Physiol. Genomics Ad Instruments
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Physiol. Genomics 34: 101-111, 2008. First published April 22, 2008; doi:10.1152/physiolgenomics.00261.2007
1094-8341/08 $8.00
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Tables
Right arrow All Versions of this Article:
34/1/101    most recent
00261.2007v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Zagorski, J.
Right arrow Articles by Watts, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zagorski, J.
Right arrow Articles by Watts, J. A.
Received 6 November 2007; accepted in final form 17 April 2008.
Physiological Genomics 34:101-111 (2008)
1094-8341/08 $8.00 © 2008 American Physiological Society

Transcriptional profile of right ventricular tissue during acute pulmonary embolism in rats

John Zagorski , Nina Sanapareddy , Michael A. Gellar , Jeffrey A. Kline and John A. Watts

Department of Emergency Medicine, James G. Cannon Research Center, Carolinas Medical Center, Charlotte, North Carolina


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Acute pulmonary embolism (PE) is the third leading cause of cardiovascular death in the United States. Moderate to severe PE can cause pulmonary arterial hypertension (PH) with resultant right ventricular (RV) heart damage. The mechanisms leading to RV failure after PE are not well defined, although it is becoming clear that PH-induced inflammatory responses are involved. We previously demonstrated profound neutrophil-mediated inflammation and RV dysfunction during PE that was associated with increased expression of several chemokine genes. However, a complete assessment of transcriptional changes in RVs during PE is still lacking. We have now used DNA microarrays to assess the alterations in gene expression in RV tissue during acute PE/PH in rats. Key results were confirmed with real-time RT-PCR. Nine CC-chemokine genes (CCL-2, -3, -4, -6, -7, -9, -17, -20, -27), five CXC-chemokine genes (CXCL-1, -2, -9, -10, -16), and the receptors CCR1 and CXCR4 were upregulated after 18 h of moderate PE, while one C-chemokine (XCL-1) and one CXC-chemokine (CXCL-12) were downregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated increased expression of many inflammatory genes. There was also a major shift in the expression of components of metabolic pathways, including downregulation of fatty acid transporters and oxidative enzymes, a change in glucose transporters, and upregulation of stretch-sensing and hypoxia-inducible transcription factors. This pattern suggests an extensive shift in cardiac physiology favoring the expression of the "fetal gene program."

heart; pulmonary hypertension; inflammation; microarray; GeneSifter


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
PULMONARY EMBOLISM (PE) is a common and potentially lethal event in about 600,000 patients each year in the US, leading to as many as 60,000 deaths (6, 7, 38). PE usually results from the formation of blood clots in the deep vasculature, often the legs, which detach and flow into the pulmonary arterial system, where they occlude blood flow (9). Major PE reduces the cross-sectional area of patient pulmonary blood vessels, resulting in an increase in pulmonary vascular resistance and pulmonary hypertension (PH). The right ventricle (RV) is then subjected to increased work, increased wall tension, shear force injury to myocytes, and compression of the coronary vessels, leading to functional ischemia (39). The presence of RV damage increases patient mortality and morbidity after PE (8, 12, 16, 19, 22, 29, 30, 32, 34).

Our laboratory is interested in the role of inflammation in promoting RV damage during PE. We have used a novel rat model of irreversible PE (3, 10, 40) to demonstrate that PH causes a severe inflammatory response in RV tissue that can be mitigated by treatment with nonsteroidal anti-inflammatory drugs (10), by neutrophil depletion with anti-rat neutrophil antiserum (37), or by antichemokine treatment (41). These results have demonstrated that PE-associated PH induces a proinflammatory condition in the RV that contributes at least in part to RV damage and potentially to RV failure. The factors promoting RV inflammation during PE have not been well characterized. Chemokines are important leukocyte recruitment factors during inflammation (1, 42) and thus obvious candidates for promoting leukocyte recruitment into RV tissue during PE. We recently showed (37) that the rat chemokines CINC-1, CINC-2, CINC-3/macrophage inflammatory protein (MIP)-2, monocyte chemoattractant protein (MCP)-1, and MIP-1{alpha} are overexpressed in RV tissue during PE and are potential contributors to the recruitment of neutrophils and monocytes into RVs that is easily observed in stained histological RV sections.

Our previous studies with PE focused on the physiological and biochemical changes associated with RV failure in experimental PE; however, a detailed understanding of the changes in gene expression during PH that may contribute to RV failure is necessary to understand the mechanisms leading to RV dysfunction. We now use DNA microarray analysis of RV tissues from our rat model of PE to examine the transcriptome-wide changes in gene expression during PE.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Animal treatments and induction of PE.
Experiments were done on male Sprague-Dawley rats weighing between 375 and 400 g. All experiments were conducted in accordance with the "Guiding Principles in the Care and Use of Animals" of the American Physiological Society and were approved by the Institutional Animal Care and Use Committee of the Carolinas Medical Center. Before use, rats had ad libitum access to food and water.

PE was induced in rats by intra-jugular vein injection of polystyrene microsphere beads (mean diameter 25 ± 1 µm; no. 7525A, Duke Scientific, Palo Alto, CA) as previously described (3, 10, 37, 40). Briefly, microspheres were sterilized with 70% ethanol, washed extensively with sterile 0.01% Tween 20, and carefully resuspended in sterile 0.01% Tween 20 to a final concentration of 13 million beads/ml. On the basis of prior work, rats were injected with either 1.3 or 2.0 million microspheres/100 g body wt (referred to as PE1.3 or PE2.0) to produce either mild or moderate PE, respectively. Vehicle control rats were injected with 0.15 ml/100 g of 0.01% Tween 20, which was equivalent in volume to the PE2.0 dose of microspheres. PE1.3 produces a mean RV systolic pressure (RVSP) of ~40 mmHg 2 h after injection (normal rat RVSP is ~25 mmHg), while PE2.0 produces a mean RVSP of >50 mmHg after 2 h (37). At appropriate times after start of PE, hearts were surgically removed from anesthetized rats by midline thoracotomy and perfused briefly with Krebs-Henseleit bicarbonate buffer (in mM: 118 NaCl, 4.7 KCl, 21 NaHCO3, 1.25 CaCl2, 1.2 MgSO4, 1.2 KH2PO4, 11 glucose, and 0.05 octanoate) gassed with 95% O2-5% CO2 on a Langendorff heart perfusion apparatus by standard procedures to remove blood (37). RVs were then carefully separated from the left ventricles and septa by manual dissection at room temperature. RVs were collected from vehicle- and PE1.3 and PE2.0 microsphere-injected rats 2, 6, or 18 h after initiation of PE (9 experimental groups in total). RV tissues were quick-frozen in liquid nitrogen and stored at –70°C after dissection. Each experimental group consisted of five rats (5 microarrays).

