed. We have also tested the effect of ODQ, an inhibitor of soluble guanylate cyclase, which is one of the major effectors of NO action in cells. We found only three instances where NO-induced gene expression was affected by ODQ. This is consistent with the reports of a negligible activity of guanylate cyclase in several fibroblast lines (15,16).
In addition to the pharmacological approach, we pursued a genetic approach by using cells that lack the p53 gene, an important effector of NO that mediates its antiproliferative and apoptosis-related functions (8,11,17). MEFs isolated from p53 knockout animals (18) or wild type animals were exposed to SNAP. Q-PCR showed that RNA levels of mdm2, IMAGE 524571, p21/WAF1, reprimo, and perlecan were lower in p53deficient MEFs than in wild type MEFs exposed to NO ( Table  I), suggesting that their activation by NO is dependent on p53. DISCUSSION We describe a comprehensive study of gene regulation by NO in a mammalian cell line. We present a list of genes whose expression is altered by NO and the temporal order of these changes. We validated these findings by performing a highly quantitative technique to estimate the magnitude of the changes induced by NO, and we showed that similar changes are induced in response to NO produced enzymatically by NO synthase. Finally, we used chemical inhibitors and cells with genetic lesions to show that NO uses multiple signaling pathways to induce gene expression. We chose a widely used cell type (NIH3T3 fibroblasts) and a common source of NO (SNAP) to establish a data set that can serve as a base-line reference for further studies. This data set may be used for further examination of selected targets of NO, for comparison with other data sets (e.g. of genes activated by various cytokines whose action is known to involve production of NO), and for elucidation of the cellular signaling pathways activated by NO.
One conclusion of this study is that there are distinct waves of gene induction events initiated by NO in mammalian cells. The first wave activates genes that are immediate targets of the NO signals. These genes (group I) include several of the FIG. 4. Induction of iNOS following exposure to cytokine mixture. NIH3T3 fibroblasts were treated with a mixture of cytokines (10 ng/ml tumor necrosis factor-␣, 200 units/ml interferon-␥, 200 pg/ml interleukin-1␤), L-NAME (5 mM), or both L-NAME and cytokines, and the cell lysates were prepared at the indicated times. A, Western blot analysis of iNOS protein detected by anti-iNOS polyclonal antibody (Transduction Laboratories). All of the blots were reprobed with antibody to ␤-actin (Sigma) as a control. Identical results were obtained in independent experiments. B, the expression level of iNOS mRNA was measured by Q-PCR in cytokine-stimulated NIH3T3 fibroblasts. The data were normalized to ␤-actin mRNA and are indicated relative to the expression level in unstimulated fibroblasts (4 h). The values shown are the means of triplicate measurements Ϯ S.E. C, NOS activity in lysates of NIH3T3 fibroblasts treated with cytokines. The activity of NOS was measured by the [ 3 H]arginine-[ 3 H]citrulline conversion assay. The activity level measured in the control sample (0 h) was subtracted from activity levels detected in experimental lysates. The data shown are the means of duplicate measurements and are expressed as counts/g of protein. D, transcript levels for HO-1, mdm2, BNIP3, and gly96 were measured by Q-PCR in cytokine-stimulated fibroblasts. All of the genes were normalized to ␤-actin, relative to unstimulated fibroblasts, and the values indicate the means from triplicate measurements.

Effect of signaling pathway blockades on NO-stimulated gene expression in NIH3T3 fibroblasts
The changes in the RNA levels in fibroblasts after the addition of NO donor in the presence of wortmannin, calphostin, SN50, and ODQ or left untreated (vehicle alone) were determined using Q-PCR analysis. MEFs deficient in p53 were compared to wild type MEFs. known immediate-early genes, such as c-fos and egr-1. Several group I genes code for transcription factors; this is consistent with the fact that this initial wave of gene activation is followed by a second wave (activation of group II genes). Group II genes may include direct targets of transcription factors activated in the first wave. Finally, we can detect a distinct third wave of gene activation which starts at ϳ12 h after the addition of the NO donor. These genes may represent the targets of the group II genes; they may also reflect changes inherent to the cell cycle arrest status induced by NO. It will be interesting to determine whether there are any key regulatory genes in these groups required for the transition to the next stage. Genes in group I are especially interesting because they represent immediate targets of NO, and their activation may reflect changes in the transcription machinery (e.g. S-nitrosylation of some transcription factors) (19,20). Most of these genes are activated within 30 min after addition of the NO donor; using Q-PCR we also found that some of them are activated as early as 10 -15 min after addition of the donor (data not shown). The regulatory regions of these genes may be good candidate sites to search for putative NO response elements; they may also lead to identification of transcription factors affected by NO.
