Conceived and designed the experiments: JB. Performed the experiments: AR VM. Analyzed the data: AR JB. Wrote the paper: AR JB.
The authors have declared that no competing interests exist.
Lung cancer is a multistage process with poor prognosis and high morbidity. Importantly, the genetics of dysplasia, a facultative cancer, at the edge of malignant transformation is unknown.
We employed laser microdissection to harvest c-Raf1- induced dysplastic as opposed to transgenic but otherwise morphologically unaltered epithelium and compared findings to non-transgenic lung. We then employed microarrays to search genome wide for gene regulatory networks. A total of 120 and 287 genes were significantly regulated, respectively. Dysplasia was exclusive associated with up-regulation of genes coding for cell growth and proliferation, cell-to-cell signalling and interaction, lipid metabolism, development, and cancer. Likewise, when dysplasia was compared with non-transgenic cells up-regulation of cancer associated genes, tight junction proteins, xenobiotic defence and developmental regulators was observed. Further, in a comparison of the data sets of dysplasia vs transgenic and dysplasia vs non-transgenic 114 genes were regulated in common. We additionally confirmed regulation of some genes by immunohistochemistry and therefore demonstrate good concordance between gene regulation and coded protein.
Our study identified transcriptional networks at successive stages of tumor-development, i.e. from histological unaltered but transgenic lungs to nuclear atypia. Our SP-C/c-raf transgenic mouse model revealed interesting and novel candidate genes and pathways that provide clues on the mechanism forcing respiratory epithelium into dysplasia and subsequently cancer, some of which might also be useful in the molecular imaging and flagging of early stages of disease.
The lung cancer epidemic was the subject of a recent editorial
Specifically, eighty percent of the lung cancers are classified as non-small cell lung cancer (NSCLC) whereas the remains 20% are small cell lung cancer (SCLC). Survival of patients diagnosed with non-small-cell lung cancer (NSCLC) is poor; over the last decades the 5-year survival rate remained less than 15%. Survival of lung cancer is, however, strongly associated with the stage of disease at the time of diagnosis. Indeed, 5- year survival rates range from 5% for patients with stage IV lesions to 70% at stage I
Previous studies had defined atypical adenomatous hyperplasia (AAH) as a preinvasive lesion that progresses from low to high grade dysplasia to invasive adenocarcinoma
Here we report findings with a transgenic mouse model, where targeted overexpression of c-RAF to respiratory epithelium resulted in lung cancer development
As of today, the genetic events associated with dysplasia are basically unknown. We therefore aimed for an identification of preneoplastic changes in a c-raf-1 lung cancer disease model. In our study, we used predominately 5 month old mice to gain information at an early stage of tumor development where isolated foci of transformed cells in distinct areas of the lung are visible. By use of laser microdissection pressure catapulting (LMPC) dysplastic cells in well defined lesions
Animals at the age of 5 month displayed morphological changes typical for dysplasia. Note,
Histological analyses of lung tissue from mice transgenic for lung-targeted expression of the cRaf-1 protein. Lung tissue from a 5-month-old SP-C-c-Raf mouse were sectioned at 10 µm, fixed in methanol/acetig acid and stained with H&E. We detected single dysplastic foci, whereas in 10-month-old SP-C-c-Raf mice were multiple foci. A: Overview presentation (magnification: ×50), B: dysplastic foci (magnification: ×200).
120 genes were significantly regulated when transgenic but otherwise unaltered cells were compared with dysplastic cells (Supplementary
Venn diagram for significantly up-regulated genes. Comparison of dysplasia and transgenic unaltered lung tissue with non-transgenic samples. 114 genes were found in dysplasia, respectively, which were at least 2-fold differentially expressed (FDR = 0.001).
non-transgenic | transgenic | dysplasia | |
0 | 18 | 287 | |
18 | 0 | 120 | |
287 | 120 | 0 |
Notably, we used this stringent cut-off because we desired a smaller number of genes with very low false positive rates on which to focus our attention. With an estimated false discovery rate of 0.1 we obtained in dysplasia 2352 significantly regulated genes (867 up-regulated and 1485 down-regulated) compared with transgenic but unaltered cells and 3311 genes (1279 up-regulated and 2032 down-regulated) in dysplasia versus non-transgenic cells. Comparison of these data sets resulted in 2207 genes regulated in common in dysplasia (data not shown).
