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Physiol. Genomics 25: 435-449, 2006. First published February 28, 2006; doi:10.1152/physiolgenomics.00315.2005
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Received 20 December 2005; accepted in final form 22 February 2006.
Physiological Genomics 25:435-449 (2006)
1094-8341/06 $8.00 © 2006 American Physiological Society

Gene expression profiling reveals the profound upregulation of hypoxia-responsive genes in primary human astrocytes

S. M. Mense 1, A. Sengupta 1, M. Zhou 1, C. Lan 1, G. Bentsman 2, D. J. Volsky 2 and L. Zhang 1

1 Department of Environmental Health Sciences, Mailman School of Public Health
2 Molecular Virology Division, St. Luke’s-Roosevelt Hospital Center and College of Physicians and Surgeons, Columbia University, New York, New York


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Oxygen is vital for the development and survival of mammals. In response to hypoxia, the brain initiates numerous adaptive responses at the organ level as well as at the molecular and cellular levels, including the alteration of gene expression. Astrocytes play critical roles in the proper functioning of the brain; thus the manner in which astrocytes respond to hypoxia is likely important in determining the outcome of brain hypoxia. Here, we used microarray gene expression profiling and data-analysis algorithms to identify and analyze hypoxia-responsive genes in primary human astrocytes. We also compared gene expression patterns in astrocytes with those in human HeLa cells and pulmonary artery endothelial cells (ECs). Remarkably, in astrocytes, five times as many genes were induced as suppressed, whereas in HeLa and pulmonary ECs, as many as or more genes were suppressed than induced. More genes encoding hypoxia-inducible functions, such as glycolytic enzymes and angiogenic growth factors, were strongly induced in astrocytes compared with HeLa cells. Furthermore, gene ontology and computational algorithms revealed that many target genes of the EGF and insulin signaling pathways and the transcriptional regulators Myc, Jun, and p53 were selectively altered by hypoxia in astrocytes. Indeed, Western blot analysis confirmed that two major signal transducers mediating insulin and EGF action, Akt and MEK1/2, were activated by hypoxia in astrocytes. These results provide a global view of the signaling and regulatory network mediating oxygen regulation in human astrocytes.

microarray; hypoxic response; signaling pathways


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
OXYGEN IS VITAL for the development and survival of mammals. The brain is the most sensitive organ to hypoxia, and it consumes 20% of the body’s oxygen (114). In response to hypoxia, the brain initiates numerous adaptive responses at the molecular and cellular levels as well as at the whole organ level (79, 109, 142). These responses include the activation of the brain stem sites that inhibit respiration at the organ level (18, 79), inhibition of oxygen-sensitive K+ and Ca2+ channels at the cellular level (111, 131), and alteration in gene expression at the molecular level (75, 109, 138). Lack of oxygen and oxygen-sensing mechanisms have been implicated in the pathology of many central nervous system disorders, including stroke, head trauma, neural developmental problems associated with preterm birth, neoplasia, and neurodegenerative diseases (1, 72, 112). Thus previous studies have been performed to investigate the molecular mechanisms of oxygen sensing in neuronal cell models, such as PC12 (23, 24, 107, 108, 110, 118) and RN46A cells (140). In addition, numerous studies using rodent brain slices have been performed to extensively characterize the morphological and metabolic responses of the brain, including neuronal cells and astrocytes, to hypoxia-ischemia (5, 81, 84, 89). However, a comprehensive identification of hypoxia-responsive genes at the genomic level and a global characterization of genomic response may be limited by the complexity of brain tissues, which include neuronal and glial cells. Thus it would be useful to investigate the molecular responses of purified neuronal and glial cells.

Particularly, astrocytes are sensors of the brain environment; astrocytes immediately react to the changes in the environment at the genomic level (gene expression) as well as the nongenomic level (19, 52, 77, 124). Astrocytes account for at least 15% of oxidative metabolism in the brain, and their respiratory rates are maintained at 85% of the basal level until oxygen is virtually exhausted (10, 28, 81). Astrocytes play active roles in regulating synaptic activity, synaptogenesis, and neurogenesis and in neuroprotection (3, 4, 21, 32, 39, 78, 80, 81, 87, 91, 117, 124), in addition to their roles in maintaining ion and pH homeostasis, in the synthesis and removal of neurotransmitters (such as GABA and glutamate), and in providing glucose supply and antioxidant defense for the brain (68). The ratio of astrocytes to neurons increases with brain complexity, and, in humans, astrocytes outnumber neurons (91). The way by which astrocytes respond to hypoxia would significantly affect the response of the brain to hypoxia and the extent of brain injury in pathological conditions involving hypoxia/ischemia (17, 52, 83, 84, 124). Thus it is important to elucidate the molecular events underlying the global response of human astrocytes to hypoxia at the genomic level.

