Construction of a computable cell proliferation network focused on non-diseased lung cells
- Jurjen W Westra1,
- Walter K Schlage2,
- Brian P Frushour1,
- Stephan Gebel2,
- Natalie L Catlett1,
- Wanjiang Han3,
- Sean F Eddy1,
- Arnd Hengstermann2,
- Andrea L Matthews1,
- Carole Mathis3,
- Rosemarie B Lichtner2,
- Carine Poussin3,
- Marja Talikka3,
- Emilija Veljkovic3,
- Aaron A Van Hooser1,
- Benjamin Wong1,
- Michael J Maria1,
- Manuel C Peitsch3,
- Renee Deehan1 and
- Julia Hoeng3Email author
© Westra et al; licensee BioMed Central Ltd. 2011
Received: 20 February 2011
Accepted: 2 July 2011
Published: 2 July 2011
Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work.
To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data.
To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.
The immediate goal of this work was to construct a computable network model for cell proliferation in non-diseased lung. Lung epithelial cells are stimulated to proliferate upon injury as a mechanism for renewal . Alterations in the control of cell proliferation play a pivotal role in lung diseases including cancer, COPD, and pulmonary fibrosis. Cancer results from both gains of inappropriate growth signaling as well as the loss of mechanisms inhibiting proliferation . Hyperplasia of mucus-producing goblet cells and airway smooth muscle contribute to COPD pathology . Pulmonary fibrosis is characterized by excessive proliferation of lung fibroblasts, resulting in impaired lung function . Thus, increasing the molecular understanding of the regulation of cell proliferation in the lung will serve to aid in the treatment and prevention of several lung diseases.
Comprehensive and detailed pathway or network models of the processes that contribute to lung disease pathology are needed to effectively interpret modern "omics" data and to qualitatively and quantitatively compare signaling across diverse data sets. The ultimate goal of this work is to evaluate the biological impact of xenobiotics and environmental toxins on experimental systems such as lung cell cultures or whole rodent lung. Network models representing key biological processes as they occur in non-diseased cells are crucial for this effort. Tumor cell lines and other cell contexts representing advanced disease states have genetic changes and altered signaling networks that may not be present in normal, non-diseased cells. Thus, the network model described in this report is focused on biological signaling pathways expected to be functional and to regulate cell proliferation in non-diseased lung.
Many different approaches can be taken to develop biological models. Biological pathways such as those captured by KEGG (Kyoto Encyclopedia of Genes and Genomes)  are manually drawn pathway maps linking genes to pathways; KEGG pathways have limited computational value for analysis of systems biology data sets beyond directly mapping observed changes to pathways and assessing over-representation. Dynamic biochemical models, such as those commonly encoded in SBML (systems biology markup language) , are useful for assessing the dynamic behavior of biochemical systems. However, because dynamic biochemical models require a large number of parameters, they are generally limited to representation of simplified and well-constrained biological processes, and are thus not well suited to the comprehensive evaluation of complex systems consisting of multiple inter-related signaling processes.
Reverse Causal Reasoning (RCR) is a systems biology methodology that evaluates the statistical merit that a biological entity is active in a given system, based on automated reasoning to extrapolate back from observed biological data to plausible explanations for its cause. RCR requires an extensive Knowledgebase of biological cause and effect relationships as a substrate. RCR has been successfully applied to identify and evaluate molecular mechanisms involved in diverse biological processes, including hypoxia-induced hemangiosarcoma, Sirtuin 1-induced keratinocyte differentiation, and tumor sensitivity to AKT inhibition [7–9]. These previously published applications of RCR to experimental data have involved the analysis of diseased states. Here, we apply RCR to evaluate the biological process of cell proliferation in normal, non-diseased pulmonary cells. The lung-focused Cell Proliferation Network described in this paper was constructed and evaluated by applying RCR to published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and related cell types.
The Cell Proliferation Network reported here provides a detailed description of molecular processes leading to cell proliferation in the lung based on causal relationships obtained from extensive evaluation of the literature. This novel pathway model is comprehensive and integrates core cell cycle machinery with other signaling pathways which control cell proliferation in the lung, including EGF signaling, circadian clock, and Hedgehog. This pathway model is computable, and can be used for the qualitative systems-level evaluation of the complex biological processes contributing to cell proliferation pathway signaling from experimental gene expression profiling data. Construction of additional pathway models for key lung disease processes such as inflammatory signaling and response to oxidative stress is planned in order to build a comprehensive network of pathway models of lung biology relevant to lung disease. Scoring algorithms are under development to enable application of this Cell Proliferation Network and other pathway models to the quantitative evaluation of biological impact across data sets for different lung diseases, time points, or environmental perturbations.
Results and Discussion
Cell Proliferation Network construction overview
Cell Proliferation Network content
Cell Proliferation Network statistics
Network boundaries, assumptions, and structure
When constructing the model using content derived from the Selventa Knowledgebase, some initial boundary conditions and a priori assumptions relating to tissue context and biological content were established to constrain the substance of the model to its most salient details.
