Causal reasoning identifies mechanisms of sensitivity for a novel AKT kinase inhibitor, GSK690693

MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny

BACKGROUND

Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from.

RESULTS

To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein-protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action.

CONCLUSIONS

MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN .

Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis

BACKGROUND

Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks.

RESULTS

According to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of direct targets. With respect to the recovery of signalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 'landmark' genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets.

CONCLUSIONS

Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

Treatment With Suboptimal Dose of Benznidazole Mitigates Immune Response Molecular Pathways in Mice With Chronic Chagas Cardiomyopathy

Chronic Chagas cardiomyopathy (CCC) is the most frequent and severe form of Chagas disease, a neglected tropical illness caused by the protozoan Trypanosoma cruzi, and the main cause of morbimortality from cardiovascular problems in endemic areas. Although efforts have been made to understand the signaling pathways and molecular mechanisms underlying CCC, the immunological signaling pathways regulated by the etiological treatment with benznidazole (Bz) has not been reported. In experimental CCC, Bz combined with the hemorheological and immunoregulatory agent pentoxifylline (PTX) has beneficial effects on CCC. To explore the molecular mechanisms of Bz or Bz+PTX therapeutic strategies, C57BL/6 mice chronically infected with the T. cruzi Colombian strain (discrete typing unit TcI) and showing electrocardiographic abnormalities were submitted to suboptimal dose of Bz or Bz+PTX from 120 to 150 days postinfection. Electrocardiographic alterations, such as prolonged corrected QT interval and heart parasite load, were beneficially impacted by Bz and Bz+PTX. RT-qPCR TaqMan array was used to evaluate the expression of 92 genes related to the immune response in RNA extracted from heart tissues. In comparison with non-infected mice, 30 genes were upregulated, and 31 were downregulated in infected mice. Particularly, infection upregulated the cytokines IFN-γ, IL-12b, and IL-2 (126-, 44-, and 18-fold change, respectively) and the T-cell chemoattractants CCL3 and CCL5 (23- and 16-fold change, respectively). Bz therapy restored the expression of genes related to inflammatory response, cellular development, growth, and proliferation, and tissue development pathways, most probably linked to the cardiac remodeling processes inherent to CCC, thus mitigating the Th1-driven response found in vehicle-treated infected mice. The combined Bz+PTX therapy revealed pathways related to the modulation of cell death and survival, and organismal survival, supporting that this strategy may mitigate the progression of CCC. Altogether, our results contribute to the better understanding of the molecular mechanisms of the immune response in the heart tissue in chronic Chagas disease and reinforce that parasite persistence and dysregulated immune response underpin CCC severity. Therefore, Bz and Bz+PTX chemotherapies emerge as tools to interfere in these pathways aiming to improve CCC prognosis.

A nontoxic fungal natural product modulates fin regeneration in zebrafish larvae upstream of FGF-WNT developmental signaling

BACKGROUND

The regeneration of larvae zebrafish fin emerged as a new model of regeneration in the last decade. In contrast to genetic tools to study fin regeneration, chemical probes to modulate and interrogate regeneration processes are not well developed.

RESULTS

We set up a zebrafish larvae fin regeneration assay system and tested activities of natural product compounds and extracts, prepared from various microbes. Colomitide C, a recently isolated product from a fungus obtained from Antarctica, inhibited larvae fin regeneration. Using fluorescent reporter transgenic lines, we show that colomitide C inhibited fibroblast growth factor (FGF) signaling and WNT/β-catenin signaling, which were activated after larvae fin amputation. By using the endothelial cell reporter line and immunofluorescence, we showed that colomitide C did not affect migration of the blood vessel and nerve into the injured larvae fin. Colomitide C did not show any cytotoxic activities when tested against FGF receptor-amplified human cancer cell lines.

CONCLUSION

Colomitide C, a natural product, modulated larvae fin regeneration likely acting upstream of FGF and WNT signaling. Colomitide C may serve as a template for developing new chemical probes to study regeneration and other biological processes.

