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Methods for Real-Time Quantitative RT-PCR, Assessment of medal accuracy, Gene expression, copy number and survival analysis

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1–S transition. The model identified known components of the cell-cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1–S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 of 12 predicted genes to have a role in G1–S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NFκB and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NFκB and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NFκB signaling. Cancer Res; 77(7); 1575–85. ©2017 AACR.

Supplemental Figure Legends

Legend Figure S1: Schematic of the experimental framework Legend Figure S2: Predicted phenotypic value and assessment of predictive power of the model. Legend Figure S3: Taget knock-down. Legend Figure S4: Treatment with individual siRNAs against TRIB1 leads to G1-S arrest. Legend Figure S5: RNA levels of cell cycle genes. Legend Figure S6: TRIB1 knockdown results in inhibition of NFκB-responsive promoter activity: comparison of individual siRNA duplexes. Legend Figure S7: TRIB1 resistant to siRNA (TRIB1-siR) rescues the phenotype induced by TRIB1-siRNA. Legend Figure S8: Downregulation of NFkB target genes after treatment with TRIB1 siRNA. Legend Figure S9: TRIB1 knock down leads to inhibition of NFκB pathway. Legend Figure S10: TRIB1 siRNA promotes TRAIL-induced apoptosis through activation of caspase-8 cleavage. Legend Figure S11: TRIB1 knockdown results in decrease of YY1gene expression. Legend Figure S12: Kaplan-Meier plots Legend Figure S13: Hazard Ratios for BCCS distribution. Legend Figure S14: Hazard Ratios for OS distribution.

Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of >1.10 for common variants (allele frequency > 0.05) and >1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 x 10(-8) under an additive genetic model.Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.

Type 2 diabetes (T2D) is caused by loss of pancreatic β-cell mass and failure of the remaining β-cells to deliver sufficient insulin to meet demand. β-Cell glucolipotoxicity (GLT), which refers to combined, deleterious effects of elevated glucose and fatty acid levels on β-cell function and survival, contributes to T2D-associated β-cell failure. Drugs and mechanisms that protect β-cells from GLT stress could potentially improve metabolic control in patients with T2D. In a phenotypic screen seeking low-molecular-weight compounds that protected β-cells from GLT, we identified compound A that selectively blocked GLT-induced apoptosis in rat insulinoma cells. Compound A and its optimized analogs also improved viability and function in primary rat and human islets under GLT. We discovered that compound A analogs decreased GLT-induced cytosolic calcium influx in islet cells, and all measured β-cell–protective effects correlated with this activity. Further studies revealed that the active compound from this series largely reversed GLT-induced global transcriptional changes. Our results suggest that taming cytosolic calcium overload in pancreatic islets can improve β-cell survival and function under GLT stress and thus could be an effective strategy for T2D treatment.

Background Variation in response to the most commonly used class of asthma controller medication, inhaled corticosteroids, presents a serious challenge in asthma management, particularly for steroid-resistant patients with little or no response to treatment. Objective We applied a systems biology approach to primary clinical and genomic data to identify and validate genes that modulate steroid response in asthmatic children. Methods We selected 104 inhaled corticosteroid–treated asthmatic non-Hispanic white children and determined a steroid responsiveness endophenotype (SRE) using observations of 6 clinical measures over 4 years. We modeled each subject's cellular steroid response using data from a previously published study of immortalized lymphoblastoid cell lines under dexamethasone (DEX) and sham treatment. We integrated SRE with immortalized lymphoblastoid cell line DEX responses and genotypes to build a genome-scale network using the Reverse Engineering, Forward Simulation modeling framework, identifying 7 genes modulating SRE. Results Three of these genes were functionally validated by using a stable nuclear factor κ-light-chain-enhancer of activated B cells luciferase reporter in A549 human lung epithelial cells, IL-1β cytokine stimulation, and DEX treatment. By using small interfering RNA transfection, knockdown of family with sequence similarity 129 member A (FAM129A) produced a reduction in steroid treatment response ( P Conclusion With this systems-based approach, we have shown that FAM129A is associated with variation in clinical asthma steroid responsiveness and that FAM129A modulates steroid responsiveness in lung epithelial cells.