Tissue context boundaries
Our goal was to build a network model that captures the biological mechanisms controlling cell proliferation in non-diseased mammalian lung. To maintain the focus of the network on these elements, we determined and applied a set of rules for selecting network content. Ideally, all causal relationships comprising the network would be supported by published data from experiments conducted in non-diseased human, mouse, or rat whole lung. Thus, causal relationships with literature support coming from whole lung or normal lung cell types (e.g. bronchial epithelial cells, alveolar type II cells, etc.) were prioritized. However, in many cases, the results of the relevant detailed experiments have not been published. Thus, as a second priority, relationships derived from cell types that are found in the normal lung (fibroblasts, epithelial/endothelial cells), but not explicitly from lung were used. The network was focused on relationships derived from experiments done in human systems, though relationships from mouse and rat were also included. Canonical mechanisms, such as the regulation of E2F transcription factor family members by the retinoblastoma protein RB1, were included in the network even if literature support explicitly demonstrating the presence of the mechanism in lung-related cells was not identified. It was assumed that the individual relationships within canonical mechanisms (for example CDKN1A inhibiting the kinase activity of CDK2) can occur in the lung. However, if canonical relationships with specific lung contexts were found in the literature, they were used. If needed for completing critical mechanisms within the network, relationships with other tissue contexts were used, provided they reflected proliferative processes that can occur in the normal lung. Causal relationships derived from embryonic tissue contexts were included, as the embryonic lung represents a model for non-diseased lung cell proliferation [10, 11]. As a general rule, the use of causal relationships with tissue contexts from immortalized cell lines was limited to providing the molecular details for mechanisms in the network when these specific relationships were not available from normal cells; immortalized cell lines are highly amenable to experimental manipulation and are thus a valuable system for identifying signaling pathway details that are most likely conserved in normal cells. Relationships with tissue contexts derived from tumors or other diseased tissues were used sparingly in order to focus the content of the network to the pathways involved in normal lung cell proliferation.
Biological mechanism boundaries
The Cell Proliferation Network represents the biological mechanisms leading to cell proliferation in a specific organ, the lung. Thus, biological boundaries were designed to focus the network on the cellular processes and signaling pathways with a described role in regulating lung cell proliferation, with a particular emphasis on the proximal connections to core cell cycle machinery. Following an exhaustive search of the literature, a set of pathways were selected for inclusion, while other pathways with less direct relevance for proliferation were excluded, creating the mechanistic biological boundaries of the network. These biological mechanism boundaries were used to ensure that the Cell Proliferation Network represented the most relevant proliferative mechanisms that occur in the non-diseased lung.
Cell proliferation can be directly or indirectly influenced by a wide range of factors, including external biological stimuli (e.g. growth factors) and internal metabolic alterations (e.g. ATP homeostasis). The broad range of factors that can influence cell proliferation, coupled with the observation that many proteins involved in regulating cell proliferation have varying degrees of biological promiscuity (e.g. p53 also regulates the DNA damage response and apoptosis [12, 13]), necessitated some additional delineations framing the biological boundaries of the network. Therefore, in addition to defining the biological content included in the network, certain processes and pathways were explicitly excluded. Specifically, inflammatory cytokine signaling, the p53-dependent DNA damage response, and pathways regulating the induction of/escape from apoptosis were not included in the network. Finally, components of the core replication, transcription, and translation machinery (DNA/RNA polymerases, ribosomes, etc.) were considered outside the boundaries of the network.
Includes canonical elements of the core machinery regulating entry and exit from the mammalian cell cycle, including but not limited to cyclin, CDK, and E2F family members.
Includes common extracellular growth factors involved in regulating lung cell proliferation, namely EGF, TGF-beta, VEGF, and FGF family members. The EGF family members EGF and TGF-alpha play critical roles in regulating the proliferation of airway epithelial cells through EGF receptor activation [14, 15]. FGF7 and FGF10, largely through activation of FGFR2-IIIb signaling, stimulate lung epithelial cell proliferation as well as regulate branching morphogenesis in the developing lung [16, 17]. VEGF, a key regulator of normal angiogenesis and involved in regulating proliferation of human fetal airway epithelial cells,  was also included.
Intra- and Extracellular (IC/EC) Signaling
This block contains diverse elements of the common intra- and extracellular pathways involved in mediating lung cell proliferation, including the Hedgehog, Wnt, and Notch signaling pathways. Hedgehog signaling regulates cell proliferation and branching morphogenesis in the developing mammalian lung [19, 20]. Similarly, Notch signaling controls lung cell proliferation as well as differentiation . Elements of the Wnt signaling pathway are important for mediating the proliferative processes seen following lung injury . The remaining areas covered by this building block are calcium signaling, MAPK, Hox, JAK/STAT, mTOR, prostaglandin E2 (PGE2), Clock, and nuclear receptor signaling as relevant to lung cell proliferation.
Includes the signal transduction pathways leading to cell proliferation that originate from the interactions of common cell adhesion molecules (including ITGB1 complexes with ITGA1-3 chains) and extracellular matrix components (specifically collagen, fibronectin, and laminin).
Includes the main known epigenetic modulators of lung cell proliferation including the histone deacetylase (HDAC) family and DNA methyltransferase (DMT) family member DNMT1. For this block, connections from these epigenetic mediators to the core cell cycle components (e.g. CCND1, CDKN2A) were prioritized.
