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Physiol. Genomics 26: 99-108, 2006. First published April 4, 2006; doi:10.1152/physiolgenomics.00152.2005
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Received 30 June 2005; accepted in final form 22 February 2006.
Physiological Genomics 26:99-108 (2006)
1094-8341/06 $8.00 © 2006 American Physiological Society

Comparative SAGE analysis of the response to hypoxia in human pulmonary and aortic endothelial cells

D. G. Peters 1,2,5,*, W. Ning1,2,3,*, T. J. Chu4, C. J. Li1,2 and A. M. K. Choi1,2

1 Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh
2 Lung Translational Genomics Center, Department of Medicine, University of Pittsburgh, Pittsburgh
3 Model Animal Research Center, Nanjing University, Nanjing, China
4 Institute for Human and Machine Cognition, University of West Florida, Pensacola, Florida
5 Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, United Kingdom


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
We utilized serial analysis of gene expression (SAGE) to analyze the temporal response of human pulmonary artery endothelial cells (HPAECs) to short-term chronic hypoxia at the level of transcription. Primary cultures of HPAECs were exposed to 1% O2 hypoxia for 8 and 24 h and compared with identical same-passage cells cultured under standard (5% CO2-95% air) conditions. Hierarchical clustering of significant hypoxia-responsive genes identified temporal changes in the expressions of a number of well-described gene families including those encoding proteins involved in thrombosis, stress response, apoptosis, angiogenesis, and cell proliferation. These experiments build on previously published data describing the transcriptomic response of human aortic endothelial cells (HAECs) obtained from the same donor and cultured under identical conditions, and we have thus taken advantage of the immortality of SAGE data to make direct comparisons between these two data sets. This approach revealed comprehensive information relating to the similarities and differences at the level of mRNA expression between HAECs and HPAECs. For example, we found differences in the cell type-specific response to hypoxia among genes encoding cytoskeletal factors, including paxillin, and proteins involved in metabolic energy production, the response to oxidative stress, and vasoreactivity (e.g., endothelin-1). These efforts contribute to the expanding collection of publicly available SAGE data and provide a foundation on which to base further efforts to understand the characteristics of the vascular response to hypoxia in the pulmonary circulation relative to systemic vasculature.

transcriptome; temporal; serial analysis of gene expression


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
HYPOXIA is an important pathological stimulus that has profound effects on the vasculature. These include alteration of vascular tone, coagulant function, redox homeostasis, endothelial permeability, and cell proliferation. Significantly, the response to hypoxia is different in distinct vascular beds. Hypoxia elicits systemic vasodilation, yet causes acute pulmonary vasoconstriction, which, if sustained, leads to profound remodeling of the pulmonary vasculature and culminates in structural-based increases in pulmonary vascular resistance and the subsequent development of pulmonary hypertension (30, 33, 36).

Although the focus of intense research for nearly a century, the mechanisms underlying these differential vascular responses to hypoxia remain unclear (55). It has, however, been previously noted that fundamental molecular differences exist between the response of pulmonary and systemic vascular cells to hypoxia (19), but little information exists regarding the differences in the genome-wide response to hypoxic stress between pulmonary and systemic vascular endothelial cells.

Despite the intense interest in the cellular response to hypoxia, particularly in the context of vascular biology, there have been few systematic attempts to document the transcriptional response to hypoxia in primary vascular endothelial cells. We (40) previously utilized serial analysis of gene expression (SAGE) to determine the temporal response to short-term chronic hypoxia in primary cultures of human aortic endothelial cells (HAECs). The goals of the present study were to expand this database of hypoxia-responsive vascular gene expression by comprehensively characterizing the temporal response of human pulmonary artery endothelial cells (HPAECs) grown under identical conditions and to directly compare these two data sets by taking advantage of the fact that SAGE provides immortal data. Novel statistical tools were thus used to identify similarities and differences between the transcriptomic response of HPAECs and HAECs to either 8- or 24-h exposure to hypoxia (1% O2).


