Rachel Cavill

Metabolic Profiling of Human Colorectal Cancer Using High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS NMR) Spectroscopy and Gas Chromatography Mass Spectrometry (GC/MS)

Current clinical strategy for staging and prognostication of colorectal cancer (CRC) relies mainly upon the TNM or Duke system. This clinicopathological stage is a crude prognostic guide because it reflects in part the delay in diagnosis in the case of an advanced cancer and gives little insight into the biological characteristics of the tumor. We hypothesized that global metabolic profiling (metabonomics/metabolomics) of colon mucosae would define metabolic signatures that not only discriminate malignant from normal mucosae, but also could distinguish the anatomical and clinicopathological characteristics of CRC. We applied both high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) and gas chromatography mass spectrometry (GC/MS) to analyze metabolites in biopsied colorectal tumors and their matched normal mucosae obtained from 31 CRC patients. Orthogonal partial least-squares discriminant analysis (OPLS-DA) models generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from malignant samples (Q(2) > 0.50, Receiver Operator Characteristic (ROC) AUC >0.95, using 7-fold cross validation). A total of 31 marker metabolites were identified using the two analytical platforms. The majority of these metabolites were associated with expected metabolic perturbations in CRC including elevated tissue hypoxia, glycolysis, nucleotide biosynthesis, lipid metabolism, inflammation and steroid metabolism. OPLS-DA models showed that the metabolite profiles obtained via HR-MAS NMR could further differentiate colon from rectal cancers (Q(2)> 0.60, ROC AUC = 1.00, using 7-fold cross validation). These data suggest that metabolic profiling of CRC mucosae could provide new phenotypic biomarkers for CRC management.

Identification of platelet function defects by multi-parameter assessment of thrombus formation

Assays measuring platelet aggregation (thrombus formation) at arterial shear rate mostly use collagen as only platelet-adhesive surface. Here we report a multi-surface and multi-parameter flow assay to characterize thrombus formation in whole blood from healthy subjects and patients with platelet function deficiencies. A systematic comparison is made of 52 adhesive surfaces with components activating the main platelet-adhesive receptors, and of eight output parameters reflecting distinct stages of thrombus formation. Three types of thrombus formation can be identified with a predicted hierarchy of the following receptors: glycoprotein (GP)VI, C-type lectin-like receptor-2 (CLEC-2)>GPIb>α6β1, αIIbβ3>α2β1>CD36, α5β1, αvβ3. Application with patient blood reveals distinct abnormalities in thrombus formation in patients with severe combined immune deficiency, Glanzmann’s thrombasthenia, Hermansky–Pudlak syndrome, May–Hegglin anomaly or grey platelet syndrome. We suggest this test may be useful for the diagnosis of patients with suspected bleeding disorders or a pro-thrombotic tendency.

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

Genetic algorithms for simultaneous variable and sample selection in metabonomics

MOTIVATION Metabolic profiles derived from high resolution (1)H-NMR data are complex, therefore statistical and machine learning approaches are vital for extracting useful information and biological insights. Focused modelling on targeted subsets of metabolites and samples can improve the predictive ability of models, and techniques such as genetic algorithms (GAs) have a proven utility in feature selection problems. The Consortium for Metabonomic Toxicology (COMET) obtained temporal NMR spectra of urine from rats treated with model toxins and stressors. Here, we develop a GA approach which simultaneously selects sets of samples and spectral regions from the COMET database to build robust, predictive classifiers of liver and kidney toxicity. RESULTS The results indicate that using simultaneous sample and variable selection improved performance by over 9% compared with either method alone. Simultaneous selection also halved computation time. Successful classifiers repeatedly selected particular variables indicating that this approach can aid defining biomarkers of toxicity. Novel visualizations of the results from multiple computations were developed to aid the interpretability of which samples and variables were frequently selected. This method provides an efficient way to determine the most discriminatory variables and samples for any post-genomic dataset. AVAILABILITY GA code available from http://www1.imperial.ac.uk/medicine/people/r.cavill/

Delineation of the Key Aspects in the Regulation of Epithelial Monolayer Formation

ABSTRACT The formation, maintenance, and repair of epithelial barriers are of critical importance for whole-body homeostasis. However, the molecular events involved in epithelial tissue maturation are not fully established. To this end, we investigated the molecular processes involved in renal epithelial proximal-tubule monolayer maturation utilizing transcriptomic, metabolomic, and functional parameters. We uncovered profound dynamic alterations in transcriptional regulation, energy metabolism, and nutrient utilization over the maturation process. Proliferating cells exhibited high glycolytic rates and high transcript levels for fatty acid synthesis genes (FASN), whereas matured cells had low glycolytic rates, increased oxidative capacity, and preferentially expressed genes for beta oxidation. There were dynamic alterations in the expression and localization of several adherens (CDH1, -4, and -16) and tight junction (TJP3 and CLDN2 and -10) proteins. Genes involved in differentiated proximal-tubule function, cilium biogenesis (BBS1), and transport (ATP1A1 and ATP1B1) exhibited increased expression during epithelial maturation. Using TransAM transcription factor activity assays, we could demonstrate that p53 and FOXO1 were highly active in matured cells, whereas HIF1A and c-MYC were highly active in proliferating cells. The data presented here will be invaluable in the further delineation of the complex dynamic cellular processes involved in epithelial cell regulation.

Transcriptomic and metabolomic data integration

Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.

Time series analysis of oxidative stress response patterns in HepG2: a toxicogenomics approach.

