Construction of a Computable Network Model for DNA Damage, Autophagy, Cell Death, and Senescence

Abstract

Towards the development of a systems biology-based risk assessment approach for environmental toxicants, including tobacco products in a systems toxicology setting such as the “21st Century Toxicology”, we are building a series of computable biological network models specific to non-diseased pulmonary and cardiovascular cells/tissues which capture the molecular events that can be activated following exposure to environmental toxicants. Here we extend on previous work and report on the construction and evaluation of a mechanistic network model focused on DNA damage response and the four main cellular fates induced by stress: autophagy, apoptosis, necroptosis, and senescence. In total, the network consists of 34 sub-models containing 1052 unique nodes and 1538 unique edges which are supported by 1231 PubMed-referenced literature citations. Causal node-edge relationships are described using the Biological Expression Language (BEL), which allows for the semantic representation of life science relationships in a computable format. The Network is provided in .XGMML format and can be viewed using freely available network visualization software, such as Cytoscape.

Introduction

System-wide ‘omics’ data containing measurements of thousands of molecular species in a single experiment are increasingly being used to unravel the complex biological mechanisms contributing to pulmonary and cardiovascular diseases. Detailed mechanistic network models are needed to place the differential measurements obtained from molecular profiling data into the context of known biology. These mechanistic models can then be used to better understand the impact of biologically active substances/ toxicants and associated disease risks as outlined in systems toxicology settings such as the “21st Century Toxicology”.^1,2

Previously, we have reported on the construction of network models describing cell proliferation and cellular stress.^3,4 Extending on elements of these networks (eg, the Cellular Stress Network), which described the network perturbations occurring during cellular defense in response to acute exogenous or endogenous insults, we report here on the construction and evaluation of a third network model, describing the mechanisms that can be activated if these cellular defenses are overwhelmed.

The proper maintenance of homeostatic balance is essential for cell survival in a constantly changing environment. Human pulmonary tissue forms an interface between the external and internal microenvironments, and is therefore constantly exposed to both exogenous stressors including combustion products (diesel exhaust, carbon monoxide, cigarette smoke (CS)), particulate matter, ozone,^5–7 and endogenous stressors (eg, mitochondrial-derived reactive oxygen species (ROS), unfolded proteins, nutrient deprivation), all of which can alter cellular homeostasis. Pulmonary cells are equipped with a variety of defense mechanisms to aid in the preservation of cellular homeostasis in the face of such harsh conditions^8–10 as outlined in one of our previous network model describing the main CS-related cellular stress defense mechanisms in detail.³ However, these mechanisms can be overwhelmed by chronic stress, for example, ultimately culminating in the intracellular accrual of free radicals, oxidative damage to biomolecules including DNA, and the induction of the DNA damage response as a further protective mechanism. If all these responses to restore cellular homeostasis fail, compromised cells may commit to a terminal fate for the collective benefit of the surrounding tissue, adopting one of four main fates: apoptosis, necroptosis, autophagy, or senescence¹¹ to prevent the nucleation of a potentially deleterious proinflammatory microenvironment.

The DNA damage response activates DNA repair enzymes and in cycling cells, halts cell division by activating G1/S or G2/M cell cycle checkpoints, allowing time for DNA repair.¹² Apoptosis is initiated through two main pathways following appropriate extracellular or intracellular signals.¹³ It fragments a dying cell into apoptotic bodies, which are subsequently cleared from tissue by the phagocytic activity of neighboring or immune cells, minimizing local inflammation. Alternatively, cell clearance can occur through necrosis, a form of death that results in cell lysis and release of proinflammatory intracellular components into the surrounding milieu. Accumulating evidence indicates that at least some forms of necrotic cell death occur in a regulated manner, termed “necroptosis”.^14,15 In contrast to apoptosis and necroptosis, which result in the removal of damaged cells, the induction of autophagy or senescence leaves cells surviving, but qualitatively changes their phenotype or function.^16,17 During autophagy, lysosomal enzymes degrade and recycle damaged intracellular organelles and proteins in an effort to maintain nutrient and energy homeostasis.^18,19 Cellular senescence is characterized by irreversible growth arrest,^20,21 as response to a variety of external stimuli including DNA damage, oncogene amplification, and telomere dysfunction.

The DNA damage response, apoptosis, necroptosis, autophagy, and senescence are especially important in the context of CS, as smoke exposure in human pulmonary experimental systems has been shown to induce each, depending on the exposure or experimental context.^22–29 Although these cellular fates generally serve in a protective capacity, emerging research also points to a prominent role for stress-induced cell fate choices in the pathogenesis of CS-related diseases, including lung cancer, chronic obstructive pulmonary disease (COPD), and cardiovascular disease.^30–33 Understanding how this protective to pathogenic transition occurs requires both a thorough mechanistic understanding of the pathways involved and the appropriate input data.

Here we describe the construction and application of a literature-based network model depicting the DNA damage response, apoptosis, necroptosis, autophagy, and senescence, hereafter referred to by the acronym DACS (DNA damage, Autophagy, Cell death (apoptosis and necroptosis), and Senescence). The DACS Network is modular and computable with its edges supported by hundreds of scientific references. We applied the Network to an independent molecular profiling data set, verifying the content and computability of the network in the process. Together with our previously published network models, the Network will be an invaluable research tool to investigate the biological effects of environmental exposures including CS on human systems, both qualitatively and quantitatively, towards systems toxicology approaches.

Methods

Biological Expression Language (BEL)

The causal relationships in the model are expressed in the Biological Expression Language (BEL)⁴⁴, which allows for the representation of biological processes in a computable format. BEL is designed to represent scientific findings by capturing causal and correlative relationships in context, where context can include information about the biological and experimental system in which the relationships were observed, the supporting publications cited and the curation process used.

