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High-content data have the potential to inform mechanism of action for toxicants. However, most data to support this notion have been generated in vivo. Because many cell lines and primary cells maintain a differentiated cell phenotype, it is possible that cells grown in culture may also be useful in predictive toxicology via high-content approaches such as whole-genome microarray. We evaluated global changes in gene expression in primary rat hepatocytes exposed to two concentrations of ten hepatotoxicants: acetaminophen (APAP), β-naphthoflavone (BNF), chlorpromazine (CPZ), clofibrate (CLO), bis(2-ethylhexyl)phthalate (DEHP), diisononyl phthalate (DINP), methapyrilene (MP), valproic acid (VPA), phenobarbital (PB) and WY14643 at two separate time points. These compounds were selected to cover a range of mechanisms of toxicity, with some overlap in expected mechanism to address the question of how predictive gene expression analysis is, for a given mode of action. Gene expression microarray analysis was performed on cells after 24h and 48h of exposure to each chemical using Affymetrix microarrays. Cluster analysis suggests that the primary hepatocyte model was capable of responding to these hepatotoxicants, with changes in gene expression that appear to be mode of action-specific. Among the different methods used for analysis of the data, a combination method that used pathways (MOAs) to filter total probesets provided the most robust analysis. The analysis resulted in the phthalates clustering closely together, with the two other peroxisome proliferators, CLO and WY14643, eliciting similar responses at the whole-genome and pathway levels. The Cyp inducers PB, MP, CPZ and BNF also clustered together. VPA and APAP had profiles that were unique. A similar analysis was performed on externally available (TG-GATES) in vivo data for 6 of the chemicals (APAP, CLO, CPZ, MP, MP and WY14643) and compared to the in vitro result. These results indicate that transcription profiling using an in vitro assay may offer pertinent biological data to support predictions of in vivo hepatotoxicity potential., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

The skin is not only the body's largest organ but is widely considered to be the sentinel organ of the human body as it is often the first line of defense from environmental insults. Acting as both an environmental sensor and guardian, the skin responds to external cues and helps us adapt to our surroundings in many ways, such as tanning to adapt to repeated ultraviolet (UV) exposure. In order to better understand the molecular events that occur as the skin adapts to its environment, we need a deeper understanding of how the genes in the cells comprising the skin are regulated and how this regulation leads to changes in biological response. Regulation of messenger RNA (mRNA) ultimately results in changes in protein production, which is responsible for carrying out the physical and chemical reactions responsible for the skin's adaptation response. Each step in this cellular response is the subject of intense research. In fact, scientific sub-disciplines have developed around understanding each of these steps: genomics, proteomics and metabonomics. There are a myriad of applications for genomics in skin. Since capabilities, such as gene chips, provide fundamental molecular insights into skin cell biology that can be exploited for the development of new products, it is anticipated that genomics will continue to play an ever-increasing role in skin care.

Over the past several years, microarray technology has evolved into a critical component of any discovery-based program. Since 1999, the Association of Biomolecular Resource Facilities (ABRF) Microarray Research Group (MARG) has conducted biennial surveys designed to generate a profile of microarray service laboratories and, more importantly, an overview of technology development and implementation. Survey questions addressed instrumentation, protocols, staffing, funding, and work flow in a microarray facility. Presented herein are the results of the MARG 2005 survey; where possible, trends in the field are discussed and compared to data collected from previous surveys.

When small biological samples are collected by microdissection or other methods, amplification techniques are required to provide sufficient target for hybridization to expression arrays. One such technique is to perform two successive rounds of T7-based in vitro transcription. However the use of random primers, required to regenerate cDNA from the first round of transcription, results in shortened copies of cDNA from which the 5' end is missing. In this paper we describe an experiment designed to compare the quality of data obtained from labeling small RNA samples using the Affymetrix Two-Cycle Eukaryotic. Target Labeling procedure to that of data obtained using the One-Cycle Eukaryotic Target Labeling protocol. We utilized different preprocessing algorithms to compare the data generated using both labeling methods and present a new algorithm that improves upon existing ones in this setting.

Background: In a previous study, we determined the effects of 17-alpha-ethynyl estradiol (EE) on gene expression using microarrays that represented approximately 9,000 genes, which was the state of-the-art. Higher content arrays with almost double the number of genes have since become available. In order to better determine whether common sets of gene expression changes can be predictive of estrogenic activity, we have replicated the previous experiment using the more comprehensive microarray., Methods: Immature 20-day-old Sprague-Dawley rats were exposed to 0.1, 1, and 10 microg EE/kg/day ( subcutaneously [s.c.]), for four days, dosing from postnatal day (PND) 20-23). Changes in a more comprehensive expression level of 15,923 rat annotated genes and expressed sequence tags were evaluated on PND 24., Results: By comparing the response of the treatment groups versus controls using various statistical parameters, we determined that the expression of 1,394 genes showed a significant change with respect to control (p< or =0.0001), to at least one of the EE dosages. The tissues from animals exposed to 0.1 microg EE/kg/day showed changes in the expression of only 33 genes, whereas when they were exposed to 1 or 10 microg EE/kg/day, the expression of 409 and 548 genes was modified, respectively. A dose-dependent analysis indicated that 592 genes showed a robust and significant response to EE exposure (increased or decreased). Our analysis confirmed the regulation of previously identified estrogen-sensitive genes, and clearly identified novel mediators of estrogen actions, both in the uterus as well as in the ovary., Conclusions: This compendium of genes represents the largest compilation of estrogen-responsive genes that has ever been identified for the immature uterus and ovary of any species, and can be used to generate testable hypothesis to improve the understanding of the molecular pathways associated with physiological and pathophysiological responses to exposure to chemicals with estrogenic properties.