Sample processing.
Frozen RVs were crushed with a custom-built stainless steel mortar and pestle chilled with liquid nitrogen. Approximately 100 mg of tissue powder was then processed into RNA by the TRIzol method (Invitrogen). The extracted RNAs were purified on Qiagen RNeasy columns according to the manufacturer's instructions. Five micrograms of total RNA was converted to double-stranded cDNA with a Superscript double-stranded cDNA synthesis kit (Invitrogen). The cDNA was then transcribed into biotin-labeled cRNA by in vitro transcription (IVT) with the Affymetrix IVT Labeling Kit. The biotin-labeled cRNAs were fragmented nonenzymatically according to Affymetrix procedures. Each fragmented sample was spiked with bioB, bioC, bioD, and cre, which served as hybridization controls. The fragmented cRNAs were then hybridized to Affymetrix Rat Genome 230 v2.0 microarrays in Affymetrix hybridization buffer for 16 h at 45°C. The hybridized arrays were washed and fluorescently stained in the Affymetrix Fluidics Station 400 according to Affymetrix procedures. Each array was scanned twice by the Agilent Gene Array Scanner G2500A (Agilent Technologies, Palo Alto, CA).

Data analysis.
Microarray data were analyzed with GeneSifter web-based software (VizXlabs, Seattle, WA). Affymetrix ".cel" files were uploaded to the GeneSifter web site with GC robust multiarray average (RMA) normalization. Hierarchical clustering of data from the nine treatment groups (2-, 6-, and 18-h vehicle, PE1.3, and PE2.0) was done with the GeneSifter Project Analysis function by ANOVA. A minimum 1.5-fold ratio was chosen as an initial filter to identify differentially expressed genes between the groups (P < 0.05), and a Benjamini and Hochberg adjustment was used to correct for false positive discovery rates. Statistical pairwise analyses of 2-, 6-, and 18-h PE2.0-to-vehicle and PE1.3-to-vehicle expression ratios were done with t-tests (P < 0.05) and Benjamini and Hochberg adjustments. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) reports were evaluated by z-scores. z-Scores greater than or equal to +2.0 or less than or equal to –2.0 were taken as suggestive of biological significance, indicating that more (z = positive no.) or fewer (z = negative no.) genes in a particular KEGG/GO pathway were altered in expression than would be expected by random occurrence. z-Scores were calculated by GeneSifter with the formula z = (r – n R/N)/Formula(R/N)(1 – R/N)[1 – (n – 1/N – 1)], where R is the total number of genes meeting selection criteria, N is the total number of genes measured, r is the number of genes meeting selection criteria with the specified GO term, and n is the total number of genes measured with the specific GO term.

Ontology analyses were done with database annotations current to 7/13/07. KEGG reports were current to 8/7/07. Some annotations may have changed since the preparation of this manuscript. All microarray data have been deposited in the NIH/NCBI GEO database (http://www.ncbi.nlm.nih.gov/projects/geo; GEO accession no. GSE6104). Complete lists of gene expression changes for GoTables 2, 3, and 4 are available in the supplemental data for this article.1


View this table:
[in this window]
[in a new window]

  		Table 1. Total number of genes significantly influenced by PE/PH

 

View this table:
[in this window]
[in a new window]

  		Table 2. Genes most altered after 2 h of PE2.0

 

View this table:
[in this window]
[in a new window]

  		Table 3. Genes most altered after 6 h of PE2.0

 

View this table:
[in this window]
[in a new window]

  		Table 4. Genes most altered after 18 h of PE2.0

 
Real-time RT-PCR.
Reverse transcription of 10 RNA samples (5 vehicle and 5 PE2.0) was performed with TaqMan reverse transcription reagents and oligo(dT) RT-PCR primers (Applied Biosystems, Foster City, CA). Real-time PCR was used to confirm the cDNA expression levels of 11 genes of interest with TaqMan Gene Expression Assays and TaqMan Universal PCR Master Mix (Applied Biosystems) for the genes of interest and 3 housekeeping genes (β-actin, GAPDH and 18S rRNA). The 25-µl real-time reactions were carried out under standard cycling conditions on the ABI Prism 7000 Sequence Detection System. One reverse transcriptase (RT)-negative reaction was run on each plate to verify that no genomic DNA was amplified in the reactions. Each reaction was run in triplicate with equal amounts of cDNA. With the REST-384 v2 program, cycle threshold values for the two sample groups were normalized with the 3 housekeeping genes to determine relative expression changes of the 11 genes (27).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Gene clustering.
We examined gene expression in RV tissue isolated from rats embolized with 1.3 million microspheres/100 g body wt (mild PE, PE1.3), 2.0 million microspheres/100 g body wt (moderately hypertensive PE2.0) or Tween 20 vehicle control, at 2, 6, and 18 h after treatments (n = 5/group). Microarray expression data from the nine treatment groups were compared with the GeneSifter Project Analysis function by ANOVA. The data were then subjected to hierarchical clustering by GeneSifter to compare the nine groups. These data are shown in Fig. 1. The three 2-h treatment groups were closely clustered, indicating that there were minimal alterations in gene expression at this time point. The 6-h PE2.0 group was slightly separated from the 6-h PE1.3 and vehicle control groups, indicating the beginnings of a more noticeable transcriptional response to PE2.0-induced hypertension at this time. It was evident that the 18-h PE2.0 group was very distinct from the other groups, indicating large changes in gene expression.


Figure 1
View larger version (11K):
[in this window]
[in a new window]

  		Fig. 1. Summary of microarray data. Affymetrix ".cel" files were uploaded to the GeneSifter web site with GC-RMA microarray normalization. The 9 treatment groups were then compared with the Project Analysis function with ANOVA and Benjamini and Hochberg statistical analyses. Data were then subjected to hierarchical clustering. PE1.3, PE2.0, pulmonary embolism with 1.3 or 2.0 million microspheres/100 g body wt; Veh, vehicle.