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1–S transition. The model identified known components of the cell-cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1–S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 of 12 predicted genes to have a role in G1–S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NFκB and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NFκB and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NFκB signaling. Cancer Res; 77(7); 1575–85. ©2017 AACR.

T cell migration toward sites of antigen exposure is mediated by G protein signaling and is a key function in the development of immune responses. Regulators of G protein signaling (RGS) proteins modulate G protein signaling; however, their role in the regulation of adaptive immune responses has not been thoroughly explored. Herein we demonstrated abundant expression of the Gi/Gq-specific RGS3 in activated T cells, and that diminished RGS3 expression in a T cell thymoma increased cytokine-induced migration. To examine the role of endogenous RGS3 in vivo, mice deficient in the RGS domain (RGS3ΔRGS) were generated and tested in an experimental model of asthma. Compared with littermate controls, the inflammation in the RGS3ΔRGSmice was characterized by increased T cell numbers and the striking development of perivascular lymphoid structures. Surprisingly, while innate inflammatory cells were also increased in the lungs of RGS3ΔRGSmice, eosinophil numbers and Th2 cytokine production were equivalent to control mice. In contrast, T cell numbers in the draining lymph nodes (dLN) were reduced in the RGS3ΔRGS, demonstrating a redistribution of T cells from the dLN to the lungs via increased RGS3ΔRGST cell migration. Together these novel findings show a nonredundant role for endogenous RGS3 in controlling T cell migration in vitro and in an in vivo model of inflammation.

This protocol details the use of the mode-of-action by network identification (MNI) algorithm to identify the gene targets of a drug treatment based on gene-expression data. Investigators might also use the MNI algorithm to identify the gene mediators of a disease or the physiological state of cells and tissues. The MNI algorithm uses a training data set of hundreds of expression profiles to construct a statistical model of gene-regulatory networks in a cell or tissue. The model describes combinatorial influences of genes on one another. The algorithm then uses the model to filter the expression profile of a particular experimental treatment and thereby distinguish the molecular targets or mediators of the treatment response from hundreds of additional genes that also exhibit expression changes. It takes approximately 1 h per run, although run time is significantly affected by the size of the genome and data set.

Cell-surface proteins are attractive targets for the development of novel antifungals as they are more accessible to drugs than are intracellular targets. By using a computational biology approach, we identified 180 potential cell-surface proteins in Candida albicans, including the known cell-surface adhesin Als1 and other cell-surface antigens, such as Pra1 and Csa1. Six proteins (named Csf1-6 for cell-surface factors) were selected for further biological characterization. First, we verified that the selected CSF genes are expressed in the yeast and/or hyphal form and then we investigated the effect of the loss of each CSF gene on cell-wall integrity, filamentation, adhesion to mammalian cells and virulence. As a result, we identified Csf4, a putative glycosidase with an apparent orthologue in Saccharomyces cerevisiae (Utr2), as an important factor for cell-wall integrity and maintenance. Interestingly, deletion of CSF4 also resulted in a defect in filamentation, a reduction in adherence to mammalian cells in an in vitro adhesion assay, and a prolongation of survival in an immunocompetent mouse model of disseminated candidiasis. A delay in colonization of key organs (e.g. kidney) was also observed, which is consistent with a reduction in virulence of the csf4-deletion strain. These data indicate a key role for extracellular glycosidases in fungal pathogenesis and represent a new site for therapeutic intervention to cure and prevent fungal disease. Copyright © 2004 John Wiley & Sons, Ltd.