Network verification and expansion
Selection of published cell proliferation transcriptomic data sets for verification
Data sets analyzed for verification and expansion of the cell proliferation literature model
Data Set ID
glucocorticoid receptor null
Affymetrix Human Genome U133A 2.0
Affymetrix Mouse Genome U74A v2
Affymetrix Mouse Genome 430A
GE Healthcare CodeLink Mouse Whole Genome Bioarray
day 18.5 embryonic lung
day 18.5 embryonic lung
# State changes
Reverse Causal Reasoning on transcriptomic data sets identifies proliferative mechanisms and verifies the literature model
RCR-predicted hypotheses appearing within the Cell Cycle block of literature model nodes provided verification that the proximal mechanisms regulating cell proliferation were 1) correctly present in the literature model and 2) detectable using this computational approach. However, equally important were the predictions for nodes in the peripheral building blocks, which 1) identify additional mechanistic detail for the proliferative pathways modulated and 2) can be used together with the hypothesis predictions in the core Cell Cycle block to assess the coverage of the literature model by all four data sets (see "Evaluation of the Cell Proliferation Network"). For the purposes of highlighting the peripheral mechanisms involved in lung cell proliferation, hypotheses within the growth factors building block were especially well represented, including predicted increases in PDGF, FGFs 1, 2 and 7, HGF, and EGF and its receptors (Figure 7). In particular, hypotheses for decreased FGF1 and FGF7 (also known as KGF (keratinocyte growth factor)) were predicted in the EIF4G1 data set, directionally consistent with the experimental observation of decreased proliferation observed in MCF10A epithelial cells. Both FGF1 and FGF7 are critical for promoting epithelial cell proliferation in the developing respiratory epithelium [36, 37]. Several EGF receptor complexes and their ligands, which also play central roles in regulating normal lung cell proliferation, were also predicted as hypotheses in this analysis [38–40]. These hypotheses were especially noticeable in the RhoA data set, which used NIH3T3 cells as an experimental model. Although NIH3T3 cells normally express low levels of EGF family receptors and are minimally responsive to EGF, RhoA activation has been shown to decrease EGFR endocytosis, which could lead to increased levels of EGF family responsiveness in RhoA overexpressing cells [41–44]. Hypotheses from many of the other blocks of the cell proliferation literature model are also predicted in directions consistent with the observed direction of cell proliferation in the four data sets, with nodes from the cell interaction (FN1, SRC activity), MAPK signaling (MAPK 1/3 activity, MEK family), Hedgehog (Hedgehog family, GLI 1/2 activity), and WNT/beta-catenin (CTNNB1 activity, WNT3A) blocks being particularly well represented.
Despite the large number of RCR-derived hypotheses corresponding to nodes in the Cell Proliferation Network predicted in directions consistent with increased cell proliferation, some showed a different pattern. Figure 8 shows the RCR-derived hypotheses corresponding to nodes in the Cell Proliferation Network that were predicted in a direction that is opposite to what we expected based on their literature-described roles in regulating lung cell proliferation. Many of these hypotheses are pleiotropic signaling molecules, which are involved in other processes in addition to proliferation, and may result from the perturbation of non-proliferative areas of biology in the data sets examined. For example, the "response to hypoxia" and transcriptional activity of HIF1A (taof(HIF1a)) predictions may be more indicative of angiogenesis than proliferation. Additionally, some of these hypotheses may be predicted in unexpected directions due to feedback mechanisms or other forms of regulation. Finally, these predictions may also result from alternative activities of these signaling molecules that have not been described in the literature, such as the microRNA MIR192, which is still in the early stages of research into its functions. It is important to note that none of the hypotheses predicted in unexpected directions are nodes in the core Cell Cycle block, an observation that further verifies the cell proliferation literature model.
This analysis supported the model as an accurate and comprehensive representation of cell proliferation in the lung. Predictions for nodes in the core Cell Cycle and Growth Factor blocks are especially robust, consistent with the key role these elements play in cell proliferation. The analysis also confirms the ability of RCR to predict proliferative mechanisms based on transcriptomic data from multiple, independent data sets. Therefore, the proliferation literature model (and the framework used to create it) appears to be very well-suited for the evaluation of mechanisms guiding lung cell proliferation using gene expression microarray data sets.
Expansion of the literature model using data set-derived nodes to create the integrated model
In addition to verifying the cell proliferation literature model, RCR on the four cell proliferation data sets was used to identify other mechanisms impacting cell proliferation in the lung. The prediction of a hypothesis in a cell proliferation data set may suggest involvement in proliferation; however, they may also reflect other biological processes that are affected by the experimental perturbations in these data sets. Therefore, each of the hypotheses predicted by RCR in these four data sets that were not already included in the model was investigated to determine its role in lung proliferation. Hypotheses that were determined to play a role in lung proliferation based on surveys of the literature were then further examined to determine how they could best be integrated into the existing literature model. These nodes (33 in total) were then added to the model, creating a more robust and comprehensive network of lung proliferation. The literature model supplemented with these data set-derived nodes is referred to in this paper as the integrated Cell Proliferation Network, as it takes into account not only known proliferative mechanisms operating in the lung from the literature, but also additional mechanisms determined to play a role in lung cell proliferation identified by RCR on cell proliferation data sets. For example, the transcriptional activity of Zbtb17 (MIZ-1), was predicted to be increased in the CTNNB1 data set (Figure 6). MIZ-1 is ubiquitously expressed during embryonic development and has the ability to induce growth arrest . Recently, it has been reported that the physical interaction of MIZ-1 with MYC blocks the ability of MIZ-1 to induce growth arrest, partially through removing the ability of MIZ-1 to activate p15INK4b gene expression . While Zbtb17 is known to influence the transcriptional activity of MYC , and cell proliferation in other cell types, it does not yet have a direct literature-described role in regulating normal lung cell proliferation. The data set-derived nodes added to the literature model as a result of their prediction as hypotheses in the cell proliferation data sets are designated in Figure 6 and 7 by the "D" in the 'Origin' column.