To this end, we decided to systematically identify and categorize hypoxia-responsive genes in purified primary human astrocytes by using microarray gene expression profiling and sophisticated computational data-analysis algorithms. We took advantage of previous studies of hypoxia and compared the expression profiles of human astrocytes with those of human carcinoma HeLa cells. HeLa cells have been widely used as a model system for studying transcriptional regulation and for studying the molecular mechanisms of oxygen sensing, for example, the mechanism by which the hypoxia-inducible factor (HIF) family transcription factors promote oxygen sensing (46, 47, 127, 129). Also, we compared astrocytic hypoxia-responsive genes with those hypoxia-responsive and HIF-1-regulated genes in primary human pulmonary artery endothelial cells (ECs), identified previously by Manalo and colleagues (69). We also examined whether previously known clusters of hypoxia-responsive genes are also similarly affected by hypoxia in purified human astrocytes. These analyses provide important insights into the molecular events underlying astrocytic responses to hypoxia.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Human primary astrocytes.
Human fetal astrocytes (HFAs) were isolated as previously described by Dr. Volsky and colleagues (15, 130). Astrocytes were isolated from second-trimester (gestational age: 16–19 wk) human fetal brains obtained from elective abortions in full compliance with National Institutes of Health guidelines and under an exemption from Institutional Review Board review. Pure preparations of astrocytes were obtained by using high-density culture conditions in the absence of growth factors in F12-DMEM (Invitrogen) containing 10% FBS, penicillin, streptomycin, and gentamycin. Subsequently, cells were maintained in this medium at 2–5 x 104 cells/cm2 and were subcultured weekly up to six times. Cultures were regularly monitored by immunofluorescence staining for the expression of the astrocytic marker glial fibrillary acidic protein (GFAP) and HAM-56 to identify cells of the monocyte lineage. Only cultures that contained ≥99% GFAP-positive astrocytes and no detectable HAM-56-positive cells were used in the experiments. HeLa cells (American Type Culture Collection) were maintained in DMEM (GIBCO-Invitrogen) containing 10% FBS, penicillin, streptomycin, and gentamycin. Cells were maintained in this medium at 2–5 x 104 cells/cm2 and subcultured every 3–4 days.

Hypoxia treatment.
Before hypoxia treatment, human astrocytes and HeLa cells were maintained to ensure a healthy state of growth with about 60–70% confluence, and medium was refreshed. Cells were then maintained under hypoxic (1% O2-5% CO2-94% N2) or normoxic (95% air-5% CO2) conditions for 24 h. Hypoxic treatment was performed in a O2-CO2 incubator with controls for O2 and CO2 (NAPCO; Winchester, VA). The O2 level was calibrated by using an O2 monitor (Coy Laboratories; Grass Lake, MI) and also confirmed by analyzing HIF-1{alpha} protein levels in various mammalian cells. This hypoxia treatment condition was selected based on an extensive survey of previous studies of hypoxia responses in cultured cells. These studies almost always used 1% O2 and time periods ranging from 4 to 48 h (8, 26, 33, 41, 46, 47, 59, 60, 106, 127–129, 132). Thus we chose 1% O2 in our experiments. We chose the 24-h time period to ensure that the hypoxia responses of most genes were optimally induced (69, 104, 105). At this time point, previous studies, particularly those of HIF-1, showed that HIF-1{alpha} activity was most strongly induced (8, 26, 33, 41, 46, 47, 59, 60, 106, 127–129, 132). The effects of hypoxia on certain proteins or genes, such as p53, can be detected only after a relatively long induction time (42, 98). Notably, our condition is also the same as that used in the genomic study of human ECs by Manalo and colleagues (69), who performed extensive studies of hypoxia responses in cell lines as well as in animals. The identical treatment conditions make the data more comparable.

RNA extraction and Affymetrix GeneChip expression analysis.
Primary human astrocytes and HeLa cells grown under hypoxic (1% O2-5% CO2-94% N2) or normoxic (95% air-5% CO2) conditions for 24 h were observed to ensure that they retained their normal shape and remained adherent. Separate assays (e.g., TdT-mediated dUTP nick-end labeling) were performed to confirm that the cells were not apoptotic after hypoxia treatment. Total RNA was extracted by using TRIzol reagent (GIBCO-BRL Life Technologies). We isolated RNA from three independent batches of human astrocytes, which were isolated separately from different subjects. No identifiable information from these subjects was available, in keeping with the guidelines on human subjects. Three independent batches of astrocytes from three different subjects, not the same subject, were used to ensure that our data and conclusions would not totally rely on one unidentified subject, who may have experienced influential environmental or genetic conditions. To keep the conditions comparable with astrocytes, we also isolated RNA from three independently cultured HeLa cells at different times.