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Primary endothelial cell culture and RNA isolation.
For SAGE and RT-PCR/Northern blot data (GoFigs. 2 and 3), HPAECs derived from a female donor (52 yr old) were obtained at passage 4 from Clonetics (San Diego, CA). These cells were from the same donor as those previously used to assess the response to short-term chronic hypoxia in endothelial cells derived from the systemic circulation (40) to minimize the potential for detecting genome-specific expression differences. This donor had a history of heart disease and Type 2 diabetes and was a nonsmoker. It is not known whether the donor was hypertensive. For the endothelin RT-PCR (Fig. 4), HPAECs and HAECs were derived from a second female donor (5 yr old). These were obtained at passage 3 from Clonetics. This donor had no known pathology. Cells were maintained in growth medium EBM-2 supplemented with EGM-2 (Clonetics) in a humidified incubator containing an atmosphere of 5% CO2-95% air at 37°C. Cells were split twice, grown to 90% confluence, and then exposed to hypoxia (1% O2-5% CO2-balance N2) for 8 and 24 h in a tightly sealed modular chamber (Billups-Rothenberg). Subsequently, total RNA from normoxic and hypoxic endothelial cells was isolated by the TRIzol reagent method according to the manufacturer's instructions (Invitrogen; Carlsbad, CA).


Figure 1
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  		Fig. 1. Normalized tag count plotted against the duration of exposure to hypoxia for representative genes from each of clusters 1–9 (A–I, respectively).

 

Figure 2
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  		Fig. 2. A: relative expression levels of selected genes in HPAECs after 8 and 24 h of exposure to hypoxia (1% O2) confirmed by Taqman RTP-CR. CAV1, caveolin 1; MET, met protooncogene; MMP2, matrix metalloproteinase 2; SERPINE1, plasminogen activator inhibitor type 1; CTGF, connective tissue growth factor. Normalized tag counts are displayed on the vertical axis. B: comparisons between fold changes in mRNA expression between samples determined by sequential analysis of gene expression (SAGE) and TaqMan RT-PCR. Expression changes are shown relative to time 0.

 

Figure 3
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  		Fig. 3. Northern blot analysis of paxillin expression in human pulmonary artery endothelial cells (HPAECs) and human aortic endothelial cells (HAECs) at 0, 8, and 24 h of exposure to hypoxia (1% O2). *Corresponding SAGE tag counts.

 

Figure 4
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  		Fig. 4. Relative expression levels of endothelin 1 in HAECs and HPAECs after 8 and 24 h of exposure to hypoxia (1% O2) confirmed by Taqman RT-PCR.

 
SAGE.
Twenty micrograms of total RNA were used to construct each SAGE library, as previously described (44) with some minor modifications (40). In brief, double-stranded cDNA was synthesized from mRNA bound to oligo (dT) magnetic beads (Dynal Biotech; Lake Success, NY) using Superscript II reverse transcriptase (Invitrogen). cDNAs were cleaved with NlaIII (anchoring enzyme), and the most-3' terminal cDNA fragments were captured with magnetic beads and divided into two pools. Each pool was ligated to 5' biotinylated linker A or B (51), which contained a recognition site for the tagging enzyme BsmFI. After ligation, beads were washed, and SAGE tags released from both pools by digestion with BsmFI. Tags were blunted at their 3' ends and combined to form the 104-bp ditags-linker products, which were then amplified by PCR. The amplified ditags-linkers products were redigested with NlaIII to remove the linkers, and the ditags (26 bp) were isolated by gel electrophoresis, purified through Spin X tubes (VWR; West Chester, PA), and concatemerized by self-ligation. Concatemers with sizes between 500 and 2,500 bp were obtained by gel purification, cloned into the SphI site of vector pZero (Invitrogen), and transformed into Escherichia coli strain DH10B (Invitrogen) by electroporation. For each library, about 1,200 colonies were randomly picked, and plasmids with concatemer inserts were cycle sequenced with Big Dye terminator chemistry (Big Dye version 1, Applied Biosystems; Foster City, CA) and analyzed on a 3700 Applied Biosystems DNA sequencer (Applied Biosystems).

SAGE data analysis.
The sequence file generated by the automated sequencer was analyzed using SAGE 2000 software (version 4.12, kindly provided by K.W. Kinzler and colleagues). After the elimination of linker sequences and duplicate ditags, the software was used to extract tags from the sequence file and create a report of the sequence and occurrence of each of the transcript tags. Tags were matched to gene database entries using the Cancer Genome Anatomy Project SAGEGenie database (http://cgap.nci.nih.gov/SAGE). Each specific transcript abundance was then determined by its unique tag count. Tag counts were normalized to 30,000 for each library.

