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Published ahead of print on August 29, 2007, doi:10.1164/rccm.200705-683OC
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American Journal of Respiratory and Critical Care Medicine Vol 176. pp. 1108-1119, (2007)
© 2007 American Thoracic Society
doi: 10.1164/rccm.200705-683OC

Original Article

Comparative Expression Profiling in Pulmonary Fibrosis Suggests a Role of Hypoxia-inducible Factor-1{alpha} in Disease Pathogenesis

Argyris Tzouvelekis1,*, Vaggelis Harokopos2,*, Triantafillos Paparountas2,*, Nikos Oikonomou2, Aristotelis Chatziioannou3, George Vilaras4, Evangelos Tsiambas4, Andreas Karameris4, Demosthenes Bouros1 and Vassilis Aidinis2

1 Department of Pneumonology, Medical School, Democritus University of Thrace, and University Hospital of Alexandroupolis, Alexandroupolis, Greece; 2 Institute of Immunology, Biomedical Sciences Research Center "Alexander Fleming", Athens, Greece; 3 Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Athens, Greece; and 4 Department of Pathology, Veterans Administration Hospital (N.I.M.T.S), Athens, Greece

Correspondence and requests for reprints should be addressed to Vassilis Aidinis, Ph.D., Institute of Immunology, B.S.R.C. Alexander Fleming, 34 Fleming Street, 16672, Athens, Greece. E-mail: v.aidinis{at}fleming.gr


   ABSTRACT
TOP
 ABSTRACT
AT A GLANCE COMMENTARY
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Rationale: Despite intense research efforts, the etiology and pathogenesis of idiopathic pulmonary fibrosis remain poorly understood.

Objectives: To discover novel genes and/or cellular pathways involved in the pathogenesis of the disease.

Methods: We performed expression profiling of disease progression in a well-characterized animal model of the disease. Differentially expressed genes that were identified were compared with all publicly available expression profiles both from human patients and animal models. The role of hypoxia-inducible factor (HIF)-1{alpha} in disease pathogenesis was examined with a series of immunostainings, both in the animal model as well as in tissue microarrays containing tissue samples of human patients, followed by computerized image analysis.

Measurements and Main Results: Comparative expression profiling produced a prioritized gene list of high statistical significance, which consisted of the most likely disease modifiers identified so far in pulmonary fibrosis. Extending beyond target identification, a series of meta-analyses produced a number of biological hypotheses on disease pathogenesis. Among them, the role of HIF-1 signaling was further explored to reveal HIF-1{alpha} overexpression in the hyperplastic epithelium of fibrotic lungs, colocalized with its target genes p53 and Vegf.

Conclusions: Comparative expression profiling was shown to be a highly efficient method in identifying deregulated genes and pathways. Moreover, tissue microarrays and computerized image analysis allowed for the high-throughput and unbiased assessment of histopathologic sections, adding substantial confidence in pathologic evaluations. More importantly, our results suggest an early primary role of HIF-1 in alveolar epithelial cell homeostasis and disease pathogenesis, provide insights on the pathophysiologic differences of different interstitial pneumonias, and indicate the importance of assessing the efficacy of pharmacologic inhibitors of HIF-1 activity in the treatment of pulmonary fibrosis.

Key Words: idiopathic pulmonary fibrosis (IPF) • expression profiling • tissue microarrays • hypoxia-inducible factor-1{alpha} (HIF-1{alpha})



   AT A GLANCE COMMENTARY
TOP
 ABSTRACT
 AT A GLANCE COMMENTARY
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Scientific Knowledge on the Subject
Despite intense research efforts, the etiology and pathogenesis of idiopathic pulmonary fibrosis remain poorly understood, which is reflected in the lack of effective treatment.

What This Study Adds to the Field
Our results suggest an early primary role of HIF-1 in alveolar epithelial cell homeostasis and disease pathogenesis, provide insights on the pathophysiologic differences of different interstitial pneumonias, and indicate the importance of assessing the efficacy of pharmacologic inhibitors of HIF-1 activity in the treatment of pulmonary fibrosis.

 
Idiopathic interstitial pneumonias (IIPs) are a heterogeneous group of diseases comprising seven distinct clinical and pathologic entities. Idiopathic pulmonary fibrosis (IPF) and cryptogenic organizing pneumonia (COP) represent two of the most prevalent members of the disease group, with major differences in pathogenesis, clinical course, and prognosis (1). IPF is a refractory and lethal IIP characterized by fibroblast proliferation, extracellular matrix deposition, and progressive lung scarring, and comprises the histopathologic pattern of usual interstitial pneumonia (UIP). The incidence of IPF is estimated at 6.8 to 16.3 cases per 100,000 per year in the United States, and the mean survival from the time of diagnosis is 3 to 5 years regardless of treatment (2–4). Although the etiology and pathogenesis of IPF remain poorly understood, current research suggests that the mechanisms driving IPF reflect abnormal, deregulated wound healing in response to multiple sites of ongoing alveolar epithelial injury, involving increased activity and possibly exaggerated responses by a spectrum of proinflammatory and profibrogenic factors (3, 5).