The synthesis of cDNA and biotin-labeled cRNA were carried out exactly as described in the Affymetrix GeneChip Expression Analysis Technical Manual (2000). The human genome U133 plus 2.0 arrays were purchased from Affymetrix. Probe hybridization and data collection were carried out by the Columbia University Affymetrix GeneChip processing center. Specifically, the Affymetrix GeneChip Hybridization Oven 640 and the next-generation GeneChip Fluidics Station 450 were used for hybridization and chip processing. Chip scanning was performed by using the GeneChip scanner 3000. Initial data analysis was performed by using Affymetrix GCOS1.2 software.

Computational analysis of microarray data.
Complete sets of microarray data have been submitted to the GEO repository (http://www.ncbi.nlm.nih.gov/geo/info/linking.html with GEO Accession Nos. GSE3045 and GSE3051). We analyzed microarray expression data by applying a series of quality control, statistical, filtering, gene ontology, and pathway analysis algorithms. First, by using GCOS1.2 with the advanced probe logarithmic intensity error algorithm, we calculated and examined the parameters reflecting the image quality of the arrays. Arrays with a high background level in any region were discarded and replaced. The average noise or background level was limited to <5%. The average intensity for those genes judged to be present was at least 10-fold higher than those judged to be absent. Also, arrays that deviated considerably in the percentage of present and absent genes from the majority of the arrays were replaced. Arrays with a ß-actin 3'-to-5' ratio of >2 were replaced. Next, microarray data were uploaded to GeneSpring 7.0 (Silicon Genetics) for further quality and statistical analysis. Data were normalized again by using the stringent per chip and per gene normalization algorithms; genes with low control signal (less than average) or not present in one-third of the samples were dropped out before statistical analysis. Data were then analyzed by nonparametric two-way ANOVA (one parameter is cell, another parameter is treatment). The Benjamini and Hochberg false discovery rate multiple testing correction was applied with a false discovery rate of <1%. Two-way ANOVA was chosen for statistical analysis because the variations of astrocytes from different subject can be very significant and, in certain cases, may exert stronger effects than the treatment of cells. Two-way ANOVA would take account of this and identify statistically significant genes (P < 0.01) whose transcript level was consistently altered by hypoxia. Second, to identify genes whose transcript level was significantly altered by hypoxia, we applied four consecutive filtering processes, which allowed us to identify genes whose overall expression level in three independent batches of HFAs was altered at least 2-fold (hypoxia treated vs. control) and whose expression level in each batch of HFAs was altered by more than 1.5-fold.

Third, we analyzed and categorized the identified hypoxia-altered genes by using various biological annotation and gene ontology programs, including the DAVID/EASE program (National Institute of Allergy and Infectious Diseases) and Gene Ontology algorithm in GeneSpring. The gene lists (hypoxia-responsive and HIF-1-regulated genes) identified by the microarray study in human pulmonary artery ECs were uploaded to GeneSpring and compared with hypoxia-responsive genes in human astrocytes and HeLa cells. Finally, to identify signaling pathways or transcriptional regulators that may mediate the effect of hypoxia on gene expression, we used another computational program called PathwayAssist (Stratagene software).

Quantitative real-time RT-PCR.
Oligonucleotide primers for measuring the transcript levels of genes were designed based on the sequences used to design microarray probes by using the Primer3 program (http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi). ß-Actin was used as a control for the relative quantification of transcripts, because our microarray data and previous studies showed that the ß-actin transcript level is unaffected by hypoxia. RT-PCRs were performed by using a Roche LightCycler and the SYBR green kit according to specified protocols. Calculations were done by using Roche LightCycler software (14, 36, 67). Primer sequences used for real-time PCR are listed in Table 1.