Knowledgebase

The nodes and edges comprising the DACS Network were assembled from the Selventa Knowledgebase, a comprehensive repository containing over 1.5 million nodes (biological processes and entities) and over 7.5 million edges (assertions about causal and non-causal relationships between nodes). The assertions in the Selventa Knowledgebase are derived from peer-reviewed scientific literature as well as other public and proprietary databases. Specifically, each assertion describes an individual experimental observation from an experiment performed in a human, mouse or rat species context, either in vitro or in vivo. Assertions in one species (eg, human) are homologized to another species (eg, mouse) in cases where each element of an assertion has an orthologous counterpart in both species. Assertions also capture information about the referring source (eg, the PubMed ID (PMID) for journal articles listed in MEDLINE), as well as key contextual information including the species (human, mouse, or rat) and the tissue or cell line from which the experimental observation was derived. An example causal assertion is the increased transcriptional activity of TP53 (tumor protein p53) causes an increase in the mRNA expression of CDKN1A (cyclin-dependent kinase inhibitor 1A) [fibroblast; Human; PMID 15616590]. The Knowledgebase contains causal relationships derived from healthy tissues and disease areas such as inflammation, metabolic diseases, cardiovascular injury, liver injury and cancer.

Analysis of transcriptomic data sets

Four previously published data sets, GSE6206,³⁴ E-MEXP-1968,³⁵ GSE13330,³⁶ and GSE19018 were used to construct the DACS Network. A fifth data set, GSE28464,³⁷ was used to evaluate the DACS Network, with a specific focus on the relevant senescence sub-models (Supplementary Table 1). All data sets were downloaded either from Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/gds) or from ArrayExpress (http://www.ebi.ac.uk/arrayexpress). RNA expression data were analyzed using the “affy”, “lumiHumanIDMapping”, and “limma” packages of the Bioconductor suite of microarray analysis tools available for the R statistical environment.^38–42 Robust Microarray Analysis (RMA) background correction and quantile normalization were used to generate microarray expression values for the Affymetrix platform (CEL files from GSE6206, E-MEXP-1968, GSE13330, and GSE19018), while log2 transformation and quantile normalization were used to generate expression values for the Illumina platform (non-normalized data file from GSE28464). 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.⁴³ The Benjamini-Hochberg False Discovery Rate (FDR) method was then used to correct for multiple testing effects.

For Affymetrix data sets, probe sets were considered to have statistically significant changed expression levels in a specific comparison if they had an adjusted P-value of less than 0.05, an absolute fold change greater than 1.3, and average expression intensity greater than 150 in either treatment group. NetAffx version na32 feature annotation files, available from Affymetrix (http://www.Affymetrix.com), were used to map probe sets to genes. For the Illumina platform, the criteria used for statistical significance in changed gene expression were if they had an adjusted P-value of less than 0.05 and an absolute fold change greater than 1.3. In our analysis, 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’ ie, they were upregulated or downregulated, respectively, in response to the experimental condition. The number of State Changes for each data set is listed in Supplementary Table 1.

Reverse causal reasoning (RCR): automated hypothesis generation

Reverse causal reasoning (RCR) analysis of the five DNA damage and senescence transcriptomic data sets was used to generate lists of nodes that were predicted to be increased or decreased, and these lists of nodes were used to aid in the selection of nodes for inclusion in the DACS Network, as well as to evaluate the DACS Network using the data set. RCR interrogates the Selventa Knowledgebase to identify potential upstream controllers of entities observed to change significantly in an experiment (see Selventa 2010⁴⁴ and Additional File 1 for specific detail on RCR). Here we applied RCR to the mRNA State Changes in the five transcriptomic data sets to predict potential upstream controllers for the expression changes. These potential upstream controllers identified by RCR are called HYPs as they represent statistically significant hypotheses that are potential explanations for the observed downstream mRNA State Changes. Specifically, the upstream HYP is a potential explanation for the subset of State Changes that are causally downstream of the HYP in individual assertions in the Selventa Knowledgebase.

Each HYP is scored according to two probabilistic scoring metrics: richness and concordance. Richness is the probability that the number of observed mRNA State Changes connected to a given HYP could have occurred by chance alone, calculated using the hyper-geometric distribution. Concordance is the probability that the number of observed RNA State Changes that match the direction of the HYP (eg, increased or decreased activity or abundance of a node) could have occurred by chance alone, calculated using a binomial distribution. HYPs meeting both richness and concordance P-value cutoffs of 0.1 were considered to be statistically significant. When performing control analyses, applying these significance cutoffs to randomly generated data (with similar numbers of RNA State Changes as the experimental data) generally produces less than 5% of the number of HYPs meeting both significance criteria than are observed for experimental data (not shown). For the purposes of network model construction, top scoring HYPs meeting the minimum statistical cutoffs for richness and concordance were evaluated and selected for integration based on their biological plausibility and relevance to the perturbation and biological context (eg, cell type) of the experiment. For data set interrogation, scored HYPs meeting these same statistical cutoffs were considered, with the understanding that as potential explanations for a subset of State Changes, the connectivity and consistency of direction of individual HYPs needed to be considered within context of the models (Selventa 2010⁴⁴ and Additional File 1).

Results

Network structure and content

We constructed a network model focused on DNA damage response and the four main cellular fates induced by overwhelming stress: autophagy, apoptosis, necroptosis, and senescence (Fig. 1, Supplementary Fig. 1). The complete DACS Network is provided in Additional File 2 as an excel file and in Additional Files 3–7 in .XGMML format. The .XGMML format can be viewed using freely available network visualization software, such as Cytoscape.⁴⁵ The DACS Network was constructed using a highly modular design, where the larger network is divided into sub-models. Discrete mechanisms affecting cell fate (eg, ‘NFKB signaling’ describing the prosurvival effects of NFKB-mediated transcriptional upregulation of anti-apoptotic genes) in the five DACS Network areas are described by 34 sub-models (Fig. 1).

