Computational Systems Biology Approach Research Articles

Following Transcriptome to Uncover FOXO Biological Functions.

Two and half million red blood cells (RBC) are generated every second in a healthy adult. The process of RBC production known as erythropoiesis requires a meticulous synchrony between signaling processes and the activity of many transcription factor complexes. FOXO3 is a transcription factor that is responsive to signaling processes and essential for the erythroid proliferation and maturation, RBC formation, and lifespan. Here, we discuss how using an integrated computational and experimental systems biology approach new and unanticipated FOXO3 functions in terminal erythropoiesis were uncovered. These combinatory approaches identified FOXO3 as a key regulator of terminal erythropoiesis. As a result, a new mode of FOXO3 participation in erythroid transcription complex formation has been proposed.

Computational systems biology approach to identify novel pharmacological targets for diabetic retinopathy

Diabetic retinopathy was included by the World Health Organization in the eye disease priority list. Up to now, only proliferative diabetic retinopathy can be treated with approved drugs, such as intravitreal anti-vascular endothelial growth factor (VEGF) agents or steroids. In this perspective, there is the urgent need to explore novel pharmacological targets for treatment of diabetic retinopathy. Drug discovery todays exploits the noticeable ability of computational systems biology methods to identify novel drug targets in complex pathologies bearing multifactorial etiology and wide and varying symptomatology. This is especially true for diseases, where the identification of specific molecular mechanisms, and thus drug targets, is a challenging, when not impossible, task. Within this framework, we applied a systems biology approach to identify novel drug targets for diabetic retinopathy. The complexity of diabetic retinopathy was investigated through the analysis of transcriptomics data, retrieved from Gene Expression Omnibus Dataset repository (GEO) datasets. Analysis of GEO datasets was carried out with an enrichment-information approach, which gave as output a series of complex gene-pathway and drug-gene networks. Analysis of these networks identified genes and biological pathways related with inflammation, fibrosis and G protein-coupled receptors that are potentially involved in development of the disease. This analysis provided new clues on novel pharmacological targets, useful to treat diabetic retinopathy.

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections.

A systems biology approach to predictive developmental neurotoxicity of a larvicide used in the prevention of Zika virus transmission

The need to prevent developmental brain disorders has led to an increased interest in efficient neurotoxicity testing. When an epidemic of microcephaly occurred in Brazil, Zika virus infection was soon identified as the likely culprit. However, the pathogenesis appeared to be complex, and a larvicide used to control mosquitoes responsible for transmission of the virus was soon suggested as an important causative factor. Yet, it is challenging to identify relevant and efficient tests that are also in line with ethical research defined by the 3Rs rule (Replacement, Reduction and Refinement). Especially in an acute situation like the microcephaly epidemic, where little toxicity documentation is available, new and innovative alternative methods, whether in vitro or in silico, must be considered. We have developed a network-based model using an integrative systems biology approach to explore the potential developmental neurotoxicity, and we applied this method to examine the larvicide pyriproxyfen widely used in the prevention of Zika virus transmission. Our computational model covered a wide range of possible pathways providing mechanistic hypotheses between pyriproxyfen and neurological disorders via protein complexes, thus adding to the plausibility of pyriproxyfen neurotoxicity. Although providing only tentative evidence and comparisons with retinoic acid, our computational systems biology approach is rapid and inexpensive. The case study of pyriproxyfen illustrates its usefulness as an initial or screening step in the assessment of toxicity potentials of chemicals with incompletely known toxic properties.

Modulators of Redox Metabolism in Head and Neck Cancer.