Expression profiling, the estimation of the expression level of thousands of genes by DNA microarrays, is a powerful tool for biologists, bioinformaticians, and statisticians in their attempt to decipher the complex organization of biological phenomena. In this context, and to identify genes and/or cellular pathways involved in the initiation and progression of IPF, we used the bleomycin (BLM)-induced animal model, the closest equivalent of the human disease. RNA lung samples were isolated at different endpoints in the development of the disease and hybridized to cDNA microarrays. After robust statistical selection of differentially expressed genes (DEGs), results were compared with all publicly available microarray datasets in IPF (6–15), both from mice and humans, thus creating a unique list of likely disease modifiers. Furthermore, gene ontology and pathway analysis revealed hypoxia signaling among the most statistically important deregulated pathways. Prompted by the meta-analysis results, we investigated the role of hypoxia-inducible factor (HIF)-1{alpha} in disease pathogenesis, in the animal model as well as in human patients, to reveal an early primary role of HIF-1{alpha} in IPF development. Some of the results of these studies have been previously reported in the form of abstracts (16, 17).


   METHODS
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 AT A GLANCE COMMENTARY
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Animals
All mouse strains were bred and maintained in the C57/Bl6 background for over 20 generations in the animal facilities of the Biomedical Sciences Research Center "Alexander Fleming" (Athens, Greece) under specific pathogen–free conditions, in compliance with the Declaration of Helsinki principles. Mice were housed at 20–22°C, with 55 ± 5% humidity, and a 12-hour light:dark cycle; food and water was given ad libitum. All experimentation was approved by an internal institutional review board, as well as by the Veterinary Service and Fishery Department of the local governmental prefecture. Pulmonary fibrosis was induced by a single tail vein injection of BLM hydrogen chloride (100 mg/kg body weight; 1/3 of lethal dose, 50% [1/3LD50]; Nippon Kayaku Co. Ltd., Tokyo, Japan) to 6- to 8-week-old mice as previously reported in detail (18).

Expression Profiling
Total RNA from the right lobe of lung specimens was isolated by homogenization in ice-cold TRIzol reagent (Invitrogen Life Sciences, Carlsbad, CA) followed by a single passage through an RNeasy column (QIAGEN GmbH, Hilden, Germany). Isolated total RNA was reverse transcribed with Superscript Reverse Transcriptase II (Invitrogen), and the cDNA was indirectly labeled using the amino-allyl cDNA labeling method. Experimental samples were mixed with equimolar amounts of the baseline sample (which was used as a common reference sample throughout) and hybridized in quadruplicates to cDNA glass microarray slides (Riken, Yokohama, Japan) interrogating 18,816 genes. After image analysis, all microarray data were subjected to preprocessing, lowess normalization, centering, and/or averaging. To select statistically significant DEGs, and because there is no international consensus on the most appropriate method for statistical selection, we used simultaneously the two most widely used methods: a parametric and a nonparametric analysis of variance (Kruskal-Wallis), using proprietary algorithms implemented in MATLAB (version 7.1, release 14; The MathWorks, Inc., Natick, MA). Reverse transcriptase–polymerase chain reaction (RT-PCR) gene validation was performed using MMLV reverse transcriptase and an oligo-dT(15) primer (Promega, Madison, WI). Detailed information on expression profiling, including gene ontology and pathway analysis, are provided in the online supplement.

Human Subjects
In total, 45 newly diagnosed patients with IIPs of two different histopathologic patterns (IPF/UIP, and COP/organizing pneumonia [COP/OP]) were recruited in our study. The diagnosis of IIPs was based on the consensus statement of the American Thoracic Society/European Respiratory Society in 2002 (1, 19). Subjects were separated according to the histopathologic pattern of the IIPs as shown in Table 1. All patients were treatment naive at study inclusion. Paraffin-embedded surgical lung specimens (open lung biopsy or by video-assisted thoracoscopic surgery) from two different fibrotic regions of each individual were sampled. All patients were fully informed and signed an informed consent form in which they agreed to the anonymous usage of their lung samples for research purposes.