Oxidative stress plays an important role in chemically induced liver injury, however, our insight into molecular responses to different oxygen radicals is fragmentary. Since these cellular responses will differ over time, examining time-dependent changes in gene expression, and correlating these with markers for oxidative stress, may provide new insights into responses to oxidants. We used the human hepatoma cell line HepG2 to investigate the effects of oxidative stress on the transcriptome level by micro-arrays at seven time points (0.5, 1, 2, 4, 6, 8 and 24h) following exposure to the oxidants menadione, hydrogen peroxide and tert-butyl hydroperoxide including the effects on cell cycle and apoptosis by flow cytometry, protein carbonyl formation by spectrophotometry and oxidative DNA damage by FPG-comet. In total, 3429 genes were differentially expressed, including 136 genes that were significantly modified by all oxidants. Time-dependent biological pathway analysis showed that these genes were particularly involved in inflammatory responses, cell cycle processes and glutathione signaling. These responses were confirmed and supported by phenotypic anchoring to the different cellular endpoints. In addition, using an innovative temporal analysis we established an oxidative stress-related gene expression time cluster. Altogether, this study provides new insights in temporal oxidative stress mechanisms and demonstrates sequential cellular responses that may contribute to a better hazard identification and the mechanisms of toxicological responses in the liver induced by oxidative stress.

Extensive temporal transcriptome and microRNA analyses identify molecular mechanisms underlying mitochondrial dysfunction induced by multi-walled carbon nanotubes in human lung cells

Abstract Understanding toxicity pathways of engineered nanomaterials (ENM) has recently been brought forward as a key step in twenty-first century ENM risk assessment. Molecular mechanisms linked to phenotypic end points is a step towards the development of toxicity tests based on key events, which may allow for grouping of ENM according to their modes of action. This study identified molecular mechanisms underlying mitochondrial dysfunction in human bronchial epithelial BEAS 2B cells following exposure to one of the most studied multi-walled carbon nanotubes (Mitsui MWCNT-7). Asbestos was used as a positive control and a non-carcinogenic glass wool material was included as a negative fibre control. Decreased mitochondrial membrane potential (MMP↓) was observed for MWCNTs at a biologically relevant dose (0.25 μg/cm2) and for asbestos at 2 μg/cm2, but not for glass wool. Extensive temporal transcriptomic and microRNA expression analyses identified a 330-gene signature (including 26 genes with known mitochondrial function) related to MWCNT- and asbestos-induced MMP↓. Forty-nine of the MMP↓-associated genes showed highly similar expression patterns over time (six time points) and the majority was found to be regulated by two transcription factors strongly involved in mitochondrial homeostasis, APP and NRF1. In addition, four miRNAs were correlated with MMP↓ and one of them, miR-1275, was found to negatively correlate with a large part of the MMP↓-associated genes. Cellular processes such as gluconeogenesis, mitochondrial LC-fatty acid β-oxidation and spindle microtubule function were enriched among the MMP↓-associated genes and miRNAs. These results are expected to be useful in the identification of key events in ENM-related toxicity pathways for the development of molecular screening techniques.

DTW4Omics: Comparing Patterns in Biological Time Series

When studying time courses of biological measurements and comparing these to other measurements eg. gene expression and phenotypic endpoints, the analysis is complicated by the fact that although the associated elements may show the same patterns of behaviour, the changes do not occur simultaneously. In these cases standard correlation-based measures of similarity will fail to find significant associations. Dynamic time warping (DTW) is a technique which can be used in these situations to find the optimal match between two time courses, which may then be assessed for its significance. We implement DTW4Omics, a tool for performing DTW in R. This tool extends existing R scripts for DTW making them applicable for “omics” datasets where thousands entities may need to be compared with a range of markers and endpoints. It includes facilities to estimate the significance of the matches between the supplied data, and provides a set of plots to enable the user to easily visualise the output. We illustrate the utility of this approach using a dataset linking the exposure of the colon carcinoma Caco-2 cell line to oxidative stress by hydrogen peroxide (H2O2) and menadione across 9 timepoints and show that on average 85% of the genes found are not obtained from a standard correlation analysis between the genes and the measured phenotypic endpoints. We then show that when we analyse the genes identified by DTW4Omics as significantly associated with a marker for oxidative DNA damage (8-oxodG), through over-representation, an Oxidative Stress pathway is identified as the most over-represented pathway demonstrating that the genes found by DTW4Omics are biologically relevant. In contrast, when the positively correlated genes were similarly analysed, no pathways were found. The tool is implemented as an R Package and is available, along with a user guide from http://web.tgx.unimaas.nl/svn/public/dtw/.

Effect of the Histone Deacetylase Inhibitor Trichostatin A on the Metabolome of Cultured Primary Hepatocytes

Trichostatin A (TSA) is a histone deacetylase inhibitor that has antiproliferative and differentiation-inducing effects on cancer cells, and in cultures of primary hepatocytes has been shown to maintain xenobiotic metabolic capacity. Using an NMR-based metabolic profiling approach, we evaluated if the endogenous metabolome was stabilized and the normal metabolic phenotype retained in this model. Aqueous soluble metabolites were extracted from isolated rat hepatocytes after 44 and 92 h exposure to TSA (25 muM) together with time-matched controls and measured by (1)H NMR spectroscopy. Multivariate analysis showed a clear difference in the global metabolic profile over time in control samples, while the TSA treated group was more closely clustered at both time points, suggesting that treatment reduced the time related effect on metabolism that was observed in the control. TSA treatment was associated with decreases in glycerophosphocholine, 3-hydroxybutyric acid, glycine and adenosine, an increase in glycogen, and a reduction in the decrease of inosine, hypoxanthine, and glutathione over time. Collectively, our data suggest that TSA treatment reduces the loss of a normal metabolic phenotype in cultured primary hepatocytes, improving the model as a tool to study endogenous liver metabolism, xenobiotic metabolism, and potentially affecting the accuracy of all biological assays in this system.