Head and neck squamous cell cancer (HNSCC) is a complex disease characterized by high genetic and metabolic heterogeneity. Radiation therapy (RT) alone or combined with systemic chemotherapy is widely used for treatment of HNSCC as definitive treatment or as adjuvant treatment after surgery. Antibodies against epidermal growth factor receptor are used in definitive or palliative treatment. Recent Advances: Emerging targeted therapies against other proteins of interest as well as programmed cell death protein 1 and programmed death-ligand 1 immunotherapies are being explored in clinical trials. The disease heterogeneity, invasiveness, and resistance to standard of care RT or chemoradiation therapy continue to constitute significant roadblocks for treatment and patients' quality of life (QOL) despite improvements in treatment modality and the emergence of new therapies over the past two decades. As reviewed here, alterations in redox metabolism occur at all stages of HNSCC management, providing opportunities for improved prevention, early detection, response to therapies, and QOL. Bioinformatics and computational systems biology approaches are key to integrate redox effects with multiomics data from cells and clinical specimens and to identify redox modifiers or modifiable target proteins to achieve improved clinical outcomes. Antioxid. Redox Signal.

Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future "Cognitive Developmental Robotics".

OPINION article Front. Neurorobot., 15 November 2017 Volume 11 - 2017 | https://doi.org/10.3389/fnbot.2017.00063

Systems approach to the pharmacological actions of HDAC inhibitors reveals EP300 activities and convergent mechanisms of regulation in diabetes

ABSTRACTGiven the skyrocketing costs to develop new drugs, repositioning of approved drugs, such as histone deacetylase (HDAC) inhibitors, may be a promising strategy to develop novel therapies. However, a gap exists in the understanding and advancement of these agents to meaningful translation for which new indications may emerge. To address this, we performed systems-level analyses of 33 independent HDAC inhibitor microarray studies. Based on network analysis, we identified enrichment for pathways implicated in metabolic syndrome and diabetes (insulin receptor signaling, lipid metabolism, immunity and trafficking). Integration with ENCODE ChIP-seq datasets identified suppression of EP300 target genes implicated in diabetes. Experimental validation indicates reversal of diabetes-associated EP300 target genes in primary vascular endothelial cells derived from a diabetic individual following inhibition of HDACs (by SAHA), EP300, or EP300 knockdown. Our computational systems biology approach provides an adaptable framework for the prediction of novel therapeutics for existing disease.

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors

Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.

A Computational Systems Biology Approach for Identifying Candidate Drugs for Repositioning for Cardiovascular Disease.

We report an in silico method to screen for receptors or pathways that could be targeted to elicit beneficial transcriptional changes in a cellular model of a disease of interest. In our method, we integrate: (1) a dataset of transcriptome responses of a cell line to a panel of drugs; (2) two sets of genes for the disease; and (3) mappings between drugs and the receptors or pathways that they target. We carried out a gene set enrichment analysis (GSEA) test for each of the two gene sets against a list of genes ordered by fold-change in response to a drug in a relevant cell line (HL60), with the overall score for a drug being the difference of the two enrichment scores. Next, we applied GSEA for drug targets based on drugs that have been ranked by their differential enrichment scores. The method ranks drugs by the degree of anti-correlation of their gene-level transcriptional effects on the cell line with the genes in the disease gene sets. We applied the method to data from (1) CMap 2.0; (2) gene sets from two transcriptome profiling studies of atherosclerosis; and (3) a combined dataset of drug/target information. Our analysis recapitulated known targets related to CVD (e.g., PPARγ; HMG-CoA reductase, HDACs) and novel targets (e.g., amine oxidase A, δ-opioid receptor). We conclude that combining disease-associated gene sets, drug-transcriptome-responses datasets and drug-target annotations can potentially be useful as a screening tool for diseases that lack an accepted cellular model for in vitro screening.

Systems Biology of Immunomodulation for Post-Stroke Neuroplasticity: Multimodal Implications of Pharmacotherapy and Neurorehabilitation.

