Jiri Klema

Genotoxicity but not the AhR-mediated activity of PAHs is inhibited by other components of complex mixtures of ambient air pollutants.

In this study, we compared the genotoxicity and aryl hydrocarbon receptor (AhR)-dependent transcriptional changes of selected target genes in human lung epithelial A549 cells incubated for 24 h, either with extractable organic matter (EOMs) from airborne particles <2.5 μm (PM2.5) collected at four localities from heavily polluted areas of the Czech Republic or two representative toxic polycyclic aromatic hydrocarbons (PAHs) present in EOMs, benzo[a]pyrene (B[a]P) and benzo[k]fluoranthene (B[k]F). Genotoxic effects were determined using DNA adduct analysis or analysis of expression of selected AhR-related genes involved in bioactivation of PAHs (CYP1A1, CYP1B1) and transcriptional repression (TIPARP). Sampled localities differing in the extent and source of air pollution did not exhibit substantially different genotoxicity. DNA adduct levels induced by three subtoxic EOM concentrations were relatively low (1-5 adducts/10(8) nucleotides), compared to levels induced by similar concentrations of B[a]P, while B[k]F gave very low DNA adduct levels. Here, we compared genotoxicity and gene deregulation induced by complex mixtures containing PAHs with the effects of the comparable concentrations of individual PAHs. Our results suggested inhibition of formation of B[a]P-induced DNA adducts compared to individual B[a]P, probably attributable to competitive inhibition by other non-genotoxic EOM components. In contrast, induction of AhR target genes appeared not to be antagonized by the components of complex mixtures, as induction of CYP1A1, CYP1B1 and TIPARP transcripts reached maximum levels induced by PAHs.

Combining Sequence and Itemset Mining to Discover Named Entities in Biomedical Texts: A New Type of Pattern

Biomedical named entity recognition (NER) is a challenging problem. In this paper, we show that mining techniques, such as sequential pattern mining and sequential rule mining, can be useful to tackle this problem but present some limitations. We demonstrate and analyse these limitations and introduce a new kind of pattern called LSR pattern that offers an excellent trade-off between the high precision of sequential rules and the high recall of sequential patterns. We formalise the LSR pattern mining problem first. Then we show how LSR patterns enable us to successfully tackle biomedical NER problems. We report experiments carried out on real datasets that underline the relevance of our proposition.

Mining Plausible Patterns from Genomic Data

The discovery of biologically interpretable knowledge from gene expression data is one of the largest contemporary genomic challenges. As large volumes of expression data are being generated, there is a great need for automated tools that provide the means to analyze them. However, the same tools can provide an overwhelming number of candidate hypotheses which can hardly be manually exploited by an expert. An additional knowledge helping to focus automatically on the most plausible candidates only can up-value the experiment significantly. Background knowledge available in literature databases, biological ontologies and other sources can be used for this purpose. In this paper we propose and verify a methodology that enables to effectively mine and represent meaningful over-expression patterns. Each pattern represents a bi-set of a gene group over-expressed in a set of biological situations. The originality of the framework consists in its constraint-based nature and an effective cross-fertilization of constraints based on expression data and background knowledge. The result is a limited set of candidate patterns that are most likely interpretable by biologists. Supplemental automatic interpretations serve to ease this process. Various constraints can generate plausible pattern sets of different characteristics

Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification

The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed.

Comparative Analysis of Toxic Responses of Organic Extracts from Diesel and Selected Alternative Fuels Engine Emissions in Human Lung BEAS-2B Cells