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  		TABLE 1. DEMOGRAPHIC AND SPIROMETRIC CHARACTERISTICS OF PATIENTS WITH IPF/UIP, PATIENTS WITH COP/OP, AND CONTROL SUBJECTS

 
Tissue Microarrays, Immunohistochemistry, and Computerized Image Analysis
Tissue microarrays (TMAs) were constructed from 85 tissue samples consisting of 45 lung specimens from two different histopathologic patterns of IIPs and 40 control tissues derived from the normal part of lungs removed for benign lesions. After epitope demasking, TMAs were immunostained with a number of antibodies against HIF-1{alpha}, surfactant protein A (SP-A), vascular endothelial growth factor (VEGF), p53, and DNA fragmentation factor (DFF). Signal intensities were quantified with computerized image analysis using a semiautomated system. Statistical analysis was performed using SPSS 13.0 software (SPSS, Inc., Chicago, IL). Details on these methodologies can be found in the online supplement.


   RESULTS
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 ABSTRACT
 AT A GLANCE COMMENTARY
 METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Expression Profiling
To identify genes and/or cellular pathways involved in the initiation and progression of IPF, we performed expression profiling of disease progression in the animal model of BLM-induced pulmonary inflammation and fibrosis (18, 20). In this model, and as reported previously (18), BLM administration results in progressive subpleural/peribronchial pulmonary inflammation, which subsequently diffuses into the parenchyma. Inflammation is followed by the development of mainly subpleural and peribronchial fibrotic patches, characterized by alveolar septa thickening and focal dilation of respiratory bronchioles and alveolar ducts. Concomitantly, collagen accumulation peaks 23 days post–BLM injection (Figure E4 of the online supplement). The model is very reproducible, using standardized procedures and dedicated functional readouts exhibiting minimal variation (18). RNA lung samples were isolated at 7, 15, and 23 days post–BLM administration, corresponding to the inflammatory, intermediate, and fibrotic phases of the disease (Figure E4). Similarly, as a baseline control, RNA lung samples were isolated from littermate mice 23 days after administration of saline alone.

Equimolar amounts of purified RNA from five mice per endpoint were pooled, to minimize biological diversity, and fluorescently labeled using the amino-allyl indirect labeling method as described in METHODS. Identical labeled samples from the same pool were mixed with the labeled common reference sample (wt/saline) and hybridized in (technical) quadruplicates to cDNA glass microarray slides, interrogating 18,816 genes. After image acquisition and analysis, microarray data were analyzed as outlined in Figure E1, using proprietary algorithms implemented in MATLAB. Briefly, and as described in detail in METHODS, after preprocessing, lowess normalization and quality control (Figure E2), centering was applied either before or after averaging, thus producing two gene matrices. These two matrices were further analyzed with two different statistical selection methods, one parametric and one nonparametric, thus ending up with four different lists of likely DEGs. The 1,172 genes identified as differentially expressed from all methods (having therefore a very high statistical significance and a very low false discovery rate) are shown in Table E1. The differential expression of a small number of genes (clu, Hba-a1, spp1, slc6a6, nish, mt1) was further confirmed with semiquantitative RT-PCR (at three different RNA concentrations in the linear range of the reaction) in separate pools of five experimental animals and their controls (Figure E3).

Comparative Expression Profiling and Meta-analysis
To validate our list of DEGs (Table E1) in a high-throughput mode, and to compare results from different animal models as well as from human patients, we collected (through database searching and personal communications) all publicly available information from published expression profiling datasets on IPF (6–15), each one with different levels of data quality, annotation, and availability. Mouse and human Entrez-Gene IDs for all reported DEGs from the different datasets/studies were retrieved using the Ingenuity Pathways Analysis (IPA version 5.0; Ingenuity Systems, Redwood City, CA) software. Comparisons were performed separately for both human and mouse Entrez-Gene IDs and results were fused together to avoid exclusions due to species nonconcordance. Strikingly, and although the compared data were obtained from various models and organisms (which conceptually are governed by different pathogenetic mechanisms), using different microarray platforms (containing different genes) and statistical methods, we identified a large number of genes in common between our dataset and the published genes in pairwise comparisons (Table 2 and Table E2). Therefore, the combined gene list (Table E3) containing 296 (nonredundant) genes (common DEGs [cDEGs]) identified as differentially expressed from at least two independent studies (our own and a published one) is self-validated, has a high statistical significance, and therefore is a valuable resource of likely disease-modifying genes. Among them, 35 genes were identified from three different datasets and 6 genes from four datasets (as highlighted in Table E3), prioritizing these genes even further.