 
Largest changes in expression.
We next used pairwise analyses to compare gene expression ratios for the PE1.3/vehicle and PE2.0/vehicle groups at each of the three time points to identify the magnitudes of the transcriptional responses to PE. A summary of these data is presented in Table 1. GeneSifter software identified the total number of genes with expression ratios altered by at least 1.5-fold with statistical significance (P < 0.05, Benjamini and Hochberg correction) between the experimental groups. Two features were immediately evident from this data. First, nonhypertensive PE1.3 had little effect on gene expression relative to vehicle controls at all time points. A modest 83 genes had altered expression at 18 h, while only 2 genes were altered at 6 h (an unknown gene and SERTAD1, both with minimal positive ratios of 1.59 and 1.58-fold, respectively) and none was altered at 2 h. Of the 83 genes in the 18-h PE1.3/vehicle comparison, only 19 had ratios >2.0. The largest ratio was for Rho family GTPase-1 (up 3.88-fold), followed by chemokine CCL-7 (up 2.82-fold) and LIM domain protein-5 (up 2.64-fold). In contrast, 2 h of PE2.0 resulted in 76 genes altered in expression; 6 h resulted in 821 genes altered, while 18 h of PE2.0 resulted in 5,939 genes altered. The magnitude of the transcriptional response to PE 2.0/vehicle compared with PE1.3/vehicle compelled us to focus on the hypertensive PE2.0 treatment groups.

PE2.0/vehicle expression changes at 2 h.
Only 36 genes had PE2.0-to-vehicle gene ratios >2.0 out of the 76 genes with ratios of >1.5, indicating that although the very early stages of PE did induce alterations in gene expression this response was modest (Table 2). Surprisingly, the majority of gene expression ratios were down, suggesting a rather general depression in gene expression early in PE, possibly due to RV tissue trauma. Also, the magnitude of gene expression ratios was high for only a handful of genes, with only 11 genes having an expression ratio >3. The most downregulated gene was EEA1 (early endosome antigen-1; down 9.13-fold). Use of the GeneCards database (www.genecards.org) identified several proposed EEA1-interacting proteins including ras oncogene-related proteins (RAB5A, RAB14, and RAB22A), another G protein-associated protein (GIT1), and insulin-like growth factor II. The relationship of EEA1 to PE/PH is not obvious, and the GeneCards "virtual" Northern blot of EEA1 tissue-specific mRNA expression does not support heart expression of this gene. The most upregulated gene was ANKRD1 (ankyrin repeat domain protein 1), a muscle-expressed transcription factor implicated in cardiac failure that has been proposed to be a primary signal between muscle stretch and transcriptional activity (18, 24, 43).

PE2.0/vehicle expression changes at 6 h.
Gene expression ratios were substantially altered after 6 h of PE, and in contrast to the expression at 2 h most of the expression changes resulted from increased expression (Table 3). The pattern indicated that 821 genes had >1.5-fold changes in expression at 6 h, while 272 genes were altered by >2.0-fold. A total of 28 genes had expression ratios higher than fivefold, all with increased in expression during PE. The most upregulated gene was ATF3 (cyclic AMP-dependent transcription factor-3), followed by EGR1 (early growth response-1), a zinc finger transcription factor. Expression of cardiomyopathy-associated protein-1 (CMYA1), a protein known to be involved in cardiac development, was also increased (5.06-fold; Refs. 11, 33). The gene for the neutrophil chemoattractant CXCL-1 (CINC-1; cytokine-induced neutrophil chemoattractant-1) was also sharply elevated by 12.52-fold, PE2.0/vehicle, 6 h after PE. The related chemokine CXCL-2 (CINC-2) was also elevated, although to a lesser ratio (up 5.12-fold). ANKRD1 maintained elevated expression at 6 h (up 4.59-fold), and ankyrin repeat domain proteins 2 and 17b (ANKRD2, ANKRD17b) were also elevated by 7.79-fold and 2.12-fold, respectively. The protooncogene transcription factors FBJ (c-fos) and myelocytomatosis viral homolog (c-myc) were also elevated.

Expression changes at 18 h.
Compared with the changes in gene expression 2 or 6 h after PE2.0 there was a massive alteration in gene expression after 18 h (Table 4); 5,939 genes were altered with ratios >1.5-fold. These included a smaller number of genes that had even higher expression ratios: 3,182 genes >2.0-fold; 389 genes >5.0-fold; 94 genes >10-fold; 16 genes >25-fold; 8 genes >50-fold. Thus hypertensive PE is associated with a remarkable change in the transcriptional activity in RV tissues after only 18 h. A list of the genes with the largest PE2.0-to-vehicle expression ratios is shown in Table 4. All of the genes in the list were upregulated. The two S100 calcium binding proteins A8 and A9 were upregulated by 83.18-fold and 92.89-fold, respectively. Ankyrin repeat domain protein-2 was upregulated 87.87-fold at 18 h compared with its rise of 7.79-fold at 6 h. Numerous proinflammatory genes were highly upregulated: chemokines CCL-2, CCL-7, CXCL-1 (CINC-1), and CXCL-2 (CINC-2), Interleukin-1β, Interleukin-1 receptor type II, and Interleukin-6 to name a few. Many more proinflammatory and proliferative genes were expressed at lower gene ratios and could not be included in this table.

Gene ontology analyses of 18-h PE2.0.
The complexity of gene expression changes during PE required us to use a more general analysis of the PE transcriptome by focusing on alterations in well-cataloged expression pathways and gene ontologies using GO and KEGG summary functions. GeneSifter allowed examination of GO pathways grouped into three broad categories: Biological Process, Cellular Components, and Molecular Functions. The Biological Process ontologies influenced by PE are listed in Table 5. We used z-scores as a guide for initial screening of ontologies that might have significance for the initiation and/or progression of PE/PH. We set a threshold that z-scores greater than +2.0 or less than –2.0 were indicative of pathways containing a disproportionate number of genes within an ontology with altered expression during PE. This threshold is suggested by GeneSifter, but the larger the z-score the more likely that the genes in an ontology are diverging from randomly expected changes in expression. z-Scores were assigned for disproportionate upregulation ({blacktriangleup}) and disproportionate downregulation ({blacktriangledown}) of genes within each GO pathway. Gene ontologies are listed in descending order of the number of genes within each GO.