The content of the Knowledgebase (the substrate used to build the proliferation network) used in this study is constantly updated with the latest scientific information. As such, the proliferation model itself is dynamic, and has the flexibility to represent a contemporary view of lung cell proliferation as scientific knowledge advances. RCR prediction of a given node using gene expression data sets requires a minimum of four observed RNA expression changes that are consistent with the predicted change in node activity in the Knowledgebase. Thus, one reason that a network node may not be predicted as a hypothesis using RCR on the cell proliferation data sets is that the Knowledgebase contains too few causal connections from the node to downstream RNA expressions. To address this, we took advantage of the dynamic property of the Knowledgebase to perform targeted knowledge curation around specific nodes in order to increase the likelihood of detecting them as hypotheses using RCR. The extent of these curation efforts was limited to a subset of nodes in the proliferation network, however the structural framework used in the construction of this network allows for additional knowledge to be incorporated in the future.
Evaluation of the Cell Proliferation Network
In order to evaluate the content of the Cell Proliferation Network we assessed the coverage of network nodes predicted by RCR (on the four cell proliferation data sets) as a percentage of total network nodes that were capable of being predicted. In all, 229 of the 848 nodes in the Cell Proliferation Network met the minimum criteria to be predicted changed by RCR (i.e. there were four or more observed RNA expression changes consistent with the predicted change contained in the Knowledgebase) and are termed the "possible nodes". Of these 229 "possible nodes", RCR predicted changes in 102 (45%) in at least one of the four cell proliferation data sets. Seventy one (31%) were predicted based on the RhoA data set alone, while 31 (14%), 19 (8%) and 47 (21%) were predicted based on the CTNNB1, NR3C1, and EIF4G1 data sets, respectively. Notably, many of the nodes for which a prediction was not possible exert their influences on proliferation via non-transcriptional events, such as phosphorylation, degradation, etc., or have limited published information regarding their influences on gene expression. As such, these nodes would be more likely predicted to increase or decrease when using a combination of systems biology data types (e.g. gene expression and phosphoproteomic arrays). These results further verify the Cell Proliferation Network, as well as the method of using RCR to predict proliferative mechanisms using systems biology data.
As noted in the "Network verification and expansion" section, the ideal publicly available data set for verifying the network would have adhered to collection of quality control criteria including 1) non-diseased lung tissue focus, 2) simple perturbation of primarily cell proliferation (as opposed to other biological processes such as apoptosis), 3) relevant endpoint data, and 4) statistical soundness. The data sets used for evaluating the model were chosen because they all met criteria 2-4 detailed above, and were also done in lung cell relevant contexts. In fact, two of the data sets were derived from experiments done in embryonic lung, and two were done in cell types that best approximated the biology occurring in lung cells.
The network described here is the first step in the larger objective of creating an integrated network of lung biology. The Cell Proliferation Network portrays the signaling pathways involved in normal lung cell proliferation with expanded coverage relative to existing representations. However, it relates only a subset of the processes involved in many complex lung diseases. For example, lung cancer is a disease of uncontrolled cell proliferation, but also involves response to DNA damage and apoptosis evasion components, among others [48, 49]. Similarly, chronic lung diseases such as asthma and COPD involve not only alterations in the proliferative aspects of cell populations, but also profound alterations in the inflammatory response [50, 51]. In this light, a truly systemic evaluation of diseases such as these will require networks that cover multiple biological processes in a lung focused and interconnected manner. As such, the Cell Proliferation Network is the first of several planned networks that will be built over the coming months to capture the known universe of biological processes relevant for lung disease in a comprehensive, centralized, and computable structure (Additional file 2 corresponds to the entire Cell Proliferation Network in a computable format).
Cell proliferation is a complex biological process with relevance to several common lung diseases. Modern systems biology data, such as transcriptomics, are useful in unraveling the detail embedded in complex processes like cell proliferation, but require the appropriate tools. The publicly available lung focused Cell Proliferation Network described here represents the most comprehensive and fully referenced mechanistic representation of the signaling pathways that regulate normal lung cell proliferation in existence, and is compatible with analysis using systems biology data. The adaptable and computable structure of the network makes it a useful tool for a wide variety of research investigators across broad scientific disciplines.
Knowledgebase and Knowledge Assembly Models
The human KAM is the set of causal assertions from human sources that has been augmented with orthologous causal assertions derived from either mouse or rat sources, and is competent for RCR (see Reverse Causal Reasoning (RCR): Automated Hypothesis Generation). Similarly, the mouse KAM is the set of causal assertions derived from mouse sources that has been augmented with orthologous causal assertions derived from either human or rat sources. Each KAM contains approximately 90,000 total nodes and 400,000 total edges, incorporating information from over 35,000 distinct citations. An example causal assertion is increased transcriptional activity of EGR1 (early growth response 1) causing an increase in the expression of CCND1 (cyclin D1). Each such causal assertion has a specific scientific citation, and the assembled collection of these causal assertions is referred to as either the human or mouse KAM in this paper. The Selventa Knowledgebase and KAMs provide a framework for developing computable, qualitative models of specific areas of biology.
When analyzing public gene expression data sets for the construction and verification of the network, the full human KAM was used as the substrate for RCR; however the Cell Proliferation Network itself (the nodes and edges that make up the physical network connectivity) reflects a subset of all the causal assertions in the human KAM.