Recent studies indicate that anti-inflammatory drugs, act as a double-edged sword, not only exacerbating secondary brain injury but also contributing to neurological recovery after stroke. Our aim is to explore whether there is a beneficial role for neuroprotection and functional recovery using anti-inflammatory drug along with neurorehabilitation therapy using transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), so as to improve functional recovery after ischemic stroke. We develop a computational systems biology approach from preclinical data, using ordinary differential equations, to study the behavior of both phenotypes of microglia, such as M1 type (pro-inflammatory) vis-à-vis M2 type (anti-inflammatory) under anti-inflammatory drug action (minocycline). We explore whether pharmacological treatment along with cerebral stimulation using tDCS and rTMS is beneficial or not. We utilize the systems pathway analysis of minocycline in nuclear factor kappa beta (NF-κB) signaling and neurorehabilitation therapy using tDCS and rTMS that act through brain-derived neurotrophic factor (BDNF) and tropomyosin-related kinase B (TrkB) signaling pathways. We demarcate the role of neuroinflammation and immunomodulation in post-stroke recovery, under minocycline activated-microglia and neuroprotection together with improved neurogenesis, synaptogenesis, and functional recovery under the action of rTMS or tDCS. We elucidate the feasibility of utilizing rTMS/tDCS to increase neuroprotection across the reperfusion stage during minocycline administration. We delineate that the signaling pathways of minocycline by modulation of inflammatory genes in NF-κB and proteins activated by tDCS and rTMS through BDNF, TrkB, and calmodulin kinase (CaMK) signaling. Utilizing systems biology approach, we show that the activation pathways for pharmacotherapy (minocycline) and neurorehabilitation (rTMS applied to ipsilesional cortex and tDCS) results into increased neuronal and synaptic activity that commonly occur through activation of N-methyl-d-aspartate receptors. We construe that considerable additive neuroprotection effect would be obtained and delayed reperfusion injury can be remedied, if one uses multimodal intervention of minocycline together with tDCS and rTMS. Additive beneficial effect is, thus, noticed for pharmacotherapy along with neurorehabilitation therapy, by maneuvering the dynamics of immunomodulation using anti-inflammatory drug and cerebral stimulation for augmenting the functional recovery after stroke, which may engender clinical applicability for enhancing plasticity, rehabilitation, and neurorestoration.

Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process.

Novel computational systems biology tools such as common targets analysis, common regulators analysis, pathway discovery, and transcriptomic-based hotspot discovery provide new opportunities in understanding of apoptosis molecular mechanisms. In this study, after measuring the global contribution of microRNAs in the course of apoptosis by Affymetrix platform, systems biology tools were utilized to obtain a comprehensive view on the role of microRNAs in apoptosis process. Network analysis and pathway discovery highlighted the crosstalk between transcription factors and microRNAs in apoptosis. Within the transcription factors, PRDM1 showed the highest upregulation during the course of apoptosis, with more than 9-fold expression increase compared to non-apoptotic condition. Within the microRNAs, MIR1208 showed the highest expression in non-apoptotic condition and downregulated by more than 6 fold during apoptosis. Common regulators algorithm showed that TNF receptor is the key upstream regulator with a high number of regulatory interactions with the differentially expressed microRNAs. BCL2 and AKT1 were the key downstream targets of differentially expressed microRNAs. Enrichment analysis of the genomic locations of differentially expressed microRNAs led us to the discovery of chromosome bands which were highly enriched (p<0.01) with the apoptosis-related microRNAs, such as 13q31.3, 19p13.13, and Xq27.3 This study opens a new avenue in understanding regulatory mechanisms and downstream functions in the course of apoptosis as well as distinguishing genomic-enriched hotspots for apoptosis process.

Systems Biology Approaches to the Study of Biological Networks Underlying Alzheimer's Disease: Role of miRNAs.

MicroRNAs (miRNAs) are emerging as significant regulators of mRNA complexity in the human central nervous system (CNS) thereby controlling distinct gene expression profiles in a spatio-temporal manner during development, neuronal plasticity, aging and (age-related) neurodegeneration, including Alzheimer's disease (AD). Increasing effort is expended towards dissecting and deciphering the molecular and genetic mechanisms of neurobiological and pathological functions of these brain-enriched miRNAs. Along these lines, recent data pinpoint distinct miRNAs and miRNA networks being linked to APP splicing, processing and Aβ pathology (Lukiw et al., Front Genet 3:327, 2013), and furthermore, to the regulation of tau and its cellular subnetworks (Lau et al., EMBO Mol Med 5:1613, 2013), altogether underlying the onset and propagation of Alzheimer's disease. MicroRNA profiling studies in Alzheimer's disease suffer from poor consensus which is an acknowledged concern in the field, and constitutes one of the current technical challenges. Hence, a strong demand for experimental and computational systems biology approaches arises, to incorporate and integrate distinct levels of information and scientific knowledge into a complex system of miRNA networks in the context of the transcriptome, proteome and metabolome in a given cellular environment. Here, we will discuss the state-of-the-art technologies and computational approaches on hand that may lead to a deeper understanding of the complex biological networks underlying the pathogenesis of Alzheimer's disease.

