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In platelets, elevated cytosolic Ca 2+ is a crucial second messenger, involved in most functional responses, including shape change, secretion, aggregation and procoagulant activity. The platelet Ca 2+ response consists of Ca 2+ mobilization from endoplasmic reticulum stores, complemented with store-operated or receptor-operated Ca 2+ entry pathways. Several channels can contribute to the Ca 2+ entry, but their relative contribution is unclear upon stimulation of ITAM-linked receptors such as glycoprotein VI (GPVI) and G-protein coupled receptors such as the protease-activated receptors (PAR) for thrombin. We employed a 96-well plate high-throughput assay with Fura-2-loaded human platelets to perform parallel [Ca 2+] i measurements in the presence of EGTA or CaCl 2. Per agonist condition, this resulted in sets of EGTA, CaCl 2 and Ca 2+ entry ratio curves, defined by six parameters, reflecting different Ca 2+ ion fluxes. We report that threshold stimulation of GPVI or PAR, with a variable contribution of secondary mediators, induces a maximal Ca 2+ entry ratio of 3-7. Strikingly, in combination with Ca 2+-ATPase inhibition by thapsigargin, the maximal Ca 2+ entry ratio increased to 400 (GPVI) or 40 (PAR), pointing to a strong receptor-dependent enhancement of store-operated Ca 2+ entry. By pharmacological blockage of specific Ca 2+ channels in platelets, we found that, regardless of GPVI or PAR stimulation, the Ca 2+ entry ratio was strongest affected by inhibition of ORAI1 (2-APB, Synta66) > Na +/Ca 2+ exchange (NCE) > P2× 1 (only initial). In contrast, inhibition of TRPC6, Piezo1/2 or STIM1 was without effect. Together, these data reveal ORAI1 and NCE as dominating Ca 2+ carriers regulating GPVI- and PAR-induced Ca 2+ entry in human platelets.

Mathematical modeling plays an important role in our understanding of therapy resistance mechanisms in cancer. The polymorphic Gompertzian model, analyzed theoretically by Viossat and Noble, describes a heterogeneous cancer population consisting of therapy sensitive and resistant cells. This theoretically promising model has not previously been validated with real-world data. In this study, we provide this validation. We demonstrate that the polymorphic Gompertzian model successfully captures trends in bothin vitroandin vivodata on non-small cell lung cancer (NSCLC) dynamics under treatment. For thein vivodata of tumor dynamics in patients undergoing treatment, we compare the polymorphic Gompertzian model to the classical oncologic models, which were previously identified as the models fitting this data the best. We showed that the polymorphic Gompertzian model can successfully capture the U-shape trend in tumor size during cancer relapse, which can not be fitted with the classical oncologic models. In general, the polymorphic Gompertzian model corresponds well to bothin vitroandin vivoreal-world data, suggesting it as a candidate for improving the efficacy of cancer therapy, for example through evolutionary/adaptive therapies.

BACKGROUND AND AIMS: This study aims to identify sex-specific transcriptional differences and signaling pathways in circulating monocytes contributing to cardiovascular disease.METHODS AND RESULTS: We generated sex-biased gene expression signatures by comparing male versus female monocytes of coronary artery disease (CAD) patients (n = 450) from the Center for Translational Molecular Medicine-Circulating Cells Cohort. Gene set enrichment analysis demonstrated that monocytes from female CAD patients carry stronger chemotaxis and migratory signature than those from males. We then inferred cytokine signaling activities based on CytoSig database of 51 cytokine and growth factor regulation profiles. Monocytes from females feature a higher activation level of EGF, IFN1, VEGF, GM-CSF, and CD40L pathways, whereas IL-4, INS, and HMGB1 signaling was seen to be more activated in males. These sex differences were not observed in healthy subjects, as shown for an independent monocyte cohort of healthy subjects (GSE56034, n = 485). More pronounced GM-CSF signaling in monocytes of female CAD patients was confirmed by the significant enrichment of GM-CSF-activated monocyte signature in females. As we show these effects were not due to increased plasma levels of the corresponding ligands, sex-intrinsic differences in monocyte signaling regulation are suggested. Consistently, regulatory network analysis revealed jun-B as a shared transcription factor activated in all female-specific pathways except IFN1 but suppressed in male-activated IL-4.CONCLUSIONS: We observed overt CAD-specific sex differences in monocyte transcriptional profiles and cytokine- or growth factor-induced responses, which provide insights into underlying mechanisms of sex differences in CVD.

