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. Author manuscript; available in PMC: 2010 Apr 20.
Published in final edited form as: Physiol Genomics. 2005 May 31;22(3):292–307. doi: 10.1152/physiolgenomics.00217.2004

Gene expression and phenotypic characterization of mouse heart after chronic constant or intermittent hypoxia

Chenhao Fan 1, Dumitru A Iacobas 2, Dan Zhou 1, Qiaofang Chen 1, James K Lai 3, Orit Gavrialov 1, Gabriel G Haddad 1,2
PMCID: PMC2856928  NIHMSID: NIHMS193595  PMID: 15928208

Abstract

Chronic constant hypoxia (CCH), such as in pulmonary diseases or high altitude, and chronic intermittent hypoxia (CIH), such as in sleep apnea, can lead to major changes in the heart. Molecular mechanisms underlying these cardiac alterations are not well understood. We hypothesized that changes in gene expression could help to delineate such mechanisms. The current study used a neonatal mouse model in CCH or CIH combined with cDNA microarrays to determine changes in gene expression in the CCH or CIH mouse heart. Both CCH and CIH induced substantial alterations in gene expression. In addition, a robust right ventricular hypertrophy and cardiac enlargement was found in CCH- but not in CIH-treated mouse heart. On one hand, upregulation in RNA and protein levels of eukaryotic translation initiation factor-2α and -4E (eIF-2α and eIF-4E) was found in CCH, whereas eIF-4E was downregulated in 1- and 2-wk CIH, suggesting that eIF-4E is likely to play an important role in the cardiac hypertrophy observed in CCH-treated mice. On the other hand, the specific downregulation of heart development-related genes (e.g., notch gene homolog-1, MAD homolog-4) and the upregulation of proteolysis genes (e.g., calpain-5) in the CIH heart can explain the lack of hypertrophy in CIH. Interestingly, apoptosis was enhanced in CCH but not CIH, and this was correlated with an upregulation of proapoptotic genes and downregulation of anti-apoptotic genes in CCH. In summary, our results indicate that 1) the pattern of gene response to CCH is different from that of CIH in mouse heart, and 2) the identified expression differences in certain gene groups are helpful in dissecting mechanisms responsible for phenotypes observed.

Keywords: patterns of hypoxia, cardiac hypertrophy, cDNA microarray, apoptosis, initiation factors


a variety of experimental and clinical studies have demonstrated that chronic hypoxia, whether constant (CCH) or intermittent (CIH), has major effects on heart structure and function (8, 21). Although CCH from pulmonary disease, congenital heart disease, or high altitude is not an infrequent clinical occurrence, the molecular mechanisms that lead to cardiac injury or adaptation are not fully established. For instance, it has not been very clear which molecular mechanism(s) induces cardiac hypertrophy during chronic hypoxia. Furthermore, different tissue responses have been found in patients suffering from intermittent compared with sustained blood gas disturbances. For example, systemic hypertension is more frequently seen in patients with sleep apnea than in patients with chronic obstructive pulmonary diseases (21). These differences between intermittent and sustained blood gas disturbances, such as hypoxia, have not been studied in detail and, indeed, have been limited to phenotypic descriptions.

Technologies like microarrays have not yet been used to study differences between CCH and CIH in heart. Because 1) there has been a paucity of studies on the effect of chronic hypoxia on heart in early life, which may be different from that in the adult, and 2) it is possible that CCH and CIH have a different impact on the heart, we performed transcriptomic analyses and compared differences in gene expression between these two types of hypoxia using the neonatal mouse.

MATERIALS AND METHODS

Hypoxia treatment

CD1 mice (Charles River) were placed in a hypoxia chamber (Biospherix) with their mother starting on the second day after birth (P2) for 1, 2, or 4 wk. In CCH experiments, an O2 concentration of 11% was applied continuously. In CIH, we alternated O2 concentration between 21% for 4 min and 11% for another 4 min. The cycling was continuous for 24 h/day for the period desired. At the end of each period, mice were anesthetized by inhalation of isoflurane (Baxter Pharmaceutical Products). The hearts were removed and quickly frozen in liquid nitrogen. Parameters such as body weight, organ weight, and hematocrit were collected. The surgical procedures and protocols were approved by the Albert Einstein College of Medicine (AECOM) Animal Care and Use Committee.

Histology

A total of nine hearts (3 hearts/group) from animals exposed to CCH, CIH, or room air for 4 wk were obtained for histological examination. Fresh hearts were fixed in 4% paraformaldehyde overnight and transferred to 75% ethanol with double-distilled H2O for paraffin embedding. The sections were stained with hematoxylin and eosin. The sizes of cardiomyocytes were measured as transverse areas (μm2) of the cells in at least 10 fields of sections (×400 magnitude) using the image AxioVision 4.1 software (Zeiss, Thornwood, NY).

Microarrays

Arrays were hybridized with cDNA from four individual animals at each age (1, 2, or 4 wk) and treatment (CCH, CIH, or normoxia), as shown in Supplemental Fig. S1A (available at the Physiological Genomics web site).1 The slides (28,704 spots, representing 7,455 distinct genes with known protein products in http://genome-www5.stanford.edu/cgi-bin/source//sourceBatchSearch, with several spotted sequences probing the same gene), 11,686 expressed sequence tags (ESTs) whose annotation was incomplete at the date of the study (eliminated from the expression analysis), and 192 bacterial sequences for quality control of the arrays were obtained from the Microarray Facility of AECOM. The hybridization process was performed according to the instructions of the core facility. Briefly, total RNA (60 μg), extracted with TRIzol (Invitrogen), was used to synthesize a fluorescently labeled cDNA probe by direct incorporation with either Cy3 or Cy5 fluorescent dye (Amersham Biosciences) in separate reactions. Fluorescent cDNA probes were prehybridized with blocking solution for 1 h before being applied to pretreated and prehybridized microarray slides. Hybridization was done in GeneMachines HybChamber and incubated overnight at 50°C. After incubation, each slide was washed to remove unbound cDNAs and SDS, dried, and scanned with a GenePix 4100A scanner (Axon Instruments) at 600 V (635 nm) and 550 V (532 nm).

We adopted an experimental strategy (experimental design and flow chart in Supplemental Fig. S1A) similar to that used in previous studies (3). This strategy was termed “multiple yellow” (MYS), since most spots on the hybridized slide should appear yellow in an 8-bit pseudocolor image (example in Supplemental Fig. S1B). As presented in the discussion section and in the Supplemental Material, MYS provides a similar detection accuracy of the regulated genes compared with the widely used dye swapping (DSS) and reference sample (RSS) strategies (16) but has a considerable advantage in cost and flexibility. Each slide was hybridized with heart cDNA obtained from a male mouse (labeled with Cy5) and a female mouse (labeled with Cy3), both of which were subjected to the same treatment for the same period of time. Thus all comparisons between hypoxia and normoxia used animals of the same gender composition.

Images were acquired and primarily analyzed with GenePix Pro 4.1 software. The background-subtracted signals were normalized with an in-house developed iterative algorithm similar to those used in previous publications (12, 13), alternating within-array normalization and interarray normalization until the average-corrected ratio differed by <5% from the previous one (14). Individual measurements of genes for all 12 mice studied in each period (1, 2, or 4 wk) were further divided by the average of the corresponding normoxic values, and then the results for each group of four mice (i.e., normoxic, CCH, and CIH) were rescaled with respect to the average of that group. The ratios obtained by proportioning the normalized green and red fluorescence intensities of a spot with hypoxic cDNAs to the normalized green and red fluorescence intensities of a matched spot with corresponding normoxic cDNAs were averaged for both channels. In the case of a gene probed in multiple spots, the expression ratio was the weighted average ratios, as previously described (12). Detection of significantly regulated genes relied on both fold changes in expression ratio (limited by the technical noise of the method and expression variability among animals) and the statistical significance of the two-tailed t-test with a Bonferroni-type adjustment applied to the redundancy groups (14). The data set (series no. GSE2271) was deposited in the Gene Expression Omnibus (GEO) database: http://www.ncbi.nlm.nih.gov/geo/. Profiling of the data was accomplished using hierarchical clustering algorithm, with the software available from http://rana.lbl.gov/index.htm.