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  		Table 1. Primer sequences used for real-time PCR

 
Western blot analysis.
Human astrocytes were grown as described above, and proteins were extracted. Briefly, astrocytes were washed twice in PBS, and whole cell extracts were prepared by adding 10 packed cell volumes of 1x sample buffer [2% SDS, 100 mM dithiothreitol, 60 mM Tris (pH 6.8), and 10% glycerol] and boiled for 5 min. Protein concentrations were determined by using the bicinchoninic acid protein assay kit (Pierce). Approximately 100 µg of protein were analyzed by 9% SDS-PAGE and transferred onto an Immunoblot polyvinylidene difluoride membrane (Bio-Rad). Membranes were probed with polyclonal antibodies, followed by detection with a chemiluminescence Western blotting kit (Boehringer Mannheim). Polyclonal antibodies against phospho(Ser217/221)-MEK1/2 (p-MEK), phospho(Ser473)-Akt (p-Akt), total Akt (Akt), and ß-actin were purchased from Cell Signaling Technology.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Microarray gene expression profiling suggests that human astrocytes respond to hypoxia in a distinctive manner.
To gain a global view of the genomic response of astrocytes to hypoxia, we examined the gene expression profiles of hypoxic and normoxic primary human astrocytes. Gene expression profiling was performed by using RNA samples isolated from three independent batches of primary human astrocytes grown under hypoxic and normoxic conditions. We used Affymetrix human genome U133 plus 2.0 arrays, each of which contains probe sets that allow the detection of over 50,000 transcripts representing >38,000 genes. Various computational and statistical algorithms were applied to identify genes whose overall expression level in three independent batches of astrocytes was altered at least 2-fold by hypoxia (compared with controls) and whose expression level in each batch of astrocytes was similarly altered at least 1.5-fold (see MATERIALS AND METHODS). We analyzed and categorized these hypoxia-responsive genes to determine whether previously known clusters of hypoxia-induced genes, such as those encoding enzymes involved in carbohydrate metabolism and those encoding angiogenic growth factors, were also induced in human astrocytes and HeLa cells. We compared the hypoxia-responsive genes identified in primary human astrocytes with those identified in HeLa cells. Furthermore, we compared the identified hypoxia-responsive genes with those hypoxia-responsive and HIF-1-regulated genes identified in primary human pulmonary artery ECs by Manalo et al. (69).

The main results from our analyses and comparisons are summarized in Table 2. Note that about half of the genes are not annotated, i.e., no functional information about them is available (Table 2). Preliminary real-time RT-PCR analysis of several hypoxia-responsive genes, such as VEGF, adrenomedullin (ADM), and pyruvate dehydrogenase kinase (PDK1), in both HFAs and HeLa cells supported the results from microarray analyses. Figure 1 shows the results from real-time RT-PCR analysis with RNA prepared from three independent batches of astrocytes. The relative degrees of hypoxia-induced changes in transcript levels were generally consistent between microarray and RT-PCR data, although the exact folds of change obtained from RT-PCR were not identical to those from microarray analysis.


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  		Table 2. Summary of hypoxia-responsive genes in HFAs and HeLa cells

 

Figure 1
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  		Fig. 1. Real-time RT-PCR analysis of the induction of the transcript levels of phosphoglycerate kinase 1 (PGK1), glucose phosphate isomerase (GPI), adrenomedullin (ADM), pyruvate dehydrogenase kinase 1 (PDK1), solute carrier family 16 member 3 (SLC16A3), and VEGF by hypoxia in human astrocytes. Total RNA from 3 independent sets of human astrocytes was isolated, and real-time RT-PCR analysis was performed as described in MATERIALS AND METHODS. ß-Actin was used as an internal standard. The folds of induction of the indicated transcript levels were calculated by using Roche software, as described in MATERIALS AND METHODS.

 
We found that 1,168 genes (562 are annotated, i.e., with certain known structural or function information) were altered in primary human astrocytes in response to hypoxia, whereas 2,047 genes (1,006 are annotated) were altered in HeLa cells (Table 2). Notably, in primary human astrocytes, five times as many genes were induced as suppressed, whereas in HeLa cells, the number of suppressed genes was slightly higher than that of induced genes. In astrocytes, 979 genes were induced by hypoxia and 189 genes were suppressed. In HeLa cells, 903 genes were induced and 1,144 genes were suppressed (Table 2). This pattern of the hypoxia response in HeLa cells was very similar to that observed by Manalo et al. (69) in human ECs. They found that in ECs, 845 genes were induced by hypoxia, whereas 1,072 genes were suppressed (69). Like the astrocytes used here, ECs are primary human cells, whereas HeLa cells are carcinoma cells. These results illustrate that the patterns of gene expression during hypoxia in ECs and HeLa cells are much more similar to one another than they are to the gene expression pattern of astrocytes, suggesting that the response of human astrocytes to hypoxia is distinctive.