Reverse Causal Reasoning (RCR): Automated hypothesis generation
Reverse causal reasoning (RCR) was used to verify and expand the Cell Proliferation Network using cell proliferation experiments with publicly available transcriptomic profiling data. RCR interrogates a species-specific KAM to identify upstream controllers of the RNA State Changes (see Analysis of transcriptomic data sets section) observed in the data set. These upstream controllers are called "hypotheses", as they are statistically significant potential explanations for the observed RNA State Changes. Hypothesis generation is performed automatically by a computer program that utilizes the KAM to identify hypotheses that explain the input RNA State Changes, prioritized by multiple statistical criteria. The substrate for analysis of RNA State Changes observed in the cell proliferation data sets is a species-specific KAM, which is derived from the global Selventa Knowledgebase. For the EIF4G1 data set, the human KAM was used, while the mouse KAM was used for the RhoA, CTNNB1, and NR3C1 data sets.
Each hypothesis is scored according to two probabilistic scoring metrics, richness and concordance, which examine distinct aspects of the probability of a hypothetical cause explaining a given number of RNA State Changes (see next section). Richness is the probability that the number of observed RNA State Changes connected to a given hypothesis could have occurred by chance alone. Concordance is the probability that the number of observed RNA State Changes that match the directionality of the hypothesis (e.g., increased or decreased kinase activity for a kinase, increased or decreased transcriptional activity for a transcription factor, etc.) could have occurred by chance alone. A scored hypothesis is considered to be statistically (although not necessarily biologically) significant if it meets richness and concordance p-value cutoffs of 0.1. Following automated hypothesis generation (which can generate hundreds of hypotheses for a given data set analysis), each scored hypothesis meeting the minimum statistical cutoffs for richness and concordance is evaluated and prioritized by a group of scientists based on its biological plausibility and relevance to the experimental perturbation used to generate the data. Evaluation and prioritization was based on multiple criteria, including the mechanistic proximity of the hypothesis to non-diseased lung biology and evidence that the hypothesis is present or has activity in normal lung or lung-related cells. When constructing this network, each hypothesis was collaboratively evaluated by teams of scientists from both Philip Morris International (PMI) and Selventa. For a more comprehensive and detailed explanation on hypothesis scoring and evaluation, please refer to .
Many hypotheses identified using RCR on the cell proliferation data sets were already represented in the literature model; those that were not represented in the literature model were investigated by evaluation of their biological relevance to the lung context and whether they are causally linked to phenotypes and processes relevant to cell proliferation in the literature. Hypotheses meeting the above criteria were then added to the literature model as data set-driven nodes, creating the integrated network model. Thus, RCR allowed for verification, testing, and expansion of the Cell Proliferation Network using publicly available proliferation data sets.
Analysis of transcriptomic data sets
Four previously published cell proliferation data sets, GSE11011 (EIF4G1), GSE5913 (RhoA), PMID15186480 (CTNNB1), and E-MEXP-861 (NR3C1), were used for the verification and expansion of the Cell Proliferation Network (Table 2). These data sets was chosen for a variety of reasons, including 1) the relevance of the experimental perturbation to modulating the types of cell proliferation that can occur in cells of the normal lung, 2) the availability of raw gene expression data, 3) the statistical soundness of the underlying experimental design, and 4) the availability of appropriate cell proliferation endpoint data associated with each transcriptomic data set. In addition, the perturbations used to modulate cell proliferation in these experiments covered mechanistically distinct areas of the Cell Proliferation Network, ensuring that robust coverage of distinct mechanistic pathways controlling lung cell proliferation were reflected in the network. Data for GSE11011 and GSE5913 were downloaded from Gene Expression Omnibus (GEO) http://www.ncbi.nlm.nih.gov/gds, while data for E-MEXP-861 was downloaded from ArrayExpress http://www.ebi.ac.uk/microarray-as/ae/. The data from PMID15186480 was obtained from a link within the online version of the paper http://jbiol.com/content/3/3/11. Raw RNA expression data for each data set were analyzed using the "affy" and "limma" packages of the Bioconductor suite of microarray analysis tools available for the R statistical environment [53–56]. Robust Microarray Analysis (RMA) background correction and quantile normalization were used to generate microarray expression values for the Affymetrix platform data sets, EIF4G1, RhoA, and CTNNB1. Quantile normalization was applied to analysis of the GE Codelink platform data set, NR3C1. An overall linear model was fit to the data for all sample groups, and specific contrasts of interest were evaluated to generate raw p-values for each probe set on the expression array . The Benjamini-Hochberg False Discovery Rate (FDR) method was then used to correct for multiple testing effects.
Probe sets were considered to have changed qualitatively in a specific comparison if an adjusted p-value of 0.05 was obtained and they had an absolute fold change greater than 1.3. An additional expression abundance filter was applied to three of the data sets; probe set differences were considered significant only if the average expression intensity was above 250 in either the control or treated group for the EIF4G1 and RhoA data sets, and above 10 for the NR3C1 data set. No abundance threshold was applied to the CTNNB1 data set. These criteria were applied to optimize State Change numbers for RCR. NetAffx version na30 feature annotation files, available from Affymetrix http://www.Affymetrix.com, were used for mapping of probe sets to genes. Genes represented by multiple probe sets were considered to have changed if at least one probe set was observed to change. Gene expression changes that met these criteria are called "State Changes" and have the directional qualities of "increased" or "decreased", i.e., they were upregulated or downregulated, respectively in response to the experimental perturbation. The number of State Changes for each data set is listed in Table 2.