Quantitative real-time RT-PCR

The two-step quantitative real-time RT-PCR (QRT-PCR) SYBR Green method (Applied Biosystems) was used to compare and confirm the levels of selected interesting genes. Primers were devised with the software Primer 3 and synthesized at Invitrogen. The cDNA synthesis and QRT-PCR were done according to previously described methods (29). Relative ratios of fluorescent intensities of products from hypoxia to normoxia were calculated by using the 2−ΔΔCt method, where Ct is cycle threshold (17), and β-actin amplicons were used as loading control. Specific primers are listed in Supplemental Table S5.

Western blotting

Total protein was prepared using buffer as previously described (1). The concentration of protein lysates was determined with the bicinchoninic acid protein assay kit. Protein samples (20 μg) were isolated through SDS-PAGE electrophoresis using 10% Novex Bis-Tris gel and then electrophoretically transferred to a polyvinylidene difluoride membrane. Nonspecific binding sites were blocked, and the membranes were incubated overnight at 4°C with primary antibodies [eukaryotic translation initiation factor (eIF)-4E and eIF-4E (Ser209) from Cell Signaling, eIF-2α and eIF-2α (Ser52) from Abcam, and internal control Hsc70 from Stressgen]. The signals were visualized by incubating with horseradish peroxidase (HRP)-conjugated secondary antibody followed by enhanced chemiluminescence. Band densities were quantified using the Personal Densitometer SI scanner (Molecular Dynamics, Sunnyvale, CA) and analyzed with the aid of ImageQuaNT image analysis software (Molecular Dynamics).

Apoptosis detection

In situ terminal deoxynucleotide transferase-mediated dUTP nick-end labeling (TUNEL) assay (Roche Applied Science) was used to detect apoptotic nuclei and quantified as percentage of apoptotic nuclei per total nuclei. Sections were first deparaffinized and rehydrated, and then the manufacture’s instructions were followed. Briefly, sections were stripped of protein by incubation with pepsin (0.25%, pH 2.0) for 15–20 min at 37°C. For positive control, a section of normoxic control heart was treated with DNase I to produce artificially fragmented nuclear DNA. Samples were incubated with TUNEL reaction mix for 60 min at 37°C in a dark, humidified chamber. Total nuclei were counterstained with DAPI. Samples were first observed under a fluorescence microscope, then treated with anti-fluorescein antibody conjugated with alkaline phosphatase (AP), and observed under a light microscope. Nuclei were counted using the software AxioVision 4.1. Results are expressed as mean values ± SD. Differences in means were considered statistically significant if P < 0.05, using unpaired Student’s t-test.

RESULTS

Weights, hematocrits, and light microscopy of heart

CCH and CIH animals had a lower body weight than controls at 1 and 2 wk after initiation of hypoxia, but a catch-up in body growth was found at ~4 wk of age in CCH mice (Fig. 1A). Heart weight and size increased significantly in CCH animals but remained unchanged in CIH compared with normoxic controls (Fig. 1B and Fig. 2A). This increased heart weight in CCH mice was significant after 1 wk in hypoxia, and this difference continued to be pronounced at 2 and 4 wk of age (Fig. 1B). A similar pattern was also detected in total protein/heart in CCH mice but not CIH mice (Fig. 2F). The ratio of heart weight to body weight increased in both CCH and CIH, but the difference from control was greater in CCH (Fig. 1C). Hematocrit increased in both CCH and CIH, but the difference was more significant in CCH at all ages (Fig. 1D).

Fig. 1.

Fig. 1

Changes in body weight, heart weight, and hematocrit in mice with chronic constant hypoxia (CCH) and chronic intermittent hypoxia (CIH). A: growth of mice was decreased in both CCH (n = 8/treatment) and CIH (n = 8/treatment) compared with normoxic control (NC; n = 16/age-matched group), but there was a catch-up growth in CCH treatment for 4 wk. B: heart weight was much higher in CCH, but CIH mice were similar to NC mice. C and D: ratios of heart weight to body weight and hematocrit increased in CCH and CIH but more so in CCH. Statistical significance was calculated by Student’s t-test. Values are means ± SD. *P < 0.05 and **P < 0.01, CCH or CIH compared with NC. +P < 0.05 and ++P < 0.01, CCH compared with CIH.

Fig. 2.

Fig. 2

Fig. 2

Effect of chronic hypoxia treatment on heart size/weight and cardiomyocyte size in mice. A: representative images show larger heart size in CCH compared with age-matched NC. B: coronal midline sections show the apparently thicker right ventricular wall in CCH but little change in CIH (arrows) compared with age-matched NC. C: in CIH with a 7.5% O2 level as the nadir in each cycle, heart size became even smaller after 1 wk of hypoxia exposure compared with age-matched NC (death occurred in prolonged hypoxia period). D: light microscopy (×400) shows markedly thicker right ventricular muscle fibers in CCH but not CIH and broader interstitium with leukocyte infiltration in both CCH and CIH. E: in transverse section of cardiomyocytes, the cell size (mean ± SE) in the right ventricle was robustly thicker in CCH (P < 0.05) and thicker, but to a much lesser extent, in CIH compared with NC. F: total protein/heart changes over time under NC, CCH, and CIH. Neonatal P2 mice (2nd day after birth) were weighed, and mice of similar weight were separated into 3 groups and treated under NC, CCH, or CIH. Hearts were obtained after 1, 2, and 4 wk of hypoxia, and total proteins were measured in individual hearts (n = 4). Hearts of mice treated with CCH contained much more protein compared with NC and CIH hearts at the same time points. G and H: heart weight (n = 8) was lower in CIH than in NC when 7.5% O2 rather than 11% O2 was applied. The size of cardiomyocytes in the transverse section was smaller than in NC after 1 wk of hypoxia exposure. Values are means ± SD. *P < 0.05 and **P < 0.01, CCH or CIH compared with NC.

The midline sections of the heart had thicker free wall of the right ventricle in CCH and CIH animals compared with controls, and this difference was more apparent in CCH mice (Fig. 2B). Right ventricular muscle fibers in CCH hearts were larger than those in controls or CIH, based on data obtained from transverse sections of cardiomyocytes (Fig. 2, D and E). The cardiomyocytes from the left ventricles in CCH also became larger than in controls, but there was no difference in cell size between left ventricles in CIH and the controls (Fig. 2E). To further study the influence of CIH on cardiac hypertrophy and size, we decreased the O2 concentration from 11 to 7.5% to induce a severer stress. Contrary to our expectation, heart size became much smaller than in controls (Fig. 2C), and right ventricular hypertrophy was not observed (Fig. 2, G and H). Another interesting feature in the muscle histopathology is that the interstitium became broader with leukocyte infiltration in both CCH and CIH (Fig. 2D).

Overview of gene expression using cDNA microarray

Our results showed that a substantial number of genes have altered their expression in the hearts of both CCH- and CIH-treated mice. Both individual variability and reproducibility of gene expression pattern of mice subjected to the same treatment are illustrated in Fig. 4A, Supplemental Table S6, and Supplemental Fig. S4. We found that a total of 549 genes were upregulated and 375 genes downregulated in CCH heart (Fig. 3A). A substantial number of genes were also altered in CIH, but the majority were downregulated: 294 genes upregulated and 440 genes downregulated (Fig. 3B). At 1, 2, and 4 wk with CCH, there were 272, 856, and 294 upregulated genes and 110, 613, and 303 downregulated genes, respectively. Likewise, with CIH there were 375, 440, and 150 upregulated and 440, 795, and 68 downregulated genes at these same time points. Remarkably, in both treatments, the largest number of altered genes was after 2 wk of exposure to hypoxia. Genes that altered their expression at all three time points are listed in Supplemental Tables S1 and S2.

Fig. 4.