Figure 1.

Overview of the DACS Subnetworks.

Notes: The DACS Network is comprised of 34 submodels that represent relevant signaling within five areas of biology – apoptosis, autophagy, DNA damage response, necroptosis, and senescence. Each of the 34 submodels describes the molecular signaling mechanisms shown to activate or inhibit the end process (eg, in the submodel ‘Replicative senescence,’ increased CDKN2A and CDKN1A protein abundances lead to the induction of replicative senescence, while increased abundance of WRN protein inhibits replicative senescence). The left panel lists the names of the submodels involved in each area (eg, ‘Replicative senescence’ under Senescence), and the right panel shows an agglomerated diagram of all submodels involved in each area, with different submodels highlighted in unique colors.

In total, the DACS Network contains 1052 unique nodes and 1538 unique edges (959 causal edges and 579 non-causal edges), which are supported by 1231 PubMed-referenced literature citations (Table 1, Additional Files 2–7). Nodes in the DACS Network are biological entities such as protein abundances, mRNA expression levels, and protein activities. In addition, nodes can also represent biological processes (eg, protein biosynthesis). Edges are relationships between the nodes, and are categorized as either causal or non-causal. Causal edges are directional cause-effect relationships between nodes (eg, NFKB directly increases the gene expression of BCL2), whereas non-causal edges connect different forms of a biological entity, such as gene expression to the related protein abundance. Node-edge relationships in the DACS Network are described using the BEL which allows for the semantic representation of life science relationships in a computable format (Selventa 2010⁴⁴ and Additional File 1). Overall, the DACS Network provides a comprehensive, detailed representation of the causal pathways involved in the DNA damage response, apoptosis, necroptosis, autophagy, and senescence.

Table 1.

DACS Network statistics.

Nodes	1052
mRNAs	138
Proteins	392
Phosphoproteins	105
Activities	224
Complexes	22
Protein families	25
Biological processes/GO terms	48
Chemicals/small molecules	22
Other	76
Total edges	1538
Causal	959
Non-casual	579
Unique PMIDs	1231
Submodel name	Total nodes (predictable)
DNA Damage	272 (72)
Autophagy	161 (49)
Apoptosis	280 (112)
Necroptosis	94 (30)
Senescence	365 (186)

Notes: Summary of relevant statistics describing the contents of the DACS Network. For each DACS Network area, the total number of unique nodes in the agglomerated model is given, along with the number of those nodes that are capable of prediction by RCR (in parentheses).

Network construction

The DACS Network was constructed using the same iterative process used to create previously published network models.^3,4 Using this strategy, the network is populated with nodes and edges from two main sources: prior knowledge described in the scientific literature, and results obtained from the computational analysis of transcriptomic profiling data via RCR (Selventa 2010⁴⁴ and Additional File 1) (Fig. 2).

Figure 2.

Workflow used to construct and evaluate the DACS Network.

Notes: The DACS Network is a literature based model containing content derived from two main sources. The literature model was constructed from causal relationships extracted from relevant scientific literature following the definition of network boundaries. The literature model was then augmented with additional nodes derived from Reverse Causal Reasoning (RCR) analysis of transcriptomic data sets, forming the integrated model. In this step, RCR analysis was also used to verify the placement of existing nodes in the literature model. Manual review and refinement of the integrated model resulted in the final network model. The final network model was evaluated using RCR analysis of an independent test transcriptomic data set.

In order to build a network model that describes the DACS-related biological mechanisms in non-diseased pulmonary and cardiovascular cells/tissues, we first defined and applied a set of criteria for selecting network content similar to those used in previously published network models.^3,4 Starting with a list of nodes identified by a survey of published literature in the five DACS Network areas, we searched for causal relationships describing the mechanistic relationships between these nodes with literature support from normal lung and cardiovascular cell types. In cases where the relevant experiments have not been published in these contexts, relationships derived from non-lung contexts using cell types found in normal lung (fibroblasts, epithelial cells, endothelial cells, etc.) were used. Canonical mechanisms that are well-known in the literature were also included in the network model even if literature support explicitly demonstrating the presence of the mechanism in normal lung or cardiovascular tissues was not found (eg, the catalytic activity of the FAS receptor increasing the catalytic activity of FADD in the activation of TNFR signaling). For direct and proximal connections such as a kinase phosphorylating a residue on a target or protein-protein interactions, evidence from cell free in vitro systems, which lack a single specified tissue context, were also used when normal lung or cardiovascular tissues were not available. Lastly, relationships derived from human and rodent (specifically mouse and rat) systems were included and homologized, with human contexts prioritized (see Methods).

Using these network boundaries, a literature model was created by compiling causal relationships extracted from the Selventa Knowledgebase, a unified collection of over 1.5 million elements of biological knowledge captured from public literature and other resources (see Methods). When critical causal connections did not exist in the Knowledgebase, they were identified and manually curated from literature into the Knowledgebase. During the course of model building, over 7,500 new causal relationships related to DNA damage, cell death, and senescence from 685 unique literature references were added to the Knowledgebase to support the biology reflected in the DACS Network. Following this effort, the literature model encompassed experimentally proven and well-established mechanistic signaling within the five DACS areas.