A comparison of the hypoxia-responsive genes in astrocytes and HeLa cells showed that they shared 133 common hypoxia-responsive genes (Table 2). Among these common genes, 129 genes were induced, whereas only 4 genes were suppressed (Supplemental Table S1; available at the Physiological Genomics web site).1 A comparison of the hypoxia-responsive genes in astrocytes with the previously identified HIF-1-regulated and hypoxia-responsive genes in primary human ECs identified 58 HIF-1-regulated hypoxia-responsive genes in human astrocytes (Tables 2 and 3). Among those HIF-1-regulated genes in astrocytes, 54 genes were induced and only 4 genes were suppressed (Table 2). In HeLa cells, 41 hypoxia-responsive genes were also HIF-1-regulated: 34 genes were induced and 7 genes were suppressed (Table 2). These results suggest that HIF-1 is an important regulator mediating oxygen regulation of multiple genes in human astrocytes, as expected.


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  		Table 3. List of HIF-regulated hypoxia-responsive genes in Astrocytes

 
To better understand the effects of hypoxia on gene expression in primary human astrocytes and HeLa cells, we used the DAVID/EASE program and Gene Ontology algorithm in GeneSpring to categorize the annotated hypoxia-responsive genes. On the basis of the hypoxia-responsive gene clusters identified in previous studies, we identified several functionally distinctive classes of hypoxia-responsive genes (6, 12, 25, 40, 50, 69, 71, 75, 86, 92, 99, 100). These included genes involved in carbohydrate metabolism, genes involved in apoptosis, and genes encoding angiogenic growth factors and cytokines, transporters, signal transducers, and transcriptional regulators (Table 2).

Hypoxia induces the expression of genes encoding functions involved in carbohydrate metabolism and growth factors.
It is well known that hypoxia induces the expression of genes encoding functions involved in carbohydrate metabolism in diverse mammalian cells (99, 103, 138). Indeed, our microarray expression profiling (Table 2) showed that 33 genes in astrocytes (Table 4) and 19 genes in HeLa cells, encoding mainly enzymes involved in glycolysis, were induced. No genes involved in carbohydrate metabolism were suppressed. Eight of these genes, such as aldolase, enolase, and phoshpoglycerate kinase, were common to both astrocytes and HeLa cells. Notably, not only the number of this class of genes was greater in astrocytes than in HeLa cells but also their folds of induction were generally higher in astrocytes than in HeLa cells (Fig. 2). Likewise, glucose transporters, including glucose transporter 1 (SLC2A1) and glucose transporter 14 (SLC2A14), were induced in astrocytes (Tables 1 and S2) as well as in HeLa cells (Table 2). Furthermore, consistent with previous findings in astrocytes, the expression of several genes encoding monocarboxylic acid transporters (MCTs for lactate transport) (9, 28, 37, 44, 85), including astrocytic low-affinity MCT1 (SLC16A1) and MCT4 (SLC16A3; Table S2), were induced by hypoxia in human astrocytes.


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  		Table 4. List of hypoxia-induced genes involved in carbohydrate metabolism in astrocytes

 

Figure 2
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  		Fig. 2. Induction of genes involved in carbohydrate metabolism in HeLa cells and human fetal astrocytes (HFAs). Plotted here are the folds of hypoxia induction of genes involved in carbohydrate metabolism in HeLa cells and primary HFAs. The folds were calculated by averaging data from 3 independent batches of HFAs or HeLa cells. Gene names are omitted to save space; the scales for the folds of hypoxia induction are identical.

 
Another class of well-known genes induced by hypoxia in diverse mammalian cells consists of genes that encode angiogenic growth factors and cytokines (1, 99, 103, 138). Indeed, a series of such genes encoding ADM, angiopoietin-like factor, and VEGF, were induced in both astrocytes (Tables 2 and 5) and HeLa cells (Table 2). Certain proinflammatory factors were also induced in astrocytes (Table 5), as expected from previous studies (48, 63, 121, 122, 124). Together, these results, which fit nicely with previous data regarding hypoxia-induced gene expression changes, strongly support the validity of our microarray analysis.


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  		Table 5. List of hypoxia-responsive genes encoding growth factors and cytokines in astrocytes

 
Hypoxia causes changes in the expression of diverse genes.
Genes encoding other functions were also altered by hypoxia. For example, 12 genes encoding functions involved in apoptosis were induced in human astrocytes, whereas 20 such genes were induced in HeLa cells (Table 2). Many of these induced genes, such as BCL2L11, BNIP3, BNIP3L, and DATF1 (Table S3), are proapoptotic factors (11, 34, 56, 136). Intriguingly, contrary to other classes of hypoxia-induced genes, more apoptotic genes were induced in HeLa cells than in human astrocytes (Table 2).