Fig. 4

Fig. 4

Alteration in gene expression and protein level of eukaryotic translation initiation factors (eIFs) after chronic hypoxia treatment. A: profiles of gene expression and regulation of eIFs in 4 individual mice subjected to normoxia (N1–N4), CCH (C1–C4), and CIH (I1–I4) for 1, 2, or 4 wk. Each value is represented by a colored square. Duration of the treatment is indicated before the letter of treatment, (e.g., 1I2 = 1 wk CIH, 2nd mouse), while the green/red color of the square shows down/upregulation, with brighter colors for higher regulation. Note both the variability and the reproducible pattern among the mice subjected to the same treatment. Note also the darker colors of the normoxic values, since they were closer to the average used in normalization. B: Western blot analysis of eIF-2α and phosphorylated eIF-2α (Ser52) in CCH, CIH, and age-matched NC. Results were reproduced in 3 independent experiments and averaged. C and D: statistical analysis (t-test) of densitometric analyses of Western results of eIF-2α and phosphorylated eIF-2α (Ser52). The y-axis depicts the relative protein expression level as a ratio of the protein to its HSC70 density per 40 μg of total protein. Values are means ± SD (n = 3). E: Western blot analysis of eIF-4E and phosphorylated eIF-4E (Ser209) in CCH, CIH, and age-matched NC. F and G: statistical analysis (t-test) of densitometric analyses of Western results of eIF-4E and phosphorylated eIF-4E (Ser209). *P < 0.05 compared with normoxic control. **P < 0.01 compared with normoxic control. †P < 0.01 compared with CIH.

Fig. 3.

Fig. 3

A and B: profiles of gene expression in mouse heart subjected to CCH or CIH. More genes were upregulated in CCH, and more genes were downregulated in CIH. C and D: results of microarray and quantitative RT-PCR are consistent for 8 selected genes from mouse hearts at 2 wk after CCH or CIH treatment. Bnip3l, Slc6a8, and Slc12a2 were all upregulated in CCH and CIH. Note the opposite alterations of Madh4 and Solh in CCH- and CIH-treated hearts. E: percent differences between the fold change in male and female mice subjected for 1 wk to CIH plotted against the significant regulation ratios I1/N1 (negative values for downregulation) of the entire set of 4 mice. Note that no difference exceeds 50% of the average fold change for the entire set of 4 mice (meaning that both genders were regulated in the same sense), most of the differences do not exceed 25% (no statistically significant difference between the fold change in the 2 genders), and the approximate symmetry of the differences i.e., the no. of genes with a higher fold change in males than in females (points above the horizontal axis) is close to the no. of genes with a higher regulation in females than in males (points below the horizontal axis) for both types of regulations (upregulations in the positive side of the horizontal axis and downregulations in the negative one).

We first categorized the altered genes based on magnitude of change and found that most differentially expressed genes changed approximately two- to threefold (Fig. 3, A and B). However, in CCH, there were 6 genes that were highly upregulated and 21 genes that were highly downregulated, i.e., over fivefold. Similarly, in CIH, there were 20 upregulated and 6 downregulated genes, over fivefold (Supplemental Tables S3 and S4). To characterize the major influence on biological processes after CCH or CIH treatment, we used MAPPFinder (a component of GenMAPP version 2.0) (2, 5, 6). MAPPFinder produced a statistically ranked list (based on P value) of Gene Ontology (GO) categories associated with each treatment from which the significant categories are listed. In each treatment, several highly significant, nonsynonymous, biological process categories were identified and are listed in Table 1 (permutation P < 0.05). Most of the significantly altered gene clusters were related to signal transduction and metabolism. The gene cluster related to regulation of translational initiation was found to be significant when comparing CCH- with CIH-treated animals throughout all time points (Table 2).

Table 1. Summary of changes and significance levels for specific GO biological process categories after different periods of CCH and CIH treatments.

GO ID GO Name No.
Changed
No.
Measured
No. in GO Percent
Changed
P Value
CCH 1 wk
15031 protein transport 35 159 325 22.01 0.000
51179 localization 77 438 1,444 17.58 0.000
6810 transport 74 432 1,431 17.13 0.000
7264 small GTPase-mediated signal transduction 17 69 180 24.64 0.003
7242 intracellular signaling cascade 38 217 623 17.51 0.005
51051 negative regulation of transport 2 2 6 100.00 0.007
7265 Ras protein signal transduction 4 8 18 50.00 0.011
188 inactivation of MAPK 3 5 7 60.00 0.013
7010 cytoskeleton organization and biogenesis 16 77 192 20.78 0.019
15758 glucose transport 2 3 13 66.67 0.028
51248 negative regulation of protein metabolism 4 11 33 36.36 0.034
165 MAPKKK cascade 5 16 55 31.25 0.034
6820 anion transport 9 38 138 23.68 0.036
43161 proteasomal ubiquitin-dependent protein catabolism 2 3 5 66.67 0.043
6515 misfolded or incompletely synthesized protein catabolism 2 3 5 66.67 0.043
30433 ER-associated protein catabolism 2 3 5 66.67 0.043
CCH 2 wk
51246 regulation of protein metabolism 28 42 115 66.67 0.003
46907 intracellular transport 80 144 304 55.56 0.004
6809 nitric oxide biosynthesis 5 5 7 100.00 0.016
9891 positive regulation of biosynthesis 5 5 29 100.00 0.017
9889 regulation of biosynthesis 19 29 81 65.52 0.017
6457 protein folding 38 67 140 56.72 0.022
6417 regulation of protein biosynthesis 17 26 77 65.38 0.024
188 inactivation of MAPK 5 5 7 100.00 0.025
50808 synapse organization and biogenesis 4 4 15 100.00 0.026
46483 heterocycle metabolism 11 15 44 73.33 0.029
6986 response to unfolded protein 11 15 30 73.33 0.037
7169 transmembrane receptor protein tyrosine kinase signaling
pathway
12 17 76 70.59 0.037
6605 protein targeting 27 45 94 60.00 0.037
42278 purine nucleoside metabolism 4 4 4 100.00 0.038
6836 neurotransmitter transport 6 7 27 85.71 0.041
8104 protein localization 87 168 342 51.79 0.041
6357 regulation of transcription from RNA polymerase II promoter 25 42 119 59.52 0.041
7017 microtubule-based process 20 33 74 60.61 0.042
18108 peptidyl-tyrosine phosphorylation 7 9 20 77.78 0.043
17015 regulation of transforming growth factor beta receptor
signaling pathway
4 4 5 100.00 0.047
9893 positive regulation of metabolism 25 42 104 59.52 0.047
CCH 4 wk
6082 organic acid metabolism 29 89 260 32.58 0.001
19752 carboxylic acid metabolism 28 88 258 31.82 0.002
43174 nucleoside salvage 3 3 3 100.00 0.004
43101 purine salvage 3 3 3 100.00 0.004
6631 fatty acid metabolism 12 35 91 34.29 0.016
35050 embryonic heart tube development 2 2 5 100.00 0.029
6420 arginyl-tRNA aminoacylation 2 2 3 100.00 0.030
6519 amino acid and derivative metabolism 16 53 174 30.19 0.031
30042 actin filament depolymerization 2 2 3 100.00 0.033
51016 barbed-end actin filament capping 2 2 3 100.00 0.033
30835 negative regulation of actin filament depolymerization 2 2 3 100.00 0.033
9966 regulation of signal transduction 10 29 81 34.48 0.036
19058 viral infectious cycle 2 2 6 100.00 0.038
9308 amine metabolism 17 59 193 28.81 0.040
6471 protein amino acid ADP ribosylation 3 5 15 60.00 0.043
51050 positive regulation of transport 3 5 23 60.00 0.046
CIH 1 wk
51187 cofactor catabolism 8 11 19 72.73 0.001
6412 protein biosynthesis 58 164 389 35.37 0.004
6099 tricarboxylic acid cycle 6 9 14 66.67 0.011
9060 aerobic respiration 6 9 14 66.67 0.011
188 inactivation of MAPK 4 5 7 80.00 0.014
7088 regulation of mitosis 3 3 12 100.00 0.020
19538 protein metabolism 199 712 2,007 27.95 0.022
45333 cellular respiration 6 10 19 60.00 0.023
6461 protein complex assembly 9 19 104 47.37 0.025
6084 acetyl-CoA metabolism 7 13 21 53.85 0.030
6874 calcium ion homeostasis 4 6 20 66.67 0.035
6650 glycerophospholipid metabolism 4 6 17 66.67 0.037
6820 anion transport 15 38 138 39.47 0.042
42278 purine nucleoside metabolism 3 4 4 75.00 0.044
44272 sulfur compound biosynthesis 3 4 17 75.00 0.049
CIH 2 wk
7001 chromosome organization and biogenesis (sensu Eukaryota) 30 50 172 60.00 0.001
16571 histone methylation 5 5 6 100.00 0.006
6886 intracellular protein transport 53 108 209 49.07 0.009
7229 integrin-mediated signaling pathway 10 14 43 71.43 0.014
51093 negative regulation of development 9 13 41 69.23 0.023
6928 cell motility 22 41 124 53.66 0.029
7411 axon guidance 6 8 38 75.00 0.034
8277 regulation of G protein-coupled receptor protein signaling
pathway
3 3 13 100.00 0.035
16477 cell migration 18 33 101 54.55 0.037
6334 nucleosome assembly 6 8 72 75.00 0.042
19884 antigen presentation, exogenous antigen 3 3 14 100.00 0.048
9142 nucleoside triphosphate biosynthesis 13 22 47 59.09 0.048
9108 coenzyme biosynthesis 15 27 65 55.56 0.048
6461 protein complex assembly 11 19 104 57.89 0.048
6355 regulation of transcription, DNA dependent 124 296 1199 41.89 0.049
CIH 4 wk
187 activation of MAPK 3 4 14 75.00 0.000
43149 stress fiber formation 2 2 3 100.00 0.001
45859 regulation of protein kinase activity 5 14 43 35.71 0.002
6915 apoptosis 15 110 295 13.63 0.004
46822 regulation of nucleocytoplasmic transport 2 2 10 100.00 0.005
6637 acyl-CoA metabolism 2 2 6 100.00 0.006
48511 rhythmic process 3 8 35 37.50 0.008
74 regulation of cell cycle 10 60 177 16.67 0.009
51246 regulation of protein metabolism 8 42 115 19.05 0.010
16043 cell organization and biogenesis 22 200 562 11.00 0.013
6605 protein targeting 7 45 94 15.56 0.029
19222 regulation of metabolism 34 367 1,374 9.26 0.036
6888 ER to Golgi transport 2 5 11 40.00 0.039
6417 regulation of protein biosynthesis 5 26 77 19.23 0.041
8283 cell proliferation 7 47 173 14.89 0.043
8361 regulation of cell size 5 30 71 16.67 0.043
6820 anion transport 6 38 138 15.79 0.044
7519 myogenesis 2 6 21 33.33 0.046
43037 translation 7 48 100 14.58 0.049

