Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Toxicity prediction from toxicogenomic data based on class association rule mining

While the recent advent of new technologies in biology such as DNA microarray and next-generation sequencer has given researchers a large volume of data representing genome-wide biological responses, it is not necessarily easy to derive knowledge that is accurate and understandable at the same time. In this study, we applied the Classification Based on Association (CBA) algorithm, one of the class association rule mining techniques, to the TG-GATEs database, where both toxicogenomic and toxicological data of more than 150 compounds in rat and human are stored. We compared the generated classifiers between CBA and linear discriminant analysis (LDA) and showed that CBA is superior to LDA in terms of both predictive performances (accuracy: 83% for CBA vs. 75% for LDA, sensitivity: 82% for CBA vs. 72% for LDA, specificity: 85% for CBA vs. 75% for LDA) and interpretability.

Biological networks for predicting chemical hepatocarcinogenicity using gene expression data from treated mice and relevance across human and rat species

BACKGROUND

Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases.

OBJECTIVES

To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions.

METHODS

Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans.

RESULTS

Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions.

CONCLUSIONS

Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.

A model of early human embryonic stem cell differentiation reveals inter- and intracellular changes on transition to squamous epithelium

The molecular events leading to human embryonic stem cell (hESC) differentiation are the subject of considerable scrutiny. Here, we characterize an in vitro model that permits analysis of the earliest steps in the transition of hESC colonies to squamous epithelium on basic fibroblast growth factor withdrawal. A set of markers (GSC, CK18, Gata4, Eomes, and Sox17) point to a mesendodermal nature of the epithelial cells with subsequent commitment to definitive endoderm (Sox17, Cdx2, nestin, and Islet1). We assayed alterations in the transcriptome in parallel with the distribution of immunohistochemical markers. Our results indicate that the alterations of tight junctions in pluripotent culture precede the beginning of differentiation. We defined this cell population as "specified," as it is committed toward differentiation. The transitional zone between "specified" pluripotent and differentiated cells displays significant up-regulation of keratin-18 (CK18) along with a decrease in the functional activity of gap junctions and the down-regulation of 2 gap junction proteins, connexin 43 (Cx43) and connexin 45 (Cx45), which is coincidental with substantial elevation of intracellular Ca2+ levels. These findings reveal a set of cellular changes that may represent the earliest markers of in vitro hESC transition to an epithelial phenotype, before the induction of gene expression networks that guide hESC differentiation. Moreover, we hypothesize that these events may be common during the primary steps of hESC commitment to functionally varied epithelial tissue derivatives of different embryological origins.

Use of chemoprotectants in chemotherapy and radiation therapy: the challenges of selecting an appropriate agent

Chemoprotection refers to the protection from the toxicity of one chemical by the intervention of another. Conflicting preclinical and clinical reports make it difficult to either ignore or accept the use of chemoprotectants during cancer chemotherapy or radiotherapy. The selection of anticancer drugs depends on the type and stage of cancer development. However, very little attention has been paid to the selection of chemoprotectants. The answer to the use of chemoprotectants during cancer therapy lies in their appropriate selection in a case-specific and/or issue-specific manner. The need of the hour is to find better answers on the rationality of chemoprotectants selection during cancer therapy using cutting-edge science. In this commentary, we have presented few examples to justify our view-points.