Distribution of the counting of a tag.
The analysis of SAGE data assumes that the distribution of tag counts follows a binomial distribution. Given a SAGE library of size n, the count of a type of tag t has a binomial distribution with parameters (n,p), where p is the relative frequency of tag t or, ideally, the gene represented by tag t in the original tissue/cell population (9).

Test for differentially expressed genes in HPAEC alone.
Suppose we have s SAGE libraries. Let ni be the size of the ith library and Xi the counting of tag t in the ith library. Pearson's {chi}2-statistic is then defined as follows:

Formula
where

Formula
Asymptotically, under the null hypothesis that t is not differentially expressed, T has a {chi}s – 12 distribution. Simulation studies have shown that for SAGE data, the asymptotic distribution is a good approximation to the exact distribution of T (under the null hypothesis). In this study, we used the following level 5% test: a tag t is differentially expressed if the T-statistic for this tag is >95% quantile of the {chi}s – 12 distribution.

Control of the false discovery rate.
Because we were testing the expression levels of thousands of tags simultaneously, we needed to control the false discovery rate (FDR), i.e., among the tags claimed to be differentially expressed, the (average) percentage of the tags that actually were not differentially expressed. We used the Benjamini and Hochberg's linear step up multiple-comparison procedure (BH procedure) (4). The BH procedure first sorts the p values of the test statistics p(1) ≤...≤ p(k) in ascending order, where k is the number of tests. To keep the average FDR below the given level {alpha}, we searched for the largest i such that p(i) ≤ {alpha}i k and rejected all the null hypotheses whose p values were smaller than p(i). Using this procedure, all the tags whose T-statistics were greater than the 1 – p(i) quantile of the {chi}s – 12 distribution could be considered differentially expressed. We applied the BH procedure only to the tags that were at least moderately expressed in one library, because we knew in advance that a tag barely expressed in both the libraries was not likely to be differentially expressed. Genes that would not be considered differentially expressed when FDR was controlled at 5% but would be considered differentially expressed without the FDR control were included in cases where they matched genes of potential biological significance.

Test for differentially expressed genes between HPAECs versus HAECs.
The following test was used to identify genes that displayed different patterns of expression over the time course in the two groups of libraries. Let p1, p2, and p3, and q1, q2, and q3 be the true concentration levels of gene G in the three pulmonary tissues and three aortic tissues, respectively. Pearson's {chi}2-statistic can be used to test the null hypothesis that there is a constant r such that pi = rqi for i = 1, 2, 3. The test statistic is as follows:

Formula
where pi and r are the maximum likelihood estimates of pi and r under the null hypothesis obtained using the iterative method. We chose a significance level of 5% and accepted the alternative hypothesis that a gene's expression level changes over the time course of 24 h following different patterns in the two groups of libraries if the T2-statistic for this gene is >5.99, the 95% quantile of the {chi}s – 12 distribution. Table 2 lists the genes whose T2-statistic is ≥5.99.

Real-time quantitative RT-PCR.
Total RNAs were purified by the RNeasy Mini Kit (Qiagen; Valencia, CA). Residual genomic DNA was eliminated by the DNA-free kit (Ambion; Austin, TX) according to the manufacturer's protocol and quantified by spectrophotometry (Beckman DU 640). The optimal RT was carried out in 100-µl volumes as previously described (10) and two RNA inputs (100 and 400 ng). No-reverse transcriptase controls were carried out with 400 ng of RNA. Quantitative PCR was performed on this cDNA on the ABI 7700 Sequence Detection Instrument (Applied Biosystems) using TaqMan MGB probes. Quantitative RT-PCR was carried out for four genes that were identified as being hypoxia-inducible genes in HPAECs by SAGE analyses. PCR primers and probe were ordered from Applied Biosystems [matrix metalloproteinase 2 (MMP2): Hs00234422_m1, plasminogen activator inhibitor type 1 (SERPINE1): Hs00167155_m1, caveolin (CAV): Hs00184697_m1, met protooncogene (MET): Hs00228845_m1, and connective tissue growth factor (CTGF): Hs00170014_ml]. PCR amplification of cDNA derived from HPAECs (n = 2) was performed in duplicate in 50-µl volumes as previously described (10) with the optimal primer and probe concentrations used for each gene (300 nM for primer and 100 nM for probe). Gene expressions were measured relative to an endogenous reference gene, human ß-glucuronidase (ß-GUS), using the comparative cycle threshold method described previously (10).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
We sequenced a total of 95,623 tags from SAGE libraries derived from primary cultures of HPAECs grown under standard conditions (5% CO2-95% air) or exposed to 8 and 24 h of hypoxia (1% O2), respectively. Full data sets are available at the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/).