GO, Gene Ontology; CCH, chronic constant hypoxia; CIH, chronic intermittent hypoxia.

Table 2. Summary of differences and significance levels for specific GO biological process categories between CCH- and CIH-treated animals.

GO ID GO Name No.
Changed
No.
Measured
No. in GO Percent
Changed
P Value
CCH vs. CIH 1 wk
9116 nucleoside metabolism 7 10 21 70.00 0.015
19883 antigen presentation, endogenous antigen 5 6 14 83.33 0.016
42325 regulation of phosphorylation 5 6 20 83.33 0.017
6811 ion transport 48 112 522 42.86 0.023
9893 positive regulation of metabolism 21 42 104 50.00 0.025
9081 branched chain family amino acid metabolism 3 3 6 100.00 0.029
6446 regulation of translational initiation 5 7 10 71.43 0.030
30199 collagen fibril organization 3 3 4 100.00 0.031
6968 cellular defense response 4 5 38 80.00 0.032
6732 coenzyme metabolism 24 50 110 48.00 0.033
19885 antigen processing, endogenous antigen via MHC class I 5 7 13 71.43 0.037
6521 regulation of amino acid metabolism 4 5 17 80.00 0.037
6235 dTTP biosynthesis 3 3 4 100.00 0.039
9212 pyrimidine deoxyribonucleoside triphosphate biosynthesis 3 3 4 100.00 0.039
19882 antigen presentation 6 9 29 66.67 0.040
46777 autophosphorylation 4 5 15 80.00 0.041
18108 peptidyl-tyrosine phosphorylation 6 9 20 66.67 0.047
9891 positive regulation of biosynthesis 4 5 29 80.00 0.047
9889 regulation of biosynthesis 15 29 81 51.72 0.047
51246 regulation of protein metabolism 20 42 115 47.62 0.049
CCH vs. CIH 2 wk
46907 intracellular transport 102 144 304 70.83 0.000
6886 intracellular protein transport 75 108 209 69.44 0.007
6413 translational initiation 18 21 35 85.71 0.007
82 G1/S transition of mitotic cell cycle 7 7 21 100.00 0.015
19538 protein metabolism 431 712 2,007 60.53 0.019
51258 protein polymerization 13 15 27 86.67 0.020
6796 phosphate metabolism 134 209 564 64.11 0.021
19226 transmission of nerve impulse 15 18 124 83.33 0.022
7049 cell cycle 84 127 341 66.14 0.024
51246 regulation of protein metabolism 31 42 115 73.81 0.025
6446 regulation of translational initiation 7 7 10 100.00 0.026
6259 DNA metabolism 82 124 359 66.13 0.027
51169 nuclear transport 20 26 64 76.92 0.036
7017 microtubule-based process 25 33 74 75.76 0.038
CCH vs. CIH 4 wk
6406 mRNA nucleus export 6 7 12 85.71 0.000
16070 RNA metabolism 31 88 210 35.23 0.002
6396 RNA processing 26 69 162 37.68 0.003
6605 protein targeting 18 45 94 40.00 0.007
8154 actin polymerization and/or depolymerization 6 9 12 66.67 0.008
8380 RNA splicing 12 26 62 46.15 0.008
16071 mRNA metabolism 18 48 93 37.50 0.010
6836 neurotransmitter transport 5 7 27 71.43 0.011
42591 antigen presentation, exogenous antigen via MHC class II 3 3 10 100.00 0.012
16485 protein processing 4 5 21 80.00 0.012
9725 response to hormone stimulus 4 6 7 66.67 0.017
51248 negative regulation of protein metabolism 6 11 33 54.55 0.018
9892 negative regulation of metabolism 17 46 139 36.96 0.020
279 M phase 16 44 103 36.36 0.036
42742 defense response to bacteria 3 4 31 75.00 0.037
6941 striated muscle contraction 2 2 6 100.00 0.042
42326 negative regulation of phosphorylation 2 2 3 100.00 0.043
19222 regulation of metabolism 98 367 1,374 26.70 0.046
31324 negative regulation of cellular metabolism 15 42 126 35.71 0.046
6939 smooth muscle contraction 4 7 14 57.14 0.047
42158 lipoprotein biosynthesis 2 2 9 100.00 0.048
6446 regulation of translational initiation 4 7 10 57.14 0.048
7260 tyrosine phosphorylation of STAT protein 2 2 6 100.00 0.049

Eight interesting genes were chosen from different functional categories in CCH or CIH for further quantitative real-time PCR analysis (Fig. 3, C and D). The PCR reactions for each of these genes were repeated at least three times using 2-wk-treated samples. Variation in the number of Ct for a gene was <1. Results of QRT-PCR for the selected genes were consistent with the microarray data.

The experimental design allowed us to compare gene expression in the two genders. Fig. 3E presents the fold-change difference between male and female mice subjected to 1 wk of CIH in the entire set of four mice. We found no difference in the type of regulation between the two genders (all differences <50% fold change) and no significant bias of fold change toward one gender or another (symmetrical distribution of differences).