A large group of genes encoding transcriptional regulators and signal transducers was altered by hypoxia. In astrocytes, 76 genes encoding transcriptional regulators were altered: 61 genes were induced, whereas only 15 genes were suppressed (Table 2). This is consistent with a greater overall number of genes being induced than suppressed in astrocytes. In HeLa cells, about 165 genes encoding transcriptional regulators were altered by hypoxia: 83 genes were induced, whereas 82 genes were suppressed (Table 2). Similarly, 108 genes encoding signal transducers were altered: 98 genes were induced, whereas only 10 genes were suppressed (Table 2). In HeLa cells, 122 genes encoding signal transducers were altered by hypoxia: 65 genes were induced, whereas 52 genes were suppressed (Table 2). The effects of hypoxia on the expression of these transcriptional regulators and signal transducers may mediate the effects of hypoxia on the expression of other identified genes.

Insulin and EGF signaling pathways appear to play important roles in mediating oxygen regulation of gene expression.
To uncover common signaling or regulatory pathways that may mediate the oxygen regulation of multiple genes, we used another computational program called PathwayAssist. Intriguingly, we found that a significant number of hypoxia-responsive genes are targets of EGF and insulin signaling pathways (Table 2). Specifically, in astrocytes, 43 targets of the EGF signaling pathway were altered by hypoxia; 37 of them were induced, and 8 are known to be affected by HIF-1 (Tables 2 and 6). In HeLa cells, 54 targets of the EGF signaling pathway were altered by hypoxia; 41 of them were induced (Table 2 and data not shown). Among the target genes of the insulin signaling pathway, 64 were altered by hypoxia in astrocytes (Tables 2 and 7), whereas 31 were altered in HeLa cells (Table 2). Fifty-nine of the 64 altered genes in astrocytes were induced; 12 of them are known to be regulated by HIF-1 (Tables 2 and 7). Eighteen of the 31 altered genes in HeLa cells were induced (Table 2). These results suggest that the EGF and insulin signaling pathways are altered by hypoxia in both astrocytes and HeLa cells. The upregulation of most of these target genes by hypoxia, particularly in astrocytes, suggests that these signaling pathways may be activated by hypoxia.


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  		Table 6. List of hypoxia-responsive genes that are regulated by the EGF signaling pathway in astrocytes

 

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  		Table 7. List of hypoxia-responsive genes that are regulated by the insulin signaling pathway in astrocytes

 
Analysis by PathwayAssist also revealed several transcriptional regulators that may play important roles in mediating oxygen regulation of gene expression. In astrocytes, 26 target genes of Myc, 8 target genes of p53, and 33 target genes of Jun were altered by hypoxia (Tables 2 and 8). The predominant majority of these targets were induced by hypoxia (Table 8). Likewise, in HeLa cells, 11 targets of Myc, 10 targets of Jun, and 47 targets of p53 were altered by hypoxia (Table 2). Myc, Jun, and p53 are also controlled by the signaling pathways mediating EGF and insulin action (38, 97, 133). Thus these results fit very well with each other. Together, they suggest that the EGF and insulin signaling pathways and their related regulators may play important roles in mediating oxygen regulation of gene expression in human astrocytes as well as in HeLa cells. The upregulation of the predominant majority of targets of insulin and EGF in astrocytes (Tables 2, 6, and 7) may suggest that pathways mediating their actions are activated by hypoxia.


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  		Table 8. List of hypoxia-responsive genes that are regulated by Myc, Jun, or p53 in astrocytes

 
Western blot analysis shows that Akt and MEK1/2, key components of EGF and insulin signaling pathways, are activated by hypoxia.
The Ras-ERK1/2 and phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathways are two major pathways mediating insulin and EGF actions in diverse mammalian cells (65, 70, 90, 94, 97, 133, 143). The effects of hypoxia on the target genes of insulin and EGF may be mediated by the components of such signaling pathways. Akt is a key component of the PI3K-Akt pathway, whereas MEK1/2 is a key component of the Ras-ERK1/2 pathway. Because both MEK1/2 and Akt are activated by serine phosphorylation, the levels of their phosphorylated proteins are reflective of their activity. We therefore measured the levels of phosphorylated proteins by Western blot analysis (Fig. 3). We repeatedly found that the levels of phosphorylated MEK1/2 increased progressively with increasingly hypoxia treatment time, being the highest at 24 h postinduction (Fig. 3). The level of phosphorylated Akt increased at 2 h postinduction. At 4 h, the level of phosphorylated Akt dropped, but, at 24 h, its level peaked again (Fig. 3). As controls, we showed that the levels of ß-actin and total Akt were unaffected by hypoxia (Fig. 3). These results support the results from microarray gene expression profiling and PathwayAssist analysis suggesting that the signaling pathways mediating insulin and EGF action may be activated in astrocytes.