Next, the literature model was augmented with additional nodes derived from the computational analysis of molecular profiling data using RCR. RCR-derived HYPs were included as new nodes in the DACS Network model if they had literature support for a mechanistic role in the process of interest. RCR analysis was done to confirm the relevance of nodes already present in the literature model, and to uncover relevant nodes that were not identified during the construction of the literature model. RCR-based augmentation of the DACS Network was performed using four transcriptomic data sets (two for DNA damage and two for senescence), referred to as ‘building’ data sets (Supplementary Table 1). Ideally, transcriptomic data sets addressing all five DACS areas would be used in order to maximize network coverage. However, because three of the DACS Network areas (apoptosis, autophagy and necroptosis) have not been classically described as driven by or executed through transcriptomic changes, we focused our efforts on transcriptomic data from experiments describing DNA damage response and the induction of senescence. Candidate data sets for RCR analysis were selected from public gene expression data repositories GEO and ArrayExpress. We prioritized data sets according to three main criteria: (1) whether the biological process relevant to the DACS Network was induced in non-diseased cell types found in normal lung, (2) whether phenotypic endpoint data was available to provide additional verification of the experimental setup/transcriptomic data, and (3) the statistical rigor of the design of transcriptomic profiling experiments. The four building data sets (Supplementary Table 1) were all derived from in vitro experiments done in human or mouse fibroblasts, and represent the response to DNA damage, induction of replicative senescence (RS) and stress-induced premature senescence (SIPS). Applying RCR to the four network building data sets, 575 HYPs were evaluated for biological plausibility. From this initial list of 575 HYPs, 63 were considered biologically plausible in the context of previous literature reports, and were placed into the appropriate sub-model(s) based on their mechanistic connections to the DACS areas (Supplementary Table 2). The literature model augmented with the data-driven nodes formed the integrated model. As a final step in the construction of the DACS Network, the nodes and edges were manually reviewed and refined (eg, by additional specific literature curation), producing the final DACS Network model (Fig. 2).

Application of the DACS Network to an independent data set

Following network finalization, the DACS Network was applied to investigate a transcriptomic test data set, not included in the construction process, from a well-accepted model of senescence induction ie, oncogene-induced senescence through tamoxifen-inducible HRAS G12V expression in lung fibroblasts (GSE28464)³⁷ (Supplementary Table 1).^46–48 This data set also met the boundary criteria for data set selection described above. Although the test data set did not reflect biological activity occurring in all areas of the DACS Network, it enabled a detailed proof-of-principle evaluation of a specific portion of the network (ie, relevant senescence sub-models) as a means to ensure that the nodes and edges placed into the network through manual curation provided an accurate reflection of currently known biology. The four senescence sub-models representing the biology most closely related to the experimental perturbation (constitutively active HRAS by G12V mutation) were selected for investigation using this data set: oncogene-induced senescence (OIS), regulation of CDKN2A expression, regulation by tumor suppressors, and transcriptional regulation of the senescence-associated secretory phenotype (SASP). The OIS sub-model directly reflects the mechanism expected to be seen given the GSE28464 experimental perturbation. The other three sub-models describe mechanisms that are generally applicable to all modes of cellular senescence.

In total, the four senescence sub-models used for evaluation contain 259 unique nodes, 126 (49%) of which were eligible for prediction (meaning that they contain four or more downstream gene expression relationships and thus are capable of prediction as a hypothesis) by RCR. Eighty three of the 126 RCR-capable nodes (66%) are predicted as HYPs in the test set, 79 of which (95%) are predicted in directions consistent with increased oncogene-induced senescence that was experimentally observed.

In particular, the oncogene-induced senescence sub-model describes the upstream signaling pathways associated with the induction of OIS as well as the unique SASP proteins produced by cells following OIS.⁴⁹ When GSE28464 was used to interrogate this sub-model, 30 of the 43 RCR-capable nodes (70%) comprising this sub-model were predicted as HYPs, with 28 of the 30 (93%) predicted in directions consistent with increased OIS (Table 2). These directionally consistent HYP predictions include increased HRAS mutated at G12V, oncogene-induced senescence, and cell aging, all of which match the experimental perturbation from the test data set.³⁷ Increased p38 MAPK activity, FOXO1 activity, RAF1 activity, and HBP1 abundance are all involved in known pathways leading to OIS (Fig. 3).^8,46,50,51 Several SASP proteins were also predicted increased in abundance, including OSM, MIF, VEGFA, IL1A, LIF, PPBP and IFNG, consistent with what has been observed following OIS.⁴⁹ The two directionally inconsistent predictions are for KRAS abundance and KRAS mutated at G12V (which can lead to OIS, but are predicted to be decreased). These inconsistencies were further clarified by reviewing the underlying State Change support for the KRAS HYPs. First, we performed a Gene Ontology (GO) biological process enrichment query on the State Changes supporting both the HRAS mutated at G12V and KRAS mutated at G12V HYPs using the Database for Annotation, Visualization and Integrated Discovery (DAVID). While the State Changes supporting the HRAS mutated at G12V HYP converged on GO biological processes indicative of cell cycle modulation known to be affected during cellular senescence, the State Changes supporting the KRAS at G12V HYP did not converge on any specific biological process (data not shown). In addition, underlying evidence for the KRAS HYP comes, at least in part, from transformed cells that have already bypassed senescence during the transformation process, thus excluding the KRAS HYP from further consideration in the OIS sub-model.

Table 2.

Nodes from the oncogene-induced senescence submodel of the DACS Network that are predicted as HYPs by RCR on the GSE28464 test data set.

Oncogene Induced Senescence HYPs	Expected Direction	Test Data set
		GSE28464
Predicted in consistent directions
BNIP3L
cell aging
ETS2
FOXO1
gtpof(Ras family Hs)
HBP1
HRAS
HRAS mutated at G12V
IFNG
IL1A
kaof(MAP2K1)
kaof(MAP2K6)
kaof(MEK Family Hs)
kaof(p38 MAPK family Hs)
kaof(RAF1)
LIF
MAP2K1
MAP2K6
MIF
Oncogene induced senescence
OSM
PPBP
RAF1
RAS Family Hs
SMARCB1
taof(ETS2)
taof(FOXO1)
VEGFA
Predicted in inconsistent directions
KRAS
KRAS mutated at G12V

Notes: Expected direction is based on internal causality of the oncogene-induced senescence submodel. Yellow = predicted increase in abundance or activity; blue = predicted decrease in abundance or activity. Submodel nodes that are shared with other senescence models are bolded.

