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Nucleic acids duplex formation via hybridization is a crucial reaction in many processes and application across different disciplines. In life sciences the detection of mutations is an important application for which hybridization is used, e.g. in diagnostics via single-nucleotide variants (SNVs). This paper deals with the physicochemical aspects of hybridization-based detection of low-abundance mutations, which is challenging due to unavoidable competitive hybridization of high-abundant wild type sequence with the low-abundant variants. We apply two experimental methods based on theoretical hybridization models to show how sensing of DNA mutation can be significantly improved. This is implemented on two SNV biomarkers for which we first select a reference capture probe. This is a probe designed to match neither the wild type nor the SNV sequence, but to have an equal affinity to the wild-type as the SNV-matching probe. This allows the mutation-specific signal to be expressed as a ratiometric quantity, leading to increased assay robustness. Secondly, we selectively deplete the wild-type species by introducing an excess of wild-type-specific capture probes, and account for these depletion effects in the theoretical model. We demonstrate the detection of 0.05% mutant species in a wild-type background, which is an improvement of an order of magnitude in the limit of detection in comparison with the nodepletion case. This sensitivity is comparable with digital PCR results, showing performance suitable for e.g. clinical applications in liquid biopsy context. The principles of this work apply to a wide range of hybridizationbased DNA biosensing technologies, irrespective of the underlying transducer principle. This work was supported by the Research Foundation Flanders (FWO) and the Flemish Institute for Technological Research VITO nv (grant numbers 1S69320N, 1159719N ) and by the EU H2020 M3DLoC project (grant number 760662).

For differential expression studies in all omics disciplines, data normalization is a crucial step that is often subject to a balance between speed and effectiveness. To keep up with the data produced by high-throughput instruments, researchers require fast and easy-to-use yet effective methods that fit into automated analysis pipelines. The CONSTANd normalization method meets these criteria, so we have made its source code available for R/BioConductor and Python. We briefly review the method and demonstrate how it can be used in different omics contexts for experiments of any scale. Widespread adoption across omics disciplines would ease data integration in multiomics experiments.

In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets with about 2 million count records in less than a second whilst removing unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd’s simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.

Rationale: The current methods for identifying peptides in mass spectral product ion data still struggle to do so for the majority of spectra. Based on the experimental setup and other assumptions, such methods restrict the search space to speed up computations, but at the cost of creating blind spots. The proteomics community would greatly benefit from a method that is capable of covering the entire search space without using any restrictions, thus establishing a baseline for identification. Methods: We conceived the "mass pattern paradigm" (MPP) that enables the creation of such an identification method, and we implemented it into a prototype database search engine "PRiSM" (PRotein-Spectrum Matching). We then assessed its operational characteristics by applying it to publicly available high-precision mass spectra of low and high identification difficulty. We used those characteristics to gain theoretical insights into trade-offs between sensitivity and speed when trying to establish a baseline for identification. Results: Of 100 low difficulty spectra, PRiSM and SEQUEST agree on 84 identifications (of which 75 are statistically significant). Of 15 of 100 spectra not identified in a previous study (using SEQUEST), 13 are considered reliable after visual inspection and represent 3 proteins (out of 9 in total) not detected previously. Conclusions: Despite leaving noise intact, the simple PRiSM prototype can make statistically reliable identifications, while controlling the false discovery rate by fitting a null distribution. It also identifies some spectra previously unidentifiable in an "extremely open" SEQUEST search, paving the way to establishing a baseline for identification in proteomics. Department of Economy, Science and Innovation, Flanders; Vlaamse Instelling voor Technologisch Onderzoek, Grant/Award Number: UH_PhD_1701

Previous studies have shown that selective synthetic cell receptors can be produced by cell imprinting on polymer layers. However, knowledge on the fundamental detection mechanisms remains limited. In this article, while using yeast cells (Saccharomyces cerevisiae) as model cells, the factors influencing cellular recognition by surface-imprinted polymers (SIPs) are studied by means of spectroscopic and microscopy techniques and a transducer platform based on interfacial thermal transport, the so-called heat-transfer method (HTM). These analyses indicate that cell imprinting creates selective binding sites on the surface of the SIP layer in the form of binding cavities that match the cells in shape and size. Also, we show that phospholipid moieties are incorporated into the SIP cavities during imprinting, while membrane proteins do not seem to be transferred. More importantly, we demonstrate that the incorporated phospholipids significantly enhance cell adhesion to the SIP, and thus play a significant role in the cell-SIP binding mechanism. Furthermore, the hydrophobicity of the SIP layer was found to be considerably higher when compared with a non-imprinted polymer layer (NIP), an effect that could not be attributed to the presence of cavities on the surface of the SIP layer. Therefore, we suggest that the role of phospholipids in the SIP recognition mechanism is mediated by long range hydrophobic forces. ispartof: Sensors and Actuators B, Chemical vol:255 issue:1 pages:907-917 status: published