Figure 3
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  		Fig. 3. Effects of hypoxia on protein levels of MEK1/2 and Akt. HFAs were maintained under hypoxia (1% O2) for 0, 2, 4, 8, and 24 h, respectively. Protein extracts were then prepared and subjected to Western blot analysis. The polyvinylidene difluoride membranes were probed with antibodies against phospho(Ser217/221)-MEK1/2 (p-MEK), phospho(Ser473)-Akt (p-Akt), total Akt (Akt), and ß-actin, respectively. Equal amounts of cellular proteins were loaded in every lane.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
Astrocytes play active roles in the brain (126). While the proper functioning of astrocytes is important for neuronal function and neuroprotection (43, 124), the dysregulation of astrocyte function is associated with the pathogenesis of many neurological diseases, such as prion, Alzheimer’s and Parkinson’s Diseases, and human immunodeficiency virus-1-associated dementia (13, 20, 35, 51, 73, 130). Likewise, the response of astrocytes to cerebral ischemia/hypoxia is critical for determining the outcome of cerebral ischemia (19, 52, 77). Here, to gain a global view of astrocytic response to hypoxia, we systematically examined and analyzed the global gene expression responses of primary human astrocytes to hypoxia. We also compared the astrocytic responses with those detected in widely used human HeLa cells and in the human pulmonary artery ECs characterized by Manalo et al. (69). The results revealed several interesting characteristics of hypoxic responses in astrocytes.

First, the number of hypoxia-suppressed genes in astrocytes was much smaller than that in HeLa cells, whereas the numbers of hypoxia-induced genes were similar in astrocytes and HeLa cells (Table 2). The effect of hypoxia on gene expression profiles in HeLa cells is very similar to that detected in primary human pulmonary artery ECs (69). In both cells, the number of hypoxia-suppressed genes was slightly higher than that of induced genes. It is tempting to speculate that the overwhelming effect of hypoxia on enhanced gene expression may contribute to the relative resistance of astrocytes to hypoxia/ischemia.

Second, our results are consistent with previous studies investigating gene expression in astrocytes in response to brain ischemia. For example, consistent with previous studies of brain tissues (for reviews, see Refs. 28 and 124), we found that genes encoding glucose transporters, lactate transporters, glycolytic enzymes, and angiogenic growth factors are all induced by hypoxia. Although these genes were also induced in HeLa cells, the extent of these changes was greater in astrocytes than in HeLa cells. For example, many more genes involved in glycolysis, glucose transport, and lactate transport were significantly induced in astrocytes compared with HeLa cells (Fig. 2). Also, their fold of induction was greater in astrocytes compared with HeLa cells (Fig. 2). This characteristic of gene expression may contribute to the resistance of astrocytes to hypoxia.

Third, our data support the previous idea that oxygen regulation of gene expression is cell type and species dependent (29, 69, 102, 115). As discussed above, our analysis identified a series of previously known genes that are induced by hypoxia in diverse cells, such as those encoding angiogenic factors and glycolytic enzymes. However, our data also suggest that many hypoxia-responsive genes identified in other cells are not hypoxia responsive in primary human astrocytes and vice versa. For example, only about 10% of those oxygen- and HIF-1-regulated genes identified in primary human pulmonary artery ECs (69) were also regulated by oxygen in primary human astrocytes and HeLa cells (Table 2). Likewise, only 133 genes were regulated by oxygen in the same manner in human astrocytes and HeLa cells (Table 2). Curiously, the transcript levels of a class of stress proteins, including heme oxygenase-1 [HO-1 or heat shock protein (HSP)33], oxygen-related protein 150 (ORP150), glucose-related protein 94 (GRP94), GRP78, and HSP28, that were found to be induced by hypoxia in rat astrocytes (45, 58, 120) were not significantly affected by hypoxia in the purified human astrocytes used here. This difference is likely attributable to the interspecies differences between humans and rodents (115). Indeed, oxygen regulation of HO-1 gene expression has been shown to be different in rodents and humans. Hypoxia induces HO-1 in rodent, bovine, and monkey cells but represses HO-1 expression in several human cell lines, including lung cancer A549 cells, umbilical vein endothelial cells, and glioblastoma cells (49, 53, 64, 76, 113, 115, 119, 135). Similarly, consistent with our results, a previous study (120) showed that ORP150 was induced in only in astrocytes of stroke patients after chronic ischemia (i.e., at least 3 days).