Abbreviations: gtpof(X), GTP-bound activity of X; kaof(X), kinase activity of X; taof(X), transcriptional activity of X.

Figure 3.

Graph showing the oncogene-induced senescence submodel as depicted using the BEL framework and colored according to the GSE28464 test data set.

Notes: Yellow = predicted increase in abundance or activity; blue = predicted decrease in abundance or activity.

Abbreviations: catof(X), catalytic activity of X; exp(X), mRNA expression of X; gtpof(X), GTP-bound activity of X; kaof(X), kinase activity of X; paof(X), phosphatase activity of X; sec(X), cell secretion of X; taof(X), transcriptional activity of X.

The regulation of CDKN2A expression sub-model includes direct transcriptional regulators of CDKN2A, a cyclin-dependent kinase inhibitor whose increased expression at the gene and protein levels are hallmarks of cellular senescence.⁵² When interrogated using the test data set, 17 of the 33 RCR-capable nodes (52%) in this sub-model were predicted as HYPs, all in directions consistent with increased CDKN2A expression (Supplementary Table 3). Notably, the prediction for increased CDKN2A protein abundance was also supported by the observed increase in CDKN2A mRNA levels in the test data set. Both positive (SMARCB1, HBP1, ETS1, ETS2, SP1, and PPARG) and negative (HDAC3 and GLI2) regulators of CDKN2A expression were predicted in directions consistent with their previously reported roles.^51,53–57 Finally, members of both the Polycomb Repressive Complexes 1 and 2 (PRC1/2) were predicted decreased (YY1 and BMI1 for PRC1, EED and EZH2 for PRC2), consistent with their known role as negative regulators of CDKN2A expression (Fig. 4).^58,59

Figure 4.

Graph showing the regulation of CDKN2A expression submodel as depicted using the BEL Framework and colored according to the GSE28464 test data set.

Notes: Yellow = predicted increase in abundance or activity; blue = predicted decrease in abundance or activity; red = observed increase in mRNA expression.

Abbreviations: catof(X), catalytic activity of X; exp(X), mRNA expression of X; taof(X), transcriptional activity of X.

Next, we interrogated the regulation by tumor suppressors sub-model, which describes the cell cycle exit characteristic of senescence regulated by the E2F/Rb axis and CDK inhibitors.⁶⁰ Thirty-eight of the 49 RCR-capable nodes (78%) in this sub-model were predicted as HYPs in the test data set, all but two (95%) in directions consistent with cell cycle exit and increased senescence (Supplementary Table 4). The consistent HYPs include predictions for decreased abundance/activity of cell cycle activators (E2F family members and CCND1) and conversely, increased abundance/activity of cell cycle inhibitors (RB1, CDKN1A, and CDKN2A). The content of the regulation by tumor suppressors sub-model also included SASP proteins that are shared between multiple modes of senescence, and several of these are predicted increased at the HYP level as well, including CCL2, CSF2, CXCL1, HGF, IL1B, IL6ST, IL8, IL13, and TNFRSF1A (Fig. 5).⁴⁹ The inconsistent HYPs are IL6, which is predicted to be decreased, and PTEN, which is predicted to be increased (Supplementary Table 4). Upon further exploration of the directionality of the IL6 HYP, we noted that a large fraction of the supporting State Changes (74 out of 173) were IL6 targets related to cell proliferation in multiple myeloma, which falls outside of the network boundaries.^61,62 Because many of these proliferative genes were observed to be downregulated (presumably as a consequence of senescent cells exiting the cell cycle), this set of genes could account for the RCR prediction of decreased IL6. When the directionality of the IL6 HYP was re-evaluated excluding this set of genes, it was predicted increased in abundance (Supplementary Fig. 2), consistent with its role as a proinflammatory mediator. Due to those findings, the related literature evidence (Supplementary Fig. 2) will be excluded from future RCR analysis in this model. PTEN is a multifunctional protein and the prediction for increased abundance may be reflective of its role in areas outside of senescence.

Figure 5.

Graph showing the regulation by tumor suppressors submodel as depicted using the BEL framework and colored according to the GSE28464 test data set.

Notes: Yellow = predicted increase in abundance or activity; blue = predicted decrease in abundance or activity; red = observed increase in mRNA expression. IL6 is shown as predicted increased in this figure, in contrast to the initial prediction by RCR (See Section 3.3 Application of the DACS Network to an Independent Data Set for additional detail and Supplementary Fig. 2).

Abbreviations: exp(X), mRNA expression of X; kaof(X), kinase activity of X; sec(X), cell secretion of X; taof(X), transcriptional activity of X.

To further evaluate the mechanisms responsible for the expression of the SASP proteins, we interrogated the transcriptional regulation of the senescence-associated secretory phenotype sub-model, which centers on the transcriptional activities of NFKB and CEBPB upstream of the mRNA expression of SASP proteins. Of the 23 RCR-capable nodes in this sub-model, 16 (70%) are predicted as HYPs in GSE28464, and 15 (94%) are predicted in directions consistent with an increased SASP (Supplementary Table 5). In addition, the transcriptional activity of both NFKB and CEBPB are predicted to be increased, consistent with their roles as central transcriptional mediators of the SASP.^63,64 SASP proteins such as CCL2, CCL5, CXCL1, IFNG, IL1A, IL13, IL8, IL6, and VEGFA are all predicted increased. Complementing the RCR predictions, six of these proteins (CCL2, CXCL1, IL1A, IL8, IL6 and VEGFA) are also observed increased at the mRNA level in GSE28464 (Supplementary Fig. 3).

In summary, the evaluation of the transcriptomic profiling data set from human lung cells expressing oncogenic HRAS (GSE28464) using four relevant sub-networks from the DACS Network reveals molecular processes known to be involved in the major hallmarks of oncogene-induced senescence, eg, decreased abundance/activity of cell cycle activators (E2F family members and CCND1), increased abundance/activity of cell cycle inhibitors (RB1, CDKN1A, and CDKN2A), and induction of SASP proteins via activation of the transcription factors NFKB and CEBPB.