CD34+ hematopoietic progenitor cells (HPCs) offer great opportunities to develop new treatments for numerous malignant and non-malignant diseases. Nanoparticle (NP)-based strategies can further enhance this potential, and therefore a thorough understanding of the loading behavior of HPCs towards NPs is essential for a successful application. The present study focusses on the interaction kinetics of 40 nm sized carboxylated polystyrene (PS) NPs with HPCs. Interestingly, a transient association of the NPs with HPCs is observed, reaching a maximum within 1 hour and declining afterwards. This behavior is not seen in dendritic cells (CD34-DCs) differentiated from HPCs, which display a monotonic increase in NP load. We demonstrate that this transient interaction requires an energy-dependent cellular process, suggesting active loading and release of NPs by HPCs. This novel observation offers a unique approach to transiently equip HPCs. A simple theoretical approach modeling the kinetics of NP loading and release is presented, contributing to a framework of describing this phenomenon.

Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access.

DNA surface-hybridization biosensors utilize the selective hybridization of target sequences in solution to surface-immobilized probes. In this process, the target is usually assumed to be in excess, so that its concentration does not significantly vary while hybridizing to the surface-bound probes. If the target is initially at low concentrations and/or if the number of probes is very large and have high affinity for the target, the DNA in solution may get depleted. In this paper we analyze the equilibrium and kinetics of hybridization of DNA biosensors in the case of strong target depletion, by extending the Langmuir adsorption model. We focus, in particular, on the detection of a small amount of a single-nucleotide "mutant" sequence (concentration $c_2$) in a solution, which differs by one or more nucleotides from an abundant "wild-type" sequence (concentration $c_1 \gg c_2$). We show that depletion can give rise to a strongly-enhanced sensitivity of the biosensors. Using representative values of rate constants and hybridization free energies, we find that in the depletion regime one could detect relative concentrations $c_2/c_1$ that are up to three orders of magnitude smaller than in the conventional approach. The kinetics is surprisingly rich, and exhibits a non-monotonic adsorption with no counterpart in the no-depletion case. Finally, we show that, alongside enhanced detection sensitivity, this approach offers the possibility of sample enrichment, by substantially increasing the relative amount of the mutant over the wild-type sequence., Comment: 27 pages, 4 figures

In Mycobacterium tuberculosis (Mtb) the detection of single nucleotide polymorphisms (SNPs) is of high importance both for diagnostics, since drug resistance is primarily caused by the acquisition of SNPs in multiple drug targets, and for epidemiological studies in which strain typing is performed by SNP identification. To provide the necessary coverage of clinically relevant resistance profiles and strain types, nucleic acid-based measurement techniques must be able to detect a large number of potential SNPs. Since the Mtb problem is pressing in many resource-poor countries, requiring low-cost point-of-care biosensors, this is a non-trivial technological challenge. This paper presents a proof-of-concept in which we chose simple DNA-DNA hybridization as a sensing principle since this can be transferred to existing low-cost hardware platforms, and we pushed the multiplex boundaries of it. With a custom designed probe set and a physicochemical-driven data analysis it was possible to simultaneously detect the presence of SNPs associated with first- and second-line drug resistance and Mtb strain typing. We have demonstrated its use for the identification of drug resistance and strain type from a panel of phylogenetically diverse clinical strains. Furthermore, reliable detection of the presence of a minority population (

In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on, f.i., mixed models can take a lot of (computing) time. MS-based proteomics labs are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at https://qcquan.net/) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow, and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

The capture and translocation of biomolecules through nanometer-scale pores are processes with a potential large number of applications, and hence they have been intensively studied in the recent years. The aim of this paper is to review existing models of the capture process by a nanopore, together with some recent experimental data of short single- and double-stranded DNA captured by Cytolysin A (ClyA) nanopore. ClyA is a transmembrane protein of bacterial origin which has been recently engineered through site-specific mutations, to allow the translocation of double- and single-stranded DNA. A comparison between theoretical estimations and experiments suggests that for both cases the capture is a reaction-limited process. This is corroborated by the observed salt dependence of the capture rate, which we find to be in quantitative agreement with the theoretical predictions., Published in JPCM Special Issue "Transport in Narrow Channels"

In hybridization based nucleic acid sensors the stringency of hybridization poses a challenge to design and experiment. For a given set of experimental parameters the affinity window of probe-target interaction is always limited and vice versa for a given probe set design, changes in experimental conditions can easily bring some measurements out of detection range. In this paper we introduce and apply a strategy to extend this dynamic range for affinity sensors, sensors which measure the amount of hybridized molecules after equilibrium is reached. The method relies on concepts of additivity of nucleic acids hybridization free energies and on equilibrium isotherms. It consists in combining the measurements from probes with different lengths, by appropriately rescaling the measured signals. We test the validity of the approach on experiments and show that by combining probes with hybridizing regions of length 21, 23 and 25 nucleotides we manage to extend the dynamic range of the intensity signals by a factor of 25. The presented concept is easy to extend, platform free and applies to any hybridization based affinity sensor. publisher: Elsevier articletitle: Dynamic range extension of hybridization sensors journaltitle: Biosensors and Bioelectronics articlelink: http://dx.doi.org/10.1016/j.bios.2014.09.043 content_type: article copyright: Copyright © 2014 Elsevier B.V. All rights reserved. ispartof: Biosensors & Bioelectronics vol:64 pages:411-415 ispartof: location:England status: published

We present a general analytical model for the intensity fluctuation autocorrelation function for second and third harmonic generating point scatterers. Expressions are derived for a stationary laser beam and for scanning beam configurations for specific correlation methodologies. We discuss free translational diffusion in both three and two dimensions. At low particle concentrations, the expressions for fluorescence are retrieved, while at high particle concentrations a rescaling of the function parameters is required for a stationary illumination beam, provided that the phase shift per unit length of the beam equals zero.