A Systems Approach Identifies Essential FOXO3 Functions at Key Steps of Terminal Erythropoiesis.

Circulating red blood cells (RBCs) are essential for tissue oxygenation and homeostasis. Defective terminal erythropoiesis contributes to decreased generation of RBCs in many disorders. Specifically, ineffective nuclear expulsion (enucleation) during terminal maturation is an obstacle to therapeutic RBC production in vitro. To obtain mechanistic insights into terminal erythropoiesis we focused on FOXO3, a transcription factor implicated in erythroid disorders. Using an integrated computational and experimental systems biology approach, we show that FOXO3 is essential for the correct temporal gene expression during terminal erythropoiesis. We demonstrate that the FOXO3-dependent genetic network has critical physiological functions at key steps of terminal erythropoiesis including enucleation and mitochondrial clearance processes. FOXO3 loss deregulated transcription of genes implicated in cell polarity, nucleosome assembly and DNA packaging-related processes and compromised erythroid enucleation. Using high-resolution confocal microscopy and imaging flow cytometry we show that cell polarization is impaired leading to multilobulated Foxo3 -/- erythroblasts defective in nuclear expulsion. Ectopic FOXO3 expression rescued Foxo3 -/- erythroblast enucleation-related gene transcription, enucleation defects and terminal maturation. Remarkably, FOXO3 ectopic expression increased wild type erythroblast maturation and enucleation suggesting that enhancing FOXO3 activity may improve RBCs production. Altogether these studies uncover FOXO3 as a novel regulator of erythroblast enucleation and terminal maturation suggesting FOXO3 modulation might be therapeutic in disorders with defective erythroid maturation.

The rise in computational systems biology approaches for understanding NF-κB signaling dynamics.

A study by Cheng et al. in this issue of Science Signaling highlights the distinct single-cell signaling characteristics conferred by pathways mediated by the adaptor proteins MyD88 and TRIF in the TLR4-dependent activation of the transcription factor nuclear factor κB (NF-κB).

Systems Biology Reveals Cigarette Smoke-Induced Concentration-Dependent Direct and Indirect Mechanisms That Promote Monocyte-Endothelial Cell Adhesion.

Cigarette smoke (CS) affects the adhesion of monocytes to endothelial cells, a critical step in atherogenesis. Using an in vitro adhesion assay together with innovative computational systems biology approaches to analyze omics data, our study aimed at investigating CS-induced mechanisms by which monocyte-endothelial cell adhesion is promoted. Primary human coronary artery endothelial cells (HCAECs) were treated for 4 h with (1) conditioned media of human monocytic Mono Mac-6 (MM6) cells preincubated with low or high concentrations of aqueous CS extract (sbPBS) from reference cigarette 3R4F for 2 h (indirect treatment, I), (2) unconditioned media similarly prepared without MM6 cells (direct treatment, D), or (3) freshly generated sbPBS (fresh direct treatment, FD). sbPBS promoted MM6 cells-HCAECs adhesion following I and FD, but not D. In I, the effect was mediated at a low concentration through activation of vascular inflammation processes promoted in HCAECs by a paracrine effect of the soluble mediators secreted by sbPBS-treated MM6 cells. Tumor necrosis factor α (TNFα), a major inducer, was actually shed by unstable CS compound-activated TNFα-converting enzyme. In FD, the effect was triggered at a high concentration that also induced some toxicity. This effect was mediated through an yet unknown mechanism associated with a stress damage response promoted in HCAECs by unstable CS compounds present in freshly generated sbPBS, which had decayed in D unconditioned media. Aqueous CS extract directly and indirectly promotes monocytic cell-endothelial cell adhesion in vitro via distinct concentration-dependent mechanisms.