Discussion

Comparison with other DACS-related computational networks

Several different modeling approaches have been used to build models of biological systems depending on the biological complexity being captured, the specific goals of the study, and the experimental details involved. The DACS Network was constructed using a prior knowledge of causal relationships from literature, and augmented with nodes derived from RCR, a data-driven method that infers pathway activity based on differentially expressed entities and knowledge of their upstream regulators. Here, we compare and contrast three previously published networks that share features with the DACS Network.^65–67

Behrends et al performed a systematic proteomic analysis and utilized existing protein interaction databases to construct an autophagy interaction network (AIN).⁶⁵ Like the DACS Network, the AIN consists of functional sub-networks, representing unique biological areas of autophagy. In contrast to the protein-protein interactions of the Behrends AIN, the DACS Network shows directionality through mechanistic causal relationships between proteins and other entities including genes, protein activities, biological processes, complexes, etc. Additionally, the DACS Network incorporates transcriptomic data through the integration of computationally derived nodes to infer pathway activity.

Caron et al manually constructed a comprehensive, detailed network of mTOR signaling based on 522 published articles and a protein interaction network (PIN) using 85 key mTOR proteins and protein-protein interactions from multiple databases.⁶⁶ Comparable to the DACS Network, the mTOR network represents biochemical modifications, directionality, biological entities, and annotations (cell lines, cited literature references). While the integrated mTOR network provides a highly granular view of mTOR signaling, the DACS Network covers a wider range in addition to basic mTOR signaling.

Finally, Passos et al utilized several ‘omics’ approaches to investigate cellular senescence.⁶⁷ Using target gene inhibition, in silico interactome analysis based on the BioGrid database, and statistical inference, they identified a signaling pathway involving TP53, CDKN1A, GADD45A, MAPK14, GRB2, SRC, DAB2, TGFRB2, and TGFβ. Overlaying these results with those from previous gene expression analysis, they were able to confirm the upregulation of these pathway genes in senescent MRC5 fibroblasts. Similarly, the DACS Network uses transcriptomic data, but applies a computational approach to infer the activity of upstream controllers that fall in a pathway rather than overlaying the genes onto the network itself. Although the Passos network depicts the interconnections between senescence-related entities, it is undirected, as BioGrid interactions lack inherent directionality.

Thus, although the DACS Network shares many features with other previously published networks, we believe the inherent computability conferred upon it by the BEL Framework and the ability to evaluate biological mechanisms by RCR (as opposed to direct mapping of differentially expressed genes onto pathways) differentiates the DACS Network from previously existing resources. In addition, the broad scientific coverage of five distinct yet overlapping biological areas makes the DACS Network a unique resource for the scientific community.

The knowledgebase used to build the network model contains information curated from published literature. We concede that the peer review process is far from perfect and any errors that exist in the public literature could be translated to the knowledgebase. However, the prior knowledge encoded in the knowledgebase has been subject to two additional layers of peer review by PhD level curators. We believe that any inaccuracies that exist in the knowledgebase constitute a minor fraction and occur without a systematic bias that would profoundly affect the results presented here.

While the results shown here indicate that network models have utility in evaluating ‘omics’ data, there are some elements that could be improved in the future. The methodology depends on up-to-date prior knowledge of both the signaling pathways that are represented by the network models and the genes that are regulated by network components. As new discoveries in these areas are made and published, a process for maintaining the connectivity of the network models will need to be put in place to ensure the networks constantly reflect the current state of the field; being dynamic and updatable, any new knowledge can be added to the existing DACS network.

Future application in systems biology-based risk assessment

Understanding how exposure to chemical products affects biological systems is a key first step in the development of effective risk assessment programs. Historically, chemical mechanism-of-action (MOA) studies used simple in vitro or in vivo models and measured a relatively limited number of biological entities. Modern toxicological assessment using system-wide ‘omics’ approaches can now generate thousands of biological data points for a single experiment, and the field of systems toxicology has evolved in order to distil discrete MOA information from this sea of data.^68,69 Detailed mechanistic network models are needed to place the differential measurements obtained from molecular profiling data into the context of known biology. These mechanistic models can then be used to better understand the impact of biologically active substances/toxicants and associated disease risks. We are currently developing an application of these network models to derive quantitative measures of network perturbations to compare the impact of biologically active substances, including CS, on human systems in order to assess relative disease risk.

The biological mechanisms represented in the DACS Network, combined with its inherent computability, make it an ideal resource in systems toxicology approaches. For example, the DACS Network could be used in combination with molecular profiling data from human in vitro toxicological studies to characterize the degree to which a simple chemical entity induces a DNA damage response or initiates cell death pathways. In addition, the DACS Network could be used with molecular profiling data from rodents exposed to environmental toxicants in vivo in order to identify the mechanisms whose activation or suppression precedes the development of known genotoxic markers. In each case, the information obtained by combining systems-level data with network-level analyses would provide invaluable mechanistic insight into the biological effects of potentially harmful exposures, and would serve to aid in the development of risk assessment pipelines.

Conclusions

We have presented here a network model that broadly covers the biology within five distinct yet overlapping cellular processes: DNA damage and the main cell fates resulting from cellular stress. The computability enabled by BEL and the broad coverage of toxicologically relevant biology make the DACS Network an exceptional, open-source tool for evaluating modern ‘omics’ data.

Supplementary Materials

Table S1.

Data sets analyzed by RCR for model augmentation and evaluation.