Membrane binding and unbinding dynamics play a crucial role in the biological activity of several non-integral membrane proteins, which have to be recruited to the membrane in order to perform their functions. By localizing to the membrane, these proteins are able to induce downstream signal amplification in their respective signaling pathways. Here we present a 3D computational approach using reaction-diffusion equations to investigate the relation between membrane localization of Focal Adhesion Kinase (FAK), Ras homolog family member A (RhoA) and signal amplification of the YAP/TAZ signaling pathway. Our results show that the theoretical scenarios, in which FAK is membrane-bound, yield robust and amplified YAP/TAZ nuclear translocation signals. Moreover, we predict that the amount of YAP/TAZ nuclear translocation increases with cell spreading, confirming the experimental findings in the literature. In summary, our in silico predictions show that when the cell membrane interaction area with the underlying substrate increases, for example through cell spreading, this leads to more encounters between membrane-bound signaling partners and downstream signal amplification. Since membrane activation is a motif common to many signaling pathways, this study has important implications for understanding the design principles of signaling networks.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Fritz Thyssen Stiftung T cells have a prominent role in the pathogenesis of atherosclerosis, although their function in atherosclerotic plaques is only partly understood. In this study, we utilize the advantages of high-throughput techniques and data analytic strategies to compare the inherent biological changes of T cells during plaque transition from a stable, non-haemorrhaged (low-risk) to a rupture-prone, haemorrhaged (high-risk) phenotype. We classified 43 human carotid arterial lesions into high- and low-risk plaques based on the presence/absence of intraplaque hemorrhages. RNA from these lesions was isolated and microarray gene expression data was obtained and analyzed by Weighted Gene Co-expression Network Analysis. A strong T cell signalling signature was identified in high- versus low-risk plaques, influencing angiogenesis and interferon-related processes. Bayesian network inference, cell type deconvolution and single-cell RNA sequencing analysis revealed that the T cell-associated gene program was linked to effector-memory cytotoxic, CD8+ T cells. This gene program appeared driven by CD8+ T cell-related transcription factors, including RUNX3, IRF7 and most importantly PRDM1. To validate these findings, we demonstrated in a murine model that T cell PRDM1 plays a key role in plaque formation, as atherosclerotic mice with a T cell specific Prdm1 deficiency developed larger and more advanced atherosclerotic plaques compared to control mice. In conclusion, our study unveils a clear PRDM1-regulated effector-memory cytotoxic CD8+ T cell footprint in plaque development and the shift from low- to high-risk plaques, thereby revealing CD8+ T cells and PRMD1 as potential targets for intervention in adverse T cell responses in human atherosclerotic lesions.

Game theory is a powerful theory to model strategic decision-making, but also complex biological systems, such as cancer. In the past decades, game-theoretical models have helped to understand cancer, its response to various treatments, and to design better therapies. However, to fully utilize the potential of game-theoretical modelling in designing better anti-cancer therapies, we need more information on cancer population (ecological) and strategy (evolutionary) dynamics in response to treatment. Here we explore how transcriptomics data can be utilized as inputs of game-theoretic models for predicting evolutionary response to irradiation using patient-derived glioblastoma organoids. For that purpose, we utilize both supervised and unsupervised methods to disentangle cell mixtures from publicly available datasets and identify proportions of relevant cancer cell types in patient-derived glioblastoma organoids treated with radiotherapy. We then fit these proportions to the replicator dynamics, the most classic evolutionary game dynamics, to predict both transient evolutionary dynamics and evolutionary stable cell proportions in these organoids. Hereby we demonstrate a methodology that can be utilized to design evolutionary therapies, i.e. therapies that anticipate and prevent evolution of therapy resistance in cancer cells. Our predictions in glioblastoma suggest that hypoxia is the most important factor in identifying short-term response to irradiation, while this seems much less relevant for the long-term response. Further, we conclude that supervised methods are the best way to estimate cancer evolutionary dynamics when therapy resistance is a qualitative evolutionary trait. We provide a pipeline for designing better therapies through testing evolutionary responses in patient-derived organoids, while in parallel the ecological response can be tracked through serum biomarkers and imaging in the corresponding patients.