Identification of differentially expressed tags.
We identified 342 tags whose expressions varied significantly between the three experimental conditions. Within these 342 tags, 324 tags matched human Unigene clusters, 41 tags matched established sequence tags or other uncharacterized cDNA clones, 18 tags had no match to any UniGene entry, and the remaining 283 tags matched known genes. The entire list of 346 differentially expressed tags, their database matches (if any), and relevant gene function are shown in Supplemental Table S1.1

Hierarchical clustering to identify genes whose expression patterns are similarly affected by hypoxia.
We next performed hierarchical clustering analysis to identify clusters of genes whose expressions varied in a similar fashion after an exposure to hypoxia. We identified nine major clusters (clusters 1–9) of genes, and these could be broadly defined as follows. Cluster 1 includes genes whose expressions were moderately increased or unchanged within 8 h and then increased between 8 and 24 h. Cluster 2 includes genes whose expressions were decreased between 0 and 8 h and then moderately reduced or unchanged between 8 and 24 h. Cluster 3 includes genes whose expressions were dramatically increased between 0 and 8 h and then dramatically decreased back to (or just above) baseline between 8 and 24 h. Cluster 4 contains genes that were relatively unchanged between 0 and 8 h and then dramatically decreased between 8 and 24 h. Cluster 5 contains genes that were increased between 0 and 8 h and then decreased to below baseline between 8 and 24 h. Cluster 6 contains genes whose expressions were moderately decreased between 0 and 8 h and then increased between 8 and 24 h. Cluster 7 contains genes whose expressions were dramatically reduced between 0 and 8 h and then dramatically increased between 8 and 24 h. Cluster 8 contains genes whose expressions were slightly reduced between 0 and 8 h and then further reduced between 8 and 24 h. Finally, cluster 9 contains genes that were increased between 0 and 8 h and then relatively unchanged or moderately increased between 8 and 24 h. These data are summarized in Supplemental Table S1, and examples of cluster-specific gene expression patterns are shown in Fig. 1.

Functional characteristics of hypoxia-responsive genes in HPAECs.
We utilized expression analysis systematic explorer (EASE) (20) to match differentially expressed genes to gene ontology terms using the "biological process" category. EASE is able to perform "theme discovery," defined as the identification of terms or phrases that describe statistically significant genes in a list of genes (or in our case, a cluster) with respect to the number of genes described by the term or phrase in the population of genes (the entire list of differentially expressed genes) from which the list is derived.

Using EASE, we found a number of statistically significant enriched biological themes in specific clusters of genes (Table 1). Significance was based upon EASE scores of <0.05 (20). For example, cluster 8 was enriched for genes encoding proteins involved in cell growth. The fact that the expressions of genes in cluster 8 were dramatically reduced by hypoxia in our data strongly suggests a coordinated reduction in cell cycle progression, which is consistent with our previous findings in aortic endothelial cells (40). Cluster 5 contained a significant number of genes involved in the response to oxidative stress, specifically, periredoxin 1 and 6, which is consistent with previous observations (26, 41) but notably distinct from previous results in HAECs (40). Cluster 6 contained an overrepresentation of genes involved in cell adhesion/integrin-mediated signaling, cluster 1 contained genes that encode proteins involved in cell communication and signal transduction, and cluster 2 contained a preponderance of genes involved in protein and RNA biosynthesis.


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  		Table 1. EASE analysis: matching differentially expressed genes to gene ontology terms using the "Biological Process" category

 
Hypoxia elicits a prothrombotic endothelial phenotype.
Genes involved in promoting thrombosis were increased by exposure to hypoxia in cluster 1 (SERPINE1 and von Willebrand factor) and cluster 3 (protein C receptor), and there was a corresponding and dramatic reduction between 0 and 8 h in the expression of tissue factor pathway inhibitor (TFPI), which is involved in the inhibition of coagulation. Notably, TFPI was then dramatically increased between 8 and 24 h, which suggests the possibility of a homeostatic feedback loop to regulate thrombotic potential in endothelial cells. Similar results have been previously observed in HAECs (40).