List of abbreviations used
v-akt murine thymoma viral oncogene homolog
Biological Expression Language
cyclin-dependent kinase inhibitor
chronic obstructive pulmonary disease
catenin, beta 1
E2 transcription factor
epidermal growth factor
early growth response 1
eukaryotic translation initiation factor 4 gamma, 1
false discovery rate
fibroblast growth factor
fibroblast growth factor receptor 2
Gene Expression Omnibus
glioma-associated oncogene family zinc finger
hepatocyte growth factor
hypoxia inducible factor 1, alpha subunit
v-Ha-ras Harvey rat sarcoma viral oncogene homolog
intracellular and extracellular
insulin-like growth factor 2
interleukin 1, beta
Knowledge Assembly Model
Kyoto Encyclopedia of Genes and Genomes
lymphoid enhancer-binding factor 1
mitogen-activated protein kinase
Michigan Cancer Foundation cell line 10A
mitogen-activated protein kinase kinase
messenger ribonucleic acid
mammalian target of rapamycin
v-myc myelocytomatosis viral oncogene homolog
v-myc myelocytomatosis viral related oncogene, neuroblastoma derived
National Institutes of Health cell line 3T3
nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor)
tumor protein p53
platelet derived growth factor
Reverse Causal Reasoning
ras homolog gene family, member A
Robust Microarray Analysis
Systems Biology Markup Language
v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog
signal transducer and activator of transcription
transforming growth factor
vascular endothelial growth factor
wingless-type MMTV integration site family
zinc finger and BTB domain containing 17 (MIZ-1).
The authors would like to thank Sam Ansari and Florian Martin for reviewing the manuscript and Lynda Conroy for editorial support.
The research described in this article was supported by Philip Morris International in a collaborative project with Selventa.
- Crosby LM, Waters CM: Epithelial repair mechanisms in the lung. Am J Physiol Lung Cell Mol Physiol. 2010, 298: L715-731. 10.1152/ajplung.00361.2009PubMed CentralView ArticlePubMedGoogle Scholar
- Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100: 57-70. 10.1016/S0092-8674(00)81683-9View ArticlePubMedGoogle Scholar
- Chung KF, Adcock IM: Multifaceted mechanisms in COPD: inflammation, immunity, and tissue repair and destruction. Eur Respir J. 2008, 31: 1334-1356. 10.1183/09031936.00018908View ArticlePubMedGoogle Scholar
- Hetzel M, Bachem M, Anders D, Trischler G, Faehling M: Different effects of growth factors on proliferation and matrix production of normal and fibrotic human lung fibroblasts. Lung. 2005, 183: 225-237. 10.1007/s00408-004-2534-zView ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010, 38: D355-360. 10.1093/nar/gkp896PubMed CentralView ArticlePubMedGoogle Scholar
- Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novere N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics. 2003, 19: 524-531. 10.1093/bioinformatics/btg015View ArticlePubMedGoogle Scholar
- Laifenfeld D, Gilchrist A, Drubin D, Jorge M, Eddy SF, Frushour BP, Ladd B, Obert LA, Gosink MM, Cook JC, Criswell K, Somps CJ, Koza-Taylor P, Elliston KO, Lawton MP: The role of hypoxia in 2-butoxyethanol-induced hemangiosarcoma. Toxicol Sci. 2010, 113: 254-266. 10.1093/toxsci/kfp213PubMed CentralView ArticlePubMedGoogle Scholar
- Blander G, Bhimavarapu A, Mammone T, Maes D, Elliston K, Reich C, Matsui MS, Guarente L, Loureiro JJ: SIRT1 promotes differentiation of normal human keratinocytes. J Invest Dermatol. 2009, 129: 41-49. 10.1038/jid.2008.179PubMed CentralView ArticlePubMedGoogle Scholar
- Kumar R, Blakemore SJ, Ellis CE, Petricoin EF, Pratt D, Macoritto M, Matthews AL, Loureiro JJ, Elliston K: Causal reasoning identifies mechanisms of sensitivity for a novel AKT kinase inhibitor, GSK690693. BMC Genomics. 2010, 11: 419- 10.1186/1471-2164-11-419PubMed CentralView ArticlePubMedGoogle Scholar
- Hilfer SR: Morphogenesis of the lung: control of embryonic and fetal branching. Annu Rev Physiol. 1996, 58: 93-113. 10.1146/annurev.ph.58.030196.000521View ArticlePubMedGoogle Scholar
- Price WA, Stiles AD: New insights into lung growth and development. Curr Opin Pediatr. 1996, 8: 202-208. 10.1097/00008480-199606000-00002View ArticlePubMedGoogle Scholar
- Rogoff HA, Kowalik TF: Life, death and E2F: linking proliferation control and DNA damage signaling via E2F1. Cell Cycle. 2004, 3: 845-846. 10.4161/cc.3.7.976View ArticlePubMedGoogle Scholar
- Evan G, Littlewood T: A matter of life and cell death. Science. 1998, 281: 1317-1322.View ArticlePubMedGoogle Scholar
- Casalino-Matsuda SM, Monzon ME, Forteza RM: Epidermal growth factor receptor activation by epidermal growth factor mediates oxidant-induced goblet cell metaplasia in human airway epithelium. Am J Respir Cell Mol Biol. 2006, 34: 581-591. 10.1165/rcmb.2005-0386OCPubMed CentralView ArticlePubMedGoogle Scholar