Similarities in gene expression between CCH and CIH

During chronic hypoxia, whether CCH or CIH, some of the regulated genes responded qualitatively in a similar fashion in the heart (Table 3). These included stress-responding genes (e.g., heat shock and redox genes), genes involved in vascular dilation, angiogenesis, and heme biosynthesis. For example, the gene that encodes a thioredoxin-interacting protein inhibits the function of thioredoxin; therefore, downregulation of this gene by 2.3-fold in CCH and 1.6-fold in CIH suggests enhancement of antioxidant function. A recent report has shown that a downregulation of this gene is involved in cardiac hypertrophy (28). The gene EGL nine homolog 1, which is involved in the degradation of the protein of hypoxia-inducible factor (HIF), was upregulated by 5-fold in CCH and 2.4-fold in CIH, whereas EGL nine homolog 3 was downregulated by 1.7-fold in CCH and 1.8-fold in CIH (7).

Table 3. Examples of similarly regulated genes in CCH and CIH hearts.

Gene Name 1I/N 1C/N 2I/N 2C/N 4I/N 4C/N P(1I/N) P(1C/N) P(2I/N) P(2C/N) P(4I/N) P(4C/N) P(C1/I) P(C2/I) P(C4/I)
Acetyl-CoA acetyltransferase 1 1.62 1.90 1.83 1.56 0.008 0.002 0.001 0.000 0.002
Adrenergic receptor kinase,
 beta 1
−2.77 −2.21 1.85 1.81 −1.60 −1.85 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Alpha tetoprotein −1.87 −1.71 1.82 2.19 0.000 0.000 0.000 0.000
Amyotrophic lateral sclerosls 2
 (juvenile) chromosome
 region, candidate 2 homolog
 (human)
1.91 1.78 −1.89 −2.10 0.000 0.001 0.000 0.000 0.001
Ankyrin repeat domain 6 −2.03 −2.17 0.000 0.000
Apolipoprotein C-II −1.57 −2.03 0.004 0.000
Aryl-hydrocarbon receptor-
 interacting protein
−1.66 −2.04 1.69 2.45 0.007 0.002 0.000 0.000 0.002 0.000
BCL2/adenovirus E1B 19kDa-
 interacting protein 3-like
2.45 3.66 1.70 2.17 0.000 0.000 0.001 0.005 0.002
Brachyury 2 5.49 3.24 1.99 1.66 2.73 2.19 0.000 0.000 0.001 0.003 0.000 0.011 0.000
Carbonyl reductase 2 −2.08 −1.79 −3.49 −1.52 0.001 0.003 0.000 0.001 0.003 0.000
Carnitine palmitoyltransferase
 1, liver
−1.80 −1.63 1.54 1.52 0.003 0.006 0.000 0.000 0.006
CD24a antigen 1.68 1.90 3.04 2.02 2.01 0.000 0.000 0.000 0.001 0.002 0.000 0.008
Chemokine (C-X-C motif)
 ligand 12
−2.63 1.74 2.37 −1.52 0.00 0.00 0.00 0.02 0.000
Choline kinase 1.92 1.59 −1.51 −3.43 0.001 0.000 0.000 0.000 0.000 0.000
Crystallin, alpha C −1.92 −1.59 1.53 1.57 0.001 0.005 0.000 0.000 0.005
Cytochrome P450, family 2,
 subfamily a, polypeptide 5
1.86 1.67 −1.50 −1.82 0.000 0.000 0.000 0.000 0.000 0.004
Dihydrolipoamide S-
 acetyltransferase (E2
 component of pyruvate
 dehydrogenase complex)
−1.62 −1.55 2.31 1.53 0.005 0.008 0.000 0.000 0.008 0.001
Dual-specificity tyrosine-(Y)-
 phosphorylation regulated
 kinase 1a
−1.75 −1.88 1.62 1.75 0.001 0.000 0.000 0.000 0.000
EGL nine homolog 1 (C.
 elegans)
2.37 4.97 0.00 0.00 0.000
EGL nine homolog 3 (C.
 elegans)
−1.73 −1.82 0.00 0.00
Erythrocyte protein band 4.1 1.57 3.41 2.59 1.57 0.001 0.000 0.000 0.025
FK506 binding protein 4 −1.59 1.84 1.83 2.27 0.00 0.00 0.00 0.00 0.000
Glucosamine-6-phosphate
 deaminase 2
1.81 1.56 −1.88 −2.10 0.000 0.000 0.000 0.000 0.000
Golgi apparatus protein 1 −1.57 −3.73 1.70 1.52 0.012 0.000 0.000 0.000 0.000
Guanine deaminase 3.21 1.98 1.76 2.27 1.71 0.000 0.000 0.000 0.000 0.019 0.000
Heat shock protein 1
 (chaperonin)
−1.79 1.71 1.83 −1.53 0.001 0.000 0.000 0.001
Hepatoma-derived growth
 factor
−1.94 −1.50 2.58 −1.69 −1.73 0.000 0.000 0.000 0.003 0.003 0.000
Heterogeneous nuclear
 ribonucleoprotein K
−8.69 −3.73 1.68 1.97 0.000 0.000 0.000 0.000 0.000 0.004
Histocompatibility 2, L region −2.58 −1.51 −2.53 −2.82 0.000 0.002 0.000 0.000 0.002 0.004
Immunoglobulin heavy chain 6
 (heavy chain of IgM)
2.91 2.20 1.78 −1.63 0.00 0.00 0.00 0.00 0.005
Interferon-stimulated protein 2.06 2.32 1.83 4.29 2.35 2.13 0.000 0.000 0.003 0.000 0.000 0.003 0.000 0.000
Kinasin family member 22 2.95 5.14 2.52 −1.92 −1.56 0.000 0.000 0.000 0.026 0.006
Makorin, ring finger protein, 1 2.11 2.26 1.54 2.27 0.000 0.000 0.005 0.004 0.000
Mitochondrial solute carrier
 protein
4.97 2.49 1.98 1.79 2.10 0.000 0.000 0.000 0.002 0.021 0.000
Peroxiredoxin 2 1.84 1.60 −1.71 −1.59 0.000 0.006 0.000 0.000 0.006
Pre-mRNA processing factor β −1.54 −2.24 1.56 1.91 0.005 0.000 0.000 0.000 0.000 0.001
Proteaseome (prosome,
 macropain) 28 subunit, 3
2.18 2.12 0.000 0.000
Proteasome (prosome,
 macropain) inhibitor subunit
 1
2.19 −1.77 −1.84 −1.77 −1.96 0.000 0.000 0.000 0.000 0.000
RAB4B, member RAS
 oncogene family
3.92 1.65 2.17 −1.67 −1.62 0.000 0.000 0.000 0.000 0.001 0.000
Ras-related associated with
 diabetes
2.08 1.71 1.69 1.54 0.000 0.000 0.000 0.005 0.001
Reticulocalbin 1.91 4.01 1.71 −1.65 −4.56 0.000 0.000 0.000 0.009 0.000 0.000 0.000
Rho guanine nucleotide
 exchange factor (GEF) 1
−1.55 −2.15 −2.04 −2.72 0.003 0.000 0.002 0.001 0.000 0.000
Ribosomal protein S6 3.88 1.63 −1.70 −2.51 0.000 0.000 0.000 0.000 0.000 0.002
Ribosome binding protein 1 −1.51 −1.99 −1.50 −1.52 0.004 0.000 0.000 0.000 0.000
RNA-binding region (RNP1,
 RRM) containing 1
2.23 2.19 0.000 0.000
SH3 domain binding glutamic
 acid-rich protein-like 3
−1.78 −1.52 1.96 1.51 −1.64 −2.42 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.007 0.000
Signal recognition particle
 receptor, B subunit
−1.70 −1.51 −1.94 −2.20 0.002 0.007 0.000 0.000 0.007
Small nuclear
 ribonucleoprotein
 polypeptide F
2.53 2.03 −1.80 −2.23 1.88 0.000 0.000 0.000 0.000 0.000 0.000 0.003
Solute carrier family 12,
 member 2
3.05 3.68 −1.58 0.00 0.00 0.00 0.000
Solute carrier family 25
 (mitochondrial carrier;
 adenine nucleotide
 translocator), member 13
2.02 2.15 0.000 0.000
Solute carrier family 25
 (mitochondrial
 deoxynucleotide carrier),
 member 19
−1.88 −1.98 2.03 1.81 0.002 0.002 0.000 0.000 0.002
Special AT-rich sequence
 binding protein 1
−2.64 −4.18 −2.01 −2.70 −3.11 −1.91 0.006 0.002 0.009 0.003 0.006 0.028 0.002 0.001 0.002
Succinate-CoA ligase, GDP-
 forming, alpha subunit
−2.03 −1.50 1.93 2.32 0.000 0.001 0.000 0.000 0.002
Tescalcin −2.41 −3.23 1.57 2.87 0.002 0.001 0.000 0.000 0.001 0.000
Tissue inhibitor of
 metalloproteinase 2
1.89 1.68 1.68 −1.97 −1.56 0.000 0.000 0.000 0.001 0.003 0.003
Transferrin receptor 1.94 3.34 1.72 2.33 0.000 0.000 0.000 0.000 0.000
TXK tyrosine kinase −1.66 −1.58 −1.51 −2.22 0.000 0.000 0.000 0.000 0.000 0.001
Ublquintin c-terminal
 hydrolase related
polypeptide −1.67 −1.58 −1.69 −1.58 0.000 0.000 0.004 0.006 0.000
WD repeat domain 22 1.59 −2.22 −2.40 −1.53 −1.52 0.010 0.000 0.000 0.000 0.000
WD repeat domain 7 1.98 1.77 −1.70 −2.16 0.000 0.000 0.000 0.000 0.000