We examined a dataset of 590 Non-Small Cell Lung Cancer patients treated with either chemotherapy or immunotherapy using a game-theoretic model that includes both the evolution of therapy resistance and a cost of resistance. We tested whether the game-theoretic model provides a better fit than classical mathematical models of population growth (exponential, logistic, classic Bertalanffy, general Bertalanffy, Gompertz, general Gompertz). To our knowledge, this is the first time a large clinical patient cohort (as opposed to only in-vitro data) has been used to apply a game-theoretic cancer model. The game-theoretic model provided a better fit to the tumor dynamics of the 590 Non-Small Cell Lung Cancer patients than any of the non-evolutionary population growth models. This was not simply due to having more parameters in the game-theoretic model. The game-theoretic model was seemingly able to fit more accurately patients whose tumor burden exhibit a U-shaped trajectory over time. We explained how this game-theoretic model provides predictions of future tumor growth based on just a few initial measurements. Using the estimates for treatment-specific parameters, we then explored alternative treatment protocols and their expected impact on tumor growth and patient outcome. As such, the model could possibly be used to suggest patient-specific optimal treatment regimens with the goal of minimizing final tumor burden. Therapeutic protocols based on game-theoretic modeling can help to predict tumor growth, and could potentially improve patient outcome in the future. The model invites evolutionary therapies that anticipate and steer the evolution of therapy resistance.

Prostate-specific antigen (PSA) is the most common serum marker for prostate cancer. It is used to detect prostate cancer, to assess responses to treatment and recently even to determine when to switch treatment on and off in adaptive therapy protocols. However, the correlation between PSA and tumor volume is poorly understood. There is empirical evidence that some cancer cell types produce more PSA than others. Still, recent mathematical cancer models assume either that all cell types contribute equally to PSA levels, or that only specific subpopulations produce PSA at a fixed rate.Here, we compare time to competitive release of the PSA-based adaptive therapy protocol by Zhang et al. with that of the standard of care based on continuous maximum tolerable dose under different assumptions on PSA production. In particular, we assume that androgen dependent, androgen producing, and androgen independent cells may contribute to the PSA production to different extents.Our results show that, regardless the assumption on how much each type contributes to PSA production, the time to competitive release is always longer under adaptive therapy than under the standard of care. However, in some cases, e.g., if the androgen-independent cells are the only PSA producers, adaptive therapy protocol by Zhang et al. cannot be applied, because the PSA value never reaches half of its initial size and therefore therapy is never discontinued.Furthermore, we observe that in the adaptive therapy protocol, the number of treatment cycles and their length strongly depend on the assumptions about the PSA contribution of the three types. Our results support the belief that a better understanding of patient-specific PSA dynamics will lead to more successful adaptive therapies.