14-3-3 family of proteins plays a key regulatory role in signal transduction, checkpoint control, apoptotic, and nutrient-sensing pathways. 14-3-3 proteins act by binding to partner proteins, and this binding often leads to the altered subcellular localization of the partner. 14-3-3 proteins promote the cytoplasmic localization of many binding partners, including the pro-apoptotic protein BAD and the cell cycle regulatory phosphatase Cdc25C, but they can also promote the nuclear localization of other partners, such as the catalytic subunit of telomerase (TERT). In some cases, 14-3-3 binding has no effect on the subcellular localization of a partner. 14-3-3 may affect the localization of a protein by interfering with the function of a nearby targeting sequence, such as a nuclear localization sequence (NLS) or a nuclear export sequence (NES), on the binding partner.

14-3-3 family members are dimeric phosphoserine-binding proteins that participate in signal transduction and checkpoint control pathways. In this work, dominant-negative mutant forms of 14-3-3 were used to disrupt 14-3-3 function in cultured cells and in transgenic animals. Transfection of cultured fibroblasts with the R56A and R60A double mutant form of 14-3-3zeta (DN-14-3-3zeta) inhibited serum-stimulated ERK MAPK activation, but increased the basal activation of JNK1 and p38 MAPK. Fibroblasts transfected with DN-14-3-3zeta exhibited markedly increased apoptosis in response to UVC irradiation that was blocked by pre-treatment with a p38 MAPK inhibitor, SB202190. Targeted expression of DN-14-3-3eta to murine postnatal cardiac tissue increased the basal activation of JNK1 and p38 MAPK, and affected the ability of mice to compensate for pressure overload, which resulted in increased mortality, dilated cardiomyopathy and massive cardiomyocyte apoptosis. These results demonstrate that a primary function of mammalian 14-3-3 proteins is to inhibit apoptosis.

The protein kinase KSR-1 is a recently identified participant in the Ras signaling pathway. The subcellular localization of KSR-1 is variable. In serum-deprived cultured cells, KSR-1 is primarily found in the cytoplasm; in serum-stimulated cells, a significant portion of KSR-1 is found at the plasma membrane. To identify the mechanism that mediates KSR-1 translocation, we performed a yeast two-hybrid screen. Three clones that interacted with KSR-1 were found to encode the full-length γ10 subunit of heterotrimeric G-proteins. KSR-1 also interacted with γ2and γ3 in a two-hybrid assay. Deletion analysis demonstrated that the isolated CA3 domain of KSR-1, which contains a cysteine-rich zinc finger-like domain, interacted with γ subunits. Coimmunoprecipitation experiments demonstrated that KSR-1 bound to β1γ3 subunits when all three were transfected into cultured cells. Lysophosphatidic acid treatment of cells induced KSR-1 translocation to the plasma membrane from the cytoplasm that was blocked by administration of pertussis toxin but not by dominant-negative Ras. Finally, transfection of wild-type KSR-1 inhibited β1γ3-induced mitogen-activated protein kinase activation in cultured cells. These results demonstrate that KSR-1 translocation to the plasma membrane is mediated, at least in part, by an interaction with βγ and that this interaction may modulate mitogen-activated protein kinase signaling.

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28., Author Summary The collection and analysis of clinical data has played a key role in providing insights into the diagnosis, prognosis and treatment of disease. However, it is imperative that molecular and genetic data also be collected and integrated into the creation of network models, which capture underlying mechanisms of disease and can be interrogated to elucidate previously unknown biology. Bringing data from the clinic to the bench completes the cycle of translational research, which we demonstrate with this work. We built disease models from genetics, whole blood gene expression profiles and the component clinical measures of rheumatoid arthritis using a data-driven approach that leverages supercomputing. Genetic factors can be utilized as a source of perturbation to the system such that causal connections between genetics, molecular entities and clinical outcomes can be inferred. The existing TNF-α blocker treatments for rheumatoid arthritis are only effective for approximately 2/3 of the affected population. We identified novel therapeutic intervention points that may lead to the development of alternatives to TNF-α blocker treatments. We believe this approach will provide improved drug discovery programs, new insights into disease progression, increased drug efficacy and novel biomarkers for chronic and complex diseases.