We have validated our findings by quantitating the NOinduced changes using Q-PCR technique. Furthermore, we found that the tested genes induced by exogenous NO donor were also induced by the mixture of cytokines, which gave rise to endogenously produced NO. We also found that the degree of contribution of the NO signaling pathways varies widely from fully underlying the action of cytokines on gene expression (e.g. in the case of HO-1 and mdm2) to mediating only a part of the signaling cascades that lead to gene activation (e.g. BNIP3 and gly96). The overlap between the sets of genes activated in NIH3T3 cells by NO and by cytokines may reflect an important role for NO in the response of fibroblasts to cytokines in vivo during inflammation and tissue repair. It will be interesting to compare these results with the transcriptional profiles of cells exposed to individual cytokines (21)(22)(23)(24)(25) to estimate the relative contribution of NO in the action of these effectors.
Recent reports assessed the contribution of NO to gene activation in several settings: macrophage response to mycobacteria and interferon-␥ (21), hepatocyte response to the introduction of iNOS gene (26), and cardiac hypertrophy in iNOS and neuronal NOS (nNOS)-deficient mice (27). Although these experimental models and the experimental details (e.g. types of microarrays, sets of the tested genes, etc.) are different, a subset of genes that we have identified in our experiments were also found in other screens (e.g. HO-1, c-fos, cyclins E and G, proliferating cell nuclear antigen, ribonucleotide reductase, macrophage migration inhibitory factor, plakoglobin, adrenomedullin, hsp70, N-acetylglucosamintransferase, and others). This overlap provides additional confidence in the results of transcription profiling and underscores the role of NO in diverse physiological responses of animal cells.
We used both pharmacological inhibitors and genetic lesions to identify specific signaling pathways used by NO to activate gene expression (Fig. 5). We identified specific groups of genes that require the activity of PI 3-kinase, PKC, or NF-B to be induced by NO. These data correspond well to reports of the involvement of these proteins in the physiological changes induced by NO or changes in the enzymatic activity of these proteins induced by NO (28 -30). We also found, using cells from mutant animals, a distinct group of genes whose activation by NO was prevented by the lack of p53. These data correspond well to previous results from our group and others (8,11,12,14), showing that the p53 protein is up-regulated in response to NO and plays a role in the antiproliferative function of NO. This provides further support for the relevance of the profiling data in explaining the long term biological effect of NO.
Activation of some genes by NO was not affected by any of the inhibitors we tested or by the absence of p53, suggesting that they are induced through other, as yet unidentified NOactivated pathways. At the same time, activation of some genes was sensitive to more than one inhibitor, suggesting that the pathways identified by each inhibitor may cooperate to transduce the NO signal to the transcriptional machinery; e.g. inhibition by wortmannin of ATM kinase (which belongs to the family of PI 3-kinases and which regulates activity of p53) may explain the fact that activation by NO of most of the p53-dependent genes is also sensitive to wortmannin.
Together, our data demonstrate that NO can introduce profound changes in cell physiology by eliciting a transcriptional response and altering the mRNA profiles of the cells. These data also highlight the potential benefits of combining RNA profiling with the use of transgenic animals carrying defined genetic lesions and the use of specific chemical inhibitors to study the long term effects of NO action. We show that NO can employ multiple signaling pathways and their combinations to control gene activity. These data may be used as a starting point for elucidating the components of the signaling cascades that lead from NO to gene transcription.