Hypoxia causes a coordinated elevation in stress response genes.
We also found a clear and coordinated increase in the expressions of a number of genes that encode stress response proteins. These data are consistent with previously reported observations in HAECs (40). The majority of these were coexpressed in cluster 3 (Table S1) and included, for example, heat shock 70-kDa protein 8; heat shock 90-kDa protein 1, {alpha}; DnaJ (Hsp40) homolog, subfamily B, member 1; DnaJ (Hsp40) homolog, subfamily C, member 8; heat shock 70-kDa protein 5; heat shock 70-kDa protein 1A; and glutathione-S-transferase-{pi}. Cluster 3 also contained basic helix-loop-helix domain containing, class B2, and it has previously been suggested that this transcription factor is a critical component of the cellular response to hypoxia (37). We also found a number of other stress response genes whose expressions were elevated by hypoxia that did not reach statistical significance but have been previously shown to be responsive to hypoxia in HAECs (40). These include heat shock 60-kDa protein 1 (~2-fold); heat shock 10-kDa protein 1 (~3-fold); heat shock 105-kDa (~6-fold); and chaperonin-containing t-complex protein 1, subunit 6A (~1.7-fold).

Short-term chronic hypoxia elicits coordinated changes in the expressions of apoptotic genes in HPAECs.
In keeping with our previous observations in HAECs, we found that hypoxia resulted in gene expression changes that were consistent with a proapoptotic molecular phenotype. For example, as shown in Supplemental Table S1, lymphotoxin-ß receptor, reticulon 4, etoposide-induced 2.4 mRNA, peptidylprolyl isomerase F, and programmed cell death 4 were all significantly increased by hypoxic exposure. Similarly, a number genes previously identified in HAECs were elevated, although these did not reach statistical significance. These include apoptosis-inducing factor (~4-fold) and Bcl2/adenovirus E1B 19-kDa interacting protein 3-like (~3-fold). Notably, antiapoptotic factor Bcl2/adenovirus E1B 19-kDa interacting protein 2 was significantly reduced in HPAECs within 8 h after the onset of hypoxia, and this reached significance in our HPAEC data (Supplemental Table S1).

Exposure to hypoxia results in an antiproliferative phenotype in HPAECs.
Exposure to hypoxia resulted in both an increase in the expressions of genes encoding antiproliferative factors and a reduction in genes encoding proteins involved in cell cycle progression in HPAECs (Supplemental Table S1). For example, the antiproliferative genes sialomucin and IGF-binding protein 7 were both found to be significantly elevated by hypoxia. Similarly, there were decreases in the expressions of a number of cell cycle-associated genes previously identified as hypoxia inducible in HAECs such as cyclin D1 (~2-fold reduction at 24 h), minichromosome maintenance deficient 2 (~5-fold reduction at 24 h), and enhancer of rudimentary homolog (~2-fold reduction at 8 h) (Supplemental Table S1). As previously demonstrated in HAECs (40), there was a concomitant increase in the negative cell cycle regulator retinoblastoma-binding protein 1 (~3-fold reduction).

Hypoxia causes changes in the expression of genes encoding extracellular matrix factors.
Short-term chronic hypoxia also caused significant elevations in the expressions of genes encoding extracellular matrix factors (Supplemental Table S1). These included procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2, lysyl oxidase-like 2, microfibrillar-associated protein 2, CTGF, MMP2, and EGF-containing fibulin-like extracellular matrix protein 1. With the exception of microfibrillar-associated protein 2, all these genes were also significantly upregulated by hypoxia in HAECs (40).

Other notable hypoxia-responsive genes in HPAECs.
A number of genes of significant functional interest were also altered by exposure of HPAECs to hypoxia. These included angiopoietin-like 4 (ANGPTL4) in cluster 1 [which we previously found to be similarly elevated in HAECs (40)] and cysteine-rich motor neuron 1 in cluster 6, both of which have been shown to be involved in angiogenesis (16, 27). The elevation of ANGPTL4 in response to hypoxia is consistent with previous reports (29) and may indicate an angiogenic response to hypoxia. Furthermore, we found that the transcription factor signal transducer and activator of transcription (STAT)3 was undetectable at 0 h but rapidly induced by 8 h of hypoxia in HPAECs, whereas STAT5B was reduced between 0 and 8 h of hypoxia. STAT3 has been shown to be involved in the protective cellular response to hypoxic injury (47) and is involved in the hypoxia-inducible factor (HIF)-1{alpha}-dependent induction of VEGF in renal (25) and prostate carcinoma cells (18). STAT5B has recently been shown to be involved in cell proliferation in vascular endothelial cells (13).