Human disease genes differ significantly from their rodent orthologs with respect to their overall levels of conservation and their rates of evolutionary change. Rodent orthologs of human trinucleotide repeat-expansion disease genes were also found to contain substantially fewer such repeats., Background Model organisms have contributed substantially to our understanding of the etiology of human disease as well as having assisted with the development of new treatment modalities. The availability of the human, mouse and, most recently, the rat genome sequences now permit the comprehensive investigation of the rodent orthologs of genes associated with human disease. Here, we investigate whether human disease genes differ significantly from their rodent orthologs with respect to their overall levels of conservation and their rates of evolutionary change. Results Human disease genes are unevenly distributed among human chromosomes and are highly represented (99.5%) among human-rodent ortholog sets. Differences are revealed in evolutionary conservation and selection between different categories of human disease genes. Although selection appears not to have greatly discriminated between disease and non-disease genes, synonymous substitution rates are significantly higher for disease genes. In neurological and malformation syndrome disease systems, associated genes have evolved slowly whereas genes of the immune, hematological and pulmonary disease systems have changed more rapidly. Amino-acid substitutions associated with human inherited disease occur at sites that are more highly conserved than the average; nevertheless, 15 substituting amino acids associated with human disease were identified as wild-type amino acids in the rat. Rodent orthologs of human trinucleotide repeat-expansion disease genes were found to contain substantially fewer of such repeats. Six human genes that share the same characteristics as triplet repeat-expansion disease-associated genes were identified; although four of these genes are expressed in the brain, none is currently known to be associated with disease. Conclusions Most human disease genes have been retained in rodent genomes. Synonymous nucleotide substitutions occur at a higher rate in disease genes, a finding that may reflect increased mutation rates in the chromosomal regions in which disease genes are found. Rodent orthologs associated with neurological function exhibit the greatest evolutionary conservation; this suggests that rodent models of human neurological disease are likely to most faithfully represent human disease processes. However, with regard to neurological triplet repeat expansion-associated human disease genes, the contraction, relative to human, of rodent trinucleotide repeats suggests that rodent loci may not achieve a 'critical repeat threshold' necessary to undergo spontaneous pathological repeat expansions. The identification of six genes in this study that have multiple characteristics associated with repeat expansion-disease genes raises the possibility that not all human loci capable of facilitating neurological disease by repeat expansion have as yet been identified.

The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.

14-3-3 proteins are intracellular, dimeric molecules that bind to and modify the activity of several signaling proteins. We used human 14-3-3zeta as a bait in the yeast two-hybrid system to screen a murine embryonic cDNA library. One interacting clone was found to encode the carboxyl terminus of a putative protein kinase. The coding sequence of the human form (protein kinase Ualpha, PKUalpha) of this protein kinase was found in GenBank(TM) on the basis of sequence homology. The two-hybrid clone was also highly homologous to TOUSLED, an Arabidopsis thaliana protein kinase that is required for normal flower and leaf development. PKUalpha has been found by coimmunoprecipitation to bind to 14-3-3zeta in vivo. Our confocal laser immunofluorescence microscopic experiments revealed that PKUalpha colocalizes with the cytoplasmic intermediate filament system of cultured fibroblasts in the G(1) phase of the cell cycle. PKUalpha is found in the perinuclear area of S phase cells and in the nucleus of late G(2) cells. Transfection of cells with a dominant negative form of 14-3-3eta promotes the nuclear localization of PKUalpha. These results suggest that the subcellular localization of PKUalpha is regulated, at least in part, by its association with 14-3-3.