Using microRNA Networks to Understand Cancer

Human cancers are characterized by deregulated expression of multiple microRNAs (miRNAs), involved in essential pathways that confer the malignant cells their tumorigenic potential. Each miRNA can regulate hundreds of messenger RNAs (mRNAs), while various miRNAs can control the same mRNA. Additionally, many miRNAs regulate and are regulated by other species of non-coding RNAs, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs). For this reason, it is extremely difficult to predict, study, and analyze the precise role of a single miRNA involved in human cancer, considering the complexity of its connections. Focusing on a single miRNA molecule represents a limited approach. Additional information could come from network analysis, which has become a common tool in the biological field to better understand molecular interactions. In this review, we focus on the main types of networks (monopartite, association networks and bipartite) used for analyzing biological data related to miRNA function. We briefly present the important steps to take when generating networks, illustrating the theory with published examples and with future perspectives of how this approach can help to better select miRNAs that can be therapeutically targeted in cancer.

Expanding primary cells from mucoepidermoid and other salivary gland neoplasms for genetic and chemosensitivity testing

Restricted availability of cell and animal models is a rate-limiting step for investigation of salivary gland neoplasm pathophysiology and therapeutic response. Conditionally reprogrammed cell (CRC) technology enables establishment of primary epithelial cell cultures from patient material. This study tested a translational workflow for acquisition, expansion and testing of CRC-derived primary cultures of salivary gland neoplasms from patients presenting to an academic surgical practice. Results showed that cultured cells were sufficient for epithelial cell-specific transcriptome characterization to detect candidate therapeutic pathways and fusion genes, and for screening for cancer risk-associated single nucleotide polymorphisms (SNPs) and driver gene mutations through exome sequencing. Focused study of primary cultures of a low-grade mucoepidermoid carcinoma demonstrated amphiregulin-mechanistic target of rapamycin-protein kinase B (AKT; AKT1) pathway activation, identified through bioinformatics and subsequently confirmed as present in primary tissue and preserved through different secondary 2D and 3D culture media and xenografts. Candidate therapeutic testing showed that the allosteric AKT inhibitor MK2206 reproducibly inhibited cell survival across different culture formats. By contrast, the cells appeared resistant to the adenosine triphosphate competitive AKT inhibitor GSK690693. Procedures employed here illustrate an approach for reproducibly obtaining material for pathophysiological studies of salivary gland neoplasms, and other less common epithelial cancer types, that can be executed without compromising pathological examination of patient specimens. The approach permits combined genetic and cell-based physiological and therapeutic investigations in addition to more traditional pathologic studies, and can be used to build sustainable bio-banks for future inquiries.This article has an associated First Person interview with the first author of the paper.

Transcription factor HBP1 is a direct anti-cancer target of transcription factor FOXO1 in invasive oral cancer

Either FOXO1 or HBP1 transcription factor is a downstream effector of the PI3K/Akt pathway and associated with tumorigenesis. However, the relationship between FOXO1 and HBP1 in oral cancer remains unclear. Analysis of 30 oral tumor specimens revealed that mean mRNA levels of both FOXO1 and HBP1 in non-invasive and invasive oral tumors were found to be significantly lower than that of the control tissues, and the status of low FOXO1 and HBP1 (< 0.3 fold of the control) was associated with invasiveness of oral tumors. To investigate if HBP1 is a direct transcription target of FOXO1, we searched potential FOXO1 binding sites in the HBP1 promoter using the MAPPER Search Engine, and two putative FOXO1 binding sites located in the HBP1 promoter –132 to –125 bp and –343 to –336 bp were predicted. These binding sites were then confirmed by both reporter gene assays and the in cellulo ChIP assay. In addition, Akt activity manipulated by PI3K inhibitor LY294002 or Akt mutants was shown to negatively affect FOXO1-mediated HBP1 promoter activation and gene expression. Last, the biological significance of the FOXO1-HBP1 axis in oral cancer malignancy was evaluated in cell growth, colony formation, and invasiveness. The results indicated that HBP1 knockdown potently promoted malignant phenotypes of oral cancer and the suppressive effect of FOXO1 on cell growth, colony formation, and invasion was alleviated upon HBP1 knockdown in invasive oral cancer cells. Taken together, our data provide evidence for HBP1 as a direct downstream target of FOXO1 in oral cancer malignancy.

Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547

The challenge of developing effective pharmacodynamic biomarkers for preclinical and clinical testing of FGFR signaling inhibition is significant. Assays that rely on the measurement of phospho-protein epitopes can be limited by the availability of effective antibody detection reagents. Transcript profiling enables accurate quantification of many biomarkers and provides a broader representation of pathway modulation. To identify dynamic transcript biomarkers of FGFR signaling inhibition by AZD4547, a potent inhibitor of FGF receptors 1, 2, and 3, a gene expression profiling study was performed in FGFR2-amplified, drug-sensitive tumor cell lines. Consistent with known signaling pathways activated by FGFR, we identified transcript biomarkers downstream of the RAS-MAPK and PI3K/AKT pathways. Using different tumor cell lines in vitro and xenografts in vivo, we confirmed that some of these transcript biomarkers (DUSP6, ETV5, YPEL2) were modulated downstream of oncogenic FGFR1, 2, 3, whereas others showed selective modulation only by FGFR2 signaling (EGR1). These transcripts showed consistent time-dependent modulation, corresponding to the plasma exposure of AZD4547 and inhibition of phosphorylation of the downstream signaling molecules FRS2 or ERK. Combination of FGFR and AKT inhibition in an FGFR2-mutated endometrial cancer xenograft model enhanced modulation of transcript biomarkers from the PI3K/AKT pathway and tumor growth inhibition. These biomarkers were detected on the clinically validated nanoString platform. Taken together, these data identified novel dynamic transcript biomarkers of FGFR inhibition that were validated in a number of in vivo models, and which are more robustly modulated by FGFR inhibition than some conventional downstream signaling protein biomarkers. Mol Cancer Ther; 15(11); 2802-13. ©2016 AACR.

Construction of biological networks from unstructured information based on a semi-automated curation workflow

Capture and representation of scientific knowledge in a structured format are essential to improve the understanding of biological mechanisms involved in complex diseases. Biological knowledge and knowledge about standardized terminologies are difficult to capture from literature in a usable form. A semi-automated knowledge extraction workflow is presented that was developed to allow users to extract causal and correlative relationships from scientific literature and to transcribe them into the computable and human readable Biological Expression Language (BEL). The workflow combines state-of-the-art linguistic tools for recognition of various entities and extraction of knowledge from literature sources. Unlike most other approaches, the workflow outputs the results to a curation interface for manual curation and converts them into BEL documents that can be compiled to form biological networks. We developed a new semi-automated knowledge extraction workflow that was designed to capture and organize scientific knowledge and reduce the required curation skills and effort for this task. The workflow was used to build a network that represents the cellular and molecular mechanisms implicated in atherosclerotic plaque destabilization in an apolipoprotein-E-deficient (ApoE(-/-)) mouse model. The network was generated using knowledge extracted from the primary literature. The resultant atherosclerotic plaque destabilization network contains 304 nodes and 743 edges supported by 33 PubMed referenced articles. A comparison between the semi-automated and conventional curation processes showed similar results, but significantly reduced curation effort for the semi-automated process. Creating structured knowledge from unstructured text is an important step for the mechanistic interpretation and reusability of knowledge. Our new semi-automated knowledge extraction workflow reduced the curation skills and effort required to capture and organize scientific knowledge. The atherosclerotic plaque destabilization network that was generated is a causal network model for vascular disease demonstrating the usefulness of the workflow for knowledge extraction and construction of mechanistically meaningful biological networks.