- Korfhagen TR, Swantz RJ, Wert SE, McCarty JM, Kerlakian CB, Glasser SW, Whitsett JA: Respiratory epithelial cell expression of human transforming growth factor-alpha induces lung fibrosis in transgenic mice. J Clin Invest. 1994, 93: 1691-1699. 10.1172/JCI117152PubMed CentralView ArticlePubMedGoogle Scholar
- Portnoy J, Curran-Everett D, Mason RJ: Keratinocyte growth factor stimulates alveolar type II cell proliferation through the extracellular signal-regulated kinase and phosphatidylinositol 3-OH kinase pathways. Am J Respir Cell Mol Biol. 2004, 30: 901-907.View ArticlePubMedGoogle Scholar
- Bellusci S, Grindley J, Emoto H, Itoh N, Hogan BL: Fibroblast growth factor 10 (FGF10) and branching morphogenesis in the embryonic mouse lung. Development. 1997, 124: 4867-4878.PubMedGoogle Scholar
- Brown KR, England KM, Goss KL, Snyder JM, Acarregui MJ: VEGF induces airway epithelial cell proliferation in human fetal lung in vitro. Am J Physiol Lung Cell Mol Physiol. 2001, 281: L1001-1010.PubMedGoogle Scholar
- Pepicelli CV, Lewis PM, McMahon AP: Sonic hedgehog regulates branching morphogenesis in the mammalian lung. Curr Biol. 1998, 8: 1083-1086. 10.1016/S0960-9822(98)70446-4View ArticlePubMedGoogle Scholar
- Bellusci S, Furuta Y, Rush MG, Henderson R, Winnier G, Hogan BL: Involvement of Sonic hedgehog (Shh) in mouse embryonic lung growth and morphogenesis. Development. 1997, 124: 53-63.PubMedGoogle Scholar
- Tsao PN, Vasconcelos M, Izvolsky KI, Qian J, Lu J, Cardoso WV: Notch signaling controls the balance of ciliated and secretory cell fates in developing airways. Development. 2009, 136: 2297-2307. 10.1242/dev.034884PubMed CentralView ArticlePubMedGoogle Scholar
- Ramirez-Valle F, Braunstein S, Zavadil J, Formenti SC, Schneider RJ: eIF4GI links nutrient sensing by mTOR to cell proliferation and inhibition of autophagy. J Cell Biol. 2008, 181: 293-307. 10.1083/jcb.200710215PubMed CentralView ArticlePubMedGoogle Scholar
- Berenjeno IM, Nunez F, Bustelo XR: Transcriptomal profiling of the cellular transformation induced by Rho subfamily GTPases. Oncogene. 2007, 26: 4295-4305. 10.1038/sj.onc.1210194PubMed CentralView ArticlePubMedGoogle Scholar
- Okubo T, Hogan BL: Hyperactive Wnt signaling changes the developmental potential of embryonic lung endoderm. J Biol. 2004, 3: 11- 10.1186/jbiol3PubMed CentralView ArticlePubMedGoogle Scholar
- Bird AD, Tan KH, Olsson PF, Zieba M, Flecknoe SJ, Liddicoat DR, Mollard R, Hooper SB, Cole TJ: Identification of glucocorticoid-regulated genes that control cell proliferation during murine respiratory development. J Physiol. 2007, 585: 187-201. 10.1113/jphysiol.2007.136796PubMed CentralView ArticlePubMedGoogle Scholar
- Chen HZ, Tsai SY, Leone G: Emerging roles of E2Fs in cancer: an exit from cell cycle control. Nat Rev Cancer. 2009, 9: 785-797. 10.1038/nrc2696PubMed CentralView ArticlePubMedGoogle Scholar
- Yang XH, Sladek TL: Overexpression of the E2F-1 transcription factor gene mediates cell transformation. Gene Expr. 1995, 4: 195-204.PubMedGoogle Scholar
- Benaud CM, Dickson RB: Adhesion-regulated G1 cell cycle arrest in epithelial cells requires the downregulation of c-Myc. Oncogene. 2001, 20: 4554-4567. 10.1038/sj.onc.1204609View ArticlePubMedGoogle Scholar
- Lutterbach B, Hann SR: Overexpression of c-Myc and cell immortalization alters c-Myc phosphorylation. Oncogene. 1997, 14: 967-975. 10.1038/sj.onc.1200920View ArticlePubMedGoogle Scholar
- Okubo T, Knoepfler PS, Eisenman RN, Hogan BL: Nmyc plays an essential role during lung development as a dosage-sensitive regulator of progenitor cell proliferation and differentiation. Development. 2005, 132: 1363-1374. 10.1242/dev.01678View ArticlePubMedGoogle Scholar
- Goodrich DW: The retinoblastoma tumor-suppressor gene, the exception that proves the rule. Oncogene. 2006, 25: 5233-5243. 10.1038/sj.onc.1209616PubMed CentralView ArticlePubMedGoogle Scholar
- Crosby ME, Almasan A: Opposing roles of E2Fs in cell proliferation and death. Cancer Biol Ther. 2004, 3: 1208-1211. 10.4161/cbt.3.12.1494PubMed CentralView ArticlePubMedGoogle Scholar
- Abbas T, Dutta A: p21 in cancer: intricate networks and multiple activities. Nat Rev Cancer. 2009, 9: 400-414. 10.1038/nrc2657PubMed CentralView ArticlePubMedGoogle Scholar
- Collado M, Blasco MA, Serrano M: Cellular senescence in cancer and aging. Cell. 2007, 130: 223-233. 10.1016/j.cell.2007.07.003View ArticlePubMedGoogle Scholar
- Mason DX, Jackson TJ, Lin AW: Molecular signature of oncogenic ras-induced senescence. Oncogene. 2004, 23: 9238-9246.PubMedGoogle Scholar