Confirmation of HPAEC expression ghanges by RT-PCR.
Real-time TaqMan RT-PCR was carried out on five genes (CAV1, MET, MMP2, SERPINE1, and CTGF) to confirm the transcriptional changes identified by SAGE. These were chosen for further analysis because they are all well-characterized genes and representative of a broad range of functional classes. It can be seen from Fig. 2, A and B, that the hypoxia-responsive differential expression identified by SAGE was quantitatively corroborated by RT-PCR for these five genes.

Direct comparison of the response to short-term chronic hypoxia in HPAECs versus HAECs.
We took advantage of the digital nature of SAGE data to formally compare our HPAEC data with previously published SAGE data derived from HAECs grown under identical conditions and using cells obtained from the same donor to reduce potential confounding results due to polymorphic genetic variation. Genes whose expressions were found to differ significantly between HPAECs and HAECs are shown in Supplementary Table S2.

In general, we found marked similarity between HPAEC and HAEC transcriptomes under both normoxic and hypoxic conditions. Despite this, however, there was limited overlap between the genes flagged as significant when SAGE data from HPAEC and HAECs were analyzed independently. This likely reflects the high stringency at which significant genes were flagged because overall trends between the two cell types were highly similar. Specifically, 25 of 354 (7%) significantly altered pulmonary endothelial genes were found to overlap with an identical analysis of aortic endothelial genes. Table 2 shows that the genes that did significantly overlap were strongly representative of a small number of functional groups including those involved in extracellular matrix structure and remodeling, metabolic energy production, and the response to stress. Other important groups such as blood clotting and angiogenesis were also represented.


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  		Table 2. Statistically significant genes common to both HAECs and HPAECs

 
Supplemental Table S2 shows that the genes that distinguish HPAECs from HAECs include representatives from a variety of functional classes. One interesting feature of the comparison between the HPAECs and HAECs under hypoxic conditions is the response of genes encoding factors involved in metabolic energy production. For example, ATP synthase, H+ transporting, mitochondrial F0 complex, subunit c (subunit 9), isoform 1 (ATP5G1) and ATP synthase, H+ transporting, mitochondrial F1 complex, subunit O (ATP5O), which encode components of the F-type ATPase complex, were differentially modulated in the two cell types. Specifically, we found a strong reduction in expression after 24-h exposure to hypoxia in HPAECs versus either an induction (ATP5O) or only moderate reduction (ATP5G1) in HAECs. Similarly, we found that transcription of suppressor of S. cerevisiae gcr2, which is a transcriptional regulator of glycolytic genes, was reduced by hypoxia in HPAECs and induced in HAECs and adenylate kinase 1 was elevated in HPAECs and reduced in HAECs. Notably, glycerol-3-phosphate dehydrogenase 2, which is involved in lipid metabolism, was elevated by hypoxia in HPAECs and reduced in HAECs. Patatin-like phospholipase domain containing 2, which is also involved in lipid metabolism, showed a similar pattern of expression to that of glycerol-3-phosphate dehydrogenase 2.

Significant transciptional changes between the two cell types in a number of genes involved in the response to oxidative stress were observed. For example, thioredoxin reductase 1 and thioredoxin-like 1 were elevated in HPAECs and reduced in HAECs. The ferridoxin reductase gene was reduced by hypoxia in HPAECs and induced in HAECs. Similarly, peroxiredoxin 6 was induced between 0 and 8 h in HPAECs and then dramatically reduced between 8 and 24 h, whereas it was relatively unchanged in HAECs.

Genes encoding cytoskeletal factors were also differentially responsive to short-term chronic hypoxia in HPAECs versus HAECs (Supplemental Table S2). For example, LIM domain kinase 2 was repressed in HPAECs and induced in HAECs by hypoxia, as were a number of other genes, including emerin and titin-cap. One gene of particular interest in this context is paxillin (PXN). Although PXN differential expression in HPAECs (tag 5'-ATTTTCAAAA-3') did not reach statistical significance, we found its expression to be induced 6.5-fold between 0 and 8 h (Supplemental Table S1). We (40) have previously shown that PXN is not altered at the level of transcription by hypoxia in HAECs. We further explored this apparent difference between HPAECs and HAECs at the level of transcription. The Northern analysis data presented in Fig. 3 shows that this cell type-specific induction is indeed confined to HPAECs.