Model building data sets					Model evaluation data set
Process	DNA damage		Senescence		Senescence
Data set ID	GSE6206	E-MEXP-1968	GSE13330	GSE19018	GSE28464
PubMed ID	19584263	19363488	19155301	Unpublished	21512002
Species	Mouse	Mouse	Human	Human	Human
Context	In vitro	In vitro	In vitro	In vitro	In vitro
Cell type	Embryonic fibroblasts	Dermal fibroblasts	Foreskin BJ fibroblasts	IMR90 lung fibroblasts	IMR90 lung fibroblasts
Perturbation	Cisplatin (16 μM)	UV irradiation (4 J/m2)	Bleomycin (100 μg/mL) Long term culture	Long term culture in 20% oxygen	Tamoxifen-inducible HRAS G12V expression
Timepoint(s)	24 hr	6 hr after UV exposure	24 hr Late passage	48 population doublings (old)	Day 4 post HRAS G12V induction
Control	Untreated	Non-irradiated (0 J/m2)	Early passage (young)	30 population doublings (young)	Day 0
# State changes	3684	472	3355 2799	2257	3691

Notes: Four data sets relating to two different DACS Network areas (DNA damage and Senescence) were analyzed by RCR for the data-driven phase of model construction (Model Building Data Sets). One senescence data set was analyzed by RCR for model evaluation. This table provides a summary of the experimental details and comparisons used for each data set as well as the number of gene expression State Changes observed in each data set.

Figure S1.

The DACS Network.

Notes: Graphical representation of the five central biological areas covered by the DACS Network. An agglomerated view of all subnetwork nodes and edges relating the mechanisms involved in the cellular DNA damage response (green) and the induction of senescence (teal), necroptosis (red), autophagy (magenta), and apoptosis (purple), forming an interconnected network of shared and non-overlapping biology.

Table S2.

Data-driven nodes that were predicted as HYPs by RCR on the GSE6206, E-MEXP-1968, GSE13330 RS, GSE13330 SIPS, and GSE19018 building data sets.

Data-Driven Nodes Added to DACS Network	Expected Direction	DNA Damage Data Sets		Senescence Data Sets			Submode I
		GSE6206	E-MEXP-1968	GSE13330 RS	GSE13330 SIPS	GSE19018
ATF2							DNA Damage—Double—strand break response
BACH1							Senescence—Stress—induced premature senescence
BIRC5							Apoptosis—NFKB signaling
BNIP3L							Senescence—Oncogene—induced senescence
catof(proteasome complex (sensu Eukarya) Hs)							Senescence—Stress—induced premature senescence
catof(PTGS2)							Senescence—Stress—induced premature senescence
CCL5							Senescence—Transcriptional regulation of the SASP
CCND1							DNA Damage—Double—strand break response
CTNNB1							Senescence—Regulation by tumor suppressors
DDIT3							Apoptosis—ER stress—induced apoptosis
DNMT3A							Senescence—Replicative senescence
ENO1							Senescence—Regulation by tumor suppressors
EP300							Apoptosis—MAPK signaling
ETS2							Senescence—Oncogene—induced senescence
FHIT							DNA Damagee—single—strand break response
gtpof(RAC1)							Apoptosis—MAPK signaling
gtpof(RHOA)							DNA Damagee—single—strand break response
gtpof(RHOB)							DNA Damagee—single—strand break response
HDAC1							Senescence—Regulation by tumor suppressors
HDAC3							Senescence—Regulation of p16INK expression
IFNA1							Senescence—Regulation by tumor suppressors
IL1A							Senescence—Transcriptional regulation of the SASP
IRF1							Senescence—Regulation by tumor suppressors
IRF3							Senescence—Regulation by tumor suppressors
IRF5							Senescence—Replicative senescence
kaof(MAP2K1)							Senescence—Oncogene—induced senescence
kaof(PKC Family Hs)							Apoptosis—PKC signaling
kaof(RAF1)							Senescence—Oncogene—induced senescence
MAP2K1							Senescence—Oncogene—induced senescence
MYC							DNA Damage—Double—strand break response
PKC Family Hs							Apoptosis—PKC signaling
PPARG							Senescence—Regulation of p16INK expression
proteasome complex (sensu Eukarya) Hs							Senescence—Stress—induced premature senescence
PTGS2							Senescence—Stress—induced premature senescence
RAC1							Apoptosis—MAPK signaling
RAF1							Senescence—Oncogene—induced senescence
RASSF1							DNA Damage—Double—strand break response
RHOA							DNA Damage—Double—strand break response
RHOB							DNA Damage—Double—strand break response
SMARCB1							Senescence—Regulation of p16INK expression
SP1							Senescence—Regulation of p16INK expression
taof(ATF2)							DNA Damage—Double—strand break response
taof(BACH1)							Senescence—Stress—induced premature senescence
taof(CTNNB1)							Senescence—Regulation by tumor suppressors
taof(EP300)							Apoptosis—MAPK signaling
taof(ETS2)							Senescence—Oncogene—induced senescence
taof(IRF1)							Senescence—Regulation by tumor suppressors
taof(IRF3)							Senescence—Regulation by tumor suppressors
taof(IRF5)							Senescence—Replicative senescence
taof(PPARG)							Senescence—Regulation of p16INK expression
taof(SP1)							Senescence—Regulation of p16INK expression
taof(TFDP1)							Senescence—Regulation by tumor suppressors
taof(TP63)							DNA Damage—Componenets affecting TP63 activity
taof(TP73)							DNA Damage—Componenets affecting TP73 activity
taof(XBP1)							Apoptosis—ER stress—induced apoptosis
taof(YY1)							DNA Damage—Componenets affecting TP53 activity
TFDP1							Senescence—Regulation by tumor suppressors
TP63							DNA Damage—Componenets affecting TP63 activity
TP73							DNA Damage—Componenets affecting TP73 activity
TWIST1							Senescence—Stress—induced premature senescence
VHL							Senescence—Stress—induced premature senescence
XBP1							Apoptosis—ER stress—induced apoptosis
YY1							DNA Damage—Componenets affecting TP53 activity

Notes: These data-driven nodes were added to the indicated submodels of the DACS Network based on their mechanistic connections to the processes reflected by the submodels. Expected direction is based on internal causality of the indicated submodels. Yellow = predicted increase in abundance or activity, blue = predicted decrease in abundance or activity.