Background: KSR (kinase suppressor of Ras) is a recently identified putative protein kinase that positively mediates the Ras signaling pathway in the invertebrates Caenorhabditis elegans and Drosophila melanogaster . The function of vertebrate KSR is not well characterized biochemically or biologically. Results: We examined the physiological role of KSR in vertebrate signal transduction using Xenopus laevis oocytes. Overexpression of KSR, in combination with overexpression of the intracellular dimeric protein 14-3-3, induced Xenopus oocyte meiotic maturation and cdc2 kinase activation; the effect of KSR and 14-3-3 on oocyte maturation was blocked by co-expression of dominant-negative Raf-1. We noted that KSR contains multiple potential binding sites for 14-3-3, and we used the yeast two-hybrid system and co-immunoprecipitation experiments to show that KSR can bind to 14-3-3. Furthermore, we demonstrated that KSR can form a complex with Raf kinase both in vitro and in cultured cells. Cell fractionation studies revealed that KSR formed a complex with 14-3-3 in both the membrane and cytoplasmic fractions of cell lysates; however, KSR only formed a complex with Raf-1 in the membrane fraction. Conclusions: Our finding suggest that KSR, 14-3-3 and Raf form an oligomeric signaling complex and that KSR positively regulates the Ras signaling pathway in vertebrate organisms.

Background: Lenvatinib is an oral tyrosine kinase inhibitor targeting VEGFR1-3, FGFR1-4, RET, KIT and PDGFRβ. Anti-tumor activity has been observed in melanoma patients in Phase I studies. We applied an integrative supercomputer-driven analysis approach to identify biomarkers of lenvatinib treatment response in melanoma patients. Methods: Clinical data sets including tumor response data (RECIST), progression free survival (PFS), pharmacokinetic parameters (PK) and molecular data sets including baseline tumor gene expression (Affymetrix U133Plus2) and BRAF and NRAS mutational status, were collected from 18 patients with metastatic melanoma who received lenvatinib 10 mg orally twice daily in 28-day cycles. These clinical and molecular data sets were used to generate computational models developed using REFS (Reverse Engineering and Forward Simulation) modeling platform, which utilizes Bayesian network inference and simulations. Simulations were performed to identify biomarkers of lenvatinib treatment response. These potential predictive biomarkers were then tested for their ability to predict response to lenvatinib in preclinical models. Results: Using a model comprising gene expression, mutational status and PK data, REFS identified a panel of 18 potential predictive biomarkers of lenvatinib treatment response. Identified biomarkers were able to predict up to 89% of the observed variance in the tumor response data. A total of 32 identified genes including 6 candidate predictive biomarkers (TARBP2, CACNA1, C7ORF, RAP2A, SHMT1, IL22RA2) were further validated in a preclinical melanoma model system (n=12) and tissue bank samples with matched normal adjacent tissue (n=21). Expression of 14 genes correlated with relative tumor volume (r>0.35 or r2). Conclusions: Potential predictive biomarkers of lenvatinib treatment response in melanoma patients were identified by computational modeling and validated in a preclinical model system and tumor tissue bank samples. The identified biomarkers will be tested for their predictive value in an ongoing Phase 2 trial. Citation Format: Tadashi Kadowaki, Yasuhiro Funahashi, Junji Matsui, Kumar Pavan, Pallavi Sachdev, Jim O'Brien, Heming Xing, Paul D. McDonagh, Iya Khalil, Razelle Kurzrock, David S. Hong, John Nemunaitis. Data-driven computational modeling to identify biomarkers of response to lenvatinib (E7080) in melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4025. doi:10.1158/1538-7445.AM2013-4025

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate non-intuitive behaviors. We applied a novel computational strategy to decipher causal relationships between signaling network components. Gene networks were constructed from global gene expression profiles to model G1-S cell cycle arrest as a consequence of inhibition of MEK, a key component of the oncogenic RAF-MEK-ERK signal transduction pathway. Simulation of causally driven models generated a ranked list of 12 testable hypotheses, which confirmed role of known cell cycle regulators (CCND1, CCNE2, and CDC25A) and identified novel drivers of G1-S transition (IER2, TRIB1, C14ORF133). Validation confirmed 11 out 12 predictions. Second model which inferred causal relationships between genes lead to discovery of TRIB1 (tribbles homolog of Drosophila) as a regulator of NFkB-dependent gene expression. TRIB1 also mediates cross-talk to the NFκB pathway by enhancing p100 and IκBα-phosphorylation and degradation thus regulating cell survival and expression of cytokines crucial for tumor-stroma interaction. In agreement with this, knock-down of TRIB1 results in induction of apoptosis and sensitizes cells to cell killing induced by the death-receptor agonist TRAIL via upregulation of TRAIL receptor, DR5. Thus TRIB1 is a central regulator of cell cycle and survival and represents a potential therapeutic target. Therefore, applying REFS (Reverse Engineering Forward Simulation) to analyze gene expression profiles maximizes our ability to extract knowledge from experimental data.