Bioinformatics in protein kinases regulatory network and drug discovery

Protein kinases have been implicated in a number of diseases, where kinases participate many aspects that control cell growth, movement and death. The deregulated kinase activities and the knowledge of these disorders are of great clinical interest of drug discovery. The most critical issue is the development of safe and efficient disease diagnosis and treatment for less cost and in less time. It is critical to develop innovative approaches that aim at the root cause of a disease, not just its symptoms. Bioinformatics including genetic, genomic, mathematics and computational technologies, has become the most promising option for effective drug discovery, and has showed its potential in early stage of drug-target identification and target validation. It is essential that these aspects are understood and integrated into new methods used in drug discovery for diseases arisen from deregulated kinase activity. This article reviews bioinformatics techniques for protein kinase data management and analysis, kinase pathways and drug targets and describes their potential application in pharma ceutical industry.

Causal analysis approaches in Ingenuity Pathway Analysis

Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.

Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets.

Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com).

Supplementary information:Supplementary material is available at Bioinformatics online.

Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data

Background

Gene expression profiling and other genome-scale measurement technologies provide comprehensive information about molecular changes resulting from a chemical or genetic perturbation, or disease state. A critical challenge is the development of methods to interpret these large-scale data sets to identify specific biological mechanisms that can provide experimentally verifiable hypotheses and lead to the understanding of disease and drug action.

Results

We present a detailed description of Reverse Causal Reasoning (RCR), a reverse engineering methodology to infer mechanistic hypotheses from molecular profiling data. This methodology requires prior knowledge in the form of small networks that causally link a key upstream controller node representing a biological mechanism to downstream measurable quantities. These small directed networks are generated from a knowledge base of literature-curated qualitative biological cause-and-effect relationships expressed as a network. The small mechanism networks are evaluated as hypotheses to explain observed differential measurements. We provide a simple implementation of this methodology, Whistle, specifically geared towards the analysis of gene expression data and using prior knowledge expressed in Biological Expression Language (BEL). We present the Whistle analyses for three transcriptomic data sets using a publically available knowledge base. The mechanisms inferred by Whistle are consistent with the expected biology for each data set.

Conclusions

Reverse Causal Reasoning yields mechanistic insights to the interpretation of gene expression profiling data that are distinct from and complementary to the results of analyses using ontology or pathway gene sets. This reverse engineering algorithm provides an evidence-driven approach to the development of models of disease, drug action, and drug toxicity.

Systems biology in drug discovery and development

The complexity of human biology makes it challenging to develop safe and effective new medicines. Systems biology omics-based efforts have led to an explosion of high-throughput data and focus is now shifting to the integration of diverse data types to connect molecular and pathway information to predict disease outcomes. Better models of human disease biology, including more integrated network-based models that can accommodate multiple omics data types, as well as more relevant experimental systems, will help predict drug effects in patients, enabling personalized medicine, improvement of the success rate of new drugs in the clinic, and the finding of new uses for existing drugs.

Leveraging systems biology approaches in clinical pharmacology

Computational modeling has been adopted in all aspects of drug research and development, from the early phases of target identification and drug discovery to the late-stage clinical trials. The different questions addressed during each stage of drug R&D has led to the emergence of different modeling methodologies. In the research phase, systems biology couples experimental data with elaborate computational modeling techniques to capture lifecycle and effector cellular functions (e.g. metabolism, signaling, transcription regulation, protein synthesis and interaction) and integrates them in quantitative models. These models are subsequently used in various ways, i.e. to identify new targets, generate testable hypotheses, gain insights on the drug's mode of action (MOA), translate preclinical findings, and assess the potential of clinical drug efficacy and toxicity. In the development phase, pharmacokinetic/pharmacodynamic (PK/PD) modeling is the established way to determine safe and efficacious doses for testing at increasingly larger, and more pertinent to the target indication, cohorts of subjects. First, the relationship between drug input and its concentration in plasma is established. Second, the relationship between this concentration and desired or undesired PD responses is ascertained. Recognizing that the interface of systems biology with PK/PD will facilitate drug development, systems pharmacology came into existence, combining methods from PK/PD modeling and systems engineering explicitly to account for the implicated mechanisms of the target system in the study of drug–target interactions. Herein, a number of popular system biology methodologies are discussed, which could be leveraged within a systems pharmacology framework to address major issues in drug development.