In combination with microspotting, whole-blood microfluidics can provide high-throughput information on multiple platelet functions in thrombus formation. Based on assessment of the inter-and intra-subject variability in parameters of microspot-based thrombus formation, we aimed to determine the platelet factors contributing to this variation. Blood samples from 94 genotyped healthy subjects were analyzed for conventional platelet phenotyping: i.e. hematologic parameters, platelet glycoprotein (GP) expression levels and activation markers (24 parameters). Furthermore, platelets were activated by ADP, CRP-XL or TRAP. Parallel samples were investigated for whole-blood thrombus formation (6 microspots, providing 48 parameters of adhesion, aggregation and activation). Microspots triggered platelet activation through GP Ib-V-IX, GPVI, CLEC-2 and integrins. For most thrombus parameters, inter-subject variation was 2-4 times higher than the intra-subject variation. Principal component analyses indicated coherence between the majority of parameters for the GPVI-dependent microspots, partly linked to hematologic parameters, and glycoprotein expression levels. Prediction models identified parameters per microspot that were linked to variation in agonist-induced alpha(IIb)beta(3) activation and secretion. Common sequence variation of GP6 and FCER1G, associated with GPVI-induced alpha(IIb)beta(3) activation and secretion, affected parameters of GPVI-and CLEC-2-dependent thrombus formation. Subsequent analysis of blood samples from patients with Glanzmann thrombasthenia or storage pool disease revealed thrombus signatures of aggregation-dependent parameters that were subject-dependent, but not linked to GPVI activity. Taken together, this high-throughput elucidation of thrombus formation revealed patterns of inter-subject differences in platelet function, which were partly related to GPVI-induced activation and common genetic variance linked to GPVI, but also included a distinct platelet aggregation component.

Scientific reports 11, 12358 (2021). doi:10.1038/s41598-021-91661-x, Published by Macmillan Publishers Limited, part of Springer Nature, [London]

In haemostasis and thrombosis, platelet, coagulation and anticoagulation pathways act together to produce fibrin-containing thrombi. We developed a microspot-based technique, in which we assessed platelet adhesion, platelet activation, thrombus structure and fibrin clot formation in real time using flowing whole blood. Microspots were made from distinct platelet-adhesive surfaces in the absence or presence of tissue factor, thrombomodulin or activated protein C. Kinetics of platelet activation, thrombus structure and fibrin formation were assessed by fluorescence microscopy. This work revealed: (1) a priming role of platelet adhesion in thrombus contraction and subsequent fibrin formation; (2) a surface-independent role of tissue factor, independent of the shear rate; (3) a mechanism of tissue factor-enhanced activation of the intrinsic coagulation pathway; (4) a local, suppressive role of the anticoagulant thrombomodulin/protein C pathway under flow. Multiparameter analysis using blood samples from patients with (anti)coagulation disorders indicated characteristic defects in thrombus formation, in cases of factor V, XI or XII deficiency; and in contrast, thrombogenic effects in patients with factor V-Leiden. Taken together, this integrative phenotyping approach of platelet–fibrin thrombus formation has revealed interaction mechanisms of platelet-primed key haemostatic pathways with alterations in patients with (anti)coagulation defects. It can help as an important functional add-on whole-blood phenotyping.

In clinical trials, animal and cell line models are often used to evaluate the potential toxic effects of a novel compound or candidate drug before progressing to human trials. However, relating the results of animal and in vitro model exposures to relevant clinical outcomes in the human in vivo system still proves challenging, relying on often putative orthologs. In recent years, multiple studies have demonstrated that the repeated dose rodent bioassay, the current gold standard in the field, lacks sufficient sensitivity and specificity in predicting toxic effects of pharmaceuticals in humans. In this study, we evaluate the potential of deep learning techniques to translate the pattern of gene expression measured following an exposure in rodents to humans, circumventing the current reliance on orthologs, and also from in vitro to in vivo experimental designs. Of the applied deep learning architectures applied in this study the convolutional neural network (CNN) and a deep artificial neural network with bottleneck architecture significantly outperform classical machine learning techniques in predicting the time series of gene expression in primary human hepatocytes given a measured time series of gene expression from primary rat hepatocytes following exposure in vitro to a previously unseen compound across multiple toxicologically relevant gene sets. With a reduction in average mean absolute error across 76 genes that have been shown to be predictive for identifying carcinogenicity from 0.0172 for a random regression forest to 0.0166 for the CNN model (p < 0.05). These deep learning architecture also perform well when applied to predict time series of in vivo gene expression given measured time series of in vitro gene expression for rats.