The knowledge of genomic DNA variations in patient samples has a high and increasing value for human diagnostics in its broadest sense. Although many methods and sensors to detect or quantify these variations are available or under development, the number of underlying physico-chemical detection principles is limited. One of these principles is the hybridization of sample target DNA versus nucleic acid probes. We introduce a novel thermodynamics approach and develop a framework to exploit the specific detection capabilities of nucleic acid hybridization, using generic principles applicable to any platform. As a case study, we detect point mutations in the KRAS oncogene on a microarray platform. For the given platform and hybridization conditions, we demonstrate the multiplex detection capability of hybridization and assess the detection limit using thermodynamic considerations; DNA containing point mutations in a background of wild type sequences can be identified down to at least 1% relative concentration. In order to show the clinical relevance, the detection capabilities are confirmed on challenging formalin-fixed paraffin-embedded clinical tumor samples. This enzyme-free detection framework contains the accuracy and efficiency to screen for hundreds of mutations in a single run with many potential applications in molecular diagnostics and the field of personalised medicine.

A major conceptual breakthrough in cell signaling has been the finding of EV as new biomarker shuttles in body fluids. Now, one of the major challenges in using these nanometer-sized biological entities as diagnostic marker is the development of translational methodologies to profile them. SPR offers a promising label-free and real time platform with a high potential for biomarker detection. Therefore, we aimed to develop a uniform SPR methodology to detect specific surface markers on EV derived from patient with CHD. EVs having an approximate size range between 30 and 100 nm (similar to 48.5%) and 100-300 nm (similar to 51.5%) were successfully isolated. The biomarker profile of EV was verified using immunogold labeling, ELISA and SPR. Using SPR, we demonstrated an increased binding of EV derived from patients with CHD to anti-ICAM-1 antibodies as compared to EV from healthy donors. Our current findings open up novel opportunities for in-depth and label-free investigation of EV. (C) 2017 The Authors. Published by Elsevier Inc. This work was co-financed by the EU through the Interreg IV Flanders-the Netherlands project VaRiA (IVA-VLANED-3.65) and Interreg V Flanders-the Netherlands project Trans Tech Diagnostics (TTD) and Johannes Waltenberger is supported by Cells-in-Motion Cluster of Excellence (EXC 1003-CiM), University of Munster, 48149 Munster, Germany.

VITOSENS™ is an in vitro assay based on dendritic cell (DC) activation that is a key event in the adverse outcome pathway (AOP) on skin sensitization. Exposure induced expression changes of gene transcripts are quantified in DC derived from CD34+ progenitor cells in human cord blood. The assay was initially designed as a classifier and it is able to discriminate chemical skin sensitizers from non-sensitizers. By combining different VITOSENS™ variables in vitro potency values are modelled that closely fit in vivo-derived data, and this over the entire range from weak to extremely sensitizing chemicals. As such the assay can provide valuable information in the context of chemical risk assessment. Furthermore, the biomarkers in the assay display a concentration-dependent response relation that shifts when non-sensitizing compounds (e.g. irritants, endotoxin, anti-oxidants) are added to the chemical sensitizer. This indicates that our assay can be applied for mixture assessment and efficacy testing. Finally, a reliable test system should be based on key events of the in vivo disease process it is screening for. The functional relevance of the VITOSENS™ gene markers in the skin sensitization pathways are demonstrated. In a first step their differential protein expression was shown, and in a second phase changes in DC surface molecules were altered by counteracting the sensitizer-induced activity of VITOSENS™ markers. In conclusion, these results point to the feasibility of applying VITOSENS™ in an integrated approach for in vitro assessment of the sensitizing risk of chemical ingredients and mixtures. The assay represents the key event of DC maturation in the AOP of chemical-induced skin sensitization.

The Table 1 (EU declaration signatory and membership status) of the original article published online contained several errors: the IARC was incorrectly listed in the table, which only includes countries, and footnote; Croatia, Lithuania, Poland and Portugal were not listed as full EMBL members; Estonia is now listed as a Prospect Member of EMBL; Hungary, Ireland and Israel were incorrectly listed as BBMRI-ERIC members; Iceland was omitted from the table. Hyphens stand for ‘not applicable’; the table and footnote have been corrected accordingly. These errors have now been corrected online and in print.