View this table:
[in this window]
[in a new window]

  		Table 5. Biological process ontologies of PE2.0-to-vehicle gene expression ratios after 18 h of PE

 
Examination of the Biological Process ontologies immediately identified the "Immune System Process" ontology as a GO with the highest positive z-score in the {blacktriangleup} column, indicating that a disproportionate number of genes within this ontology were upregulated by PE (i.e., more than would be randomly expected). Likewise, this ontology had a strongly negative z-score in the {blacktriangledown} column, indicating that a disproportionate number of genes in this category were not downregulated. Together, these complementary data indicated that genes within this ontology were preferentially activated during PE-mediated RV dysfunction.

GeneSifter allowed further exploration of GO pathways by examining more specific ontologies that were defined within the primary GO terms such as Immune System Process. Opening this ontology revealed the secondary GO terms contained within it and the new z-scores for these pathways. This process is illustrated in Fig. 2. Seven secondary GO pathways were contained within the Immune System Process GO, with positive z-scores ranging from {blacktriangleup} +7.07 to {blacktriangleup} +2.39. The GO with the largest z-score was Leukocyte Migration. This GO itself consisted of the tertiary GO terms Leukocyte Chemotaxis ({blacktriangleup} z = +5.81) and Cellular Extravasation ({blacktriangleup} z = +3.58). The former term contained 14 genes, including CINC-1 and CINC-2 as well as CCL-3, integrin {alpha}M, integrin β2, and IL-1β, all with positive PE-to-vehicle ratios.


Figure 2
View larger version (11K):
[in this window]
[in a new window]

  		Fig. 2. Gene Ontology (GO) tree of Biological Process/Immune System Process ontologies. Only the Leukocyte Migration GO, which had the largest z-score, was followed to its end points to avoid overloading the figure. Only GO pathways with z-scores greater than +2.0 were included in this figure. Although some GO pathways did meet the z-score criterion of less than –2.0, all of these pathways also met the criterion of z greater than +2.0.

 
The use of z-scores as an initial screen to select GO terms for further examination was extremely useful but can be misleading since assignment of individual genes to GO terms during database annotation may be a subjective decision. We next examined the GO term Response to Stimulus and followed the sequence of subsequent terms as follows: Response to Stimulus ({blacktriangleup}z = +4.32) -> Response to External Stimulus ({blacktriangleup}z = +7.25) -> Taxis ({blacktriangleup}z = +7.67) -> Chemotaxis ({blacktriangleup}z = +7.67). The Chemotaxis GO term consisted of 90 genes on the microarray of which 45 met the selection criteria of our experiment; 39 genes had positive PE2.0-to-vehicle expression ratios, while 9 were negative. The upregulated genes included nine CC-chemokines, four CXC-chemokines (CXCL-1, -2, -9, -10), and one CXC-receptor (CXCR4). The chemokines XCL1 and CXCL-12 were modestly downregulated. Following the Chemotaxis GO led to the next series of GO terms: Positive Chemotaxis ({blacktriangleup} z = +2.81), Regulation of Chemotaxis ({blacktriangleup} z = +3.14), and Leukocyte Chemotaxis ({blacktriangleup} z = +5.81), the latter of which was identical to the tertiary term identified by following the Immune System Process primary GO above.

The Cellular Component (Table 6) and Molecular Function (Table 7) ontologies provided somewhat less insight into the biology of PE than Biological Process. We followed the Membrane-Enclosed Lumen pathway ({blacktriangleup} z = +4.09) within the Cell Component GO term through several subterms: Organelle Lumen ({blacktriangleup} z = +4.09) -> Nuclear Lumen ({blacktriangleup} z = +6.69 and {blacktriangledown} z = –4.18) -> Nucleolus ({blacktriangleup} z = +10.10). This final GO term contained 85 genes on the microarray of which 46 met the original PE2.0-to-vehicle cutoff ratio and statistical criteria (45 {blacktriangleup}, 1 {blacktriangledown}). Not surprisingly, many of the individual genes identified in the Nucleolus GO were proteins involved in rRNA processing (i.e., fibrillarin and snRNP subunits). The relevance of this observation to the pathophysiology of PE is not clear, although it may simply reflect the influx of translationally active leukocytes into RV tissue as part of the inflammatory response. The Cellular Component -> Envelope GO was followed through the series Organelle Envelope ({blacktriangleup} z = +3.95) -> Mitochondrial Envelope ({blacktriangleup} z = +4.70 and {blacktriangledown} z = –4.10) -> Mitochondrial Membrane ({blacktriangledown} z = +4.75 and {blacktriangleup} z = –3.72) -> Mitochondrial Inner Membrane ({blacktriangledown} z = +4.26 and {blacktriangleup} z = –3.94). This final GO term contained 193 genes on the microarray of which 50 met the selection criteria (41 {blacktriangledown}, 9 {blacktriangleup}). The genes in this GO term encoded a diverse number of protein functionalities but included genes for several membrane-associated fatty acid transferases and for members of Solute Carrier Family-25 (e.g., SLC25-1, -22, -25, -30) and Solute Carrier Family-39 (SLC39-1).


View this table:
[in this window]
[in a new window]

  		Table 6. Cellular component ontologies of PE2.0-to-vehicle gene expression ratios after 18 h of PE

 

View this table:
[in this window]
[in a new window]

  		Table 7. Molecular function ontologies of PE2.0-to-vehicle gene expression ratios after 18 hours of PE

 
Within the Molecular Function ontology we followed only two GO terms: Binding -> Protein Binding ({blacktriangleup} z = +6.56) -> Protein Complex Binding ({blacktriangleup} z = +3.89; 86 genes on array, 31 met criteria, 25 {blacktriangleup}, 6 {blacktriangledown}) and Translation Regulator Activity -> Translation Factor Activity ({blacktriangleup} z = +4.41; 70 genes on array, 29 met criteria, 23 {blacktriangleup}, 6 {blacktriangledown}). Most of the genes in this last GO were subunits of translation initiation, elongation, or termination factors, including initiation factors 1–5. These data may be suggestive of significant translational regulation of protein expression in resident cardiac cells or merely reflect the change in RV tissue cell population as leukocytes are massively recruited into RVs.