Poly(2-alkyl-2-oxazoline)s (PAOx) are regaining interest for biomedical applications. However, their full potential is hampered by the inability to synthesise uniform high-molar mass PAOx. In this work, we proposed alternative intrinsic chain transfer mechanisms based on 2-oxazoline and oxazolinium chain-end tautomerisation and derived improved polymerization conditions to suppress chain transfer, allowing the synthesis of highly defined poly(2-ethyl-2-oxazoline) s up to ca. 50 kDa (dispersity (D)

Platelet interaction with collagens, via von Willebrand factor, is a potent trigger of shear-dependent thrombus formation mediated by subsequent engagement of the signaling collagen receptor glycoprotein (GP)VI, enforced by integrin &alpha, 2&beta, 1. Protein tyrosine kinase Syk is central in the GPVI-induced signaling pathway, leading to elevated cytosolic Ca2+. We aimed to determine the Syk-mediated thrombogenic activity of several collagen peptides and (fibrillar) type I and III collagens. High-shear perfusion of blood over microspots of these substances resulted in thrombus formation, which was assessed by eight parameters and was indicative of platelet adhesion, activation, aggregation, and contraction, which were affected by the Syk inhibitor PRT-060318. In platelet suspensions, only collagen peptides containing the consensus GPVI-activating sequence (GPO)n and Horm-type collagen evoked Syk-dependent Ca2+ rises. In whole blood under flow, Syk inhibition suppressed platelet activation and aggregation parameters for the collagen peptides with or without a (GPO)n sequence and for all of the collagens. Prediction models based on a regression analysis indicated a mixed role of GPVI in thrombus formation on fibrillar collagens, which was abolished by Syk inhibition. Together, these findings indicate that GPVI-dependent signaling through Syk supports platelet activation in thrombus formation on collagen-like structures regardless of the presence of a (GPO)n sequence.

Imbalance between high reactive oxygen species formation and antioxidant capacity in the colon and liver has been linked to increased cancer risk. However, knowledge about possible cell line-specific oxidative stress-mechanisms is limited. To explore this further, gene expression data from a human liver and colon cell line (HepG2/Caco-2), both exposed to menadione and H2O2 at six time points (0.5-1-2-4-8 and 24 h) were compared in association with cell cycle distribution. In total, 3164 unique- and 1827 common genes were identified between HepG2 and Caco-2 cells. Despite the higher number of unique genes, most oxidative stress-related genes such as CAT, OGG1, NRF2, NF-kappa B, GCLC, HMOX1 and GSR were differentially expressed in both cell lines. However, cell-specific regulation of genes such as KEAP1 and GCLM, or of the EMT pathway, which are of pathophysiological importance, indicates that oxidative stress induces different transcriptional effects and outcomes in the two selected cell lines. In addition, expression levels and/or -direction of common genes were often different in HepG2 and Caco-2 cells, and this led to very diverse downstream effects as confirmed by correlating pathways to cell cycle changes. Altogether, this work contributes to obtaining a better molecular understanding of cell line-specific toxicity upon exposure to oxidative stress-inducing compounds.

Motivation: Comparing time courses of gene expression with time courses of phenotypic data may provide new insights in cellular mechanisms. In this study, we compared the performance of five pattern recognition methods with respect to their ability to relate genes and phenotypic data: one classical method (k-means) and four methods especially developed for time series [Short Time-series Expression Miner (STEM), Linear Mixed Model mixtures, Dynamic Time Warping for -Omics and linear modeling with R/Bioconductor limma package]. The methods were evaluated using data available from toxicological studies that had the aim to relate gene expression with phenotypic endpoints (i.e. to develop biomarkers for adverse outcomes). Additionally, technical aspects (influence of noise, number of time points and number of replicates) were evaluated on simulated data. Results: None of the methods outperforms the others in terms of biology. Linear modeling with limma is mostly influenced by noise. STEM is mostly influenced by the number of biological replicates in the dataset, whereas k-means and linear modeling with limma are mostly influenced by the number of time points. In most cases, the results of the methods complement each other. We therefore provide recommendations to integrate the five methods. Availability: The Matlab code for the simulations performed in this research is available in the Supplementary Data (Word file). The microarray data analysed in this paper are available at ArrayExpress (E-TOXM-22 and E-TOXM-23) and Gene Expression Omnibus (GSE39291). The phenotypic data are available in the Supplementary Data (Excel file). Links to the pattern recognition tools compared in this paper are provided in the main text. Contact: d.hendrickx@maastrichtuniversity.nl Supplementary information: Supplementary data are available at Bioinformatics online.

