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Background: Oligonucleotide arrays have become one of the most widely used high-throughput tools in biology. Due to their sensitivity to experimental conditions, normalization is a crucial step when comparing measurements from these arrays. Normalization is, however, far from a solved problem. Frequently, we encounter datasets with significant technical effects that currently available methods are not able to correct.Results: We show that by a careful decomposition of probe specific amplification, hybridization and array location effects, a normalization can be performed that allows for a much improved analysis of these data. Identification of the technical sources of variation between arrays has allowed us to build statistical models that are used to estimate how the signal of individual probes is affected, based on their properties. This enables a model-based normalization that is probe-specific, in contrast with the signal intensity distribution normalization performed by many current methods. Next to this, we propose a novel way of handling background correction, enabling the use of background information to weight probes during summarization. Testing of the proposed method shows a much improved detection of differentially expressed genes over earlier proposed methods, even when tested on (experimentally tightly controlled and replicated) spike-in datasets.Conclusions: When a limited number of arrays are available, or when arrays are run in different batches, technical effects have a large influence on the measured expression of genes. We show that a detailed modelling and correction of these technical effects allows for an improved analysis in these situations. © 2010 Hulsman et al; licensee BioMed Central Ltd.

This article provides methodological and technical considerations to researchers starting to develop computational model-based diagnostics using clinical chemistry data.These models are of increasing importance, since novel metabolomics and proteomics measuring technologies are able to produce large amounts of data that are difficult to interpret at first sight, but have high diagnostic potential. Computational models aid interpretation and make the data accessible for clinical diagnosis. We discuss the issues that a modeller has to take into account during the design, construction and evaluation phases of model development.We use the example of Particle Profiler development, a model-based diagnostic tool for lipoprotein disorders, as a case study, to illustrate our considerations. The case study also offers techniques for efficient model formulation, model calculation, workflow structuring and quality control. © The Author 2010. Published by Oxford University Press.

We introduce the metabolomics and proteomics based Postprandial Challenge Test (PCT) to quantify the postprandial response of multiple metabolic processes in humans in a standardized manner. The PCT comprised consumption of a standardized 500 ml dairy shake containing respectively 59, 30 and 12 energy percent lipids, carbohydrates and protein. During a 6 h time course after PCT 145 plasma metabolites, 79 proteins and 7 clinical chemistry parameters were quantified. Multiple processes related to metabolism, oxidation and inflammation reacted to the PCT, as demonstrated by changes of 106 metabolites, 31 proteins and 5 clinical chemistry parameters. The PCT was applied in a dietary intervention study to evaluate if the PCT would reveal additional metabolic changes compared to non-perturbed conditions. The study consisted of a 5-week intervention with a supplement mix of anti-inflammatory compounds in a crossover design with 36 overweight subjects. Of the 231 quantified parameters, 31 had different responses over time between treated and control groups, revealing differences in amino acid metabolism, oxidative stress, inflammation and endocrine metabolism. The results showed that the acute, short term metabolic responses to the PCT were different in subjects on the supplement mix compared to the controls. The PCT provided additional metabolic changes related to the dietary intervention not observed in non-perturbed conditions. Thus, a metabolomics based quantification of a standardized perturbation of metabolic homeostasis is more informative on metabolic status and subtle health effects induced by (dietary) interventions than quantification of the homeostatic situation. © 2011 The Author(s).