KEGG report of gene expression during 18-h PE2.0.
The large number of genes influenced by PE in our model made systematic analysis of pathways very difficult with ontology screening alone, even when we employed the z-score Report function of GeneSifter (data not shown). KEGG is an alternative to ontology analyses. We used the KEGG Report function of GeneSifter to summarize KEGG pathways influenced by PE. These data are summarized in Table 8. Forty-five KEGG pathways met the selection criteria described in Table 8 out of 170 total KEGG pathways in the GeneSifter database. Several pathways had large z-scores in either the positive ({blacktriangleup}) or negative ({blacktriangledown}) PE2.0-to-vehicle ratio columns. It was noteworthy that many of the GO terms with large z-scores in the {blacktriangleup} column were associated with inflammation or cell responses to stimuli. These included Regulation of Actin Cytoskeleton, Cytokine-Cytokine Receptor Interactions, Apoptosis, Toll-Like Receptor Signaling, B-Cell Receptor Signaling, and Hematopoietic Cell Lineage. GO terms with large z-scores in the {blacktriangledown} column included pathways involved in metabolism including Fatty Acid Metabolism, Tryptophan Metabolism, Valine-Leucine-Isoleucine Degradation, Glutathione Metabolism, Lysine Degradation, Butanoate Metabolism, β-Alanine Metabolism, Fatty Acid Elongation in Mitochondria, and Captolactam Degradation. Overall, the KEGG data indicated that RVs that were stressed by PH not only experienced a major inflammatory response but also underwent a diverse adjustment in metabolism.


View this table:
[in this window]
[in a new window]

  		Table 8. 18-h PE2.0/vehicle KEGG report

 
Validation of gene expression changes by real-time RT PCR.
Gene expression was also evaluated by real-time RT PCR for comparison with microarray findings (Table 9). Genes were selected to represent each of the three major pathways of change found in the microarray data, including inflammatory, metabolic, and transcription factor genes. The selected genes also represented a wide range of values, from 36-fold upregulation to 5.5-fold downregulation in the microarray data.


View this table:
[in this window]
[in a new window]

  		Table 9. Comparison of fold changes detected by microarray and real-time PCR in right ventricular tissue from animals receiving 18 h of PE compared with vehicle treatment

 
All 11 genes showed significant changes in expression by real-time RT-PCR (P < 0.05). Values for 10 of the 11 genes showed significant correlation between the fold increase obtained by microarray and real-time RT PCR techniques (P = 0.0008, r = 0.88, r2 = 0.77). Thus there was close correlation in the data from both methods for genes involved in all three general processes being described. One of the genes, Fosl1, showed a larger difference in the increase detected by real-time PCR (230.3-fold) compared with the microarray (23.7-fold increase) measurement. A close examination of the real-time PCR data suggests a possible explanation for this discrepancy. The cycle time value in the vehicle-treated hearts was much longer than the other 10 genes and was close to the limits of detection for the real-time technique. Thus the denominator of the ratio of PE2.0 to vehicle may have been subject to an error that could make large differences in the reported value of fold increase in expression. Both methods, however, are consistent in detecting a large increase in expression of this Fosl1 gene.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
We have examined the temporal and severity-related changes in cardiac gene expression associated with experimental PE in rats with DNA microarrays. Alterations in gene expression were more profound with increasing PH (PE2.0 vs. PE1.3) and with time after PE (18 h vs. 6 h vs. 2 h). PE2.0 had a much more profound impact on the RV transcriptome than PE1.3, causing almost 6,000 genes to be altered in expression when 18-h PE2.0 was compared with 18-h vehicle controls. Three major areas of gene changes were observed, including acute inflammation, alterations in pathways for metabolic substrate oxidation, and transcription factor signaling pathways. Results of changes in expression of genes selected from each of these three pathways were confirmed with real-time RT PCR methodology.

Inflammation.
GO and KEGG screening of microarray data revealed a strong bias toward expression of proinflammatory GO pathways and individual genes during PE2.0. These data support our previous findings (37) that experimental PE was associated with a robust inflammatory response with massive recruitment of neutrophils and monocytes into RV tissues, which was associated with increased expression of the chemokines CINC-1, CINC-2, MCP-1, and MIP-1{alpha}.

The present microarray study adds to our understanding of the types of inflammatory genes associated with inflammation in RV tissue following experimental hypertensive PE, particularly with respect to chemokine expression. Nine CC-chemokine genes (CCL-2, -3, -4, -6, -7, -9, -17, -20, -27), five CXC-chemokine genes [CXCL-1, -2, -9, -10, -16 (note that CXCL-16 was present in the PE2.0-to-vehicle ratios but was not included in the annotation for the Chemotaxis GO)], and the receptors CCR1 and CXCR4 were upregulated after 18 h of PE2.0, while one C-chemokine (XCL-1) and one CXC-chemokine (CXCL-12) were downregulated. IL-1β ({blacktriangleup} 21.08-fold), IL-6 ({blacktriangleup} 53.19-fold), and E-selectin ({blacktriangleup} 24.54-fold) were also sharply elevated. We showed previously (37) that treatment of rats with an anti-neutrophil antiserum blocks RV inflammation and is sufficient to ameliorate RV dysfunction. The expression of the common CXC-chemokines CINC-1 and CINC-2 in RV tissue during PE makes them likely candidates for neutrophil recruitment factors in our model, and we recently demonstrated (41), using blocking anti-CINC-1 antibodies, that CINC-1 contributes to RV inflammation.

The role of monocytes in rat PE2.0 has yet to be determined. CD68+ monocytes accumulate in RV tissue during acute PE2.0, but unlike neutrophil accumulation, which is resolved after ~1 wk of PE, monocytes are maintained in RVs for at least 6 wk. We speculate that monocytes play an adaptive role in chronic PE by promoting wound healing, tissue repair, and tissue remodeling after the acute damage caused by neutrophil recruitment is resolved. Microarray data indicate nine CC-chemokine genes as potential (acute) monocyte recruitment factors in our model, which will make identifying the primary chemoattractants responsible for monocyte recruitment into RVs very complex. Preliminary data with an anti-rat MCP-1 MAb (31) did not demonstrate an influence on the outcome of acute (18–20 h) PE2.0. Rats that survive with PE for 6 wk appear healthy but show obvious signs of RV tissue remodeling, and the roles of monocytes in this process are not yet determined. Likewise, the role of acute neutrophil-mediated inflammation on the outcome of chronic RV remodeling has yet to be determined.