The expression levels were analyzed by the GCOS and ArrayTrack software. We initially examined the data in a 34,000 genes ×15 sample matrix. The PCA classified the data into 3 major groups, namely non-transgenic, transgenic and dysplasia (
Principal component analysis of transformed cells from transgenic SP-C/c-raf mouse model in comparison to unaltered lung tissue of transgenic and non-transgenic mice were conducted using the autoscaled method within ArrayTrack. orange; dysplasia; blue, transgenic non-tumor sample; green, non-transgenic samples.
We also applied hierarchical gene cluster analyses after SAM analysis. The closest pair of expression values of 2909 differentially expressed genes was grouped together. Consequently, the data are organized in a phylogenetic tree in which the branch lengths represent the degree of similarity between the expression values. A clear segregation of the analyzed groups (dysplasia, transgenic but unchanged lung tissue and non-transgenic lung tissue) was obtained (
The normalized data were used for the Ward's Minimum Variance linkage clustering algorithm. A total of 2909 differentially expressed genes (mean channel intensity >100, FDR: 0.1, Bad Flags: 5) were used in the cluster dendogram to obtain a clear segregation of the analyzed groups (dysplasia, transgenic and non-transgenic). The similarity of gene expression profiles among experimental samples is summarized in a dendogram above the cluster, in which the pattern and the length of the branches reflect the relatedness of the samples. Groups (dysplasia, transgenic and non-transgenic) are presented by columns, and genes in rows. Expression values were colour coded with a red green scale. Green, transcript levels below the median; black, equal to the median and red, greater than median.
We employed the Ingenuity Pathways Analysis software and over 70% of regulated genes were mapped to different networks in the IPA database. These networks describe functional relationships among gene products based on findings presented in peer-reviewed biological pathways. Taken collectively, 12 and 16 networks could be defined for the comparison dysplasia vs transgenic and dysplasia vs non-transgenic cells. Based on pathway analysis the top 2 and top 3 networks reached a score of 25 or higher and contained 15 or more genes in the comparison dysplasia vs transgenic (
Ingenuity networks generated by mapping the focus genes that were differentially expressed between dysplasia and transgenic unaltered lung tissue. Each network is graphically displayed with genes/gene products as nodes (different shapes represent the functional classes of the gene products) and the biological relationships between the nodes as edges (lines). The length of an edge reflects the evidence in the literature supporting that node-to-node relationship. The intensity of the node color indicates the degree of up- (red) or down-regulation (green) of the respective gene. A solid line without arrow indicates protein-protein interaction. Arrows indicate the direction of action (either with or without binding) of one gene to another. IPA networks were generated as follows: Upon uploading of genes and corresponding fold-change expression values (done separately for dysplasia vs transgenic and dysplasia vs non-transgenic differentially expressed genes), each gene identifier was mapped to its corresponding gene object in the IPA Knowledge Base (part of the IPA algorithm). Fold-change expression values were used to signed genes whose expression was differentially regulated; these “focus genes” were overlaid onto a global molecular network contained in the IPA Knowledge Base. Networks of these focus genes were then algorithmically generated based on their connectivity and scored according to the number of focus genes within the network as well as according to the strength of their associations.
Ingenuity networks generated by mapping the focus genes that were differentially expressed between dysplasia and non-transgenic unaltered lung tissue (descriptions see
Cell-to-cell interactions are also important for the regulation of cell proliferation and differentiation. Indeed, expression of cell adhesion molecules is programmed during development to provide positional and migratory information for cells. Disruption of these adhesion events leads to increased cell motility and potential invasiveness trough remodelling of the extra cellular matrix.