Examples of similarly regulated genes in CCH and CIH hearts. Nos. in columns labeled 1I/N–4C/N indicate expression fold change (negative for downregulation) with respect to the corresponding normoxic mice (N) after 1, 2, and 4 wk of CCH (C/N columns) or CIH (I/N columns), whereas nos. in columns labeled P(I1/N)–P(C4/I) are the P values of the significant differences when comparing the individual measurements at 1, 2, and 4 wk of the indicated treatments, using a heteroscedastic Student’s t-test. Missing values indicate not-significant regulation (i.e., absolute fold change <1.5 and/or P value > 0.05). Note that, although the significant [P(I1,2,4/N) and P(C1,2,4/N) <0.05) regulation with respect to normoxia had the same orientation in both hypoxic treatments, the fold change was different, and in most cases the difference was statistically significant [P(C1,2,4/1) < 0.05].

Most genes related to fibrosis were upregulated in both CCH and CIH, and this synergistic upregulation of the collagen gene family suggested that fibrosis would be enhanced during hypoxia (Supplemental Fig. S2F). Genes related to immune response were also upregulated in both CCH and CIH. Chemokine (C-X-C motif) ligand 12, immunoglobulin heavy chain (J558 family), interferon-stimulated protein, FK506 binding protein 4, and beclin 1 (coiled-coil, myosin-like BCL2-interacting protein) are some genes regulating diverse biological functions, the expression levels of which were significantly altered by chronic hypoxia treatment. Lastly, solute carrier family 6, member 8 (creatine transporter), and solute carrier family 12 (sodium/potassium/chloride transporters) were upregulated in both CCH and CIH, suggesting that ionic homeostasis may have been altered, as would be expected in hypoxia (20, 23).

Divergent transcriptomic effects of CCH and CIH

Some gene families were differently altered in the two conditions. These genes are likely to be involved in inducing distinct phenotypes between CCH and CIH hearts. For example, genes encoding eukaryotic initiation factors and genes encoding ribosomal protein subunits were mostly upregulated in CCH and downregulated in CIH. We identified a total of 23 eIFs that were regulated by hypoxia in the mouse heart, with more upregulated genes in CCH and more downregulated genes in CIH hearts. This may explain the increased protein synthesis in CCH heart and subsequent myocardial hypertrophy (Fig. 2F and Fig. 4A). Indeed, previous studies have indicated that eIFs and their phosphorylation are important in cardiac hypertrophy (4). CCH and CIH induced similar regulation of genes such as eIF3s, eIF4g2, and eIF4el3 but opposite regulations of genes such as eIF3s10, eIF3s2, and eIF2c2. To determine whether eIF proteins increase and possibly play a role in cardiac hypertrophy, eIF-2α and eIF-4E were studied in this work. Western blotting showed that both eIF-2α and eIF-4E increased ~1.5-fold at 1 wk in CCH. We also showed that phosphorylated eIF-4E (Ser209) increased by ~1.8- to 2.0-fold at 1 and 2 wk in CCH, and this increase was more remarkable than the increase in total protein level of eIF-4E. The changes of total as well as phosphorylated eIF-2α and eIF-4E in CIH heart were not significant. The gene eIF-4E, along with the upregulation of eIF-4E binding protein 2, an inhibitor of eIF-4E, control the translation efficiency and are likely to be important in cardiac hypertrophy in CCH (4).

The divergent effects of CCH and CIH on heart gene expression were also observed when apoptotic and Rho/MAPK signaling genes were considered. For example, most of the proapoptotic genes were upregulated and most of the anti-apoptotic genes were downregulated in CCH but not in CIH (Fig. 5, A and B, and Table 4). This suggested that myocardial apoptosis might be enhanced in the CCH model. To further test this hypothesis, TUNEL staining was performed in both CCH and CIH heart sections. At least 20 consecutive high-magnification images were captured from each section of CCH, CIH, or control hearts. The ratio of apoptotic nuclei to total nuclei was significantly higher in the heart after 4 wk of CCH treatment (0.86%) compared with the age-matched normoxic controls (0.34%, P < 0.05; Fig. 5, C and E). No significant difference was found in the heart samples after 4 wk of CIH treatment (0.44%, P > 0.05; Fig. 5, C and F). This result correlated well with the changes in proapoptotic gene as well as anti-apoptotic gene expression in CCH and CIH. Furthermore, some genes, the function of which is related to either the Rho pathway or MAPK pathway, were differentially regulated in CCH and CIH hearts. Most members related to the Rho pathway were upregulated in CCH, but all were downregulated in CIH (Supplemental Fig. S2C); most of the altered MAPK pathwayrelated genes were upregulated in CCH but not in CIH (Supplemental Fig. S2D).

Fig. 5.

Fig. 5

Alteration in proapoptotic and anti-apoptotic genes in CCH- and CIH-treated mouse heart. A and B: proapoptotic genes were mostly upregulated in CCH hearts, whereas the anti-apoptotic genes were dominantly upregulated in CIH-treated hearts. C: ratio of apoptotic nuclei to total nuclei shows that apoptotic nuclei were significantly increased in CCH-but remained unchanged in CIH-treated mouse hearts. After treatment with converter-alkaline phosphatase, the apoptotic nuclei could be detected as dark spots (arrows in E, F, G) under a light microscope: apoptotic nuclei are clearly seen in CCH-treated (E) but are rarely seen in age-matched NC (D) and CIH-treated (F) mouse hearts. In C, **P < 0.01 and +P < 0.05. G: positive control. A heart section from an NC mouse treated with DNase I. Many nuclei with fragmented DNA were labeled by TUNEL. H: fluorescent microscope picture of apoptotic nuclei in CCH that were stained with green fluorescein and colocalized with the nuclei dye DAPI (blue). Scale bars = 20 μm.

Table 4. Examples of differently regulated genes in CCH and CIH hearts.