Metabolism.
The KEGG and GO reports yielded insight into metabolic aspects of the responses of the RV to PE. The predominant substrates for the conversion of metabolic energy into mechanical energy in heart muscle under normal aerobic conditions are fatty acids (21). Fatty acids normally contribute 60–90% of cardiac energy conversion, while glucose provides 10–40% and amino acids 1–5% of total production (17). Fatty acids contain large stores of energy for ATP production; however, myocardial efficiency (work performed/oxygen consumed) is lower during the consumption of fatty acids compared with the oxidation of carbohydrates (5, 21). Metabolic efficiency may influence cardiac function under pathological conditions such as in the development of progressive left ventricular heart failure, where shifting metabolism away from fatty acid oxidation and favoring carbohydrates improves contractile function and delays the development of cardiac dysfunction (5, 21). A similar reprogramming of metabolism is also observed in the RV during chronic pressure overload, which is created by pulmonary artery banding (4). This change in usage of carbon sources for energy is consistent with a conversion to a "fetal program" of metabolism and gene expression, which may be reflective of dependence on glucose and lactate, since little oxygen is available in utero (28). The present data show that genes for oxidation of fatty acids and amino acids were decreased by PE, suggesting a shift in metabolism toward a preference for carbohydrates. Further examination of our 18-h PE2.0 data revealed other gene expression changes in substrate transporters that are consistent with "fetal" reprogramming of metabolism: carnitine palmitoyltransferase-1a (CPT1a), {blacktriangledown} 1.71-fold; CPT1c, {blacktriangledown} 2.10; CPT2, {blacktriangledown} 2.03; solute carrier protein 2a4/glucose transporter-4 (GLUT4), {blacktriangledown} 2.95. The primary oxygen-sensing pathway that leads to fetal reprogramming involves hypoxia-inducible transcription factors HIF-1{alpha} and HIF-1β, forming a heterodimeric complex, which binds to hypoxia-responsive elements to alter transcription (17). The present data show a significant increase in HIF-1{alpha} after 18 h of PE2.0 ({blacktriangleup}1.91 cutoff in Table 4), supporting this pathway for reprogramming of metabolism. In addition, gene BE109501 (hypoxia-inducible protein-2; HIG-2) was also upregulated ({blacktriangleup} 3.49; cutoff in Table 4) after 18 h.

The decrease in gene expression for the enzymes involved in fatty acid metabolism observed in the present studies (Table 8) suggests an adaptation of the hearts toward a preference for carbohydrates, a more efficient source of metabolic energy for ATP synthesis. This provides rationale for manipulation of metabolism as a therapeutic intervention (36). Experimental interventions that increase oxidation of carbohydrates relative to fatty acids result in improved recovery of cardiac function under acute pathological conditions such as ischemia and reperfusion (20, 35), hemorrhagic shock (14), and septic shock (15). Direct measurements of oxygen consumption, substrate oxidation, and RV work would be required to assess this concept in the setting of PE.

Transcription factors.
The regulation of transcription factors by PE/PH was also noticeably influenced by PE. These expression changes are likely to be quite important for the initiation and progression of inflammation in RV tissue and for the response of the RV to PH. In particular, we observed increased expression of several transcription factors during the early time points of 2-h and 6-h PE2.0. Transcription factors altered in expression at 2 h (Table 2) included GATA zinc finger domain 2B (aka p66/p68), Kruppel-like factor 6, CREB-binding protein (symbol CREBBP; cAMP-responsive element binding protein-binding protein), c-myc protooncogene, and immediate-early response gene-5 (IER-5). Kruppel, c-myc, and IER-5 were increased in expression, while GATA-2B and CREB-binding protein were decreased, indicating selective reprogramming of the RV transcriptome as early as 2 h after start of PE. Expression of c-myc and IER-5 was increased at 6 h (Table 3) above the levels seen at 2 h, and additional transcription factors were expressed (Table 3 cutoff at ≥4-fold PE2.0-to-vehicle ratios): c-fos and c-jun protooncogenes, activating transcription factor-3, and early growth response-1. The increased expression of early response genes such as c-myc and c-fos and the hypoxia-inducible transcription factor HIF-1{alpha} are consistent with the conversion to a fetal gene expression program.

Of additional interest is the increased expression of three ankyrin repeat domain proteins: ANKRD1, {blacktriangleup} 4.88-fold at 2 h; BE117929, similar to ANKRD17b, {blacktriangleup} 2.12 at 2 h; and ANKRD2, {blacktriangleup} 7.79 at 6 h. ANKRD1 [also known as CARP, cardiac ankyrin repeat protein (24)] is thought to be a negative transcriptional regulator of cardiac-specific gene expression (2), and it is tempting to hypothesize that it is involved in cardiac adult-to-fetal reprogramming since such reprogramming involves downregulation of adult genes rather than upregulation of fetal genes (28). ANKRD2 is a stretch-responsive nuclear protein expressed in both skeletal and cardiac muscle (13, 25), suggesting that it may respond to tissue stress during PH by altering myocyte gene expression. "Virtual" Northern blot data summarized on the www.genecards.org web site indicate that ANKRD17 is expressed in skeletal but not cardiac muscle tissue. However, the gene identifier BE117929 (similar to ANKRD17b) is annotated by NCBI/PubMed as an expressed sequence tag, so the true identity of the gene expressed in our experiments may be questionable.

Our findings indicate that the activation of new transcriptional pathways early in PE/PH accounts for the large number of transcriptome changes observed at 18 h after PE. The present studies demonstrate large genomic changes in acute inflammation and alterations in the genetic regulation of metabolism toward the fetal gene program. Future experiments will examine signal processes initiating these changes and the effect of treatments that manipulate these events for possible translation into the clinical setting.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was supported in part by a grant from the Education and Research Committee of the Charlotte-Mecklenburg Health Services Foundation to J. Zagorski.


   ACKNOWLEDGMENTS
 
We thank the members of the Cannon Research Center Microarray Core Facility for performing the GeneChip hybridizations: Zari Bahrani-Mostafavi, Judy Vachris, Kris Bennett, and Dr. Lowell Rayburn. We also thank Kris Bennett for real-time PCR analyses. The anti-rat MCP-1 mouse hybridoma cell line C4 was a generous gift of Dr. Teizo Yoshimura, National Cancer Institute, National Institutes of Health, Frederick, MD.