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  		TABLE 2. LIST OF COMPARED DATASETS

 
To prioritize the cDEGs systematically, an in-depth meta-analysis was conducted. Initially, a very extensive literature search with automated text mining using the Biolab Experiment Assistant software (Version 2.7; Biovista, Athens, Greece) and manual (PubMed) search revealed a total of 81 genes that have been found to play a direct role in the development of the disease (Table E4). This set of genes was used as a training set for the software application Endeavour (SymBioSys, Center for Computational Systems Biology, Katholieke Universiteit Leuven, Leuven, The Netherlands), which performs computational prioritization of "test genes," based on a set of "training genes" (21). Endeavour uses nine different data sources including both vocabulary-based (e.g., Gene Ontology [GO]) as well as other data sources (e.g., BLAST and microarray databases). The ranking of a test gene for a given data source is calculated based on its similarity with the training genes, whereas the final prioritization is calculated based on order statistics of the individual rankings (21). The statistically more significant (according to Endeavour, P < 0.05) cDEGs are shown in Figure 1.


Figure 1
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  		Figure 1. Endeavour-prioritized differentially expressed genes, identified from at least two independent datasets. One and eight genes were identified in three and four different datasets, respectively. Queried data source, in order of headings on left side of figure: Du, Dummy model Ouzunis; Du, Dummy model propector; BI, BLAST; En; ENSEMBL EST model; IP, INTERPRO; KE, KEGG; EX, microarray expression database 1; EX, microarray expression database 2; Go, GO analysis; Mo, transcriptional motifs and cis-regulatory elements; pV, Endeavour P value.

 
To get additional functional insights on a potential role of DEGs in the development of the disease, we then examined whether any of these genes are tumor necrosis factor (TNF) or transforming growth factor (TGF) targets, the major proinflammatory or profibrogenic factor, respectively, with a definite role in disease induction (18, 22, 23). TNF or TGF DEGs were identified from published microarray datasets (24–28) and compared with IPF DEGs (Table E1; 1,172 genes). As shown in Table 2 (and Table E2 in detail), 19 and 37 DEGs, respectively, were found to be TNF or TGF targets. Remarkably, 14 (of 19) and 21 (of 37) TNF/TGF targets that were found as IPF DEGs were also included in the cDEG list (Table E3), highlighting the role of TNF and TGF in disease development. Five and seven of them, respectively, are also included in the statistically significant Endeavour-prioritized cDEG list (highlighted in Figure 1).

Finally, in an attempt to combine expression profiling with genetic linkage studies, IPF DEGs (Table E1) were compared with possible susceptibility genes from identified quantitative trait loci for BLM-induced pulmonary fibrosis (Blmpf1 and 2; References 29–31). Eleven of 22 genes from the blmpf1/2 loci, respectively, have been identified as DEGs (highlighted in Table E1) and three of five of these were also included in the cDEG list (highlighted in Table E3).

GO and Pathway Analysis
In parallel with the statistical identification of DEGs and their prioritization, and to (1) prove the validity and extend the utility of the expression data analysis even further, (2) infer deregulated biological functions from the gene expression data, and (3) define functional criteria for further gene selection, the selected genes (Table E1) were annotated in the form of the GO terms, in the categories Molecular Function and Biological Process. GO term frequencies in the selected gene list were then calculated and their statistical significance (expressed as a P value) were estimated (through their hypergeometric distribution) as reported previously, and as described in detail in METHODS. As shown in Table 3, a number of well-expected functions and processes were found to be deregulated during the pathogenesis of BLM-induced pulmonary inflammation and fibrosis, such as inflammatory response, and chemokine, cytokine, and growth factor activity. As anticipated, GO analysis indicated multiple levels of gene expression regulation during pathogenesis (RNA helicase activity, transcription corepressor activity, transcription factor binding, magnesium ion binding; RNA processing, nuclear mRNA splicing, mRNA processing). The adhesion–cytoskeleton axis was also highlighted from the analysis, as indicated (directly or indirectly) from a number of deregulated functions and processes (respectively: GTPase activity, actin binding; actin filament severing, cell matrix adhesion). Notably, oxygen transport was indicated as the most significant deregulated GO function as well as GO process, indicating hypoxia as a pathogenic insult that could lead to (or exacerbate) pulmonary fibrosis.


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  		TABLE 3. GENE ONTOLOGY AND PATHWAY ANALYSIS OF DIFFERENTIALLY EXPRESSED GENES

 
In a similar, complementary effort, the software program IPA (Ingenuity Systems) was used for automated gene expression data integration in cellular canonical pathways, as these are (pre)defined and curated by IPA. DEGs (Table E1) were examined for their participation in IPA canonical pathways, followed by a statistical test to examine if the pathway association could be observed by chance alone. The statistically significant (P < 0.05) deregulated canonical pathways are shown in Table 3. Remarkably, integrin and hypoxia signaling were ranked first in the list of statistically significant deregulated pathways, further supporting the GO analysis results.