Gene Name 1I/N 1C/N 2I/N 2C/N 4I/N 4C/N P(1I/N) P(1C/N) P(2I/N) P(2C/N) P(4I/N) P(4C/N) P(C1/I) P(C2/I) P(C4/I)
5-Phosphorylase kinase, gamma 2
 (testis)
2.20 −2.01 −1.70 0.00 0.00 0.00 0.000
A disintegrin and metalloproteinase
 domain 17
−1.63 −1.68 2.28 2.10 0.00 0.00 0.00 0.00 0.000
A kinase (PRKA) anchor protein
 (gravin) 12
−1.84 −1.58 1.50 0.00 0.00 0.00 0.000
Actin related protein 2/3 complex,
 subunit 4
−1.65 1.81 2.26 0.00 0.00 0.00 0.000
Adenosine kinase −1.72 2.95 1.51 0.00 0.00 0.00 0.000
ADP-ribosylation factor 1 −1.51 −1.57 1.61 0.00 0.00 0.01 0.000
ADP-ribosylation factor interacting
 protein 2
7.07 −1.66 2.53 −6.04 −1.69 0.00 0.00 0.00 0.00 0.00 0.001 0.000
Aldehyde reductase (aldose
 reductase)-like 6
−2.03 1.66 −2.68 3.50 0.00 0.00 0.00 0.00 0.000 0.000
Aldolase 2, B isoform −1.54 1.50 2.53 −2.46 −1.86 0.00 0.00 0.00 0.00 0.00 0.000 0.000
Aldolase 3, C isoform −1.56 1.54 2.55 −1.55 0.00 0.00 0.00 0.00 0.000 0.000
Anaphase-promoting complex subunit
 2
−1.60 1.67 2.12 −2.01 −2.59 0.00 0.00 0.00 0.00 0.00 0.000 0.000
Ankyrin repeat domain 10 −1.83 1.51 −1.70 −2.22 2.36 0.00 0.00 0.00 0.00 0.00 0.000 0.000
ATPase, H+ transporting, V1 subunit
 A, Isoform 1
1.59 −1.80 −1.55 1.52 1.78 0.00 0.00 0.00 0.00 0.00 0.000 0.000
ATP-binding cassette, sub-family D
 (ALD), member 4
−1.99 1.63 −1.69 1.50 0.00 0.00 0.00 0.00 0.000 0.000
Basic leucine zipper and W2 domains
 1
−2.56 1.56 2.60 −1.69 0.00 0.00 0.00 0.00 0.000 0.000
B-cell CLL/lymphoma 9 −2.27 1.51 −2.13 1.93 0.00 0.00 0.00 0.00 0.000 0.000
Bernardinelli-Seip congenital
 lipodystrophy 2 homolog (human)
−2.18 1.51 1.90 −1.55 0.00 0.00 0.00 0.00 0.000 0.000
Branched chain ketoacid
 dehydrogenase kinase
−1.97 3.47 −2.20 2.53 0.00 0.00 0.00 0.00 0.000 0.000
Cellular nucleic acid binding protein −2.25 1.69 1.75 −1.90 −2.06 −2.77 0.00 0.00 0.00 0.00 0.00 0.00 0.000 0.000 0.010
CK2 interacting protein 1 −1.85 1.74 3.53 −2.23 −1.96 −4.47 0.00 0.00 0.00 0.00 0.00 0.00 0.000 0.000 0.000
Coactivator-associated arginine
 methyltransferase 1
−1.70 1.57 −1.71 2.24 1.57 2.52 0.00 0.00 0.00 0.00 0.00 0.00 0.000 0.000 0.001
Complement component 1, q
 subcomponent, gamma polypeptide
−2.04 1.56 1.98 −1.78 −1.75 −3.20 0.00 0.00 0.00 0.00 0.00 0.00 0.000 0.000 0.000
Component of oligomeric golgi
 complex 1
−1.67 1.57 2.20 −1.86 −1.76 −3.54 0.00 0.00 0.00 0.00 0.01 0.00 0.003 0.000 0.000
Component of oligomeric golgi
 complex 6
−2.58 1.91 2.33 −2.13 −1.60 −2.07 0.00 0.00 0.00 0.00 0.01 0.00 0.000 0.000 0.013
Crystallin, alpha C 2.81 −1.55 −3.58 0.00 0.00 0.00 0.000
Cysteine and glycine-rich protein 2 −2.46 2.22 −1.81 −1.77 0.00 0.00 0.00 0.00 0.000
Cytochrome b-5 1.59 −1.52 2.81 2.07 0.00 0.00 0.00 0.00 0.000
Cytochrome c oxidase, subunit VIb −1.58 2.20 0.00 0.00 0.000
DAZ associated protein 2 −1.55 2.13 1.89 0.01 0.00 0.00 0.000
DEAD (Asp-Glu-Ala-Asp) box
 polypeptide 24
1.55 −1.57 1.54 0.00 0.00 0.00 0.000
Dimethylarginine
 dimethylaminohydrolase 2
1.69 −2.37 0.00 0.00 0.000
DnaJ (Hsp40) homolog, subfamily B,
 member 9
1.74 −1.51 −1.90 0.00 0.00 0.00 0.000
Dudlin 2 −2.19 2.08 −3.86 −2.48 0.00 0.00 0.00 0.00 0.000
EBNA1 binding protein 2 −1.81 2.01 2.00 0.00 0.00 0.00 0.000
Elongation protein 4 homolog (S.
cerevisiae)
−1.82 1.58 −2.27 0.00 0.00 0.00 0.001
Enoyl CoA hydratase domain
 containing 1
1.64 −1.95 2.04 0.00 0.00 0.00 0.000
Era (G protein)-like 1 (E. coli) −1.73 1.85 0.00 0.00 0.000
Gene trap locus 6 −2.01 1.71 −2.23 2.86 0.00 0.00 0.00 0.00 0.000
GLI pathogenesis-related 2 2.24 −1.95 1.81 2.08 0.00 0.00 0.00 0.00 0.000
High mobility group nucleosomal
 binding domain 1
−1.70 1.64 0.00 0.00 0.000
Homeodomain interacting protein
 kinase 1
1.64 −1.74 1.57 0.00 0.00 0.00 0.000
Lymphocyte antigen 6 complex, locus
 C
−1.60 1.63 −2.34 1.66 0.00 0.00 0.00 0.00 0.000
MAD homolog 4 (Drosophila) −1.90 1.85 2.15 0.00 0.00 0.00 0.000
Metastasis associated 1 1.57 −2.01 1.68 0.00 0.00 0.00 0.000
Methyl CpG binding protein 2 1.77 −1.76 −1.61 1.72 0.00 0.00 0.00 0.00 0.000
Mitochondrial ribosomal protein L35 −1.52 1.72 −2.06 −1.54 0.00 0.00 0.00 0.00 0.000
Mortality factor 4 like 1 1.72 −1.89 −1.61 0.00 0.00 0.00 0.000
Neighbor of Punc E11 −2.19 −1.52 0.00 0.00 0.001
Notch gene homolog 1 (Drosophila) 1.58 −1.69 2.25 1.58 0.00 0.00 0.00 0.00 0.000
Nucleosome binding protein 1 −2.36 1.64 −1.97 0.00 0.00 0.00 0.000
Peroxiredoxin 6 2.04 1.92 −1.66 0.00 0.00 0.00 0.000
Purine rich element binding protein A −2.84 1.61 0.00 0.00 0.000
Rho guanine nucleotide exchange
 factor (GEF) 10
2.04 1.66 1.87 −2.03 0.00 0.00 0.00 0.00 0.000
Rho guanine nucleotide exchange
 factor (GEF) 17
−1.59 −1.92 1.98 2.24 0.00 0.00 0.00 0.00 0.000
Rho-guanine nucleotide exchange
 factor
−1.51 1.68 1.74 0.00 0.00 0.00 0.000
Small optic lobes homolog
 (Drosophila)
1.63 1.72 −1.54 0.00 0.00 0.00 0.000
Speckle-type POZ protein −1.64 −1.82 1.61 2.04 0.00 0.00 0.00 0.00 0.000
Spermidine/spermine N1-acetyl
 transferase 2
−1.54 2.05 1.92 0.00 0.00 0.00 0.000
Transformation related protein 53 −1.70 1.56 1.85 0.00 0.00 0.00 0.000
Ubiquilin 1 −1.54 1.53 −1.77 1.79 0.00 0.00 0.00 0.00 0.000

Examples of differently regulated genes in CCH and CIH hearts. Nos. in columns labeled 1I/N–4C/N indicate expression fold change (negative for downregulation) with respect to the corresponding normoxic mice (N) after 1, 2, and 4 WK of CCH (C/N columns) or CIH (I/N columns), whereas nos. in columns labeled P(I1/N)–P(C4/I) are the P values of the significant differences when comparing the individual measurements at 1, 2, and 4 WK of the indicated treatments, using a heteroscedastic Student’s t-test. Note the opposite regulation with respect to normoxia and the statistical significance of the difference between the 2 hypoxic treatments [P(C1,2,4/I) <0.05].