Amines have potential to be used in CO 2 capture and storage (CCS) technology, but as they can be released into the environment and be degraded into more toxic compounds, such as nitrosamines and nitramines, there have been concerns about their negative impact on human health. We investigated the potential toxic effects from acute exposure to dimethylnitramine (DMA–NO 2 ), methylnitramine (MA-NO 2 ), ethanolnitramine (MEA-NO 2 ) and 2-methyl-2-(nitroamino)-1-propanol (AMP-NO 2 ). The eye irritation, and skin sensitization, irritation and corrosion potential of these substances have been evaluated in vitro using the Bovine Corneal Opacity and Permeability (BCOP) assay, VITOSENS® assay, Reconstructed Human Epidermis (RHE) skin irritation test and Corrositex Skin corrosion test, respectively. Exposure to DMA–NO 2 induced a mild eye irritation response, while MA-NO 2 , MEA-NO 2 and AMP-NO 2 were shown to be very severe eye irritants. MA-NO 2 and MEA-NO 2 were tested for skin sensitization and found to be non-sensitizers to the skin. In addition, none of the four test substances was irritant or corrosive to the skin.

The developmental neurotoxic potential of the majority of environmental chemicals and drugs is currently undetermined. Specific in vivo studies provide useful data for hazard assessment but are not amenable to screen thousands of untested compounds. In this study, methods which use zebrafish embryos, eleutheroembryos and larvae as model organisms, were proposed as alternatives for developmental neurotoxicity (DNT) testing. The evaluation of spontaneous tail coilings in zebrafish embryos aged 2426 hours post fertilization (hpf) and the swimming activity of eleutheroembryos at 120 and larvae at 144 hpf, i.e. parameters for locomotor activity, were investigated as potential endpoints for DNT testing, according to available standard protocols. The overall performance and predictive value of these methods was then examined by testing a training set of 10 compounds, including known developmental neurotoxicants and compounds not considered to be neurotoxic. The classification of the selected compounds as either neurotoxic or non-neurotoxic, based on the effects observed in zebrafish embryos and larvae, was compared to available mammalian data and an overall concordance of 90% was achieved. Furthermore, the specificity of the selected endpoints for DNT was evaluated as well as the potential similarities between zebrafish and mammals with regard to mechanisms of action for the selected compounds. Although further studies, including the screening of a large testing set of compounds are required, we suggest that the proposed methods with zebrafish embryos and larvae might be valuable alternatives for animal testing for the screening and prioritization of compounds for DNT.

Detection of point mutations and single nucleotide polymorphisms in DNA and RNA has a growing importance in biology, biotechnology, and medicine. For the application at hand, hybridization assays are often used. Traditionally, they differentiate point mutations only at elevated temperatures (>40 degrees C) and in narrow intervals (Delta T = 1-10 degrees C). The current study demonstrates that a specially designed multistranded DNA probe can differentiate point mutations in the range of 5-40 degrees C. This unprecedentedly broad ambient-temperature range is enabled by a controlled combination of (i) nonequilibrium hybridization conditions and (ii) a mismatch-induced increase of equilibration time in respect to that of a fully matched complex, which we dub "kinetic inversion". We thank Dr. Yulia V. Gerasimova for helpful discussions. D.M.K. was funded by NSF CCF 1423219 and NIAID R15AI10388001A1. J.H. received funding from the Research Fund - Flanders (FWO K226314N). A.B. was supported by the UCF startup funds. The computational time for the project was provided by the UCF STOKES cluster.

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters. peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=ieru20 ispartof: Expert Review of Proteomics vol:13 issue:5 pages:495-511 ispartof: location:England status: published

In quantitative proteomics applications, the use of isobaric labels is a very popular concept as they allow for multiplexing, such that peptides from multiple biological samples are quantified simultaneously in one mass spectrometry experiment. Although this multiplexing allows that peptide intensities are affected by the same amount of instrument variability, systematic effects during sample preparation can also introduce a bias in the quantitation measurements. Therefore, normalization methods are required to remove this systematic error. At present, a few dedicated normalization methods for isobaric labeled data are at hand. Most of these normalization methods include a framework for statistical data analysis and rely on ANOVA or linear mixed models. However, for swift quality control of the samples or data visualization a simple normalization technique is sufficient. To this aim, we present a new and easy-to-use data-driven normalization method, named CONSTANd. The CONSTANd method employs constrained optimization and prior information about the labeling strategy to normalize the peptide intensities. Further, it allows maintaining the connection to any biological effect while reducing the systematic and technical errors. As a result, peptides can not only be compared directly within a multiplexed experiment, but are also comparable between other isobaric labeled datasets from multiple experimental designs that are normalized by the CONSTANd method, without the need to include a reference sample in every experimental setup. The latter property is especially useful when more than six, eight or ten (TMT/iTRAQ) biological samples are required to detect differential peptides with sufficient statistical power and to optimally make use of the multiplexing capacity of isobaric labels. This work was supported by SBO grant "InSPECtor" (120025) of the Flemish agency for Innovation by Science and Technology (IWT).