Present address: N. Sanapareddy, Program in Bio-Informatics, University of North Carolina at Charlotte, Charlotte, NC.


   FOOTNOTES
 
Address for reprint requests and other correspondence: J. A. Watts, Dept. of Emergency Medicine, Carolinas Medical Center, James G. Cannon Research Center, 1542 Garden Terr., Rm. 302, Charlotte, NC 28203 (e-mail: jwatts{at}carolinas.org).

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 The online version of this article contains supplemental material. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

  1. Charo IF, Ransohoff RM. The many roles of chemokines and chemokine receptors in inflammation. N Engl J Med 354: 610–624, 2006.[Free Full Text]
  2. Chu W, Burns DK, Swerlick RA, Presky DH. Identification and characterization of a novel cytokine-inducible nuclear protein from human endothelial cells. J Biol Chem 270: 10236–10245, 1995.[Abstract/Free Full Text]
  3. Courtney DM, Watts JA, Kline JA. End-tidal CO2 is reduced during hypotension and cardiac arrest in a rat model of massive pulmonary embolism. Resuscitation 53: 83–91, 2001.[CrossRef][Web of Science]
  4. Faber MJ, Dalinghaus M, Lankhuizen IM, Bezstarosti K, Verhoven AJM, Dunker DJ, Helbing WA, Lamers JMJ. Time dependent changes in cytoplasmic proteins of the right ventricle during prolonged pressure overload. J Mol Cell Cardiol 43: 197–209, 2007.[CrossRef][Web of Science][Medline]
  5. Fragasso G. Inhibition of free fatty acids metabolism as a therapeutic target in patients with heart failure. Int J Clin Pract 61: 603–610, 2007.[CrossRef][Web of Science][Medline]
  6. Goldhaber SZ, Elliott CG. Acute pulmonary embolism: part I: epidemiology, pathophysiology, and diagnosis. Circulation 108: 2726–2729, 2003.[Free Full Text]
  7. Goldhaber SZ, Elliott CG. Acute pulmonary embolism: part II: risk stratification, treatment, and prevention. Circulation 108: 2834–2838, 2003.[Free Full Text]
  8. Goldhaber SZ, Visani L, De Rosa M. Acute pulmonary embolism: clinical outcomes in the International Cooperative Pulmonary Embolism Registry (ICOPER). Lancet 353: 1386–1389, 1999.[CrossRef][Web of Science][Medline]
  9. Hirsh J, Hoak J. Management of deep vein thrombosis and pulmonary embolism: a statement for healthcare professionals. Circulation 93: 2212–2245, 1996.[Free Full Text]
  10. Jones AE, Watts JA, Debelak JP, Thornton LR, Younger JD, Kline JA. Inhibition of prostaglandin synthesis during polystyrene microsphere-induced pulmonary embolism in the rat. Am J Physiol Lung Cell Mol Physiol 284: L1072–L1081, 2003.[Abstract/Free Full Text]
  11. Jung-Ching LJ, Gustafson-Wagner EA, Sinn HW, Choi S, Jaacks SM, Wang DZ, Evans S, Li-Chun LJ. Structure, expression and function of a novel intercalated disk protein, Xin. J Med Sci 25: 215–222, 2005.[Medline]
  12. Kasper W, Konstantinides S, Geibel A, Tiede N, Krause T, Just H. Prognostic significance of right ventricular after-load stress detected by echocardiography in patients with clinically suspected pulmonary embolism. Heart 77: 346–349, 1997.[Abstract/Free Full Text]
  13. Kemp TJ, Sadusky TJ, Saltisi F, Carey N, Moss J, Yang SY, Sassoon DA, Goldspink G, Coulton GR. Identification of Ankrd2, a novel skeletal muscle gene coding for a stretch-responsive ankyrin-repeat protein. Genomics 66: 229–241, 2000.[CrossRef][Web of Science][Medline]
  14. Kline JA, Maiorano PC, Schroeder JD, Grattan RM, Vary TC, Watts JA. Activation of pyruvate dehydrogenase improves heart function and metabolism after hemorrhagic shock. J Mol Cell Cardiol 29: 2465–2474, 1997.[CrossRef][Web of Science][Medline]
  15. Kline JA, Thornton LR, Lopaschuk GD, Barbee RW, Watts JA. Lactate improves cardiac efficiency after hemorrhagic shock. Shock 14: 215–221, 2000.[Web of Science][Medline]
  16. Kline JA, Hernandez-Nino J, Rose GA, Norton HJ, Camargo CA Jr. Surrogate markers for adverse outcomes in normotensive patients with pulmonary embolism. Crit Care Med 34: 2773–2780, 2006.[CrossRef][Web of Science][Medline]
  17. Kodde IF, van der Stok J, Smolenski RT, deJong JW. Metabolic and genetic regulation of cardiac energy substrate preference. Comp Biochem Physiol A 146: 26–39, 2007.[CrossRef][Medline]
  18. Kojic S, Medeot E, Guccione E, Krmac H, Zara I, Martinelli V, Valle G, Faulkner G. The ankrd2 protein, a link between the sarcomere and the nucleus in skeletal muscle. J Mol Biol 339: 313–325, 2004.[CrossRef][Web of Science][Medline]
  19. Kreit JW. The impact of right ventricular dysfunction on the prognosis and therapy of normotensive patients with pulmonary embolism. Chest 125: 1539–1545, 2004.[CrossRef][Web of Science][Medline]
  20. Lopaschuk GD. Regulation of carbohydrate metabolism in ischemia and reperfusion. Am Heart J 139: S115–S119, 2000.[CrossRef][Web of Science][Medline]
  21. Lopaschuk GD. Optimizing cardiac fatty acid and glucose metabolism as an approach to treating heart failure. Semin Cardiothorac Vasc Anesth 10: 228–230, 2006.[Abstract/Free Full Text]
  22. Lualdi JC, Goldhaber SZ. Right ventricular dysfunction after acute pulmonary embolism: pathophysiologic factors, detection, and therapeutic implications. Am Heart J 130: 1276–1280, 1995.[CrossRef][Web of Science][Medline]
  23. Maeda T, Supulveda J, Chen HH, Stewart AF. Alpha1-adrenergic activation of the cardiac ankyrin repeat protein gene in cardiac myocytes. Gene 297: 1–9, 2002.[CrossRef][Web of Science][Medline]
