Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

Background: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. Methods. We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Results: Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Conclusions: Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect

Identification by Gene Coregulation Mapping of Novel Genes Involved in Embryonic Stem Cell Differentiation

A combined analysis of data from a series of literature studies can lead to more reliable results than that based on a single study. A common problem in performing combined analyses of literature microarray gene expression data is that the original raw data are not always available and not always easy to combine in one analysis. We propose an approach that does not require analyzing original raw data, but instead takes literature gene sets derived from (supplementary) tables as input and uses gene co-occurrence in these sets for mapping a co-regulation network. An algorithm for this method was applied to a collection of literature-derived gene sets related to embryonic stem cell (ESC) differentiation. In the resulting network, genes involved in similar biological processes or expressed at similar time points during differentiation were found to cluster together. Using this information, we identified 43 genes not previously associated with cardiac ESC differentiation for which we were able to assign a putative novel biological function. For 6 of these genes (Apobec2, Cth, Ptges, Rrad, Zfp57, and 2410146L05Rik), literature data on mouse knockout phenotypes support their putative function. Three other genes (Rcor2, Zfp503, and Hspb3) are part of major pathways within the network and therefore likely mechanistically relevant candidate genes. We anticipate that these 43 genes can help to improve the understanding of the molecular events underlying ESC differentiation. Moreover, the approach introduced here can be more widely applied to identify possible novel gene functions in biological processe

Dose response analysis of monophthalates in the murine embryonic stem cell test assessed by cardiomyocyte differentiation and gene expression

The embryonic stem cell test (EST) is based on compound-induced inhibition of cardiomyocyte differentiation of pluripotent stem cells. We examined the use of transcriptomics to assess concentration-effect relationships and performed potency ranking within a chemical class. Three embryotoxic phthalate monoesters, monobutyl phthalate (MBuP), monobenzyl phthalate (MBzP) and mono-(2-ethylhexyl) phthalate (MEHP) and the non-embryotoxic monomethyl phthalate (MMP) were studied for their effects on gene expression. Effects on gene expression were observed at concentrations that did not inhibit cardiomyocyte differentiation or induce cytotoxicity. The embryotoxic phthalate monoesters altered the expression of 668 commonly expressed genes in a concentration-dependent fashion. The same potency ranking was observed for morphology and gene expression (MEHP > MBzP > MBuP > MMP). These results indicate that integrating transcriptomics provides a sensitive method to measure the dose-dependent effects of phthalate monoester exposure and enables potency ranking based on a common mode of action within a class of compounds. Transcriptomic approaches may improve the applicability of the EST, in terms of sensitivity and specificity

Dynamic changes in energy metabolism upon embryonic stem cell differentiation support developmental toxicant identification

Embryonic stem cells (ESC) are widely used to study embryonic development and to identify developmental toxicants. Particularly, the embryonic stem cell test (EST) is well known as in vitro model to identify developmental toxicants. Although it is clear that energy metabolism plays a crucial role in embryonic development, the modulation of energy metabolism in in vitro models, such as the EST, is not yet described. The present study is among the first studies that analyses whole genome expression data to specifically characterize metabolic changes upon ESC early differentiation. Our transcriptomic analyses showed activation of glycolysis, truncated activation of the tricarboxylic acid (TCA) cycle, activation of lipid synthesis, as well as activation of glutaminolysis during the early phase of ESC differentiation. Taken together, this energy metabolism profile points towards energy metabolism reprogramming in the provision of metabolites for biosynthesis of cellular constituents. Next, we defined a gene set that describes this energy metabolism profile. We showed that this gene set could be successfully applied in the EST to identify developmental toxicants known to modulate cellular biosynthesis (5-fluorouracil and methoxyacetic acid), while other developmental toxicants or the negative control did not modulate the expression of this gene set. Our description of dynamic changes in energy metabolism during early ESC differentiation, as well as specific identification of developmental toxicants modulating energy metabolism, is an important step forward in the definition of the applicability domain of the EST