Novel pancreatic endocrine maturation pathways identified by genomic profiling and causal reasoning

We have used a previously unavailable model of pancreatic development, derived in vitro from human embryonic stem cells, to capture a time-course of gene, miRNA and histone modification levels in pancreatic endocrine cells. We investigated whether it is possible to better understand, and hence control, the biological pathways leading to pancreatic endocrine formation by analysing this information and combining it with the available scientific literature to generate models using a casual reasoning approach. We show that the embryonic stem cell differentiation protocol is highly reproducible in producing endocrine precursor cells and generates cells that recapitulate many aspects of human embryonic pancreas development, including maturation into functional endocrine cells when transplanted into recipient animals. The availability of whole genome gene and miRNA expression data from the early stages of human pancreatic development will be of great benefit to those in the fields of developmental biology and diabetes research. Our causal reasoning algorithm suggested the involvement of novel gene networks, such as NEUROG3/E2F1/KDM5B and SOCS3/STAT3/IL-6, in endocrine cell development We experimentally investigated the role of the top-ranked prediction by showing that addition of exogenous IL-6 could affect the expression of the endocrine progenitor genes NEUROG3 and NKX2.2.

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 (TNFα) 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-TNFα 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-TNFα-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.

Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

Background

High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.

Results

Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.

Conclusions

The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

Determination of minimal transcriptional signatures of compounds for target prediction

The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.

Comparative transcriptional network modeling of three PPAR-α/γ co-agonists reveals distinct metabolic gene signatures in primary human hepatocytes

AIMS

To compare the molecular and biologic signatures of a balanced dual peroxisome proliferator-activated receptor (PPAR)-α/γ agonist, aleglitazar, with tesaglitazar (a dual PPAR-α/γ agonist) or a combination of pioglitazone (Pio; PPAR-γ agonist) and fenofibrate (Feno; PPAR-α agonist) in human hepatocytes.

METHODS AND RESULTS

Gene expression microarray profiles were obtained from primary human hepatocytes treated with EC(50)-aligned low, medium and high concentrations of the three treatments. A systems biology approach, Causal Network Modeling, was used to model the data to infer upstream molecular mechanisms that may explain the observed changes in gene expression. Aleglitazar, tesaglitazar and Pio/Feno each induced unique transcriptional signatures, despite comparable core PPAR signaling. Although all treatments inferred qualitatively similar PPAR-α signaling, aleglitazar was inferred to have greater effects on high- and low-density lipoprotein cholesterol levels than tesaglitazar and Pio/Feno, due to a greater number of gene expression changes in pathways related to high-density and low-density lipoprotein metabolism. Distinct transcriptional and biologic signatures were also inferred for stress responses, which appeared to be less affected by aleglitazar than the comparators. In particular, Pio/Feno was inferred to increase NFE2L2 activity, a key component of the stress response pathway, while aleglitazar had no significant effect. All treatments were inferred to decrease proliferative signaling.

CONCLUSIONS

Aleglitazar induces transcriptional signatures related to lipid parameters and stress responses that are unique from other dual PPAR-α/γ treatments. This may underlie observed favorable changes in lipid profiles in animal and clinical studies with aleglitazar and suggests a differentiated gene profile compared with other dual PPAR-α/γ agonist treatments.

