1 Institute of Pharmacology and Toxicology, University of Zurich
2 Institute of Anesthesiology, University Hospital Zurich, Zurich, Switzerland
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ABSTRACT |
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preconditioning; gene array analysis; ischemia
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INTRODUCTION |
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Volatile anesthetics emerged as a model class of agents eliciting PPC with low toxicity and high clinical applicability (51, 53). Even small doses of volatile anesthetics are capable of producing profound cardioprotection. PPC by volatile anesthetics and IschPC similarly augment postischemic functional recovery, decrease infarct size, elicit a "second window of protection" (41, 45), and, most importantly, were shown to occur in humans with coronary artery disease (20). The signaling cascades of both types of preconditioning involve several G-protein-coupled receptors and alterations in nitric oxide and free oxygen radical formation and point to the key role of protein kinase C (47) as signal amplifier and to KATP channels as the main end effectors in preconditioning (52). Conversely, despite the same degree of structural and functional protection, differences with respect to key signaling steps were also reported (5, 47). These include differential activation and translocation of protein kinase C isoforms to subcellular targets (47) as well as the role of other intracellular kinases (5) in triggering and mediating preconditioning-induced cardioprotection.
Functional genomics aims at analyzing the regulation of genes in response to changes in physiological parameters. Microarray technology revolutionized the analysis of gene expression in biological processes by enabling to assess gene activity on a genome-wide scale in a single experiment. Both early and late preconditioning affect gene expression in the heart. While protection by late preconditioning directly depends on altered gene expression, early preconditioning modulates the adverse consequences of prolonged ischemia at the gene expression level. Given the complex interactions in cardioprotection by preconditioning, preconditioning might be better characterized by the expression patterns of protective and antiprotective genes. Despite distinct signaling pathways between different types of preconditioning, there may exist overlapping genetic modifiers. Thus the transcriptional comparison might unravel novel protective genes expressed in a coordinated manner and shared across different types of preconditioning. In the quest for novel mechanisms underlying preconditioning, IschPC and PPC elicited by the volatile anesthetic isoflurane were compared with respect to their pre- and postischemic genomic responses.
The data presented herein provide additional new insights into the molecular similarities of the transcriptional responses between different types of preconditioning in the myocardium and ultimately aid to conceptualize the molecular events surrounding the remarkable protection achieved by preconditioning.
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MATERIALS AND METHODS |
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Langendorff Isolated Heart Preparation
Male Wistar rats (250 g) were heparinized (500 U ip) and 20 min later were decapitated without prior anesthesia. The hearts were rapidly removed and perfused in a noncirculating Langendorff apparatus with Krebs-Henseleit buffer (in mmol/l: 155 Na+, 5.6 K+, 138 Cl, 2.1 Ca2+, 1.2 PO43, 25 HCO3, 0.56 Mg2+, and 11 glucose) gassed with 95% O2-5% CO2 and maintained at a pH of 7.4 and a temperature of 37°C. Perfusion pressure was set to 80 mmHg. A water-filled balloon was inserted into the left ventricle and inflated to set an end-diastolic pressure of 05 mmHg during the initial equilibration. Data were recorded as previously described in detail (5, 47).
Perfusion Protocols and Hemodynamics
Hearts were allowed to equilibrate for 10 min and to beat spontaneously in all experiments (Fig. 1). PPC was induced by the volatile anesthetic isoflurane (APC-TRI, APC) administered for 15 min at 1.5 MAC (minimum alveolar concentration, 2.1 vol%). The buffer solution was equilibrated with isoflurane using an Isotec 3 vaporizer (Datex-Ohmeda, Tewksbury, MA) with an air bubbler. Applied concentration of isoflurane was measured in the buffer solution using a gas chromatograph (PerkinElmer, Norwalk, CT): isoflurane 2.1% (vol/vol) (1.5 MAC in rats at 37°C), 0.52 mM (SD 0.04). IschPC (IPC-TRI, IPC) was induced by 3 cycles of 5 min ischemia interspersed by 5 min of reperfusion. Preconditioned hearts were subjected to 40 min of ischemia followed by 180 min of reperfusion (APC, IPC: mediator/effector responses) or followed by 220 min of perfusion only (APC-TRI, IPC-TRI: trigger responses). Nonpreconditioned hearts subjected to ischemia and reperfusion served as ischemic control (ISCH). Control group (CTL) consisted of time-matched perfused hearts (a total of 270 min of perfusion). For each experimental group, five hearts were prepared and functional parameters were recorded (Fig. 1). Repeated-measures analysis of variance was used to evaluate differences over time between groups. Paired t-tests were used to compare within groups over time, and unpaired t-tests were used to compare groups at identical time points (SigmaStat v. 2.0; SPSS Science, Chicago, IL). Post-hoc Bonferroni test for multiple comparisons was used. Corrected P < 0.05 was considered to be statistically significant. Data are presented as means with SD in parentheses.