ABSTRACT Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate non-intuitive behaviors. We applied a novel computational strategy to decipher causal relationships between signaling network components. Gene networks were constructed MEK from global gene expression profiles to model G1-S cell cycle arrest as a consequence of inhibition of MEK, a key component of the oncogenic RAF-MEK-ERK signal transduction pathway. Through forward-simulation of reverse-engineered networks candidate MEK-dependent regulators of the cell cycle were identified. The potential of the approach to infer causality is demonstrated by confirmed predictions of differential effects of two early-response transcription factors, IER2 and EGR1. Conventional linear regression analysis shows strong correlation of expression of both with MEK inhibition and cell cycle distribution, while only IER2 is predicted by our model and experimentally validated to have a causal effect on G1-S transition. Furthermore, we identify TRIB1 (tribbles homologue 1 drosophila) as a potent and novel cell cycle regulator. TRIB1 also mediates cross-talk to the NFkB pathway by enhancing p100 and IkBa-phosphorylation and degradation thus regulating cell survival and expression of cytokines crucial for tumor-stroma interaction. In agreement with this, knock-down of TRIB1 results in induction of apoptosis and sensitizes cells to cell killing induced by the death-receptor agonist TRAIL via upregulation of TRAIL receptor, DR5. Thus TRIB1 is a central regulator of cell cycle and survival and represents a potential therapeutic target. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 986. doi:1538-7445.AM2012-986

Model organisms have contributed substantially to our understanding of the etiology of human disease as well as having assisted with the development of new treatment modalities. The availability of the human, mouse and, most recently, the rat genome sequences now permit the comprehensive investigation of the rodent orthologs of genes associated with human disease. Here, we investigate whether human disease genes differ significantly from their rodent orthologs with respect to their overall levels of conservation and their rates of evolutionary change. Human disease genes are unevenly distributed among human chromosomes and are highly represented (99.5%) among human-rodent ortholog sets. Differences are revealed in evolutionary conservation and selection between different categories of human disease genes. Although selection appears not to have greatly discriminated between disease and non-disease genes, synonymous substitution rates are significantly higher for disease genes. In neurological and malformation syndrome disease systems, associated genes have evolved slowly whereas genes of the immune, hematological and pulmonary disease systems have changed more rapidly. Amino-acid substitutions associated with human inherited disease occur at sites that are more highly conserved than the average; nevertheless, 15 substituting amino acids associated with human disease were identified as wild-type amino acids in the rat. Rodent orthologs of human trinucleotide repeat-expansion disease genes were found to contain substantially fewer of such repeats. Six human genes that share the same characteristics as triplet repeat-expansion disease-associated genes were identified; although four of these genes are expressed in the brain, none is currently known to be associated with disease. Most human disease genes have been retained in rodent genomes. Synonymous nucleotide substitutions occur at a higher rate in disease genes, a finding that may reflect increased mutation rates in the chromosomal regions in which disease genes are found. Rodent orthologs associated with neurological function exhibit the greatest evolutionary conservation; this suggests that rodent models of human neurological disease are likely to most faithfully represent human disease processes. However, with regard to neurological triplet repeat expansion-associated human disease genes, the contraction, relative to human, of rodent trinucleotide repeats suggests that rodent loci may not achieve a 'critical repeat threshold' necessary to undergo spontaneous pathological repeat expansions. The identification of six genes in this study that have multiple characteristics associated with repeat expansion-disease genes raises the possibility that not all human loci capable of facilitating neurological disease by repeat expansion have as yet been identified.