AKT signaling pathway activated by HIN-1 methylation in non-small cell lung cancer

The purpose of this study is to determine the epigenetic changes and function of High in Normal-1 (HIN-1) in non-small cell lung cancer (NSCLC). HIN-1 expression was examined by semiquantitative RT-PCR before and after 5-aza-2'-deoxycytidine (5-aza) treatment in NSCLC cell lines. Promoter methylation status of HIN-1 was tested by methylation-specific PCR (MSP). Effect of forced expression of HIN-1 on different key molecules of AKT signaling pathway was tested by Western Blot analysis in H157 and H23 cell lines. Promoter methylations are inversely correlated with expression of HIN-1 in eight (H23, H157, 95D, H1299, H358, H1752, H460, A549) of ten NSCLC cell lines and re-expression was observed by 5-aza treatment. We then tested promoter methylation of HIN-1 in primary NSCLC tissues. Methylation was detected in 73 out of 152 (48%) NSCLC cases. Forced expression of HIN-1 in NSCLC cell lines inhibited colony formation and induce apoptosis. Furthermore, overexpression of HIN-1 reduces the expression of phosphorated-AKT (p-AKT), c-myc, Bcl-2 and cyclinD1 while Bax was increased. Our data suggest that HIN-1 is a potential tumor suppressor gene in NSCLC, silenced by promoter hypermethylation and negatively regulate AKT signaling pathway.

Construction of a computable cell proliferation network focused on non-diseased lung cells

BACKGROUND

Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work.

RESULTS

To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data.

CONCLUSIONS

To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.

Akt signalling in health and disease

Akt (also known as protein kinase B or PKB) comprises three closely related isoforms Akt1, Akt2 and Akt3 (or PKBα/β/γ respectively). We have a very good understanding of the mechanisms by which Akt isoforms are activated by growth factors and other extracellular stimuli as well as by oncogenic mutations in key upstream regulatory proteins including Ras, PI3-kinase subunits and PTEN. There are also an ever increasing number of Akt substrates being identified that play a role in the regulation of the diverse array of biological effects of activated Akt; this includes the regulation of cell proliferation, survival and metabolism. Dysregulation of Akt leads to diseases of major unmet medical need such as cancer, diabetes, cardiovascular and neurological diseases. As a result there has been substantial investment in the development of small molecular Akt inhibitors that act competitively with ATP or phospholipid binding, or allosterically. In this review we will briefly discuss our current understanding of how Akt isoforms are regulated, the substrate proteins they phosphorylate and how this integrates with the role of Akt in disease. We will furthermore discuss the types of Akt inhibitors that have been developed and are in clinical trials for human cancer, as well as speculate on potential on-target toxicities, such as disturbances of heart and vascular function, metabolism, memory and mood, which should be monitored very carefully during clinical trial.

GSK690693 delays tumor onset and progression in genetically defined mouse models expressing activated Akt

PURPOSE

Akt plays a central role in regulating tumor cell survival and cell cycle progression and is regarded as a promising therapeutic target. We used genetically defined mouse models that develop spontaneous tumors exhibiting activated Akt to determine if Akt inhibition by GSK690693 is effective in the treatment of cancer. The broad long-term objective of this project was to use preclinical cancer models with precisely defined genetic lesions to elucidate the efficacy of targeting Akt with GSK690693.

EXPERIMENTAL DESIGN

We tested the in vivo effects of GSK690693 in Lck-MyrAkt2 transgenic mice that develop lymphomas, heterozygous Pten(+/-) knockout mice that exhibit endometrial tumors, and TgMISIIR-TAg-DR26 mice that develop ovarian carcinomas, all of which exhibit hyperactivation of Akt. In addition to standard disease onset and histology, tumors arising in treated animals were examined by immunohistochemistry to verify downregulated Akt signaling relative to placebo-treated mice. When possible, drug response was evaluated in tumor cell cultures by standard proliferation and apoptosis assays and by immunoblotting with various phosphospecific antibodies.

RESULTS

GSK690693 exhibited efficacy irrespective of the mechanism of Akt activation involved. Interestingly, GSK690693 was most effective in delaying tumor progression in Lck-MyrAkt2 mice expressing a membrane-bound, constitutively active form of Akt. Both tumors and primary cell cultures displayed downregulation of the Akt pathway, increased apoptosis, and primarily decreased cell proliferation.

CONCLUSION

These results suggest that GSK690693 or other Akt inhibitors might have therapeutic efficacy in human cancers with hyperactivated Akt and/or a dependence on Akt signaling for tumor progression.