Early HIF-1{alpha} Overexpression in BLM-induced Pulmonary Inflammation and Fibrosis
To examine the role of hypoxia in the pathogenesis of pulmonary fibrosis, as indicated by the GO/IPA analysis, we then focused on the role of the HIF-1{alpha}, the major transcription factor that mediates cellular responses to hypoxia (32). Semiquantitative RT-PCR analysis indicated that the mRNA levels of Hif-1{alpha} are found to be up-regulated upon administration of BLM and the development of pulmonary inflammation and fibrosis (Figure E5). Of the 70 well-known HIF-1{alpha} targets (identified from References 33 and 34) and the OMIM [Online Mendelian Inheritance in Man] and Transfac databases), 42 were included in the microarray we used (containing 18,816 genes) and 6 of those were found to be statistically significantly overexpressed (Bnip3l, Flt1, Siah1, Bhlhb2, Vegfa, and Vegfc; Table E1, 1,172 genes), a number much higher than the one expected by chance alone. To examine if the observed enrichment of HIF-1{alpha} targets in the DEG list was statistically significant, we tested the null hypothesis as described in online METHODS, where the enrichment of HIF-1{alpha} targets was found to be statistically significant (P = 0.0153). The overexpression of VEGFa was further confirmed with RT-PCR analysis together with two more major HIF-1{alpha} targets (cxcl12, pgk1; which are not included in the microarray) (Figure E5). Moreover, immunohistochemistry for HIF-1{alpha} on lung paraffin sections from BLM-treated mice (7, 15 and 23 d postadministration) confirmed overexpression of HIF-1{alpha} during the development of the disease, localizing it mainly at the epithelium and the endothelium (Figure 2). Remarkably, both mRNA and protein levels of Hif-1{alpha} were found to be up-regulated as early as 7 days post–BLM treatment, before any destruction of lung architecture and consequent gas exchange problems, indicating an early role of Hif-1{alpha} in disease pathogenesis.


Figure 2
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  		Figure 2. Increased hypoxia inducible factor (HIF)-1{alpha} expression in bleomycin (BLM)-induced pulmonary inflammation and fibrosis. (A) Representative immunohistochemistry with an anti-HIF-1{alpha} antibody on lung paraffin sections from BLM-treated mice (7, 15, and 23 d postadministration). (B) Computerized image analysis of immunostained sections.

 
HIF-1{alpha} and HIF Target Genes Overexpression in the Pulmonary Epithelium of Patients with IPF/COP
To confirm the observed overexpression of HIF-1{alpha} in the animal model of pulmonary fibrosis, we then examined HIF-1{alpha} expression in lung sections of human patients with IPF/UIP and COP/OP (Table 1). To expedite and standardize experimental procedures, we created two TMA blocks consisting of 125 tissue cores each, derived from 25 IPF, 20 COP, and 40 normal lung samples. TMA blocks were immunostained with anti-HIF-1{alpha} antibodies and analyzed quantitatively/statistically by computerized image analysis as described in METHODS. As shown in Figure 3 (and Figure E6), a significant expression of HIF-1{alpha} expression was observed in IPF and COP samples, which was almost missing from normal lung control samples. In IPF samples, the expression of HIF-1{alpha} was localized almost exclusively in hyperplastic type II alveolar epithelial cells (AECs) (as shown with double immunostaining with SP-A; Figure E7) overlying areas of highly proliferative fibroblasts, called fibroblast foci. Interestingly, and in accordance with results from the animal model, HIF-1{alpha} was also present in the alveolar epithelium lying within areas of normal lung (Figure E6), implicating Hif-1{alpha} activation as an early event in the fibrogenic cascade. In COP samples, the expression of HIF-1{alpha} was localized both in the alveolar epithelium overlying areas of active fibrosis called Masson bodies (MBs) as well in the fibrotic interstitium, indicating differences in the pathogenetic mechanisms of IPF and COP.


Figure 3
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  		Figure 3. Increased hypoxia inducible factor (HIF)-1{alpha} expression in the pulmonary epithelium of patients with idiopathic pulmonary fibrosis (IPF) and those with cryptogenic organizing pneumonia (COP). (A) Representative immunohistochemistry with an anti-HIF-1{alpha} antibody on tissue microarrays containing 25 IPF, 20 COP, and 40 normal lung samples. (B) Computerized image analysis of immunostained sections.