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Microarray Hybridization and Scanning
Affymetrix Rat Genome U34A array (Affymetrix, Santa Clara, CA) was used for gene expression profiling. The U34A GeneChip contains a total of 8,799 probe sets representing 7,000 known rat genes and 1,000 expressed sequence tags (ESTs). Five independent GeneChips for each group were used, resulting in a total of 30 GeneChips analyzed. The biotin-labeled cRNA was fragmented in fragmentation buffer (200 mM Tris-acetate, 50 mM KOAc, 150 mM MgOAc, at pH 8.1) and hybridized to the oligonucleotides in hybridization solution containing 15 µg fragmented cRNA in MES buffer (0.1 M MES, 1.0 M NaCl, 0.01% Triton X-100, at pH 6.7) and herring sperm DNA. GeneChips were placed in a hybridization oven at 60 rpm and 45°C for 16 h. Afterward, arrays were first washed at 22°C with SSPE-T (0.9 M NaCl, 60 mM NaH2PO4, 6 mM EDTA, 0.005% Triton X-100, at pH 7.6) and subsequently with 0.1 MES at 45°C for 30 min. The GeneChips were then stained with a streptavidin-phycoerythrin conjugate (Molecular Probes, Leiden, The Netherlands) and washed. Additional staining with anti-streptavidin antibody and streptavidin-phycoerythrin conjugate was used to enhance the signals. GeneChips were scanned at a resolution of 3 µm using a confocal scanner (model 900154; Affymetrix). From the 30 U34A GeneChips analyzed, one GeneChip of the IPC-TRI group did not satisfy the stringent quality criteria and was therefore excluded from further analysis. For all other experimental groups, five GeneChips, each resulting from an individual experiment, were of high quality and entered the subsequent bioinformatics analysis. The data are available at the Gene Expression Omnibus (GEO) web site under the series number GSE1616 (http://www.ncbi.nlm.nih.gov/geo/).
Analysis of Gene Expression Data
A flowchart illustrating the individual steps of data analysis can be viewed in Supplemental Fig. S1 (the Supplemental Material for this article is available online at the Physiological Genomics web site).1
Step 1: Normalization and computation of expression values.
Normalization and computation of expression values were performed using the robust multichip average (RMA) method (19) implemented in the module affy (10) of the BioConductor open-source bioinformatics software (http://www.bioconductor.org) in the R programming environment. R (http://www.r-project.org; Ref. 18) is a widely used open-source language for statistical computing and graphics. RMA performs the following operations: 1) probe-specific background correction to compensate for nonspecific binding using perfect-match (PM) distribution rather than PM-mismatch (MM) values, 2) probe level multichip quantile normalization to unify PM distributions across all GeneChips, 3) and robust probe set summary of the log-normalized probe-level data by median polishing.
Step 2: Statistical filtering and unsupervised clustering methods.