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.

The mechanism of polycation cytotoxicity and the relationship to polymer molecular weight is poorly understood. To gain an insight into this important phenomenon a range of newly synthesised uniform (near monodisperse) linear polyethylenimines, commercially available poly(L-lysine)s and two commonly used PEI -based transfectants (broad 22 kDa linear and 25 kDa branched) were tested for their cytotoxicity against the A549 human lung carcinoma cell line. Cell membrane damage assays (LDH release) and cell viability assays (MTT) showed a strong relationship to dose and polymer molecular weight, and increasing incubation times revealed that even supposedly "non-toxic" low molecular weight polymers still damage cell membranes. The newly proposed mechanism of cell membrane damage is acid catalysed hydrolysis of lipidic phosphoester bonds, which was supported by observations of the hydrolysis of DOPC liposomes. Crown Copyright (C) 2017 Published by Elsevier B.V. All rights reserved.

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.

Toxicity testing is vital to protect human health from exposure to toxic chemicals in the environment. Furthermore, combining novel cellular models with molecular profiling technologies, such as metabolomics can add new insight into the molecular basis of toxicity and provide a rich source of biomarkers that are urgently required in a 21st Century approach to toxicology. We have used an NMR-based metabolic profiling approach to characterise for the first time the metabolome of the RPTEC/TERT1 cell line, an immortalised non-tumour human renal epithelial cell line that recapitulates phenotypic characteristics that are absent in other in vitro renal cell models. RPTEC/TERT1 cells were cultured with either the dosing vehicle (DMSO) or with exposure to one of six compounds (nifedipine, potassium bromate, monuron, D-mannitol, ochratoxin A and sodium diclofenac), several of which are known to cause renal effects. Aqueous intracellular and culture media metabolites were profiled by (1)H NMR spectroscopy at 6, 24 and 72 hours of exposure to a low effect dose (IC(10)). We defined the metabolome of the RPTEC/TERT1 cell line and used a principal component analysis approach to derive a panel of key metabolites, which were altered by chemical exposure. By considering only major changes (±1.5 fold change from control) across this metabolite panel we were able to show specific alterations to cellular processes associated with chemical treatment. Our findings suggest that metabolic profiling of RPTEC/TERT1 cells can report on the effect of chemical exposure on multiple cellular pathways at low-level exposure, producing different response profiles for the different compounds tested with a greater number of major metabolic effects observed in the toxin treated cells. Importantly, compounds with established links to chronic renal toxicity produced more diverse and severe perturbations to the cellular metabolome than non-toxic compounds in this model. As these changes can be rationalised with the different pharmacological and toxicity profiles of the chemicals it is suggested that metabolic profiling in the RPTEC/TERT1 model would be useful in investigating the mechanism of action of toxins at a low dose.

Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g., just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12 percent in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.

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 multiwalled 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 mitochondria' membrane potential (MMP)) was observed for MWCNTs at a biologically relevant dose (0.25 jLgicm2) and for asbestos at 2 lig/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 MMPI. Forty-nine of the MMP1-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 NRFI. In addition, four miRNAs were correlated with MMP I and one of them, miR-1275, was found to negatively correlate with a large part of the MMP1-associated genes. Cellular processes such as gluconeogenesis, mitochondrial LC-fatty acid [3-oxidation and spindle microtubule function were enriched among the MMP f-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.