 
To examine if the observed HIF-1{alpha} overexpression was followed by the subsequent activation of the most prominent HIF target genes, we then examined the expression patterns of VEGF (also identified and confirmed as cDEG; Table 3 and Figure E5, respectively) and p53. Remarkably, both VEGF and p53 were found to be overexpressed in IPF/COP, following the exact expression pattern of HIF-1{alpha}, exclusively in AECs overlying fibroblast foci in IPF and in both AECs and MBs in COP (Figure 4 and Figure E6), as also shown with double HIF-1{alpha}/VEGF, HIF-1{alpha}/p53 immunostainings (Figure E7).


Figure 4
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  		Figure 4. Increased expression of vascular endothelial growth factor (VEGF) and p53 in idiopathic pulmonary fibrosis (IPF) lungs. (A) Representative immunohistochemistry with an anti-VEGF and an anti-p53 antibody on tissue microarrays containing 25 IPF, 20 cryptogenic organizing pneumonia (COP), and 40 normal lung samples. (B) Computerized image analysis of immunostained sections. AECs, alveolar epithelial cells; FF, fibroblastic foci; MB, Masson's bodies.

 
Increased Apoptosis in the Epithelium of Fibrotic Lungs
The caspase-mediated pulmonary epithelial apoptosis has been proposed as one of the initiating insults in the pathogenesis of pulmonary fibrosis, whereas the relative resistance of (myo)fibroblasts to apoptosis has been suggested as one of the perpetuating stimuli of the fibrotic response. To confirm whether the increased expression of the proapoptotic tumor suppressor gene p53 in the hyperplastic epithelium results in increased epithelial apoptosis, the expression pattern of the caspase-activated DFF (or caspase-activated DNase) (35) was determined (Figure 5). Increased apoptosis was clearly noticed at the alveolar epithelium of patients with IPF and COP compared with control subjects, confirming previous results (36). Further analyzing the regional apoptotic profiles, we observed minimal apoptosis in areas of active fibrosis, compared with the surrounding hyperplastic epithelium. In addition, increased DFF expression was found within the fibromyxoid lesions of COP lung compared with fibroblastic foci seen in the histopathologic pattern of UIP, indicating distinct apoptotic profiles between these two disease entities.


Figure 5
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  		Figure 5. Expression pattern of DNA fragmentation factor (DFF) and BCL2 in idiopathic pulmonary fibrosis (IPF) lungs. (A) Representative immunohistochemistry with an anti-DFF and an anti-BCL2 antibody on tissue microarrays containing 25 IPF, 20 cryptogenic organizing pneumonia (COP), and 40 normal lung samples. (B) Computerized image analysis of immunostained sections. AECs, alveolar epithelial cells; FF, fibroblastic foci; MB, Masson's bodies.

 
To further analyze the distinct apoptotic profiles exhibited by AECs and fibroblasts in IPF lung, we assessed the expression of the antiapoptotic protein BCL2. Prominent staining of BCL2 was primarily observed within the fibrotic interstitium in IPF lung and especially within areas of accumulated fibroblast-like cells (fibroblasts and myofibroblasts) compared with the overlying hyperplastic epithelium (Figure 5). In addition, bcl2 expression was almost absent in MBs of COP lung, indicating that fibroblasts derived from IPF lung, in contrast to those seen in COP lung, are resistant to apoptosis through enhanced expression of antiapoptotic mechanisms, including bcl2, confirming previous results (36). The latter may explain differences between patients with IPF and those with COP in disease progressiveness and treatment responsiveness. Finally, BCL2 staining was almost absent within areas of normal lung and in the alveolar epithelium of control lung samples.


   DISCUSSION
TOP
 ABSTRACT
 AT A GLANCE COMMENTARY
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Comparative Expression Profiling, Gene Prioritization, and Meta-analysis
Expression profiling, the relative quantification of the expression level of thousands of genes simultaneously, is one of the most promising approaches for understanding mechanisms of differentiation, development, and disease. Especially in the case of polygenic diseases, such as interstitial pneumonias, expression profiling is a prerequisite for the generation of new working hypotheses on pathogenetic mechanisms. In this context, and to identify genes and/or cellular pathways involved in the initiation and progression of IPF, we performed expression profiling in a well-characterized animal model. After robust data analysis and statistical selection, a large number of differential expressed genes were identified. To extend beyond the subsequent confirmation of differential expression for each of the reported genes and to validate our results in a high-throughput mode, the list of DEGs was compared with all publicly available information on differential expression in either IPF or various animal models of the disease. The commonly identified genes (Table 2) have a high statistical significance and take advantage of all accumulated knowledge of differential expression in IPF. Identified DEGs were further prioritized according to their relationship with genes well known to be associated with IPF, based on the massive amount of information stored in different databases (21). The inherent statistics have identified and ranked 33 genes as highly statistically significant, representing the best candidates identified so far in IPF (Figure 1). Moreover, 10 of these genes were identified as TNF or TGF targets, the major proinflammatory and profibrotic factors, respectively, with a definite role in disease induction (18, 22, 23).