Nonequilibrium wetting transitions are observed in Monte Carlo simulations of a kinetic spin system in the absence of a detailed balance condition with respect to an energy functional. A nonthermal model is proposed starting from a two-dimensional Ising spin lattice at zero temperature with two boundaries subject to opposing surface fields. Local spin excitations are only allowed by absorbing an energy quantum (photon) below a cutoff energy E_c. Local spin relaxation takes place by emitting a photon which leaves the lattice. Using Monte Carlo simulation nonequilibrium critical wetting transitions are observed as well as nonequilibrium first-order wetting phenomena, respectively in the absence or presence of absorbing states of the spin system. The transitions are identified from the behavior of the probability distribution of a suitably chosen order parameter that was proven useful for studying wetting in the (thermal) Ising model., 12 pages, 12 figures

Cite this as: K. Bloemen, R. Van Den Heuvel, E. Govarts, J. Hooyberghs, V. Nelen, E. Witters, K. Desager and G. Schoeters, Clinical & Experimental Allergy, 2011 (41) 346–356. Summary Background Asthma is a complex clinical disease characterized by airway inflammation. Recently, various studies reported on the analysis of exhaled breath condensate (EBC) in the search for potential biomarkers for asthma. However, in a complex disease such as asthma, one biomarker might not be enough for early diagnosis or follow-up. Objective The use of proteome analysis may reveal disease-specific proteolytic peptide or protein patterns, and may lead to the identification of novel proteins for the detection of asthma. Methods Liquid chromatography and mass spectrometry were used to separate and detect proteins (proteolytic peptides) present in EBC samples from 30 healthy children and 40 children with asthma in the age group of 6–12 years. Results Support vector machine analysis resulted in differentiating profiles based on asthma status. These proteolytic peptide patterns were not correlated to some well known (spirometry, exhaled nitric oxide) and more recently described exhaled markers (EBC pH, LTB4). The more abundant proteins in EBC were identified as cytokeratins, albumin, actin, haemoglobin, lysozyme, dermcidin, and calgranulin B. Conclusion Although the exact role in the disease development or physiological state of the airways of the proteins described in the presented pattern is not clear at this moment, this is an important step in the search for exhaled biomarkers for asthma. This study shows that EBC contains proteins that are of interest for future non-invasive asthma diagnosis or follow-up.

The underlying events of how dendritic cells (DC) are capable of evoking an antigen-specific skin sensitization response are not yet understood. Recently, we revealed a set of genes in human cord blood CD34(+) DC (CD34-DC) that show a discriminating behaviour after skin sensitizing exposure. Based on their differential expression, an in vitro assay was developed to identify chemicals as sensitizing or not. This study was designed to investigate the genes' involvement in the DC response to skin sensitizers and as such gain insights in the sensitization cascade. Functional connection of the marker genes was inquired by constructing a molecular network using Ingenuity software. By real-time RT-qPCR, we established the effective expression of 3 additional gene transcripts in the generated network in CD34-DC, of which CREB1 and TNF-alpha were significantly altered in expression by sensitizing versus non-sensitizing exposure. Next, it was tested whether the discriminating response of CCR2 and COX2 marker genes was translated at the protein level in CD34-DC exposed to 3 sensitizers versus 3 non-sensitizers. Significantly differential protein expression of CCR2 and COX2 was confirmed using flow cytometry. Our results indicate that the marker genes may be functionally relevant in DC mediated skin sensitization. ispartof: Toxicology letters vol:196 issue:2 pages:95-103 ispartof: location:Netherlands status: published

The skin-sensitizing potential of chemicals is an important concern for public health and thus a significant end point in the hazard identification process. To determine skin-sensitizing capacity, large research efforts focus on the development of assays, which do not require animals. As such, an in vitro test has previously been developed based on the differential expression of CREM and CCR2 transcripts in CD34(+) progenitor-derived dendritic cells (CD34-DC), which allows to classify chemicals as skin (non-)sensitizing. However, skin sensitization is not an all-or-none phenomenon, and up to now, the assessment of relative potency can only be derived using the in vivo local lymph node assay (LLNA). In our study, we analyzed the feasibility to predict the sensitizing potency, i.e., the LLNA EC3 values, of 15 skin sensitizers using in vitro data from the CD34-DC-based assay. Hereto, we extended the in vitro-generated gene expression data set by an additional source of information, the concentration of the compound that causes 20% cell damage (IC20) in CD34-DC. We statistically confirmed that this IC20 is linearly independent from the gene expression changes but that it does correlate with LLNA EC3 values. In a further analysis, we applied a robust linear regression with both IC20 and expression changes of CREM and CCR2 as explanatory variables. For 13 out of 15 compounds, a high linear correlation was established between the in vitro model and the LLNA EC3 values over a range of four orders of magnitude, i.e., from weak to extreme sensitizers. ispartof: Toxicological sciences : an official journal of the Society of Toxicology vol:116 issue:1 pages:122-129 ispartof: location:United States status: published