Host-pathogen interactions between the human innate immune system and Candida albicans-understanding and modeling defense and evasion strategies.

The diploid, polymorphic yeast Candida albicans is one of the most important human pathogenic fungi. C. albicans can grow, proliferate and coexist as a commensal on or within the human host for a long time. However, alterations in the host environment can render C. albicans virulent. In this review, we describe the immunological cross-talk between C. albicans and the human innate immune system. We give an overview in form of pairs of human defense strategies including immunological mechanisms as well as general stressors such as nutrient limitation, pH, fever etc. and the corresponding fungal response and evasion mechanisms. Furthermore, Computational Systems Biology approaches to model and investigate these complex interactions are highlighted with a special focus on game-theoretical methods and agent-based models. An outlook on interesting questions to be tackled by Systems Biology regarding entangled defense and evasion mechanisms is given.

Supervised learning methods in modeling of CD4+ T cell heterogeneity.

BackgroundModeling of the immune system – a highly non-linear and complex system – requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales.MethodsThis study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR). Application of supervised learning methods could reduce the complexity of Ordinary Differential Equations (ODEs)-based intracellular models by only focusing on the input and output cytokine concentrations. In addition, this modeling framework can be efficiently integrated into multiscale models.ResultsOur results demonstrate that ANN and RF outperform the other two methods. Furthermore, ANN and RF have comparable performance when applied to in silico data with and without added noise. The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system. Finally, the running time of different methods was compared, which confirms that ANN is considerably faster than RF.ConclusionsUsing machine learning as opposed to ODE-based method reduces the computational complexity of the system and allows one to gain a deeper understanding of the complex interplay between the different related entities.

Oscillation of cAMP and Ca2+ in cardiac myocytes: a systems biology approach

Cyclic adenosine monophosphate (cAMP) and Ca(2+) levels may oscillate in harmony within excitable cells; a mathematical oscillation loop model, the Cooper model, of these oscillations was developed two decades ago. However, in that model all adenylyl cyclase (AC) isoforms were assumed to be inhibited by Ca(2+), and it is now known that the heart expresses multiple AC isoforms, among which the type 5/6 isoforms are Ca(2+)-inhibitable whereas the other five (AC2, 3, 4, 7, and 9) are not. We used a computational systems biology approach with CellDesigner simulation software to develop a comprehensive graphical map and oscillation loop model for cAMP and Ca(2+). This model indicated that Ca(2+)-mediated inhibition of AC is essential to create oscillations of Ca(2+) and cAMP, and the oscillations were not altered by incorporation of phosphodiesterase-mediated cAMP hydrolysis or PKA-mediated inhibition of AC into the model. More importantly, they were created but faded out immediately in the co-presence of Ca(2+)-noninhibitable AC isoforms. Because the subcellular locations of AC isoforms are different, spontaneous cAMP and Ca(2+) oscillations may occur within microdomains containing only Ca(2+)-inhibitable isoforms in cardiac myocytes, which might be necessary for fine tuning of excitation-contraction coupling.

Discovery of Key Molecular Pathways of C1 Metabolism and Formaldehyde Detoxification in Maize through a Systematic Bioinformatics Literature Review