To select probe sets with a statistical significant alteration in signal intensity, the gene expression matrix was filtered using analysis of variance (ANOVA with P value = 0.01). To investigate similarities of the expression pattern across treatments, unsupervised clustering methods (principal component analysis, hierarchical clustering) were applied to the filtered data. Principal component analysis was performed using both the entire filtered data matrix and the gene lists according to functional classifications in Gene Ontology (GO, http://www.geneontology.org; Ref. 1). Hierarchical clustering was performed using the coupled two-way clustering (CTWC) algorithm (2, 11, 12). The concept and underlying philosophy of this algorithm is based on an analogy to the physics of inhomogeneous ferromagnets and has been previously described in detail (2). The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of genes and samples into stable classes. The transcripts analyzed were rearranged as ordered by the clustering algorithm, so that transcripts with the most similar expression patterns, as measured by the Euclidean distance, were placed adjacent to each other. Gene and sample clusters were regarded as stable according to specified size and stability index. The following parameters were used to optimize the resolution of the clustering process: gene cluster size 15, sample cluster size
4, stability threshold of gene and sample clusters
T
6K with one dropout for samples and 3 dropouts for genes at a single increment in T. Expression data were preprocessed using an iterative scaling and merging algorithm described in detail elsewhere (14).
Step 3: Determination of differentially expressed genes and Venn diagrams.
Both Significance Analysis of Microarrays algorithm (SAM, Ref. 46) and the LIMMA ("linear models for microarray data"; Ref. 38) analysis package are software packages for the statistical analysis of gene expression microarray data particularly designed for the assessment of differential gene expression. SAM and LIMMA both provide ranking of genes. A false discovery rate <1% was used in SAM analyses, and P = 0.01 was used in LIMMA analyses to obtain the ranked lists of differentially expressed genes. The gene lists obtained by SAM were used to generate Venn diagrams. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways was additionally used (http://www.GenMAPP.org; Refs. 6 and 8).
Validation of Selected Gene Expression Levels By Quantitative Real-Time RT-PCR
As an independent method of measuring levels of gene expression, RT-PCR was performed for 13 selected genes to confirm microarray data. The primers are listed in Table 1. For each amplification, 20 µl of cDNA were diluted in water (1:10) before using as template for the QuantiTect SYBR Green RT-PCR kit (Qiagen, Hilden, Germany). RT-PCR quantification and determination of expression levels were performed on ABI Prism 7700 sequence detector real-time PCR machine (PerkinElmer, Foster City, CA). Amplification reactions were conducted with an initial step at 90°C for 3 min followed by 2035 cycles. All PCR reactions were performed in triplicates, and -tubulin and aminopeptidase were used as reference controls. Predicted size of PCR products was confirmed by agarose gel electrophoresis. For all controlled genes, the direction (up- and downregulation) as well as the strength of regulation agreed with RT-PCR results.
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RESULTS |
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Gene Ontology-Based Comparisons Between PPC and IschPC Identifies Distinct Genomic Responses
To determine differences between genomic trigger and mediator/effector responses and between the two types of preconditioning, principal component analysis was applied using previously established GO terms (Supplemental Figs. S2S4). The widest separation was attained by trigger vs. mediator/effector responses for genes involved in apoptosis, growth factor activity, response to external stimuli, inflammatory response, electron transport, oxidoreductase activity, biosynthesis, and protein transport (Supplemental Fig. S2). Also, although less pronounced, differences within the trigger and mediator/effector responses were observed between PPC and IschPC (Supplemental Figs. S3 and S4). Depending on the GO category, CTL was more similar to protocols with prolonged ischemia (APC, IPC, ISCH) or more similar to trigger responses (APC-TRI, IPC-TRI).
IschPC but not PPC Elicits a Postischemic Gene Expression Profile Similar to Unprotected Ischemic Myocardium
Clustering analysis including all treatment groups.