It is recognized that respiratory sensitization is a hazard of high concern. Despite international regulatory requirements there is no established protocol for the identification of chemical respiratory sensitizers. New tests should be based on mechanistic understanding and should be preferentially restricted to in vitro assays. The major goal of this study was to investigate the genetic response of human THP-1 macrophages after contact with respiratory (non-)sensitizers, and to identify genes that are able to discriminate between both groups. THP-1 macrophages were exposed during different time points to 3 respiratory sensitizers, 2 irritants, and 1 skin sensitizer. Gene expression changes were evaluated using Agilent Whole Human Genome arrays. Fisher Linear Discriminant Analysis was used to obtain a ranking of genes that reflects their potential to discriminate between respiratory (non-)sensitizing chemicals. Among the 20 most discriminating genes which were categorized into molecular and biological Gene Ontology (GO) terms, EIF4E, PDGFRB, SEMA7A, and ZFP36L2 could be associated with respiratory sensitization. When categorizing the top-1000 genes into biological GO terms, 24 genes were associated with immune function. Using a pathway analysis tool, platelet-derived growth factor signaling was observed to be activated in THP-1 macrophages in the context of respiratory sensitization.

Exhaled breath condensate (EBC) is a biological fluid that contains trace amounts of secreted pulmonary proteins, and is emerging as a potentially valuable and non-invasively obtained source of disease biomarkers. Proteome analysis of these samples could lead to the identification of prognostic indicators of airway diseases. The objective of this study was to develop a protocol for proteome analysis of EBC samples. In this report, an improved procedure for EBC sample preparation and concentration is presented, together with a method for comparison of the protein profiles between two groups. The presented approach enabled to study the condensed exhaled breath proteome for biomarker analysis, and revealed proteins not previously identified in an EBC proteomics approach. In a comparative pilot study, EBC protein profiles obtained from smokers and non-smokers showed distinct differences and are illustrative for its potential in clinical studies. EBC from smokers contained higher concentrations of the more abundant proteins, such as cytokeratins, compared to non-smokers, and calgranulin B was identified uniquely in EBC samples from smokers.

Respiratory sensitization is a concern for occupational and environmental health in consumer product development. Despite international regulatory requirements there is no established protocol for the identification of chemical respiratory sensitizers. New tests should be based on mechanistic understanding and should be preferentially restricted to in vitro assays. The major goal of this study was to investigate the alterations in gene expression of human bronchial epithelial (BEAS-2B) cells after exposure to respiratory sensitizers and respiratory non-sensitizing chemicals, and to identify genes that are able to discriminate between both groups of chemicals. BEAS-2B cells were exposed during 6, 10, and 24 h to the respiratory sensitizers ammonium hexachloroplatinate IV, hexamethylene diisocyanate, and trimellitic anhydride, the irritants acrolein and methyl salicylate, and the skin sensitizer 1-chloro-2,4-dinitrobenzene. Overall changes in gene expression were evaluated using Agilent Whole Human Genome 4× 44K oligonucleotide arrays. Fisher Linear Discriminant Analysis was used to obtain a ranking of genes that reflects their potential to discriminate between respiratory sensitizing and respiratory non-sensitizing chemicals. The 10 most discriminative genes were BC042064, A_24_P229834, DOCK11, THC2544911, DLGAP4, NINJ1, PFKM, FLJ10986, IL28RA, and CASP9. Based on the differentially expressed genes, pathway analysis was used to identify possible underlying mechanisms of respiratory sensitization. We demonstrated that in bronchial epithelial cells the canonical PTEN signaling pathway is probably the most specific pathway in the context of respiratory sensitization. Results are indicative that the BEAS-2B cell line can be used as an alternative cell model to screen chemical compounds for their respiratory sensitizing potential.

The ethical and economic burden associated with animal testing for assessment of skin sensitization has triggered intensive research effort towards development and validation of alternative methods. In addition, new legislation on the registration and use of cosmetics and chemicals promote the use of suitable alternatives for hazard assessment. Our previous studies demonstrated that human CD34+ progenitor-derived dendritic cells from cord blood express specific gene profiles upon exposure to low molecular weight sensitizing chemicals. This paper presents a classification model based on this cell type which is successful in discriminating sensitizing chemicals from non-sensitizing chemicals based on transcriptome analysis of 13 genes. Expression profiles of a set of 10 sensitizers and 11 non-sensitizers were analyzed by RT-PCR using 9 different exposure conditions and a total of 73 donor samples. Based on these data a predictive dichotomous classifier for skin sensitizers has been constructed, which is referred to as VITOSENS® . In a first step the dimensionality of the input data was reduced by selectively rejecting a number of exposure conditions and genes. Next, the generalization of a linear classifier was evaluated by a cross-validation which resulted in a prediction performance with a concordance of 89%, a specificity of 97% and a sensitivity of 82%. These results show that the present model may be a useful human in vitro alternative for further use in a test strategy towards the reduction of animal use for skin sensitization.