We found genes coding for tight junction proteins to be regulated. Identification of the molecular components of the tight junction evidenced that, in addition to their structural functions, these proteins play central roles in regulating cellular proliferation and differentiation. Under normal conditions, tight junctions act to segregate a growth factor within the apical membrane compartment away from its receptor in the basolateral membrane compartment, thereby precluding receptor activation
Next to claudins, we observed up-regulation of gap junction proteins in dysplasia by 11 and 9-fold as well (Gja3, gap junction protein alpha-3 and Gjb4, gap junction protein beta-4). Alteration in gap junctional intercellular communication results in the inability of cells to receive apoptotic, growth suppressing or differentiation signals from their neighbours. Specifically, connexins, a family of 20 trans-membrane proteins in humans, comprise the main subunits of gap junctions; – these specialised clusters of intercellular channels allow adjacent cells to directly share ions and hydrophilic molecules of up to ∼1 KDa in size. Gap junctional intercellular communication is thought to control tissue homeostasis and to coordinate cellular processes such as proliferation, migration and differentiation. Disruption of gap junctional intercellular communication or mutations in connexins is associated with several human diseases. Notably, gap junction expression is often up-regulated in hyperplastic tissues.
Furthermore, lipid metabolism is altered in cancer, including loss of body fat early in tumor growth, induction of hyperlipidemia and changes in a variety of serum lipid and lipoprotein fractions. Thus, cancer patients were found to have higher rates of fat oxidation when compared with healthy individuals with equal weight loss
In this regard we also found changes in the expression of cell surface glycolipids in dysplastic cells as compared to unaltered transgenic or to non-transgenic lung tissue. For example, Sult2b1 (sulfotransferase family 2b, member 1) was significantly over expressed. This enzyme catalyzes the sulphate conjugation of pregnenolone and cholesterol. Moreover, St8sia6 (St8 alpha-n-acetyl-neuraminide alpha-2,8-sialyltransferase 6), an enzyme which synthesize sialylglycoconjugates of glycolipids was significantly up-regulated, as well. These changes may result in new surface antigens and glycolipids in addition to altered cell-cell and cell-extracellular matrix communication followed by decreased adhesiveness and invasiveness through normal tissue barriers.
Further evidence for changes in glycosylation patterns during transformation of normal cells into malignant ones stems form the up-regulation of B4galt6 (UDP-GAL:Beta-GlcNAc Beta-1,4-Galactosyltransferase, polypeptide 6), Fut2 (Fucosyltransferase 2), Gpc6 (Glypican6) and Orm1 (orosomucoid1) as observed in dysplastic cells. The synthesis of new carbohydrate chains in dysplastic cells are due to activation of glycosyltransferase such as B4galt6, which catalyzes the reaction UDP-galactose and N-acetylglucosamine for the production of galactose beta-1,4-N-acetylglucosamine. Strikingly, these carbohydrates are absent or have low activity in normal cells. Moreover, the secretor enzyme Fut2, an
As the expression of glycoproteins is increased in many cancers, it was of no surprise that Orm1 was 11.1-fold overexpressed in dysplastic cells. Orosomucoid 1 belongs to a group of highly glycosylated glycoproteins and appears to function in modulating the activity of the immune system. Furthermore, we found glypican 6 to be induced (9.9-fold). Notably, this protein belongs to a family of glycosylphosphatidylinositol-anchored heparin sulphate proteoglycans. It is known that proteoglycans are high-molecular-weight glycoproteins and interact via their multiple binding domains with many other structural macromolecules. They are bound together with extracellular matrix components and act as cell adhesion factors by promoting organization of actin filaments in the cell cytoskeleton. Proteoglycans have been shown to undergo alterations during malignant transformation resulting in disrupted interaction between the extra cellular matrix and the transformed cells to simplify the invasion into the surrounding tissue.
We further investigated genes involved in developmental processes and found 8 genes to be 3.6–15.4-fold up-regulated in dysplasia as compared to transgenic lung tissue. Notably, 22 genes were 5.4–148.4-fold up-regulated in dysplasia as compared to non-transgenic lung tissue.