Finally, and to follow a more "systemic" approach in dissecting IPF pathogenesis (14), we used GO and pathway analysis to identify deregulated functions, processes, and pathways (Table 3). Beside the well-known or anticipated deregulated functions in IPF, a few biological hypotheses (Table 3) have emerged from our analysis. In this report, we have followed the generated hypothesis that hypoxia could be a pathogenic insult that could lead to (or exacerbate) pulmonary fibrosis.

A Primary Role for HIF-1{alpha} in the Pathogenesis of Pulmonary Fibrosis
IPF is a prototype fibrotic disease involving abnormal wound healing in response to multiple sites of ongoing alveolar epithelial injury (36–38). Hypoxia, the lack of oxygen, can modulate alveolar epithelial cell homeostasis by promoting significant and adverse effects on epithelial function, including VEGF and surfactant protein production, disruption of cytoskeleton integrity, and the triggering of apoptosis (39). Alveolar hypoxia can also promote macrophage recruitment and enhanced expression of inflammatory mediators (40). Thus, hypoxia could represent a potential fibrotic stimulus through induction of epithelial apoptosis, angiogenesis, and modulation of the inflammatory response. HIF-1 is recognized as a master regulator of hypoxic signaling by activating gene transcription of genes encoding proteins that mediate the cellular adaptive response under hypoxic conditions (32, 41). The HIF-1beta subunit is constitutively expressed, whereas the HIF-1{alpha} subunit is subject to ubiquitination and proteosomal degradation, a process that is inhibited under hypoxic conditions (42). Thus, we examined HIF-1{alpha} expression in BLM-induced pulmonary fibrosis in serial time points following disease progression. The mRNA levels of HIF-1{alpha}, as well as of HIF-1 target genes vegfa, cxcl12, pgk1, were found to be up-regulated upon administration of BLM and the development of pulmonary inflammation and fibrosis. Immunohistochemistry for HIF-1{alpha} confirmed overexpression of HIF-1{alpha}, localizing it mainly at the epithelium. Notably, HIF-1{alpha} (and HIF-1 target genes) overexpression was observed early in the pathologic cascade, before any deterioration of lung architecture and consequent gas exchange problems, indicating an early role for HIF-1 in the development of the modeled disease.

Because the BLM animal model is not fully representative of IPF due to its self-limiting nature and rapidity of development, we then investigated HIF-1{alpha} immunolocalization in lung samples from patients with IPF/UIP and COP/OP, two histopathologic patterns of pulmonary fibrosis with different clinical course and prognosis. We used the pioneering technology of tissue microarrays, which allowed us the simultaneous analysis of up to 85 samples in a single experiment under highly standardized conditions. Thus, all tissue samples were analyzed in an identical, unbiased fashion, with minimal tissue damage and precise positioning of arrayed samples, which not only facilitates manual interpretation of the staining but also serves as an ideal basis for automated analysis amenable to robust statistics. A significant expression of HIF-1{alpha} was observed in IPF and COP samples, which was almost missing from normal lung control samples. Surprisingly, HIF-1{alpha} was localized not only in the hyperplastic alveolar epithelium surrounding areas of active fibrosis but also within areas of IPF lung that appear histologically normal, suggesting that HIF-1{alpha} induction is an early event in the pathogenesis. On the contrary, HIF-1{alpha} was almost absent within fibroblastic foci underlying hyperplastic epithelium, whereas it was present within fibromyxoid lesions characterizing the pattern of COP/OP. In addition, positive staining was localized in type II AECs immediately adjacent to MBs in COP lung.