Fourth, many targets of the insulin and EGF signaling pathways were altered in astrocytes as well as in HeLa cells. The predominant majority of these targets were induced in astrocytes (Tables 2, 6, and 7), and most of these targets have been shown to be induced by insulin and EGF action in human cells. Thus this result suggests that the signaling pathways mediating insulin and EGF action may be activated. Indeed, our Western blot data showed that two major signaling components mediating insulin and EGF action, MEK1/2 and Akt (82, 95, 96, 116, 134), were activated by hypoxia in a time-dependent manner. Previous studies have shown that both insulin and EGF can activate hypoxia-induced genes via HIF-1-dependent and -independent pathways in certain mammalian cells (7, 31, 74, 88, 137, 139). These previous results are consistent with our data, suggesting that these pathways are involved in oxygen regulation of gene expression.

Finally, our analysis suggests that besides HIF-1, the transcriptional regulators Myc, Jun, and p53 are involved in oxygen regulation in astrocytes and HeLa cells. More known targets of Myc and Jun were altered in astrocytes, whereas more targets of p53 were altered in HeLa cells (Tables 2 and 8). Previous studies have shown that all three transcriptional regulators can affect the expression of hypoxia-responsive genes in mammalian cells (2, 22, 27, 42, 54, 55, 57, 61, 62, 93, 98, 123). Jun may act to control transcription via both HIF-1-dependent and -independent pathways (2, 22, 61, 62, 93). Myc may be a part of a novel HIF-1 pathway that controls a distinct set of Myc target genes (123). Under certain conditions, HIF-1 may displace Myc binding to its target genes such as p21cip1 and counteract the effect of Myc on gene expression, leading to cell cycle arrest (54, 55). Under severe hypoxia, p53 accumulates and counteracts HIF-1, causing apoptosis (27, 42, 57, 98). Our results are consistent with these previous findings, suggesting the involvement of these regulators in oxygen regulation. Notably, because the Ras-ERK1/2 and PI3K-Akt signaling pathways may modulate the activity of Myc, p53, and Jun (16, 30, 66, 101, 125, 141), the effects of hypoxia on those transcriptional regulators may be mediated by their effects on the signaling pathways.

Taken together, the results from microarray, computational, and biochemical analyses allow us to envision the regulatory network that may mediate oxygen regulation of gene expression in human astrocytes (Fig. 4). It appears that lack of oxygen causes the activation of key components, such as MEK1/2 and Akt, of signaling pathways, such as Ras-ERK1/2 and PI3K-Akt pathways (Fig. 4). Subsequently, the activated signaling components would then cause the activation (or perhaps inactivation in certain cases) of transcriptional regulators, such as Myc, Jun, and p53. HIF-1 may be also activated by these pathways. The activation or inactivation of these transcriptional regulators would lead to the induction (or suppression in certain cases) of hypoxia-responsive genes in human astrocytes (Fig. 4). Here, our analysis allowed us to identify the Ras-ERK1/2 and PI3K-Akt signaling pathways and related transcriptional regulators as candidates in controlling the hypoxia response of a subclass of genes. However, the identification of targets of these pathways are limited by the available literature on gene regulation in human cells. With more information on gene expression in human cells becoming available, it is likely that more hypoxia-responsive genes will be identified as targets of these pathways. Thus these signaling pathways identified here may play roles in oxygen regulation of a greater number of genes in human astrocytes. Our results provide a starting point for further studies to investigate the global molecular mechanisms underlying oxygen sensing and regulation in astrocytes.


Figure 4
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  		Fig. 4. Diagram illustrating the putative O2-sensing and regulatory network in human astrocytes. Lack of oxygen may activate MEK1/2 and Akt or other components of the Ras-ERK1/2 and phosphatidylinositol 3-kinase (PI3K-Akt) pathways, directly or indirectly. These activated signal transducers may then in turn activate transcriptional regulators, such as Myc, p53, and Jun, and perhaps hypoxia-inducible factor (HIF)-1. Ultimately, this leads to the induction of the expression of their many target genes in response to hypoxia.

 

   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was supported in part by National Institutes of Health Grants HL-65568 (to L. Zhang) and NS-31492 (to G. Bentsman and D. J. Volsky).


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

Address for reprint requests and other correspondence: L. Zhang, Dept. of Environmental Health Sciences, Mailman School of Public Health, Columbia Univ., 60 Haven Ave., B-106, New York, NY 10032 (e-mail: lz2115{at}columbia.edu).

1 The Supplemental Material (Supplemental Tables S1, S2, and S3) for this article is available online at http://physiolgenomics.physiology.org/cgi/content/full/00315.2005/DC1. Back


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