In particular Hnf4α (hepatocyte nuclear factor 4-alpha), Foxa3 (forkhead box gene A3) and Foxp2 (forkhead box gene P2) were significantly over expressed in dysplasia (4.5–12.8-fold). These genes codes for proteins that belong to a family of winged-helix/forkhead DNA binding domain transcription factors and are expressed in defined neural, intestinal, and cardiovascular cell types during embryogenesis and differentiation of epithelium. Hnf4α plays a key role in a transcriptional hierarchy and controls the expression of other transcription factors such as Hnf1 (hepatocyte nuclear factor 1)
Finally, we searched for expression of oncogenes and found the proto-oncogene Ros1 (v-ros avian ur2 sarcoma virus oncogene homolog 1), which encodes a transmembrane protein with a sequence typical of tyrosine kinases to be 5.8-fold up-regulated in dysplasia. Ectopic expression of this receptor tyrosine kinase Ros1 has been reported in many tumors of the central nervous system and recently in lung and stomach cancers
To confirm the microarray data, expression of 8 genes in dysplasia, transgenic and non-transgenic samples was examined by quantitative real-time PCR using TaqMan Technology. qRT-PCR confirmed that amphiregulin (Areg), epiregulin (Ereg), fetuin beta (Fetub), apolipoprotein A1 (Apoa1), claudin 2 (Cldn2), hepatic nuclear factor 4, alpha (Hnf4α), glutathione S-transferase, alpha 4 (Gsta4) and forkhead box A3 (Foxa3) were up-regulated in dysplasia (Supplementary
The expression data generated by the oligonucleotid array and RT-PCR agreed well, therefore supporting the reliability of the array analysis (
Summary of differential expression of the eight genes verified by quantitative RT-PCR in comparison with Oligonucleotid-Array analysis. The data were analyzed statistically using Student's t-test (*p<0.05; **p<0.01). Error bars indicate standard deviation of five samples and two independent assays for each gene.
In order to validate array data as well as to add subcellular localization, we performed immunohistochemical staining for some differentially expressed genes. Immunohistochemistry using antibodies towards amphiregulin, epiregulin, hepatocyte nuclear factor 4 alpha, forkhead box A3 and forkhead box P2 showed a consistent difference in immunoreactivity between dysplastic and transgenic but otherwise unaltered cells (
Immunohistochemical staining in dysplasia in the presence of primary antibody (a) 10× magnification, b) 40× magnification) and in the presence of primary antibody preincubated with blocking peptide (c). 1 = amphiregulin (AREG), 2 = epiregulin (EREG), 3 = hepatocyte nuclear factor 4 alpha (HNF4α), 4 = forkhead box 3a (FOX3A/HNF3γ), 5 = forkhead box P2 (FOXP2). Strong, mainly cytoplasmic immunoreactivity was found in dysplastic cells, whereas unaltered cells shown only weak positivity using the amphiregulin antibody (1). Epiregulin immunostaining showed a cytoplasmatic pattern of immunoreactivity in dysplastic cells (2). The FOX3A antibody (3) showed strong nuclear positivity in dysplastic cells, while unaltered transgenic cells were negative. The HNF4α antibody showed nuclear immunoreactivity (4). Nuclear positivity was also restricted to dysplastic cells with no staining in unaltered transgenic cells using the FOXP2 antibody (5).
This study aimed for a better understanding of the genetic events associated with dysplasia in a genetic model of lung cancer induced by overexpression of the c-Raf-1 kinase.
We aimed to determine the regulatory gene networks associated with dysplasia. This enabled identification of candidate genes specifically regulated at the edge of malignant transformation. Based on hierarchical gene cluster analysis and principal-component analysis we were able to distinguish the dysplastic cells from transgenic and non-transgenic lung tissue.
To further validate the microarray data, we employed qRT-PCR of selected genes, and confirmed by immunohistochemistry regulated proteins in dysplastic foci.
Notably, RT-PCR data is suggestive for the microarray to underestimate changes in the gene expression even though both methods supported the general directions of changes. An important finding of our study was that about 10% of the differentially expressed genes regulated by ≥2 fold are already known to be associated with lung cancer.
