Jennifer Park

A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue

BackgroundHumans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate.ResultsWe have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung.ConclusionsThe results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells.

Quantitative assessment of biological impact using transcriptomic data and mechanistic network models

Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology.

Genes associated with intestinal permeability in ulcerative colitis: Changes in expression following infliximab therapy†

Background: Alterations in intestinal permeability have been implicated in ulcerative colitis (UC). Infliximab, a monoclonal anti‐tumor necrosis factor alpha (TNF&agr;) antibody, can induce clinical response in UC. Gene expression in colonic biopsies taken from responders and nonresponders to infliximab can provide insight into the mechanisms of the altered intestinal permeability at a molecular level. Methods: Colonic biopsies (n = 18 anti‐TNF&agr; naïve UC patients; n = 8 normal controls; n = 80 Active Ulcerative Colitis Trial [ACT] 1 patients) were analyzed for mRNA expression using gene expression microarrays. Computational reverse causal reasoning was applied to build causal network models of UC and response and nonresponse of UC to treatment. Quantitative reverse‐transcription polymerase chain reaction (qPCR) was used to confirm differentially expressed genes. Results: Reverse causal reasoning on mRNA expression data from anti‐TNF&agr;‐naïve UC and normal samples provided a mechanistic disease model of the biology of gene expression observed in UC. mRNA expression data from the ACT 1 study enabled construction of a mechanistic model describing the biology of nonresponders to infliximab, including evidence for increased intestinal permeability compared with normal and responder samples. Gene expression changes identified as central to intestinal permeability dysregulation were confirmed in normal, UC, and infliximab‐treated patients by qPCR analysis. Gene expression returned toward normal levels in infliximab responders, but not in nonresponders. Conclusion: Gene expression analysis and causal network modeling in combination showed that aberrant mRNA expression of genes involved in intestinal epithelial permeability for infliximab responders was restored toward levels observed in normal samples. Infliximab nonresponders showed no equivalent restoration in the expression of these genes. (Inflamm Bowel Dis 2012)

Utilization of Causal Reasoning of Hepatic Gene Expression in Rats to Identify Molecular Pathways of Idiosyncratic Drug-Induced Liver Injury

Drug-induced liver injury (DILI) represents a leading cause of acute liver failure. Although DILI can be discovered in preclinical animal toxicology studies and/or early clinical trials, some human DILI reactions, termed idiosyncratic DILI (IDILI), are less predictable, occur in a small number of individuals, and do not follow a clear dose-response relationship. The emergence of IDILI poses a critical health challenge for patients and a financial challenge for the pharmaceutical industry. Understanding the cellular and molecular mechanisms underlying IDILI is key to the development of models that can assess potential IDILI risk. This study used Reverse Causal Reasoning (RCR), a method to assess activation of molecular signaling pathways, on gene expression data from rats treated with IDILI or pharmacologic/chemical comparators (NON-DILI) at the maximum tolerated dose to identify mechanistic pathways underlying IDILI. Detailed molecular networks involved in mitochondrial injury, inflammation, and endoplasmic reticulum (ER) stress were found in response to IDILI drugs but not negative controls (NON-DILI). In vitro assays assessing mitochondrial or ER function confirmed the effect of IDILI compounds on these systems. Together our work suggests that using gene expression data can aid in understanding mechanisms underlying IDILI and can guide in vitro screening for IDILI. Specifically, RCR should be considered for compounds that do not show evidence of DILI in preclinical animal studies positive for mitochondrial dysfunction and ER stress assays, especially when the therapeutic index toward projected human maximum drug plasma concentration is low.

Construction of a Computable Network Model of Tissue Repair and Angiogenesis in the Lung

We have recently developed methodologies to quantify the biological impact of exposure to environmental toxicants via the use of computable network models and network scoring methods. In this study, we extend our collection of lung pathophysiology specific network models to tissue repair and angiogenesis. It is important to understand the molecular mechanisms of wound healing which, if unresolved, could eventually progress to irreversible disease. The Tissue Repair and Angiogenesis (TRAG) Network consists of nine modular subnetworks that describe the following processes: hypoxia-inducible factor 1 alpha (HIF1A) signaling, sprouting and tubulogenesis, Vascular Endothelial Growth Factor (VEGF)-mediated angiogenesis, growth factor-mediated angiogenesis, immune regulation of angiogenesis, immune regulation of tissue repair, cell migration, differentiation of progenitor cells and fibrosis. We used a data-driven approach to augment the initial literature-based network, and to evaluate a portion of the network using two independent gene expression data sets. This approach increases the confidence in the network’s ability to accurately describe tissue repair processes. The TRAG Network serves as a valuable research tool for assessing the biological impact of exposure to environmental insults and in understanding the initial molecular events that may lead to disease. The TRAG network, which consists of 666 nodes linked by 1215 relationships or edges covering 1371 PubMed IDs, is expressed in the Biological Expression Language and made available in computer readable formats including XGMML and .xls.