CTWC was applied to the ANOVA-filtered RMA data for all six treatment groups [APC-TRI (n = 5), IPC-TRI (n = 4), APC (n = 5), IPC (n = 5), ISCH (n = 5), CTL (n = 5)]. The main cluster G1 (2,212 genes) broke into eight stable clusters as follows (Fig. 3 and 4): cluster G2 (69 genes, predominantly upregulated in APC-TRI and IPC-TRI including cell surface receptors, ion channels, Gadd45, and many ESTs), cluster G3 (129 genes, markedly upregulated in APC-TRI and downregulated in IPC and ISCH including mitochondria-related genes such as uncoupling protein 2, carnitine palmitoyl transferase 1b and 2, and many cell defense-related genes such as Hsp8, Hsp27, crystallin
B), cluster G8 and associated subcluster G4 (103 and 83 genes, respectively, exclusively downregulated in APC, IPC, and ISCH including genes related to inflammation such as interleukin 15, tumor necrosis factor
, nuclear factor
B, vascular cell adhesion molecule, selectin), cluster G5 (58 genes, exclusively upregulated in APC, IPC, and ISCH, including many chaperones such as Hsp10, Hsp40, Hsp70, and Hsp86), cluster G6 [171 genes, upregulated in ISCH and IPC but downregulated in APC-TRI and IPC-TRI including many transcription factors such as cAMP responsive element regulator (CREB), E2F transcription factor 5, DEAF-1 related transcriptional regulator (NUDR)], cluster G7 (17 genes, exclusively upregulated in APC including many ribosomal proteins S5, S7, S8, S15a, L9, L32, L36, L37), and cluster G9 (17 genes, exclusively upregulated in ISCH including LINE, SPARC-like 1, and many genes associated with cardiac remodeling including various types of collagens, vimentin, and matrix metalloproteinase 2). The main sample cluster S1 broke into six stable clusters as follows (see Fig. 6A): cluster S2 (APC, n = 5), cluster S3 (IPC+ISCH, n = 10), cluster S4 (CTL, n = 5), cluster S5 (CTL+APC, n = 10), cluster S6 (APC-TRI, n = 5), and cluster S7 (IPC-TRI, n = 4). Principal component analysis of sample clusters established a close genomic relationship between IPC and ISCH, while APC was close to CTL (nonischemic healthy myocardium) (Fig. 3B).
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Using cluster analysis, we were able to demonstrate that similar but distinct pre- and postischemic gene expression patterns characterize PPC and IschPC in the heart. Importantly, IschPC but not PPC elicits a postischemic gene expression profile similar to unprotected ischemic myocardium.
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DISCUSSION |
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Clustering Analysis
We used the CTWC method (2, 12) to identify patterns of genes within the large database. CTWC is characterized by a high robustness against noise and a natural ability to identify stable clusters, providing insight that would have been impossible by simply looking at particular gene lists. Unique expression patterns emerged within the transcriptional responses and placed the expression of many transcripts into a more holistic context. The clusters included families of transcripts known to have similar function, suggesting that this method closely followed biological likeness. In this study, we were able to track a genomic similarity between unprotected myocardium and IschPC. Although test ischemia itself activated protective genes, this may merely reflect the transcriptional response of the highly stressed but yet surviving myocardial tissue. Irrespectively, the gene expression profile of unprotected myocardium was clearly coupled to poor functional recovery and cell death and therefore represents the characteristic postischemic profile of the unprotected state. Likewise, the gene expression pattern of untreated virgin myocardium should be regarded as archetypal for nonischemic healthy myocardium. Collectively, using a global gene discovery approach (CTWC clustering), our data support the concept that PPC may be less harmful to the myocardium and thus superior to IschPC as therapeutic strategy in cardiac protection. However, since many genes separated protected from unprotected myocardium, the molecular similarity between IschPC and unprotected myocardium may be regarded as relative rather than absolute, and its significance remains to be determined.