Specifically, in dysplasia genes coding for nucleic acid metabolism or cell cycle regulation were unchanged but two EGF-ligands, namely amphi- and epiregulin were highly significantly up-regulated. These ligands enable autocrine loops to foster undue EGF-signaling. Notably, Areg (amphiregulin) is a 252-amino acid transmembrane glycoprotein and consist of two major soluble forms of 78 and 84 amino acids, respectively. Areg was originally isolated from conditioned medium of the human breast carcinoma cell line MCF-7 and was found to be a heparin-binding growth factor. Areg promotes neoplastic growth in mammary epithelial cells, fibroblasts, and keratinocytes
A further growth factor strongly induced in dysplasia is Ereg (epiregulin). The gene codes for a transmembrane precursor before being proteolytically cleaved to release a 46-amino-acid activated protein
The fact that transformed cells grow faster than unaltered cells is also sustained by our data with regard to the number and strength of up-regulated genes affecting cellular growth. Specifically, the cell surface membrane proteins play an important role in the behaviour of cells to allow for communication with other cells, cell movement and migration, adherence to other cells or structures and recognition by the immune system. Alterations of the plasma membrane in malignant cells may thus be inferred from a variety of properties that characterize their growth and behaviour.
Our study is suggestive for changes in the glycosylation patterns and in cell surface glycolipids. Changes in glycosylation can include the presence of new carbohydrate structures that are not detected in the normal epithelial cells and/or short carbohydrate chains usually masked by larger epitopes. For example, we found St8sia6 to be up-regulated in dysplasia. St8sia6 (alpha-2,8-sialyltransferase VI) belongs to a family of sialyltransferases that synthesize sialylglycoconjugates. The most frequently described aberrant glycosylation in cancer cells include the synthesis of highly branched and heavily sialyted glycans
Changes of the glycosylation pattern in dysplasia enables further variability in molecular assembly of membranes and offers a wide range of flexibility in response to cellular environment. Their detection on the cancer cell surface may provide useful diagnostic or prognostic information but until now is incompletely understood and therefore requires further elucidation.
Changes in the expression pattern of cell-adhesion molecules and components of intercellular junctions are related to loss of epithelial organization, proper cell layer and tissue polarity. With regard to adhesion molecules, we found up-regulation of Chl1 (cell adhesion molecule with homology to L1CAM), a member of the L1 gene family of neural cell adhesion molecules, and of several claudins as exemplified by the up-regulation of Cldn2 (claudin2), Cldn4 (claudin4), and Cldn8 (claudin8), which are important components of the tight junctions. Adhesion properties greatly impacts cell-to-cell interaction and growth of cancer cells.
In this regard, the cytoskeleton, which represents a complex of interconnected fibrillar elements, has been determined as an important factor in mediating adhesion-independent and dependent signalling. During morphogenesis, they determine cell shape and polarity, and promote stable cell-cell and cell-matrix adhesions through their interactions with cadherins and integrins, respectively. Surprisingly, in dysplastic cells we did not identify regulation of genes involved in the cytoskeleton.
Instead, we identified changes in cell to cell communication. In our study Gjb3 (gap junction membrane channel protein beta 3) and Gjb4 (gap junction membrane channel protein beta 4) were significantly up-regulated. It was shown that the gap junctional intercellular communication is a form of cell–to-cell signalling thereby mediating the exchange of small molecules between neighbouring cells
We also focused on genes coding for surfactant lipids in respiratory epithelium. Surfactant is a complex mixture of lipids and proteins that reduces surface tension at the air–liquid interface and prevents alveolar collapse during respiration. Four surfactant proteins (SP) with unique properties have been identified. SP-A and SP-D are relatively hydrophilic proteins and contribute to innate defence of the lung and surfactant homeostasis. SP-B and SP-C are hydrophobic proteins that enhance surface-active properties of surfactant phospholipid films
We found Apoa1 (apolipoprotein A-1), Adcyap1 (adenylate cyclase activating polypeptide 1), Ltb4dh (leukotriene B4 12-hydroxydehydrogenase), Fst (follistatin), Inhbb (inhibin beta-B), Clu (clusterin), Hnf4α (hepatic nuclear factor 4, alpha), Prokr1 (prokineticin receptor 1), Pla2g1b (phospholipase A2, group IB), Sult2b1 (sulfotransferase family, cytosolic, 2B, member 1), St8sia6 (ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 6), Cyp1b1 (cytochrome P450, family 1, subfamily b, polypeptide 1) and the Pthlh (parathyroid hormone-like peptide) to be regulated with inferred roles in lipid metabolism.