Early Patient Stratification and Predictive Biomarkers in Drug Discovery and Development

The current drug discovery paradigm is long, costly, and prone to failure. For projects in early development, lack of efficacy in Phase II is a major contributor to the overall failure rate. Efficacy failures often occur from one of two major reasons: either the investigational agent did not achieve the required pharmacology or the mechanism targeted by the investigational agent did not significantly contribute to the disease in the tested patient population. The latter scenario can arise due to insufficient study power stemming from patient heterogeneity. If the subset of disease patients driven by the mechanism that is likely to respond to the drug can be identified and selected before enrollment begins, efficacy and response rates should improve. This will not only augment drug approval percentages, but will also minimize the number of patients at risk of side effects in the face of a suboptimal response to treatment. Here we describe a systems biology approach using molecular profiling data from patients at baseline for the development of predictive biomarker content to identify potential responders to a molecular targeted therapy before the drug is tested in humans. A case study is presented where a classifier to predict response to a TNF targeted therapy for ulcerative colitis is developed a priori and verified against a test set of patients where clinical outcomes are known. This approach will promote the tandem development of drugs with predictive response, patient selection biomarkers.

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications:

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.

Reputation-based collaborative network biology.

A pilot reputation-based collaborative network biology platform, Bionet, was developed for use in the sbv IMPROVER Network Verification Challenge to verify and enhance previously developed networks describing key aspects of lung biology. Bionet was successful in capturing a more comprehensive view of the biology associated with each network using the collective intelligence and knowledge of the crowd. One key learning point from the pilot was that using a standardized biological knowledge representation language such as BEL is critical to the success of a collaborative network biology platform. Overall, Bionet demonstrated that this approach to collaborative network biology is highly viable. Improving this platform for de novo creation of biological networks and network curation with the suggested enhancements for scalability will serve both academic and industry systems biology communities.

W1228 Genes Associated With Reduced Epithelial Permeability and Epithelial-Mesenchymal Transition: Changes in Expression Following Infliximab Therapy in Ulcerative Colitis

Background:Changes in intestinal permeability have been implicated in the pathology of ulcerative colitis (UC). Objectives: To investigate mechanismsmodulating intestinal permeability and epithelial-mesenchymal transition (EMT) following infliximab (IFX)treatment, we analyzed mRNA expression in colonic biopsies obtained from UC patients(pts), normal healthy subjects, and UC pts enrolled in the ACT1 study. ACT1 evaluated safety and efficacy of IFX in pts with UC. Methods: Colonic biopsies (n=17 UC anti-TNFα naive; n=8 nonIBD normal; n=113 from ACT1) were analyzed for mRNA expression using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays followed by quantitative polymerase chain reaction (qPCR) confirmation of the differentially expressed genes. Causal network modeling was utilized to generate models describing the biological signaling pathways that drive changes observed in the mRNA expression data. Results: mRNA expression results from anti-TNFα naive UC and normal samples analyzed using network modeling determined that genes known to affect intestinal permeability were deregulated in UC pts. Analysis of the ACT1 sample expression data demonstrated that treatment with IFX restored gene expression toward, but not completely to, normal in IFX-responder pts. Pts in ACT1 who were non-responders or placebo treated (including those diagnosed as clinical responders) did not restore these functions. qPCR analysis of the identified genes (Table) confirmed the changes in gene expression observed in IFX treated pts from the ACT1 study. Conclusion: Using the combination of expression analysis and network modeling, we found that responders to IFX restore mRNA expression levels in genes involved in intestinal epithelial permeability and fibrotic processes leading to EMT to levels comparable with those seen in normal subjects. IFX non-responders and placebo treated pts have no equivalent restoration in gene expression involved in these functions.

Abstract 4061: Computational exploration of mechanistic determinants of antibody drug-conjugate pharmacokinetics using quantitative systems pharmacology modeling strategies

The pharmacokinetics of antibody drug conjugate (ADC) therapeutics typically show a discrepancy between the PK of total antibody (conjugated and unconjugated antibody) and that of conjugated antibody, carrying one or more payload molecules. This discrepancy is often attributed to deconjugation (Kamath, 2014), however recent evidence suggests that the underlying mechanisms may be more complex. This work employs a computational quantitative systems pharmacology (QSP), or mechabistic PK/PD approach to understand the impact of drug antibody ratio (DAR) and the resulting changes in molecular properties on overall PK and relative payload disposition as observed in preclinical and clinical studies. Our work establishes the benefit of using computational models to design novel ADCs and to optimize the discovery and development of existing ADCs. Citation Format: A. Katharina Wilkins, Andrew Matteson, Lore Gruenbaum, Jennifer Park, John M. Burke, Joshua Apgar. Computational exploration of mechanistic determinants of antibody drug-conjugate pharmacokinetics using quantitative systems pharmacology modeling strategies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4061. doi:10.1158/1538-7445.AM2017-4061