  24. Miller MK, Bang ML, Witt CC, Labeit D, Trombitas C, Watanabe K, Granzier H, McElhinny AS, Gregorio CC, Labeit S. The muscle ankyrin repeat domain proteins: CARP, ankrd2/Arpp and DARP as a family of titan filament-based stress response molecules. J Mol Biol 333: 951–964, 2003.[CrossRef][Web of Science][Medline]
  25. Moriyama M, Tsukamoto Y, Fugiwara M, Kondo G, Nakada C, Baba T, Ishiguro N, Miyazaki A, Nakamura K, Hori N, Sato K, Shomori K, Takeuchi K, Satoh H, Mori S, Ito H. Identification of a novel human ankyrin-repeated protein homologous to CARP. Biochem Biophys Res Commun 285: 715–723, 2001.[CrossRef][Web of Science][Medline]
  26. Neely JR, Morgan HE. Relationship between carbohydrate and lipid metabolism and the energy balance of heart muscle. Annu Rev Physiol 36: 413–459, 1974.[CrossRef][Web of Science]
  27. Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res 30: e36, 2002.[Abstract/Free Full Text]
  28. Rajabi M, Kassiotis C, Razeghi P, Taegtmeyer H. Return to the fetal program protects the stressed heart: a strong hypothesis. Heart Fail Rev 12: 331–343, 2007.[CrossRef][Web of Science][Medline]
  29. Ribeiro A, Lindmarker P, Juhlin-Dannfelt A, Johnsson H, Jorfeldt L. Echocardiography Doppler in pulmonary embolism: right ventricular dysfunction as a predictor of mortality rate. Am Heart J 134: 479–487, 1997.[CrossRef][Web of Science][Medline]
  30. Ribeiro A, Lindmarker P, Johnsson H, Juhlin-Dannfelt A, Jorfeldt L. Pulmonary embolism: one-year follow-up with echocardiography Doppler and five-year survival analysis. Circulation 99: 1325–1330, 1999.[Abstract/Free Full Text]
  31. Sakanashi Y, Takeya M, Yoshimura T, Feng L, Morioka T, Takahashi T. Kinetics of macrophage subpopulations and expression of monocyte chemoattractant protein-1 (MCP-1) in bleomycin-induced lung injury of rats studied by a novel monoclonal antibody against rat MCP-1. J Leukoc Biol 56: 741–750, 1994.[Abstract]
  32. Schoepf UJ, Kucher N, Kipfmueller F, Quiroz R, Costello P, Goldhaber SZ. Right ventricular enlargement on chest computed tomography: a predictor of early death in acute pulmonary embolism. Circulation 110: 3276–3280, 2004.[Abstract/Free Full Text]
  33. Sinn HW, Balsamo J, Lilien J, Lin JJ. Localization of the novel xin protein to the adherens junction complex in cardiac and skeletal muscle during development. Dev Dyn 225: 1–13, 2002.[CrossRef][Web of Science][Medline]
  34. Stevinson BG, Hernandez-Nino J, Rose G, Kline JA. Echocardiographic and functional cardiopulmonary problems 6 months after first-time pulmonary embolism in previously healthy patients. Eur Heart J 28: 2517–2524, 2007.
  35. Tanaguchi M, Wilson C, Hunter CA, Pehowich DJ, Clanachan AS, Lopaschuk GD. Dichloroacetate improves cardiac efficiency after ischemia independent of changes in mitochondrial proton leak. Am J Physiol Heart Circ Physiol 280: H1762–H1769, 2001.[Abstract/Free Full Text]
  36. Watts JA, Kline JA. Bench to bedside: the role of mitochondrial medicine in the pathogenesis and treatment of cellular injury. Acad Emerg Med 10: 985–997, 2003.[CrossRef][Web of Science][Medline]
  37. Watts JA, Zagorski J, Gellar MA, Stevinson BG, Kline JA. Cardiac inflammation contributes to right ventricular dysfunction following experimental pulmonary embolism in rats. J Mol Cell Cardiol 41: 296–307, 2006.[CrossRef][Web of Science][Medline]
  38. White RH. The epidemiology of venous thromboembolism. Circulation 107: 14–18, 2003.[CrossRef]
  39. Wood KE. Major pulmonary embolism: review of a pathophysiologic approach to the golden hour of hemodynamically significant pulmonary embolism. Chest 121: 877–905, 2002.[CrossRef][Web of Science][Medline]
  40. Zagorski J, Debelak J, Gellar M, Watts JA, Kline JA. Chemokines accumulate in the lungs of rats with severe pulmonary embolism induced by polystyrene microspheres. J Immunol 171: 5529–5536, 2003.[Abstract/Free Full Text]
  41. Zagorski J, Gellar MA, Obraztsova M, Kline JA, Watts JA. Inhibition of CINC-1 decreases right ventricular damage caused by experimental pulmonary embolism in rats. J Immunol 179: 7820–7826, 2007.[Abstract/Free Full Text]
  42. Zlotnik A, Yoshie O. Chemokines: a new classification system and their role in immunity. Immunity 12: 121–127, 2000.[CrossRef][Web of Science][Medline]
  43. Zolk O, Frohme M, Maurer A, Kluxen FW, Hentsch B, Zubakov D, Hoheisel JD, Zucker IH, Pepe S, Eschenhagen T. Cardiac ankyrin repeat protein, a negative regulator of cardiac gene expression, is augmented in human heart failure. Biochem Biophys Res Commun 293: 1377–1382, 2002.[CrossRef][Web of Science][Medline]



This article has been cited by other articles:


Home page
 Physiol. GenomicsHome page
J. Zagorski, M. Obraztsova, M. A. Gellar, J. A. Kline, and J. A. Watts
Transcriptional changes in right ventricular tissues are enriched in the outflow tract compared with the apex during chronic pulmonary embolism in rats
Physiol Genomics, September 1, 2009; 39(1): 61 - 71.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Tables
Right arrow All Versions of this Article:
34/1/101    most recent
00261.2007v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Zagorski, J.
Right arrow Articles by Watts, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zagorski, J.
Right arrow Articles by Watts, J. A.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Visit Other APS Journals Online
Copyright © 2008 by the American Physiological Society.