Occupational exposure to chemicals is one of the main causes of respiratory allergy and asthma. Identification of chemicals that trigger allergic asthma is difficult as underlying processes and specific markers have not yet been clearly defined. Moreover, adequate classification of the respiratory toxicity of chemicals is hampered due to the lack of validated in vivo and in vitro test methods. The study of differential gene expression profiles in appropriate human in vitro cell systems is a promising approach to identify selective markers for respiratory allergy. As alveolar macrophages display important immunological and inflammatory properties in response to foreign substances in the lung, we aimed at gaining more insight in changes of human macrophages transcriptome and to identify selective genetic markers for respiratory sensitization in response to hexamethylene diisocyanate (HDI). In vitro cultures of human THP-1 cells were differentiated into macrophages and exposed to 55 μg/ml HDI for 6 and 10 h. Using human oligonucleotide microarrays, changes were observed in the expression of genes that are involved in diverse biological and molecular processes, including detoxification, oxidative stress, cytokine signaling, and apoptosis, which can lead to the development of asthma. These genes are possible markers for respiratory sensitization caused by isocyanates.

The assessment of the skin sensitising capacity of chemicals is up to now investigated using in vivo animal tests. However there has been an increasing public and governmental concern regarding the use of animals for chemical screening. This has raised the need for the development of validated in vitro alternatives. Langerhans cells are potent antigen-presenting cells that play a crucial role in the development of allergic contact dermatitis. We used CD34(+) progenitor-derived dendritic cells from cord blood as an in vitro alternative for Langerhans cells. The cells were exposed to four contact allergens (nickel sulphate, dinitrochlorobenzene, oxazolone and eugenol) and two irritants (sodium dodecyl sulphate and benzalkonium chloride) for 3, 6, 12 and 24h. Using microarray analyses we revealed a set of 25 genes with an altered gene expression pattern after exposure to allergens and not to irritants. Five out of these 25 genes were selected and their gene expression changes were confirmed with real-time reverse transcriptase polymerase chain reaction. The list of 25 genes represent valuable candidates to be further evaluated for their capacity to predict the sensitizing potential of different classes of chemicals in studies using a more extended set of (non) allergic substances.

Over the past years, the health impact of airborn e particulate matter (PM) has become a very topical subject. In the environmental sciences a lot of research effort goes to wards the understanding of the PM phenomenon and the ability to for ecast ambient PM concentrations. In this paper we describe the development of a neural network tool to forecast the daily average PM 10 concentrations in Belgium one day ahead . This research is based upon measurements from ten monitoring sites during the pe riod 1997 -2001 and upon ECMWF simulations of meteorological parameters. The most important input variable found was the boundary layer height . A model based on this parameter currently operational online serves to monitor the daily average threshold of 100 µg/m³. By e xtending the model with other input parameters we were able to increase the performance only slightly. This brings us to the conclusion that day to day fluctuations of PM 10 concentrations in Belg ian urban areas are to a large exten t driven by m eteorological conditions and to a lesser extend by changes in anthropogenic sources.

The stationary state of stochastic processes such as reaction-diffusion systems can be related to the ground state of a suitably defined quantum Hamiltonian. Using this analogy, we investigate the applicability of a real-space renormalization group approach, originally developed for quantum spin systems, to interacting particle systems. We apply the technique to an exactly solvable reaction-diffusion system and to the contact process (both in d = 1). In the former case, several exact results are recovered. For the contact process, surprisingly good estimates of critical parameters are obtained from a small-cell renormalization.

For the classification of respiratory sensitizing chemicals, no validated in vivo nor in vitro tests are currently available. In this study, we evaluated whether respiratory sensitizers trigger specific signals in human bronchial epithelial (BEAS-2B) cells at the level of the transcriptome. The cells were exposed during 6, 10, and 24 h to 4 respiratory sensitizers and 6 non-respiratory sensitizers (3 skin sensitizers and 3 respiratory irritants) at a concentration inducing 20% cell viability loss after 24 h. Changes in gene expression were evaluated using Agilent Whole Human Genome 4 x 44 K oligonucleotide arrays. A limited number of 11 transcripts could be identified as potential biomarkers to identify respiratory sensitizers. Three of these transcripts are associated to immune system processes (HSPA5, UPP1, and SEPRINEI). In addition, the transcriptome was screened for transcripts that are differentially expressed compared to vehicle control for each chemical. The results show that the NRF2-mediated oxidative stress response is activated in the cell line after stimulation with all of the chemicals that were selected in our study, and that - at the level of gene expression - this pathway shows no potential to discriminate between any of the three compound groups: respiratory sensitizers, skin sensitizers, or electrophilic respiratory irritants. (C) 2013 Elsevier Ltd. All rights reserved. For the classification of respiratory sensitizing chemicals, no validated in vivo nor in vitro tests are currently available. In this study, we evaluated whether respiratory sensitizers trigger specific signals in human bronchial epithelial (BEAS-2B) cells at the level of the transcriptome. The cells were exposed during 6, 10, and 24. h to 4 respiratory sensitizers and 6 non-respiratory sensitizers (3 skin sensitizers and 3 respiratory irritants) at a concentration inducing 20% cell viability loss after 24. h. Changes in gene expression were evaluated using Agilent Whole Human Genome 4. ×. 44. K oligonucleotide arrays. A limited number of 11 transcripts could be identified as potential biomarkers to identify respiratory sensitizers. Three of these transcripts are associated to immune system processes ( HSPA5, UPP1, and SEPRINE1). In addition, the transcriptome was screened for transcripts that are differentially expressed compared to vehicle control for each chemical. The results show that the NRF2-mediated oxidative stress response is activated in the cell line after stimulation with all of the chemicals that were selected in our study, and that - at the level of gene expression - this pathway shows no potential to discriminate between any of the three compound groups: respiratory sensitizers, skin sensitizers, or electrophilic respiratory irritants.