Comparison Between IschPC and PPC
In the present study, the regulation of many transcripts previously reported to be involved in preconditioning was confirmed, but some transcripts showed opposite regulation. However, preconditioning is a highly dynamic complex network of intricate mechanisms undergoing multiphase regulation. Accordingly, different mechanisms have been reported to be responsible for the protection at different time points after preconditioning (49). Also, the genomic trigger responses as measured after 3 h in this study do not necessarily reflect the transcriptional changes involved in delayed protection. To date, few studies used microarrays to uncover the complex molecular mechanisms underlying preconditioning (30, 3437, 54). Onody et al. (30) observed upregulation of chaperonin (TCP-1
) and ribosomal proteins in preconditioned rat hearts after test ischemia and reperfusion. Simkhovich et al. (37) reported the activation of a protective genetic program predominantly including various heat shock proteins and transcription factors in rat hearts after brief ischemic episodes. Albeit not directly comparable, the results of the present study in principle confirm and extend these findings as well as the results of our previous microarray study, where we investigated the trigger responses of brief episodes of ischemia compared with a prolonged isoflurane exposure (110 min) but did not investigate protocols with test ischemia and reperfusion (35). In the latter study, a differential regulation of Hsp27, Hsp70, and programmed cell death 8 was observed in response to brief ischemia compared with isoflurane exposure. In another study, Rokosh et al. (34) compared trigger responses in mouse hearts exposed to brief ischemia and nitric oxide but did not compare the postischemic genomic reprogramming of the two types of preconditioning. Hence, the current study is the first comparing comprehensively pre- and postischemic genomic responses of IschPC vs. PPC.
Chaperones
Hearts exposed to global prolonged ischemia overexpressed many heat shock proteins independent of whether preconditioning was applied or not. Hsp27 scavenges cytochrome c (3), inhibits activation of caspase 3 (26), and blocks Fas-related apoptotic pathways (15). Hsp70 together with Hsp40 prevents mitochondrial release of cytochrome c and inhibits caspase 9 activation via Apaf-1 (26). Interestingly, Hsp10 was exclusively upregulated in IPC, while Hsp20 was exclusively upregulated in APC. Hsp10 acts in collaboration with Hsp60 opposing the proapoptotic Bax (15), and Hsp20 was recently found to inhibit ß-agonist-induced cardiac apoptosis (9). In the trigger responses (APC-TRI, IPC-TRI), several chaperones including Hsp8, Hsp20, and crystallin B were jointly downregulated. Collectively, these observations provide evidence for a highly dynamic and distinct regulation of chaperones in both IschPC and PPC.
Inflammation
Surprisingly and in contrast to previous work (28), the inflammatory response was profoundly and consistently downregulated in all protocols receiving prolonged ischemia and 3 h of reperfusion independent of whether preconditioning was applied or not. Mediators of inflammation are known to be important in ischemia/reperfusion-induced myocardial damage, and their inhibition was previously implicated in the protection underlying preconditioning. In contrast, upregulation of cytokines at the late phase of IschPC may represent a cytoprotective mechanism (54). It is possible that in the Langendorff model, which is virtually devoid of blood components, the inflammatory response may be limited to a short and blunted burst of inflammatory mediators at the early reperfusion (17). Alternatively, it could be speculated that the observed delayed postischemic anti-inflammatory status reflects a counterregulatory response and simply represents the intrinsic protective response of the viable myocardium unmasked in the absence of leukocytes, macrophages, and other extrinsic proinflammatory components.
Transcription Factors
Early growth response-1 (Egr-1), an immediate-early gene zinc finger transcription factor in the vasculature (29), which triggers increased expression of transcripts encoding intercellular adhesion molecule-1, vascular cell adhesion molecule-1, and platelet-derived growth factor, was upregulated after APC but downregulated after both types of triggering. Zf36, another member of the zinc finger transcription factors, which is widely distributed in tissues, was exclusively increased in the protected myocardium. This may result in anti-inflammatory protective effects, as the zf36 knockout mouse model displays deleterious tumor necrosis factor- overexpression (42). Likewise, activating transcription factor 3 (ATF3) was exclusively upregulated in protected myocardium. ATF3 is a member of the cAMP-responsive element binding protein family, which is known to downregulate the transcription of p53 gene, thus promoting cell survival (16). Growth arrest and DNA-damage-inducible 45
(Gadd45
) controlling DNA stability and repair clustered in both trigger responses. Interestingly, enhanced E2F activity, previously linked to apoptosis (23), was observed in ISCH and IPC, but was downregulated in the trigger responses. A similar expression pattern throughout the treatment groups was observed for deformed epidermal autoregulatory factor-1 (DEAF-1).