Other significant differences between HPAECs and HAECs included the induction of I{kappa}B kinase-ß, which was reduced by 8 h in HPAECs and induced in HAECs; latent transforming growth factor-ß-binding protein 2, which was increased in HPAECs and reduced in HAECs; and propapoptotic factor tumor necrosis factor (ligand) superfamily, member 14, which was unchanged in HPAECs and dramatically increased in HAECs. Of particular interest is the observation that corin, was dramatically induced by 8 h of hypoxia in HPAECs but unaffected in HAECs. The fact that this gene encodes a protein involved in blood pressure regulation, whose absence has been shown to lead to elevated blood pressure (6), is intriguing given that hypoxia is known to have a constrictive effect in pulmonary vessels. We also noted a dramatic reduction in the expression at 8 h in HPAECs of CREBBP/EP300 inhibitor 1, which is significant given the documented involvement of CREBBP/EP300 in gene transcription via the HIF pathway (28), and the fact that we also found significant cell type-specific differences in the response of known HIF-1{alpha}-inducible genes to hypoxia (Table 3).


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  		Table 3. Comparison of SAGE-identified hypoxia-responsive genes to previously identified hypoxia-inducible factor-1-regulated hypoxia-responsive genes

 
We also found that the endothelin gene (EDN1) was differentially responsive in HPAECs compared with HAECs. Specifically, we found EDN1 to be elevated by ~2.5-fold in HPAECs between 8 and 24 h of hypoxia, whereas it was unresponsive in HAECs. This observation was confirmed by real-time PCR (Fig. 4) using RNA derived from a second female donor in which the magnitude of fold change between HPAECs versus HAECs at 24 h was 1.5-fold.

Finally, we also observed alterations in the expressions of genes involved in the pathobiology of Alzheimer's disease, including amyloid-ß (A4) precursor protein (APP); amyloid-ß (A4) precursor-like protein 2 (APLP2); ß-site APP-cleaving enzyme 2 (BACE2); and APP binding, family B, member 1 (APBB1). All of these were elevated in HPAECs and, with the exception of BACE2, were relatively unchanged (or slightly down-modulated) by hypoxia in HAECs. In contrast, anterior pharynx defective 1 homolog A (APH-1A) was significantly reduced by hypoxia in HPAECs and unchanged in HAECs. These changes were statistically significant in HPAECs except for APBB1 (2-fold increase at 24 h).

Identification of previously described hypoxia-responsive genes.
A number of known hypoxia-responsive genes are known to be regulated specifically by the activities of HIF-1{alpha}. We therefore compared our data with previous data to search for genes that might be modulated by HIF1-{alpha}. Table 3 shows that our data are in general agreement with previously published data. For example, known hypoxia-responsive genes such as adrenomedullin, aldolase A, endothelin-1, enolase 1, gluose transporter 1, glyceraldehyde phosphate dehydrogenase, hexokinase 2, lactate dehydrogenase A, p21, phosphofructokinase, phosphoglycerate kinase 1, and plasminogen activator inhibitor 1 were all found to be increased by exposure to hypoxia. However, aldolase C, endothelin-converting enzyme 1, hemeoxygenase1, and pyruvate kinase M were not, and a number of other known HIF-1{alpha}-inducible genes were expressed at levels too low to make any comparison possible.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
We present a comprehensive analysis of the temporal genomic response to short-term chronic hypoxia at the level of transcription in primary cultures of HPAECS. This global and unbiased analysis builds on previous SAGE analysis of HAECs cultured under identical conditions and provides a resource for the characterization of the endothelial cell transcriptome under pathologically important stresses. SAGE is a valuable tool in this context because the resulting data are considered to be immortal and can be readily compared with other distinct SAGE data generated at different times and in different laboratories (22). Thus, in addition to identifying a number of genes representing distinct functional classes whose expressions are modulated by short-term chronic hypoxia in HPAECs, we also identified genes whose expressions are altered in both a similar or disparate fashion in HAECs. We also identified numerous uncharacterized hypoxia-responsive genes. Clearly, these observations will require further confirmation at the level of protein expression and in vivo. Confirmation of these data is important given the complex mechanisms by which hypoxia is known to alter physiology in vivo, such as by altering shear stress via polycythemia, that cannot be reproduced in a model cell-based system such as ours.