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/.

The development of hepatoma-based in vitro models to study hepatocyte physiology is an invaluable tool for both industry and academia. Here, we develop an in vitro model based on the HepG2 cell line that produces chenodeoxycholic acid, the main bile acid in humans, in amounts comparable to human hepatocytes. A combination of adenoviral transfections for CCAAT/enhancer-binding protein beta (C/EBP beta), hepatocyte nuclear factor 4 alpha (HNF4 alpha), and constitutive androstane receptor (CAR) decreased intracellular glutamate, succinate, leucine, and valine levels in HepG2 cells, suggestive of a switch to catabolism to increase lipogenic acetyl CoA and increased anaplerosis to replenish the tricarboxylic acid cycle. Transcripts of key genes involved in bile acid synthesis were significantly induced by approximately 160-fold. Consistently, chenodeoxycholic acid production rate was increased by more than 20-fold. Comparison between mRNA and bile acid levels suggest that 12-alpha hydroxylation of 7-alpha-hydroxy-4-cholesten-3-one is the limiting step in cholic acid synthesis in HepG2 cells. These data reveal that introduction of three hepatocyte-related transcription factors enhance anabolic reactions in HepG2 cells and provide a suitable model to study bile acid biosynthesis under pathophysiological conditions.

Freshly established cultures of primary hepatocytes progressively adopt a foetal-like phenotype and display increased production of connexin43. The latter is a multifaceted cellular entity with variable subcellular locations, including the mitochondrial compartment. Cx43 forms hemichannels and gap junctions that are involved in a plethora of physiological and pathological processes, such as apoptosis. The present study was conducted with the goal of shedding more light onto the role of connexin43 in primary hepatocyte cultures. Connexin43 expression was suppressed by means of RNA interference technology, and the overall outcome of this treatment on the hepatocellular proteome and metabolome was investigated using tandem mass tag-based differential protein profiling and 1H NMR spectroscopy, respectively. Global protein profiling revealed a number of targets of the connexin43 knock-down procedure, including mitochondrial proteins (heat shock protein 60, glucose-regulated protein 75, thiosulphate sulphurtransferase and adenosine triphosphate synthase) and detoxifying enzymes (glutathione S-transferase μ 2 and cytochrome P450 2C70). At the metabolomic level, connexin43 silencing caused no overt changes, though there was some evidence for a subtle increase in intracellular glycine quantities. Collectively, these data could further substantiate the established existence of a mitochondrial connexin pool and could be reconciled with the previously reported involvement of connexin43 signalling in spontaneously occurring apoptosis in primary hepatocyte cultures.

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 24 h) 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.

The chick chorioallantoic membrane (CAM) is a powerful alternative to rodent models for the study of physiological or pathological angiogenesis. We investigated metabolic changes during the maturation of the CAM by (1)H NMR-based metabolic profiling (metabonomics/metabolomics), allowing simultaneous measurements of many metabolites in an untargeted fashion. Specifically, we examined the time course of the measured metabolites to elucidate common patterns of regulation. Three clusters of metabolites were observed that correspond to essential biological processes active in the CAM with similar dynamics. The time courses common to the metabolite clusters distinguished specific stages of vessel growth, identifying waste product metabolites being stored in the CAM and energy-related substrates decreasing during embryonic growth. Using this top-down approach, combined with existing microarray data, we could link gene expression to metabolic consequences during the growth of a vascularized organ. For example, transcriptomic analysis demonstrated that many transcripts involved in the TCA cycle were down-regulated during CAM development, which correlated with the decrease in levels of TCA precursors and intermediates seen in the metabolite data. Taken together, this paper provides the first metabonomic study in an embryonic tissue where vessel development is the most active morphogenic process.

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.

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.

Motivation: Metabolic profiles derived from high resolution 1H-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/ Contact: r.cavill@imperial.ac.uk; t.ebbels@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.