Abbreviations: catof(X), catalytic activity of X; gtpof(X), GTP-bound activity of X; kaof(X), kinase activity of X; taof(X), transcriptional activity of X.

Table S3.

Nodes from the regulation of CDKN2A expression submodel of the DACS Network that are predicted as HYPs by RCR on the GSE28464 test data set.

Regulation of CDKN2A HYPs	Expected Direction	Test Data set
		GSE28464
Predicted in consistent directions
CDKN2A
ETS1
ETS2
HBP1
PPARG
SMARCB1
SP1
taof(ETS2)
taof(PPARG)
taof(YY1)
YY1
BMI1
EED
EZH2
GLI2
HDAC3
taof(GLI2)

Notes: Expected direction is based on internal causality of the regulation of CDKN2A expression submodel. Yellow = predicted increase in abundance or activity, blue = predicted decrease in abundance or activity. Submodel nodes that are shared with other senescence models are bolded.

Abbreviation: taof(X), transcriptional activity of X.

Table S4.

Nodes from the regulation by tumor suppressors submodel of the DACS Network that are predicted as HYPs by RCR on the GSE28464 test data set.

Regulation by Tumor Suppressors HYPs	Expected Direction	Test Data set
		GSE28464
Predicted in consistent directions
BRCA1
CCL2
CDKN1A
CDKN2A
CDKN2A NP_000068
Cell aging
CSF2
CXCL1
HGF
IFNA1
IL6 *
IL13
IL1B
IL6ST
IL8
ING1
IRF1
IRF3
Kaof(RAF1)
Oncogene induced senescence
RAF1
RB1
RBL2
replicative cell aging
taof(IRF1)
taof(RB1)
TNFRSF1A
CCND1
E2F1
E2F2
E2F3
ENO1
taof(E2F family Hs)
taof(E2F1)
taof(E2F2)
taof(E2F3)
TFDP1
Predicted in inconsistent directions
PTEN

Notes: Expected direction is based on internal causality of the regulation by tumor suppressors submodel. Yellow = predicted increase in abundance or activity, blue = predicted decrease in abundance or activity. Submodel nodes that are shared with other senescence models are bolded.

^*IL6 is shown as predicted increased in this table, in contrast to the initial prediction by RCR (See Section 3.3 Application of the DACS Network to an Independent Data Set for additional detail and Supplementary Fig. 2).

Abbreviations: kaof(X), kinase activity of X; taof(X), transcriptional activity of X.

Figure S2.

Directionality investigation for the IL6 HYP in the DACS Network Model test data set.

Notes: The scored IL6 HYP is shown for the DACS Network evaluation data set GSE28464 before (left) and after (right) removal of proliferation-related genes in multiple myeloma as reported in two previously published studies. Circles represent observed gene expression State Changes (red for increased, green for decreased), and edges connecting the central IL6 HYP node to State Changes represent literature-derived causal relationships between IL6 and its downstream target genes. Solid lines indicate relationships that support the HYP prediction (counted as “Score”) and dotted lines indicate relationships that are contradictory to the HYP prediction (counted as “Contra”). When proliferation-related genes are retained in the IL6 HYP, RCR produces a prediction for decreased IL6 abundance. Excluding this set of proliferation-related genes results in an RCR prediction for increased IL6 abundance.

Table S5.

Nodes from the transcriptional regulation of the senescence-associated secretory phenotype (SASP) submodel of the DACS Network that are predicted as HYPs by RCR on the GSE28464 test data set.

Transcriptional Regulation of the SASP HYPs	Expected Direction	Test Data Set
		GSE28464
Predicted in consistent directions
CCL2
CCL5
CEBPB
CXCL1
IFNG
IL1 Family Hs
IL6 *
IL13
IL1A
IL8
kaof(p38 MAPK family Hs)
NFKB Complex Hs
RELA
taof(CEBPB)
taof(NFKB Complex Hs)
VEGFA

Notes: Expected direction is based on internal causality of the transcriptional regulation of the SASP submodel. Yellow = predicted increase in abundance or activity, blue = predicted decrease in abundance or activity. Submodel nodes that are shared with other senescence models are bolded.

^*IL6 is shown as predicted increased in this table, in contrast to the initial prediction by RCR (See Section 3.3 Application of the DACS Network to an Independent Data Set for additional detail and Supplementary Fig. 2).

Abbreviations: kaof(X), kinase activity of X; taof(X), transcriptional activity of X.

Figure S3.

Graph showing the transcriptional regulation of the senescence-associated secretory phenotype (SASP) submodel as depicted using the BEL framework and colored according to the GSE28464 test data set.

Notes: Yellow = predicted increase in abundance or activity, blue = predicted decrease in abundance or activity, red = observed increase in mRNA expression. IL6 is shown as predicted increased in this figure, in contrast to the initial prediction by RCR (See Section 3.3 Application of the DACS Network to an Independent Data Set for additional detail and Supplementary Fig. 2).

Abbreviations: exp(X), mRNA expression of X; kaof(X), kinase activity of X; taof(X), transcriptional activity of X.

Additional Files

Additional File 1 Reverse Causal Reasoning.pdf

Additional File 2 DACS Network.xlsx

Additional File 3 DNA Damage-Agglomerated.cy.xgmml

Additional File 4_Autophagy-Agglomerated.cy.xgmml

Additional File 5_Apoptosis-Agglomerated.cy.xgmml

Additional File 6_Necroptosis-Agglomerated.cy.xgmml

Additional File 7_Senescence-Agglomerated.cy.xgmml