For example, Adcyap1 (adenylate cyclase activating polypeptide 1) is a member of the secretin/glucagons/vasoactive intestinal peptide (VIP) family of peptides. It has been localized by immunohistochemistry to the central nervous system, digestive tract and was shown to exhibit a variety of biological activities. It is involved in synthesis of sulfatides, glycolipid, sphingolipid and plays a role in the accumulation of estrogen and progesterone. It was also reported that Adcyap1 can regulate the proliferation and differentiation
Because cellular growth and differentiation is depended on highly co-ordinated transcriptional networks we also searched for master regulatory proteins of respiratory epithelium. Notably, morphogenesis and branching of respiratory epithelium depends on the timely expression and activity of transcription factors. Dynamic changes in transcriptional activation of lung-specific genes are required to get an appropriate positioning of respiratory epithelial cells with the mesenchyme-derived endothelial cells. If this critical process is disturbed, this can lead to malignancy
In our study some of these transcription factors were highly significantly regulated in dysplasia i.e. Hnf4α, Foxa3 and Foxp2. Notably, Hnf4α (hepatocyte nuclear factor 4 alpha), a member of the steroid/thyroid hormone receptor superfamily is a transcriptional activator
Among the genes regulated by HNF4α we found at least 11 target genes up-regulated in dysplasia i.e. Arg2 (arginase type 2), Apoa1 (apoliporotein A1), Cyp1b1 (cytochrome P450, family 1, subfamily B, polypeptide 1), Cldn2 (claudin 2), Fetub (fetuin beta), Gpx2 (glutathione peroxidase 2), Gsta4 (glutathione S-transferase A4), Gpc6 (glypican 6), Hpn (hepsin), Orm1 (orosomucoid 1) and Lad1 (ladinin1)
Likewise, we found Foxa3 (forkhead box A3) to be induced 7.5-fold. This protein belongs to a group of endoderm-related developmental factors that are members of the forkhead box (Fox) superfamily of transcription factors. They were first discovered by their ability to bind to promoters of liver specific genes encoding α1-antitrypsin and transthyretin
We also found Foxp2 (forkhead box P2) to be up-regulated 4.5-fold. This protein is a member of the new subfamily of winged-helix/forkhead DNA binding domain transcription factor. It could be shown that Foxp2 are expressed in the lung restricted to the distal epithelium and may regulate lung epithelial-specific gene transcription during embryonic development
Additionally, we have confirmed the up-regulation of Areg, Ereg, Hnf4α, Foxa3 and Foxp2 by immunohistochemical staining. The immunostaining showed a distinct pattern of immunoreactivity in dysplastic cells with no staining in unaltered transgenic cells. For example, immunohistochemical staining of Areg showed a cytoplasmic immunoreactivity in dysplastic cells but not each cell expressed this molecule equally strong.
In dysplasia, however, we could not evidence altered transcriptional networks of the MAPK pathway and we did not observe regulated genes coding for cell cycle or nucleotide metabolism. Both processes are highly active in tumor cells but do not play a big role in dysplastic cells prior to malignant transformation. In this context we found two EGF ligands, Areg and Ereg to be specifically up-regulated in dysplasia. While amphiregulin binds exclusively to EGFR epiregulin binds to HER4 and mediates aberrant activation of this receptor. Indeed, exaggerated EGF tyrosine kinase activity is a probable cause for lung cancer. We now evidence de novo expression of two ligands of EGFR in dysplasia that are frequently over expressed in cancers. We did, however, not observe increased transcript expression of the cancer stem cell markers CD44, CD133 and the epithelial cell adhesion molecule EpCAM. This may support the notion that dysplasia is a facultative cancer where additional events need to occur that enable malignant transformation. Taken collectively, the whole genome expression data provided important information in the multistage process of lung cancer. Our study revealed interesting novel candidate genes and pathways that dissected the programme of respiratory epithelium from transgenic into dysplasia and eventually lung cancer. In the second part of our study we report the additional genetic events that take place from dysplasia to malignantly transformed cells and thus provide a molecular rational for the multistage process in lung cancer.
