AJRCCM Articles in Press. Published on August 29, 2007 as doi:10.1164/rccm.200705-683OC
Pulmonary Fibrosis Comparative Expression Profiling Suggests Key Role of Hypoxia Inducible Factor 1a
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, 68100 Greece
2
Institute of Immunology, Biomedical Sciences Research Center “Alexander Fleming”, 34 Fleming Street, 16672 Athens/Greece
3
Institute of Biological Research and Biotechnology, National Hellenic Research Foundation. 48 Vasileos Konstantinou Avenue, 11635 Athens/Greece
4
Department of Pathology, Veterans Administration Hospital (N.I.M.T.S), 12 Monis Petraki, 11521 Athens/Greece
*
These authors equally contributed to the work
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:
[email protected]
1 Copyright (C) 2007 by the American Thoracic Society.
Supported by the Society for Respiratory Research and Treatment of Eastern Macedonia and Thrace (D.B.), a European Commission Network of Excellence grant QLRT-CT-2001-01407 (V.A.) and a Hellenic Ministry for Development grant GSRTPENED-136 (V.A.). AT is a recipient of an annual research grant in respiratory medicine provided by GlaxoSmithKline.
Running head: HIF-1a in IPF pathogenesis
Descriptor(s): •
Interstitial lung disease: clinical manifestations & basic mechanisms (75-76)
•
Cell and molecular biology of inflammation and repair: in patients & experimental models (40-41)
Word count: 4565
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org
2
Abstract Rationale: Despite intense research efforts, the etiology and pathogenesis of idiopathic pulmonary fibrosis remain poorly understood. Objective: To discover novel genes and/or cellular pathways involved in the pathogenesis of the disease. Methods/Measurements: Expression profiling of disease progression in an animal model of the disease was compared with all publicly available expression profiles both from human patients and animal models. Hypoxia inducible factor 1a expression was determined in tissue microarrays containing tissue samples of human patients, followed by computerized image analysis. Main results: A highly statistically significant, prioritized list of differentially expressed genes in idiopathic and/or modeled pulmonary fibrosis. Extending beyond target identification, a series of data meta-analyses produced a number of biological hypotheses on disease pathogenesis. From these, the role of hypoxia inducible factor 1a was further explored to reveal 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 histopathological sections, adding substantial confidence to pathological evaluations. More importantly, our results suggest an early primary role of hypoxia inducible factor 1 in alveolar epithelial cell homeostasis and disease pathogenesis, provide insights on the
pathophysiological differences of different interstitial pneumonias, and indicate the importance of assessing the efficacy of pharmacological inhibitors of HIF-1 activity in the treatment of pulmonary fibrosis.
Word count: 240
Introduction Idiopathic interstitial pneumonias (IIPs) are a heterogeneous group of diseases comprising of seven distinct clinical and pathological 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, comprising the histopathologic pattern of usual interstitial pneumonia (UIP). The incidence of IPF is estimated at 6.8-16.3 cases per 100.000 per year in the United States, and the mean survival from the time of diagnosis is 3-5 yr 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 in order to identify genes and/or cellular pathways involved in the initiation and progression of IPF, we utilized the bleomycin-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. Following robust statistical selection of differential expressed genes, 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 inducing factor 1a (HIF1a) in disease pathogenesis, in the animal model as well as in human patients, to reveal an early primary role of HIF-1a in IPF development. Some of the results of these studies have been previously reported in the form of abstracts (16, 17).
Methods Animals All mice 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” under specific pathogen-free conditions, in compliance with the Declaration of Helsinki principles. Mice were housed at 20–22°C, 55 ± 5% humidity, and a 12 h 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 Bleomycin hydrogen chloride (100 mg/kg body weight; 1/3 LD50; Nippon Kayaku Co. Ltd., Tokyo) to 6- to 8-wkold 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 followed by a single passage through an RNeasy column.
Isolated total RNA was reverse transcribed with Superscript Reverse transcriptase II 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 interrogating 18816 genes (manufactured by RIKEN, Yokohama, Japan). Following image analysis all microarray data were subjected to pre-processing, lowess normalization, centering and/or averaging. To select statistically significant differentially expressed genes, and since there is no international consensus on the most appropriate method for statistical selection, we utilized simultaneously the two most widely used: a parametric (ANOVA) and a non parametric one (Kruskal-Wallis), using proprietary algorithms implemented in MATLAB 7.1 Release 14. RT-PCR gene validation was performed using MMLV reverse transcriptase and an oligo-dT(15) primer. Additional details on expression profiling methodologies, including Gene Ontology and pathway analysis, are provided in an on-line supplement.
Human subjects In total, 45 newly diagnosed patients with idiopathic interstitial pneumonias (IIPs) of two different histopathologic patterns (idiopathic pulmonary fibrosis/usual interstitial pneumonia- IPF/UIP, and cryptogenic organizing pneumonia/organizing pneumonia COP/OP) were recruited in our study. The diagnosis of IIPs was based on the consensus statement of the ATS/ERS 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 when included in the study. Paraffin-embedded surgical lung specimens (open lung biopsy or by video assisted thoracoscopic surgery-VATS) from
two different fibrotic regions of each individual were sampled. All patients were fully informed and signed an informed consent form where they agreed to the anonymous usage of their lung samples for research purposes.
Tissue microarrays, immunocytochemistry 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. Following epitope demasking, TMAs were immunostained with a number of antibodies against HIF-1a, SPA, VEGF, p53 and DFF. Signal intensities were quantified with computerized image analysis using a semi-automated system. Statistical analysis was carried out using SPSS 13.0 software. Details on these methodologies can be found in an on-line supplement.
Results 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 bleomycin-(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 d post BLM injection (Fig. E4). The model is very reproducible, employing 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 (Fig. E4). Similarly and 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 5 mice per endpoint were pooled, to minimize biological diversity, and fluorescently labeled employing 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 18816 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, following preprocessing, lowess normalization and quality control (Fig. 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 non-parametric, thus ending up with four different lists of likely Differential Expressed Genes (DEGs). The 1172 genes identified as differentially expressed from all methods (having therefore a very high statistical significance and a very low False Discovery Rate; FDR) 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 semi-quantitative RT-PCR (at three different RNA concentrations in the linear range
of the reaction) in separate pools of five experimental animals and their controls (Fig. E3).
Comparative expression profiling and Meta-analysis To validate our list of DEGs (Table E1) in a highthroughput mode, and in order to compare results from different animal models as well as from human patients, we collected (through databases 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 utilizing the Ingenuity Pathways Analysis (IPA) (Ingenuity Systems CA, USA) software. Comparisons were carried out separately for both human and mouse Entrez-Gene IDs and results were fused together to avoid exclusions due to species non-concordance. Strikingly and although the compared data were obtained from various models and organisms (which conceptually are governed by different pathogenetic mechanisms), utilizing different microarray platforms (containing different genes) and statistical methods, we identified a large number of genes common between our data set and the published ones in pair-wise 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 consists 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.
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 (BEA; 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 utilized as a training set for the software application Endeavour, which performs computational prioritization of “test genes”, based on a set of “training genes” (21). Endeavour uses a number of nine different data sources including both vocabulary-based (such as Gene Ontology; GO) as well as other data sources (such as 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, while the final prioritization is calculated based on order statistics of the individual rankings (21). The statistically more significant (according to Endeavour; p