In general, we found that the response of HPAECs for a number of gene categories (e.g., heat shock, cell cycle, apoptosis, glycolysis/ATP, extracellular matrix, and thrombosis) was markedly similar to that previously described for HAECs cultured under identical conditions (40). The significance of these gene families in the endothelial response to hypoxia has been discussed previously (40).

There were, however, notable differences between the responses to hypoxia between HPAECs and HAECs. These include genes encoding proteins that are involved in cellular protection against oxidative stress such as peroxiredoxin 6 (7) and ferridoxin reductase, which has recently been shown to sensitize cells to oxidative stress-induced apoptosis (31). Also differentially responsive were genes involved in thioredoxin signaling including thioredoxin reductase 1 and thioredoxin-like 1. Interestingly, thioredoxin activity is thought to lead to elevated HIF-1{alpha} protein expression, resulting in increased VEGF expression and angiogenesis (52), and thioredoxin domain containing 5 has been shown to be involved in the cytoprotective response to hypoxia (45).

Another significant outcome of our comparison of the cell type-specific response to hypoxia was the observation that cytoskeletal genes are differentially expressed in HPAECs versus HAECs, including LIM kinase 2 and PXN. The LIM kinase 2 protein is phosphorylated and activated by Rho-associated, coiled-coil-containing protein kinase, a downstream effector of Rho, and once activated in this fashion it phosphorylates cofilin, inhibiting its actin-depolymerizing activity. It is thought that this pathway contributes to Rho-induced reorganization of the actin cytoskeleton (50), and, significantly, Rho signaling has been shown to be critically important for hypoxia-dependent alterations in endothelial cell structural alterations (2). Furthermore, it has been previously demonstrated that HAECs display significantly greater motility in response to hypoxia than do HPAECs (38). In keeping with this, Tian and Phillips (48) showed that PXN expression is inversely correlated with motility. The fact that we observed an elevated expression of PXN in HPAECs supports these findings.

The fact that EDN1 was only hypoxia responsive in HPAECs is significant. Endothelin is a well-characterized vasoconstrictor whose hypoxia-responsive mRNA induction has been previously described in umbilical vein endothelial cells (21). Furthermore, it is known that EDN1 is a major mediator of hypoxia-induced pulmonary vasoconstriction (8). This cell type-specific hypoxia-responsive induction of EDN1 clearly deserves further investigation at the functional level.

Also significant with regard to the difference between the HPAEC- and HAEC-specific responses to hypoxia is the fact that four genes associated with Alzheimer's disease pathobiology (APP, BACE2, APBB1, and APLP2) were coordinately elevated by exposure to hypoxia, whereas APH-1H was reduced. These changes were statistically significant. In contrast, in our previous analysis of the hypoxia-responsive transcriptome in HAECs (40), we did not find these genes to be significantly altered, although BACE2 was upregulated at 24 h. Numerous reports (3, 39) have linked the expression of Alzheimer's disease-associated genes with hypoxia and ischemia, but these observations have almost exclusively been made in neuronal tissue.

In conclusion, we used SAGE to characterize the global temporal response of HAECs to short-term chronic hypoxia at the level of transcription. This identified numerous hypoxia-responsive genes representing a variety of functional classes. This information can be collated to build up a relatively detailed picture of the way in which HAEC molecular physiology is reprogrammed after exposure to hypoxia. In addition to providing comprehensive data regarding the hypoxia-responsive HCAEC transcriptome in vitro, it provides a foundation for further studies of the molecular mechanisms by which cells respond to hypoxic stress. Further experiments will require validation of our findings in experimental systems that more closely represent physiological conditions. Until then, the present data provide a reference point for biologists interested in the genomic response to hypoxia in an in vitro vascular model system.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was supported by National Aeronautics and Space Administration Grant NCCI-1227 (to D. G. Peters).


   ACKNOWLEDGMENTS
 
We thank Elisa O'Hare and Birgit Suppe for technical advice.


   FOOTNOTES
 
Article published online before print. See website for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: D. G. Peters, Dept. of Pharmacology and Therapeutics, The Sherrington Bldg.s, Univ. of Liverpool, Ashton St., Liverpool L69 3GE, UK (e-mail: david.peters{at}liverpool.ac.uk).

* D. G. Peters and W. Ning contributed equally to this work. Back

1 Supplemental Material for this article is available at the Physiological Genomics web site. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

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