There were also other examples showing divergent effects on gene expression in CCH- and CIH-treated hearts. For instance, MAD homolog 4 (Drosophila) and Notch homolog 1 (Drosophila), genes that are important in cell fate and cell proliferation, were upregulated by ~1.5- to 2.3-fold in CCH but downregulated by a similar magnitude in CIH. The homeodomain-interacting protein kinase 1, a suppressor of homeodomain transcription factor, which is involved also in development, was downregulated by 2.2-fold in CCH but upregulated by ~1.6- to 2.2-fold in CIH. In addition, the upregulation of GATA-2 in CCH but its downregulation in CIH may explain the different effects of CCH and CIH on cardiac muscle size (19). Furthermore, the small optic lobes homolog gene, which contains a calpain domain, was upregulated 7.0-fold in CIH but downregulated 5.8-fold in CCH. This suggests that the small optic lobe gene may be important in hypoxia-reoxygenation-induced injury or proteolysis (26).

DISCUSSION

We used cDNA microarray to study the alteration of gene expression in hearts of neonatal mice subjected to CCH and CIH vs. normoxia on a large genomic scale, identifying also transcriptomic similarities and dissimilarities between CCH and CIH. The multiple yellow strategy that we used was validated in previous studies (3). It improves intrachip normalization, since the mRNA content of the starting total RNA was affected only by the biological variability among animals, matched by gender, age, and condition. Indeed, the green and red fluorescence signals, which were obtained with the same scanner setting for all slides, were compared separately, thus avoiding the inherent nonuniform bias toward one tag. This allowed all possible comparisons among conditions, time points, and genders. Our results show for the first time that CCH and CIH have dramatic effects on the mouse heart transcriptome, exhibiting both similar and opposite alterations of gene expression. It should be emphasized that Supplemental Table S6 illustrates 1) the reproducible pattern within each set of four animals subjected to the same condition, 2) variability among individuals in each set of animals, and 3) distinct expression profiles among the three experimental treatments, namely, normoxia, CIH, and CCH for the same duration.

In the current study, several clusters of genes that are related to certain specific biological processes were significantly altered by the hypoxia treatments. One of the altered gene clusters is related to the translational initiation factors in CCH and CIH. In CCH, genes encoding eIFs as well as ribosomal proteins were mostly upregulated, as measured by microarrays and QRT-PCR as well as by Western blot analysis [eIF-2α, eIF-2α (Ser52), eIF-4E, and eIF-4E (Ser209)]. Upregulation of these genes and their proteins enhances protein synthesis. Protein levels and phosphorylated proteins of eIFs may also have an effect on translation and protein synthesis. While the relation between phosphorylated eIF-2α and protein synthesis may not be well understood, that of phosphorylated eIF-4E is well known. For example, Tuxworth et al. (25) found that eIF-4E phosphorylation and protein synthesis are increased concomitantly in response to stimuli that induce hypertrophic growth in adult cardiocytes (25). This is consistent with our in vivo results: both eIF-4E protein level and phosphorylated eIF-4E (Ser209) increased in CCH after 1 and 2 wk, an increase that is expected to promote protein synthesis. In CIH, eIF-4E was downregulated at both 1 and 2 wk, a condition that explains the absence of cardiac hypertrophy. Therefore, we raise the distinct hypothesis that the enhanced protein synthesis machinery (via eIFs) plays an important role in the hypertrophy of heart in CCH. The eIF RNA and protein results and the hypothesis of increased protein synthesis in CCH are further supported by our other data showing increased cell size of cardiac myocytes as well as increased total protein (Fig. 2F).

Signaling pathways that induce hypertrophy and enlargement of heart size include two gene families: the Rho GTPases and the MAPKs. Because 1) several members of Rho GTPases have been reported to be involved in cardiac hypertrophy (11), and 2) two members of the Rho GTPases (Arhgap10 and Arhgap18) were upregulated in CCH, we believe that such pathways actually contributed in inducing cardiac hypertrophy in CCH. Indeed, most members of MAPK have been identified in our work to be upregulated in CCH but not in CIH. Such changes may be related to increased heart mass in CCH (24). Combined with other results from our microarray study, such as the downregulation of thioredoxin-interacting protein and upregulation of GATA-2, which are already known to be involved in cardiac hypertrophy (28, 19), we believe that hypertrophy of cardiac myocytes in CCH is the result of coordinated regulation on expression of various gene families.

Of great interest is the fact that the increase in protein synthesis in the heart in hypoxia contrasts to the decrease in protein synthesis in most organs (such as brain and kidney; Supplemental Fig. S3, A and B). The question of how different is protein synthesis in the hypoxic heart compared with other organs is intriguing. We have indeed alluded to this difference in our previous work (18). Interestingly, the lungs also increase in weight or at least do not reduce their weights in hypoxia, as do the kidneys and to a lesser degree the brain (Supplemental Fig. S3, C and D, and unpublished observations), suggesting that the heart and lungs behave in a similar manner and enhance protein synthesis for adaptation to the hypoxic stress. Although muscle fiber stretching such as in hypertension or overload can induce cardiac hypertrophy, we believe that hypoxia directly induces the hypertrophy. This partly agrees with in vitro studies showing that mild hypoxia (10% O2) induces hypertrophy of cardiomyocytes of rat (15).

Because hypoxia can change cell fate, we further asked whether programmed cell death takes place, especially because we have evidence that, in CCH heart, the genes involved in apoptosis are regulated. In situ TUNEL staining confirmed that changes in gene expression paralleled those in apoptosis. This result further supports the notion that, during CCH, the heart undergoes remodeling that is not restricted only to hypertrophy. There is indeed a more complicated process that induces apoptosis (9, 27).

Although the increase in cardiac and cell size in CIH was not impressive, we did additional experiments to determine whether a more severe hypoxia in the intermittent model (7.5% O2 instead of 11% O2) would induce a hypertrophy similar to CCH. With this more severe paradigm, the heart and cell size were even much smaller than in controls, suggesting that the lack of hypertrophy in CIH is due to the nature of this particular stress model. Downregulation of most subunits of mitochondrial complex I in CIH but not in CCH suggested possible mitochondrial functional inhibition and a resultant shortage of ATP supply in the organ (10). Along with down-regulation of several genes involved in cardiac development (Supplemental Figs. S2, E and G), these may constitute the underlying molecular mechanisms in CIH.

In conclusion, our results show that CCH and CIH have different impacts on heart phenotype and that the respective genetic responses provide a molecular basis for these phenotypic differences. In CCH, the heart is characterized by a robust right ventricular hypertrophy and larger cardiac mass. This phenotype creates an imbalance with the continuous relative shortage of O2 supply and with an induction of proapoptotic genes, which may constitute a major mechanism for heart failure. By contrast, in CIH, mitochondrial dysfunction and cardiac growth inhibition in early life may be more important.

Supplementary Material

Supplementary Fig.3
Supplementary Figure Legends; Figures S1-S3; Tables S1-S5
Supplementary Table S6

ACKNOWLEDGMENTS

We are grateful for the technical assistance of Cate Muenker. We also thank P. E. Aldo Massimi from the Microarray Facility at AECOM for consultative help.

GRANTS

This work was supported by National Heart, Lung, and Blood Institute Grant R01-HL-66327.

Footnotes

1

The Supplemental Material for this article (Supplemental Figs. S1S4 and Supplemental Tables S1S6) is available online at http://physiolgenomics.physiology.org/cgi/content/full/00217.2004/DC1.

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Supplementary Materials

Supplementary Fig.3
Supplementary Figure Legends; Figures S1-S3; Tables S1-S5
Supplementary Table S6

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