International audience; Hybridization of nucleic acids on solid surfaces is a key process involved in high-throughput technologies such as microarrays and, in some cases, next-generation sequencing (NGS). A physical understanding of the hybridization process helps to determine the accuracy of these technologies. The goal of a widespread research program is to develop reliable transformations between the raw signals reported by the technologies and individual molecular concentrations from an ensemble of nucleic acids. This research has inputs from many areas, from bioinformatics and biostatistics, to theoretical and experimental biochemistry and biophysics, to computer simulations. A group of leading researchers met in Ploen Germany in 2011 to discuss present knowledge and limitations of our physico-chemical understanding of high-throughput nucleic acid technologies. This meeting inspired us to write this summary, which provides an overview of the state-of-the-art approaches based on physico-chemical foundation to modeling of the nucleic acids hybridization process on solid surfaces. In addition, practical application of current knowledge is emphasized.

It has recently been shown that in some DNA microarrays the time needed to reach thermal equilibrium may largely exceed the typical experimental time, which is about 15h in standard protocols (Hooyberghs et al. Phys. Rev. E 81, 012901 (2010)). In this paper we discuss how this breakdown of thermodynamic equilibrium could be detected in microarray experiments without resorting to real time hybridization data, which are difficult to implement in standard experimental conditions. The method is based on the analysis of the distribution of fluorescence intensities I from different spots for probes carrying base mismatches. In thermal equilibrium and at sufficiently low concentrations, log I is expected to be linearly related to the hybridization free energy $\Delta G$ with a slope equal to $1/RT_{exp}$, where $T_{exp}$ is the experimental temperature and R is the gas constant. The breakdown of equilibrium results in the deviation from this law. A model for hybridization kinetics explaining the observed experimental behavior is discussed, the so-called 3-state model. It predicts that deviations from equilibrium yield a proportionality of $\log I$ to $\Delta G/RT_{eff}$. Here, $T_{eff}$ is an effective temperature, higher than the experimental one. This behavior is indeed observed in some experiments on Agilent arrays. We analyze experimental data from two other microarray platforms and discuss, on the basis of the results, the attainment of equilibrium in these cases. Interestingly, the same 3-state model predicts a (dynamical) saturation of the signal at values below the expected one at equilibrium., Comment: 27 pages, 9 figures, 1 table

We consider mixtures of two DNA sequences t and t' differing by a single nucleotide, which are analyzed by an Agilent custom DNA microarray. In particular we focus on the case in which t, the "wild type", is predominantly abundant and t' the "mutant" is at very low concentrations compared to t. We show that by using appropriately designed arrays it is possible to accurately quantify the presence of t' even at low relative concentrations (≈1%). The detection method is based on thermodynamic models of DNA hybridisation and on the analysis of a large number of hybridisation intensities from probes containing one or two mismatches with respect to t and t'.

Quantifying interactions in DNA microarrays is of central importance for a better understanding of their functioning. Hybridization thermodynamics for nucleic acid strands in aqueous solution can be described by the so-called nearest-neighbor model, which estimates the hybridization free energy of a given sequence as a sum of dinucleotide terms. Compared with its solution counterparts, hybridization in DNA microarrays may be hindered due to the presence of a solid surface and of a high density of DNA strands. We present here a study aimed at the determination of hybridization free energies in DNA microarrays. Experiments are performed on custom Agilent slides. The solution contains a single oligonucleotide. The microarray contains spots with a perfect matching complementary sequence and other spots with one or two mismatches: in total 1006 different probe spots, each replicated 15 times per microarray. The free energy parameters are directly fitted from microarray data. The experiments demonstrate a clear correlation between hybridization free energies in the microarray and in solution. The experiments are fully consistent with the Langmuir model at low intensities, but show a clear deviation at intermediate (non-saturating) intensities. These results provide new interesting insights for the quantification of molecular interactions in DNA microarrays., 31 pages, 5 figures

The dynamical activity K(t) of a stochastic process is the number of times it changes configuration up to time t. It was recently argued that (spin) glasses are at a first order dynamical transition where histories of low and high activity coexist. We study this transition in the one-dimensional contact process by weighting its histories by exp(sK(t)). We determine the phase diagram and the critical exponents of this model using a recently developed approach to the thermodynamics of histories that is based on the density matrix renormalisation group. We find that for every value of the infection rate, there is a phase transition at a critical value of s. Near the absorbing state phase transition of the contact process, the generating function of the activity shows a scaling behavior similar to that of the free energy in an equilibrium system near criticality., 16 pages, 7 figures