Journal: Archives of toxicology

Article Title: A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.

doi: 10.1007/s00204-015-1573-y

Figure Lengend Snippet: Fig. 2 Characterization of transcriptional changes induced by HDACi and mercurials. a Differentiating cells were treated for 6 days by toxicants (four samples per compound; as in Fig. 1) before RNA was prepared and gene expression was measured on Affymetrix microarrays. The 50 genes with highest variance between all samples were selected for clustering (=clustering set). Then, all samples were clustered (Euclidean distance) on the basis of gene expression values for this set. The results are represented as heatmap with each row representing one gene, and the colour of each square indicating the absolute gene expression level (blue low; green middle; yellow high). b Number of differentially expressed genes (DEG) after exposure to toxicants compared to untreated controls (detailed data are shown in supplemental material). c Recombinant active caspase-3 was incubated for 30 min with respective mercuri- als at indicated concentrations. Then, the enzymatic activity was determined by a fluoromet- ric assay. The caspase activity is represented in percentage relative to untreated control enzyme. The BMC of the respective mercurial (used in this study for microarray analy- sis) is indicated by a red line; data are mean ± SEM; n = 3; panobino, panobinostat (colour figure online)

Article Snippet: RNA was extracted, and Affymetrix chip-based DNA microarray analysis (Human Genome U133 plus 2.0 arrays) was performed as described earlier (Krug et al. 2013).

Techniques: Gene Expression, Recombinant, Incubation, Activity Assay, Control, Microarray

Journal: Archives of toxicology

Article Title: A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.

doi: 10.1007/s00204-015-1573-y

Figure Lengend Snippet: Fig. 4 Detection and visualiza- tion transcription factor (TF) networks affected by HDACi. a The CellNet database (3297 microarray sets from all major tissues) was used to construct a generic human TF network, based on statistical co-expres- sion information and graph- theoretical design principles. Each node represents a TF gene, and each edge suggests co- regulation. The edge length is driven by the number of edges on neighbouring nodes, not by the strength of co-regulation. Nodes are placed according to the Fruchterman–Reingold algorithm. Clusters (coded by same colours) were defined by an optimization algorithm that tries to maximize the modular- ity of the division of the graph into clusters. Then, GO term overrepresentation analysis was performed for each cluster to identify its biological role, and naming of the 18 clusters is based on these findings. Nodes (orange) at the rim of the network displayed in orange have not been assigned to define clusters. b The set of genes significantly up-regulated on DoD6 versus DoD0 (p < 0.05; FC ≥ 2.0) was retrieved from Balmer et al. (2014), and the TFs of this gene set were marked (red dots) in the TF network (see large, scalable version in supplemental mate- rial). c All TFs were identified amongst the HDACi consensus genes and marked in the TF network (blue down-regulated; red up-regulated). This diagram indicates, together with infor- mation from (a), which parts of the TF network are affected by at least 4 of the 6 HDACi used here. The clusters ‘forebrain development’ and ‘neuronal development’ have been encir- cled for better visualization in b and c (colour figure online)

Article Snippet: RNA was extracted, and Affymetrix chip-based DNA microarray analysis (Human Genome U133 plus 2.0 arrays) was performed as described earlier (Krug et al. 2013).

Techniques: Microarray, Construct

Journal: Archives of toxicology

Article Title: A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.

doi: 10.1007/s00204-015-1573-y

Figure Lengend Snippet: Fig. 6 Validation of the transcriptome-based classifier to iden- tify HDACi. a Differentiating cells were treated as indicated in Fig. 1 and transcriptome changes of neurally differentiating stem cells induced by HDACi and mercurials are plotted in a PCA (as in Fig. 1e) together with samples treated with 25, 150, 350, 450, 550, 650, 800 and 1 mM valproic acid (VPA) obtained from Waldmann et al. (2014). Each point represents one experiment (=data from one microarray), and the colour coding indicates the compound used in the experiment, mercurials (blue shades), HDACi (red shades) and VPA legacy data (green). The four samples from the present study (VPA classifier) have been encircled for better visualisation. The pur- ple arrow indicates the track of transcriptional changes after exposure to increasing concentrations of VPA in the Waldmann et al. (2014) data set. The SVM classifier was applied to this (green) data set, and the prediction of VPA, at indicated concentrations (25 µM–1 mM) acting on stem cell differentiation like an HDACi, is shown in the table as a mean of four replicate samples. The lower row of the table indicates whether the respective sample triggered developmental toxicity (+) or not (−), according to Waldmann et al. (2014). b The diagram shows various schedules of drug exposure. Grey bars indi- cate the period of drug exposure with 600 µM VPA or 10 nM TSA, and white open bars indicate culture periods in medium without HDACi. The samples were analysed at the times indicated. Exposures of a limited duration relative to the overall experiment were termed ‘pulsed’ treatments, and these were distinguished as early, medium and late pulse according to the exposure scheme. c, d The tables indi- cate the calculated probability of VPA or TSA acting like an HDACi when used as described in b. Probabilities >0.5 are defined as HDACi classification (green), and p < 0.5 indicates that the experimental con- dition did not show a canonical HDAC effect (colour figure online)

Article Snippet: RNA was extracted, and Affymetrix chip-based DNA microarray analysis (Human Genome U133 plus 2.0 arrays) was performed as described earlier (Krug et al. 2013).

Techniques: Biomarker Discovery, Microarray, Cell Differentiation

Journal: Molecular Vision

Article Title: Dopachrome tautomerase is a retinoblastoma-specific gene, and its proximal promoter is preferentially active in human retinoblastoma cells

doi:

Figure Lengend Snippet: Summary of published data focused on gene expression profiling in retinoblastoma tissues.

Article Snippet: Platform , Human 19K cDNA microarray (University Health Network) (∼ 19,000 genes) , GeneChip Human U133 V2.0 microarray (Affymetrix) (38,500 genes) , Human Gene 1.0ST (Affymetrix) (28,869 genes) , , Illumina TruSeq RNA Sample Prep Kit, HiSeq 2000 , Illumina TruSeq RNA Library Prep Kit, HiSeq 2500.

Techniques: Gene Expression, RNA Sequencing, Microarray, Sample Prep, Control, Expressing