Animals were kept according to the Public Health Service Policy on Humane Care and Use of Laboratory Animals and SP-C/c-raf transgenic mice were obtained from the laboratory of Prof. Ulf Rapp (University of Würzburg, Germany), who bred the mice in the C57BL/6/DBA/2 hybrid background. We kept the SP-C/c-raf transgenic mice in the C57BL/6 background for at least five generations.
Lung samples were derived from 5 SP-C/c-raf mice (aged 5–10 months); dysplastic and unaltered lung tissue were always isolated from the same dysplasia-bearing transgenic mouse (aged 5–7 months). Endogenous normal lung tissue was studied of 5 non-transgenic mice (aged 7–10 months). The non-transgenic littermates (wild-type) served as control for transgenic effects.
Mice were sacrificed and the lung tissues were immediately frozen on dry ice and stored at −80°C until further analysis.
The histopathological diagnosis was based on routinely processed hematoxylin-eosin stains.
From each frozen lung tissue 10-µm thick sections were prepared and transferred on polyethylene napthalate foil-covered slides (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany).
The sections were fixed in methanol/acetic acid and stained in hematoxylin. The desired cells were microdissected using the PALM MicroLaser systems (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany) and collected in an adhesive cap (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany). Microdissected cells were resuspended in a guanidine isothiocyanate-containing buffer (RLT buffer from RNeasy MikroKit, Qiagen, Santa Clarita, CA, USA) with 10 µl/ml β-mercaptoethanol to ensure isolation of intact RNA. Approximately an area of 6×106 µm2 were pooled from a specific layer of interest in the same animal and used for RNA extraction.
Following microdissection, total RNA-extraction was performed with the RNeasy Micro Kit (RNeasy MicroKit Qiagen, Santa Clarita, CA, USA) according to the manufacturer's instruction. A standard quality control of the total RNA was performed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, USA).
Total RNA (median: 175 ng; range: 150–200 ng) was used to generate biotin-labeled cRNA (10 µg) by means of Message Amp aRNA Premium Amplification Kit (Ambion, Austin, TX). Quality control of cRNA was performed using a bioanalyzer (Agilent 2001 Biosizing, Agilent Technologies). Following fragmentation, labeled cRNA of each sample was hybridized to Affymetrix GeneChip® Mouse Genome 430 2.0 Arrays covering over 34.000 genes and stained according to the manufacturer's instructions.
Array data was normalized using scaling or per-chip normalization to adjust the total or average intensity of each array to be approximately the same.
Microarray chips were analyzed by the GCOS (GeneChip Operating Software) from Affymetrix with the default settings except that the target signal was set to 500 and used to generate a microarray quality control and data report. CEL files exported from GCOS were uploaded into ArrayTrack software (National Center for Toxicological Research, U.S. FDA, Jefferson, AR, USA (NCTR/FDA)) and normalized using Total Intensity Normalization after subtracting backgrounds for data management and analysis. ArrayTrack software includes some tools common to other bioinformatics software (e.g., ANOVA, T-test and SAM).
To compare the normalized data from dysplasia, normal lung tissue from transgenic mouse, tumor cells and non-transgenic of different mice, we used the Significance Analysis of Microarrays (SAM) algorithm (ArrayTrack), which contains a sliding scale for false discovery rate (FDR) of significantly up- and down-regulated genes
Two clustering approaches were used to determine components of variation in the data in this study as follows.
Principal-component analysis (PCA) that was used to obtain a simplified visualization of entire datasets. PCA is a useful linear approach to obtain a simplified visualization of entire datasets, without losing experimental information (variance). PCA allowed the dimension of complex data to be reduced and highlights the most relevant features of a given dataset to be highlighted.