This study used toxicogenomics to identify the complex biological response of human lung BEAS-2B cells treated with organic components of particulate matter in the exhaust of a diesel engine. First, we characterized particles from standard diesel (B0), biodiesel (methylesters of rapeseed oil) in its neat form (B100) and 30% by volume blend with diesel fuel (B30), and neat hydrotreated vegetable oil (NEXBTL100). The concentration of polycyclic aromatic hydrocarbons (PAHs) and their derivatives in organic extracts was the lowest for NEXBTL100 and higher for biodiesel. We further analyzed global gene expression changes in BEAS-2B cells following 4 h and 24 h treatment with extracts. The concentrations of 50 µg extract/mL induced a similar molecular response. The common processes induced after 4 h treatment included antioxidant defense, metabolism of xenobiotics and lipids, suppression of pro-apoptotic stimuli, or induction of plasminogen activating cascade; 24 h treatment affected fewer processes, particularly those involved in detoxification of xenobiotics, including PAHs. The majority of distinctively deregulated genes detected after both 4 h and 24 h treatment were induced by NEXBTL100; the deregulated genes included, e.g., those involved in antioxidant defense and cell cycle regulation and proliferation. B100 extract, with the highest PAH concentrations, additionally affected several cell cycle regulatory genes and p38 signaling.

Genome-wide miRNA profiling in myelodysplastic syndrome with del(5q) treated with lenalidomide

Lenalidomide is a potent drug with pleiotropic effects in patients with myelodysplastic syndrome (MDS) with deletion of the long arm of chromosome 5 [del(5q)]. We investigated its effect on regulation of microRNA (miRNA) expression profiles in del(5q) patients with MDS in vivo.

Molecular Networks Involved in the Immune Control of BK Polyomavirus

BK polyomavirus infection is the important cause of virus-related nephropathy following kidney transplantation. BK virus reactivates in 30%–80% of kidney transplant recipients resulting in BK virus-related nephropathy in 1%–10% of cases. Currently, the molecular processes associated with asymptomatic infections in transplant patients infected with BK virus remain unclear. In this study we evaluate intrarenal molecular processes during different stages of BKV infection. The gene expression profiles of 90 target genes known to be associated with immune response were evaluated in kidney graft biopsy material using TaqMan low density array. Three patient groups were examined: control patients with no evidence of BK virus reactivation (n = 11), infected asymptomatic patients (n = 9), and patients with BK virus nephropathy (n = 10). Analysis of biopsies from asymptomatic viruria patients resulted in the identification of 5 differentially expressed genes (CD3E, CD68, CCR2, ICAM-1, and SKI) (P < 0.05), and functional analysis showed a significantly heightened presence of costimulatory signals (e.g., CD40/CD40L; P < 0.05). Gene ontology analysis revealed several biological networks associated with BKV immune control in comparison to the control group. This study demonstrated that asymptomatic BK viruria is associated with a different intrarenal regulation of several genes implicating in antiviral immune response.

Comparative evaluation of set-level techniques in microarray classification

Analysis of gene expression data in terms of a priori-defined gene sets typically yields more compact and interpretable results than those produced by traditional methods that rely on individual genes. The set-level strategy can also be adopted in predictive classification tasks accomplished with machine learning algorithms. Here, sample features originally corresponding to genes are replaced by a much smaller number of features, each corresponding to a gene set and aggregating expressions of its members into a single real value. Classifiers learned from such transformed features promise better interpretability in that they derive class predictions from overall expressions of selected gene sets (e.g. corresponding to pathways) rather than expressions of specific genes. In a large collection of experiments we test how accurate such classifiers are compared to traditional classifiers based on genes. Furthermore, we translate some recently published gene set analysis techniques to the above proposed machine learning setting and assess their contributions to the classification accuracies.

Capitalizing on Aggregate Data for Gaining Process Understanding––Effect of Raw Material, Environmental and Process Conditions on the Dissolution Rate of a Sustained Release Product

Continuous improvement of pharmaceutical manufacturing operations has not evolved at the same rate as it has in other industries. Although time-series data are routinely collected as part of equipment control systems, the data are usually not thoroughly evaluated. This article investigates batch data, in-process and release laboratory test data and time-series data from granulation, fluid-bed drying and coating operations in an effort to determine which parameters are most critical to the dissolution of a matrix-release, solid oral dosage form of a poorly soluble drug.

Evolving groups of basic decision trees

A decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification into such subsets is not possible, it is better to put the classification decision on to some other classifier. This leads to the introduction of a null classification, which simply means that no classification is possible in this step. This approach is sensible with evolutionary methods, as they can handle a number of trees simultaneously. In the process of construction, we have to address the problem of whether a classification is sensible. The performance of the proposed model has been tested on several data sets and the results presented on one such data set show its potential.