HIF-1{alpha} expression was colocalized with the up-regulated expression of VEGF, an HIF target gene and a potent inducer of angiogenesis (43). Our findings are in accordance with previous studies showing vascular heterogeneity within the fibrotic lung and, more importantly, they implicate for the first time HIF-1{alpha} as a master regulator of VEGF expression in fibrotic lungs. Although numerous studies have examined so far the interplay between aberrant vascular and matrix remodeling, the relative role of angiogenesis in the initiation and/or progression of the fibrotic cascade still remains elusive and controversial (44). p53, a tumor suppressor gene and an HIF-1{alpha} target, was also found to be up-regulated and colocalized with its transcription factor in areas of alveolar hyperplasia surrounding fibromyxoid lesions in IPF and COP biopsy samples. Upon exposure to stress, such as DNA damage or hypoxia, p53 is stabilized to promote transcription of target genes regulating cell cycle progression, apoptosis, and cellular homeostasis (45). p53 stabilization under hypoxia was shown to be HIF-1 dependent (46), whereas accumulated levels of p53 have been shown to inhibit HIF-1 activity by targeting HIF-1{alpha} for murine double minute 2 (Mdm2)-mediated ubiquitination and proteasome degradation (47). Concerning hypoxia and apoptosis, a unifying picture is still lacking, and the impact of HIF-1{alpha} remains controversial (45). Nevertheless, HIF-1{alpha} has been shown to enhance apoptosis of AECs (48), whereas we showed that the expression pattern of HIF-1{alpha}/p53 correlated with the expression pattern of DFF, a direct indicator of DNA fragmentation and apoptosis (35). Increased apoptosis was clearly noticed in the hyperplastic epithelium, whereas it was almost absent within fibroblastic foci, in the IPF lung. In line with this, fibroblastic foci exhibited enhanced expression of the antiapoptotic protein BCL2, which was almost absent in the alveolar epithelium. Our findings further support the consensus notion of the "apoptotic paradox" in IPF—apoptosis susceptibility in epithelial cells and apoptosis resistance in fibroblasts/myofibroblasts (49)—highlighting the role of the HIF-1{alpha}–p53 axis. Furthermore, the differential expression of the HIF-1{alpha}–p53 apoptotic axis within fibroproliferative areas in IPF and COP lung may explain the resolution of lesions in COP lung in response to corticosteroids and the maintenance of fibroblastic foci, resulting in dismal prognosis despite treatment, and provides a novel mechanism through which highly proliferative fibroblast-like cells exert their resistance to apoptosis by showing a cell-type–specific HIF-1{alpha} activation.

The data presented clearly support for the first time a role of HIF-1{alpha} in the pathogenesis of IPF. HIF-1{alpha} activation under the hypoxic conditions of a fibrotic lung is well expected and could promote perpetuation of the disease, most likely through modulation of preferential apoptosis of epithelial cells and subsequent angiogenesis, as shown in this article. Most important, HIF-1{alpha} overexpression was found early in the pathogenesis of the modeled disease and it was noted at histologically normal areas of human fibrotic lungs, suggesting that HIF-1{alpha} activation can represent an early event and a potential fibrotic stimulus. Animal model studies with epithelium-specific ablation of Hif-1{alpha} will most likely provide further mechanistic insights. HIF-1{alpha} activation could potentially be triggered not only from the hypoxic conditions of the fibrotic lung (and perpetuate disease) but also in normoxic conditions under the influence of various immunomodulatory and/or inflammatory factors that have been shown to play a role in the development of the disease. Insulin growth factor (IGF) and TGF can synthesize HIF-1{alpha} independent of oxygen level via phosphoinositide 3-kinase (PI3K) or mitogen-activated protein kinase (MAPK) pathways (50). TNF has been shown to induce HIF-1{alpha} protein levels (51), most likely through nuclear factor-{kappa}B activation (52) and/or the production of reactive oxygen species (53). Therefore, pharmacologic HIF-1{alpha} inactivation could prove to be beneficial for patients with IPF, both by inhibiting the perpetuating effects of fibrotic tissue hypoxia, as well as by targeting HIF-mediated primary pathogenic stimuli on alveolar epithelial cells.


   Acknowledgments
 
The authors thank Dr. Y. Hayashizaki (Genome Exploration Research Group, RIKEN Genomic Sciences Center, RIKEN Yokohama Institute, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, Japan) for his generous gift of RIKEN microarrays.


   FOOTNOTES
 
Supported by the Society for Respiratory Research and Treatment of Eastern Macedonia and Thrace (D.B.), European Commission Network of Excellence grant QLRT-CT-2001-01407 (V.A.), and Hellenic Ministry for Development grant GSRT-PENED-136 (V.A.). A.T. is a recipient of an annual research grant in respiratory medicine provided by GlaxoSmithKline.

* These authors contributed equally to this article. Back

This article has an online supplement, which is accessible from this issue's table of contents at www.atsjournals.org

Originally Published in Press as DOI: 10.1164/rccm.200705-683OC on August 30, 2007

Conflict of Interest Statement: A.T. is a recipient of a {euro}15,000 respiratory research award provided by GlaxoSmithKline in 2005. V.H. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. T.P. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. N.O. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. A.C. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. G.V. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. E.T. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. A.K. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. D.B. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. V.A. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript.

Received in original form May 8, 2007; accepted in final form August 29, 2007


   REFERENCES
TOP
 ABSTRACT
 AT A GLANCE COMMENTARY
 METHODS
 RESULTS
 DISCUSSION
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