Computational systems biology approaches provide insights to understand complex molecular phenomena in living systems. Such understanding demands the need to systematically interrogate and review existing literature to refine and distil key molecular pathways. This paper explores a methodological process to identify key molecular pathways from systematic bioinformatics literature review. This process is used to identify molecular pathways for a ubiquitous molecular process in all plant biological systems: C1 metabolism and formaldehyde detoxification, specific to maize. The C1 metabolism is essential for all organisms to provide one-carbon units for methylation and other types of modifications, as well as for nucleic acid, amino acid, and other biomolecule syntheses. Formaldehyde is a toxic one-carbon molecule which is produced endogenously and found in the environment, and whose detoxification is an important part of C1 metabolism. This systematic review involves a five-part process: 1) framing of the research question; 2) literature collection based on a parallel search strategy; 3) relevant study selection based on search refinement; 4) molecular pathway identification; and 5) integration of key molecular pathway mechanisms to yield a well-defined set molecular systems associated with a particular biochemical function. Findings from this systematic review produced three main molecular systems: a) methionine biosynthesis; b) the methylation cycle; and c) formaldehyde detoxification. Specific insights from the resulting molecular pathways indicate that normal C1 metabolism involves the transfer of a carbon group from serine through a folate-mediated pathway to methionine, and eventually the methylation of a biomolecule. In photosynthetic tissues, C1 metabolism often proceeds in reverse towards serine biosynthesis and formate oxidation. C1 metabolism, in maize, appears to be present in the developing embryo and endosperm indicating that these cells are vulnerable to perturbations in formaldehyde detoxification. These insights demonstrate the value of a systematic bioinformatics literature review process from a broad spectrum of domain literature to specific and relevant molecular pathways.

Abstract PR09: A systems biology approach to elucidate novel drug targets in KRAS mutant tumors

Abstract Mutations of RAS GTPase family oncogenes affect more than 20% of human cancers. Of the three major RAS isoforms (HRAS, KRAS, and NRAS), KRAS is the most frequently mutated, affecting 57% of pancreatic adenocarcinomas, 33% of colorectal cancers, 17% of lung adenocarcinomas, and many other tumor subtypes. Most RAS mutations abrogate its GTPase activity, thus activating aberrant downstream signals associated with worst prognosis and tumor resistance to both chemotoxic and targeted therapy. Therapies against RAS have failed, in part, because of our limited understanding of RAS-mediated signal transduction pathways. Therefore, we hypothesize that the systematic and complete dissection of RAS relevant regulatory interactions can help elucidate druggable mechanisms to abrogate its aberrant activity or elicit synthetic lethality in RAS mutated tumors. Here, we have employed computational systems biology approaches to begin to dissect KRAS regulatory pathways. Specifically, we reverse-engineered a cell context specific KRAS regulatory network from TCGA transcriptome-wide expression data. The KRAS regulatory network was used to predict KRAS activation levels across all colon and lung adenocarcinoma samples available in TCGA in order to generate a patient-by-patient estimate of KRAS activity. Genome-wide somatic mutations and copy-number variations were then tested for significant association with the predicted KRAS activity. Consistent with known KRAS biology, samples harboring KRAS or BRAF activating mutations were significantly associated with increased KRAS activity. However, we also observed that mutations in the DEAD box helicase DDX6 and the Glycolipid Transfer-domain protein GLTPD2 to be significantly associated, suggesting that these proteins may be involved in KRAS pathways. We dissected the effects of KRAS mutations on the activity of all transcription factors, identifying those more closely activated or repressed in a KRAS-specific context. KRAS is activated by its own mutations, along with known KRAS interactors, such as SOS1 and RAPGEF6. Interestingly, we found that these activating KRAS mutations significantly correlate with increased activities of the Rho-associated kinases ROCK1 and ROCK2. Moreover, the protein-protein interaction database PrePPI predicts KRAS to homodimerize, and surprisingly, to interact with ROCK1/2 through a new PPI interface. We are currently confirming these results through biochemical and biophysical analyses. This abstract is also presented as Poster B34. Citation Format: David A. Wah, Federico M. Giorgi, Donald Petrey, Mariano J. Alvarez, Tonya Silkov, Josh Broyde, Gabrielle E. Rieckhof, Barry Honig, Andrea Califano. A systems biology approach to elucidate novel drug targets in KRAS mutant tumors. [abstract]. In: Proceedings of the AACR Special Conference on RAS Oncogenes: From Biology to Therapy; Feb 24-27, 2014; Lake Buena Vista, FL. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(12 Suppl):Abstract nr PR09. doi: 10.1158/1557-3125.RASONC14-PR09