Hierarchical gene clustering (HCA) where the data points were organized in a phylogenetic tree in which the branch lengths represent the degree of similarity between the values. HCA were conducted using the ward's minimum variance linkage clustering algorithm within ArrayTrack. After normalisation and SAM analysis a total of 2909 significant genes were used for hierarchical clustering.
Lists of significantly differentially expressed genes were uploaded to Ingenuity Pathways Analysis (IPA, Ingenuity Systems Inc., Redwood City, CA, USA) (
Additionally, Venn diagrams were used to examine the overlap of resulting lists of genes differentially expressed between the different sample sets.
Corroboration of RNA expression data was performed by realtime PCR using the ABI PRISM 7500 Sequence Detection System Instrument (Applied Biosystems, Applera Deutschland GmbH, Darmstadt, Germany). Total RNA (200 ng) underwent reverse transcription using an Omniscript RT Kit (Qiagen, Santa Clarita, CA, USA) according to the manufacturer's instruction. PCR reactions were performed according to the instructions of the manufacturer using commercially available assays-on-demand (Applied Biosystems, Applera Deutschland GmbH, Darmstadt, Germany). CT values were calculated by the ABI PRISM software and relative gene expression levels were expressed as the difference in CT values of the target gene and the control gene Actin beta.
Each tumor section (8 µm in thickness) was deparaffinized in roti-histol for 2 times 8 minutes, these were dehydrogenated by means of a descending alcohol row. The following incubation steps were accomplished: 2 times 3 minutes in 96% ethanol, 2 times 2 minutes in 70% Ethanol, and 2 minutes in Aqua dest. The pre-treated slices were heated in a autoclave for 15 min in citrate buffer submitted of an antigen retrieval before the colouring first. For blocking endogenous peroxidase activity the slices covered for 30 minutes with 3% hydrogen peroxide/Methanol peroxidase blocking solution. After a wash step, the slices were incubated with the primary polyclonal anti-body against AREG, EREG, HNF4α, FOXA3 and FOXP2 (Santa Cruz, Santa Cruz Biotechnologys Inc., CA, USA) for 45 minutes. After washing, a streptavidin horseradish peroxidase detection kit (Envision DAKO, Hamburg, Germany) containing 3,3′-diaminobenzidine solution as substrate was used for immunohistochemical staining according to the manufacturer's instructions. Harris Hämatoxylin was used as the counterstaining.
The specificity of the immunostaining was confirmed by negative control staining using mouse nonimmune immunoglobulin G instead of the primary antibody.
List of genes with changed expressions that are significantly overexpressed in dysplasia versus non-altered transgenic mice: 120 significantly regulated genes. This table shows the RefSeq transcript IDs, Unigene IDs, gene titles, gene symbols, and fold changes of the significantly regulated genes.
(0.18 MB DOC)
List of genes with changed expressions that are significantly over- or under-expressed in dysplasia versus non-transgenic mice: 287 significantly regulated genes. This table shows the RefSeq transcript IDs, Unigene IDs, gene titles, gene symbols, and fold changes of the significantly regulated genes.
(0.41 MB DOC)
List of genes with changed expressions that are significantly over- or under-expressed in unaltered transgenic versus non-transgenic mice: 18 significantly regulated genes. This table shows the RefSeq transcript IDs, Unigene IDs, gene titles, gene symbols, and fold changes of the significantly regulated genes.
(0.04 MB DOC)
Ingenuity - Canonical Pathways. This figure shows the canonical pathways which were overrepresented in the group of significantly regulated genes in dysplasia versus transgenic mice.
(2.13 MB DOC)
Quantitative real-time PCR. Real-time PCR curves of eight genes assessed by Taqman technology as well as of the reference gene ACTB of a representative experiment are shown. The differences of the Ct values of target and ACTB (deltaCT) are indicated. The smaller the deltaCT, the higher the relative expression level of the target mRNA.
(6.75 MB DOC)
Quantitative real-time PCR.
(6.60 MB DOC)