Metabolic Plasticity (Supplemental Fig. S6)
Enzymes involved in long-chain fatty acid ß-oxidation were increased in APC and IPC. Also, pyruvate dehydrogenase, which determines the fate of the glycolytic product pyruvate, i.e., mitochondrial oxidation or anaerobic conversion into lactate, was upregulated exclusively in protected myocardium. Interestingly, APC but not IPC upregulated carnitine palmitoyltransferase, the rate-limiting enzyme in fatty acid ß-oxidation, possibly preventing palmitate-induced myocyte apoptosis (32). An intriguing new finding was the upregulation of many phosphoprotein phosphatases, which may be due to the regulation of multiple metabolic pathways. Collectively, enhanced substrate oxidation reflects the more robust preservation of energy production in preconditioned hearts allowing better functional recovery. In line with this view is the notion that members of the mitochondrial respiratory chain, i.e., uncoupling protein 2 in APC and uncoupling factor 6 in IPC, respectively, were upregulated. Overexpression of uncoupling protein 2 was recently shown to inhibit mitochondrial death signaling in superoxide-stressed cardiomyocytes (43). Interestingly, in both types of preconditioning, although less pronounced in APC, postischemic myocardium expressed increased levels of hexokinase. Recent research has shown that specific isoforms of hexokinase bind to mitochondrial voltage-dependent anion channel, thus suppressing the release of cytochrome c and inhibiting apoptosis (24).
Metabolic remodeling was completely different in the trigger protocols. Transcripts of enzymes involved in glycolysis and tricarboxylic acid cycle, fatty acid ß-oxidation, and mitochondrial respiration were consistently downregulated. This "state of metabolic hibernation" was more pronounced in APC than IPC. Myocardial protection by preconditioning and slowed metabolism was previously reported to coincide (31). Reduced energy demand is a feature of preconditioned myocardium and may be due to protein kinase C-mediated phosphorylation of various regulatory proteins in several energy-consuming reactions. Alternatively, downregulation of key metabolic pathways may be a regulatory response to preconditioning-induced increased glucose uptake (44). Taken together, these results confirm previous observations and extend our knowledge on metabolic plasticity of preconditioned myocardium.
Remodeling
A wide range of transcripts encoding extracellular matrix and structural proteins were enriched in a cluster archetypal for unprotected ischemic myocardium. Both types of preconditioning prevented activation of the remodeling program, a process that might be mechanistically linked to improved postischemic cardiac function and a decreased propensity for arrhythmogenesis. Consistent with this notion, angina, the clinical correlate to IschPC, was recently shown to protect patients against ventricular remodeling (39). In the current study, APC and IPC were associated with increased brain natriuretic peptide expression. Brain natriuretic peptide is known to decrease collagen synthesis and cardiac remodeling. Upregulated matrix metalloproteinase 2, responsible for collagen degradation and remodeling of the extracellular matrix, was clustering with vimentin and insulin-like growth factor II in unprotected myocardium. Metalloproteinase 2 was previously reported to cleave troponin I at reperfusion, thereby reducing contractile function (48).
Long Interspersed Nucleotide Elements
We have also uncovered a retrotransposon transcriptional burst. LINEs are long interspersed repeated retrotransposable elements and found in almost all eukaryotes (13). They contain an internal promoter for RNA polymerase II (pol II), usually two open reading frames encoding proteins of unknown function and polypeptides with reverse transcriptase and DNA endonuclease activity. After translation, the nascent reverse transcriptase binds to the LINE mRNA forming a ribonucleoprotein complex, which enters the nucleus where it initiates a process called "target-primed reverse transcription" priming reverse transcription of the LINE mRNA into the chromosome. Short interspersed retrotransposable elements (SINEs) may use the LINE machinery for retrotransposition, a process called "retropositional parasitism." Notably, retrotransposable elements are integrated in many introns of many genes accounting for 37% of the rat genome. We here report for the first time that two types of preconditioning consistently reversed ischemia-enhanced LINE expression. It is tempting to speculate that LINE activity and SINE activity may be involved in pro- and/or antiprotective gene regulation by posttranscriptional gene silencing, inhibition of transcriptional elongation, or inhibition of protein kinase R (4, 50). Interestingly, Alu, the most common LINE, is known to attract DNA fragmentation events within ORF2 at early stages of apoptosis (21). Collectively, this raises the possibility that LINEs and/or SINEs may play an important regulatory role in ischemia/reperfusion phenomena.