There is a need for representative in vitro models to assess the effects of airborne particles on lung health. The ob-jective of this study was to assess the cellular effects of cerium oxide (Ce02) particles exposed via an air—liquid interface in three relevant cell models in parallel. BEAS-2B, A549, and MucilAirTM cells were exposed for 1 hour to Ce02 particles comprising a range of doses (0.04-3.0 pg/cm2) and sizes (0.75 pm and 13.8 nm primary particle size). MucilAir cells are complex 3D cell systems with intact mucocilairy system and are cultured at air—liquid interface, in contrast to the simple cell lines A549 and BEAS 2B, which are essentially immersion models. Cell responses varied according to cell type used. BEAS-2B and A549 cells (normally cultured in liquid media) were sensitive to air exposure alone and exposure to Ce02 particles in terms of cytotoxicity, infiammation, and genotoxicity. MucilAir cells normally cultured at an air—liquid interface were insensitive to the air stream alone and only exhibited oxidative stress response after exposure to Ce02 particles. Minimal or no effects on gene ex-pression were detected in all cell types. Variations according to different sizes and doses of Ce02 were apparent but did not result in any definitive patterns. The minimal effects, except for the oxidative stress response, ob-served in the MucilAir cells are likely because of the presence of extracellular defensive mechanisms, such as intact mucocilairy system, that were not present in the other cell types. Further studies with MucilAir cells and other compounds are warranted to understand wider applicability in inhalation toxicology research

Acetaminophen is the primary cause of acute liver toxicity in Europe/USA, which led the FDA to reconsider recommendations concerning safe acetaminophen dosage/use. Unfortunately, the current tests for liver toxicity are no ideal predictive markers for liver injury, i.e. they only measure acetaminophen exposure after profound liver toxicity has already occurred. Furthermore, these tests do not provide mechanistic information. Here, 'omics techniques (global analysis of metabolomic/gene-expression responses) may provide additional insight.To better understand acetaminophen-induced responses at low doses, we evaluated the effects of (sub-)therapeutic acetaminophen doses on metabolite formation and global gene-expression changes (including, for the first time, full-genome human miRNA expression changes) in blood/urine samples from healthy human volunteers.Many known and several new acetaminophen-metabolites were detected, in particular in relation to hepatotoxicity-linked, oxidative metabolism of acetaminophen. Transcriptomic changes indicated immune-modulating effects (2. g dose) and oxidative stress responses (4. g dose). For the first time, effects of acetaminophen on full-genome human miRNA expression have been considered and confirmed the findings on mRNA level.'Omics techniques outperformed clinical chemistry tests and revealed novel response pathways to acetaminophen in humans. Although no definitive conclusion about potential immunotoxic effects of acetaminophen can be drawn from this study, there are clear indications that the immune system is triggered even after intake of low doses of acetaminophen. Also, oxidative stress-related gene responses, similar to those seen after high dose acetaminophen exposure, suggest the occurrence of possible pre-toxic effects of therapeutic acetaminophen doses. Possibly, these effects are related to dose-dependent increases in levels of hepatotoxicity-related metabolites. © 2012 Elsevier Inc.

Mesenchymal progenitor cells can be differentiated in vitro into myotubes that exhibit many characteristic features of primary mammalian skeletal muscle fibers. However, in general, they do not show the functional excitation-contraction coupling or the striated sarcomere arrangement typical of mature myofibers. Epigenetic modifications have been shown to play a key role in regulating the progressional changes in transcription necessary for muscle differentiation. In this study, we demonstrate that treatment of murine C2C12 mesenchymal progenitor cells with 10 µM of the DNA methylation inhibitor 5-azacytidine (5AC) promotes myogenesis, resulting in myotubes with enhanced maturity as compared to untreated myotubes. Specifically, 5AC treatment resulted in the up-regulation of muscle genes at the myoblast stage, while at later stages nearly 50% of the 5AC-treated myotubes displayed a mature, well-defined sarcomere organization, as well as spontaneous contractions that coincided with action potentials and intracellular calcium transients. Both the percentage of striated myotubes and their contractile activity could be inhibited by 20 nM TTX, 10 µM ryanodine, and 100 µM nifedipine, suggesting that action potential-induced calcium transients are responsible for these characteristics. Our data suggest that genomic demethylation induced by 5AC overcomes an epigenetic barrier that prevents untreated C2C12 myotubes from reaching full maturity. © FASEB.