- Cardoso WV, Itoh A, Nogawa H, Mason I, Brody JS: FGF-1 and FGF-7 induce distinct patterns of growth and differentiation in embryonic lung epithelium. Dev Dyn. 1997, 208: 398-405. 10.1002/(SICI)1097-0177(199703)208:3<398::AID-AJA10>3.0.CO;2-XView ArticlePubMedGoogle Scholar
- Rubin JS, Bottaro DP, Chedid M, Miki T, Ron D, Cheon G, Taylor WG, Fortney E, Sakata H, Finch PW, et al.: Keratinocyte growth factor. Cell Biol Int. 1995, 19: 399-411. 10.1006/cbir.1995.1085View ArticlePubMedGoogle Scholar
- Miettinen PJ, Berger JE, Meneses J, Phung Y, Pedersen RA, Werb Z, Derynck R: Epithelial immaturity and multiorgan failure in mice lacking epidermal growth factor receptor. Nature. 1995, 376: 337-341. 10.1038/376337a0View ArticlePubMedGoogle Scholar
- Madtes DK, Busby HK, Strandjord TP, Clark JG: Expression of transforming growth factor-alpha and epidermal growth factor receptor is increased following bleomycin-induced lung injury in rats. Am J Respir Cell Mol Biol. 1994, 11: 540-551.View ArticlePubMedGoogle Scholar
- Raaberg L, Nexo E, Buckley S, Luo W, Snead ML, Warburton D: Epidermal growth factor transcription, translation, and signal transduction by rat type II pneumocytes in culture. Am J Respir Cell Mol Biol. 1992, 6: 44-49.View ArticlePubMedGoogle Scholar
- Pandiella A, Beguinot L, Velu TJ, Meldolesi J: Transmembrane signalling at epidermal growth factor receptors overexpressed in NIH 3T3 cells. Phosphoinositide hydrolysis, cytosolic Ca2+ increase and alkalinization correlate with epidermal-growth-factor-induced cell proliferation. Biochem J. 1988, 254: 223-228.PubMed CentralView ArticlePubMedGoogle Scholar
- Messerle K, Schlegel J, Hynes NE, Groner B: NIH/3T3 cells transformed with the activated erbB-2 oncogene can be phenotypically reverted by a kinase deficient, dominant negative erbB-2 variant. Mol Cell Endocrinol. 1994, 105: 1-10. 10.1016/0303-7207(94)90029-9View ArticlePubMedGoogle Scholar
- Wang L, Zheng Y: Cell type-specific functions of Rho GTPases revealed by gene targeting in mice. Trends Cell Biol. 2007, 17: 58-64. 10.1016/j.tcb.2006.11.009View ArticlePubMedGoogle Scholar
- Coleman ML, Marshall CJ, Olson MF: RAS and RHO GTPases in G1-phase cell-cycle regulation. Nat Rev Mol Cell Biol. 2004, 5: 355-366.View ArticlePubMedGoogle Scholar
- Peukert K, Staller P, Schneider A, Carmichael G, Hanel F, Eilers M: An alternative pathway for gene regulation by Myc. Embo J. 1997, 16: 5672-5686. 10.1093/emboj/16.18.5672PubMed CentralView ArticlePubMedGoogle Scholar
- Adhikary S, Peukert K, Karsunky H, Beuger V, Lutz W, Elsasser HP, Moroy T, Eilers M: Miz1 is required for early embryonic development during gastrulation. Mol Cell Biol. 2003, 23: 7648-7657. 10.1128/MCB.23.21.7648-7657.2003PubMed CentralView ArticlePubMedGoogle Scholar
- Sakamuro D, Prendergast GC: New Myc-interacting proteins: a second Myc network emerges. Oncogene. 1999, 18: 2942-2954. 10.1038/sj.onc.1202725View ArticlePubMedGoogle Scholar
- Hoglund P: DNA damage and tumor surveillance: one trigger for two pathways. Sci STKE. 2006, 2006: pe2- 10.1126/stke.3172006pe2PubMedGoogle Scholar
- Unni AM, Bondar T, Medzhitov R: Intrinsic sensor of oncogenic transformation induces a signal for innate immunosurveillance. Proc Natl Acad Sci USA. 2008, 105: 1686-1691. 10.1073/pnas.0701675105PubMed CentralView ArticlePubMedGoogle Scholar
- Kool M, Lambrecht BN: Dendritic cells in asthma and COPD: opportunities for drug development. Curr Opin Immunol. 2007, 19: 701-710. 10.1016/j.coi.2007.09.003View ArticlePubMedGoogle Scholar
- Chang J, Mosenifar Z: Differentiating COPD from asthma in clinical practice. J Intensive Care Med. 2007, 22: 300-309. 10.1177/0885066607304445View ArticlePubMedGoogle Scholar
- Selventa: Reverse Causal Reasoning Methods Whitepaper. [White paper]. 2010, http://www.selventa.com/technology/white-papersGoogle Scholar
- Gentleman R: Bioinformatics and computational biology solutions using R and Bioconductor. Statistics for Biology and Health. 2005, xix: 397-420.Google Scholar
- Gentleman RC, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology. 2004, 5: R80- 10.1186/gb-2004-5-10-r80PubMed CentralView ArticlePubMedGoogle Scholar
- Irizarry RA, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4: 249-264. 10.1093/biostatistics/4.2.249View ArticlePubMedGoogle Scholar
- R Development Core Team: R: A Language and Environment for Statistical Computing. Book R: A Language and Environment for Statistical Computing (Editor eds.). City. 2007Google Scholar
- Smyth GK: Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology. 2004, 3: Article 3-View ArticleGoogle Scholar
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