Clinical Implications
Clinical studies suggest that pre-infarction angina, a clinical correlate to IschPC, increases the chances of rapid reperfusion after thrombolytic therapy, and reduces the number of hypokinetic myocardial segments and infarct size. Most recently, decreased ventricular remodeling (39) and improved cardiovascular long-term outcome (22) was observed in patients with coronary artery disease and an effective preconditioning mechanism. Despite these beneficial effects, some publications raised concerns about the safety of IschPC as a therapeutic strategy (33). In support of these concerns are experimental results obtained in old Fisher 344 rats exposed to brief ischemic episodes (36). In this model, brief ischemia promoted expression of injury- and disease-related genes. Our study now shows for the first time the close molecular relationship between IschPC and unprotected myocardium. Thus PPC, as opposed to IschPC, may have a wider "therapeutic window." There are a large number of experimental studies, and more recently an increasing number of clinical studies, demonstrating the significant cardioprotection of volatile anesthetics in patients undergoing coronary artery bypass grafting. Laboratory investigations also stress the concept that volatile anesthetics may precondition endothelial and smooth muscle cells (7), implying that systemic administration of these agents may potentially protect a variety of other vital organs. Intriguingly, preconditioning by sevoflurane was reported to attenuate cardiopulmonary bypass-associated transient renal dysfunction in coronary artery bypass graft patients (20). Together, based on our experimental results and previous clinical observations, PPC should be preferentially applied as a therapeutic strategy for cardioprotection in the clinical setting.
Study Limitations
The following remarks should be added. RT-PCR may be more powerful to detect gene regulation. Also, changes in mRNA levels may be not always correlated with respective protein levels. Although genomics has demonstrated that there is more than 85% similarity in coding regions of the rat genome compared with the human genome, data from rodent studies must be always interpreted with caution. In addition, buffer-perfused hearts have a limited long-term biologic stability and may undergo short confounding ischemic periods during the isolation procedure. Finally, the observations as obtained by the volatile anesthetic isoflurane may not be applicable for all PPC inducing agents.
Conclusions
We have used an unbiased gene discovery approach to define the global transcriptional responses surrounding preconditioning. Novel key clusters containing LINEs and transcripts related to cardiac remodeling emerged after ischemia and were effectively modulated by preconditioning. Due to the genomic similarity between unprotected myocardium and IschPC, IschPC may be less desirable as therapeutic approach, specifically in high-risk patients in whom an ischemic type of preconditioning may further jeopardize diseased myocardium. The information obtained herein by comparing PPC and IschPC might help to develop novel rational therapeutic interventions targeted to specific cardioprotective mechanisms.
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FOOTNOTES |
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Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).
Address for reprint requests and other correspondence: M. Zaugg, Institute of Anesthesiology, Univ. Hospital Zurich, Rämistrasse 100, CH-8091 Zurich, Switzerland (E-mail: michael.zaugg{at}usz.ch)
doi:10.1152/physiolgenomics.00166.2004.
1 The following additional data files are available with the online version of this article: Excel sheets containing complete lists of differentially regulated genes separated according to trigger (APC-TRI, IPC-TRI) and mediator/effector responses (APC, IPC) (Tables S4S6), lists of genes of the individual clusters resulting from CTWC (Tables S1S3), a flowchart illustrating the individual steps of data analysis (Fig. S1), additional principal component analysis results (Figs. S2S4), reordered gene expression matrices of the various clustering analyses (Fig. S5), and representative GenMAPP pathways (Fig. S6). This is available online at http://physiolgenomics.physiology.org/cgi/content/full/00166.2004/DC1.
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REFERENCES |
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